exploration des liens entre les propriétés optiques de

Transcription

Université du Québec
Institut National de la Recherche Scientifique
Centre Eau Terre Environnement
EXPLORATION DES LIENS ENTRE LES PROPRIÉTÉS OPTIQUES DE LA MATIÈRE
ORGANIQUE DISSOUTE ET LA COMPLEXATION DES MÉTAUX TRACES DANS
LES EAUX DOUCES NATURELLES
Par
Kristin K. Mueller
Thèse présentée pour l'obtention
du grade de Philosophiae doctor (Ph.D.)
en sciences de l'eau
Jury d'évaluation
Président du jury et
examinateur externe
William H. Hendershot
Department of Renewable Resources
Macdonald College, McGili University
Examinateur externe
D. Scott Smith
Chemistry Department
Wilfrid Laurier University
Examinateur interne
Bernard Vigneault
Ressources naturelles Canada
Directeur de recherche
Peter G.c. Campbell
INRS-ETE
Codirecteur de recherche
Claude Fortin
INRS-ETE
© Droits réservés de (Kristin K. Mueller), 2012
ii
Résumé
La matière organique dissoute (MOD) joue un rôle clé dans l'écotoxicologie des métaux puisqu'elle
complexe les ions métalliques libres, et diminue ce faisant la concentration de l'ion libre. Une telle
complexation atténue la biodisponibilité et la toxicité des métaux. Le modèle Windermere Humic
Aqueous Model (WHAM) est un modèle d'équilibre chimique qui incorpore la complexation des
métaux par la MOD. WHAM est partie intégrante du modèle du ligand biotique (BLM), lequel est
couramment utilisé pour prédire la biodisponibilité de métaux environnementalement importants,
tels que Cd, Cu, Ni et Zn, envers les organismes aquatiques dans les systèmes naturels. WH AM
prend en compte la quantité mesurée de MOD, mais l'utilisateur doit définir la « qualité» de la
MaD en imposant le pourcentage (%) de la MaD qui est réellement active dans la complexation des
métaux. Ici, nous proposons une approche plus rigoureuse, laquelle consiste à mesurer le % de
MaD active en utilisant des méthodes spectroscopiques bien établies. Nous avons mesuré les
concentrations de Cd 2+, Cu 2+, Ni 2+ et Zn2+ dans huit lacs et trouvé des accords raisonnables entre les
2
2
mesures et les prédictions faites par WHAM pour Cd + et Zn +. Au contraire, des différences
significatives ont été observées pour Cu 2+ et Ni 2+. Pour les métaux Cd, Ni et Zn, le % de MaD actif
requis pour ajuster les prédictions du modèle aux mesures d'ion libre est bien expliqué par les
variations inter-lacs dans la qualité spectroscopique de la MOD. La proportion de MOD active dans
la complexation de Cd et Zn augmente avec l'augmentation de la fluorescence caractéristique de la
MOD de type allochtone; une tendance inverse a été observée pour Ni, suggérant que les sites de
complexation de Cd et Zn sur la MOD sont différents que pour Ni. L'utilisation de la qualité
spectroscopique de la MaD pour estimer le % de MaD actif vis-à-vis de la complexation des métaux
2
a amélioré les capacités de WH AM à prédire les concentrations de Cu 2+ et particulièrement Ni +
libres. Nous concluons que le raffinement du modèle WHAM pour qu'il incorpore les variations dans
la qualité de la MOD doit aller au-delà du simple ajustement du pourcentage de la MOD qui est actif,
par exemple en tenant compte de l'affinité des différents sites de complexation des métaux,
notamment Cu et Ni. Nous discutons ensuite comment les variations dans la qualité de la MOD,
telles que révélées par les différences observées dans les spectres de fluorescence, peuvent être
utilisées pour améliorer les prédictions du modèle WHAM vis-à-vis des concentrations des métaux
environnementale ment importants dans les systèmes aquatiques naturels.
iii
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Abstract
Dissolved organic matter (DOM) plays a key role in metal ecotoxicology by complexing free metal
ions, an action that decreases the free metal ion concentrations and thus attenuates metal
bioavailability and toxicity. The Windermere Humic Aqueous Model (WHAM) is a chemical
equilibrium model that incorporates DOM-metal complexation and is an integral part of the Biotic
Ligand Model, which is widely used to predict the bioavailability of environmentally significant
metals, such as Cd, Cu, Ni and Zn, to aquatic organisms in natural systems. WHAM incorporates the
measured quantity of DOM, but users must define the "qua lity" of the DOM by choosing the % of
DOM that is actively involved in metal complexation. The ability to estimate this % active DOM
spectroscopically would be useful. We measured Cd 2+, Cu 2+, Ni 2+ and Zn2+ concentrations in eight
2
lakes and found reasonable agreement between measured and predicted concentrations of Cd + and
Zn2+, but there were marked differences between measured and WHAM-modeled concentrations of
Cu 2+ and Ni 2+. Relationships between the model-optimized % active DOM needed to fit modeled to
measured free metal concentrations and the lake-to-Iake variation in the spectroscopie quality of
the DOM were apparent for Cd, Ni and Zn. The proportion of DOM involved in Cd and Zn
complexation was found to increase with an increase in the allochthonous DOM fluorescence
signature; the opposite trend was observed for Ni, suggesting that the DOM binding sites involved in
Cd and Zn binding differ from those involved in Ni complexation. Improved WHAM predictions of
Cu 2+ and, especially, Ni 2+ concentrations were achieved when the DOM spectroscopie qua lity was
used to estimate the % of DOM active in metal complexation. We conclude that the refinement of
the WHAM model to incorporate variations in DOM quality must go beyond the simple adjustment
of the "percent active DOM" and possibly include changes to DOM metal binding affinities, notably
for Cu and Ni. We discuss the application of variations in DOM quality, as revealed by differences
observed in the fluorescence spectra, to refine metal speciation predictions obtained with WHAM
for environmentally significant trace metals in natural aquatic systems.
iv
Avant-propos
La thèse comprend un sommaire récapitulatif (en français) ainsi qu'une première partie qui
comporte une synthèse générale (en anglais) de l'ensemble des recherches, composée d'une
introduction, des objectifs et hypothèses de recherche, d'une discussion des résultats majeurs et
des perspectives de la recherche. La deuxième partie de la thèse consiste en trois publications
scientifiques: la première est déjà publiée, la seconde est provisoirement acceptée pour publication
(en révision), et la troisième est en préparation pour être soumise à une revue avec comité de
lecture. Finalement, la troisième partie contient une annexe qui décrit une expérience
complémentaire qui a été réalisée pour évaluer l'effet d'entreposage des échantillons sur la
spéciation des métaux et la qualité de la matière organique dissoute.
La contribution des auteurs aux différents articles s'établit comme suit:
1.
Mueller, K.K., C. Fortin, P.G.c. Campbell. 2012. "Spatial variation in the optical properties of
dissolved organic matter (DOM) in lakes on the Canadian Precambrian Shield and links to
watershed characteristics", Aquatic Geochemistry, 18(1): 21-44.
K.K. Mueller : Conception et réalisation du projet (échantillonnage, analyses, traitement et
interprétation des données); rédaction initiale et finale de l'article.
C. Fortin: Conception et réalisation du projet (contribution au traitement et l'interprétation de
données); contribution à la rédaction finale de l'article.
P.G.C. Campbell: Conception et réalisation du projet (contribution au traitement et
l'interprétation de données); contribution à la rédaction finale de l'article.
2.
Mueller, K.K., S. Lofts, C. Fortin, P.G.c. Campbell. 2012. "Trace metal speciation predictions in
natural aquatic systems: incorporation of dissolved organic matter (DOM) spectroscopic
quality", Environmental Chemistry, 9: 356-368.
K.K. Mueller : Conception et réalisation du projet (échantillonnage, analyses, traitement et
S. Lofts: Modifications du model WHAM (contribution au traitement et l'interprétation de
données; optimisation du modèle); contribution à la rédaction finale de l'article.
3.
Mueller, K.K., C. Fortin, P.G.c. Campbell. 2012. "Nickel and copper complexation by dissolved
organic matter in lake waters", en preparation.
K.K. Mueller : Conception et réalisation du projet (échantillonnage, analyses, t~aitement et
v
Forward
The thesis consists of a summary (in French) followed bya more general synthesis (in English). The
synthesis comprises the following sections: 1) an introduction; 2) the research hypothesis, objectives
and originality of the thesis; 3) the methodology; 4) the results and discussion; and finally, 4) the
main conclusions of the thesis. The second part of the thesis presents three scientific publications,
the first of which is already published, the second has been accepted with revisions, whereas the
third is in preparation for submission. The third part of the thesis is an appendix that describes the
results of an experiment on the effects of sample storage on the trace metal speciation and the
spectroscopic quality of dissolved organic matter in natural water samples.
The contributions of each of the authors of the scientific articles included in the thesis are as
follows:
1.
Mueller, K.K., C. Fortin, P.G.c. Campbell. 2012. "Spatial variation in the optical properties of
dissolved organic matter (DOM) in lakes on the Canadian Precambrian Shield and links to
watershed characteristics", Aquatic Geochemistry, 18(1): 21-44.
K.K. Mueller: Design and realization of the project (sampling, analyses, data analysis and
interpretation); initial and final writing of the article.
C. Fortin: Design and realization ofthe project (contribution to the analysis and interpretation of
data); contribution to the final writing of the article.
P.G.C. Campbell: Design and realization ofthe project (contribution to the analysis and
interpretation of data); contribution to the final writing of the article.
2.
Mueller, K.K., S. Lofts, C. Fortin, P.G.c. Campbell. 2012. "Trace metal speciation predictions in
natural aquatic systems: incorporation of dissolved organic matter (DOM) spectroscopic
quality", Environmental Chemistry, 9: 356-368.
interpretation); initial and final writing ofthe article.
S. Lofts: Modifications to the model WHAM (contribution to the analysis and interpretation of
data, model optimization); contribution to the final writing of the article.
C. Fortin: Design and realization of the project (contribution to the analysis and interpretation of
P.G.C. Campbell: Design and realization of the project (contribution to the analysis and
3.
Mueller, K.K., C. Fortin, P.G.c. Campbell. 2012. "Nickel and copper complexation by dissolved
organic matter in lake waters", in preparation.
interpretation); initial and final writing ofthe article.
C. Fortin: Design and realization of the project (contribution to the analysis and interpretation of
P.G.C. Campbell: Design and realization ofthe project (contribution to the analysis and
vi
Remerciements
J'ai été privilégiée de pouvoir travailler, tout au long de mes études doctorales, avec un groupe de
personnes exceptionnelles à l'Institut national de la Recherche scientifique.
Je remercie d'abord mon superviseur Peter Campbell pour son mentorat, son support et sa
patience. Tu as toujours été très généreux de ton temps et toujours disponible pour répondre à mes
questions et réviser mes écrits rapidement, et cela peu importe où tu te trouvais sur la planète. Je
remercie aussi mon co-superviseur, Claude Fortin, pour son support et son expertise analytique.
Ton souci du détail et ta lecture attentive de mes travaux les ont grandement améliorés. Enfin, vous
m'avez proposé des opportunités scientifiques qui m'ont permis d'élargir mes horizons et de
développer un réseau de collègues qui me servira lors de ma carrière à venir. Parmi ces
opportunités je suis particulièrement reconnaissante pour le séjour à Lancaster (Angleterre), l'atelier
de travail à Grenade (Espagne) et la conférence à Kyoto (Japon) ou j'ai pu présenter mes résultats.
Les autres professeurs du groupe de biogéochimie à l'INRS-ETE m'ont encouragée durant mes
études. Je remercie Charles Gobeil, André Tessier, Landis Hare et Isabelle Laurion non seulement
pour les discussions stimulantes, mais aussi pour les conseils sur la carrière et la vie de scientifique.
Je remercie spécialement Scott Smith pour ses conseils sur la fluorescence de la MOD, son
enthousiasme pour les sciences et sa pédagogie. Le mentorat que j'ai reçu du Professeur Ed Tipping
et de Steve Lofts sur le modèle WHAM a été essentiel au succès de cette étude. Je vous remercie
tous les deux de m'avoir généreusement accueilli au Centre for Ecology and Hydrology de Lancaster.
Je remercie finalement mon comité examinateur pour leurs commentaires qui ont grandement
amélioré la version finale de la thèse.
J'exprime aussi ma gratitude aux professionnels de recherche et au personnel technique de l'INRSÉTÉ pour leur support et leur expertise tant au laboratoire que sur le terrain: Michelle Bordeleau,
Sébastien Duval, Pauline Fournier, Philippe Girard, Pierre Marcoux, Julie Perreault, Stéfane Prémont,
Lise Rancourt et René Rodrigue. Je remercie Marc-André Robin pour les données GIS et Isabelle
Lavoie pour l'aide avec les analyses statistiques. Un support logistique essential a été fourni par
John Gunn et la Freshwater Ecology Unit de Laurentian University Cooperative, de même que par
Louis Jourdain du Ministère de Ressources naturelles et de la Faune du Québec.
Ce fut un grand plaisir de côtoyer le groupe d'étudiants et de chercheurs de l'INRS-ETE, en
particulier Julie Breton, Dany Dumont, Dominique Lapointe, Michel Lavoie, Isabelle Lavoie, Séverine·
Le Faucheur et Isabelle Prou Ix.
J'aimerais exprimer ma reconnaissance à mes amis et ma famille pour leur support et leur amour.
En particulier Kimberley Tasker, qui a été une amie si généreuse durant ces années; mes parents,
Lyle et Wendi, qui m'ont enseigné la ténacité et l'indépendance; ma sœur Johanna, à qui j'ai
toujours pu me confier malgré la distance qui nous sépare; mes beaux-parents Gilles et Sylvie, qui
m'ont généreusement accueillie durant mes nombreux séjours à Québec durant la fin de mes
études. Enfin, je remercie Raoul-Marie Couture pour ces encouragements, sa patience et son
support. J'ai apprécié tes conseils et tout le temps que tu aies passé à revoir mes écrits.
vii
Acknowledgements
1was very fortunate, throughout my doctoral studies, to have been able to work with a great group
of people at the Institut national de la Recherche scientifique.
1would first like to thank my supervisor Peter Campbell for his mentorship, guidance, support and
most of ail, his patience. Vou were always very generous with your time and 1appreciate your quick
response to my questions and written work, even from the other side of the planet! 1would also
like to my co-supervisor, Claude Fortin, for his guidance, support and analytical expertise. Vour
attention to detail in reviewing written work was remarkable and greatly improved the quality of my
work. Finally, the scientific opportunities Vou both provided me with, from an internship in
Lancaster, to a workshop in Granada and a conference in Kyoto, have helped me to broaden my
horizons and develop a network of colleagues that will serve me weil in my future career.
Other professors in the biogeochemistry research group at INRS-ETE were also encouraging during
my studies. Thank Vou to Charles Gobeil, André Tessier, Landis Hare and Isabelle Laurion for not
only stimulating scientific discussions, but also for candid discussions on career and life balance.
1would especially like to thank Scott Smith for insight and advice on DOM fluorescence, your
enthusiasm for science and your pedagogy. The training 1received from Professor Ed Tipping and
Steve Lofts on the model WHAM were essential for the completion of this work. Thank vou for
generously hosting me at the Centre for Ecology and Hydrology in Lancaster and for your continuing
encouragement. 1also thank my examining committee who took the time to evaluate my thesis and
whose comments have greatly improved this thesis.
My gratitude goes to the following technical staff at INRS-ETE for their support and expertise in the
lab and in the field: Michelle Bordeleau, Sébastien Duval, Pauline Fournier, Philippe Girard, Pierre
Marcoux, Julie Perreault, Stéfane Prémont, Lise Rancourt and René Rodrigue. 1also thank MarcAndré Robin for providing GIS data and Isabelle Lavoie for help with statistical analyses. Essential
logistical assistance in the field was provided by John Gunn and the Laurentian University
Cooperative Freshwater Ecology Unit, as weil as Louis Jourdain from the Ministère de Ressources
naturelles et de la Faune du Québec.
1was also fortune enough to work with a great group of students and researchers at INRS-ETE.
would especially like to acknowledge Julie Breton, Dany Dumont, Dominique Lapointe, Michel
Lavoie, Isabelle Lavoie, Séverine Le Faucheur and Isabelle Proulx who made daily life at INRS-ETE a
pleasure.
1am forever grateful to my friends and family for their continuai love and support. In particular
Kimberley Tasker, who has been such a generous friend over the years; my parents, Lyle and Wendi,
who taught me to be tenacious and independent; my sister, Johanna, who has always been only a
phone cali away; and my in-Iaws, Gilles and Sylvie, who graciously housed me many times during my
many trips to Québec City near the end of my studies. Finally, 1am grateful for the endless
encouragement, patience and support of Raoul-Marie Couture. Thank vou for your advice,
comments and ail the time Vou spent editing my written work.
viii
Table des matières
Résumé .........................................................................................................................................................iii
Abstract ................................... :.................................................................................................................... iv
Avant-propos .................................................................................................................................................v
Forward ........................................................................................................................................................vi
Remerciements ............................................................................................................................................ vii
Acknowledgements .................................................................................................................................... viii
Liste des figures .......................................................................................................................................... xiii
Liste des tableaux ........................................................................................................................................ xv
Sommaire récapitulatif ................................................................................................................................ 1.
1 Introduction ............................................................................................................................................. 3
1.1
Métaux et lacs d'intérêt ............................................................................................................... 3
1.2
Spéciation des métaux en eau douce ........................................................................................... 4
1.3
Matière organique dissoute (MOD) ............................................................................................. 5
1.4
Modélisation de la spéciation à l'équilibre .................................................................................. 8
2 Hypothèse de recherche, objectifs et originalité .................................................................................. 10
2.1
Hypothèse de recherche ............................................................................................................ 10
2.2
Objectifs de recherche ............................................................................................................... 10
2.3
Originalité ....................................... :........................................................................................... 11
3 Méthodologie ........................................................................................................................................ 11
3.1
Échantillonnage .......................................................................................................................... 12
3.2
Analyses ....................................................................................................................................... 12
4 Résultats et Discussion .......................................................................................................................... 13
4.1
Bassins versants .......................................................................................................................... 14
4.2
Chimie des eaux lacustres .......................................................................................................... 14
4.3
Matière organique dissoute (MOD) ........................................................................................... 15
4.4
Spéciation des métaux traces ..................................................................................................... 17
4.5
Spéciation des métaux traces mesurée et prédite ..................................................................... 17
ix
4.6
Paramètres de complexation mesurés ....................................................................................... 19
5 Conclusions ............................................................................................................................................ 22
Première partie: Synthèse ......................................................................................................................... 27
1 Introduction ........................................................................................................................................... 29
1.1
Metals of interest ........................................................................................................................ 29
1.2
Sites of interest ........................................................................................................................... 29
1.3
Freshwater trace metal speciation ............................................................................................. 31
1.3.1
Measuring freshwater trace metal speciation ............................................................. 33
1.4
Dissolved organic matter (DOM) ................................................................................................ 34
1.5
Chemical equilibrium speciation modelling ................................................................................ 39
1.5.1
WHAM field validations ............................................................................................... 41
2 Research hypothesis, objectives and origina lity .................................................................................... 44
2.1
Research hypothesis ................................................................................................................... 44
2.2
Research objectives ..................................................................................................................... 45
2.3
Originality .................................................................................................................................... 46
3 Methodology ......................................................................................................................................... 46
3.1
Sampling ...................................................................................................................................... 46
3.2
Sample analysis ........................................................................................................................... 49
3.2.1
Lake DOM quality ......................................................................................................... 49
3.2.2
Cd, Cu, Ni and Zn metal speciation .............................................................................. 50
3.2.3
DOM titrations with Cu and Ni. .................................................................................... 50
3.2.4
Effect of sample storage on metal speciation and DOM quality ................................. 51
4 Results and Discussion ........................................................................................................................... 52
4.1
Lake watersheds ......................................................................................................................... 52
4.2
Lake water chemistry .................................................................................................................. 54
4.3
Dissolved organic matter (DOM) ................................................................................................ 55
4.4
Trace metal speciation ................................................................................................................ 60
4.5
Measured vs. modeled trace metal speciation ............................ ,.............................................• 61
4.6
Measured metal binding parameters ......................................................................................... 66
x
5 Conclusions ............................................................................................................................................ 70
5.1
Improving trace metal speciation predictions ............................................................................ 70
5.2
Research perspectives ................................................................................................................ 74
Deuxième partie: Articles ......................................................................................................................... 77
Spatial variation in the optical properties of dissolved organic matter (DOM) in lakes on the
Canadian Precambrian Shield and links to watershed characteristics .................................................. 79
Trace metal speciation predictions in natural aquatic systems: incorporation of dissolved organic
matter (DOM) spectroscopic quality ................................................................................................... 117
Nickel and copper complexation by natural dissolved organic matter in lake waters ............................ 161
Troisième partie: Annexes ...................................................................................................................... 181
Appendix A: Effect of sample storage on metal speciation and DOM quality / Annexe A : Effet de
l'entreposage des échantillons sur la spéciation de métaux et la qualité de la MOD ........................ 183
Appendix B: Lake DOM spectroscopic quality results / Annexe B : Résultats de l'analyse
spectroscopique de la MOD lacustre .................................................................................................. 191
Appendix C: Effects of aluminum (AI) and iron (Fe) on WHAM VI predictions of Cd, Cu, Ni and Zn
speciation / Annexe C : Effets de l'aluminium (AI) et du fer (FE) sur les prédictions de la spéciation
du Cd, du Cu, du Ni et du Zn ................................................................................................................ 201
Quatrième partie: Bibliographie ............................................................................................................ 205
xi
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xii
Liste des figures
Figure 1.
Schematic representation of the interconnectedness between water chemistry
and bioavailability / Représentation schématique de la relation entre la chimie
des eaux naturelles et la biodisponibilité des métaux ...................................................... 32
Figure 2.
Representation of a proposed molecular structure for humic substances (from
Schulten and Schnitzer 1997) / Structure moléculaire probable de la substance
humique (tiré de Schulten and Schnitzer, 1997) .............................................................. 37
Figure 3.
Example ofthe sampling apparatus / Montage utilisé pour l'échantillonnage .............. .48
Figure 4.
Map of the two study areas showing the relative position of the two regions in
eastern Canada (panel a), the geographicallocation of the Rouyn-Noranda lakes
(panel b) and the Sudbury lakes (panel cl. Reproduced fram Mueller et al.
(2012b) / Carte de l'Est du Canada montrant les positions relatives de deux
régions étudiées (A), ainsi que l'emplacement de lacs de la région de RouynNoranda (B) et de la région de Sudbury (C). Reproduit de Mueller et al. (2012b) ........... 53
Figure
s.
Comparison of the concentrations of the free metal ion [M 2+] measured using
lET (Cd, Ni and Zn) and ISE (Cu) and those calculated with WHAM VI assuming
65% aFA (large dark circles) and a range of 33% (smaillight circles) to 130% aFA
(smaillight squares). The dashed line represents the l:lline / Comparaison
2
entre les concentrations d'ion métallique libre [M +] mesurées par lET (Cd, Ni et
Zn) ou ISE (CU) et les concentrations prédites par WHAM VI en supposant des
valeurs de % aFA de 65 % (cercles pleins) ou entre 33 et 130 % (cercles ouverts).
La ligne pointillée représente l'équivalence 1 : 1. ............................................................ 64
Figure 6.
Relationship between the WHAM-calculated percent fulvic acid active in
complexation (%aFA optimised) for Cd, Cu, Ni and Zn and the relative
contribution of the PARAFAC fluorescence components 1 (panels a-dl and 3
(panels e-h) to the overall fluorescence spectrum of lake DOM. Points of the
same shape and fill represent replicate samples. Solid lines represent regression
equations of statistical significance at the 0.05 alpha level. Reproduced from
Mueller et al. (2012c) / Pourcentage optimal d'acide fulvique actif dans la
complexation (%aFA optimisé) calculé par WHAM pour Cd, Cu, Ni et Zn en
fonction de la contribution, relative à la fluorescence totale de la MOD, des
composantes 1 (panneaux a-dl et 3 (panneaux e-h) de fluorescence PARAFAC.
xiii
Les points de même forme et de même couleur représentent des échantillons
en répliqua, et les lignes continues représentent les régressions significatives au
niveau statistique de alpha égal à 0.05. Reproduit de Mueller et al. (2012c) .................. 65
Figure 7.
z
Comparison ofthe mean (± SD, n=3) concentrations offree metal ion [M +]
calculated with WH AM VI (with the percentage active fulvic acid (%aFA)
estimated from Figure 6) and those measured (± SD, n=3) using the ion
exchange technique (lET) (Cd, Ni and Zn) or ion selective electrode {ISE) (Cu)
methods: (a) cadmium; (b) copper; (c) nickel and (d) zinc. The dashed line
represents the l:lline / Concentrations moyennes (± ET, n=3) du métal libre
z
[M +] calculé par WHAM (en employant le pourcentage d'acide fulvique actif
(%aFA) estimé d'après la Figure 6) comparées à celles mesurées (± ET, n=3) avec
la technique d'échange d'ions (IET)(Cd, Ni, Zn) ou une électrode sélective (ISE)
(Cu) : (a) cadmium; (b) cuivre; (c) nickel et (d) zinc. La ligne pointillée représente
l'équivalence 1 : 1. ............................................................................................................ 67
Figure 8.
Normalized Ni (A and C) and Cu (B and D) complexation curves (M-DOM/DOC)
for Lake Geneva (A and B) and Lake Opasatica (C and D). Measured values are
shown in colour; values calculated with WHAM VI are shown in gray triangles.
Modified from Mueller et al. (2012a) / Courbe de complexation normalisée (MMOD/MOD) pour Ni (panneaux A et D) et Cu (panneaux B et D) dans les lacs
Geneva (carrées; panneaux A et B) et Opasatica (cercles, panneaux C et D). Les
valeurs mesurées sont montrées en couleur alors que celles prédites par WHAM
VI sont représentées par des triangles gris. Modifié d'après Mueller et al.
(2012a) ............................................................................................................................. 68
xiv
Liste des tableaux
Table 1.
Lake names, sampling regions, locations and years sampled. Modified from
Mueller et al. (2012b) / Noms des lacs, ainsi que les régions, sites et dates
d'échantillonnage. Modifié d'après Mueller et al. (2012b) .............................................. 54
Table 2.
Measured pH and mean (± SD, N=3) dissolved organic matter (DOM)
concentrations, total dissolved concentrations of major cations and trace metals,
and % free metal values for lakes sampled from Rouyn-Noranda (QC) and Sudbury
(ON) in 2008/ Valeurs de pH, de concentrations moyennes (± SD, N=3) de matière
organique dissoute (MOD), de concentrations totales de cations majeurs dissous
et de métaux traces ainsi que les pourcentages (%) de métal libre pour les lacs
échantillonnés dans les régions de Rouyn-Noranda (Qué.) et de Sudbury (Ont.) en
2008 .................................................................................................................................. 56
Table 3.
Measured mean (± SD, N=3) dissolved organic matter (DOM) concentration,
specifie UV-absorbance (SUVA 24S ), fluorescence index (FI), and relative proportions
of the Cl and C3 PARAFAC components for the eighteen lakes sampled in 2007
and the eight lakes sampled in 2008/ Concentrations moyennes (± SD, N=3) de
matière organique dissoute (MOD), absorbance UV spécifique (SUVA 24S ), indice
de fluorescence (FI) et les proportions relatives des composantes de fluorescence
Cl et C3 pour les 18 lacs échantillonnés en 2007 et les 8 lacs échantillonnés en
2008 .................................................................................................................................. 59
xv
--------------------------
xvi
Sommaire récapitulatif
2
1
1.1
Introduction
Métaux et lacs d'intérêt
Plusieurs substances présentes dans l'environnent naturel sont considérées comme
potentiellement problématiques envers la santé de l'écosystème et la santé humaine. Le cuivre
(Cu), le cadmium (Cd), le nickel (Ni) et le zinc (Zn), émis par les raffineries et fonderies de cuivre
et zinc vers l'atmosphère et l'environnement terrestre et aquatique, ont été identifiés comme
« prioritaires» par la Loi canadienne sur la protection de l'environnement (CEPA, 2001)1. La
toxicité potentielle et le risque écotoxicologique de ces métaux doivent être déterminés.
Les régions du Canada qui sont les plus affectées par la présence des raffineries et fonderies de
cuivre et zinc sont les régions de Rouyn-Noranda, au Québec, et de Sudbury, en Ontario. Ces
régions sont toutes deux localisées sur le Bouclier canadien précambrien (Carignan and Nriagu,
1985). Dans la région de Rouyn-Noranda, la roche mère de type granitique est le plus souvent
recouverte d'une épaisse couche de dépôts superficiels de l'ère glacière, bien que parfois elle
soit exposée (Veillette et aL, 2005). En revanche, dans la région de Sudbury, la roche mère est
parfois recouverte d'une mince couche de dépôt «1 m), mais est le plus souvent exposée
(Barnett and Bajc, 2002). Ces différences dans l'exposition de la roche-mère contribuent aux
différences dans la topographie et, probablement, à celles dans la végétation, les sols et
l'hydrologie des bassins versants des deux régions (Sobek et aL, 2007).
Les deux régions d'intérêt possèdent un long historique d'activité minière et de raffinage de
métaux, datant du début du XIXe siècle (Carignan and Nriagu, 1985; Couillard et aL, 2004;
Gallon et aL, 2006; Nriagu et aL, 1982). Les concentrations de métaux trouvées dans les aires
entourant les raffineries sont tributaires de la direction des vents dominants et décroissent au
fur et à mesure que l'on s'éloigne de la raffinerie. Certains lacs dans la région de RouynNoranda sont aussi directement affectés par des rejets miniers et par le drainage minier acide
(Goulet and Couillard, 2009).
1
Toutes les références se trouvent à la fin de la Quatrième partie de la thèse (page 205).
3
----------------------
1.2
Spéciation des métaux en eau douce
La toxicité des métaux envers les organismes dans l'environnement aquatique dépend de leur
biodisponibilité, laquelle dépend des formes spécifiques que revêtent les métaux (Allen and
Hansen, 1996; Batley et aL, 2004; Janssen et aL, 2003). En particulier, la concentration de l'ion
métallique libre est généralement reconnue comme représentative de la biodisponibilité des
métaux envers les organismes aquatiques (Campbell, 1995). En eau douce, les ligands
inorganiques simples tels que OH-, HC0 3- et C0 32- sont souvent présents à des concentrations
2
2
permettant la formation de complexes avec les ions métalliques tels que Cu 2+, Cd +, Ni + et Zn2+
(Stumm and Morgan, 1996). Des monomères organiques comme le citrate, la glycine ou les
acides polycarboxyliques ont aussi une grande affinité pour les ions métalliques, mais était
donné leur grande labilité vis-à-vis de la dégradation par l'activité microbienne, ils ne soht
normalement pas présents en concentration suffisante, dans les eaux douces, pour influencer la
spéciation des métaux (Morel and Hering, 1993).
La complexation des métaux par la matière organique dissoute (MOD) est plus difficile à
quantifier que celle par les ligands inorganiques mentionnés précédemment. En effet,
l'hétérogénéité ainsi que les propriétés polycoordinantes et polyélectrolytiques de la MOD
rendent impossible la détermination de sites de complexation précis. Des constantes
conditionnelles sont par conséquent déterminées expérimentalement en titrant de la MOD
isolée chimiquement avec des métaux dans des conditions très spécifiques (ex. : pH, force
ionique, concentrations d'ions majeurs). Ces constantes conditionnelles ne peuvent toutefois
être utilisées dans des conditions autres que celles dans lesquelles elles ont été déterminées.
L'effet de ces différentes variables sur la complexation des métaux par la MOD a été exploré en
détail par Tipping (2002). D'abord, la complexation des métaux dépend de la concentration du
z
métal libre, [M +], laquelle dépend de la concentration totale de métal. La complexation
dépend aussi de l'affinité du métal étudié pour les sites de complexation de la MOD, ainsi que
du pH de la solution. Lorsque le pH diminue, la compétition entre le cation métallique et les
protons pour les sites de complexation augmente (ex. : Benedetti et al. (1995)). Les autres
cations en solution peuvent aussi compétitionner avec le cation métallique, en fonction de leur
4
affinité relative et de leur concentration en solution (ex. : Cao et al. (2006)). Enfin, l'effet de la
force ionique sur la complexation des métaux par la MaD est expliqué par le fait que les autres
ions atténuent les attractions électrostatiques entre la MaD et le cation métallique (Tipping,
2002).
Pour les métaux cationiques, la mesure d'une ou plusieurs espèces chimiques dans les eaux
douces naturelles n'est pas une opération de routine. Selon la composition chimique de
l'échantillon, des changements réversibles dans la coordination ou dans l'état d'oxydation des
éléments d'intérêt peuvent survenir. De plus, la mesure elle-même peut perturber l'équilibre
initialement présent. En dépit de ces difficultés, des méthodes analytiques pour déterminer la
forme spécifique des métaux dans les eaux naturelles se sont grandement améliorées au cours
des dernières décennies. Ces techniques comprennent par exemple les électrodes sélectives
(ISE), la voltampérométrie anodique (ASV) et cathodique (CSV) et la diffusion sur gel en couches
minces (DGT) (Batley et aL, 2004).
1.3
Matière organique dissoute (MOO)
La nature ubiquiste et persistante de la MaD dans l'environnement terrestre et aquatique est
responsable de son rôle clé dans plusieurs processus biogéochimiques. Le cycle du carbone
(Schiff et al., 1990), la pénétration de la lumière dans les eaux de surface (Scully and Lean,
1994), les réactions photochimiques (Molot and Dillon, 1997), l'acidification des eaux de
surface (Schindler and Curtis, 1997), les cycles des nutriments (Dillon and Molot, 1997) et la
spéciation de métaux (Buffle, 1988; Perdue and Ritchie, 2003; Wood et al., 2011) sont autant
d'exemples de processus dans lesquels la MaD joue un rôle.
La MaD se définit par un mélange complexe et désordonné de molécules polyélectrolytiques
provenant des plantes, microbes, et produits animaux à divers stades de décomposition (Ai ken
et aL, 1985; Thurman, 1985; Wetzel, 2001). C'est la présence d'une variété de groupements
fonctionnels qui permet à la MaD d'exercer un grand nombre de fonctions écologiques. La
variabilité dans les sources de la MaD, son transport et les mécanismes de sa dégradation
5
contribuent aussi à sa nature hétérogène. Dans la plupart des lacs, la MaD provient
principalement de sources allochtones extérieures au système aquatique, c'est-à-dire depuis les
sols et les plantes terrestres. La MaD allochtone consiste en un mélange de matière organique
provenant du sol, à la fois la matière organique humifiée au cours du temps et la matière
organique fraichement issue des plantes et du sol (Thurman, 1985). Dans un lac, les sources de
matière organique autochtone sont le résultat de la sécrétion, de la décomposition et la de lyse
des macrophytes, algues et cyanobactéries présents dans les eaux littorales et profondes
(Thurman, 1985; Wetzel, 2001). La composition de la MaD dans un lac reflète donc la
contribution relative de la matière organique provenant de sources autochtones et allochtones,
et la transformation de la MaD qui survient le long de son transport dans le bassin versant vers
le lac, et dans le lac lui-même (affectés par exemple par la sorption de la MaD sur les particules
de sol (Schumacher et aL, 2006) et par sa photo- et biodégradation (Këhler et aL, 2002)). Par
conséquent, les caractéristiques du bassin versant comme sa géomorphologie, son régime
hydrologique et l'existence de lacs en amont, la couverture végétative, et l'utilisation du
territoire influencent la nature de la MaD (Jaffé et aL, 2008; Mattsson et aL, 2005).
La caractérisation de la MaD requiert généralement la séparation de cette dernière en isolat en
utilisant la méthode développée par Thurman et Malcolm (1981). D'abord la MaD est séparée
en substances humiques et en substances non humiques. Les substances humiques sont
d'origine biologique et sont composées de molécules à haut poids moléculaire, réfractaires à la
biodégradation. Ces substances sont réputées représenter jusqu'à 70 ou 80 % de la MaD dans
les eaux naturelles (Thurman, 1985). Les substances humiques peuvent être ensuite séparées
en acides humiques et en acides fulviques, ou humine, en tirant avantage de leurs différentes
solubilités en fonction du pH (Thurman and Malcolm, 1981). En effet, les acides humiques sont
solubles seulement à pH plus grand que 2, alors que les acides fulviques sont solubles à tous les
pH. Les acides fulviques sont plus solubles parce qu'ils contiennent une grande proportion de
groupes fonctionnels carboxyliques et hydroxyles. Leurs masses moléculaires sont aussi plus
petites que celle des acides humiques, qui sont plus lourds et contiennent une plus grande
proportion de groupements ayant des pKa similaires à ceux des groupements phénoliques
6
(Thurman, 1985). Malgré les efforts, une description précise de la structure de la matière
organique dissoute n'est toujours pas disponible (Abbt-Braun et aL, 2004; Sutton and Sposito,
2005).
La qualité de la MaD est un paramètre qu'il est utile d'estimer afin de mieux comprendre les
rôles de la MaD dans le milieu aquatique, incluant son rôle dans la complexation et le transport
des métaux. Le terme « qualité» est utilisé ici pour classifier la MaD quant à sa composition et
à ces sources. Les propriétés optiques de la MaD, en particulier son absorbance (Gondar et aL,
2008; Weishaar et aL, 2003) et sa fluorescence (Baker, 2002; Coble, 1996; McKnight et aL,
2001), sont souvent utilisées pour distinguer entre différents types de MaD provenant
d'environnements contrastés. Par exemple, AI-Reasi et al. (2011) ont récemment publié un
article de synthèse portant sur l'utilisation des propriétés spectroscopiques de la MaD pour
distinguer entre différents types de matière organique et pour prédire leurs capacités à
protéger les organismes aquatiques contre la toxicité des métaux.
Les mesures d'absorbance et de fluorescence sont particulièrement utiles puisqu'elles sont
relativement simples, rapides et non destructives. En général, la MaD est caractérisée par la
fluorescence de type humique ou de type protéique d'origine allochtone ou d'origine
autochtone. Les mesures de fluorescence sont maintenant effectuées à l'aide de matrices
d'excitation-émission (MEE), puisque cette approche permet d'obtenir une grande quantité
d'information et de cartographier la fluorescence de la MaD en trois dimensions. De plus,
l'analyse statistique multivariée, comme l'analyse en composantes principales (ACP), a été
utilisée avec succès pour extraire de l'information des spectres MEE complexes (Andersen and
Bro, 2003; Stedmon et aL, 2003). Lorsque l'ACP est utilisée conjointement avec les MEE, des
composantes de fluorescence individuelles peuvent être identifiées ainsi que leurs
contributions relatives à la fluorescence totale. Cette approche permet ainsi de caractériser la
MaD le long des gradients environnementaux (AI-Reasi et aL, 2011; Baken et aL, 2011; Jaffé et
aL, 2008; Miller and McKnight, 2010).
7
Étant donné que la MOD forme des complexes avec les métaux traces dans l'environnement
aquatique, et puisque les caractéristiques de la MOD vis-à-vis de la complexation de métaux
dépendent du type et de la source de cette dernière (Ba ken et aL, 2011; Cao et aL, 2006; Cheng
et aL, 2005; Xue and Sigg, 1999), les propriétés optiques, en tant que mesures de la qualité de
la MOD, sont susceptibles d'être un outil puissant pour prédire l'intensité des interactions entre
la MOD et les métaux cationiques. En particulier, la qualité de la MOD, telle que déterminée en
utilisant les MEE de fluorescence et l'ACP, pourrait permettre de raffiner les modèles de
spéciation à l'équilibre conçus pour prédire la biodisponibilité des métaux dans les eaux
naturelles (Dudal and Gérard, 2004).
1.4
Modélisation de la spéciation à l'équilibre
Les modèles de spéciation à l'équilibre sont souvent utilisés pour prédire la forme spécifique
des métaux dans les eaux naturelles en utilisant une approche thermodynamique avec des
équations d'équilibres chimiques à stœchiométries précises. Toutefois, la nature hétérogène
de la MOD rend difficile la définition de constantes thermodynamiques spécifiques qui
pourraient être associées à des complexes. Néanmoins, en utilisant des hypothèses
raisonnables, basées sur les connaissances actuelles sur les groupements fonctionnels de la
MOD, sa propension à complexer les cations dans les eaux naturelles a pu être modélisée avec
un certain succès (ex. : Kinniburgh et al. (1996), Tipping (1998)). Le logiciel de spéciation à
l'équilibre Windermere Humic Aqueous Model (WHAM), combiné au modèle Humic lon-Binding
Model VI (Tipping, 1998), prend en compte l'hétérogénéité naturelle de la matière organique
naturelle (MON), la compétition des ions, la stœchiométrie des réactions, et les interactions
électrostatiques.
Le logiciel WHAM-VI incorpore des paramètres de complexation issus de l'analyse de centaines
de jeux de données existant pour la complexation du proton et des métaux par les acides
fulviques et humiques, obtenus pour une large gamme de conditions expérimentales.
WHAM-VI a été abondamment testé en laboratoire pour étudier les interactions entre la MOD
et les ions métalliques dissous (Tipping, 2002). La robustesse du modèle et de ces paramètres
8
généraux a été testée fréquemment en comparant ces prédictions avec les résultats
d'expériences en laboratoire. Toutefois, il ya peu d'exemples d'études où WHAM VI a été testé
pour les eaux naturelles (ex. : Bryan et al. 2002; Cheng et al. 2005; Guthrie et al. 2005; Fortin et
al. 2010; Doig and Liber 2007).
" est important de spécifier que le modèle WHAM-VI est basé sur l'hypothèse que l'affinité de
la MOD pour les cations dans les eaux naturelles peut être représentée par l'affinité respective
des acides humiques et fulviques, lesquels peuvent être isolés des eaux naturelles et étudiés en
laboratoire. "est de plus supposé que le rapport « matière organique dissoute: carbone
organique dissous» (MOD : COD) est de 2 : 1. En effet, puisqu'aucune technique permettant de
mesurer directement la concentration de substances humiques dans les eaux naturelles n'a été
développée, il est plus simple de mesurer le COD et d'extrapoler la quantité de MOD. Enfin,
puisqu'une telle estimation de la quantité de MOD inclut à la fois les substances humiques et
non humiques, l'utilisateur du modèle doit aussi estimer la proportion de la MOD qui
correspond aux acides fulviques et humiques menant à la complexation des métaux. Cette
,
proportion est souvent considérée comme un paramètre ajustable lors de l'ajustement du
modèle aux données mesurées. "en ressort que les choix, faits par l'utilisateur, quant à la
proportion de la MOD jouant un rôle dans la complexation des métaux (fraction dite « active»),
affectent considérablement (par 4 ordres de grandeur pour le Cu) les prédictions faites par la
suite (Kalis et al. 2006).
La sélection de la fraction active de la MOD est un défi de taille, et les études dans lesquelles ce
paramètre est réellement mesuré, plutôt que simplement estimé par ajustement des données,
sont rares. Par conséquent, des études qui mesurent à la fois la spéciation des métaux dans les
eaux naturelles et la qualité de la MOD doivent être menées à bien pour combler cette lacune
(Lofts and Tipping, 2011). De telles études permettront de mieux comprendre la qualité de la
MOD, et en principe permettront d'éliminer le besoin de faire des suppositions quant à la
proportion « active» de la MOD lors de l'utilisation des modèles. Elles permettront aussi de
diminuer les écarts entre la spéciation des métaux prédite et celle mesurée. Toutefois, il faut
9
--------
--
admettre que même si on réussit à éliminer les artefacts provoqués par l'utilisation de la MOD
isolée en laboratoire, il restera néanmoins des fluctuations temporelles et spatiales à l'échelle
du bassin versant qui induiront aussi une variabilité vis-à-vis de la complexation des métaux.
Ainsi, les modèles d'équilibres chimiques bénéficieraient de recherches dans les domaines
suivants: 1) l'obtention de données de haute qualité sur la complexation des métaux par la
matière organique sous une large gamme de conditions environnementales, 2) la mesure de la
spéciation des métaux dans les eaux naturelles, 3) la mesure de la qualité de la NOM naturelle
en ce qui a trait à son affinité pour les métaux et 4) la validation des modèles de complexation
avec des données de terrain (Tipping, 2002).
2
2.1
Hypothèse de recherche, objectifs et originalité
Hypothèse de recherche
D'après des études récentes (Fortin et aL, 2010; Guthrie et aL, 2005; Unsworth et aL, 2006), les
concentrations d'ions métalliques libres ([M z+]) mesurées pour le Cu, le Cd, le Ni et le Zn dans
les eaux naturelles divergent souvent de celles modélisées en utilisant le logiciel WHAM-VI. Sur
la base de ces résultats, nous avons fait l'hypothèse que l'échec du modèle à prédire la
spéciation est causé par des variations dans la qualité de la MOD dans les différents plans d'eau
étudiés, et que par conséquent l'incorporation de la qualité de la MOD dans le modèle de
spéciation à l'équilibre permettrait de mieux prédire la spéciation des métaux dans les eaux
naturelles.
2.2
Objectifs de recherche
L'objectif de ce projet de recherche est d'améliorer les modèles de spéciation à l'équilibre en
tenant en compte la variabilité spatiale de la qualité de la MOD. Un tel modèle pourrait ainsi
mieux prédire la spéciation, et, par conséquent la biodisponibilité du Cu, du Cd, du Ni et du Zn
dans l'environnement aquatique naturel. Pour atteindre cet objectif, il est important:
1- D'identifier une série de lacs représentant un large gradient de concentrations de
métaux, de MOD et de conditions physicochimiques.
10
2- De déterminer les qualités spectroscopiques de la MOD et de relier ces variations entre
les lacs aux caractéristiques de ces derniers et de leurs bassins versants.
3- De déterminer expérimentalement les concentrations de métal libre ([M z+]) dans des
lacs choisis et d'explorer les liens entre la concentration de métal libre et différents
facteurs environnementaux (concentration de métal total, pH, quantité et qualité de la
MOD).
4- De s'assurer que les conditions expérimentales s'approchent des conditions naturelles,
par exemple en titrant des échantillons de MOD provenant de lacs contrastés (quantité
et qualité de la MOD, spéciation des métaux) avec les métaux dont les concentrations
en ions libres prédites et mesurées divergent le plus et de comparer les constantes de
complexation conditionnelles observées avec les propriétés spectrales de la MOD.
5- D'améliorer les prédictions du modèle WHAM-VI vis-à-vis de la spéciation des métaux
dans les lacs choisis en incorporant la qualité de la MOD dans le modèle. Cette dernière
étape est effectuée en estimant la proportion de MOD active dans la complexation des
métaux (% d'acides fulviques actifs).
2.3
Originalité
À notre connaissance, cette étude est la première à utiliser l'analyse APC de matrices de
fluorescence comme une mesure de la qualité de la MOD, et à incorporer cette information
dans les modèles de spéciation chimique à l'équilibre pour les eaux douces naturelles. Il s'agit
de plus d'une des rares études à comparer les concentrations d'ions métalliques libres prédites
avec celles mesurées expérimentalement, et ce dans variété d'échantillons provenant de 18
lacs dans deux régions contrastées, dans lesquelles la qualité de la MOD et les concentrations
de Cu2+, Cd 2+, Ni 2+ et Zn2+ ont été mesurées. le projet contribue donc à une meilleure
connaissance de la biodisponibilité de métaux d'importance environnementale.
3
Méthodologie
les méthodes et le matériel employés pour l'échantillonnage et l'analyse des échantillons d'eau
étudiés sont décrits succinctement ci-dessous. Une description détaillée est donnée dans les
11
articles faisant partie de cette thèse (Mueller et aL, 2012a; Mueller et aL, 2012b; Mueller et aL,
2012c) et dans la Deuxième partie de la thèse.
3.1
ÉChantillonnage
Dix-huit lacs répartis dans les régions de Rouyn-Noranda et de Sudbury ont été échantillonnés à
l'été 2007 avec des dialyseurs (cellules à diffusion; voir la figure 3 de la Première partie, p.48).
Huit d'entre eux ont été échantillonnés à nouveau en 2008. La qualité de la MOD a été
caractérisée dans chaque lac (Mueller et aL, 2012b), puis la spéciation des métaux Cd, Cu, Ni et
Zn, mesurée et prédite, a été comparée pour les échantillons prélevés en 2008 (Mueller et aL,
2012c). Une paire de lacs témoins avec de faibles concentrations de métaux totaux (Lac
Opasatica dans la région de Rouyn-Noranda et Lac Geneva dans la région de Sudbury) ont été
échantillonnés en 2009 pour déterminer les constantes conditionnelles de complexation des
métaux Ni et Cu par la MOD à l'aide d'un titrage. Enfin, les lacs Vaudray, dans la région de
Rouyn-Noranda, et Geneva, dans la région de Sudbury, ont été échantillonnés en 2009 pour
vérifier l'effet du stockage de la MOD sur sa qualité spectroscopique et la spéciation des
métaux. Une description détaillée des lacs est donnée dans le tableau 1 (p.54).
3.2
Analyses
Le matériel de laboratoire et d'échantillonnage en plastique a été lavé à l'acide HN0 3 10% (v/v)~
pour au moins 24 h, rincé avec l'eau ultrapure (>18 Mohms·cm), et séché sous une hotte à flux
laminaire de classe 100. La verrerie a été lavée à l'acide HCI 2 N, puis rincée à l'eau ultrapure.
Les flacons de polystyrène pour l'analyse des anions ont été rincés à l'eau ultrapure
uniquement.
Des sous-échantillons ont été prélevés pour l'analyse des cations majeurs, des anions, des
concentrations totales de métaux, du carbone organique total (COT) ainsi que pour les mesures
d'absorbance et de fluorescence. La mesure d'absorbance a été utilisée pour calculer
l'absorbance spécifique à 254 nm (SUVA 254 ), tandis que les mesures de fluorescencè ont été
utilisées pour calculer l'indice de fluorescence (FI, le ratio entre l'intensité de l'émission de
12
fluorescence à 470 nm sur celle de 520 nm, pour une excitation à 370 nm) et, en utilisant
l'analyse de composantes principales (ACP ou PARAFAC pour paraI/el factor analysis), afin
d'identifier les fluorophores individuels qui contribuent à la fluorescence totale dans chaque lac
(Gondar et aL, 2008; Koster and Schmuckler, 1967; McKnight et aL, 2001). les caractéristiques
des bassins versants ont aussi été identifiées en utilisant des données hydrographiques et
topographique géospatiales intégrées dans le logiciel ArcGIS 10 (Environmental Systems
Research Institute, Inc., Redlands, CA).
les concentrations des ions Cd 2+, Ni 2+ et Zn2+ dans les lacs ont été mesurées par la technique
2
d'échange d'ions (lET), et la concentration de Cu + par électrode sélective {ISE). Enfin, les
concentrations d'ions métalliques libres dans chaque lac ont été calculées avec le logiciel
WHAM version 6.1.
les échantillons des lacs Opasatica et Geneva prélevés en 2009 ont été filtrés sur des
membranes de polycarbonate (membrane finale 0,2 ~m) et titrés avec le Cu et le Ni. Pour les
titrages avec le Cu, des quantités connues d'une solution mère de Cu (0,2 mM, Cu(N0 3h) ont
été ajoutées à l'échantillon, réparti en huit portions de 200 ml. le pH des solutions a été ajusté
à celui de l'eau naturelle du lac en utilisant de petits volumes de solutions concentrées de HN0 3
et de NaOH; les solutions étaient ensuite équilibrées pendant 12 h sous la hotte à flux laminaire
et à l'abri de lumière. la concentration de Cu 2+ libre a été ensuite mesurée par ISE. Une
approche similaire a été utilisée pour les titrages au Ni, avec l'échantillon réparti en sept
portions de 250 ml et des ajouts d'une solution de Ni(N0 3 h (1 mM), en laissant le pH
s'équilibrer pendant 12 h avant d'effectuer la mesure par la méthode lET. les mesures de Cu 2+
2
et Ni + ont été prises en triple sur un total de 27 portions pour le Cu et 24 pour le Ni. Après les
2
mesures de Cu + et Ni 2+, les sous-échantillons ont été analysés pour le Cu et le Ni total.
4
Résultats et Discussion
Cette section contient une brève discussion des résultats principaux de la thèse. Ces derniers
sont présentés en détail dans les articles de la Deuxième partie.
13
4.1
Bassins versants
La recherche a été effectuée dans des lacs de la région de Rouyn-Noranda, dans le Nord-est
québécois, ainsi que dans la région de Sudbury, dans le Nord ontarien. Les lacs ont été
échantillonnés durant l'été au cours des trois années d'études (voir la figure 4 et le tableau 1 de
la Première partie, pp. 53-54). Les caractéristiques des bassins versants des lacs diffèrent d'une
région à l'autre, et d'un lac à l'autre. En effet, les lacs de la région de Rouyn-Noranda ont un
temps de résidence plus court que les lacs de la région de Sudbury, et par conséquent ces
derniers sont moins influencés par les apports du bassin versant. Il résulte de ces différences
que la matière organique dissoute (MOD) provient davantage des lacs eux-mêmes dans la
région de Sudbury et davantage des bassins versants dans la région de Rouyn-Noranda.
4.2
Chimie des eaux lacustres
Les différentes caractéristiques des bassins versants, comme la géologie, l'hydrologie et le
couvert végétal, jouent un rôle important dans la variabilité régionale de la chimie des eaux de
surface. Les paramètres tels que le pH et la concentration des ions majeurs et des métaux sont
influencés, de même que la qualité et la quantité de la MOD (Sobek et al., 2007; Wetzel, 2001).
Néanmoins, le pH et les concentrations de cations majeurs n'ont varié que relativement peu
entre les deux régions d'échantillonnages (voir le tableau 2 de la Première partie, p.56). Les
lacs des deux régions ont un pH près de la neutralité et de faibles concentrations de Ca et Mg,
ce qui est typique des eaux douces du Bouclier canadren.
Les concentrations totales en Cd, Cu, Ni et Zn dissous varient largement entre les régions et
entre les lacs (voir le tableau 2 de la Première partie, p.56), ce qui témoigne du choix judicieux
des sites d'étude. Les plus grandes concentrations de Cd et de Zn se trouvent dans la région de
Rouyn-Noranda, de même que les plus grands écarts de concentrations entre lacs. Dans la
région de Sudbury, le Ni et le Cd montrent les plus grands gradients de concentrations, tandis
que les concentrations de Cu et de Zn sont moins variables. La variabilité inter-lacs dans les.
concentrations totales de métaux dans une région donnée est principalement due aux
14
gradients dans le dépôt atmosphérique des contaminants, ces dépôts étant influencés par les
vents dominants qui transportent les émissions des fonderies et raffineries de métaux
(Borgmann et aL, 1998; Dixit et aL, 2007). Certains lacs sont également influencés par des
sources locales de métaux, par exemple le drainage minier acide provenant de résidus miniers.
4.3
Matière organique dissoute (MOD)
les concentrations de MOD dans les lacs d'une région donnée sont influencées par la
végétation terrestre, les sols et l'hydrologie, lesquels sont en retour influencés par le climat et
la topographie. Néanmoins, à l'échelle locale, les propriétés du bassin versant et du lac luimême influencent les concentrations et la nature de la MOD (Sobek et aL, 2007). Pour les dixhuit lacs échantillonnés, la variabilité dans les concentrations de MOD est bien expliquée par les
caractéristiques des bassins versants (voir le tableau 3 de la Première partie, p.59). En général,
les lacs de Rouyn-Noranda affichent une plus grande concentration moyenne de MOD (7.2 ± 2.4
mg c·r
l
)
ainsi qu'une plus large gamme de concentrations (3.8 à 13.4 mg C'l-
l
)
que les lacs de
la région de Sudbury région (moyenne de 3.4 ± 1.1 mg c-r\ concentrations de 0.8 à 6.0 mg
C·l- l ). Nous formulons l'hypothèse que les lacs de la région de Rouyn-Noranda reçoivent de
grandes quantités de MOD de leurs bassins versants comparativement plus grands, alors que
les lacs de la région de Sudbury reçoivent moins de MOD, cette dernière étant alors produite
principalement dans le lac même (MOD autochtone).
Outre les différences dans la quantité de MOD, sa qualité, telle que mesurée par spectroscopie,
varie aussi grandement entre les lacs (voir le tableau 3 de la Première partie, p.59). les lacs de
l
la région de Rouyn-Noranda possèdent un indice SUVA 254 (8.3 ± 1.5 l·m- ·mg Cl) plus élevé et
l
un indice FI (1.32 ± 0.04) plus bas que ceux de la région de Sudbury (SUVA 254=5.3 ± 1.3 l·m- ·mg
Cl et FI=1.36 ± 0.08), témoignant d'apports accrus de MOD allochtone dans les lacs de la région
de Rouyn-Noranda. le rapport élevé entre l'aire des bassins versants et celle des lacs (BIL), la
grande proportion de couvert végétatif et l'épaisse couche de sols dans les bassins versants de
cette région font en sorte que la MOD y provienne davantage du bassin versant. Des études
précédentes ont par ailleurs trouvé une relation similaire entre les sources de MOD et la
15
fluorescence et l'absorption de cette dernière (McKnight et aL, 2001; Weishaar et aL, 2003). Par
exemple, Jaffé et al. (2008) ont trouvé des valeurs de SUVA faibles (0.5 à 5 l·m-l·mg Cl) et des
valeurs de FI élevées (1.4 to 1.75) pour des eaux de surface où il y avait une haute proportion de
matériel d'origine microbienne, associé à des sources autochtones. Dans d'autres eaux de
surface, ils ont noté des valeurs de SUVA élevées (1 à 7.5 l·m-l·mg Cl) et des valeurs de FI
faibles (1.1 à 1.5), indicatives de matière organique allochtone, provenant de sources
terrestres.
les matrices tridimensionnelles d'excitation-émission de fluorescence (MEE) contiennent des
données reflétant l'intégralité du spectre de fluorescence de la MOD. Un outil d'analyse
statistique multivariée, plus spécifiquement l'analyse de composantes principales (ACP), a été
utilisé pour identifier les quatre fluorophores distincts contribuant à la fluorescence totale de la
MOD dans les lacs, lesquels ont été attribués à différents groupements fonctionnels de la MOD
en s'appuyant sur des données acceptées dans la littérature (Coble, 1996; Cory and McKnight,
2005; Koster and Schmuckler, 1967). les composantes 1 et 2 sont attribuées aux fluorophores
typiques de la quinone réduite de la matière humique présente dans une grande variété
d'environnements aquatiques. la composante 3 est attribuée à un fluorophore typique de la
quinone oxydée et réfractaire de la matière humique d'origine allochtone, tandis que la
composante 4 est attribuée à un fluorophore typique des groupements tryptophanes des
protéines d'origine autochtone.
Selon l'analyse de la fluorescence de leur MOD, les lacs de la région de Rouyn-Noranda
possèdent une plus grande proportion de MOD d'origine allochtone que ceux de la région de
Sudbury, c'est-à-dire que ces derniers ont une plus grande proportion de MOD d'origine
autochtone. Puisque la MOD est un ligand important qui influe sur la spéciation des métaux
dans les eaux naturelles, nous formulons l'hypothèse que la spéciation des métaux dissous dans
ces systèmes est influencée par les différences dans les types de MOD.
16
4.4
Spéciation des métaux traces
Le terme « spéciation » réfère ici à la distribution des métaux entre leurs différentes formes
spécifiques. Parmi ces formes possibles, l'ion libre revêt un intérêt particulier pour les
écotoxicologues. La complexation de l'ion libre dans les eaux naturelles dépend des
concentrations de métaux et de ligand disponibles, qu'ils soient organiques ou inorganiques, de
même que de l'influence d'autres cations comme le proton et les cations majeurs qui
compétitionnent pour les sites de complexation des ligands (Stumm and Morgan, 1996). Pour
faciliter la comparaison entre les lacs, la proportion relative d'ions libres, plutôt que la
concentration en M Z+ seule, a été calculée en divisant cette dernière par la concentration totale
du métal dissous. Ainsi, dans la région de Rouyn-Noranda, le lac Opasatica possède le plus bas
z
pourcentage de métal libre (%M +) pour tous les métaux à l'exception de Cu, alors que le lac
Dasserat affiche le pourcentage le plus élevé. Dans la région de Sudbury, le lac Bethel possède
le plus petit %MZ+ pour tous les métaux, alors que le lac Raft montre les plus grands %M
z
+
pour
z
tous les métaux, à l'exception de Cu. En général, le %M + augmente lorsque la concentration de
métal total augmente et que le pH décroit, témoignant respectivement de l'importante du
rapport métal/ligand et de la compétition entre le proton et Mz+ pour des ligands.
4.5
Spéciation des métaux traces mesurée et prédite
Les modèles qui calculent la spéciation à l'équilibre des métaux, tels que le modèle WHAM,
sont très utiles pour prédire la concentration d'ions métalliques libres dans les systèmes
naturels qui contiennent de la MOD. Afin d'utiliser le modèle WHAM pour prédire la spéciation
des métaux dans les eaux naturelles, l'utilisateur doit estimer la proportion d'acides humiques
et fulviques présents dans l'eau. Cette procédure ne prend cependant pas en compte les
variations dans la qualité de la MOD provenant de différentes sources. Dans cette thèse, nous
avons d'abord prédit les concentrations d'ions métalliques libres pour Cd
2
+,
Cu
2
+,
Ni
2
+
et Zn
2
+
en
utilisant la proportion généralement suggérée de MOD active dans la complexation des métaux,
c'est-à-dire 65% (Bryan et al., 2002). Pour tous les lacs étudiés, WHAM VI prédit des
concentrations de Cd
2
+
similaires à celles mesurées (voir la figure 5 de la Première partie, p.64).
Les prédictions du modèle sont bonnes pour le Zn aussi, bien qu'à de faibles concentrations de
17
Zn total et libre, le modèle WHAM surestime ces dernières. Au contraire, dans le cas de Cu,
WHAM sous-estime l'ion libre dans la moitié des lacs, plus précisément pour ceux dans lesquels
les concentrations totales de Cu sont les plus faibles. De plus, WHAM prédit des concentrations
de Ni libre systématiquement plus élevées que celles mesurées, et ce pour tous les lacs dans
lesquels des quantités mesurables de Ni libre ont été trouvées.
Afin de prendre en compte la variabilité naturelle de la Mao entre les différents lacs, ainsi que
les effets de cette variation sur la spéciation des métaux traces, nous avons testé la sensibilité
du modèle WHAM vis-à-vis de la proportion de Mao active définie par l'utilisateur (%aFA). Un
intervalle arbitraire a été défini en diminuant de moitié (33 %) et en doublant (130 %) la
proportion de 65 % utilisée pour calculer les concentrations d'ions métalliques libres (voir la
figure 5 de la Première partie, p.64). Selon cette deuxième série de simulations, le modèle
WHAM prédit une proportion de métal libre similaire à celle mesurée pour la plupart des lacs
pour le Cd et le Zn. Toutefois, malgré la plus large gamme de %aFA employée, l'accord entre les
prédictions et les mesures fut satisfaisant seulement pour la moitié des lacs pour Cu et dans
aucun cas pour Ni. Nous avons ensuite calculé, pour chaque lac et pour chaque métal, la
proportion d'acide fulvique optimale (%aFAopt) qui permettait aux prédictions de WHAM et
aux mesures de concentrations d'ions métalliques libres de correspondre parfaitement. Bien
que l'augmentation ou la diminution de %aFA ait pour effet de modifier le nombre de sites
impliqués dans la complexation des métaux, cela n'affecte pas l'affinité des métaux pour les
sites. Pour le Cu, les valeurs de %aFA optimisés varient entre 7 et 90 % des concentrations de
Mao totales. Pour le Cd, le Zn et le Ni, les valeurs de %aFAopt sont toutes très élevées,
souvent au-dessus de la limite raisonnable de 100 % : entre 61 et 250 % pour le Cd, entre 65 et
410 % pour le Zn et entre 440 et 1900 % pour le Ni. Ces résultats suggèrent fortement que le
seul ajustement de la capacité de complexation des acides fulviques (c'est-à-dire le nombre de
sites) ne suffit pas, et qu'il faudra par conséquent ajuster aussi l'affinité de complexation
actuellement utilisée par WHAM pour le Cd, le Zn et particulièrement le Ni.
18
Enfin, nous avons comparé la proportion optimisée de la MOD active dans la complexation des
métaux aux variations inter-lacs de la qualité spectroscopique de la MOD. Une relation entre
les valeurs de %aFAopt calculées et la proportion relative de la composante de fluorescence
humique (Cl) est observée pour le Cu et le Ni; de même, une relation se dessine entre les
valeurs de %aFAopt et la composante de fluorescence allochtone (C3) pour le Cd et le Zn (voir la
figure 6 de la Première partie, p.65). De plus, la direction des relations entre les rapports Cl/CT
ou C3/CT et les %aFAopt pour le Cd, le Cu et le Zn diffère nettement de la direction de ces
relations pour le Ni. Ces tendances distinctes suggèrent que les sites de complexation de la
MOD pour le Cd, le Cu et le Zn sont différents de ceux pour le Ni.
Les relations entre les valeurs de %FAopt calculées et la proportion relative des composantes
de fluorescence Cl et C3 ont été utilisées pour calculer à nouveau la concentration des ions
métalliques libres pour le Cd, le Cu, le Ni et le Zn dans les lacs d'étude en utilisant WHAM VI.
Cette approche a permis d'améliorer les performances du modèle WHAM VI, en particulier
quant aux prédictions des concentrations de Ni libre (une amélioration jusqu'à un facteur de 6
pour le Ni; voir la figure 7 de la Première partie, p.67). Ces résultats appuient notre hypothèse
selon laquelle la qualité spectroscopique de la MOD pourra être utilisée pour prédire la
proportion de MOD active dans la complexation des métaux.
4.6
Paramètres de complexation mesurés
Les raisons suivantes peuvent expliquer les différences entre les concentrations d'ions
métalliques libres mesurées et celles prédites par le modèle WHAM : 1) la sous-estimation de la
concentration des sites de complexation de métaux par unité de carbone organique dissous, et
2) la sous-estimation de l'affinité de ces sites pour les métaux d'intérêt. La première explication
a été explorée ci-haut en calculant les valeurs de %FAopt pour chaque métal et pour chaque
lac. Toutefois pour le Cd, le Cu, le Zn et le Ni, les valeurs de %FAopt étaient supérieures à la
valeur maximale raisonnable de 100 %. Cela nous amène à supposer que c'est la sousestimation de l'affinité des sites complexation qu'il nous fait maintenant explorer. En effet, les
constantes de complexation prises en compte par WHAM sont obtenues depuis des titrages en
19
laboratoire en utilisant des isolats d'acides humiques et fulviques. De plus, de telles
expériences sont souvent réalisées en utilisant des rapports métaux: MOD beaucoup plus
élevés que ceux normalement observés dans les eaux naturelles.
Nous avons mesuré la complexation de Ni et Cu par la MOD par l'entremise de titrages pour
deux lacs où les concentrations naturelles de métaux totaux sont faibles et pour lesquels la
qualité spectroscopique de la MOD est différente. Ensuite, nous avons comparé les courbes de
titrage obtenues à celles prédites par WHAM VI en utilisant les paramètres suggérés par le
fabricant du logiciel, en fixant à 65 % la proportion de la MOD active dans la complexation des
métaux, et en supposant que toute cette fraction active pourrait être représentée par les acides
fulviques (voir la figure 8 de la Première partie, p.68). Les résultats montrent que, d'une part,
la MOD présente dans le lac Geneva complexe plus de Ni par unité de carbone que celle du lac
Opasatica, et que, d'autre part, elle complexe moins de Cu par unité de carbone. Nous nous
sommes ensuite appuyés sur ces résultats pour calculer des constantes conditionnelles pour la
complexation de Ni et de Cu par la MOD des lacs Opasatica et Geneva.
Considérons d'abord la complexation du Ni. Son affinité pour les sites faibles de la MOD du lac
Opasatica (Log Kl,Ni=6.98) est significativement plus élevée (P=0.009) que pour la MOD du lac
Geneva (Log Kl ,Ni=6.30), malgré le fait que les sites faibles dans le lac Opasatica (L l ,Ni=34.2
nmole·mg Cl) soient présents à des concentrations quatre fois plus basses (P=0.01) que dans le
lac Geneva (Ll ,Ni=137 nmole·mg Cl). L'affinité du Ni pour les sites forts dans la MOD n'est pas
significativement différente (Log K2,Ni=11.6 et 11.07 pour le lac Opasatica et Geneva,
respectivement), encore une fois malgré le fait qu'il y ait environ six fois moins (P=0.025) de
sites forts par unité de carbone dans le lac Opasatica (L 2,Ni=0.70 nmole·mg Cl) que dans le lac
Geneva (L 2,Ni=3.98 nmole·mg Cl).
Malgré que l'affinité de complexation de Cu pour les sites faibles de la MOD ne soit pas
significativement différente entre les deux lacs (Log Kl ,cu=7.59 pour le lac Opasatica et Log
Kl ,cu=7.63 pour le lac Geneva), le nombre de sites faibles par unité de carbone est environ trois
20
fois plus élevé (P<O.OOl) dans le lac Opasatica (Ll ,cu=244 nmole·mg Cl) que dans le lac Geneva
(Ll ,cu=75.6 nmole·mg Cl). Les sites forts pour la complexation du Cu, quant à eux, ont une
affinité (Log K2,cu=9.46) significativement plus forte (P=O.014) et sont deux fois plus nombreux
(P=O.003) dans la MOD du lac Opasatica que dans la MOD du lac Geneva (Log K2,cu=8.90 et
L2,cu=27.0 nmole·mg Cl). Ces résultats suggèrent fortement que la MOD diffère entre les lacs
Opasatica et Geneva en ce qui a trait à son affinité pour le Ni et le Cu.
Les mesures d'absorbance et de fluorescence suggèrent que la MOD du lac Opasatica possède
les caractéristiques d'une MOD provenant de sources allochtones, alors que celle du lac Geneva
affiche une signature d'origine autochtone. Il est par conséquent raisonnable de conjecturer
que les différentes affinités de complexation pour Cu et Ni trouvées pour la MOD de ces deux
lacs sont reliées aux différentes proportions de MOD allochtones et autochtones. La MOD de
type aromatique provenant de sources allochtones possède une affinité de complexation
significativement plus grande pour Cu - aux sites forts, K2 - que la MOD de type protéique
provenant de sources autochtones. Au contraire, une tendance opposée est observée pour Ni.
En effet, l'affinité de Ni pour les sites faibles Ki est significativement moindre pour le lac
possédant la MOD plus aromatique de type humique provenant de source allochtone. La
capacité de complexation des deux types de sites pour le Ni était significativement plus faible
pour le lac possédant la MOD aromatique de type humique provenant de source allochtone.
De nouveau, ces résultats suggèrent que le Ni et le Cu se lient à des types de sites de
complexation différents.
À notre connaissance, ces résultats sont les premiers à indiquer la présence de sites spécifiques
pour certains métaux dans la structure complexe de la MOD. Ces résultats indiquent
l'importance de spécifier la qualité de la MOD afin de mieux prédire la complexation des
métaux par la MOD dans les systèmes naturels.
21
5
Conclusions
L'objectif principal de cette thèse était de démontrer que les calculs de la spéciation des
métaux dans les eaux naturelles puissent être améliorés en incorporant des indices de la qualité
de la MOD dans les modèles de spéciation à l'équilibre existants: nous avons formulé
l'hypothèse que la qualité spectroscopique de la MOD peut être utilisée à cette fin.
Fort des résultats d'études précédentes montrant les différences entre les concentrations
d'ions libres mesurées et celles prédites par le code WHAM dans les lacs (Fortin et al., 2010;
Unsworth et al., 2006), nous avons caractérisé la spéciation des métaux dans une série des lacs
situés dans deux régions différentes du Bouclier canadien.
Malgré un accord raisonnable entre les concentrations de métaux libres prédites par WHAM
quant aux concentrations libres de Cd 2+ et Zn2+ pour la majorité des lacs, des différences
notables ont été observées pour le Cu 2+ et le Ni 2+. En particulier, des différences marquées ont
été observées pour le Cu 2+ lors que les concentrations de Cu 2+ libre étaient sous 0.4 nM (c'est-àdire pour des concentrations totales de Cu sous 40 nM). Or, de telles concentrations sont
fréquentes dans les eaux naturelles. Dans le cas de Ni, WHAM surestime systématiquement les
concentrations de Ni 2+ dans chaque lac.
Les raisons possibles pour les différences entre la spéciation mesurée et celle prédite par
WHAM ont récemment été résumées par Lofts et Tipping (2011). Ces derniers ont noté que les
facteurs suivants peuvent influencer les prédictions de WHAM : 1) la précision des intrants du
modèle ainsi que celle de ces paramètres, 2) l'incertitude sur les mesures de concentrations
d'ions métalliques libres et 3) les différences entre les propriétés complexantes des substances
humiques présentes dans les eaux naturelles et celles des isolats humiques et fulviques utilisés
pour calibrer le modèle. C'est précisément cette dernière explication qui a été explorée durant
cette thèse, en caractérisant la MOD et la spéciation des métaux dans les lacs d'étude.
22
Nous nous sommes intéressés à une méthode de caractérisation de la MOD qui, en principe,
pourrait être utilisée par des non-spécialistes. Nous avons donc choisi des techniques
spectroscopiques simples, lesquelles sont disponibles dans la plupart des laboratoires de chimie
de l'eau, par opposition aux techniques fastidieuses utilisées pour isoler les substances
humiques. Premièrement, nous avons mis en évidence des différences dans la composition de
la MOD dans un ensemble de petits lacs du Bouclier canadien en utilisant différents paramètres
optiques tels que l'absorbance spécifique UV (SUVA 254 L les indices de fluorescence (FI) et les
matrices d'excitation-émission de fluorescence (MEE) interprétées par l'analyse de
composantes principales (ACP). Nous avons ensuite exploré comment les propriétés optiques
de la MOD peuvent être utilisées pour améliorer la prédiction, par les modèles d'équilibres
chimiques, de la spéciation des métaux dans les eaux naturelles.
Nous avons fait l'hypothèse que la mesure spectroscopique de la qualité de la MOD peut être
utilisée pour estimer la proportion de MOD active dans la complexation des métaux. Comme
mentionné précédemment, l'utilisateur de WHAM doit d'abord estimer la proportion du
carbone organique dissous présent dans l'échantillon naturel considéré (mesuré en mg
c·r 1 )
qui est composé de substances humiques, ainsi que la répartition de ces substances humiques
entre acides fulviques et acides humiques. La pratique courante consiste à présumer un
rapport MOD : COD de 2 (Thurman, 1985) et à supposer que 65 % de cette matière organique
dissoute possède des propriétés de complexation des cations similaires à celles des isolats
d'acides fulviques (Bryan et al., 2002). En utilisant ces principes suggérés par les créateurs de
WHAM, nous avons trouvé de grandes différences entre les concentrations de Cu 2+ et de Ni 2+
mesurées et celles prédites par le modèle.
Nous avons travaillé à améliorer les prédictions du modèle WHAM en calculant le pourcentage
optimal d'acide fulvique impliqué dans la complexation des métaux (%FAopt) qui permet de
reproduire précisément les mesures d'ion libre pour chaque métal, et dans chaque lac. Les
relations entre la valeur de %FAopt et les propriétés spectroscopiques de la MOD ont été
explorées. Des relations significatives ont été trouvées entre la composante de fluorescence de
23
type humique (Cl) et le %FAopt pour le Cu et le Ni, et entre la composante d'origine allochtone
(C3) et le %FAopt pour le Cd et le Zn (voir la figure 6 de la Première partie, p.65).
Ainsi, nous avons amélioré les capacités de WHAM à prédire la spéciation de Cu
2
+
et Ni
2
+
libres
dans les lacs en utilisant les mesures de fluorescence pour estimer la proportion de MOD active
dans la complexation des métaux. La plus grande amélioration a été observée quant aux
prédictions de Ni 2+ libre (jusqu'à un facteur de 6), suggérant que la signature de fluorescence de
la MOD peut être utilisée comme indice de son activité de complexation dans les modèles
d'équilibres chimiques, tels que WHAM.
En fait, il serait utile de systématiquement déterminer: i) la matrice d'excitation-émission pour
une eau lacustre donnée, ii) de calculer la proportion relative de la composante de fluorescence
Cl/CT ou C3/CT, et iii) d'utiliser cette proportion pour estimer %FA dans le lac en se basent sur
une relation telle que celle illustrée dans la figure 6 de la Première partie (p.65). Bien que cette
approche soit prometteuse, elle devrait idéalement être vérifiée sur une plus large gamme
d'échantillons de MOD dans différents types d'eau naturelle.
Afin de permettre au modèle WHAM d'estimer encore plus précisément la spéciation de Cu et
de Ni, des ajustements non pas seulement à sa capacité de complexation mais aussi à son
affinité pour ces métaux seront éventuellement nécessaires. En effet, nous avons trouvé que
les différences dans la capacité de complexation de la MOD, c'est-à-dire dans le nombre de
sites, pour Cu et Ni pouvaient être expliquées par les qualités spectroscopiques de la MOD.
Dans un petit nombre de lacs (N=2) nous avons aussi trouvé que les variations entre lacs et
entre métaux de l'affinité de complexation de la MOD pour Ni et Cu pouvant également être
expliquée par la qualité spectroscopique de la MOD. Puisque le nombre de lacs utilisé pour
cette dernière observation est petit, la relation tirée est conjecturale et devrait être étudiée
dans une plus grande variété d'eaux de surface. Néanmoins, ces résultats confortent notre
hypothèse à l'effet que la qualité de la MOD, telle qu'exprimée par la signature fluorescente de
24
cette dernière, soit un paramètre clé qui permettra d'estimer le rôle de la MOD dans la
spéciation des métaux traces dans les eaux naturelles.
l'amélioration des modèles d'équilibres chimiques permet de mieux prédire la spéciation des
métaux traces dans les écosystèmes aquatiques. la justesse de ces précisions est essentielle
pour pouvoir estimer la biodisponibilitédes métaux et, par conséquent, leur toxicité envers les
organismes aquatiques. le modèle du ligand biotique (BlM) est couramment utilisé par les
scientifiques et les législateurs pour prédire la toxicité des métaux envers les organismes
aquatiques. Ce modèle utilise WHAM pour calculer la concentration d'ion libre aqueuse à
proximité du ligand biotique considéré. Bien que le BlM fonctionne raisonnablement bien
lorsqu'il considère des concentrations de métaux élevées et leur effets toxiques aigues, il doit
être amélioré pour mieux prédire la toxicité des métaux à faible concentrations de ces derniers,
c'est-à-dire lorsqu'ils sont susceptibles d'engendrer des effets toxique chroniques (Cé3mpbell et
al., 2006). Augmenter la justesse des prédictions du modèle WHAM lorsqu'il considère de
faibles concentrations de métaux permettrait directement de mieux prédire la toxicité
chronique des métaux. la portée de cette recherche est donc basée sur le fait qu'une
amélioration des prédictions de la spéciation de métaux traces dans l'environnement aquatique
est nécessaire afin de mieux estimer et gérer le risque écologique posé par les métaux traces
(luoma and Rainbow, 2008).
25
26
Première partie: Synthèse
27
28
1
1.1
Introduction
Metals of interest
Many substances present in our natural environment may be considered as prablematic with
respect to human or ecosystem health. The Canadian Environmental Protection Act defines a
toxic substance as one that "entering or may enter the environment in ~ quantity or a
concentration or under conditions that: a) have or may have an immediate or long-term
harmful effect on the environment or its biological diversity, or b) constitute or may constitute
a danger to the environ ment on which life depends, or c) constitute or may constitute a danger
in Canada to human life or health". Enviranment Canada and Health Canada published a
Priority Substances List Assessment Report in 2001 (CEPA, 2001) within which Cu, Cd, Ni and Zn
released fram copper and zinc smelters and refineries to the atmospheric, terrestrial and
aquatic environments were identified as priority substances, as defined by CEPA, the potential
toxicity of which should be determined. What was not clear fram this assessment was the
chemical speciation of the metals released and their fate in the eventual receiving water,
therefore limiting the regulators' ability to estimate their bioavailability and toxicity (Hornbrook
et al., 1986).
1.2
Sites of interest
Areas in Canada that are greatly affected by the presence of copper smelting and refining
include the Rouyn-Noranda region of Québec and the Sudbury region of Ontario. RouynNoranda, Québec, is home to the Xstrata Copper-Horne smelter, which has been in operation
since 1927, and lakes in the area have been contaminated by atmospheric de position of smelter
emissions (Couillard et al., 2004; Gallon et al., 2006). The Xstrata Copper-Horne smelter is the
highest producing copper smelter in Canada (190000 tonnes in 2001) and releases large
amounts of Cu, Zn and Cd into the atmosphere (42, 18 and 2.5 tonnes in 2001) (Bonham-Carter,
2005). Due to the changes in smelter operations and legislated emission controls, the
atmospheric emissions of Cd and Zn from the Horne smelter have decreased significantly (75%
and 90%, respectively) in recent history (1989-2001), although the emission of Cu has varied
considerably (Bonham-Carter, 2005). Furthermore, the concentrations of metals found in the
29
surrounding area depend on the direction of the prevailing winds and decrease with increasing
distance from the smelter (Bonham-Carter et aL, 2006; Telmer et aL, 2006). There have been
many studies of the total dissolved concentrations of metals in the lakes in the surrounding
area of Rouyn-Noranda (e.g., Borgmann et al. (2004), Croteau et al. (2002), Guthrie et al.
(2005)). In recent years, the concentration of total dissolved Cu in area lakes has been shown
to vary from 33 nM (Giguère et aL, 2006) to 608 nM (Fortin et aL, 2010). The concentration Cd
varies from 0.06 nM to 6.8 nM (Fortin et aL, 2010). The widest concentration range is that of
Zn from 7 nM (Bonneris et aL, 2005) to 3700 nM (Fortin et aL, 2010). Conversely, Ni varied the
least in the region with concentrations ranging from 8 to 12 nM (Giguère et aL, 2006). It should
also be noted that certain lakes in the Rouyn-Noranda area are affected by direct run-off from
past or present mining activities in addition to atmospheric de position (Goulet and Couillard,
2009).
Sudbury, Ontario, also has a long history of mining, smelting and refinery activities dating back
to 1929. There are currently two nickeljcopper smelters (Vale-Copper Cliff and Xstrata NickelSudbury) and one copper refinery (Vale-Copper Cliff) operating in the area (CEPA 2001). The
Vale-Copper Cliff smelter was reported to release 132, 9.5, 87, and 2.4 tonnesfyear of Cu, Zn, Ni
and Cd, respectively to the atmosphere in 1995 (CEPA 2001). The Xstrata Nickel-Sudbury
smelter was reported to release 9.0, 1.9, 10.2, and 4.5 tonnesfyear of Cu, Zn, Ni and Cd,
respectively to the atmosphere in 1995 (CEPA 2001). Similar to the Rouyn-Noranda lakes, the
Sudbury lakes are greatly affected by the mining and smelting activities in the area (Carignan
and Nriagu, 1985; Nriagu et aL, 1982). Moreover, the metal concentrations in lakes
surrounding the smelters decrease with increasing distance from the smelters (Keller et aL,
1999; Pyle et aL, 2005; Shuhaimi-Othman et aL, 2006). Similar to the Rouyn-Noranda region
mentioned above, a substantial decrease in lake water metal concentrations, especially Cu and
Ni, over the past 30 years has been observed following reductions in smelter emissions (Keller
et aL, 1999; Tropea et aL, 2010). Nriagu et al. (1998) found that the concentrations of total
dissolved metal in lakes in the region varied from 27-4600 nM for Cu, 0.03-11 nM for Cd, 871100 nM for Ni and 3-1300 nM for Zn.
30
Both the Rouyn-Noranda and Sudbury regions are found on the Canadian Precambrian Shield
characterized by bedrock made up of quartzite, gabbro and felsic gneisses (Carignan and
Nriagu, 1985). However, the superficial deposits covering this underlying geology vary among
regions. In the Rouyn-Noranda region, located in the Abitibi sub-province and containing the
Abitibi Greenstone Belt, the bedrock is covered with a thick layer of glacial deposits, although
clusters of bedrock do break through to the surface occasionally (Veillette et aL, 2005). In
contrast, the Sudbury region, located on three structural provinces (Grenville, Southern and
Superior) of the Shield (Rousell et aL, 2002), is covered bya thin «1 m) and discontinuous layer
of glacial deposits (Barnett and Bajc, 2002). These differences in surficial geology contribute to
differences in the sensitivity of the two regions to acid precipitation (Jeffries et al. 2003), and
they also affect the movement of organic matter from the terrestrial to the aquatic
environ ment (see section 1.4). The differences in surficial geology also contribute to
differences in the topography and thus, presumably, to the differences in vegetation, soils and
hydrology of the watersheds within these two regions (Sobek et aL, 2007).
1.3
Freshwater trace metal speciation
The toxicity of the metals to organisms in the aquatic environment depends on their
bioavailability and in turn, the bioavailability of metals in aquatic systems depends on their
chemical speciation (Allen and Hansen, 1996; Batley et aL, 2004; Janssen et aL, 2003). In
particular, it is the free metal ion that best predicts the bioavailability of a metal to aquatic
organisms (Campbell, 1995). The free metal ion may form complexes with many ligands
present in natural freshwater system. For a given complexation reaction between a metal (M)
and a ligand (L) at equilibrium,
M+L~ML
(1)
K - [ML]
- [MIL]
(2)
the equilibrium constant K,
is a measure of the stability of the formed complex (charges are omitted for brevity). In
freshwater systems, simple inorganic ligands, such as OH-, HC0 3-, CO/- are present at significant
31
2
2
concentrations and can form complexes with metals ions such as Cu 2+, Cd +, Ni + and Zn2+
(Stumm and Morgan, 1996). Simple monomeric organic ligands such as citrate, glycine or
polycarboxylates have high affinities for many metal ions, but due to their microbiallability,
these ligands are not normally present in natural freshwater systems at high enough
concentrations to influence the speciation of most metal ions (Morel and Hering, 1993). The
speciatiol1 of metal ions in simple aquatic systems (i.e. inorganic and simple organic ligands) can
be successfully modeled using computer speciation codes (Turner, 1995).
The speciation of metals in natural waters depends on their affinity for the available ligands,
and on the concentrations of these ligands. Equilibrium calculations for the four metals of
interest in this thesis (Cd, Cu, Ni and Zn) suggest that the most abundant inorganic metal
species in natural freshwater are: CuC0 30 and Cu(OHho; Cd 2+ and CdC0 30; Ni 2+ and NiC030; Zn2+
and ZnC0 3 0 (Stumm and Morgan, 1996). The carbonate ligand is very important in natural
freshwater due to its presence at relatively high concentrations and its affinity for many
diva lent metals. However, the availability of the carbonate ligand will de pend on the pH of the
system (Stumm and Morgan, 1996). Alkalinity and pH, therefore, play significant roles in the
speciation of many metals in natural aquatic systems.
....---- ,
......
,
'\
\
-..._--- .."l
'
-------------"
M( OH-, HC0 CÛ32-)
@
,,-----------------------,
3-,
Figure 1.
/
Schematic representation of the interconnectedness between water chemistry and
bioavailability / Représentation schématique de la relation entre la chimie des eaux
naturelles et la biodisponibilité des métaux.
32
The complexation of dissolved metals with natural dissolved organic matter (DOM) is less easily
quantified than is their binding to the inorganic ligands mentioned above. The heterogeneous,
multi-dentate and poly-electrolyte properties of DOM make it impossible to determine specifie
metal binding constants for DOM (Tipping, 2002). Binding characteristics of metal ions to DOM
have, however, been estimated in the past by titrating chemically isolated organic matter
fractions with metals under specifie conditions of pH, ionic strength and concentration of
competing ions (see, for example, Cabaniss and Shuman (1988), Ephraim et al. (1989), Gondar
et al. (2006), Higgo et al. (1993), Saar and Weber (1980) ). However, these conditional binding
constants define the complexation under very specifie conditions and theoretically cannot be
applied to other scenarios. Tipping (2002) summarised the effect of many variables on the
binding of metal ions to DOM. First, the binding of metal ions to DOM depends on the
concentration of the free metal ion, [M z+] (which depends on the total metal concentration)
and the affinity of the DOM binding sites for the metal in question. The binding of metal
cations to DOM will also de pend on the pH of the solution. As the pH increases, the
competition between the metal cations and protons for binding sites decreases (e.g. Benedetti
et al. (1995)). Depending on their relative concentrations and binding strengths, other cations
in the solution can also compete with metal cations for DOM binding sites (e.g. Cao et al.
(2006)). Finally, the effect of ionic strength on the binding of cations to DOM is explained by
the shielding of electrostatic attractions by other ions in solution (Tipping, 2002). Chemical
equilibrium models that take into account the binding of metal cations to DOM have been
developed and will be discussed in Section 1.5.
1.3.1
Measuring freshwater trace metal speciation
According to the International Union of Pure and Applied Chemistry (lUPAC), a chemical species
is defined as "the specifie form of an element defined as to isotopie composition, electronic or
oxidation state, and/or complex or molecular structure" and therefore, the distribution of such
chemical species in a system is referred to as "speciation" (Templeton et al., 2000). The
measurement of one or more chemical species in a natural freshwater sample is, however, not
routine. Depending on the chemistry of the sample, reversible changes in the coordinate
33
bonding or oxidation state of the element of interest may occur. Furthermore, the very act of
measuring the chemical speciation in a sample may perturb the equilibrium state initially
present. Despite these difficulties, analytical methods to measure the speciation in natural
waters have largely improved in recent decades. The review by Batley et al. (2004) gives a
detailed description of the recent improvements in the detection limits, sensitivities and
selectivities of methods, such as those using ion-selective electrodes (ISE), anodic stripping
voltammetry (ASV), cathodic stripping voltammetry (CSV), diffusive gradients in thin films (DGT)
and permeation liquid membranes (PLM).
1.4
Dissolved organic matter (DOM)
The ubiquitous and persistent nature of organic matter in both the terrestrial and aquatic
environments is responsible for its key role in many biogeochemical processes. In aquatic
systems, an arbitrary separation of dissolved organic matter (DOM) from particulate organic
matter (POM) is achieved by filtering natural water samples through 0.45 ~m filters(Wetzel,
2001). Important aquatic biogeochemical processes affected by the presence of DOM include,
carbon cycling (Schiff et aL, 1990), light penetration into surface waters (Scully and Lean, 1994),
photochemical processes (Molot and Dillon, 1997), the acidification of surface waters (Schindler
and Curtis, 1997), nutrient cycling (Dillon and Molot, 1997) and metal speciation and transport
(Buffle, 1988; Perdue and Ritchie, 2003; Wood et aL, 2011).
The foremost characteristic of DOM leading to its many important roles in the environment is
its complex heterogeneity. In order for DOM to be involved in such a vast range of ecologically
important biogeochemical processes, a heterogeneous nature is necessary (MacCarthy and
Rice, 1991). To this end, DOM is defined as a complex mixture of disordered, poly-electrolytic
molecules, generated from plant, microbial, and animal products at various stages of
decomposition (Aiken et aL, 1985; Thurman, 1985; Wetzel, 2001). The presence of a wide
variety of functional groups that, together, express average characteristics, permits the
ecological functions which rely on the presence of DOM. The importance of the nature of
organic matter is weil described by MacCarthy and Rice (1991): tiAn ecologically based
rationale for the chemically heterogeneous nature of humus: in the case of humus the mixture
34
is the message". In order to persist in aquatic systems, DOM must also be resistant to rapid
degradation (MacCarthy and Rice, 1991; Wetzel, 2001). The complex heterogeneous nature of
DOM is in part due to disorderly decomposition processes as opposed to the ordered synthesis
of specifie biological compounds (MacCarthy and Rice, 1991; Thurman, 1985).
Variability in sources, transport and degradation pathways also contribute to the
heterogeneous nature of DOM. In most lakes, DOM originates predominantly from
allochthonous sources outside the aquatic system, Le. from soil or terrestrial plant sources
(Thurman, 1985; Wetzel, 2001). Allochthonous DOM may consist of a mixture of old soil
organic matter that has been degraded and humified over a long period of time and relatively
young organic matter that has recently decomposed from plant or soil organic matter
(Thurman, 1985). Autochthonous sources of DOM from within the lake have been described as
resulting from the active secretion, decomposition and Iysis of macrophytes, algae and
cyanobacteria in littoral and open water (Thurman, 1985; Wetzel, 2001). The composition of
DOM in a given lake will reflect the relative contributions of its allochthonous and
autochthonous sources, as weil as the processing of the DOM occurring along its path from the
watershed to the lake and within the lake itself (such as adsorption to soil particles
(Schumacher et aL, 2006) and photo- and bio-degradation (Kahler et aL, 2002)). Watershed
characteristics su ch as geomorphology, hydrologie regime, land cover, upstream waterbodies,
and land use are likely to influence the composition or nature of DOM (Jaffé et aL, 2008;
Mattsson et aL, 2005). Seasonal variability will also play a role in the relative importance of
each DOM source. For example, stream and runoff flow as weil as growth and decay cycles of
terrestrial and aquatic vegetation vary greatly with seasonal temperature changes (Wetzel,
2001).
Attempts to characterize DOM often involve its separation into isolated fractions using a
method developed by Thurman and Malcolm (1981). First, DOM is separated into humic and
non-humic substances. Non-humic substances include, among other low molecular weight
organic substances, carbohydrates, proteins, peptides and amino acids, which are relatively
35
easily utilised or degraded by microorganisms and, therefore, are present at relatively low
concentrations in aquatic systems (Ai ken and Wershaw, 1985; Thurman, 1985). Humic
substances, on the other hand, are coloured, high molecular weight organic substances of
biogenic origin that are resistant to biological degradation. Humic substances are thought to
make up 70 - 80% of the DOM in natural waters (Thurman, 1985). Humic substances can be
further separated into humic acids, fulvic acids or humin based on their acid-base solubility
(Thurman and Malcolm, 1981). Humic acids are soluble only at a pH greater than 2, whereas
fulvic acids are soluble at any pH. Humin is insoluble in aqueous solutions at any pH. There has
been some debate over the wide pH range used in the isolation of fulvic and humic acids from
DOM and there is still no consensus as to whether or not isolation procedures lead to any major
chemical decomposition of the DOM (Abbt-Braun et al., 2004). Fulvie acids are thought to be
more water soluble because of their higher proportion of carboxylic and hydroxyl functional
groups and their smaller molecular size, relative to humic acids, which are generally larger and
have a higher phenolic content (Thurman, 1985). The greater aqueous solubility of fulvic acids
leads to their presence in greater proportions than humic acid in natural waters. Despite many
efforts, a precise description of the structure of DOM has not been achieved (Abbt-Braun et al.,
2004; Sutton and Sposito, 2005).
The model of DOM structure outlined by Schulten and Schnitzer (1997) illustrates its
heterogeneous nature (Figure 2). These authors point out the usefulness of such models for
the investigation of the structure and function of aqueous humic material under environmental
conditions. Sutton and Sposito (2005), however, describe a supramolecular view of the humic
substances. They describe humic substances as mixture of diverse, low molecular mass
components held together by hydrogen bonds and hydrophobie interactions, ultimately
forming micellar structures. A similar description of humic substances was first advanced by
Khan and Schnitzer (1971). The importance of describing the character of humic substances by
molecular interactions is emphasised over molecular components. Sutton and Sposito,
therefore, raise the need to re-evaluate the biogeochemical formation and pathways of humic
substances in the natural environment.
36
Figure 2.
Representation of a proposed molecular structure for humic substances (fram Schulten and
Schnitzer 1997) 2 / Structure moléculaire probable de la substance humique (tiré de
Schulten and Schnitzer, 1997).
ln order to better understand the role of DOM in the aquatic environment, including its role in
trace metal speciation and transport, the quality of DOM is often estimated. The term quality,
here, is used to describe the type of DOM based on its composition and source. Abbt-Braun et
al. (2004) present a useful review of quantitative and qualitative methods used to characterize
DOM. Spectroscopie methods of characterisation include UV-visible, Infrared (IR), nuclear
magnetic resonance (NMR) as weil as absorbance and fluorescence spectroscopy. Optical
properties of DOM have been used to distinguish among different types of DOM from
contrasting environments, including absorbance (Gondar et aL, 2008; Weishaar et aL, 2003)
and fluorescence (Baker, 2002; Coble, 1996; McKnight et aL, 2001). For example, a recent
Note that this structure does not account for the trace amounts of sulfur that normally are found in isolated
humic substances.
37
review by AI-Rea si et al. (2011) discusses the use of various spectroscopic properties of natural
organic matter (NOM) to distinguish between different sources of NOM with different
protective effects against metal toxicity towards aquatic organisms. Absorbance and
fluorescence measurements are useful tools in that they are relatively simple, rapid, sensitive
and non-destructive. In general, DOM fluorescence has been characterized as either humic-like
or protein-like and has been attributed to either allochthonous or autochthonous sources.
More recently, Cory and McKnight (2005) have used fluorescence spectroscopy to identify
oxidised and reduced quinone-like fluorophores in DOM from a variety of aquatic
environments. Fluorescence spectroscopy may therefore be used to further characterize DOM
based on its redox state.
Fluorescence excitation-emission matrices (EEMs) have become a popular method of DOM
fluorescence measurement in recent years due to the great amount of information obtained
from a three-dimensional picture of DOM fluorescence. EEMs have been used to discriminate
among different types of DOM from a wide range of environments such as river water (Baker,
2002), marine and estuarine environments (Coble, 1996) and Antarctic lakes and ponds
(McKnight et aL, 2001). Although the three-dimensional scans offer more information than
traditional single wavelength scans, these methods usually involve the visual inspection of the
spectra and identification of zone of fluorescence representative of different types of DOM.
More recently, multivariate data analyses, such as parallel factor analysis (PARAFAC) methods,
have been applied to EEM spectra to facilitate the extraction of useful data from the complex
spectra (Andersen and Bro, 2003; Stedmon et aL, 2003). When PARAFAC analysis is used in
conjunction with fluorescence EEMs, individual fluorescence components may be identified
along with their relative contribution to the overall fluorescence, allowing for a more refined
characterisation of DOM along a variety of environmental gradients (AI-Reasi et aL, 2011; Baken
et aL, 2011; Jaffé et aL, 2008; Miller and McKnight, 2010).
As DOM is known to form complexes with trace metals in aquatic environments, and since
DOM metal-complexing characteristics depend on the character and source of DOM (Baken et
38
aL, 2011; Cao et aL, 2006; Cheng et aL, 2005; Xue and Sigg, 1999), optical properties, as a
measure of DOM quality, may be an important tool for predicting the role of DOM in metal
speciation and transport. However, the percent of DOM that is actually fluorescent has been
described to be as low as 1% (Green and Blough, 1994; Senesi, 1990). Although we are unable
to directly determine the proportion of DOM that is represented by the fluorescence spectrum,
this approach has been successfully used to estimate metal-DOM complexation in titrations of
isolated organic matter samples (Saar and Weber, 1980) and in natural untreated water
samples (Yamashita and Jaffé, 2008). The incorporation of DOM quality, determined from
fluorescence EEMs and PARAFAC analysis, would be a valuable addition to existing chemical
equilibrium models used for the prediction of metal bioavailability in natural freshwater (Dudal
and Gérard, 2004).
1.5
Chemical equilibrium speciation modelling
Chemical equilibrium modelling is often employed in natural aqueous systems to predict the
chemical speciation of various aqueous solutes based on established thermodynamic
equilibrium reactions with defined stoichiometries. Simple chemical equilibrium modelling
systems, su ch as MINEQL+, calculate the speciation, precipitation-dissolution and adsorption of
aqueous inorganic chemical species using an extensive thermodynamic data base included in
the model (Schecher and McAvoy, 1992). The heterogeneous nature of DOM, however, makès
it difficult to define specific thermodynamic data associated with its complexes. Nevertheless,
with the use of sorne reasonable assumptions based on the current knowledge of the character
of DOM, the complexation of DOM with cations in natural aqueous systems has been modeled
with success (e.g. Kinniburgh et al. (1996), Tipping (1998)).
The calculation of complexation reactions in natural aquatic systems is possible using the
Windermere Humic Aqueous Model (WHAM) speciation code combined with the HumiclonBinding Model VI 3 (Tipping, 1998). The model takes into account the heterogeneity of natural
3
WHAM has very recently been updated to include the Humic lon-binding model VII (Tipping et al. 2011). The
present thesis work was, however, based on model VI which was available throughout the majority of the
research project.
39
organic matter (NOM), ion competition, reaction stoichiometry variability and electrostatic
interactions, but it is not yet weil tested in natural aquatic systems (Tipping, 2002). Tipping
(1998) describes the Model VI as a "discrete site-electrostatic model of the interactions of
protons and metals with fulvic and humic acids". The dissociation of protons on the surface of
humic and fulvic acids, assumed to be rigid spheres of uniform size, is represented byeight
groups having different acidities characterized by intrinsic equilibrium constants (Tipping,
1998).
(3)
The eight groups are further divided into four strongly acidic type A groups representing
carboxylic acids and four weakly acidic type B groups representing phenolic acids. The pK;
values associated with these groups are calculated as follows:
for i = 1-4 pK.l =pKA
.
for 1 = 4-8 pK;
+
(2i -5)
A"KA
6 ~
= pKB +
(2i -13)
6
!1pKB
(4)
(5)
where pKA and pKB are average pK values of each type of group and I1pKA and I1pKB signify the
spread of the pK values around means. Furthermore, it is written into the model that the type
A groups are twice as abundant as the type B groups. With respect to metal binding to type A
and B groups, metal ions will compete with each other and with protons. Monodentate binding
is described by the following general equation and equilibrium constants:
(6)
Type A groups; K(i) = logK MA
+ (2i -5) MK1
(7)
Type B groups; K(i) = logKMB
+ (2i -13) MK1
(8)
6
6
where ALK1 is a constant estimated from the fitting of experimental data. Bidentate and
tridentate binding is also incorporated into the model.
log K(i,j) = log K(i)+ log K(j)+ x· t.LK 2
(9)
log K(i, j,k) = log K(i)+ log K(j)+ log K(k)+ y. t.LK 2
(10)
40
4
Tridentate binding sites and adjustable parameters f).LK1 and f).LK2 were added to take into
account the heterogeneity of binding sites, resulting in 80 different sites in ail. In general,
WHAM assumes fulvic acids to have a higher density of binding sites, as weil as stronger strong
acid sites and weaker weak acid sites than humic acids (Tipping, 2002).
The non-specific binding caused by the electrostatic attraction between the generally negative
humic substances and the cations in their immediate environ ment must also be taken into
account. The accumulation of counterions in the electrical double layer formed at the surface
of humic substances is approximated bya simple Donnan sub-model. The non-specific binding
of counterions in the double layer adjacent to the binding surface is most pronounced at low
ionic strengths, such as is the case for most freshwaters on the Canadian Precambrian Shield.
A set of generic default parameters for the WHAM/Model VI has been determined by Tipping
(1998). Hundreds of existing data sets for proton and metal binding to humic and fulvic acids,
obtained over a wide range of experimental conditions, were analysed to generate average
binding parameters. Generic default parameters offer a good starting point when applying the
models to experimental data. Once the models are applied to experimental data, and based on
the overall goodness of fit, several model parameters can be adjusted. In addition, in situations
where experimental data are limited, the number of parameters needing to be optimised may
be reduced; in the case of WHAM/Model VI, the number of adjustable parameters can be
reduced to a single parameter, log
KMA'
where
K MA
is the intrinsic equilibrium constant for the
binding of metal M to the type "A" binding sites (Tipping, 1998).
1.5.1
WHAM field validations
WHAM/Model VI has been extensively applied and tested in laboratory experiments ta
investigate the interaction between DOM and dissolved metal ions (Tipping, 2002). The
robustness of this model and its default parameters have been tested by comparing simulated
4
During the development of WHAM Model VII, Tipping et al. (2011) found a coding error in Model VI, which
prompted the removal of LK1 parameters from Model VII. Ail calculations using the Model VI are still correct,
but do not refer to the model described by Tipping (1998).
41
results to those obtained from laboratory experiments. The reviews by Tipping (2002) and
Dudal and Gérard (2004) describe at length the laboratory application and optimisation of the
most recent versions of the WHAM/Model VI.
Applications of WHAM to predict the speciation of trace metals in natural waters, as opposed
to laboratory solutions, should be made while keeping in mind that it is a semi-empirical model
based on some major assumptions, such as the size and shape of the natural organic matter as
weil as density, distribution and heterogeneity of the metal-binding sites (Tipping, 2002).
Another major assumption of WHAM worth mentioning is that the cation-bindingproperties of
organic matter in natural waters can be represented by those of humic and fulvic acids that
have been isolated from natural waters and studied in the laboratory. Note too that no
technique has been developed that directly measures the concentration of humic substances in
natural waters. To circumvent this difficulty, it is normally assumed that the ratio of DOM to
DOC is about 2:1, the concentration of DOC being readily available analytically. However, since
this estimate of DOM includes both humic and non-humic components, the model user must
also estimate the proportion of DOM that corresponds to fulvic and humic acids and is active in
metal complexation. This proportion is often used as an optimisation parameter to empirically
fit model predictions to experimental data. For example, Bryan et al. (2002) optimised the
proportion of DOM as "active" in metal complexation to be used in WHAM VI as 65% by
comparing predicted and measured Cu speciation in 15 natural water samples. Cheng et al.
(2005) also found that a similar 64% of DOM was "active" in Zn complexation, but only after the
binding constant for Zn to fulvic acid was optimised to fit predicted to experimental speciation
data. Similarly, Van Laer et al. (2006) found that the best agreement between predicted and
experimental Ni speciation data for a series of natural surface water samples was found when
the fraction of FA "active" in complexing Ni was optimised to 65%, along with the KMA constant
for Ni, and the binding parameter determining the range of binding strengths of multidentate
sites,
~LK2.
It is clear that the model user's choice of the proportion of DOM that is active in
metal complexation greatly affects the predicted metal speciation in natural waters (Kalis et aL,
2006). However, choosing the "correct" active fraction of DOM is a major challenge and studies
42
where this parameter is actually measured in the natural water samples, as opposed to being
estimated àfter-the-fact by optimisation, are lacking. Studies that simultaneously measure the
speciation of metals in natural waters and the character of DOM are needed (lofts and Tipping,
2011).
One of the few studies to test the ability of chemical equilibrium speciation models (such as
WHAM) to predict measured metal speciation in natural fresh waters is described in the paper
by Unsworth et al. (2006). The free metal concentrations were measured by hollow fiber
permeable liquid membrane and Donnan membrane techniques and 50% of the DOC was
assumed to be "active" in metal complexation and to consist of 90% fulvic acids and 10% humic
acids. The authors of this study found that the model predictions of free-ion activities often
diverged from the observed values. The authors found that although there was reasonable
agreement between the predicted and measured free metal ion concentrations for Cd
2
+,
there
was a poor agreement between the predicted and measured free ion concentrations of Cu
2
+
and Pb 2+. The authors suggested that the poor prediction of free Cu 2+ and Pb 2+ concentrations
might be due to the use of default model parameters determined in laboratory experiments
with isolated humic substances. Kalis et al. (2007) found modeled free Ni
2
+
to be consistently
higher than that measured using the Donnan membrane technique (DMT) and cited the binding
of Ni bya ligand unaccounted for by the model as an expia nation. A better understanding of
the quality of DOM would in principle alleviate the need to make assumptions about the
activity of DOM and minimise the discrepancies between model predicted and measured free
metal concentrations.
The ideal application of chemical speciation models to the binding of metal ions to organic
matter in a complex, natural aquatic system draws upon an appropriate set of known
parameters that describe the binding characteristics of individual metal ions under conditions
where the pH, ionic strength, metal-ion concentration and speciation, and presence of
competing ions are known (Milne et aL, 2003). Unfortunately, this is rarely the case. One of
the key assumptions outlined by lofts and Tipping (2011) is that the cation-binding data
43
generated from laboratory experiments on samples of isolated humic substances used to
parameterise chemical equilibrium models, such as WHAM, represent the cation-binding
properties of DOM in natural waters. First, there is a large variability in the data sets used to
generate default model parameters (Tipping, 2002). The variability may result from the use of
various experimental conditions and analytical methods, and also from different sample origins.
Furthermore, there is no consensus on whether or not isolation procedures used to isolate
humic and fulvic acids from DOM affect the proportion of reactive sites and binding site
affinities. In addition to the variability in DOM that may result from laboratory procedures,
there is natural spatial and temporal variability in DOM with respect to its cation binding
character. Chemical equilibrium models wou Id, therefore, benefit from future research in the
following areas: 1) the generation of high quality data sets of metal binding to organic matter
under a wide range of environmentally significant conditions; 2) the simultaneous prediction
and measurement of metal speciation in natural samples; 3) a measure of the natural quality of
NOM with respect to metal binding activity; and 4) the validation of modeled metal binding
characteristics with reliable field data (Tipping, 2002).
2
2.1
Research hypothesis, objectives and originality
Research hypothesis
Based on recently published results (Fortin et aL, 2010; Guthrie et aL, 2005; Unsworth et aL,
2006), it was anticipated that the measured [M z+] values for Cu, Cd, Ni and Zn would not agree
weil with those predicted by the "off the shelf" or default versions of WHAM VI. It was thus
hypothesised that this failure of the models to predict the free metal ion concentrations
accurately is primarily due to variations in the quality of the dissolved organic matter (DOM) in
the different water bodies, and that the incorporation of DOM quality into the chemical
equilibrium models would result in predicted values in closer agreement with the measured
values in natural freshwater samples.
44
2.2
Research objectives
The overall objective for this research was to enhance existing chemical equilibrium models by
taking into account spatial variability in the quality of DOM. The resulting model should better
predict the speciation, and therefore, bioavailability of Cu, Cd, Ni and Zn in the natural aquatic
environment. In order to attain this proposed objective, five specifie sub-objectives were
identified.
Sub-objective 1
Identify a series of lakes representing a wide gradient of dissolved metal concentrations, DOM
concentrations and physico-chemical conditions.
Sub-objective 2
Determine the quality of DOM using UV-visible absorbance, 3D-EEM fluorescence and PARAFAC
analysis, and link variations among lakes to watershed and lake characteristics.
Sub-objective 3
Experimentally determine the concentrations of the free metal ion ([M z+]) for Cd, Cu, Ni and Zn
in the chosen lakes and explore the link between the concentration of the free metal ion and its
proportion (Le., its contribution to the total dissolved metal concentration) and various
environmental factors (total metal concentration, pH, quantity and quality of DOM).
Sub-objective 4
Based on the results of sub-objectives 2 and 3, identify a subset of lakes that exhibit marked
differences in DOM (quantity and quality) and in metal speciation, and titrate lake water
samples from these lakes with Cu and Ni under environmentally realistic conditions (Le., natural
DOM lake samples and low metal concentrations). Generate conditional binding constants for
each sample, and investigate potentiallinks between the observed binding constants and the
spectral properties of the lake water DOM.
45
Sub-objective 5
Improve WHAM VI metal speciation predictions in the chosen lakes through the incorporation
of DOM qua lity by adjusting the estimated proportion of DOM active in metal complexation (%
active FA).
2.3
Originality
Several factors contribute to the originality and significance of this doctoral thesis. First, to the
best of our knowledge, this is the first study to apply fluorescence PARAFAC analysis as a
measure of DOM quality to chemical equilibrium modelling for the prediction of metal
speciation in natural freshwater systems. Second, the study measured DOM quality and the
free Cu 2+, Cd 2+, Ni 2+ and Zn2+ concentrations in severallakes (N=18) in two different regions of
Canada with wide gradients in dissolved organic carbon and metal concentrations and water
chemistry. This is therefore one of the rare studies that compare predicted free metal
concentrations with experimentally measured concentrations in a wide variety of natural
samples. Finally, the project was designed within the research objectives of the Metals in the
Human Environment Research Network (MITHE-RN). The project yields a better understanding
of the bioavailability of environmentally significant metals.
3
Methodology
The following section briefly outlines the mater:ials and methods used for sampling and
analysing the lake water samples collected throughout the study. For a more detailed
description, the reader is referred to Mueller et al. (2012a; 2012b; 2012c) found in Part Il of the
thesis.
3.1
Sampling
As described in the Introduction section (Section 1.2), the areas of Rouyn-Noranda, Québec and
Sudbury, Ontario have been impacted by past and present copper/nickel mining, smelting and
refining activities. A detailed description of the study areas can be found in Mueller et al.
46
(2012b). lakes where chosen to represent geochemical gradients in trace metal
concentrations, pH and DOM quantity and quality.
Eighteen lakes from both of the Rouyn-Noranda and Sudbury regions were sampled in the
summer of 2007 and eight of these lakes were re-sampled in the summer of 2008. The 2007
and 2008 samples were used to characterize the DOM in the chosen lakes (Mueller et al.,
2012b), while the eight samples taken in 2008 were subsequently used to compare measured
and predicted trace metal speciation of Cd, Cu, Ni and Zn (Mueller et al., 2012c). A further two
reference lakes with low total metal concentrations (lake Opasatica from the Rouyn-Noranda
region and Lake Geneva from the Sudbury region) were sampled again in 2009 to determine
conditional binding constants for Ni and Cu to DOM by titration (Mueller et al., 2012a). Lake
Vaudray from the Rouyn-Noranda region and Lake Geneva from the Sudbury region were also
sampled in 2009 to measure the effects of long-term sample storage on DOM spectroscopie
quality and trace metal speciation. See Table 1 for a description of the lakes sampled.
Lake water was collected from the littoral zones of the chosen lakes using equilibrium diffusion
samplers in the summers of 2007 and 2008 (details can be found in Mueller et al. (2012b)). This
sampling technique is based on the equilibrium of dissolved species in the surface waters and
the ultrapure water initially held within the sampler; the diffusion occurs across a membrane of
known porosity (0.2 Ilm). The sample containers consisted of 250 ml polypropylene (Nalgene)
jars with custom-made plastic lids fitted with a 0.2 Ilm polysulfone filter membrane (HT Tuffryn,
Pail). Before being transported into the field, the assembled jars were filled with ultrapure
water (>18 Mohms) and bagged to avoid contamination. Two sample jars were horizontally
fixed to plastic rods and anchored to the lake bottom (Figure 3). Three rods (Le., six sample
jars) were installed in each lake at a depth of 1 m from the lake surface. The samplers were left
to equilibrate in the lakes for 13 to 19 days before being removed.
47
Figure 3.
Example of the sampling apparatus / Montage utilisé pour l'échantillonnage.
The membrane of each sample jar was rinsed with ultrapure water and then gently pierced with
a clean plastic pipette tip before replacing the membrane cover with a clean polypropylene
screw lid. The samplers were bagged and stored in the dark at 4°C until transported back to the
lab. In the field, lake water temperature, conductivity and pH were measured.
ln the summer of 2009, large volume grab samples were collected from the surface of the lakes
for the titration and sample storage experiments. For the water needed for the titration
experiments, two 4-L amber glass bottles (I-CHEM Brand) were used for each of the two lakes
sampled. For the sample storage experiments, one 10-L polypropylene carboy (Nalgene) was
used for each of the two lakes sampled. Ali grab samples were taken from approximately the
48
same location as the equilibrium diffusion samplers and transported back the laboratory where
they were stored in the dark and at 4°C.
3.2
Sample analysis
Ali plastic laboratory and sampling equipment was soaked in a 10% (v/v) HN0 3 solution for at
least 24 h and then rinsed a minimum of six times with ultrapure (>18 Mohms·cm) water and
dried under a Class 100 laminar flow hood. Ali glassware was soaked in a 2 N HCL solution
before being rinsed with ultrapure water. The polysulfone membrane for the diffusion
samplers was soaked in ultrapure water, changed daily, for 1 week. The polycarbonate filters
for vacuum filtration were soaked in 10% HN0 3 for at least 24 h and then ultrapure water for at
least 24 h. Polystyrene vials for anion analyses were rinsed three times with ultrapure water
only.
3.2.1
Lake DOM quality
Details of the sample preparation and analyses of the samples taken in 2007 and 2008 to
characterize the lake DOM can be found in Mueller et al. (2012b). Briefly, subsamples were
taken for analyses of major cation concentrations, total organic carbon (TOC) as weil as
absorbance and fluorescence measurements. Absorbance measurements were used to
calculate the specifie absorbance at 254 nm (SUVA 254 ), while the fluorescence measurements
were used to calculate the fluorescence index (FI; the ratio between the fluorescence emission
intensity at 470 nm over that at 520 nm, for an excitation at 370 nm) and, using parallel factor
analysis (PARAFAC), to identify the individual fluorescent components contributing to the
overall DOM fluorescence for each lake (Cory et al., 2010; Gondar et al., 2008; Koster and
Schmuckler, 1967). Lake watershed characteristics were also identified using hydrographie and
topographie geospatial data integrated using ArcGIS 10 (Environmental Systems Research
Institute, Inc., Redlands, CA).
49
3.2.2
Cd, Cu, Ni and Zn metal speciation
Details of the sample preparation and analyses of the samples taken in 2008 to compare
measured and predicted trace metal speciation ean be found in Mueller et al. (2012c). Briefly,
subsamples were taken for determination of major cation, anion and total metal
concentrations. Total organic carbon (TOC), total inorganic carbon (TOC), as weil as absorbance
(SUVA 254 ) and fluorescence (FI and PARAFAC components) measurements were also made. The
concentrations of Cd 2+, Ni 2+ and Zn 2+ in the lake samples were measured using an ion exchange
technique (lET), whereas the concentration of Cu 2+ was measured using a cupric ion selective
electrode (ISE). The concentrations of free metal ions for each lake were also calculated using
the Windermere Humic Aqueous Model (WHAM model 6.1).
3.2.3
DOM titrations with Cu and Ni
The grab samples taken from Lake Opasatica (8 l) and Lake Geneva (8 l) in 2009 were
transported back to the laboratory and vacuum filtered under a clean laminar flow hood using
polycarbonate filter holders (Advantec MFS, Inc.) and a series of 47-mm polycarbonate filters
(S, 2, 0.4 and 0.2 !lm; Millipore and AMD Manufaeturing Ine.). Before filtering the lake water
samples, 1 lof ultrapure water and a small volume of sample were filtered through the
filtration apparatus and discarded. Following filtration, the samples were stored in the dark
and at 4°C until analysis.
Samples from both lakes Opasatica and Geneva were individually titrated with Cu and Ni. For
the Cu titrations, known amounts of a stock solution of 0.2 mM copper nitrate (CU(N03h,
PlasmaCal) were added to eight 200-ml aliquots of the sample, which had been amended with
KN0 3 (99.995%, Fluka; a known amount of a 1.23 M stock solution was added to achieve a final
concentration of 10 mM KN0 3 for ail titration aliquots). The pH of the titration aliquots was
adjusted to the naturallake pH using small volumes of either HN0 3 or NaOH, and left to
equilibrate overnight under a laminar flow hood and in the dark before measuring the free Cu
2
+
concentration using a cupric ISE. An initial sample aliquot with no added Cu was also analysed.
For details on the calibration of the electrode and measurement of the free Cu
2
+
ion in natural
50
lake water samples, the reader is referred to Mueller et al. (2012c). Triplicate Cu titrations
were carried out for a total of 27 Cu titration aliquots analysed. After the measurement of the
free Cu
2
+
concentration, subsamples of each titration aliquot were taken for measurement of
the total dissolved Cu concentration.
For the Ni titrations, known amounts of a stock solution of 1 mM nickel nitrate (Ni(N0 3h,
PlasmaCal) were added to seven aliquots of 250 ml of sample, which had been amended with
Ca(N0 3 h (99.0%, Sigma-Aldrich; a known amount of a 0.025 M stock solution was added to
achieve a final concentration of 0.46 mM Ca(N0 3 h for ail titration aliquots). Again, the pH of
the titration aliquots was adjusted to the naturallake pH and left to equilibrate overnight
before measuring the free Ni
2
+
concentration using the lET. An initial sample aliquot with no
added Ni was also analysed. For details on the calibration and measurement of the free Ni 2+ ion
in naturallake water samples, the reader is referred to Mueller et al. (2012c). Triplicate Ni
titrations were carried out for a total of 24 Ni titration aliquots analysed. In the same manner
as the Cu titrations, subsamples of each titration aliquot were taken for measurement of the
total dissolved Ni concentration.
3.2.4
Effect of sam pie storage on metal speciation and DOM quality
Following the same protocol as described above (Section 3.2.3), the 10-l samples collected
from Lake Vaudray and Lake Geneva in July 2009 were filtered upon return to the laboratory.
Subsamples were taken for analyses of pH, major ion concentrations, DOM optical properties
(absorbance and fluorescence) as weil as total and free Cd, Cu, Ni and Zn concentrations
immediately after filtration and at regular intervals (approximately 3 months) over an 8-month
period. Between subsampling events, the large volume samples were stored in the dark at 4°C,
as was the case for the lake water samples collected with the in situ diffusion samplers. See
Appendix A for further details.
51
4
Results and Discussion
The following section includes a discussion of the major results found during the completion of
this thesis. More detail can be found in the papers found in Part Il of the thesis. The overall
objective of this thesis was to enhance existing chemical equilibrium models by incorporating
the quality of DOM, resulting in improved predictions of Cd, Cu, Ni and Zn speciation, and
therefore, bioavailability in natural aquatic environments. To this end, the effects of watershed
characteristics and in-Iake processes on lake water chemistry are first presented, with an
emphasis on the DOM present in the various lakes. For a subset of these lakes, chosen to
represent differences in DOM and in water chemistry (including total dissolved metal
concentrations), we then examine the free-metal ion concentrations for four metals (Cd, Cu, Ni
and Zn) and consider how the trace metal speciation varies among the metals and for a given
metal among the various lakes. The measured trace metal speciation is then compared to that
predicted by the commonly used chemical equilibrium speciation model, the Windermere
Humic Aqueous Model (WHAM) and possible reasons for differences between the measured
and WHAM modeled trace metal speciation are discussed. Finally, the conditional binding
parameters for the complexation of Cu and Ni by DOM are calculated and compared between
two lakes with low ambient concentrations of metals and spectroscopically different DOM.
4.1
Lake watersheds
During the thesis research, lakes from the Rouyn-Noranda region of north-western Québec and
from the Sudbury region of north-central Ontario were sampled during the summer over the
course of three years (Figure 4 and Table 1).
The watershed characteristics of these lakes were found to vary between sampling regions. In
general, the lakes from the more northerly Rouyn-Noranda region had a higher mean
watershed-to-Iake surface area (WjL) ratio (25), % vegetation (83%; dominated by coniferous
forest cover) and % wetland cover (1%), fewer rock outcrops and a thicker soillayer than the
lakes from the more southerly Sudbury region (mean W/L of 9; 41% vegetation; no wetland
cover and 28% rock outcrops) (see Table 2 in Mueller et al. (2012b)).
52
1 ' 4! ...
:l ...
.........
b
a.
Rouyn No nda
'lt:...
l
t
1I _
w.1
a
Hudson
by
,
,
.~
.;
v....ro.
L .. "
Quêbec
•
M
,•
,
!l-
.....
c
-,.'".
Y-- , - - - -
Onlario
~
~
y
\ 1
1---_
L
l
Sudbury
*
r i
1l1-:wrvv
Figure 4.
Map of the two study areas showing the relative position of the two regions in eastern
Canada (panel a), the geographicallocation ofthe Rouyn-Noranda lakes (panel b) and the
Sudbury lakes (panel cl. Reproduced from Mueller et al. (2012b) / Carte de l'Est du Canada
montrant les positions relatives de deux régions étudiées (A), ainsi que l'emplacement de
lacs de la région de Rouyn-Noranda (8) et de la région de Sudbury (C). Reproduit de Mueller
et al. (2012b).
It follows that the lakes from the Rouyn-Noranda region tend to have shorter water residence
times than the lakes from the Sudbury area, and accordingly are strongly influenced by inputs
from their catchments. With respect to natural organic matter, which can originate either
within the lake or in the catchment, the Rouyn-Noranda lakes receive a larger proportion of
DOM from their catchments than do the Sudbury area lakes.
53
Table 1.
Lake names, sampling regions, locations and years sampled. Modified from Mueller et al.
(2012b) / Noms des lacs, ainsi que les régions, sites et dates d'échantillonnage. Modifié
d'après Mueller et al. (2012b).
Years Sam pied
Lake
Region
Location
2007
2008
Adeline
Rouyn-Noranda
48°12'12" N
79°10'17" W
y
Bousquet
Rouyn-Noranda
48°12'56" N
78°37'06" W
y
D'Alembert
Rouyn-Noranda
48°23'01" N
79°00'35" W
y
Dasserat
Rouyn-Noranda
48°15'06" N
79°24'24"W
y
y
Dufault
Rouyn-Noranda
48°18'30" N
78°59'25" W
y
y
Dufay
Rouyn-Noranda
48°01'42" N
79°27'33" W
y
Joannès
Rouyn-Noranda
48°10'53" N
78°40'02" W
y
Opasatica
Rouyn-Noranda
48°04'32" N
79°17'49" W
y
y
Vaudray
Rouyn-Noranda
48°05'45" N
78°40'50" W
y
y
Bethel
Sudbury
46°46'17" N
80°57'43" W
y
y
Crowley
Sudbury
46°23'06" N
80°58'55" W
y
Geneva
Sudbury
46°45'52" N
81°32'46" W
y
Hannah
Sudbury
46°26'36" N
81°02'17" W
y
Middle
Sudbury
46°26'22" N
81°01'26" W
y
Nelson
Sudbury
46°43'36" N
81°05'39" W
y
Raft
Sudbury
46°24'35" N
80°56'44" W
y
Ramsey
Sudbury
46°28'57" N
80°57'00" W
y
Whitson
Sudbury
46°35'03" N
80°58'20" W
y
4.2
y
2009
y
y
y
y
Lake water chemistry
Differences in watershed characteristics, such as geology, hydrology and vegetation cover,
between regions can play an important role in the regional variability of surface water
chemistry parameters such as the pH and the concentrations of major ions and metals, as weil
as the quantity and quality of DOM (Sobek et al., 2007; Wetzel, 2001). However, the water
chemistry, Le. pH and major cation concentrations, varied only slightly between the two
54
pHs,with low
sampling
regions(seeTable2). Lakes
from both regionshavecircumneutral
concentrations
of Caand Mg,typicalof softwaterlakeson the Canadian
Shield.
Thetotaldissolved
concentrations
of Cd,Cu,Ni andZn did varybetweenregionsandamong
the eightlakessampledin 2008(seeTable2),aswasthe aimof the experimental
design.The
Rouyn-Noranda
regionhadthe highestconcentrations
of CdandZn andshowedthe largest
gradients
concentration
amongthe lakesfor thesemetals,whereasCuand Ni concentrations
variedless.LakesDufaultand Dasserat
hadthe highestconcentrations
of totaldissolved
metals,whileLakeOpasatica
hadthe lowest.In the Sudburyregion,Ni and Cdshowedthe
gradients
largestconcentration
amongthe lakes,whereasCuandZn variedless.LakesBethel
andGenevahadthe lowestconcentrations
of metals,whileLakesRaftandWhitsonhadthe
highest.Thelake-to-lake
variability
in measured
total metalconcentrations
withina given
regionis dueto variations
in atmospheric
deposition
down-wind,
with respectto the distance
presentin individual
from miningandsmeltingactivities,
andto localpointsourcedischarges
(Borgmann
catchments
et al.,1998;Dixitet al.,2007;GouletandCouillard,
2009).
4.3
Dissolvedorganicmotter (DOM)
Dissolved
organicmatterplaysan importantrolein controlling
the speciation
and bioavailability
of manytracemetalsin naturalaquaticsystems,
by complexing
the metalsand loweringthe
concentration
of the free metalion (Buffle,1984).lt is reasonable
to hypothesise,
however,
giventhe recognized
diversity
of the originsof DOM,that its abilityto complextracemetals
mayvaryfrom laketo lake,dueto differences
in precursor
materialwith differentmetalgroups.Forexample,
specific
functional
the Cubindingby DOM,aswellasthe uptakeof Cuby
fishgills,wasfoundto differamongsamples
with differentsources
of DOM(Luideret al.,2004).
55
Table2.
MeasuredpH and mean(+ SD,N=3)dissolved
organicmatter(DOM)concentrations,
total dissolvedconcentrations
of majorcations
andtrace metals,ando/o
(QC)and Sudbury(ON)in 2OO8
free metalvaluesfor lakessampledfrom Rouyn-Noranda
/Valeursde pH,
de concentrations
moyennes(t SD,N=3)de matidreorganiquedissoute(MOD),de concentrations
totalesde cationsmajeurs
(%)de m6tallibrepour leslacs6chantillonn6s
dissouset de m6tauxtracesainsique lespourcentages
danslesr6gionsde RouynNoranda(Qu6.1et
de Sudbury(Ont.)en2008.
Dasserat
pH
6.92
7.59
(mg
DOM
C'L-1) 4.85r 0 03
5 . 2x 0 . 1
Ca (uM)
2 0 7r . 1
4 0 4t 3
Ms (pM)
93.7i 0.3
118*1
Cd (nM)
0 . 8i 0 . 2
2 . 0r . 0 . 2
Cu (nM)
112t3
246t3
Ni(nM)
8 r.2
5 . 1i 0 . 3
Zn (nM)
464 t 51
342r.60
cd2.1nM;
r
0.5 0.2
1 . 1 4i 0 . 0 6
cu2.1nM;
0.37r 0.04 0.44r 0.09
< 0.62
Ni2-1nM;
0.6'
zn2.1nM;
279 x32
188r23
o/o Cd2*
o/o Cu2*
%oNi2*
o/oZnz*
Opasatica
Vaudray
7.69
6.81
6 . 3r 0 . 1
8 . 1 0i 0 . 0 8
218r.2
83rl
1 1 4r . 1
3 7 . 1r 0 . 6
0.06r 0.01 0.608r 0.005
43t1
3 5 . 1* 0 . 2
1 2 . 5t 0 . 4
12t1
15x4
5 2x s
0.021r 0.008 0.26t0.02
0.08r 0.01
0.20r 0 08
< 0.61
<0.12
2 . 3i 0 . 9
31i18
63r36
58i5
3 7x 1 7
42t 4
0.32t 0.04 0.17r 0.04 0.21t 0.04
0.47t 0.2
NA
NA
NA
8'
60f10
5 5r . 1 2
15t7
60r36
8.08
4.89i 0.03
4 6 4t 3
274t3
0 . 0 1 8i 0 . 0 0 9
24.9t 0.1
226 r.2
12t3
6.9
2 . 9* . 0 . 1
68r3
2 6t 1
0.021r 0.005
1 0 . 5r 0 . 6
1 8r . 2
2 1t 8
0.05r 0.01
7.3f09
2 . 3t 0 . 4
NA
0.48r 0.09
4 . 0t 0 . 7
1 0 . 7i 0 . 9
70
4 . 6r 0 . 9
22ts
51 t20
o.o1'
0.20r 0.05
3.2 tO.4
20r6
Raft
6.4
2.03r 0.03
8 1t 2
46r1
0.56r 0.02
8 6 . 0i 0 . 9
775 i.4'l
62t22
0 402 t 0.004
3 . 2r 0 . 5
545r9
5 1r 1 6
71t2
3.7r 0.6
70t4
82i39
Whitson
6.9
3.77x0.05
1 4 3* 1
78.4x0.4
0.51i 0.04
1 6 5t 3
1136i 54
52t2
0.31r 0.05
1 . 0r 0 . 1
489t72
32r.4
61r10
0.57i 0.08
43t7
62r8
An asterisk* indicatesonly one sampleabovelimit of quantification
(LOQ;calculated
asthreetimesthe standarddeviationof triplicateanalyses
of each
lake).NA indicatesno samplesabovethe LOQ.Modifiedfrom Muelleret al.(2012c).
(LOQ;calculdecomme6tanttrois fois l'6carttype sur
Lesymbole* d6noteun r6pliquadont lesconcentrations
sontau-delide la limitede quantification
une analyseen triple danschaquelac)et le symboleNAd6notequ'aucunechantillonn'estau-delide la LOQ.Modifi6d'aprEsMuelleret al. (2012c).
DOM Quontity
Theconcentration
of DOM,hereafterexpressed
in termsof DOC(mgC.L-1),
in lakesin a given
regionis influenced
by terrestrialvegetation,
soilsand hydrology,
whichare in turn regulated
by climateandtopography.Howeverat a morelocalscale,catchment
andlakeproperties
influence
the DOMconcentration
in individual
lakes(Sobeket al.,2007).Indeedfor the
playedan importantrolein
eighteenlakessampledin 2007,the lakewatershed
characteristics
the variability
in DOMconcentration
amonglakes(seeTable3). In general,
the lakesof the
Rouyn-Noranda
regionhadhighermeanconcentrations
of DOM(7.2 t 2.4 mgc.t-1)and
1)
spanneda widerconcentration
range(3.8to 13.4mg C.L thanthe lakesof the Sudburyregion
(meanof 3.4t 1.1mg C.L-l;rangefrom 0.8to 6.0mg C.t-1).LakesBousquet
andJoannds,
in the
Rouyn-Noranda
region,hadthe highestconcentrations
of DOM(13.0and 10.0mg C.L-1,
(1.4
respectively),
whereasLakeNelson,
of the Sudburyregion,hadthe lowestconcentration
mg C.L1). As described
in section4.L,we suspected
that the lakesfrom Rouyn-Noranda
region
receivehighquantities
whereaslakesfrom the Sudbury
of DOMfrom their largecatchments,
regionreceiveloweramountsof DOMfrom theircatchments,
and relatively
higheramounts
from withinthe watercolumn.Thewiderangeof DOMconcentrations
in the lakessampled
fulfilledoneof our objectives
a seriesof lakesrepresenting
of identifying
a widegradientof
DOMconcentrations.
DOM Quality
In additionto differences
in the quantityof organicmatter,the qualityof the lakeDOM(as
measured
spectroscopically)
alsodifferedbetweenregionsandamonglakes(seeTable3). The
(8.31 L.5L.m1'mgC-1)
Rouyn-Noranda
lakesdisplayed
higherSUVAzsq
andslightlylowerFl
values(1,.32
!0.04) thanthe Sudburylakes(5.3t 1.3L.m-l.mg
L.36t 0.08,respectively),
C-1and
indicative
of greaterinputsof allochthonous
DOMto the Rouyn-Noranda
lakesthanthe
Sudburylakes.Thehighwatershed-to-lake
area(WL) ratiosandthe largeproportionof
surfacevegetation
in the catchments
lakesalsopointto a higher
of the Rouyn-Noranda
proportionof DOMoriginating
from the catchment
thanin the caseof the Sudburylakes.
Amonglakes,LakesBousquet,
VaudrayandJoannds
hadthe highestSUVA254
valuesandthe
57
lowestFlvalues,
whileLakesBetheland Ramsey
valuesandthe highest
hadthe lowestSUVAzsq
Flvalues.Lakes
Joannds
andVaudraybothflow into LakeBousquet
andthe priorin-lake
processing
of DOMin theselakespresumably
natureof the DOM
to the recalcitrant
contributes
presentin LakeBousquet.In otherwords,the DOMoriginating
from the catchments
of Lakes
Joannds
andVaudray,andfor that generated
to
withintheirwatercolumnsis exposed
pathways
degradation
on itswayto LakeBousquet.A lowerproportionof easilydegradable
DOManda higherproportionof recalcitrant
to be presentin Lake
DOMarethereforeexpected
Bousquet.BothLakeRamsey
and LakeBethel,a smalleutrophiclakethat flowsinto Lake
Ramsey,
arelikelyproducing
DOM,thusleadingto their
a highproportionof autochthonous
low specific
absorptivities
and highFlvalues.Previous
studieshavefoundsimilarrelationships
(McKnight
betweenthe sourceof DOMandsimpleabsorption
measurements
andfluorescence
et al.,2001;Weishaar
et al.,2003).Forexample,
Jaff6et al.(2008)foundlow SUVAvalues(0.5
t
to 5 L.m-l.mg
C ) and highFl (1.4to 1.75)values
for surfacewaterswith a highmicrobial
foundin autochthonous
contribution
samples.In othercases,
theyfoundhighSUVAvalues(1
to 7.5L.m-l.mg
C-1)
andlow Flvalues(1.1to L.5),indicative
materialfound
of terrestrial-derived
in allochthonous
samples.
Balcarczyk
et al.(2009)alsofounda rangeof stream,springand
thermokarst
samples
to exhibitFlvaluesto rangebetween1.3and 1.4,representative
of
terrestrially-derived
allochthonous
DOM.
Unlikethe simpleabsorbance
andfluorescence
measurements
that useonlya smallportion
(two-dimensional)
partof the absorbance
or fluorescence
spectrum,
three-dimensional
excitation-emission
matrix(EEM)fluorescence
incorporates
datafrom the entire
spectroscopy
DOMfluorescence
spectrum.Theuseof a multivariate
statistical
analysis
tool,i.e.parallel
factor(PARAFAC)
analysis,
on EEMfluorescence
four distinctfluorescent
spectraidentified
components
contributing
to the overalllakeDOMfluorescence.
Components
l and2 were
presentin a widevarietyof aquatic
ascribed
to reducedquinone,humic-like
fluorophores
quinone,humic-like
environments,
component3 to refractory,
fluorophores
of
oxidized
proteinfluorescence
allochthonous
origin,andcomponent
4 to tryptophan-like
of
58
Table3.
Measuredmean(+ SD,N=3)dissolved
(SUVAzas),
organicmatter(DoM)concentration,
fluorescence
index(Fl),
specific
UV-absorbance
and relativeproportions
of the C1andC3PARAFAC
for the eighteenlakessampledin 2OO7
components
andthe eightlakessampledin
2OO8
moyennes(t SD,N=3)de matidreorganiquedissoute(MOD),absorbance
indicede
UV sp6cifique(SUVA2or),
/ Concentrations
(Fl)et lesproportionsrelativesdescomposantes
fluorescence
de fluorescence
C1et C3 pour les18 lacs6chantillonn6s
en 2007et les8
lacs6chantillonn6s
en 2008.
Lake
Region
DOC
suvA 254
SAC340
(mg t-'')
(L m-l mg c'1)
(t m'l mg c-1)
FI
cLlcf
czlcr
c3lcr
c4lcr
Adeline
R o u y n - N o r a n d a 4 . 6 0! O . 7 9
6 . 9 6! r . 2 7
r . 4 6! 0 . 2 5
1 . 3 3r 0 . 0 1 0 . 2 7! 0 . o 2 0.33r 0.02 0.26t 0.02 0.15r 0.01
Bousquet
R o u y n - N o r a n d a 1 2 . 5 8r 0 . 6 9
1 1 . 0 3r 0 . 6 2
3 . 5 2I 0 . 2 0
1 . 2 9r 0 . 0 1 0.15r 0.02 0.07r 0.01 0.76r 0.05 0.0210.00
D'Alembert Rouyn-Noranda 8.00t 0.80
8.67t 0.90
2 . 2 0! 0 . 2 2
1 . 3 0r 0 . 0 1 0 . 2 1 1 0 . 0 30.24r 0.03 0.48r 0.07 0.08r 0.01
Dasserat
Rouyn-Noranda 5 . 1 4r 0 . 5 8
8.00r 0.40
1 . 9 8i 0 . 1 8
1 . 3 5I 0 . 0 3 0.25r 0.19 0.34r 0.25 0 . 3 0 1 0 . 3 2 0.10t 0.07
Dufault
Rouyn-Noranda 5 . 1 0r 0 . 3 6
6.86i 0.70
L . 4 8! 0 . 2 6
1 . 3 2r 0 . 0 3 o . 2 7! O . L 2 0 . 3 3 1 0 . 1 4o . 2 7! O . 7 0 0 . 1 4 1 0 . 0 5
Dufay
Rouyn-Noranda 8 . 2 51 0 . 3 7
8.36r 0.44
2 . 1 1I 0 . 1 3
1 . 3 0 1 0 . 0 2 o.22! O.03 0.24r 0.03 0.45! 0.05 0.09r 0.01
Joannds
Rouyn-Noranda 1 0 . 2 6! 0 . 2 6
9 . 9 8 10 . 2 5
2 . 9 0 1 0 . 0 6 1 . 2 8I 0 . 0 1 0.18i 0.04 0.16r 0.03 0.61t 0.16 0.05r 0.01
Opasatica
Rouyn-Noranda 6.00r 0.40
7 .47! 0.34
1 . 6 5i 0 . 1 4
1 . 3 5r 0 . 0 2 0.26r 0.05 0.32r 0.05 0.29r 0.05 o.tz!o.o2
Vaudray
Rouyn-Noranda 8 . 0 7 1 0 . 0 9
9 . 8 31 0 . 4 3
2 . 8 8! 0 . 2 4
1 . 2 61 0 . 0 1 0.27r 0.08 0 . 2 4! O . O 7 0.43t 0.10 0.07r 0.02
Bethel
Sudbury
5 . 2 2! 0 . 4 6
4.64t0.47
0 . 8 4r 0 . 2 5
1 . 4 91 0 . 0 5 0.2410.08 0.37t 0.13 0 . 1 5 1 0 . 0 5 0.23i 0.09
Crowley
Sudbury
2 . 5 9I 0 . 2 8
4.82! 0.44
1 . 0 2! 0 . 1 2
1 . 3 31 0 . 0 8 0.29!0.o7 0 . 3 6 1 0 . 0 80.16r 0.05 0.19r 0.04
Geneva
Sudbury
3 . 2 6r 0 . 3 9
5 . 2 31 0 . 2 8
1.0910.25
1 . 3 31 0 . 0 6 0.29r 0.09 0.37r 0.10 0 . 1 81 0 . 0 7 0.17r 0.06
Hannah
Sudbury
3.61!O.r2
5.05r 0.26
1.03r 0.08
1.31t 0.04 0.28i 0.04 0.34r 0.04 0.18r 0.03 0.2010.03
Middle
Sudbury
3.32r 0.40
5 . L 7! 0 . 5 7
1.01r 0.08
1.35i 0.07 0 . 2 5 1 0 . 0 30.34r 0.04 0.20r 0.03 0.20t 0.03
Nelson
Sudbury
1 . 3 6t 0 . 4 6
4 . 5 3! 2 . O 2
0 . 8 31 0 . 4 2
1.32I 0.03 0.28r 0.08 0.35t 0.09 0.14r 0.03 0.2310.07
Raft
Sudbury
2 . 2 6! O . 3 r
5 . 9 9! 2 . 3 2
1 . 3 5r 0 . 7 1
I . 2 8 ! O . 0 7 0.29r 0.07 0.34t 0.08 0.15t 0.16 0.22r 0.06
Ramsey
Sudbury
3 . 5 5r 0 . 5 3
4 . 3 21 0 . 6 0
0 . 7 41 0 . 0 9
1 . 4 1 1 0 . 0 2 0.24t 0.05 0.33r 0.05 0.17r 0.04 0.25r 0.05
Whitson
Sudbury
3 . 8 2! 0 . L 2
6 . 8 7r 0 . 5 6
t . 7 3! 0 . 3 4
7 . 3 4! O . 0 4 0.30r 0.06 0.36r 0.08 0.19r 0.04 0.15r 0.03
values(Coble,1996;
literature
autochthonous
origin,basedon the comparison
with established
Coryand McKnight,2005;Kosterand Schmuckler,
L9671.
DOMfluorescence
from the lakesof the Rouyn-Noranda
regionhada higherrelativeproportion
of allochthonous
DOMfluorescence
thanthe Sudburylakes,whereasthe Sudburylakeshada
higherproportionof autochthonous
DOMfluorescence.
OnceagainLakesBetheland Ramsey
origin,
werefoundto havehighproportions
DOMof autochthonous
of proteinfluorescent
fluorescent
whereasLakes
Vaudray,
Joannds
hadhighproportions
of humic-like
and Bousquet
DOMof allochthonous
the speciation
origin.Giventhat DOMis an importantligandregulating
metalsin
of tracemetalsin naturalwaters,we hypothesised
of dissolved
that the speciation
freshwatersystemswouldbe sensitiveto the differenttypesof DOMpresent.
4.4
Tracemetalspeciation
Forthe purposeof the thesis,tracemetalspeciation
of trace
is definedasthe distribution
is the free metal
metalsamongsttheirchemical
forms;of particular
interestto ecotoxicologists
ion. Thecomplexation
of the freemetalion in naturalwaterswill dependon the concentration
of the metalandavailable
ligands(bothorganicandinorganic),
of other
aswellasthe influence
cations(suchas protonsandmajorcations)competing
for complexation
siteson suchligands
(Stummand Morgan,1996).Of the lakesstudied,thosethat hadthe highesttotal dissolved
(seeTable2). The
metalconcentrations
alsohadthe highestfreemetalion concentrations
relativeproportionof the freemetalion in individual
in orderto
lakeswasalsoconsidered
accountfor differences
in the totalmetalconcentrations
amonglakes.In the Rouyn-Noranda
region,LakeOpasatica
hadthe lowestTofreemetalforall metals(exceptCu)and LakeDasserat
hadthe highest.In the Sudburyregion,LakeBethelhadthe lowesto/ofree
metalfor all metals
and LakeRaft,the highestpercentmetals(exceptCu).Thepercentfreemetalincreased
as a
pH,reflecting
functionof increasing
totalmetalconcentration
the importance
anddecreasing
of the metal-to-ligand
ratioandprotoncompetition
respectively.
on metalspeciation,
50
on the DOMwill varyasa functionof the metal-toThestrengthof the bindingsitesavailable
bindingsites,the available
ligandratio. At low M/L ratios,i.e.earlyin the titrationof available
highaffinityfor the metal.At highM/L ratiosthe strongbindingsites
siteswill havea relatively
siteswill tendto haveloweraffinityfor the
available
will havebeenoccupied
andthe remaining
metalthanthosethat wereoccupied
earlyin the titration.lt followsthat the effectivebinding
andaccordingly
the %freemetalwill
constantwill becomeweakerasthe M/L ratioincreases,
that asthe pH of naturalwaters
be higherundertheseconditions.lt is alsowelldocumented
with metalionsfor bindingsites
increases
andcompetes
the protonconcentration
decreases,
L991).
ligands,
the percentfreemetalion (NelsonandCampbell,
on available
thusincreasing
higherin the
the percentfreemetalwasgenerally
to resultsfoundin the literature,
Compared
lakes,mostlikelydueto lowerpH andalkalinity
QuebecandOntariolakesthan in the European
lakessampled(Kaliset al.,2006;
ratiosin the Canadian
values,aswellas highermetal:NOM
Sigget al.,2006;Van Laeret al.,2006).
4.5
Meosuredvs.modeledtrocemetol speciotion
the
models,suchasWHAM,areextremelyusefulin predicting
Chemical
equilibrium
speciation
containing
DOM.WHAMassumes
that
free-metal
concentration
of cationsin naturalsystems
DOMis bestrepresented
by fulvicandhumicacidsthat bindcationsat typeA sites
to phenolicgroups
acidsgroupsandtype B sitesthat correspond
corresponding
to carboxylic
(Tipping,
2OO2).
Whenapplying
the WHAMmodelto a naturalwatersample,the usermust,
on the
however,estimatethe fulvicand humicacidcontentof the water. Basedon the analysis
(lakes,
ponds,streamsand rivers),Bryanet al.
samples
averageCubindingof 15freshwater
(2002)suggested
that an averageof 65%of the DOMis activelyinvolvedin metalcomplexation
therefore,doesnot take
by fulvicacidsonly. Thisapproach,
andthat it couldbe represented
of variousorigins.
intoaccountvariations
in DOMqualityamongsamples
of Cd2*,Cu2*,
In the presentwork,we startedby predictingthe free metalion concentrations
Forallthe lakes
of DOMactivein metalcomplexation.
Ni2*andZn2*usingthe suggest
ed 650/o
that weresimilarto thosemeasured
freeCd2*concentrations
WHAMVl predicted
sampled,
61
(seeFigure5). Modelpredictions
werealsosimilarto measured
valuesfor 7n,althoughat low
totalandfreeZn concentrations
WHAMslightlyover-predicted
of freeZn2*.
the concentration
Forthe Cu,however,WHAMunder-predicted
in halfof the lakes
itsfreeion concentration
sampled,
i.e.,thosewith the lowesttotalCuconcentrations.
Worsestill,WHAMsystematically
over-predicted
the concentration
of free Ni2*in all lakesfor whichthe free Ni2*concentration
waswithinthe analytically
measurable
window.
In an attemptto accountfor the variability
in the natureof the DOMamonglakesanditseffect
on theirtracemetalspeciation,
we testedthe sensitivity
of WHAMto the user-defined
proportionof DOMactive(%aFAlin metalcomplexation;
an arbitraryrangeof halving(33%l
anddoublingf.39%l
the 65%valuewasusedto calculate
the freemetalion concentrations
(seeFigure5). WHAMpredicted
a similaro/ofree
metaltothat measured
in mostlakesfor Cd
andZn,but despitethe widerangeof %aFAemployed,
for
wasstillonlysatisfactory
agreement
halfof the lakesfor Cuandfor noneof lakesfor Ni. We thencalculated
the optimalpercentFA
(%aFAopt)
activein metalcomplexation
and
that wasrequiredto exactlymatchthe predicted
measured
freemetalionconcentrations.
The%aFAopt
valuesfor eachmetalandeachlake
werecalculated
by repeatedly
runningWHAMVl andadjusting
untilthe
the FAconcentration
predicted
(seeMuelleret al.(2072cl
freemetalion concentrations
matchedthosemeasured
for moredetailson howthe %aFAopt
valueswerecalculated).
Increasing
the
or decreasing
%aFAchanges
the numberof bindingsitesinvolvedin metalcomplexation,
but the intrinsic
metalbindingaffinities
at thesebindingsitesremainthe same.lt shouldbe notedthat
increasing
the %aFAto beyond100%simplyincreases
the numberof fulvicacidbindingsites
involvedin metalcomplexation
to a valuehigherthanthe defaultcapacity
assumed
by WHAM.
ForCu,the %aFAoptvaluesvariedbetween8 and90%of the total DOMconcentration.For
Cdz*
Ni2*,however,the %aFAopt
valueswereall quitehigh,often
, Zn2'andparticularly
exceeding
the notionallimit of LOO%:
6Ito 25O%
for Cd2*;65 to 4IO%forZnz*;440to I1OO%
(i.e.,the number
for Ni. Theseresultssuggest
that in additionto adjusting
the bindingcapacity
of bindingsites)of the fulvicacid,the defaultbindingaffinities
Ni will
for Cd,Zn andespecially
needto be adjustedin WHAM.
62
In a finalstep,we compared
the optimisedproportionof DOMactivein metalcomplexation
to
qualityof the DOM. Relationships
the lake-to-lake
variationin the spectroscopic
betweenthe
calculated
%aFAopt
valuesandthe relativeproportionof the ubiquitous
humic-like
DOM
fluorescence
PARAFAC
component(C1)werefoundfor Cuand Ni;similarly,
relationships
between%aFAopt
andthe allochthonous
fluorescence
PARAFAC
component(C3)werefound
for CdandZn (seeFigure6). Moreover,the trendsin the relationships
between%aFAopt
and
the Cl./Crand C3/Crratiosfor Ni were foundto be oppositeto thosefor the samerelationships
for Cd,CuandZn. Thesedistinctly
for the firsttime,that the DOM
differenttrendssuggest,
bindingsitesactivein Cd,CuandZn complexation
aredifferentfrom thosebindingNi.
Admittedlythe relationships
shownin Figure6 will haveto be exploredovera widerrangeof
DOMsamples,
but theydo supportour originalideathat the proportionof DOMactivein metal
complexation
couldbe estimatedon the basisof itsfluorescence
signature
andthen introduced
into chemical
equilibrium
modelssuchasWHAM. Usingthe relationships
foundbetweenthe
calculated
%aFAopt
valuesandthe relativeproportionof the allochthonous
fluorescence
component(C3),a C3-optimised%aFAwas
usedto calculate,
anew,the free metalCd2*,
Ni2*
andZn2*
concentrations
in the sampledlakesusingWHAMVl. An improvement
in the WHAM
(Figure7).
Vl prediction
of the freemetalion concentration
wasobserved
for Ni especially
Theseresultssupportour hypothesis
qualityof DOM
that,especially
for Ni,the spectroscopic
maypossibly
be usedasa proxyfor the proportionof DOMactivein metalcomplexation.
yieldedan improvement
Althoughthisapproach
in the WHAMVl prediction
of the freemetal
ion concentrations,
it is apparentthat thereis stillroomfor improvement,
especially
for the
lakeswith low ambientmetalconcentrations.
63
o
65% aFA
33% aFA
130%aFA
a
I
1e-8
I
f
3
1)3'"
1e-9
+
=
+
(\|
,/-
N
tt
./..
o
E t e -ro
o
ttq)
1e-10
t'
./
o
o 1e-11
o
'o1
E
E
o
t./
?'
oO'
f-
'/'.'r
/.t
,
3
1e-12
1e-11
1e-11
1e-10
1e-9
1e-6
,/
lr
1e-12 1e-11 1e-10
1e-9
1e.8
1)
2
+
a ft-7
q|.
t
z
.r/
o
€
o
Q ,r/
t
1e-7
,r/
?/
'-1'
N
g
N
E
E
[ 1e-8
E
o
It
;O
1e-8
1e-9
1e-9
1e-8
1e-7
MeasuredM2+(M)
Figure5.
1e-6
1e-9
1e-9
1e-8
1e-7
MeasuredM2+(M)
Comparison
of the concentrations
of the free metalion [M2*]measuredusingIET(Cd,Ni
andZn)andISE(Cu)andthosecalculated
with WHAMVl assuming
65%aFA(largedark
circles)and a rangeof 33%(smalllightcircles)to L30%aFA(smalllightsquares).
The
dashedlinerepresents
the 1:1line/ Comparaison
d'ionm6tallique
entrelesconcentrations
par IET(Cd,Ni et Zn)ou ISE(CU)et lesconcentrations
pr6ditespar
libre[M2.]mesur6es
WHAMVl en supposantdesvaleursde % aFAde 65 % (cerclespleins)ou entre33 et I3Oo/o
(cercles
ouverts).Lalignepointill6erepr6sentel'6quivalence
L : 1.
64
R'?=068
!
o
I'--
250
o
E
150
<
r
o
100
"-
50
\
024
o26
028
030
032
010
034
100
o20
Y
I
v
v
o80
030
040
tr
tr
A
R60
E
o40
A
L
o
Szo
.t
"€
o24
05
o2a
030
032
010
034
o20
030
040
R'z=0 34
o
2
1500
o
N
E
a 1000
o
024
026
028
030
o32
034
005
010
015
t(o
a
N
€
E
o
soo
a
a
N
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oo
I
-o
i
o
F
- nv
100
'A
024
026
028
C1/C1
Figure5.
030
032
034
010
020
030
040
C3/CT
percentfulvicacidactivein complexation
Relationship
betweenthe WHAM-calculated
(%aFAoptimised)for Cd,Cu,Ni and Zn andthe relativecontributionof the PARAFAC
fluorescence
L (panels
a-d)and3 (panelse-h)to the overallfluorescence
components
spectrumof lakeDOM,Pointsof the sameshapeand fill representreplicatesamples.Solid
significance
linesrepresentregression
equationsof statistical
at the 0.05alphalevel.
Reproduced
from Muelleret al. (2OL2cl
optimald'acidefulviqueactifdansla
/ Pourcentage
(o/oaFA
compfexation
optimis6)calcul6par WHAMpourCd,Cu,Ni et Zn en fonctionde la
1 (panneaux
contribution,
relativei la fluorescence
totalede la MOD,descomposantes
apoints
(panneaux
Les
fluorescence
PARAFAC.
de
m6me
forme
et
de
m6me
d) et 3
e-h)de
en r6pliqua,et leslignescontinuesrepr6sentent
les
couleurrepr6sentent
des6chantillons
r6gressions
au niveaustatistiquede alpha6gald 0.05.Reproduitde Muelleret
significatives
al. (2012c).
65
4.6
porameters
Measuredmetalbinding
Possible
reasons
for discrepancies
betweenmeasured
andWHAMmodeledfree metalion
includet) the underestimation
sitesper
concentrations
of metal-binding
of the concentration
unitof dissolved
carbon,or 2) the underestimation
sites
of the strengthof thesemetal-binding
for the metalsof interest.Theformerreasonwasaddressed
of the
abovewith the calculation
valuesfor eachmetalandeachlake.ForCd,Znand Ni,however,
values
%aFAopt
the %aFAopt
exceededthe notionallimit of LOOo/o.
A possiblereasonfor the underestimation
of the strength
of the bindingsitesmightbe that the genericbindingconstants
usedin WHAMweregenerated
from laboratory
titrationsof isolatedhumicandfulvicacidsandoftenat highermetal:DOC
ratiosthanthosenormallyencountered
in naturalwaters.Underthesetitrationconditions,
onlythe highlyabundantsiteswith low metal-binding
affinitywouldbe probed.
Theneedto generatebindingaffinities
for the bindingof metalsto DOMat
andcapacities
environmentally
relevantmetalconcentrations
occurring
DOMhasalsobeen
to naturally
expressed
by Tippingand Lofts(2011)in theirreviewof the performance
of WHAMwhen
appliedto naturalwaters.We thereforemeasured
the bindingof Ni andCuto DOMthrough
titrationsfor two lakeswith spectroscopically
metal
DOMand low ambientdissolved
differ.ent
concentrations,
andthencompared
the resulting
titrationcurvesto thosepredictedby WHAM
(seeFigure8).
Vl usingdefaultparameters
and650/o
of fulvicacidsactivein metalcomplexation
In general,at ambientmetalconcentrations
LakeGenevawasfoundto bindmoreNi per unit
DOCthan LakeGenevaDOM. In contrast,LakeOpasatica
DOMwasfoundto bindmoreCuper
unit DOCthan LakeGenevaDOM.Thetitrationcurveswerethen usedto calculate
conditional
bindingparameters
for Ni andCubindingto DOMin LakesOpasatica
andGeneva.
66
1e-8
(a)
^
DU'
1e-9
,/q
+
E
3
&=
1e-9
(J
N
()
E
o
$ te-to
E
o
o
E
o
Iz. te-to
E
E
o
o
.N
N
E
E te-tt
E
ô te-1t
o
o
aYt
o
1e-11
e
1e-6
1e-10
(d)
rc/
,r2
/wh
z
p 1e-7
-g
o
E
o
=
+
N
c
N
E 1 0e-7
o
o)
T'
o
=
ff 1e-8
N
E
E
o
.N
E 1.0e-8
o
CL
o
1e-11
1.0e-6
(c)
+
F
1e-12
1e-12
1e-9
1e-10
N
o
(b)
o
1e-9
(t
o
1e-9
1e-8
1e-7
MeasuredM2.(M)
Figure7.
1.0e-9
1 0e-9
1 0e-8
'1.0e-7
MeasuredM2.(M)
with
of free metalion [Mz*]calculated
Comparison
of the mean(t SD,n=3)concentrations
estimatedfrom Figure6)and those
astivefulvicacid(o/oaFAl
WHAMVl (withthe percentage
technique(lET)(Cd,Ni andZn)or ion selective
measured(t SD,n=3)usingthe ion exchange
electrode(lSE)(Cu)methods:(a)cadmium;(b)copper;(c)nickeland (d)zinc.Thedashed
moyennes(t ET,n=3)du m6tallibre[Mz.]
linerepresents
the L:Lline/ Concentrations
par WHAM(enemployantle pourcentage
d'acidefulviqueactif(%aFA)
estim6
calcu16
(t
ET,
n=3)
la
d'6change
avec technique
i cellesmesur6es
d'aprdsla Figure6) compar6es
(lSE)(Cu): (a)cadmium;(b)cuivre;(c)
s6lective
Ni,Zn)ou une6lectrode
d'ions(lET)(Cd,
repr6sente
l'6quivalence
1 : L.
nickelet(d)zinc.Lalignepointill6e
67
I
v
-4
-3.5
Lake Geneva
WHAM modeled
I
lv
o-5
o
w
.fil
-4.0
o
o
o
tfo
J
ow
I
I
z-o
o
ct
o
o)
o
w
-7
-11
.
a
-4
-10
-9
-7
o
o
o
olA
o
aA
I
I
aoo
z-o
o)
o
o
g
-8
-9
-7
-6
D
-4.0
A
6-
A
a
4.5
A
et)
o
-5.0
-7
EF
-3.5
G
..ttf
o-5
-5.0
-6.0
-10
-6
LakeOpasatica
WHAMmodeled
o
gE
-5.5
-8
Ett.il
ry
-4.5
#B
6
4
6
A
-8
-5.5
-11
-10
-7
log Ni2+
Figure8.
-6
-11
-10
-9
-8
-7
-6
log Cu2+
Normalized
Ni (A andC)andCu(BandD) complexation
curves(M-DOM/DOC)for
LakeGeneva(A and B) and LakeOpasatica
(Cand D).Measuredvaluesare shown
in colour;valuescalculated
with WHAMVl are shownin graytriangles.Modified
(M-MOD/MOD)
from Muelleret al. (z}L2al/ Courbede complexation
normalis6e
pour Ni (panneaux
A et D) et Cu(panneaux
B et D) dansleslacsGeneva(carr6es;
panneaux
(cercles,
panneauxC et D).Lesvaleursmesur6es
A et B) et Opasatica
sontmontr6esen couleuralorsquecellespr6ditesparWHAMVl sont
par destrianglesgris.Modifi6d'aprdsMuelleret al. (2012a).
repr6sent6es
68
Considering
Ni bindingfirst,the affinityof the weaksites(LogKr,rui=6.98)
on DOMfrom Lake
is significantly
higher(P=0.009)
Opasatica
thanfor LakeGeneva(LogKr,rui=6.30),
but theseweak
(Lr,r.ri=34.2
sitesare presentat a concentration
nmole'mgC-1)
nearlyfour timeslower(P=0.01)
nmole.mgC 1).the Ni bindingaffinities
for the strongsitesin
than in LakeGeneva(Lr,r.ri=137
for LakesOpasatica
both lakesare not significantly
different(LogKz,r,ri=11.6
and L'1..7
and
Geneva,
respectively),
althoughthereare nearlysixtimesless(P=0.025)
of thesebindingsites
per unit DOCin LakeOpasatica
(12,x;=9.70
nmole.mgC-t),comparedto the DOMfrom Lake
Geneva(Lz,rrri=3.98
nmole'mgC-1).
Althoughthe strengthof the bindingof Cuto weaksiteson the DOMwasnot significantly
for LakeOpasatica
and LogKr,cu=7.63
differentfor both lakes(LogKr,c,=7.59
for LakeGeneva),
the numberof Lrsitesper unit DOCwasapproximately
threetimeshigher(P<0.001)
in Lake
(Lt,cu=244
Opasatica
nmole.mgC 1;thanin LakeGeneva(11,6u=75.6
nmole'mgC-1).Therewere
(P=0.014)
significantly
stronger(LogKz,cu=9.46)
andapproximately
twiceas many(P=0.003)
nmole'mgC 1)on the DOMfrom LakeOpasatica
strongbindingCusitesper unit DOC(12,6u=2t4.5
comparedto LakeGeneva(LogK2,6u=8.90
nmole'mgC-1).Theseresultsstrongly
and L2,gy=27.O
suggest
that the DOMfrom LakesOpasatica
andGenevaaredifferentin termsof their Ni and
Cucomplexation
affinityandcapacity.
Basedon the absorbance
measurements
described
above(section
4.3),Lake
andfluorescence
Opasatica
DOMwasfoundto be largelycharacteristic
of allochthonous
sources,
whereasLake
GenevacontainsDOMfrom largelyautochthonous
sources.lt is thustemptingto speculate
that the differences
in Cuand Ni bindingexhibitedby the DOMin the two lakesare relatedto
the differentweightingof allochthonous
andautochthonous
originsin the two lakes.Themore
mayhavea significantly
aromatichumic-like
DOMfrom allochthonous
sources
higher
for Cuthanthe moreproteincomplexation
affinity(at the strongbindingsites,K2)andcapacity
likeDOMfrom autochthonous
a contrasting
trendwasfoundfor Ni. The
sources.Interestingly,
Ni bindingaffinityat the weakbindingsites(Kr)wassignificantly
lowerfor the lakewith the
69
morearomatichumic-like
at both
sources.Thebindingcapacities
typesof bindingsiteswassignificantly
lowerfor the lakewith the morearomatichumic-like
sources,
againsuggesting
that Ni andCuare bindingto different
for
typesof bindingsiteson DOM. Bakenet al. (2011)foundhigherCu,Ni,Zn andCdaffinities
morearomaticDOM(basedon SUVAmeasurements),
althoughthe affinityvalueswerebased
on total complexedmetal,not at two differentbindingsites.Thesuggestion
from the titration
studythat Ni andCuarebindingto differentDOMsitesis analogous
to the earliersuggestion,
basedon the relationships
between% aFAoptandthe CI/CrandC3/Crratios,that Ni binding
sitesare distinctfrom thoseoccupiedby CdandZn. To our knowledge,
theseareamongthe
firstresultsto demonstrate
siteswithinthe DOM
the presence
metal-binding
of independent
mosaic.Theseresultsfurtherindicatethe importance
the qualityof DOMwhen
of specifying
predicting
the complexation
of tracemetalsby DOMin naturalsystems.
5
5.1
Conclusions
lmprovingtrocemetol speciotionpredictions
Themainobjective
of the thesiswasto demonstrate
of tracemetal
that the calculation
in naturalwaterscouldbe improvedby incorporating
speciation
a measure
of the qualitvof
quality
DOMintoexisting
chemical
equilibrium
models;we hypothesised
that the spectroscopic
of DOMmightbe usedto thisend. Eachof the followingsub-objectives
wasaddressed
during
the courseof the thesis:1) a seriesof lakesrepresenting
metal
a widegradientof dissolved
concentrations,
DOMconcentrations
2)we
and physico-chemical
conditions
wasidentified;
qualityof the lakewaterDOM,whichwaslinkedto
successfully
determined
the spectroscopic
lakeand watershedcharacteristics;
Ni2*and Zn2*were
3) the concentrations
of free Cdz*,Cu2*,
measured
in the seriesof lakesandcomparedto variousenvironmental
factors(e.g.,pH,total
quantityandqualityof DOM);4) linksbetweenthe DOMconditional
metalconcentration,
bindingconstants
for CuandNi andthe spectralproperties
DOMwere
of the lakewater
exploredfor two lakesthat exhibiteddifferentDOMcharacteristics
andfor whichmetal
speciation
alsodiffered;and5) WHAMpredictions
were
of the free metalionconcentrations
improved(esp.for Ni)by estimating
the proportionof DOMactivein metalcomplexation
using
70
qualityof the DOM.Themainconclusions
the spectroscopic
of the thesisare described
in more
detailin the followingparagraphs.
Basedon previous
studiesthat showeddiscrepancies
betweenmeasured
andWHAM-predicted
free metalconcentrations
in lakes(Fortinet al.,2010;Unsworthet al.,2006),we setout to
characterize
the free metalspeciationin a seriesof lakesfrom two differentgeographical
regionson the Canadian
Shield.Althoughgoodagreement
wasfoundbetweenmeasured
and
WHAMpredictedfree metalconcentrations
for Cd2*and Znz*for mostlakes,departuresfrom
thisagreement
wereobserved
for Cu2*and Ni2*.Markeddivergences
werefoundfor Cu2*
whenthe freeCu2*concentration
wasbelow0.4 nM (i.e.,belowtotaldissolved
Cu
concentrations
of about40 nM);similarlow freeCu2*concentrations
areoftenfoundin natural
waters.In the caseof Ni,WHAMconsistently
the free Ni2*concentration
over-predicted
in all
lakes.
Thepresence
of non-humic
origin,sometimes
foundin natural
organicligandsof anthropogenic
waters(butnot accounted
for in chemical
modelssuchasWHAM),hasbeen
equilibrium
suggested
asa reasonfor discrepancies
betweenmeasured
andmodeledfree metal
(Bakenet al.,20L1).Thisexplanation
concentrations
was,however,discounted
for the present
studyas remotelakes,littleinfluenced
by anthropogenic
sources,
werepurposely
chosen.
Otherpossible
reasons
for discrepancies
betweenmeasured
andWHAM-modeled
speciation
valueswererecentlysummarised
by LoftsandTipping(2011).Theseauthorsnotedthat the
followingfactorscouldinfluence
the accuracy
of WHAMpredictions:
1) the precision
of model
inputvariables
in measured
andmodelparameters,
2)the uncertainty
free metal
properties
concentrations,
and3) differences
betweenthe metal-complexing
of the humic
presentin naturalwatersandthe isolatedhumicandfulvicacidsusedto calibrate
substances
the model.lt wasthislatterexplanation
that wasexploredin thisthesis,by characterising
the
DOM,in additionto the metalspeciation,
in the studylakes.
7I
We wereinterested
in a DOMcharacterisation
be applied
methodthat could,in principle,
naturalDOMusingsimple
broadlyby non-specialists.
We,therefore,choseto characterize
laboratories,
asopposedto the
spectroscopic
techniques
available
in typicalwaterchemistry
that
moreinvolvedhumicsubstances
isolation
Moreover,it hasbeensuggested
techniques.
thesehumicsubstance
isolation
techniques
mayalterthe structureand metal-complexing
properties
of naturalorganicmatter(Abbt-Braun
et al.,2004;De Haan,1992).First,we showed
in the composition
Shield
that differences
of DOMin a smallsetof lakeson the Precambrian
(SUVAzsa),
(specific
weredetectable
usingvariousopticalparameters
UVabsorbance
fluorescence
indices(Fl)andexcitation-emission
measurements
(PARAFAC)).
interpreted
with parallelfactoranalysis
We thenexploredhowtheseoptical
parameters
predictions
mightbe usedto improvemetalspeciation
in naturalwatersusing
chemical
equilibrium
models.
We hypothesised
that the spectroscopic
measurements
of DOMqualitycouldbe usedto
estimatethe proportionof DOMactivein metalcomplexation.
As described
earlier(section
4.5),WHAMusersmustfirstestimatewhat proportionof the dissolved
organiccarbonpresent
in a naturalwatersample(measured
andthen
is constituted
of humicsubstances,
as mg C.L-l)
estimatewhat proportionof this humicmaterialis presentas humicvs.fulvicacids.Current
practiceisto assumethat humicsubstances
1985)
areapproximately
50%carbon(Thurman,
properties
andthat "65Yo
of the DOCis dueto organicmatterthat possesses
the cation-binding
(asmentioned
of averageisolated
fulvicacids"(Bryanet al.,2002).Usingtheseassumptions
earlierin thissection)we foundmarkeddivergences
andWHAMmodeled
betweenmeasured
concentrations
of free Cu2*and Ni2*.
Thepossibility
of improving
the optimal
the agreement
wasthenexploredby calculating
percentof FAactivelyinvolvedin metalcomplexation
(%aFAopt)
requiredto exactlypredictthe
measured
freemetalionconcentration
for eachmetalandfor eachlakesampled.
properties
Relationships
betweenthis%aFAopt
for a givenlakeand its DOMspectroscopic
wereinvestigated.
Themostsignificant
relationships
werefoundbetweenthe ubiquitous
72
humic-like
DOMfluorescence
component(C1)andthe %aFAopt
for Cuand Ni,andbetweenthe
humic-like
fluorescence
componentof allochthonous
origin(C3)andthe %aFAopt
for CdandZn
(Figure6). Giventhe measured
fluorescence
of thesecomponents
for a givenlake,its %aFAopt
wasthen estimatedandusedto predictthe speciation
of Cd,Cu,Ni andZn.
Whenfluorescence
measurements
wereusedto estimatethe proportionof DOMactively
involvedin metalcomplexation,
the WHAMpredictions
of free Ni2*concentration
were
(upto a factorof 6),comparedto the resultsobtainedwith
improvedfor allthe lakesmeasured
versionof WHAMandthe arbitrary65%aFAvalue.TheWHAMpredictions
the off-the-shelf
of
the freeCu2*concentration
improvedby a factorof 17f or LakeBethelandby a factorof 2 f or
LakesOpasatica
however,no improvementwasobserved.The
andVaudray.ForCd2*andZn2*,
fluorescence
signatureof DOMmay,therefore,potentiallybe usedas a proxyfor its metal
bindingactivityin chemical
equilibrium
modelssuchasWHAM. Ultimately,
it wouldbe of
practical
useto determinethe EEMfingerprint
of a givenlakewater,calculate
the relative
proportionof eitherthe ubiquitous
fluorescence
component(Cl/Cr)or the allochthonous
fluorescence
component(C3/Cr),
andusethisvalueto estimatethe %aFAfor the givenlake
waterfrom a relationship
similarto thoseshownin Figure6. Althoughthisapproach
appears
to be promising,
it wouldfirsthaveto be exploredovera widerrangeof DOMsamples
and
shownto holdtrue for othernaturalwaters.
It wasalsoapparentthat in orderto furtherimprovethe WHAMprediction
of the speciation
of
Cuandespecially
Ni,adjustments
to the metalbindingcapacity
of the DOMandthe affinity
constants
Indeed,we foundthat differences
in the
usedby WHAMwouldbe necessary.
i.e.the numberof bindingsites,for Cuand Ni
experimentally
measured
DOMbindingcapacity,
in the spectroscopic
character
couldbe explained
by differences
of DOM. In a smallnumberof
lakes(N=2)wefoundbetween-lake
and between-metal
differences
in the bindingaffinities
of
Ni andCuthat couldalsobe relatedto differences
character
of the DOM.
Dueto the smallnumberof samples,
theserelationships
arespeculative
onlyandshouldbe
furtherinvestigated
theseresultssupport
for a widevarietyof surfacewaters.Nonetheless,
73
our hypothesis
that the qualityof DOMis importantin determining
its rolein the speciation
of
tracemetalin naturalwatersandthat the fluorescence
signature
of DOMmaybe usedasa
proxyfor its metalbindingactivityin chemical
modelssuchasWHAM. However,
equilibrium
we havecalculated
the conditional
bindingproperties
of DOMfor Cuand Ni for onlytwo lakes.
A muchlargersetof watersamples
wouldbe requiredto comparethe WHAMVl Cuand Ni
bindingparameters
studieshave
to thoseexperimentally
measured
here.Someprevious
compared
the conditional
in the sampledlakewatersto
bindingparameters
for DOMmeasured
the average
intrinsicequilibrium
with datasetson the
constants
usedby WHAMVl (calibrated
bindingof metalsto isolatedhumicsubstances)
for each
WHAMmodelparameters
by adjusting
lakeuntila bestfit betweenmeasured
was
andpredicted
freemetalion concentrations
(Bryanet al.,2002;Chenget al.,2005;VanLaeret al.,2006).Following
achieved
thismethod,
properties
relationships
betweenthe fittedWHAMKyavaluesandmeasured
of
spectroscopic
DOMin naturalwaterscouldbe compared.
5.2
Researchperspectives
Themainobjectiveof thisthesiswasto enhance
modelsby taking
equilibrium
existingchemical
intoaccountthe spatialvariability
of the qualityof DOM. Alongtheselines,we successfully
demonstrated
the importance
of takingintoaccountthe qualityof DOMwhenusingchemical
equilibrium
modelsto predictthe speciation
of tracemetalsin naturalwaters.We also
"signature",
demonstrated
that the opticalproperties
may
itsfluorescence
of DOM,including
(i.e.,the numberof bindingsitesper unitof
be usedto estimateits metalbindingcapacity
organiccarbon).Forexample,
by measuring
of differentPARAFAC
the relativeproportions
fluorescent
components
in a givensample,the % of MODactivein the complexation
of Cd,Cu,
Ni andZn in the samplemaybe estimated
inputparameterin
a prioriandusedasa measured
chemical
speciation
models,suchasWHAM. lmprovements
in the WHAMpredictions
of the
concentration
of the free metalrangedfrom up to a factorof 6 for Ni and up to factorof 17 for
Cu. Theconditional
bindingconstants
for the metaI-DOM
complexation
of certaintracemetals
may,however,needto be updatedfor conditions
wherelow metalconcentrations
arefound
(i.e.,whereIM]/DOCratiosare low).
74
Enhancing
modelsleadsto improvedpredictions
chemical
equilibrium
of tracemetalspeciation
in aquaticsystems.Theaccurateprediction
of the speciation
of tracemetalsin aquaticsystems
is essential
for predicting
theirbioavailability
and,thus,theirtoxicityto aquaticorganisms.
The
BioticLigandModel(BLM)is widelyusedamongscientific,
regulated
andregulatory
(Paquin
communities
to predictthe toxicityof tracemetalstowardsaquaticorganisms
et al.,
2OO2l.TheBLMusesWHAMto calculate
in the aqueous
the free metalion concentration
phasenearthe bioticligandconsidered.
Althoughthe BLMworksreasonably
wellfor high,
improvements
acutelytoxicmetalconcentrations,
areneededto betterpredictmetaltoxicity
at low,chronictoxicitylevels(Campbell
et al.,2006).Our resultsshowimprovements
in WHAM
(upto a factorof 5 for Ni)that candirectly
speciation
calculations
at low metalconcentrations
improveour abilityto predictchronicmetaltoxicity.Thewiderimplication
of our research
is
that improvedprediction
in naturalaquatic
systems
is neededfor
toxicitytesting,environmental
impactassessment,
environmental
monitoring,
andecological
(Luomaand Rainbow,
riskassessment
2008).
and management
Althoughthe resultsandconclusions
of the thesisaresignificant
and novel,moreresearch
questions
perspective,
wereraised.Firstfrom an analytical
are currentanalytical
techniques
lmprovements
ableto measure
the free metalion accurately?
are neededin the analytical
(Tipping,
measurement
of manydifferentfreemetalionsat low environmental
concentrations
2002),,
including
the measurement
of free FeandAl concentrations
in naturalwaters,so asto
improvethe waychemical
equilibrium
modelsaccountfor FeandAl competition
for
complexation
signature
sites.Second,
canthe DOMfluorescence
be usedto estimatethe metal
bindingactivityof othertypesof DOM(e.g.,non-lakeDOM)andfor metalsotherthanCd,Cu,
Zn and Ni? In orderto address
thisquestion,
the comparison
betweenmeasured
and modeled
free metalionconcentrations
shouldbe conducted
for moretypesof
and DOMfluorescence
DOM(e.g.,moreautochthonous
andfor moremetals.Third,do the metalend-members)
humicsubstances
usedby WHAMsufficiently
represent
metaI-DOM
bindingparameters
bindingin naturalwaters?Shouldfutureresearch
effortsmoveawayfrom usingisolatedhumic
75
naturalDOMin chemicalspeciation
modelsandworkon building
substances
to represent
intodetermining
modelsusingDOMas it existsin naturalwatersamples?Again,moreresearch
the bindingparameters
of varioustypesof DOMwith manymetals,for widerconcentration
rangesof totalandfreemetaland DOM,is needed.Finally,
what isthe bestwayto incorporate
DOMqualityintochemical
speciation
modelssuchasWHAM?Fornow,thiscanbe done
"signature",
but
externally
by estimating
the metalbindingactivityfrom the DOMfluorescence
a newsample-specific
inputparameter
for DOMqualitywouldbe mostuseful.
76
partie: Articles
DeuxiEme
78
Article1
Spatialvariationin the opticalpropertiesof dissolvedorganicmatter (DOM)in
lakeson the CanadianPrecambrian
Shieldand linksto watershedcharacteristics
KristinK.Mueller,ClaudeFortinand PeterG.C.Campbell
Canada
INRSEauTerreet Environnement,
Universit6
du Qu6bec,490 de la Couronne,
Qu6bec(Qu6bec),
G1K9A9
18(1):2t-44.
Publi6en 2012dansAquaticGeochemistry
-y
DOf: 10.L007/ stO498-OLL-9t47
79
Abstract
In the presentstudywe exploredthe useof variousopticalparameters
in
to detectdifferences
the composition
of the dissolved
organicmatter(DOM)in a setof lakesthat areall locatedon
predictions
the Canadian
Precambrian
were
Shield,but withinwhichCuand Ni speciation
previously
shownto divergefrom measured
valuesin somelakesbut not in others.Water
(N = 18 lakes)and2008(N = 8
werecollected
samples
with in situdiffusionsamplers
in 2OO7
lakes).Significant
differences
in DOMqualitywereidentifiedbetweenthe sampling
regions
(Rouyn-Noranda,
organic
Ontario)and
amonglakes,basedon dissolved
Qu6becandSudbury,
([DOC]),
(SUVAz5a),
carbonconcentrations
fluorescence
indices(Fl)and
specificUVabsorbance
excitation-emission
measurements.
Parallelfactor
analysis
(PARAFAC)
quinone
of the EEMspectrarevealed
four components,
two of which(C3,oxidized
proteinfluorescence
fluorophore
of allochthonous
origin,andC4,tryptophan-like
of
autochthonous
origin)showedthe greatestinter-regionalvariation.
Theinter-lake
differences
in DOMqualitywereconsistent
from
with the regional
watershed
characteristics
asdetermined
satelliteimagery(e.g.,watershed-to-lake
of surface
surfacearearatiosand relativepercentages
water,rockoutcrops,vegetativecover,and urbandevelopment).Sourceapportionmentplots,
builtuponPARAFAC
components
ratioscalculated
for our lakes,wereusedto discriminate
amongDOMsources
andto comparethemto sources
identifiedin the literature.Theseresults
haveimplications
for otherareasof research,
variations
in the
lake-to-lake
suchasquantifying
influence
of organicmatteron the speciation
of traceelementsin naturalaquatic
environments.
80
1
Introduction
with
in the aquaticenvironment,
organicmatter(DOM)is ubiquitous
Naturaldissolved
rangingfrom 0.5 mg C L-lin the openoceanto morethan30 mg C L-1in bog
concentrations
1985).Althoughthe quantityof DOMis importantwith respectto its
waters(Thurman,
processes,
for exampleasan
its rolein biogeochemical
transportandflux withinan ecosystem,
(Aeschbacher
et al.,2O7Ol,
et al.,1990),asa mediatorof redoxreactions
organicacid(Shuman
(Campbellet
will depend
ligand(Tipping,
2OO2l
asa metal-binding
al.,L9971and
asa surfactant
With respectto the latterrole,attemptshavebeen
on its nature(i.e.,sourceandcomposition).
fractions
of DOM,e.g.fulvicand humicacids,in
madeto includevariousisolatedchemical
models(DudalandG6rard
equilibrium
chemical
,2OO4l.Thesemodels(e.g.,the Windermere
Adsorption
Competitive
et al.,1998)or the Non-ldeal
HumicAqueousModel,WHAM(Tipping
laboratory
titrationdata,but whenusedto
Model,NICA(Milneet al.,2003))canreproduce
(Unsworth
et
predictmetalspeciation
theyare muchlesssuccessful
in naturalwatersamples
in applying
thesemodelsto naturalwaters
al.,2006).Oneof the majorproblemsencountered
of "active"fulvicor humicacidpresentin the natural
is the needto estimatethe concentration
organiccarbon
isthe concentration
of dissolved
information
water. Oftenthe onlyavailable
(tDOCl)
asto the natureof the
havehadto makeassumptions
andin suchcasesresearchers
of the fulvicor humicacidsandthe other
andthe relativecontributions
DOCin theirsamples
dissolved
organicconstituents.
between
Theimpetusfor the presentstudywasthe recentobservationof markeddifferences
Precambrian
Shield(Fortin
in lakeson the Canadian
measured
andpredictedmetalspeciation
the WHAMand
et al.,2010;Muelleret al.,2010).On the basisof the ambientwaterchemistry,
ion concentrations
of a
modelswereusedto predictthe free-metal
NICAchemicalequilibrium
, u ,N i a n dZ n )i n l a k e si n t h e R o u y n - N o r a n(dQau 6 b e c ) a n d
numbeo
r f t r a c em e t a l s( C d C
valueswasgenerally
and predicted
betweenmeasured
Sudbury(Ontario)areas.Agreement
werewellbelowthe
ion concentrations
goodfor CdandZn,but for Cuthe predicted
free-metal
the measured
valuesconsistently
exceeded
valueswhereasfor Ni2*the predicted
measured
betweenmeasured
free-ionconcentration.In the lattertwo cases,the degreeof divergence
81
valuesvariedfrom laketo lake.We speculated
in
and predicted
that lake-to-lake
differences
the composition
to the differences
of the DOMmightbe oneof the majorfactorscontributing
betweenobserved
andpredictedmetalspeciation.
polyelectrolytic
Dissolved
organicmatterhasbeendefinedasa complexmixtureof disordered,
generated
molecules,
from plant,microbial,
andanimalproductsat variousstagesof
(Wetzel
decomposition
TheDOMpoolin lakesmaycontainsomesimplelabile,low
,2OO1-).
molecular
weightorganiccompounds,
high
but normallyis dominatedby recalcitrant,
molecular
weightfulvicand humicmaterial(Thurman,
L985).With respectto the originof
(production
DOM,bothallochthonous
from the
sources
environment
of DOMin the terrestrial
(production
degradation
of soiland plantorganicmatter),andautochthonous
within
sources
the watercolumnfrom the excretion,
algaeand
decomposition
andlysisof macrophytes,
cyanobacteria
in the littoraland pelagic
zones)contributeto the poolof DOMin naturalaquatic
(Thurman,
environments
1985;Wetzel,2001).Thecomposition
of DOMin a givenlakewill
reflectthe relativecontribution
of eachof thesesources,
but it will alsodependon the
processes
to whichthe organicmolecules
havebeensubjected
on theirpathfrom the
watershed
to the lakeandin the lakeitself(e.g.,differential
sorptionon inorganic
soilhorizons
(Schumacher
et al.,2006);photochemical
in the tributary
degradation
and biodegradation
streamsfeedingthe lakeand in the lakewatercolumn(Kcihler
et al.,2002;Yoshioka
et al.,
2OO7D.lt followsthat watershedcharacteristics,
hydrologicregime,
suchasgeomorphology,
landcover,the presence
of upstreamlakesand landuseare likelyto influence
the quality(i.e.,
the natureor composition)
of DOM(Jaff6et al.,2008;Mattssonet al.,2005).
Thespectroscopic
character
of DOMhasprovedusefulin assessing
itssourceandquality.
Opticalproperties
of DOMthat havebeenusedto distinguish
betweenthe typesof DOM
(Gondaret al.,2008;Weishaar
(Coble,1-996;
includeabsorbance
et al.,2003)andfluorescence
KosterandSchmuckler,
l-967;McKnight
et al.,2001).Absorbance
andfluorescence
spectroscopy
areparticularly
usefultoolsin the analysis
of DOMqualityin that theyare
relatively
simple,rapid,sensitive
andnon-destructive.
Moststudies,
however,have
\
82
with DOMfrom verycontrasting
discriminated
amonga largenumberof samples
andto the
for examplecomparing
DOMfrom wetlandsto rivers,to estuaries,
environments,
openocean(Jaff6et al.,2008).
to detect
Thepurposeof the presentstudywasto explorethe useof variousopticalparameters
closeto one
in the qualityof the DOMin a smallsetof lakesthat aregeographically
differences
predictions
Precambrian
Shield,andwithinwhichCuand Ni speciation
anotheron the Canadian
in DOM
values.We reporton the variability
havebeenshownto divergefrom measured
measurements,
in lakes
andfluorescence
opticalquality,asdeterminedby simpleabsorbance
Precambrian
Shield.Parallel
from the Rouyn-Noranda
andSudburyregionson the Canadian
(PARAFAC)
that contributeto the overall
factoranalysis
is alsousedto identifyfluorophores
to the spectra.Clusteranalysis
DOMfluorescence
andto estimatetheirrelativecontributions
are usedto identifygroupsof lakeswith similarDOM
and principal
components
analysis
properties,
catchments
areexplored.A source
and linksto the landusewithinthe individual
trendsandto identifythe lakes
approach
is then usedto illustratelake-to-lake
apportionment
alongthe spatialgradientof the lakessampled.
that constitute"end-members"
Methodology
1.1
Studyorea
The lakewater qualitydatain this studywerecollectedfrom two differentregionsof the
in north-western
and
Precambrian
Canadian
Shield:one centredon Rouyn-Noranda
Qu6bec,
Ontario(Figure1). In termsof the underlying
the otherlocatednearSudburyin north-central
gabbroandfelsic
geology,
Shield,madeup of quartzite,
both regionsarefoundon the Canadian
(Carignan
gneisses
is foundin the Abitibisuband Nriagu,1985).Thecityof Rouyn-Noranda
provinceof the Canadian
Belt. Forthe mostpart,the
the AbitibiGreenstone
Shieldcontaining
glaciofluvial
andglaciolacustrian
deposits,
bedrockis coveredwith a thicklayerof glacial,
et al.,2005).
breakthroughthe surfacedeposits(Veillette
althoughbedrockdoesoccasionally
is dominatedby
Shield,the locallandscape
Whilethe cityof Sudburyis alsoon the Canadian
83
glacialdeposits(Barnett
the presenceof exposedbedrockcoveredby thin and discontinuous
and Bajc,2@2; Carignan
and Nriagu,1985).
:\
L- -"_)-:,'^
a
ln'r
i*
:
t
-f
=lY
Figure1.
Mapof thetwo studyareasshowing
position
in eastern
the relative
of thetwo regions
(panela),thegeographical
Canada
lakes(panelb)andthe
location
ofthe Rouyn-Noranda
Sudbury
lakes(panelc).
Bothstudyareasare richin ore depositsand havebeengreatlyimpactedby metalminingand
smeltingactivities,particularlyby atmospheric
depositionof acidand metals(Borgmannet al.,
1998;Dixitet al.,2007).ThepH of the lakestendsto decrease
with a decreasein the downwind distancefrom the metalsmelters,particularlyin the Sudburyarea(Yanand Miller,1984).
Acidminedrainage
from pointsources,
suchasabandoned
minesor mineralized
may
outcrops,
alsodecrease
the pH of lakewater locally.In eachregion,lakeswere chosento representa
gradientin water quality(pH,[DOC]andtracemetalconcentrations).
84
2.2
Lakewoter sompling
Lakewatersamples
weretakenfrom ninelakesin eachof the studyareasduringJulyand
Augustof 2OO7
siteswherechosenin the littoralzonesof small,
and2008(Table1). Sampling
well mixedlakesandthe sampling
to be representative
siteswereconsidered
of the wholelake
of waterquality(i.e.,pH andconcentrations
of DOCand
epilimnion.Thespatialvariability
majorcations)is knownto be minimalwithinmanyof the sampledlakes(Fortinet al.,2010).
in Fortinet al. (Fortin
Lakewaterwassampledusingequilibrium
diffusionsampling
asdescribed
jars(Nalgene)
plasticlids
et al.,2010).Briefly,250mL polypropylene
toppedwith custom-made
fittedwith a 0.2prmfiltermembrane(HTTuffryn,Pall)werefilledwith ultrapurewater(>18
to the lakebottom.Thesamplers
Mohmscm)andfixedto plasticrodsthat wereanchored
for 14 to 19 days.
wereinstalled
at a depthof 1 m from the lakesurfaceandleftto equilibrate
period,the samplers
werebroughtto the surface,
Following
thisequilibration
the membrane
piercedwith a cleanplasticpipettetip and
surfacerinsedwith ultrapurewater,the membranes
with cleanpolypropylene
the customjar lidsreplaced
screwlids.Thesamplejarswerebagged
and storedin the darkat 4oCin fieldcoolersfor a maximumof 12 daysuntiltheywere
were
transported
backto the laboratory.LakewaterpH,conductivity
andtemperature
measured
in the fieldat the time of samplecollection.
85
Table1.
Location,average(t SD)measuredpH,dissolvedorganiccarbon(DOC)concentration,
dissolvedconcentrations
of Caand Mg and
yearsthe lakesweresampled(N=3-6).
Lake (code)
Region
Location
pH
DOC
(mg'L-')
Ca
(rrM)
Mg
(uM)
Years Sampled
2007
Adeline(AD)
Rouyn-Noranda 48"12',12"
N
7 9 " 1 0 ' 1 7W"
7.53
4.6f 0.8 1 7 2 . 6t 0 . 8
86.7r 0.5
Y
Bousquet(BO)
Rouyn-Noranda 48"12'56"
N 78'37'06"W
6.35
1 3 . 0r 0 . 7 1 2 2 . 2* . 0 . 4
54.4r.0.1
Y
D'Alembert(DA)
Rouyn-Noranda 48'23'01"N
79'00'35"W
7.48
8 . 0* 0 . 8 1 8 0 . 8r 0 . 5
6 2 . 5r 0 . 1
Y
Dasserat(DS)
Rouyn-Noranda 48"15'06"
N 79"24',24"W
6.64
5.2t 0.7 208.9t.2.1
96.4r 3.0
Y
Dufault(DU)
Rouyn-Noranda 48'18'30"N 78"59'25"
W
7.41
5 . 1i 0 . 4 413.2*.10.9 1 2 2 . 7i . 4 . 9
Y
Dufay(DF)
Rouyn-Noranda 48'01'42"N
79"27',33"
W
6.60
8 . 3r 0 . 4
7 5 . 9r 0 . 5
55.6r 0.2
Joannes(JO)
Rouyn-Noranda 48"10',53"
N 78"40'02"W
6.82
1 0 . 0i 0 . 3 1 9 0 . 0i 1. 4
77.6r 0.3
Opasatica(OP)
Rouyn-Noranda 48"04'32"N 79"17',49"
W
7.47
6 . 0r 0 . 4 215.4r.3 O 1 1 4 . 2r . 0 . 9
Vaudray(VA)
Rouyn-Noranda 48'05'45"N 78'40'50"W
6.59
81r0.1
8.93
5 . 2r 0 . 5 466.1r 3.3 282.6t9.4
8 3 . 6i 0 . 8
Bethel(BE)
Sudbury
46'46'17"N
Crowley(CR)
Sudbury
46'23'06',N 80'58'55'W 6.46
2 . 6r 0 . 3
Geneva(GE)
Sudbury
46"45'52',N 81'32',46"
W
6.89
3 . 4r . 0 . 4 6 8 . 6r 1 . 9
Hannah(HA)
Sudbury
46'26'36"N
W
81"02'17"
Middle(Ml)
Sudbury
46'2d22" N
Nelson(NE)
Sudbury
Raft (RA)
80"57'43"W
57.4t0.2
3 7 . 9i 1 . 1
Y
Y
Y
Y
Y
34.2i.0.1
Y
2 7 . 3r . 1 . 2
Y
7.40
3 . 6i 0 . 1 2 7 4 . 9t 1 . 0 1 5 6 . 9i 0 . 3
Y
81'01'26"W
7 .46
3 . 3r 0 . 4 246.9r 0.3 140.5t0.4
Y
46"43'36"N
81'05'39"W
5.86
1 . 4r 0 . 5
5 8 . 9r 0 . 1
28.3r01
Y
Sudbury
46'24'35"N
80'56'44"W
6.91
23r0.3
8 1 . 6r 1 . 1
4 7 . 7t 1 . 6
Y
Ramsey(RM)
Sudbury
46'28'57"N
80"57'00"w
7.83
3.6i 0.5 395.0r 0.9 198.7t O.4
Y
Whitson(WH)
Sudbury
46'35'03"N
80'58'20"W
6.24
3 . 8r 0 . 1 145.1t 1.9
Y
8 0 . 5r 2 . 3
2008
Y
Y
2.3
Sompleanalysis
All plasticlaboratory
andsampling
equipmentwassoakedinLO%(w/v)nitricacidfor at least24
h, rinseda minimumof sixtimeswith ultrapurewateranddriedundera ClassL00laminarflow
hood. Similarly,
allglasswarewassoakedin 2 N HCIsolutionbeforebeingrinsedwith ultrapure
water.
jarshadarrivedin the laboratory,
Oncethe polypropylene
theywereopenedundera clean
ClassL00laminarflow hoodandthe followingsubsamples
weretaken.A 1.0-mL
samplewas
transferred
to a polypropylene
vialandacidified
to 2%(v/v)with concentrated
ultra-trace
nitric
acid(BDHAristar,VWRInternational)
for analysis
of majorcations(Ca,Mg)by atomicemission
(VistaAXCCDSimultaneous
spectroscopy
ICP-AES,
Varian).
Next,20-mLsubsamples
weretransferred
to amberborosilicate
vials(l-ChemBrand)for total
(TOC-Vcpn,
(Cary300BioUV-Visible
organiccarbonanalysis
aswellasabsorption
Shimadzu)
(CaryEclipse
Spectrophotometer,
Varian)andfluorescence
Fluorescence
Spectrophotometer,
Varian)measurements.
All opticalparameters
weremeasured
at the naturalpH of the sample
andwithoutdilution.Absorption
spectrawererecordedusing1-cmpolymethacrylate
cuvettes.
Thespectrawere measuredfrom 24Oto 700 nm at intervalsof L nm with ultrapurewateras a
reference.Excitation-emission
spectraweremeasured
using1-cm
quartzcuvettesat excitation
wavelengths
from 2L0to 400 nm at increments
of 5 nm andat
emission
wavelengths
from 300to 580nm at increments
of 2 nm. Theprotocolfor data
collection
andtreatmentrecommended
in Stedmonand Bro(Stedmon
and Bro,2008)was
(usingcorrection
followed,including
filesprovidedby the instrument
spectralcorrection
(McKnight
manufacturer),
innerfiltercorrection
et al.,200I;Mobedet al.,1996),calibration
to
Ramanunits(Lawaetz
2009)andfinallythe subtraction
andStedmon,
of ultrapurewaterblank
and removalof spectralscatter.
(SUVAzsa,
Absorbance
measurements
wereusedto calculate
the specificUVabsorbance
L ffi'1
mg C t) by dividingthe absorbance
at 254nm in inversemeters(m-t)by the concentration
of
87
DOCmeasured
in milligrams
of carbonper litre(mgC L-')(Weishaar
et al.,2003).The
fluorescence
indexwascalculated
emission
intensityat
asthe ratiobetweenthe fluorescence
470 nm overthat at 520nm for an excitation
et al.,
at 370nm (Coryet al.,2010;McKnight
(PARAFAC),
2001).Finally,
the multivariable
statistical
was
tool,parallelfactoranalysis
employedusingthe procedure
in a
outlinedby Stedmonand Bro(2008)as implemented
MATLABenvironment,usingthe "N-waytoolboxfor MATLAB"(Andersson
and Bro,2000).
Excitation-emission
wavelengths
from 240
spectrafor 87 individual
lakesamples
with excitation
to 400nm andemission
wavelengths
from 350to 550nm wereincludedin the PARAFAC
model.Themeasured
valuesat excitation
wavelengths
from 210to 235nm andemission
wavelengths
from 300to 348nm werenot includedin the modelso asto reducethe influence
of datawith a low signal-to-noise
with
ratioandto constrain
the modelto the wavelengths
appreciable
fluorescence.
Thewiderangeof DOCconcentrations
resulted
in the lakesamples
(in Ramanunits,RU).To avoidthe
in a correspondingly
widerangeof fluorescence
intensities
identification
of the DOMfluorescence
intensityextremesasoutliersby PARAFAC
andto
compareallof the samples
on the samescale,the normalized
fluorescence
intensityforagiven
samplewascalculated
by dividingthe measured
fluorescence
intensityat eachpairof
excitation
andemission
wavelengths
intensityof the entire
by the average
fluorescence
spectrumfor eachlake.Following
the identification
numberof PARAFAC
of the appropriate
fluorescence
components,
the maximumfluorescence
fluorescent
intensities
of the identified
components
for eachlakewerethen restoredby multiplication
by the average
spectrum
fl uorescence
intensity.
2.4
Lokewatershedcharacteristics
Lakewatershed
delimitation
wasachieved
data
usingNationalHydroNetwork(NHN)geospatial
portalGeobase
from the geospatial
andthe NationalTopographic
DataBase(NTDB)generated
(NRCan).
geospatial
by NaturalResources
Canada
Usinghydrographic
andtopographic
data,
the waterdividelinewasidentified
wascalculated.
andthe surfaceareaof eachwatershed
Watershedlandcover(%surfacewater,% rockoutcrops,% vegetationand%wetland)and
landuse(%agriculture
ando/ourbandevelopment)
for each
werealsocalculated
aspercentages
88
Thelandcover
landcoverdataprovidedby Geobase.
usinggeospatial
of the lakewatersheds
Landsatortho-images.Dataintegration
dataconsistedof vectorizedrasterdatafrom classified
Research
Systems
weredoneusingArcGlS10 (Environmental
andsurfaceareacalculation
Institute,Inc.,Redlands,
CA).
2.5
Stotisticolanalysis
level
at the alphaequals0.05confidence
werecalculated
Thefollowingstatistical
analyses
lL). Parametric
correlation
analyses,
t-tests
1L.0(Systat
SoftwareInc.,Chicago,
usingSigmaPlot
procedures
usingthe Holm-Sidak
with pairwisemultiplecomparison
andone-wayANOVA's
where
of variances
amongdatasets
methodwereusedwhenthe normalityand homogeneity
Rank
testswereemployed(Spearman
non-parametric
otherwise,
the appropriate
confirmed;
Mann-Whitney
RankSumtest in placeof
correlation,
in placeof the Pearson
Ordercorrelation
one-wayANOVAon Rankswith pairwisemultiplecomparison
the t-tesqKruskal-Wallis
procedures
usingDunn'smethod,insteadof an ANOVA).Percentlandcoverand usagedata,as
werearcsinesquare
components,
intensities
of the PARAFAC
wellasthe relativefluorescence
tests.
root transformedbeforeperformingthesestatistical
(pH,the
of waterchemistry
basedon the similarity
A hierarchical
clusteranalysis
(%surfacewater,% rockoutcrops,%
of Caand Mg),watershedcharacteristics
concentrations
([DOC],
Flandthe
SUVA25a,
and DOMproperties
vegetation
cover,and% urbandevelopment)
Ct, C2,C3andC4)wasconductedusingthe
components
relativeproportions
of the PARAFAC
priorto analysis.
distance.All datawerestandardized
Wardlinkagemethodwith Euclidean
lL).A principal
SoftwareInc.,Chicago,
wereperformedusingSystat12 (Systat
Clusteranalyses
(PCA)
represent
the distribution
of the
to graphically
wasalsoconducted
componentanalysis
gradients,
inputdata.The
usingthe above-mentioned
studiedlakesalongthe environmental
International,
Wageningen,
NL;
version4.5(PlantResearch
PCAwasperformedusingCANOCO
in the
(2002)).Theresultsof thesemultivariate
analyses
are presented
ter BraackandSmilauer
S u p p l e m e n t aM
r ya t e r i a(lS M ) .
89
3
Results
Thenaturalvariability
in dissolved
organicmatter(DOM)wasassessed
for the sampledlakes
andthe resultsare presented
in the followingfour sections:
1) the generalwaterqualityof the
lakessampled,
2) the lakewatershed
characteristics,
by
3) the quantityof DOMas measured
the concentration
of DOC,4) the qualityof DOMasevaluated
on the basisof its absorbance
andfluorescence,
and5) the statistical
analysis
of theselatterdatato identifyDOM
components.
3.1
Generallake water quality
In general,
the waterqualityof the lakessampled
from the two regionsis quitesimilar(Table
1). Lakes
regionarecircumneutral,
with a meanpH of 6.79anda
narrowrangebetweenpH 6.35and7.53.ThemeanpH for the Sudburylakes(6.53)is similarto
that of the Rouyn-Noranda
lakes,but theyspana widerpH range(5.86to 8.93).Thelakesin
bothsampling
regionshavesimilarly
low concentrations
of calcium(Ca:57 to 41.3pM) and
(MC:27to 199pM),contributing
magnesium
to generally
softwater. LakesRamsey
and Bethel,
from the Sudburyregion,hadthe highestpH andthe highestCaand Mg concentrations
of all
the lakes.
3.2
Lokewatershedchoracteristics
Thewatershed
characteristics
of the lakessampledweresignificantly
differentbetween
sampling
regions(Table2). Thewatershed-to-lake
surfacearearatios(W/L)aregenerally
higherin the Rouyn-Noranda
region(mean= 25)thanin the Sudburyregion(mean= 9).
Moreover,lakewatersheds
in the Rouyn-Noranda
regionhavea significantly
highermean
percentvegetationcover(83%landwetlandcover(L%),whereaspercentsurfacewater (L3%l
androckoutcrops(O%larelowerthanin the Sudburyregion(4L%,O%,23%and28%,
respectively).
Forestcoverdominated
the vegetative
landcoverfor the watersheds
near
(69-99o/olandSudbury(96- IOO%),
Rouyn-Noranda
from shrubsbeingmuch
the contribution
lessimportant.Themorenortherlywatersheds
nearRouyn-Noranda
alsocontained
a higher
90
proportionof coniferous
with the moresoutherly
Sudburyregion
forests,compared
watersheds.
3.3
DOMquontity
differedsignificantly
betweenthe two study
Thequantityof DOM,i.e.the DOCconcentration,
(Figure2a). ThemeanDOCconcentration
lakeswashigher
of Rouyn-Noranda
areas(P<0.001)
(7.2mg L-1)andspanneda widerrange(3.8to 13.4mg L-1)
thanin the lakesfrom the Sudbury
1;
region(meanof 3.4mg L rangefrom0.8to 6.0mg L-1).Withinthe entiredataset,Lakes
regionhadthe highestIDOC],
whereasLake
Bousquet
andJoannds
(Figure
2a). Significant
Nelson,
from the Sudburyregion,hadthe lowestconcentration
(P<0.001)
in [DOC]werefoundamongmostlakes.In anygivenyear,the variability
differences
(N=3)DOCvaluesin a givenlakewaslow (i.e.,coefficient
of variation
of replicate
, CV< L7yol,
whichhada CVof 33%.Forthoselakesthat weresampled
exceptin LakeNelson(Sudbury),
variability
in the [DOC]waslessthanI4%.
duringmorethanoneyear,the inter-annual
3.4
DOM quolity
(SUVAzsq),
the specificUVabsorbance
To probethe qualityof the DOM,we calculated
valuesvariedsignificantly
between
measured
at a wavelength
of 254 nm. TheSUVAzs4
(Figure
lakes
vdlu€for the Rouyn-Noranda
2b). ThemeanSUVA25a
samplingregions(P<0.00L)
1
but
washigher(8.3L m mg C-1)
thanfor the lakesfrom the Sudburyregion(5.3L m-1mg C-1),
thanthat for the Sudburylakes(3.2to 10.3
L m-tmg C-1)
spanneda narrowerrange(5.8to 11-.5
(P<O.OOl)were
foundbetween
differences
L m-l mg C-1).Amongall lakesamples,
significant
(P<0.05);
hadthe highestSUVAzsa
LakeBousquet
LakesBetheland Ramsey
and LakeBousquet
fromthe Sudburyregionandwhichhada moderate
value,whereasLakesBetheland Ramsey,
of replicate
values.In anygivenyear,the variability
[DOC],hadsomeof the lowestSUVAzsa
(N=3)SUVA25a
similarto that of the [DOC](i.e.,CV< t8%1,
v?lu€sin a givenlakewasgenerally
Forthoselakes
respectively).
exceptin two Sudburylakes(Nelsonand RafUCVs45%and57%o,
in the SUVAzs4
values
variability
that weresampledduringmorethanoneyear,the inter-annual
was lessthan L0%,exceptfor LakeRaftfor whichthe CVwas39%.
91
Tabfe 2.
Lake (code)
Adeline(AD)
Watershed-to-lake
surfacearearatio (W/L),percentwatershedlandcover(%surfacewater,% rockoutcrops,% vegetationand%
wetland)and landuse(%agriculture
ando/o
urbandevelopment)for
the lakessampled.
Region
Rouyn-Noranda
w/L
7
Land Goverage
Land Usage
% surface
o/o foCk
water
16
outcrops
0
82
development
0
% vegetation % wetland
% agricurture
7ourban
Bousquet(BO) Rouyn-Noranda
129
6
0
90
1
(DA) Rouyn-Noranda
D'Alembert
22
o
0
81
1
Dasserat(DS)
Rouyn-Noranda
19
16
0
83
1
Dufault(DU)
Rouyn-Noranda
7
21
0
69
7
Dufay(DF)
Rouyn-Noranda
11
13
0
86
0
Joannds(JO)
Rouyn-Noranda
10
11
0
87
0
Opasatica(OP) Rouyn-Noranda
11
13
0
78
1
Vaudray(VA)
Rouyn-Noranda
8
15
0
81
I
Bethel(BE)
Sudbury
5
25
18
29
29
Crowley(CR)
Sudbury
23
19
28
52
0
Geneva(GE)
Sudbury
5
25
1
74
0
Hannah(HA)
Sudbury
6
18
63
11
7
Middle(Ml)
Sudbury
11
17
70
6
6
Nelson(NE)
Sudbury
4
34
0
66
0
Raft(RA)
Sudbury
15
23
25
52
0
Ramsey(RM)
Sudbury
6
25
26
24
25
Whitson(WH)
Sudbury
7
19
21
50
8
16
t Sudbury
Rouyn-Noranda
14
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1.5
a
tr 1.4
1.3
1.2
as
8g'3:8
o
a
o
c
"o
a
$'a:u\2"$e.s'"$.9-*"""
*q$b"*&\ffi
Figure2.
(panela), specificUV
Replicate
measures
of dissolved
organiccarbon(DOC)concentration
(SUVA254)
(panel
(Fl)
(panel
index
b) andfluorescence
c) for samplestakenfrom
absorbance
lakesin 2007(opencircles;N=18)and 2008(closedcircles;N=8).Lakesto the left of the
region,whereasthoseto the rightarefrom the
dashedlinearefrom the Rouyn-Noranda
Sudburyregion.
93
Thequalityof DOMwasalsoevaluated
by excitation-emission
Thefluorescence
spectroscopy.
index(Fl)iatio (inherently
wasused
normalized
to the tDOCI)
(Fl>1.4)
to distinguish
betweenorganicmatterfrom autochthonous
sources
andallochthonous
(Coryet al.,2OIO;
sources(Fl<1.4)
McKnight
between
et al.,200L).TheFldifferedsignificantly
(Figure2c);the meanFlfor the Rouyn-Noranda
samplingregions(P<0.05)
lakes(1.32)was
lowerthanthat for the Sudburylakes(1.36),andspanneda narrowerrange(L.26to 1.38)than
did the Flfor the Sudburylakes(1.20to 1.54).Amongall lakesamples,
significant
differences
(P<0.001)
werefoundbetweenLakesBethelandVaudrayonly(P<0.05).
Thewithin-year
(N=3)Flvaluesin a givenlakewasminimal(CV<7%1,aswastemporal
variability
of replicate
(CV< 6%).
variability
Furtherevidence
for differences
betweenthe two sampling
areaswasfoundwhenthe bivariate
relationships
betweenthe DOMquantity([DOC]),
andthe qualityparameters
SUVAzsa
and Fl
(Figure3). A positiverelationship
wereexamined
is apparentbetweenSUVAzsa
and [DOC]for
all lakes(Figure
3a). No relationship
betweenthe Fland [DOC]is apparentfor the RouynNorandalakes,whereasa positiverelationship
betweenFland [DOC]is apparentfor the
Sudburylakes(Figure
3b). Notethat sincebothSUVAz5a
with respectto
dnd Flare normalized
the [DOC],it wouldbe statistically
inappropriate
coefficient
or
to calculate
a correlation
(r = regression
betweenSUVAzsa
or Fland[DOC].A weak,but significant
negative
correlation
0.38,P = 0.001)wasfoundbetweenthe two DOMqualityparameters,
(Figure
FlandSUVAzsq
4).
Therelationship
is anchoredby LakesBetheland Ramsey
valuesand
at highFland low SUVAzs4
by LakesBousquet,
Joannds
andVaudraywith low Fland highSUVA254
values.
In summary,
theserelatively
([DOC],
routinemeasurements
5UVA254
and Fl)allowedusto
identifyLakesBousquet,
Joannds
andVaudrayfrom the Rouyn-Noranda
region,and Lakes
Betheland Ramsey
from the Sudburyregion,as lakesthat consistently
emergefrom the general
trendsandarethereforeof particular
interest.Furtherevidence
for inter-lake
differences
in
DOMqualitywasobtainedusingPARAFAC
to characterize
the DOMtakenfromthe lakes
sampled.
94
12
1.55
a
1.50
510
o
o)
E
I
1.45
8
1.40
E
J
tr
1.35
s6
(\
1.30
't, *F
1.25
Jo
J4
a
Bo
1.20
12
46810
14
DOC(mgC L-1)
Figure3.
468101214
DoC (mgc L-1)
(panela) and Fl (panelb) with DOCconcentration
Relationships
betweenSUVA254
for
(squares)
samplestakenfrom lakesin the Rouyn-Noranda
and Sudbury(circles)
areas.Error
(N=3-7)coveringall sampling
barsindicatethe standarddeviationof replicatemeasures
years.
1.55
1.50
145
140
LL
1.35
1.30
1.25
1.20
12
SUVA254(L m-1 mg 6-1;
Figure4.
Relationship
for samplestakenfrom lakesin the Rouyn-Noranda
betweenFl and SUVA25a
(squares)and
Errorbarsindicate
Sudbury(circles)areas.
the standard
deviation
of replicate
(N=3-7)
years.
measures
covering
all sampling
95
(PARAFAC)
Fouruniquefluorescent
components
wereidentifiedusingparallelfactoranalysis
year(N=87)(Figure5). Component
1 (C1)
on replicate
EEMspectrafor eachlakeandsampling
peakat 250nm anda secondary
peakat 340nm. Thiscomponentalso
hasa primaryexcitation
peakfrom 480to 500nm. Component
hasa broademission
2 (C2lhasa similarfluorescence
peakat 310nm.
peakat just below240nm anda secondary
signature
with a primaryexcitation
Itsemission
is,however,blueshiftedto a shorterwavelength
3
of 410to 430nm. Component
(C3)hasan excitation
peakbelow240nm anda verybroademission
peakfrom 420to 450 nm.
peakat
peakat lessthan240nm anda secondary
4 (C4lhasa primaryexcitation
Component
peakoccursat 355nm.Someresidualfluorescence
285nm. ltsemission
in the upperleft
fluorescence
cornerof the spectrumwasunaccounted
for by the PARAFAC
model.Thisresidual
model
is mostlikelya resultof unresolved
secondary
Rayleigh
scatter.Overall,
the PARAFAC
explained
998%of the variancein the EEMspectra.
In severalof our lakesthe absorbance
valuesexceeded
0.3at 254 nm (3 of 18 lakesin 2007and
(Milleret
L of 8 lakesin 2008),andthusthe innerfiltercorrection
mayhavebeenincomplete
results,
we
al.,20L0).To checkfor the possible
influence
of theselakeson the PARAFAC
removedthemfrom the datasetandreranthe PARAFAC
the samefour components
analysis;
wereidentifiedin the smallerdatasetandthe % explained
remainedabove99%.We
variance
alsoperformeda split-half
validation
exercise
andthe splitdatagaveverysimilarresults(see
sM).
Forcomparison
was
amonglakesamples,
the maximumfluorescence
of eachcomponent(F,n.,)
normalized
by dividingby the totalfluorescence
the lakes
of allfour components.In general,
proportionof C3(40t
regionhavea significantly
higher(P<0.001)
L6%)thando the Sudburylakes(17t 5%)(Figure
6a),whereasthe Sudburylakeshavea
proportionof C (2014%)thanthe Rouyn-Noranda
higher(P<0.00L)
significantly
lakes(10t
4%)(Figure
6b);in particular,
LakesBethel,Nelson,Raftand Ramsey
hadthe highest
from thislatterfluorescent
contribution
component.
96
E
g
o,
c
o
E
tu!i;
G
550-
3
.9
U'
.g
TU
(nm)
Excitation
Wavelength
Figure5.
Parallelfactor
components1 (panela), 2 (panelb), 3 (panelc) and 4
analysis(PARAFAC)
(paneld) identifiedfrom the excitation-emission
spectraof DOM
fluorescence
from all lakesoverall samplingyears.Contourlinesrepresentincreasing
intensityin arbitraryunits.
97
1.0
o
E
+ 0.8
co.
o
E 0.6
E
x
(U
E 0.4
t.|c
o
c
g. o.2
E
o
o
0.0
1.0
"**i""*.j.1*O"lasee{ato,}r"'}\\outulrunnnlou+t'\uuduJ
c
o
t
A 0.8
co.
I
E 0.6
E
x
(5
IL
E o.+
E
o
c
g 0.2
E
()o
0.0
Figure6.
(F..,) of PARAFAC
Proportionof maximumfluorescence
components1 (diagonallines),2
(cross-hatch),
3 (horizontallines)and4 (verticallines)relativeto the total component
(panela) and Sudbury(panelb) regions
fluorescence
for lakesin the Rouyn-Noranda
sampledin2OO7.
98
Table3.
Component
number
(this study)
peakin parentheses)
Literaturebaseddescriptionand positionof the fluorescence
maxima(secondary
and descriptionof the four
fluorescentcomponentsidentifiedby the PARAFAC
model.
Excitation
Emission
max (nm)
max (nml
Component
Gomponent
Component
assignmentby
assignmentby
assignmentby
Stedmonand
Cory and
Goble(1996)
Markager
McKnight
(2005)
(200s)
Description
Ubiquitous
origin.
c1
250(340)
480to 500
c
SQ1
Humic-like
fluorescence.
Reducedquinonefluorophore
associated
with higherplantmatter.
origin.
Ubiquitous
c2
<240(310)
410 to 430
M
so2
Humic-likefluorescence.
Reducedquinonefluorophoreassociated
with microbialorganicmatter.
Allochthonous
origin.
c3
<240
420 to 450
A
Q2
Humic-like
fluorescence.
Oxidizedquinonefluorophore.
Refractory.
c4
<250(285)
355 to 355
Tryptophan-like
Autochthonous
origin.
proteinfluorescence.
Tryptophan-like
4
Discussion
Significant
differences
in the quantityandqualityof DOMwerehighlighted
in the preceding
section,
bothamonglakesandbetweenregions.Theobservation
is consistent
with the
of suchdifferences
(2007),who concluded
analysis
of Sobekandco-workers
from theirstudyof a verylargelake
(7574lakes
database
from sixcontinents)that
regulate
terrestrialvegetation,
climateandtopography
soilsand hydrology,
andthat thesemastervariables
in turn determinethe rangeof possible
DOM
concentrations
in lakesin a givenregion.The[DOC]of eachindividual
lakewithinthe regionwill be
affectedby the locallakeandcatchment
characteristics,
suchasthe proportionof wetlandsin the
catchment,
the presence
of upstreamlakes,andwaterretentiontime. Usingthisconstruct,
in the
followingsections
we will firstrelatedifferences
in [DOC]to the characteristics
of the lake's
watershed,suchasthe watershed-to-lake
surfacearearatio(hereafternotedasW/L; seeTable2)
(SUVAzsa
andwatershed
landcover.We will thenapplythe routinelymeasured
opticalproperties
and Fl),alongwith the relativeproportionof fluorescence
asidentifiedusingPARAFAC,
components,
to characterize
the DOMin the sampledlakes,identifyits possible
its upstream
originsanddiscuss
andin-lakeprocessing.
Althoughthereis a knowneffectof pH on the absorbance
over
andfluorescence
of humicsubstances
a widerange(generally
2 to 10)of pH values(Mobedet al.,1996),littleeffectis reportedfor lake
naturalorganicmattersamples
withinthe pH rangereportedfor our lakes(Senesi,
1990;Weishaar
et
al.,2003).In fact,Weishaar
et al. (2003)suggest
that withinthe pH rangeof 2 to 8.6thereis no need
to adjustthe pHto a constantvaluewhencomparing
UVabsorbance
valuesamongsamples.Baker
(2OO2l
alsofound no variabilityin DOMfluorescence
as a resultof changesin pH for a seriesof
circumneutral
riversites.Therefore,
allopticalproperties
weremeasured
at the naturalpH of the
(SUVA25a,
sample.We foundno correlations
betweenpH andanyof the opticalmeasurements
Fl,
PARAFAC
components;
datanot shown),suggesting
that pH effectson the opticalmeasurements
are
not significant
for theselakessamples
andat their naturalpH valuesreported.
L00
4.1
Lakewatershedcharacteristics(regional
differences)
Thedatain thisstudywerecollected
fromtwo geographically
differentregionson the Canadian
Precambrian
of theseregionsplayan importantrolein determining
Shield;
the geologyand hydrology
variability
suchas pH,hardness,
the inter-lake
of surfacewaterqualityparameters
and DOMquantity
andquality.Indeed,the observed
difference
in meanlake[DOC]betweenthe two sampling
regions
(Figure2a: [DOC]p
is consistentwith the differentwatershedcharacteristics
in the
r,r> [DOC]sudourv)
regionhavehigherW/L
two regions.Thewatersheds
of the lakessampledfrom the Rouyn-Noranda
ratios,higherpercentvegetation
coverandlowerpercentrockoutcrops,
compared
to the
watersheds
of the lakessampledfromthe Sudburyregion(Table2). lt hasalsobeenreportedthat
the Rouyn-Noranda
regionhasgreatersoilcoverthanthe Sudburyregion(Veillette
et al.,2005),
whichhasverylittlesoilcoverandwherebedrockoutcroppings
are muchmorefrequent(Barnett
and Bajc,2002l..Also,because
of acidification
anderosionof the soilin the Sudburyareadueto past
(Barnettand
miningand smeltingactivities,
lossof soilA- and B-horizons
therehasbeena significant
Bajc,2OO2).
we conclude
that the Rouyn-Noranda
lakesreceivea larger
Basedon thisinformation,
proportionof DOMfrom theircatchments
in the Sudburyregion,whereas
thando theircounterparts
producea higherproportionof DOMwithinthe watercolumn.
the Sudburylakespresumably
4.2
DOM quolity
4.2.1 SpecificUVAbsorbonce(SUVA25a)
lndex(Fl)
ond Fluorescence
Opticalproperties
haveoftenbeenusedin the pastto characterize
DOMand identifyits source(s)
(Coble,1996;KosterandSchmuckler,
1967;McKnight
et al.,2001).Althoughthe absorption
spectra
of naturalDOMare relatively
featureless
andsimilaramongnaturalDOMenvironments,
the
(SUVA254)
calculation
of the specificUVabsorption
canbe usedasa tool to estimateandcomparethe
qualityof DOMfrom differentsources
(Weishaar
et al.,2003).We successfully
discriminated
values(Figure2b)andon the trendsin
betweenthe two sampling
regionsbasedon the SUVA254
valuesfor the lakesin the Rouynwith increasing
SUVAzsq
[DOC](Figure3). ThehigherSUVAzs+
giventheir higherW/L ratiosand
Norandaregion,comparedto the Sudburyregion,wereexpected
(i.e.,forestcoverage)
in the region.Thesefactorswouldbe
the greateramountof vegetation
DOM,knownto havehigheraromaticity
expected
and
to favourgreaterinputsof allochthonous
101
absorptivity
thanautochthonous
DOM(Chinet al.,1994;McKnight
et al.,
et al.,2001;Weishaar
2003).Temperateconiferousforestsof the type foundin the Rouyn-Noranda
regionare recognized
of leachable
values(Sobek
sources
organiccarbonwith relatively
higharomaticity
and highSUVA254
et al.,2007).In addition,giventhe greatersoildepthin the Rouyn-Noranda
region,somepartitioning
of the leachable
organicmatterlikelyoccursin the lowermineralsoilhorizons,
with consequent
removalof the morehydrophobic
components.In theirstudyof DOMmovementin a largeAlaskan
permafrost,
catchment
underlain
with discontinuous
Balcarczyck
et al. (2009)suggested
that such
selective
adsorption
of hydrophobic
components
wouldleadto a furtherenrichment
of hydrophilic
moietiesin solution,
with higharomaticity
values.Notehoweverthat in theirstudy
andhighSUVA254
of the originsof dissolved
organicmatterin streamsof the YukonRiverbasin,O'Donnell
et al.(2010)
suggested
the oppositetrend. Duringwinterlow flowsin theirsystem,SUVA25a
decreased
andthey
attributedthischangeto selective
removalof aromaticcompounds,
suchas lignin-derived
polyphenols,
in the deepersoillayers.
In additionto thisdirecteffecton the relativeproportions
DOM
andautochthonous
of allochthonous
enteringa lake,differences
in WL ratioswouldalsobe expected
to affectDOMindirectly.For
example,
the higherthe WL ratio,the higherthe relativeinputof allochthonous
DOM,but alsothe
lowerthe residence
time of the lakeand,therefore,
of in-lakeprocessing
of
the lowerthe likelihood
DOM(Sobeket al.,2OO7l,.
LakeBousquetwasfoundto have,by far, the highestW/L ratio(129)of all
the lakessampled,
andthe SUVAzs+
valuefor the DOMin this lakewasthe highestvaluethat we
recorded.lt shouldalsobe notedthat Lakes
and
Joannds
andVaudraybothflow into LakeBousquet
therefore,
contributeto its DOMload. Microbialprocessing
of the DOMin the upstreamlakes,with
consequent
removalofthe morelabilefractions,
wouldbe expected
to leadto an increased
proportionof recalcitrant
DOMin LakeBousquet.On the otherhand,lakesNelsonand Bethelhad
the lowestW/L ratios(4 and5, respectively).
Despitethesesimilarlow W/L ratiosfor the two lakes,
the SUVAzsa
valuefor LakeBethelwaslowerthanthat for LakeNelson.LakeNelsonis a clear,
oligotrophic
lakewith a low [DOC],
whereasLakeBethelis a coloured,
eutrophiclakewith a moderate
(Table
isfoundin a watershed
that is highlydeveloped
[DOC].LakeBethel,aswellas LakeRamsey,
2). Furthermore,
LakeBethel,a smalllakethat flowsinto LakeRamsey,
wasusedasa sewagelagoon
LOz
until1986(Pearson
et al.,2002),whichcontributed
to itseutrophication.
BothLakesBetheland
Ramsey
are,therefore,mostlikelyproducing
a higherproportionof autochthonous
DOMthan Lake
Nelson,
thusleadingto theirlowerspecific
absorptivities.
SimilarspecificUVabsorbance
valueshavebeenreportedfor otherlakesystems.Forexample,Miller
et al. (20L0)foundlow SUVA254
valuesfor alpinelakessampledin Colorado
duringthe summer
rangingbetween0.5and2.5L m-1mg-t,indicative
of autochthonous
DOMproducedby in-lake
processes.
biogeochemical
higherSUVAzsc
Theyalsoreportsignificantly
valuesfor an adjacent
t),
subalpine
streamduringthe summermonths(2.5to 4.0 L m-l mg attributedto the inputsof
allochthonous
DOM.
We werealsoableto discriminate
regionsbasedon Flvalues
amonglakesfromthe two sampling
(Figure2c). Fluorescence
spectraof naturalDOMare morecomplexthanabsorption
vary
spectra,
amongenvironments
indicative
andrevealstructural
characteristics
of DOMsourcematerial(Fellman
et al.,2010).TheFlvaluesmeasured
for the lakesvariedfrom 1.3to L.5,enoughto indicate
significant
differences
in the sourceof DOM(Balcarczyk
et al.,2009).Thelakesfrom the RouynNorandaregionhavea low meanFl,indicative
DOM,whereasthe lakesfromthe
of allochthonous
Sudburyregionhavea slightlyhighermeanFl,indicative
of a greatercontribution
by autochthonous
DOM. Ourcalculated
Flvaluesaresimilarto thosereportedfor otherfreshwater
environments
in
centralandsouthernOntario(Keltonet al.,2007).
Thedifferences
betweenthe two samplingregionsarealsoevidentwhenwe comparethe quantityof
DOM([DOC])
to the DOMqualityparameters
SUVAzsa
and Fl (Figure
3). Forthe Sudburylakes,
SUVAzsq
and Fl increased
with a highersteady-state
of the Sudbury
[DOC]in the lake.Thewatersheds
areahavelow WL ratios,low percentvegetation
cover,highpercentrockoutcropsanda thin soil
layer,andthereforeanyincrease
in the DOCconcentration
in the lakesis likelyattributable
to an
increased
contribution
of autochthonous
DOMin theselakes.Forthe Rouyn-Noranda
lakes,the
increases
SUVAzsq
with an increase
in [DOC],
whereasthe Fl appears
to decrease.
A shiftto higher
DOM)at higher[DOC]suggests
SUVA25a
valuesand lowerFlvalues(indicative
of allochthonous
an
103
is
increase
in the proportionof allochthonous
DOMderivedfromthe catchment.Thisreasoning
consistentwith the highW/L ratios,higherpercentvegetationcover,lowerpercentrockoutcrops
areaascomparedto the
andthickersoillayerin the catchments
of the lakesin the Rouyn-Noranda
Sudburyarea.
(Fl)
parameters
Otherstudieshaveshownthat the relationship
andfluorescence
betweenSUVAzs+
canbe usedto identifyautochthonous
end members(Jaff6et al.,2008;Millerand
andallochthonous
whichallowedusto
McKnight,
2010).We thereforecompared
thesetwo DOMqualityparameters,
isolateend membersin termsof allochthonous
sources
of DOMwithinour
andautochthonous
dataset(Figure
4). Again,LakesBetheland Ramsey,
foundin the upperleft handcornerof Figure4,
DOM.ln contrast,
are isolated.TheirhighFland low SUVA25a
of autochthonous
valuesare indicative
Lakes
Vaudray,
Joannds
and Bousquet
arefoundin the lowerrighthandcornerwith low Fland high
valuesindicative
SUVAzsq
of allochthonous
DOM.Theseresultsaresupportedby the resultsof the
clusterand PCAanalyses
whichfoundLakesBousquet,
andVaudray(alongwith Lakes
Joannds
(see
D'Alembert
and Dufay)tobe groupedtogetherbasedon theirDOMopticalproperties
Material,SM).
Supplementary
4.2.2 PARAFAC
Althoughsimpleabsorption
andfluorescence
DOMqualityparameters
havefrequentlybeenusedin
the pastto characterize
naturalDOM(Gondar
et al.,2008;Greenand Blough,1994),theyare not
alwaysthe besttoolsto employ.Forexample,
aresubjectto
absorbance
measurements
interferences
from particles
and inorganic
et al.,2003),
species
suchas ironand nitrate(Weishaar
(Greenand
theseinterferences
beingmoreimportantat low [DOC]thanin high[DOC]situations
Blough,1994;Millerand McKnight,
20L0).Furthermore,
whetherSUVAzsa
or Fl is usedto describe
the qualityof DOM,both parameters
arecalculated
usingonlya smallportionof the available
informationin eitherthe absorbance
therehas
or fluorescence
spectrum.In recentyears,however,
(PARAFAC)
beena significant
increase
in the useof parallelfactoranalysis
asa statistical
tool to
interpretexcitation
emission
fluorophores
or
spectraand identifyspecific
groupsof fluorophores
(Stedmon
(Jaff6et al.,2008;Miller
et al.,2003)tocharacterize
DOMsources
104
described
above,thisapproach
2010;Yamashita
et al.,201.0).Unlikethe techniques
and McKnight,
spectrum.
incorporates
datafrom the entireDOMfluorescence
are similarto thosefound
identifiedin our lakesusingPARAFAC
TheDOMfluorescence
components
(Coble,1996;Coryand McKnight,
2005;Koster
environments
in the literaturefor similarfreshwater
we ascribeCLandC2to reducedquinone,
L967).Basedon theseauthors'findings,
andSchmuckler,
C3to refractory,
humic-like
fluorophores
that are presentin a widevarietyof aquaticenvironments,
protein
quinone,humic-like
originandC4to tryptophan-like
fluorophores
of allochthonous
oxidized
fluorescence
of autochthonous
origin(Table3).
fluorescence
component(C3)in the Rouyn-Noranda
Therelativeproportionof the allochthonous
whereasthe
higherthan in the Sudburylakes(17! 5 o/ol,
lakes(40t L6%;Figure6a)wassignificantly
lower(10t 4 compared
from the proteinfluorescence
component(C ) wassignificantly
contribution
with the anticipated
trendsareconsistent
to 20 t 4 %)(P< 0.001in bothcases).Theseinter-regional
lowerinputsof DOMfromthe catchments
of the Sudburylakes,giventheir lowerWL ratios,lower
percentvegetation
cover,higherpercentrockoutcropsandthinnersoilcover.Whenallthe lakes
(P<0.001)
in
statistically
significant
differences
of variance,
werepooledandsubjected
to an analysis
and Bethel,and betweenLakes
the relativeproportions
of C3werefoundbetweenLakesBousquet
in the relativeproportions
of C4werefound
differences
and Bethel.Similarly,
significant
Joannds
Nelsonand Bethel.LakeBethelhasthe highest
betweenLakeBousquet
and LakesRamsey,
proportionof the protein-like
with its
component,
C4,consistent
fluorescent
autochthonous
increase
in primaryproduction.The
earlier,andthe consequent
eutrophicnature,asdescribed
as lakeswith
supportthe groupingof LakesBetheland Ramsey
resultsof the clusterandPCAanalyses
from LakesBousquet
and
origin,andtheirseparation
highproportions
of DOMof autochthonous
DOM(seeSM).
of allochthonous
characterized
by highcontributions
Joannds,
105
5 0.8
E
o
o.
o
o-
I o.o
(U
E
x
G'
E
rr- 0.4
c
o
c
o
o-
E
o ^^
o u.z
Vaudray
Figure7.
Joannes
Bousquet
(F.",)of PARAFAC
Proportionof maximumfluorescence
components1 (diagonallines),2 (crosshatch),3 (horizontallines)and4 (verticallines)relativeto the total componentfluorescence,
and
(opencircles)for three hydrologically
DOCconcentration
connectedlakesin the Rouyn-Noranda
areasampledin 2007.Errorbarsindicatethe standarddeviationof three replicateDOCmeasures.
Vaudray,
Lakes
Joannds
and Bousquet
areall locatedin closeproximityandareconnected
hydrologically;
theyarecharacterized
component
by relatively
highproportions
of the allochthonous
C3andlow proportions
of ubiquitous
componentC2andthe proteincomponentC4. Figure7
presents
for
the trendsin the relativeproportions
aswellas [DOC],
components,
of the fluorescence
thesethreelakes.Asthe [DOC]increases
the relative
from LakeVaudrayto LakeBousquet,
proportionof the allochthonous
VaudrayandJoannds
both
componentC3increases.
Lakes
independently
flow into LakeBousquet.Therefore,
must
in [DOC]in LakeBousquet
the increase
represent
inputsfrom its immediatewatershed.Notethat thisreasoning
is alsosupportedby the
factthat LakeBousquet
is
hasthe highestWL ratio(L29,or 83 if onlyits immediate
watershed
(10)andthen LakeVaudray(8).
followedby LakeJoannds
considered),
plotsto furtherdiscriminate
We usedsourceapportionment
betweenallochthonous
and
autochthonous
sources
of DOMfor our studylakes.Thefourfluorescence
components
are
represented
with threedifferentcomponentratiosandvisuallypresented
in two plots:C3/C1vs.
L05
C2/CL(Figure8a)andC4/CLvs.C2/Ct(Figure8b). The ratioC2/CLwaschosenfor the dependent
2005),i.e.,CLandC2exhibit
axison the basisof itssimilarity
to the Fl (Coryand McKnight,
for an excitation
respectively,
wavelength
of
emission
at 470and520 nm wavelengths,
appreciable
Flvaluesfor the lakes
370nm (seeMethods).Indeed,plottingCZ/CLratiosagainstthe calculated
(r=0.65,
yieldeda strongpositivecorrelation
P<0.001),
supporting
the analogybetweenC2/CLand Fl.
betweenthe allochthonous
Thefluorescence
and
componentratioplotsclearlydiscriminate
quinone
autochthonous
sources
of DOMto the lakesstudied.ln Figure8a,we comparethe oxidized
(C1andC2).Asthe
fluorophores
of allochthonous
origin(C3)to the reducedquinonehumic-like
relativeproportionof C2to CLincreases,
the relativeproportionof C3decreases.LakesBousquet,
in the upperleft handcornerof the plotwherethereis
Joannds
andVaudrayanchorthe relationship
whereasLakesBetheland Ramsey
fluorophores,
anchorthe
a higherproportionof the allochthonous
fluorophore.ln Figure8b,
lowerrighthandcornerwherethereis lessinfluence
proteinfluorescence
(C4)to the reduced
we comparethe relativeproportionof the tryptophan-like
quinonehumic-like
(CLandC2):asthe relativeproportionof C2to CLincreases,
fluorophores
the
relativeproportionof C4increases.
Again,lakesBousquet,
Joannds
andVaudrayanchorthe
relationship
in the lowerleft handcornerof the plotwherethereis a lowerproportionof the
fluorophores,
whereaslakesBetheland Ramsey
anchorthe upperrighthandcorner
autochthonous
fluorophore.
wherethereis higherproportionof the autochthonous
107
0.2
o.4 0.6 0.8 1 . 0 1 . 2 1 . 4 1 . 6
c2tc1
1.0
()
0.8
!t
o 0.6
0.4
Jo-olL
0.2
VA-08
0.0
0 .2 0 .4 0 . 6 0 . 8
1.0
1.2 1.4 1.6
C2IC1
Figure8.
(panel
(panela) andautochthonous
Variation
in fluorescence
component
ratiosfor allochthonous
b)DOMfrom lakesin the Rouyn-Noranda
andSudburyregions
sampledin2007(-7)and2008(-8).
Reference
valuesarefrom Stedmonet al. 2003(X=forest
stream;upwardtriangle=agricultural
stream;downwardtriangle=lake;
square=marine).
star=wetland;
circle=urban
drainage;
108
of DOM,we haveaddedthe ratiosof known
the DOMin our lakeswith sources
In orderto associate
valuesfor
signature
from Stedmonet al. (2003)toFigure8, as reference
calculated
components
quinoneof
the oxidized
differentDOMsources.With respectto C3(Figure8a),whichrepresents
andVaudrayarefoundin the sameregionas DOM
Joannds
origin,LakesBousquet,
allochthonous
arefoundnearthe regionfor DOMfrom urban
from foreststreams.LakesBetheland Ramsey
similarto thosefor DOM
drainage.Thelakesfoundbetweentheseend membersexhibitsignatures
tryptophanlands.ForC4(Figure
8b),representing
from lakesandfrom streamsdrainingagricultural
arefoundnearthe
origin,LakesBetheland Ramsey
of autochthonous
likeproteinfluorescence
VaudrayandJoannds
areonceagainfoundnear
DOM. LakesBousquet,
for urbandrainage
signature
for foreststreamDOM.Thelakeslyingbetweentheseend membersarefoundnear
the signature
streams.
for DOMfrom lakes,wetlandsandagricultural
the signatures
with the resultsof the
Therelationships
andend membersidentifiedin theseplotsareconsistent
in Figure3. To our knowledge,
thisis the
betweenour SUVAzs+
and Flvaluespresented
relationship
derivedDOMhasbeen
andautochthonous
betweenallochthonous
firsttime that the discrimination
plotsbuiltuponPARAFAC
components.
usingsourceapportionment
demonstrated
5
Conclusions
(SUVAzs+;
fluorescence
In thisresearch
we setout to explorethe useof variousopticalparameters
to detect
coupledto PARAFAC
analysis)
spectroscopy
matrixfluorescence
index;excitation-emission
Precambrian
Shield
of the DOMin a smallsetof lakeson the Canadian
differences
in the composition
predictions
values.On
hadbeenshownto divergefrom measured
withinwhichCuand Ni speciation
revealed
lakesto Sudburylakes,allthreetechniques
Rouyn-Noranda
a regionalscale,i.e.,comparing
areas.
in the composition
of the lakewaterDOMbetweenthe two sampling
distinctdifferences
quinonefluorophore
of allochthonous
origin")andC4
C3("oxidized
PARAFAC
components
proteinfluorescence
origin")werethosethat showedthe
("tryptophan-like
of autochthonous
characteristics
with the regionalwatershed
greatestinter-regional
a resultthat is consistent
variation,
the optical
to the lake.Bycombining
andthe presumedroutingof the DOMfrom the watershed
and
usingclusteranalysis
parameters,
landcoverinformation,
waterqualitydataand percentage
109
principal
components
analysis,
we wereableto identifygroupsof lakeswith similarDOM
composition
andrelatethesegroupings
to the localwatershed
characteristics
of the lakes.The
relativepercentages
of vegetative(forest)cover,rockoutcropsand surfacewaterwereshownto be
the keywatershedcharacteristics.
Currentmetalspeciation
modelsthat includedissolved
organicmatter,suchasthe Windermere
HumicAqueousModel(WHAM),incorporate
quantityof DOM,but usersmustdefine
the measured
in DOMquality(i.e.,chemical
the variability
character
andsource)by choosing
the % of DOMthat is
(Bryanet al.,2002).Thepresentresultsopenthe doorto the
activelyinvolvedin metalcomplexation
useof opticalmeasures
of DOMqualityto refineuserestimates
of thiskeyparameter.
Acknowledgements
Theauthorsacknowledge
providedby M.G.Bordeleau,
the technical
assistance
S.DuvalandJ.
Perreault
in the laboratory
and P.Girardand P.Marcouxin the field. We alsothankM.-A.Robinfor
providing
GISdata,aswellasS.SmithandC.Stedmonfor helpfuldiscussions
on PARAFAC
modelling.
l. Lavoieprovidedinvaluable
assistance
with the multivariate
was
statistical
analyses.
Sampling
greatlyfacilitatedby the personnelof the Laurentian
Freshwater
EcologyUnit,
UniversityCooperative
led by JohnGunn,in the Sudburyareaand by L.Jourdainof the Ministdredesressources
noturelleset
de lofaunedu Quibecin the Rouyn-Noranda
providedby R.-M.Coutureon earlier
area.Comments
versions
of the MSweregreatlyappreciated.
Financial
supportwasprovidedby the NaturalSciences
and Engineering
Research
Councilof Canada
Research
andby the Metalsin the HumanEnvironment
Network(www.mithe-rn.ors).
P.G.C.
Campbell
andC.Fortinaresupportedby the Canada
Research
ChairProgram.Theconstructive
commentsof two anonymous
referees
aregratefully
acknowledged.
110
Material
Supplementary
6
PARAFACmodelvalidation
6.1
matrix(EEMlfluorescence
modelbasedon the excitation-emission
Thefour-component
PARAFAC
followingthe stepsoutlinedin Stedmonand Bro
lakesamples
wasvalidated
spectraof 87 individual
(2008).Outliertests,comparing
in the excitation
andemission
data,
the sumof squaredresiduals
the resultsof the
wereperformed.FigureS1presents
and randominitialisation
split-half
analysis
procedure
PARAFAC
model.Thevalidation
of the chosenfour-component
splithalfvalidation
Sla,bandS1c,d)andcomparing
the excitation
and
involvedsplittingthe dataintotwo halves(Figures
models(compareFigure51-panelsa
Thetwo individual
loadings
for the four components.
emission
modelwas,therefore,foundto
and b with panelsc andd) are nearlyidentical.Thefour-component
most(>99%lof the variancein the data.
adequately
describe
the dataandexplained
alt
E)
to
o
fr
o
l!
450
Em (nm1
Ex (nm)
0.6
v.z
o
&
€ ors
X n,
E)
d
o
E
o.r
o
o
* 0.2
E oos
5so
FigureS1.
-\----
t
o
IJJ
400
450
Em (nm)
500
550
0r
,
250
300
Ex (nm)
350
400
PARAFAC
model.Panels
a andc showthe emission
Splithalfvalidation
of the four-component
for the two
loadingsand panelsb and d showthe excitationloadingsof the four components
1,
the
dashed
line
is
for
component
2, the
independent
models.Thesolidlineisfor component
dottedlineis for component3 and the dash-dotlineis for component4.
111
6.2
Multivariotestatisticalanolyses
6.2.1 ClusterAnalysis
Clusteranalysis
wasemployedto identifygroupsof lakeswith similarwaterquality,watershed
characteristics
andDOMopticalproperties.First,by considering
waterchemistry
data(pHandCa
and Mg concentrations)
and watershedlandcoverdata(%surfacewater,Torockoutcrops,%
vegetation
and%urbandevelopment
S2a).
)only,threemaingroupsof lakeswereidentified(Figure
All of the lakesfrom the Rouyn-Noranda
regionweregroupedtogether(Group3),whilethe lakes
from the Sudburyregionwereseparated
intoonegroupcontaining
LakesWhitson,Raft,Crowley,
Genevaand Nelson(Group2),andanothergroupcontaining
LakesMiddle,Hannah,
Ramsey
and
Bethel(Group1-).A secondclusteranalysis
wasconducted
usingthe DOMdataonly(DOC
(Figure
concentration,
SUVA25a,
Flandthe relativeproportions
of the PARAFAC
components
S2b)).
groupedthe Rouyn-Noranda
Thissecondclusteranalysis
D'Alembert,
Dufay
LakesBousquet,
Joannds,
andVaudraytogether(Group1'). Theremaining
lakesweregroupedtogetheralong
Rouyn-Noranda
with the SudburyLakeWhitson(Group2'). TheSudburyLakesRaft,Geneva,
Crowley,
Nelsonwere
re-grouped
togetherwith LakesHannah,
Middle,Ramsey
andBethel(Group3').
In the firstanalysis,
(Group1, FigureS2a)
the SudburyLakesHannah,
Middle,Betheland Ramsey
were clearlydistinct,reflectingthe higho/ourbandevelopmentin their watersheds,
their highpH
valuesandtheirhighdissolved
concentrations
of Caand Mg. However,
if the clusteranalysis
was
limitedto the DOMopticalproperties,
theselakesweremixedintothe generalSudburylakegroup
(Group3', FigureS2b).Theanalysis
usingonlythe DOMdataidentified
Lakes
the Rouyn-Noranda
Bousquet,
Joannds,
Vaudray,
D'Alembert
and Dufay(GroupL', FigureS2b)asa distinctgroup.These
lakeshavehighDOCconcentrations,
highSUVA254
valuesandhighproportions
fluorescence
provides
component
C3. Theclustering
of lakesbasedon DOMopticalproperties
additional
information
on the natureof lakeDOM,whichmightotherwisehaveremainedhidden.
1.t2
BE-07
BE-08
RM.O7
HA-07
Ml-07
NE-07
GE-07
GE.08
cR-07
RA-08
RA.O7
WH-08
WH-07
BO-07
VA-07
VA.O8
JO-07
DF.O7
DS-o7
DS-08
AD-07
DA-07
DU.07
DU.08
oP-08
oP-07
BO-07
JO-07
DA-07
DF-07
VA.O7
VA-08
DS-07
oP-08
oP-07
DU-07
AD.O7
WH-07
DU-08
DS-08
WH-08
RA.O7
RA-08
GE.O8
GE-07
cR-07
HA-07
Mt-07
NE
RM-
Lakesare identifiedby a two-lettercode(see
resultingfrom the clusteranalyses.
Figure52. Dendrograms
of the analysisperformedusingwaterchemistry(pH,
TableL) and a two-digityearcode.Results
andwatersheddata(%surfacewater,rockoutcropsvegetation,and
Caand Mg concentrations)
urbandevelopment)
are shownin panela). Resultsof the analysisusingDOMproperties(DOC
components
C1,C2,C3
Fl andthe relativeproportionsof the PARAFAC
concentration,
SUVA25a,
the levelat whichthe clusterswere
and C4)are presentedin panelb).Thedashedlinerepresents
established
andthe numbersrepresentthe groupsidentified.
Analysis
6.2.2 PrincipolComponents
(PCA)wasemployedto graphically
the lakesalong
distribute
analysis
Principal
component
in site
gradients
(Figure
8L%of the variance
S3).Thefirsttwo PCAaxesexplained
environmental
ThePCAordinationshowsthat the gradientof maximumvariance(Axis1) is associated
distribution.
the relative
rangingfrom highvaluesfor DOCconcentrations,
variables
with a seriesof environmental
proportions
in quadrant| (Q-l)tohighvaluesfor the relative
of C3,SUVA25a
and%vegetation
proportions
of CL,C2andC4,aswellasfor % surfacewaterand%rockoutcropsin quadrantll (Q-ll).
(Caand Mg
variables
Thesecondgradient(Axis2) spannedfrom highvaluesof waterchemistry
and Fl in quadrantlV (Q-lV)to highvaluesof
andpH)aswellas%ourbandevelopment
concentrations
in quadrantll (Q-ll).
% vegetation
113
Thelakeswerefirstdiscriminated
DOMgradientspanning
alongAxis1, definedasan allochthonous
diagonally
from Q-l (withhighvaluesfor DOCconcentration,
from C3andSUVAzs+)
the contribution
to Q-lfl(with highcontributions
from C4,C2,C7,losurface
waterand%rockoutcrops).Mostof the
Rouyn-Noranda
lakeswerefoundin Q-1,reflecting
the importance
of allochthonous
sources
of DOM
for theselakes.ln contrast,nearlyallof the Sudburylakeswerefoundin Q-lll,reflecting
the lesser
importance
of allochthonous
DOMin theselakes.On a secondlevel,the lakeswerediscriminated
gradientspanning
alongAxis2, definedasan autochthonous
from Q-lV,with highvaluesfor Fl,%
urbandevelopment,
Mg andCaconcentration,
andpH to Q-llwith high% vegetation.LakesBethel
and Ramsey
werefoundat the top of thisgradientQ,-lV,
indicating
the importance
of the
production
graphically
autochthonous
of DOMin theselakes.Thisordination
integrated
the site
distribution
we achieved
(Figure
with the two independent
clusteranalyses
S2a,b).General
discrimination
gradientandsecondarily
basedprimarilyon the allochthonous
on the autochthonous
gradientis illustrated
by the schematic
in the lowerrighthandcornerof Figure53.
tt4
o-/\/
C\f
BE-070
.2
x
nnn
DU47
\a
Ml-07r
M-07 o
oDF-07
Torock
.afif.=-.--;-t
.DS-08
ve-;s---
Crl
/o
/
Q-/,J
Figure53.
Axis1
%veg
CR-07o
GE{g
/
C1
vA-07
RA-08.
ruf,oz
*"rf'@
Q-1/
watershed
Principal
componentanalysisusingwaterchemistry(pH,Caand Mg concentrations),
and DOM
information(%surfacewater,rockoutcrops,vegetationand urbandevelopment)
properties(DOCconcentration,
components
SUVA,Fl andthe relativeproportionof the PARAFAC
axesare labeledAxis1 and Axis2, respectively.
CL,C2,C3and C4).Theprimaryandsecondary
variablescontributingto the gradientsandthe dark
Thegreyarrowsrepresentthe environmental
circlesrepresentthe individuallakesamples(identifiedby lakecode- yearsampled).Each
quadrantof the figureis enumeratedQ-1,Q-ll,Q-lllor Q-lV.Theschematicin the lowerrighthand
gradientsidentified.
the majorenvironmental
cornerof Q-ll illustrates
115
116
Article2
Tracemetalspeciationpredictionsin naturalaquaticsystems:incorporationof
quality
dissolvedorganicmatter (DOM)spectroscopic
KristinK.Mueller',StephenLofts',ClaudeFortin'andPeterG.C.Campbell'
'
490de la Couronne,
Universit6
du Qu6bec,
Qu6bec(Qu6bec),
Canada
G1K9A9
2
Centrefor Ecology
Lancaster
Environment
and Hydrology,
Centre,LibraryAvenue,Bailrigg,
Lancaster,
LA14AP,UnitedKingdom
Publi6en 2Ot2 dansEnvironmentalChemistry9: 356-368
DOI:1-0.1071/8N11156
Abstract
To calculate
metalspeciation
in naturalwaters,modellers
mustchoosethe proportionof dissolved
organicmatter(DOM)thatis activelyinvolvedin metalcomplexation,
definedhereasthe % active
fulvicacid(FA);to be ableto estimatethisproportionspectroscopically
wouldbe veryuseful.In the
presentstudy,we determinedthe free Cd2*,Cu2*,Ni2*andZn2*
concentrations
in eightCanadian
Shieldlakesandcompared
thesemeasured
concentrations
to thosepredictedby the Windermere
proportions
HumicAqueousModel(WHAMVl). Forsevenof the eightlakes,the measured
of Cd2*
andZnz*fell withinthe rangeof valuespredictedby WHAM;the measuredproportionof Cu2*fell
withinthis rangefor onlyhalfof the lakessampled,
whereasfor Ni,WHAMsystematically
the proportionof Ni2*.With the aim of ascribing
overestimated
betweenmeasured
the differences
and modeledmetalspeciation
to variationsin DOMquality,the % activeFAneededto fit modeledto
measuredfree metalconcentrations
variationin the spectroscopic
wascomparedto the lake-to-lake
qualityof the DOM,as determined
measurements.
Relationships
by absorbance
andfluorescence
between% activeFAand DOMqualitywere apparentfor Cd,Cu,Ni andZn,suggesting
the possibility
of estimating
the % activeFAspectroscopically
to refinemodel
andthen usingthisinformation
predictions.Therelationships
for Ni differedmarkedlyfrom thoseobservedfor the other metals,
suggesting
that the DOMbindingsitesactivein Cd,Cuand Zn complexation
are differentfrom those
involvedin Ni complexation.
To our knowledge,
hasbeen
thisisthe firsttime that sucha distinction
resolved
in naturalwatersamples.
118
RunningHead
Predicting
metalspeciationin lakewaters
EnvironmentalContext
To assess
contaminants
suchas metals,one needsto be ableto
the riskposedby environmental
identifythe keychemical
stumbling
species
that prevailin naturalwaters.Oneof the recognized
dissolved
organicmatter(DOM).Here
blocksis the needto quantifythe influence
of heterogeneous
we explorethe possibility
of DOMto determineits quality,to alleviate
of usingthe opticalsignature
properties,andto improvethe predictionof
aboutits metal-binding
the needto makeassumptions
in naturalwaters.
tracemetalspecies
distributions
r.19
1
Introduction
as a keyto
Knowledge
of the speciationof tracemetalsin naturalwatersis widelyrecognized
impacts.
In natural
boththeirgeochemical
mobilityandtheirecotoxicological
understanding
including
the common
freshwater
systems,
tracemetalsmayinteractwith a varietyof ligands,
of natural
inorganic
anions(e.g.,OH-,HCOg-,
organicmolecules
CO32-,
Cl-,F-,SOaz-),
simplemonomeric
suchas
origin(e.g.,metabolites
or anthropogenic
suchascitrateor glycine,or polycarboxylates
(fulvicandhumicacids)(Batleyet al.,2004).
nitrilotriacetic
acid),anddissolved
humicsubstances
perspective,
Froma geochemical
or ecotoxicological
the keyspeciesis the free metalion (M'*),which
ligands
andwith
is usuallypresumed
or particulate
to be in equilibrium
with thesevariousdissolved
from which
it is the masterspecies
the epithelial
or cellsurfaces
of the residentaquaticorganisms;
the concentrations
of all otherspecies
of interestcanin principlebe calculated.
in natural
A numberof approaches
havebeenusedto determinefree metalion concentrations
(lET)hasbeenemployedto determineCd2*,Ni2*and
waters.Forexample,
equilibrium
ion exchange
(Doigand Liber,2007;Fortinet al.,2010;Wormsand
Zn2*concentrations
in environmental
samples
potentiometry
Wilkinson,
2008).Variouselectrochemicaltechniques
havealsobeenused,including
(Rachou
with ion-selective
electrodes
etal'.,2007;
XueandSunda,1997)andcathodicstripping
(XueandSigg,1993;XueandSunda,L9971,
voltammetry
technique
as hasthe Donnanmembrane
(Sigget al.,2006;Wenget al.,2011). However,giventhe complexityof the analyticalmatricesin
whichthe free metalion is found,andthe oftenvery low ambientmetalconcentrations,
the
determination
of [M'*] in naturalwatersis not a routinemeasurement.
A complementary
approach
to suchanalytical
wouldbe the useof chemical
determinations
equilibrium
modelsto calculate
Forsimplesystems,
containing
the freemetalion concentration.
inorganic
is reasonably
cationsandanionstogetherwith simplemonomericligands,
suchan approach
straightforward.
However,
for waterscontaining
naturaldissolved
organicmatter(DOM),the
presence
chemical
equilibrium
calculations
mustdealwith the challenging
of fulvicandhumicacids.
Theseubiquitous
but poorlydefinedmaterials
ligands
and poly-electrolytes
behavelikemulti-dentate
in solution.DudalandG6rard(2004)havereviewedvariousapproaches
to
that havebeendeveloped
L20
two that are
equilibrium
models,including
accountfor naturalorganicmatterin aqueouschemical
HumicAqueousModel(WHAM)andthe Non-ldeal
currentlywidelyused:the Windermere
(NICA)-Donnanmodel. In additionto fulvicandhumicacids,DOMalso
Adsorption
Competitive
(aminoacids,proteins,
of whichtend
carbohydrates),
the concentrations
includes
biogenicmolecules
and uptakeby heterotrophic
at verylow levelsdueto theirdegradation
to be maintained
of the DOMpoolnormallywill not affecttrace
microorganisms.
Asa result,theseothercomponents
elementspeciation(Moreland Hering,1993). In watersaffectedby wastewatereffluents,the DOM
origin.
agentsof anthropogenic
mayalsoincludestrongchelating
measuredin naturalaquaticsystemswith the
Recentcomparisons
of free metalion concentrations
predictedfor thesesystems
modelshave
with the NICAandWHAMspeciation
concentrations
majordifferences
betweenmeasuredand predictedvaluesfor somemetals,notably
demonstrated
Cu,Ni and Pb(Fortinet al.,2010;Guthrieet al.,2005;Unsworthet al.,2006).LoftsandTipping
(2011)havediscussed
possible
including
errorsin measuring
the free
reasons
for thesedifferences,
that are usedas inputdatafor the model,anddifferences
the variables
metalion,errorsin measuring
and humicacids
betweenthe DOMpresentin the naturalwatersandthe typesof (isolated)fulvic
models.In
the chemical
equilibrium
or parameterize
beenusedto calibrate
that havetraditionally
the presentpaperwe haveexploredthis latterfactor.
equilibrium
modelto a naturalwateris to
involvedin applyinga chemical
Oneof the challenges
modelsthat include
estimatethe fulvicandhumicacidcontentof the water. Currentspeciation
quantityof DOM,but
the measured
dissolved
organicmatter(DOM),suchasWHAM,incorporate
the percentof the DOMthat is activelyinvolvedin
usersmustdefinethe DOMqualityby choosing
what proportions
of this"active"DOMshouldbe designated
metalcomplexation,
and by deciding
would be very
active"DOMspectroscopically
fulvicand humicacid. To be ableto estimatethis"%o
a measureof the
of the DOM(SUVA25a),
ultraviolet
absorbance
the specific
useful.Forexample,
aromaticity
of the organicmatter,hasbeenusedto estimatethe proportionof DOMthat is present
in the complexation
of Cuin reconstituted
as humicor fulvicacidin solutionandthat participates
of Cu
et al.,2001)and in the complexation
naturalwatersamples(Luideret al.,2004;Richards
tzL
(Ameryet al.,2008)andCd(Cornuet al.,2009;Cornuet al.,2011)in soilextracts.A bettero priori
of the qualityof DOMin a givensamplewouldalleviate
the needto makeassumptions
understanding
free
betweenpredicted
andmeasured
aboutthe activityof DOMand mightreducethe discrepancies
metalconcentrations.
(i) to determinethe free Cd2*,Cu2*,Ni2*and Zn2*
Thepurposeof this paperis thusthr:ee-fold:
gradient;(ii)to comparethese
in a setof lakeslocatedalonga metalcontamination
concentrations
predictedby WHAMVl; and(iii)to
measured
with the free metalconcentrations
concentrations
of estimatingthe
explorethe useof the opticalsignatureof the DOMin eachlakeasa mea"ns
proportionof the naturalDOMthat is activein metalcomplexation.
2
Methodology
2.1 Studyareo
Althougha detaileddescription
a brief
of the studysitescanbe foundin Muelleret al.(2012b);
descriptionis givenhere. Lakewaterwascollectedfrom lakesin two differentregionson the
Canadian
Shield:nearRouyn-Noranda
in north-western
Qu6becandnearSudburyin north-central
Ontario.Bothstudyareashavebeengreatlyimpactedby metalminingandsmeltingactivities,
particularly
by atmospheric
deposition
et al.,1998;Dixitet al.,2007).
of acidand metals(Borgmann
ThepH of the lakestendsto decrease
tend to increasewith a
andtotal metalconcentrations
particularly
decrease
in the down-winddistance
in the Sudburyarea(Yan
from the metalsmelters,
andMiller,1984).Acidminedrainage
from pointsources,
minesor mineralized
suchasabandoned
mayalsodecrease
outcrops,
the pH of lakewaterlocally.In eachregion,lakeswerechosento
represent
a gradientin waterquality(pH,dissolved
tracemetal
organiccarbon(DOC))and
concentrations.
2.2
Lakewatersampling
Lakewatersamples
weretakenfromfour lakesin eachof the studyareasduringJulyandAugustof
2008.Sampling
siteswerechosenin the littoralzonesof small,well mixedlakesandthe sampling
siteswereconsidered
to be representative
of
of the wholelakeepilimnion.Thespatialvariability
r22
(i.e.,pH andconcentrations
waterchemistry
of DOCandmajorcations)is knownto be minimal
withinmanyof the sampledlakes(Fortinet al.,2010).
in Fortinet al.
Lakewaterwascollectedpassively
usingequilibrium
diffusionsampling
asdescribed
jars(Nalgene,
(20L0)and Muelleret al.(Muelleret al.,20L2b).Briefly,250-mLpolypropylene
Nalge
plasticlidsfittedwith a
NuncInternational
Corporation,
Rochester,
NY)toppedwith custom-made
PallCanadaLtd,VilleSt-Laurent,
were
0.2-pmfiltermembrane(HTTuffrynMembrane,
QC,Canada)
MA) ultrapurewater(>18Mohms-cm)
filledwith Milli-Q(Millipore,
EMDMillipore,Billerica,
andfixed
wereinstalled
to plasticrodsthat wereanchored
to the lakebottom.Thesamplers
at a depthof 1 m
period,the
from the lakesurfaceandleftto equilibrate
for 13to 14 days.Following
thisequilibration
samplers
werebroughtto the surface,
the membrane
surfacerinsedwith ultrapurewater,the
piercedwith a cleanplasticpipettetip andthe customjar lidsreplaced
with clean
membranes
polypropylene
screwlids. Thesamplejarswere baggedand storedin the darkat 4oCin field coolers
for a maximumof 12 daysuntilthey were transportedbackto the laboratory.Notethat lakewater
pH,conductivity
weremeasured
in the fieldat the time of samplecollection.
andtemperature
2.3 Sompleanolysis
d in tO%(v/v)nitricacidfor at least24 h and
All plasticlaboratory
andsampling
equipmentwassoake
then rinseda minimumof sixtimeswith ultrapurewateranddriedundera Class100laminarflow
vialsusedfor storingthe subsamples
hood. Theonly exceptionwasfor the polystyrene
for anion
wassoakedin a
analyses;
thesevialswererinsedthreetimeswith ultrapurewateronly. Allglassware
2 N HCIsolutionbeforebeingrinsedwith ultrapurewater.
jarshadarrivedin the centrallaboratory,
theywereopenedin a cleanClass
Oncethe polypropylene
werecollected
asfollows.A 1-0-mL
subsample
was
L00laminarflow hood,andsubsamples
ultra-tracenitricacid
vialand acidifiedto2o/o(v/v)with concentrated
transferredto a polypropylene
(BDHAristarUltra,VWRlnternational,
Mississauga,
ON,Canada)for
analysis
of majorcations(Ca,
(lCP-OES,
VarianVistaAXCCD,AgilentTechnologies
Canada
Mg)by atomicemission
spectroscopy
Inc.,Mississauga,
coupledplasmamass
ON)andtotal metals(Cd,Cu,Ni andZn)by inductively
t23
(lCP-MS,
A secondL0ElementX Series,
spectrometry
ThermoScientific,
Mississauga,
ON,Canada).
(F-,
mLsubsample
wastransferred
of anionconcentrations
to a polystyrene
vialfor the determination
(DionexICS-2000,
SOa2and POa3-)
ThermoScientific).
Cl-,NO3-,
by ion chromatography
Detailsconcerning
organicmatter
the quantitativeand qualitativecharacterization
of dissolved
(DOM)canbe foundin Muelleret al. (Muelleret al.,2OL2b).Briefly,20-mLsubsamples
were
transferredto amberborosilicate
vials(l-ChemBrand)for total organiccarbonanalysis(TOC-Vcpn,
(1.mL)weretransferred
Shimadzu
Scientific
Instruments,
to 3-mLglass
Columbia,
MD). Subsamples
(Kendall
vacutainers
Monoject,Mansfield,
carbonanalysis
by gas
MA)for totalinorganic
(VarianUV-Vis
(Varian3800GC,AgilentTechnologies
chromatography
lnc.).Theabsorption
Canada
(VarianCaryEclipse
Cary300Spectrophotometer,
AgilentTechnologies
CanadaInc.)and fluorescence
Fluorescence
Spectrophotometer,
AgilentTechnologies
CanadaInc.)spectraof lakewater DOMwere
(SUVA,
ThespecificUVabsorbance
alsomeasured.
by dividingthe
L m-1mg C-1)was
alsocalculated
measuredabsorbance
at254 nm by the concentration
et al.,2003). Excitationof DOC(Weishaar
emission
spectraweremeasured
using1-cmquartzcuvettesat excitation
wavelengths
from 210to 400 nm at incrementsof 5 nm and at emissionwavelengths
from 300to 580
nm at increments
of 2 nm. Thefluorescence
indexwascalculated
asthe ratiobetweenthe
fluorescence
emission
intensityat 47Onm overthat at 520nm for an excitation
at 370nm (Coryet
al.,2OLO;
McKnight
et al.,2001).Finally,
tool,parallelfactoranalysis
the multivariable
statistical
(PARAFAC),
wasemployedto deconvolute
the EEMspectrausingthe procedure
outlinedby Stedmon
and Bro(2008)as implementedin a MATLABenvironment,usingthe "N-waytoolboxfor MATLAB"
(Andersson
and Bro,2000).
2.4
Metol speciationmeosurements
All sub-samples
for metalspeciation
filter;AMD
measurements
werefiltered(0.2-pmpolycarbonate
Manufacturing
Inc.,Mississauga,
was
ON)undera laminarflow hoodbeforeanalysis.
A precipitate
just priorto the metal
observed
in a few samples,
whichprompteda filtrationstepfor all samples
speciation
analyses.
WHAMmetalspeciation
simulations
wereperformedusingthe element
concentrations
measured
on thesefilteredsamples.All calibration
werepreparedusing
solutions
r24
ultrapurewater. Stocksolutions
of Cd,Cu,Ni andZn wereprepared
from ICP-MS
standardsolutions
(4%HNO3,
PlasmaCAL,
SPCScience,
BaieD'Urf6,QC,Canada).
To takeinto accountthe variable
naturalconditions
of eachlakesampled,
e.g.pH,ionicstrengthandtotal metalconcentrations,
a
(LOQ)wascalculated
lake-specific
limitof quantification
for the measurement
of the free metalion
for eachlake,basedon the standarddeviationof triplicatemeasurements.
Whenthe free metal
valuefor a lakefell belowits LOQ,it wasdiscardedandthe free metalion was not reported.
2.4.7 lon exchangetechnique(lET)
Theconcentrations
of Cd2*,Ni2*and Zn2*were measuredfollowingthe methodsdescribedby Fortin
(L998)and updatedby Fortinet al. (2010).Briefly,
andCampbell
the ion-exchange
technique
involves
the equilibration
of the free metalionsin an unknownsamplewith a cationexchange
resin
(Dowexmesh,Sigma-Aldrich
CanadaLtd.,Oakville,
ON,Canada)that
hasbeencalibrated
with
standardsolutions
according
to the followingreaction,
M2n +(3 -;r) . R.CatZ RrM + (3 - x).Cat'*
(1)
whereM 2*is the free metalion, R is the resin,R,Cat is the concentration
of resinbindingsitesfilled
by a mono-or divalentcation,.r is the chargeon the cationandR"M is the concentration
of resin
bindingsitesfilledby the metalion of interest.Theconditional
equilibrium
constant, Kfu, for the
abovereactionis calculated
usingthe followingequation,
u, _IR,M ]. [*,'.
KL=ffi
]'-'
(2)
Underswamping
electrolyte
conditions,
to remainconstantand
lcot'.1 anO[n.Cot] areassumed
equation2 is rearranged
to givethe resindistributioncoefficientat fixedionicstrengthand pH, Ao.,.,,
(t-.e').
Lo,i.pn : Kio
.fcot'-ft-'
=[!rrJ
(3)
[R,cor]'-' lu'.l
Theresinwascalibrated
with standardsolutions
with a pH rangeof 5.3to 8.9andwith known
concentrations
of free Cd2*(0.24to 42 nM), Ni2.(13to 202nM) and zn2*14.9to 148 nM) andthe
125
resindistributioncoefficient(tr)wascalculated
for eachmetalbeforeapplyingthe methodto the
(seeSupplementary
naturalunknownsamples
Material,FigureS1).
Experimentally,
the resinwaspre-equilibrated
with a matrixsolutionthat hadan ionicstrength
(Ca(NOa)z
asthe swamping
solutionandthe natural
electrolyte)
and pH similarto the calibrating
(99.0%,
samples;
a sufficient
volumefrom a 25 mM Ca(NOg)z
Sigma-Aldrich
CanadaLtd.)stock
solutionwasalsoaddedto eachnaturalsampleso asto obtaina constantCaconcentration
of 0.46
mM in all naturalsamples.By levelling
we ensuredthat the
the ionicstrengthof the naturalsamples,
and
time neededto reachequilibrium
betweenthe resinandthe samplewassimilarfor all samples
that a singlemetal-specific
distributioncoefficientcouldbe used.Aftera steadystatehad been
reachedbetweenthe resinandthe sample(190mL),the resinwasrinsedwith ultra-pure
waterand
elutedwith a volume(V,6 mL)of IO%(v/v)ultra-tracenitricacid(BDHAristarUltra,VWR
lnternational).
Theamountof free metaloriginally
boundto the resinat steadystate( R M ) was
calculated
usingthe followingequation,
ln t11 lMr,*,"f'V
m,
LRzM l7!_____!JJ!!!r!_
(4)
where lM u,.*] is the concentration
of metalmeasuredin the eluateand m,is the massof the resin
used(approximately
7 mg;weighedprecisely).
3 and4, the free
Finally,
by combining
equations
metalconcentration
in the samplewascalculated
usingequation5.
lu*l=Wu'*"|l'
(s)
Ao,i,pH'm,
2.4.2 Cupricion selectiveelectrode(Cu ISE)
Because
the lETtendsto overestimate
freeCu2*whenthe lakepH isgreaterthan6.5(Fortinet al.,
2OLO),
we determinedCu2*in the lakesamplesusinga cupricion selectiveelectrode(CulSE,Orion,
ThermoScientific)
followingthe methodsoutlinedby Rachou
et al.(2007).TheCuISEwascalibrated
dailywith a solutioncontaining
0.1 mM coppernitrate(Cu(NOr)2,
SPCScience),
1 mM
PlasmaCAL,
iminodiacetic
acid(lDA,Sigma-Aldrich
hydrogenphthalate
Canada
Ltd.),2.5mM potassium
(KHCaHaO+,99.95%,
Sigma-Aldrich
Fluka,
Canada
Ltd.),10 mM potassium
nitrate(KNO3,99.995%,
126
(NaOH,98%,
CanadaLtd.)and6 mM sodiumhydroxide
Sigma-Aldrich
Sigma-Aldrich
CanadaLtd.).
Thecalibration
solutionwassplitinto 5 to 11 subsamples
with pH valuesvaryingfrom2 to 11-,
adjustedby the additionsmallknownamountsof eitherHNOgor NaOH,andallowedto equilibrate
overnight.Thefree Cu2*concentration
calculatedrangedfrom the methoddetectionlimit (three
timesSDof sixreplicate
blankmeasurements)
of 2.5x10-11to
6.3x10-s
M. Thecalibration
subsamples
with pH valuesabove6 were bubbledwith Nzgas,to avoida decreasein pH with the dissolutionof
carbondioxide,COz(aq).
atmospheric
ThepH andtemperature
of samples
weremonitoredusingan
Orion(Orion,ThermoScientific)
electrode
and probe,respectively.
DuringCuISEmeasurements,
constantstirringandtemperature
conditions
werealsomaintained.
Thecalibrated
response
of the
wasdetermined
CuISEelectrode
fromthe plot of the measured
ISEpotentialagainstthe calculated
pCufor eachcalibration
subsample.
Forthe naturalwatersamples,
the concentration
of Cu2*was
then calculated
usingthe CuISEcalibration
equationandthe measured
electrodepotential.The
naturalsamples
wereamendedwith KN03(99.995Yo,Fluka,
Sigma-Aldrich
Canada
Ltd.;a specific
volumeof a 1.0M stocksolutionwasaddedto achievea finalconcentration
of 10 mM KNOa)
to
ensurea constantionicstrengthamongthe naturalsamples.
2.5
Metal speciationcalculations
Thefree Cd2*,Cuz*,Ni2*andZn2*concentrations
measuredfor eachlakewerecomparedto values
usingthe Windermere
calculated
HumicAqueousModel(WHAM,modelversion6.1)(Tipping,
1-998).
Several
weremadein the application
assumptions
of the modelto the lakewatersamples.
First,we
that the DOC:DOM
assumed
ratiowas2 (Buffle,1988),that 65%of the DOMwasactivein the
complexation
of metals(Bryanet al.,2002),andthat thisactivefractionwascomposed
of fulvicacids
only. We alsoassumed
that both Fe(lll)andAl(lll)activities
arecontrolledby the solubility
of their
hydroxides,
as calculatedusingthe empiricalequationsgivenby Loftset al. (2008)andTipping(2005),
respectively.
Othermeasured
WHAMinputparameters
includedpH andthe totalconcentrations
of
Na,Mg, K,Ca,Cr,Co,Ni, Cu,7n,Cd,Cl,NO3,SOa,CO3(measured
astotal inorganiccarbon)and F. The
measured
surfacetemperature
of the lakessampled{usedin the WHAMsimulations)
variedbetween
20 and 23 degrees
Celsius.
Separate
WHAMsimulations
wererun for lakesamples
analysed
usingthe
IETfree ion measurement
method(forthe analysis
of freeCdz*,Ni2*andZn2*)andfor thoseanalysed
L27
corresponded
with the ISEmethod(forthe analysis
of freeCu");the inputdatafor thesesimulations
for
thusaccounting
exactlyto the composition
of the samples
usedfor the freemetalion analyses,
(additions
the differences
in theirioniccomposition
and KN03,respectively).
of Ca(NOe)z
2.6
Statisticalanalysis
All statisticalanalyses
were calculated
at the alphaequals0.05confidencelevelusingSigmoPlot11.0
(SystatSoftwareInc.,SanJose,CA). Parametric
were
t-testsand linearand non-linearregression
usedwhenthe normalityand homogeneity
amongdatasetswere confirmed;otherwise,
of variances
the appropriate
RankSumtest,in placeof the
non-parametric
testswereemployed(Mann-Whitney
percent
t-test. Notethat for the regression
equationsgeneratedbetweenthe WHAMcalculated
(%aFAootimized)
fulvicacidactivein metalcomplexation
and the relativecontributionof PARAFAC
fluorescence
componentsl and 3 to the overallfluorescence
spectrumof lakeDOM (seesection
3.4),the assumptions
wereconfirmed.Theonlyexception
of normalityandhomogeneity
of variance
wasfor Ni,wherethe variance
amongthe datasetswasnot constant(P=0.01).
3
3.1
Resultsand Discussion
Waterquality
Thespeciationof dissolved
of DOM,
metalsin freshwatersystemsis sensitive
to the concentration
whichtendsto complexfree metalions,but mayalsobe affectedby otherwater qualityvariables,
notablythe concentrations
of Ca,Mg,Al, Feand the H*-ion,thesebeingpotentialcompetitorsfor the
cationbindingsiteson the DOM(Tipping
et al.,2002).In addition,for a waterof givencomposition,
the total dissolved
concentration
of the metalmayaffectits speciation(i.e.,a limitednumberof high
affinitybindingsites).In the followingtwo subsections
how the waterchemistry
varied
we describe
amongthe studiedlakes.
3.2
Backgroundwater chemistry
pH)of the lakeswasdescribed
Thewaterquality(i.e.[majorcations],
in Muelleret al. (2012b)for
2007and2008.Onlythe 2008dataareconsidered
measurements
here,sincethe metalspeciation
(pH,Ca,Mg and DOCconcentrations)
datefrom that year.Widerangesin generalwaterchemistry
L28
werefoundamongthe eightlakessampled(Table1),reflecting
both regionalgeological
differences
and localdifferences
at the watershed
scale(Muelleret al.,2OL2bl.Thelakesin bothsampling
regionshavelow concentrations
of calcium(Ca:68 to 464prM)and magnesiumlMg:27 to 274 gMl,
contributingto generallysoftwater. LakeBethel,from the Sudburyregion,hadthe highestpH and
the highestCaand Mg concentrations
of all the lakes;for the IETmeasurements,
all otherlakewater
(seesection2.4.tl'.
werethusadjustedupwardsto theseCaconcentrations
samples
Lakesfrom the Rouyn-Noranda
regionare circumneutral,
with a meanpH of 7.25anda narrowrange
of valuesbetweenpH 6.81and7.69.ThemeanpHfor the Sudburylakes(7.07)is slightlylowerthan
that of the Rouyn-Noranda
lakesand pH valuesspana widerrange(6.40to 8.08).Historically,
the
Rouyn-Noranda
lakeswere lessaffectedby anthropogenic
acidification
than werethosein the
Sudburyarea(Dupont,t9921,despitemassiveSOzemissions
from the Hornesmelter;the RouynNorandalakesare locatedin the Abitibiclaybelt and are effectivelybetterbufferedagainstacid
pH of the lakesin the Sudburyregionsis evidence
deposition.Althoughthe average
circumneutral
of
the recoveryof theselakesfrom historicalacidification
in recentyears(Kelleret al., 2OO7l,pH
values
in the lakesof thisregionspanneda widerrangethan in the Rouyn-Noranda
lakes.
(1-4pg chl-aL-1;
Basedon their historical
chlorophyll-a
concentrations
Campbell,
unpublished
data),
the four studiedlakesin the Rouyn-Noranda
regionareoligotrophic,
asare LakesRaftandWhitsonin
the GreaterSudburyarea(2-3pg chl-aL-1;
h t t p :" v w w . g r e a t e r s u d b u r v . c a / c m s ' i n d e x . c f m ? al apkpe=wd ai vt e r q u a l i t v & l a n g = e n & c u r r l D = 6 1 9 .
accessed
7 August2OL2).We were unableto find comparabledatafor LakeGeneva,whichlies
outsidethe GreaterSudburyarea,but giventhe lackof development
in itswatershed,
it is alsolikely
oligotrophic.Theonlyexceptionis LakeBethel,whichis muchmore productive(L2-24pg chl-aL-1;
http://www.greatersudburv.ca/cms/index.cfm?app=div
lakewaterqualitv&lang=en&currlD=61-9,
7 August2OL2)and canbe classified
accessed
aseutrophic.
I29
3.3
Dissolvedorgoniccarbon
ThemeanDOCconcentration
of Rouyn-Noranda
lakeswashigher(6.1mg C t-1)andspanneda wider
range(4.9to 8.1mg C t-1)thanin the lakesfrom the Sudburyregion(meanof 3.4mg L 1;rangefrom
regionthan
2.Oto4.9 mg t{). the higherDOCconcentration
in the lakesin the Rouyn-Noranda
arearatiosfor the
Sudburylakesreflectsthe greatersoilcoverandthe higherwatershed-to-lake
Rouyn-Noranda
lakes.As described
in Muelleret al. (z)Lzbl,thequantityandthe qualityof DOMin
andaswellas DOM
the lakessampledvaryasa functionof regionalwatershed
characteristics
processing
withinthe watershed
andwithinthe lakeitself.
DOMopticalquality(SUVA254
between
and Fluorescence
Index)wasfoundto differsignificantly
regionsandamonglakes(seeTable51 in the Supplementary
sampling
Materialformoredetails)and
(Muelleretal.,2OI2b).Theauthorsalsousedparallelfactor
is described
elsewhere
analysis
(PARAFAC)
to extractfluorescence
componentsfrom excitation-emission
including
spectrameasured
for 19 lakesin the Rouyn-Noranda
the eightlakes
andSudburyregions,
retainedfor the presentstudy. Of particularinterestwerethe humic-likefluorescence
components
of ubiquitous
origin(C1)andallochthonous
to a reduced
CLcorresponds
origin(C3).Component
quinonefluorophore
to an oxidized
associated
with higherplantmatter,whereasC3corresponds
quinonefluorophore(Muelleret al.,2OL2b).We usedthe proportipnof eachof theseDOM
fluorescence
i.e.,
components(relativeto the sumof all four PARAFAC
fluorescence
components;
as proxies
CI/C1or C3/Cr)as measures
of the qualityof DOMsampledin our lakes,andin particular
for the fractionof DOMactivein the complexation
3.3.2).
of traceelements(section
3.4
Lake-to-lakevariationsin metal concentrationsand metol speciotion
3.4.1 Totoldissolvedmetal ion concentrations
(Cd,Cu,Ni,Zn)variedmarkedlyamongthe eightlakes
Thetotaldissolved
metalconcentrations
gradientratios(i.e.,the
sampled(Table1). Forlakesin the Rouyn-Noranda
region,the concentration
+ minimummetalconcentration,
ratioof the maximummetalconcentration
IM]r."/[M]rin)
decreased
in the sequence
Cd(33)- Zn (31)> Cu(7)> Ni (2.5).In the Sudburyregion,the
ratiosdecreased
in the orderNi (63)> Cd(3L)> Cu(16)> Zn (5). Lakesin the Rouyn[M]r.,./[M]n';n
130
Norandaregionhadsignificantly
highermeantotal concentrations
of Cd (P=0.004)
and Zn (P=0.030)
thanthe lakesin the Sudburyarea,but lowerconcentrations
of Ni (P<O.OOl).
No significant
differencein total dissolvedCuwasfound betweenthe samplingregions.
Thelake-to-lake
variability
in the measured
totaldissolved
metalconcentrations
withina givenregion
is largelydueto variations
in atmospheric
loadingfrom the localminingandsmeltingactivities
(Borgmann
et al.,1998;Couillard
et al.,2004).Despite
the markedreductions
in smelteremissions
that havebeenachievedoverthe past30 years,lakesin closeproximityto and down-windfrom the
metalsmelters(e.g.,LakesDufaultand Dasserat
in the Rouyn-Noranda
region,LakesRaftand
Whitsonin the Sudburyarea)stillhaveconsiderably
highertotaldissolved
metalconcentrations
than
do thoselocatedupwindor far downwind.
3.4.2 Freemetal ion concentrotions
Thefree Cdz*,Cu2*,
Ni2*and Zn2*concentrations
alsovariedamonglakes(TableL). The ratioof
for the Rouyn-Noranda
lakesdecreased
asfollows:Zn (I2Il > Cd (54)> Cu(5.5).
[M2.],nr"/[M2*]n,;n
FreeNi2*concentrations
wereonlymeasurable
in one replicate
samplefrom one lakefor the Rouyngradientratiowascalculated.The ratioof
Norandaregionand,therefore,no concentration
for the Sudburylakesdecreased
in the sequence
Ni (136)> Cu(64)> Cd(40)> Zn
[M2*],.*/[M2*],n;n
(22).Asanticipated,
lakesthat hadthe highestconcentrations
metalalsoexhibitedthe
of dissolved
highestfree metalion concentrations.
131
Table1.
MeasuredpH and mean(t SD,n=3)dissolved
organiccarbon(DOC),
total dissolved
concentrations
of majorcationsand trace metals,aswell
'
(QC)andSudbury(ON)in 2008.
as measured
free metalconcentrations
and%treemetalvalues
for lakessampledfrom Rouyn-Noranda
(LOQcalculated
indicatesonlyone sampleabovelimit of quantification
asthreetimesthe standarddeviationof triplicateanalyses
of each
lake).NA indicatesno samplesabovethe LOQ.
Dasserat(DS)
pH
DOC(mg.L-l)
Ca(pM)
MS (rrM)
Cd(nM)
Cu(nM)
tt
E
=
o
o
c)
=
Ni(nM)
Zn (nM)
cd'?.(nM)
cu2*1nM;
Ni2*(nM)
zn2*1nM;
o/o Cdz*
o/oCu2'
7o Niz*
o/oZn2*
6.92
4.85r 0.03
2 0 7! 1
93.7r 0.3
0.8i 0.2
112t3
8x2
464t 51
0.5r 0.2
0.37r 0.04
0.6'
279!32
63r36
0.32r 0.04
8
60r10
Rouyn-Norandaregion lakes
Dufault(DU) Opasatica(OP)
7.59
5 . 2t 0 . 1
4 0 4! 3
118!1
7.69
6 . 3r 0 . 1
2 1 8t 2
Vaudray(VA)
Bethel(be)
6.81
8 . 1 0r 0 . 0 8
8.08
4.89i 0.03
464r.3
83r1
Sudbury region lakes
Geneva(ge)
Raft (ra)
6.4
2 . 0 3r 0 . 0 3
6.9
3 . 7 7r 0 . 0 5
68r3
81t2
0.56r 0.02
86.0r 0.9
143x1
78.4t0.4
0 . 5 1r 0 . 0 4
165r3
7 7 5! 4 1
1136r54
2 7 4t 3
26L1
0 . 0 1 8 1 0 . 0 0 9 0.021r 0.005
2 4 . 9t O . 1
10.5r 0.6
226t2
1 8! 2
114t1
0 . 0 6r 0 . 0 1
3 5 . 1r 0 . 2
3 7 . 1r 0 . 6
0.608r 0.005
43t1
12.5t 0.4
15!4
1 2r . 1
52t5
1 2t 3
1 . 1 4r 0 . 0 6
0.021* 0.008
NA
0.44r 0.09
0 . 0 8i 0 . 0 1
0.26r 0.02
0.20i 0.08
21xB
0.01'
NA
1 8 8+ 2 3
NA
NA
0 . 0 5r 0 . 0 1
7 . 3r 0 . 9
0.48r 0.09
4.0t0.7
2 " 3r 0 . 9
3 7r . 1 7
31r18
2 . 3t 0 . 4
1 0 . 7* 0 . 9
51r16
42t4
0.47t0.2
NA
0.20i 0.05
70
4 . 6r 0 . 9
7 1t 2
3 . 7r 0 . 6
2 . 0! O . 2
2 4 6x 3
5 . 1r 0 . 3
342 t60
58r5
0 . 1 7t 0 . 0 4
O.21t0.04
Whitson(wh)
6.9
2 . 9r 0 . 1
46t1
62t22
0.402r.0.004
3.2 i 0.5
545r9
5 2t 2
0 . 3 1f 0 . 0 5
1 . 0r 0 . 1
489r.72
32r.4
61*10
NA
NA
NA
3 . 2t 0 . 4
22t5
70t4
0.57r 0.08
43t7
55!12
1 5! 7
60r36
20r6
51t20
82r39
62!8
In the presentcontext,wherewe arecomparing
measured
andpredictedmetalspeciation
in
differentlakes,it is importantto considerthe relativeproportionof the free metalion in eachlake,as
in totaldissolved
metalconcentrations
this measure
takesinto accountthe differences
amonglakes.
thanthe otherthreemetals- the mean
Asanticipated,
copperwasmuchmorestronglycomplexed
percentfree metalfor copperwasonly L.3%o,whereas
the meanpercentages
for Cd2*(54%),Ni2*
(29%)andZnz*(50%)weremuchhigher(Table1). Forall four metals,and in boththe Rouyn-Noranda
pointingto
asthe pH increased,
andSudburyregions,
the percentfreemetaltendedto decrease
competitionby protonsasan importantfactoraffectingmetalspeciation.A general
complexation
for Cd,Cuand Ni asthe DOCconcentration
declinein the percentfree metalwasalsoobserved
presumably
ratio(seeFigs.53 and 54 in the
increased,
reflectingan increasein the ligand-to-metal
Materialfor thesepH and DOCrelationships).
Supplementary
3.4.3 Comporisonof the observedmetol speciationwith literature volues
In comparingmetalspeciationin differentfreshwaterenvironments,
the reportedfree metalion
are not particularly
concentrations
useful,sincetheywill varyaccording
to the metalloadings
affectingthe water body. Comparisons
of free metalion proportionsare a more meaningfulway of
evaluating
how metalspeciation
variesfrom one lakeor riverto another.In Table2, we have
compiledfreemetalion percentages
that havebeenreportedin the literaturefor Cd,Cu,Ni andZn
(the recentreviewpaperby LoftsandTipping(201L)was usedasa startingpointto identifypapers
wherefree metalion concentrations
for naturalwatershad beenreported).Table2 cannotclaimto
be comprehensive,
asthe representation
of surfacewatersfrom Switzerland
and Canadais
high,but the compilation
in metal
abnormally
doesoffersomeinsightinto metal-to-metal
differences
speciation.
is the mostextensive
Copperis the metalfor whichthe Table2 database
andit is alsoconsistently
the moststronglycomplexedmetal,followedby Ni > Cd> Zn. Fora givenmetal,however,there are
distinctregional
differences;
the percentfree metalis virtuallyalwayslowerin the alkalineSwiss
Precambrian
lakesthan in moreacidicand moredilutelakeson the Canadian
Shield.On a relative
wherethe percentage
is particularly
scale,thisdifference
notablefor Cu2*,
canbe lessthan0.00L%in
133
manyof the Swisslakesbut consistently
above1%in the Canadian
surfacewaters. Factors
(carbonate
and
contributing
to thistrendincludethe aforementioned
is pH andalkalinity
differences
in the
hydroxide
complexation),
but alsothe metalloadings.
metalconcentrations
Totaldissolved
lakesare muchhigherthan in the Swisssurfacewaters(Table2), presumably
reflecting
Canadian
Shield.Highertotal
inputsfrom the miningandsmeltingactivities
that are prevalent
on the Canadian
to leadto lower
metalconcentrations,
highermetal:DOM
andthe resulting
ratios,wouldbe expected
degreesof complexation.
3.5
Lake-to-lokevoriotionsin predictedmetal speciotionond compariion with measuredvolues
3.5.1 lnitialWHAMsimulations
Freemetalion concentrotions.TheWHAMVl chemicalequilibriummodelhasbeendescribedin
(ModelV), Model
Tippinget al.,2011).In comparisons
detailby Tipping(2OO2;
with its predecessor
Vl doesa betterjob of simulating
metalbindingto fulvicandhumicacids,particularly
at low metal
concentrations,
for low [M]/DOCratios,andfor metalsthat tendto bindto the strongmetal-binding
groups)that wereexpressly
sites(putativeN andS functional
introduced
into ModelVl. In brief,
fulvicand humicacidsareassumed
to be rigidspheres
of uniformsize(radiiof 0.8and 1-.7nm,
groupspositioned
respectively)with
two classes
of ion-binding
typeA
randomlyon theirsurface:
sitescorrespond
to monoproticcarboxylic
acidgroups,whereastype B sitesare modeledas weaker
(e.g.phenolic)
products(MOUtn-tl*,
acidgroups.Metalionsandtheirfirsthydrolysis
competewith
eachother,andwith protons,for the typeA andtype B groups.Bi-dentate
sitesare
metal-binding
generatedby combiningpairsof proton-binding
sites(A-Aor A-Bcombinations).Onlyproton-binding
sitesthat are sufficiently
closeto eachother are ableto form bi-dentatesites(s 0.45nm apart;
estimated
statistically).
sites.
Similarrulesare usedto generatetri-dentate
metal-binding
L34
Table2.
Freemetalion percentages
in variousnaturalwatersamples.
Total
dissolved
Metal Sampledescription
cd
Ni
Cu
7n
Percentfree
metal ion
Mean
SD
Reference
54
13
(thisstudy)
Canadian
Shield
7
tMl (nMl
0.06-2.0
C a n a d i aSnh i e l d
13
0.07-9.3
72
11
Fortinet al.(20L0)
Canadian
Shield
4
0.9-8.0
20
16
Guthrieet al.(2005)
Swisslake,UKriver
4
0.07-0.20
3.7
3.5
Sigget al.(2006)
Swisslake,UKriver
5
0.06-0.87
4.9
3.5
Kaliset al.(2006)
Canadian
Shield
5
8-1140
29
28
(thisstudy)
Canadian
Shield
6
490-1760
lL
6
Gopalapillai
et al. (2008)
Swisslake,UKriver
4
6-20
6.6
Swisslake,UKriver
7
6-s9
3.0
Belgiansurface
6
44-75
Canadian
Shield
8
Canadian
Shield
1,.4
'1,.4
Sigget al.(2005)
22
16
VanLaeret al.(2006)
Lr-246
1.3
1.8
(thisstudy)
2
42-L50
1-534
Swisslake,UKriver
4
L9-30
0.25
o.r2
Sigget al.(2006)
Swisslake,UKriver
7
L3-76
o.2\
0.22
Kaliset al.(2006)
Swisslakes(neutral) 2 5
4-L7
<0.0010
Swisslakes(acidic)
5
3-71.
Swissrivers
20
1_0-68
<0.0010
Swisslake
2
65-71.
0.3
O.2
XueandSunda(1997)
C a n a d i aSnh i e l d
8
L2-464
50
23
(thisstudy)
Canadian
Shield
10
10-2010
86
L5
Fortinet al.(2010)
Canadian
Shield
4
46-1300
25
4
Guthrieet al.(2005)
Swisslake,UKriver
4
28-282
10
6
Kaliset al.(2006)
Swisslakes
6
Lt-25
8
L
XueandSieg(1-994)
Kaliset al.(2006)
Guthrieet al.(2005)
Xueand Siee(L996)
4
XueandSiee(1996)
XueandSiee(1996)
oThese
" Concentration
range(N=2)ratherthan the meanvalue.
very low valueswere obtainedby competitiveligand
exchange
and cathodicstrippingvoltammetry.LoftsandTipping(2011)haverecentlysuggested
that thistechniquemay
underestimate
free Cu2*concentrations.
135
Whenusingthe WHAMmodelto predictthe free metalconcentrations
in the studiedlakes,we
initiallyassumed
in metalcomplexation
that the components
of the DOCpoolthat participate
reactions
couldbe represented
entirelyby fulvicacid(100%FA,O%HA),andthat this FAportion
corresponds
to 65%of the DOC;thesechoiceswere basedon the recommendation
of Bryanet
wasobserved
al.(Bryan
et al.,2002)forcoppercomplexation
in naturalwaters.Goodagreement
range
betweenpredictedand measuredvaluesof [Cd2.](Figure1a),overa 1-00-fold
concentration
(0.01(Figure1d),overa similar100-fold
to 1.1 nM). Agreementfor l1n2*lwasalsoreasonable
(2 to 280 nM) that were considerably
higher
concentration
rangebut for free Zn2*concentrations
thanfor Cd2*.In the leastcontaminated
the predicted
lakes(Geneva,
Betheland notablyOpasatica),
freeZnz*concentrations
that WHAMVl
were somewhathigherthan the measuredvalues,suggesting
mayunderestimate
Zn complexation
at low dissolved
Zn:DOC
molarratios.
(> 0.4nM) our ISEmeasurements
At highfreeCu2*concentrations
andthe freeCu2*valuespredicted
by WHAMVl were in verygoodagreement(Figure1b),but belowthisthreshold,in LakesVaudray,
Geneva,
Opasatica
and Bethel,our measured
valueswere4 to 22 timeshigherthanthe predicted
values.In theirstudyof Cuspeciation
Cu
in threenaturalwaterswith similarlow totaldissolved
(19-30nM),Unsworthet al. (2006)alsoreportedmeasured
concentrations
valuesof Cu2*that
exceeded
WHAMVl predictions;
techniques
in theirstudy,freeCu2*asestimatedby two independent
(Donnanmembrane;
permeable
Hollow-fibre
liquidmembrane)
wasL to 3 ordersof magnitude
higherthanthe WHAMVl predictions.
136
1e-8
A
B
A
_,/
' t"ll' r
uxy to"
+
N
E
9
o/
*n/
1e-9
,r/r,
+
N
,/
1e-10
lo-rn
VA
/./ ' a '
o)
o
q)
,/
o
/
1e-11
|ege
1e-11
top
/
l--U
i{'oe
,/
1 e - 10
1e-'t1
1e-12
1e-12
1e-9
cv
r-o be
1e-11
1e-9
1e-10
1e-8
1e-6
D
DS
./'"
DUj
n
2_ te-z
'le-7
{_
z
+
be
l,/
:o
o
o)
E
1e-8
o
||,/
*^q(^
N
,/
c
N
o
-9
o
/
E
ge/
o
2
l,/
Ds/
T
1e-8
+#+
/vA
n"tf/
/^
+{ /
iA/
OPr
7b"
1e-9
1e-8
1e-7
Measured
M" (M)
Figure1.
1e-9
1.0e-9
1.0e-8
1.Oe-7
1.0e-6
Measured
M'. (M)
of the free metalion [Mz*]calculated
with WHAM
of mean(t SD,n=3)concentrations
Comparison
(withthe 65%aFAassumption)
andthosemeasured
usingIET(Cd,Ni andZn)and ISE(Cu).The
dashedlinerepresents
the 1:1line.
Thegenericconstantsusedin WHAMVl havebeengeneratedfrom laboratorytitrationsof isolated
ratiosin thesetitrationsareveryoftenhigherthanthose
humicandfulvicacids.Copper/DOC
with the
andthusone mighthaveexpected
calculations
encountered
in naturalwatersamples,
genericconstants
we observethe oppositeresult
However,
to underestimate
coppercomplexation.
(overestimation
of Cucomplexatioh,
aswasthe casefor Unsworthet al. (2006)).Clearlymore
L37
to determinethe conditional
experiments
equilibrium
for the complexation
of Cuto DOM
constants
shouldbe carriedout underconditions
approaching
thosefoundin naturalwaters(e.g.,low Cu/DOC
ratios;naturalratherthanisolatedorganicmatter).
In our experiments,
explanation
but not in thoseof Unsworthet al. (2006)thereis a secondpossible
of an analytical
naturefor the discrepancy
betweenmeasured
and modeledfreeCu2*concentrations.
In the four lakeswith low total Cuconcentrations,
may be below
the total dissolved
Cuconcentration
that whichis necessary
to properlymeasure
the freeCu2*concentration
usingan ion-selective
electrode(-fO-t M) in a partiallybufferedsystem(Buffle,1988;Mota and CorreiaDosSantos,1995;
Rachouet al.,2007). In suchsystems,if the bufferingcapacityis insufficient,
from
Cucontamination
duringthe potentiometric
the electrode
increase
the apparent[Cu2.]
measurement
mayartificially
value.However,
if thiswerethe case,allfour lakesmightbe expected
to exhibitsimilarCu2*
concentrations,
whichis not the case.Notetoo that in all our lakes,the freeCu2*concentration
is
bufferedby the DOMnaturallypresentin the samples.
Finally,
WHAMVl consistently
over-predicted
in allthe lakeswherethe
ambientconcentration
wasabovethe methoddetectionlimit (FigureLc),i.e.the oppositeresultfrom
that with Cu,suggesting
that the defaultequilibrium
of Ni to natural
constants
DOMaretoo low for the environmental
conditions
foundin our lakes.VanLaeret al. (2006)cameto
a similarconclusion
in theirstudyof the complexation
surface
of Ni by the DOMpresentin sixBelgian
(40to 80 nM),the measuredfree-ionfraction,as
waters.At their background
Ni concentrations
measured
usingthe Donnanmembrane
variedfrom 4 to 45%;WHAMVl overestimated
technique,
the free-ionproportionmorethan 2-fold,evenif it wasassumedthat allthe DOMwaspresentas
fulvicacidandactivelyparticipating
in Ni complexation.
138
100
A
t
80
Tre
I;
660
o
E
IL
s40
T
+
L
I
t
1,1
TI
1 . r '"
l:
7''
t':
It
lF
T
7
*''
!,,:
!F
'
*
a
*
i!:
-
't'
7
i:.
20
120
z60
q)
E
IL
s40
D
.80
N
o
,fl uo
s
u$*o%suj)"."$*"i.."]',"odu"o u$*o{.$..'$*"i".{"*0"*uuo
Figure2.
Comparison
betweenmeasuredpercentfree Cd2*(A),Cu2'(B),Ni2*(C)and Zn2-1D1and WHAM
predictedpercentfree Mz*for eightlakessampledfrom the regionsof Rouyn-Noranda,
QCand
(LOQ)are not shown.
Sudbury,ON.Measuredpointsbelowthe methodlimit of quantification
inputvalues.
for a rangeof percentactivefulvicacid(%aFA)
WHAMpredictions
were calculated
Lakeson the x-axisare arrangedfrom highpH on the left to low pH on the right.Solidcirclesare
measuredo/ofree
Mz*valueswith error barsrepresentstandarddeviationof triplicatemeasures.
Greyverticalbarsrepresentthe rangeof WHAMpredicted%free Mz*valuesusing33%to t3O%
aFA.Solidhorizontallinesare WHAMpredicted%free Mz*valuesusing65%aFA.Dashed
horizontallinesrepresentthe LOQ.
139
Freemetolion percentages. A comparison
wasalsomadebetweenmeasuredand modeled
specific
"percentfree metal"valuesto take into accountdifferences
in the total metalconcentrations
(LOQ)wascalculated
percentfreemetallimitof quantification
2). A lake-specific
to eachlake(Figure
for eachlakebasedon threetimesthe standarddeviations
of boththe
of triplicatemeasurements
freemetalandtotal metalconcentrations
measured
for eachlake.Anypercentfree metalvalue
calculated
for a lakethat yieldeda valuebelowits LOQwasdiscarded.
ln additionto usingthe defaultassumption
that 65%of the DOMis presentasfulvicacid(FA)andis
activein metalcomplexation,
we alsocalculateda rangeof modelpercentfree metalpredictionsby
the %aFA
halving(33o/o)
arbitrarily
anddoubline$3O%)
thisvalue.lt shouldbe notedthat increasing
to a valuehigherthan 100%simplyincreases
the numberof fulvicacidbindingsitesinvolvedto a
(pmolbindingsitesper unitcarbon)of the defaultfulvic
valuethat exceeds
the complexation
capacity
(Figure
percentfree
acid.In the presentation
of the resultsof thesesimulations
2),the calculated
metalis depictedas an envelope
to the default
of valuesfor eachlake:the middlevaluecorresponds
valueobtainedwith the assumption
to
of 65%aFA,whereasthe upperandlowervaluescorrespond
the percentfree metalcalculated
asthe proportionsof DOMactive
with 33%andI3O%,respectively,
in metalcomplexation.
Thebracketof upperand lower%freemetalvaluesincludedthe measured
percentfree metalvaluesfor both Cdand Zn in most lakes(7 of 8). Thelakesfor whichthe measured
valuefell outsidethe predictedenvelopewerethosewith the lowesttotal andfree metal
concentrations.
Evenwith the assumption
WHAMVl
of only33%aFA,i.e.,minimumcomplexation,
stillpredicted
a lowerpercentage
valuefor the lakeswith the lowest
freeCu2*thanthe measured
(4 of 8). Similarly,
totalCuconcentrations
i.e.,maximum
of L30%aFA,
evenwith the assumption
complexation,
the measured
valuesof percentfree Ni2*allfell belowthe minimumpredicted
value.
3.5.2 Optimizationof WHAM Vl predictions
Thechemical
equilibrium
simulations
discussed
to this pointwererunwith WHAMVl in its default
mode,usingthe assumptions
that wereoutlinedearlier.Amongthe adjustable
WHAMinput
parameters
proportionof DOMactivein metalcomplexation
are(i)the user-defined
and(ii)the
relativeproportions
of humicandfulvicacids(FAand HA)makingup the DOMthat is activein metal
140
complexation.The latter parametervariesasa functionof the type of organicmatter,e.g.,whether
it is derivedfrom soilwater,peatwater,lakewater,etc.(Bryanet al.,2002).The proportionof DOM
that is activein metalcomplexation
is usuallyunknownandmustbe estimatedby the userof WHAM.
At this point,we retainedthe assumption
that the DOMactivelyinvolvedin metalcomplexation
asfulvicacid,but we removedanyconstrainton the "percentactiveFA"and
couldbe represented
for eachlakeandeachmetal,individually.
Theoptimalpercentof FA
ran multiplesimulations
(%aFAool)
requiredto exactlypredictthe measuredfree metal
activelyinvolvedin metalcomplexation
adjusting
ion wascalculated
by repeatedly
runningWHAM/ModelVl,
the FAconcentration
untilthe
predictedfree metalion concentration
equalledthe observedvalue. A computercodewas usedto
run WHAMandoptimisethe valuesof %aFA.The%aFAoot
automatically
wascalculated
in this
mannerfor eachmetalandfor eachlakeas
o/aF\,=
(mgL-1)
(mgt 1).
isthe optimisedFAconcentration
where[FA]opt
and [DOCIis the DOCconcentration
fn a givenlakeandfor a particular
metal,an optimalo/oaFAfor
eachof the triplicatesamples
was
allthe measured
calculated
by findingthe FAconcentration
that bestdescribed
freeion
(Table3). Thiswasdoneby adjusting
for eachsamplesimultaneously
concentrations
the modelFA
as before,and minimising
concentration,
the errorterm
:
) ([MJ,0,,, [M].,",,)'
freeion concentrations
where[M]our,
andcalculated
for metali.
i and [M]carc,
i ar€the observed
Byincreasing
or decreasing
numberof FAbindingsitesinvolvedin metalcomplexation
the %oaFA,the
is eitherincreased
or decreased,
whereasthe intrinsicmetalbindingaffinities
at thesebindingsites
ln otherwords,the ligandconcentration
is adjusted.Ascanbe seenin Table3, the
remainconstant.
reflecting
interoptimal%aFAvaluesobtainedin thismannervariedfrom laketo lake,presumably
valuesall fell in the 8to 90%range,
lakedifferences
in the qualityof the DOM. ForCu2*the %aFAoo,
147
the notional
Ni2*,the valueswereall muchhigher,oftenexceeding
but for Cd2*,2n2*
andparticularly
limit of too%:6t to 250%for cd2*;65 to 4Lo%f or zn2t;44oto Lgoo%for Ni.
3.5.3 Possiblereasonsfor the divergencebetweenWHAMcalculationsand measuredmetal
speciation
Althoughincreasing
the %aFA,andthusaugmenting
the numberof bindingsiteson the FA(as
thisvalueup to three
in the preceding
section),
is an acceptable
increasing
described
adjustment,
that in
Thisresultsuggests
higherthanthe defaultvalueis clearlyunrealistic.
ordersof magnitude
to adjust
additionto adjustingthe bindingcapacitvof the fulvicacid,it wilt tiketyalsobe necessary
Ni. Theremaywell be natural
metalbindingaffinities(Kvd (Tipping,2002)
for Cd,Zn and especially
ligandspresentin the lakewaterthat havea higheraffinityfor Ni thandoesfulvicacid. Further
to determinethe bindingconstants
experiments
of thesemetalswith naturalDOM,at
realistic
environmentally
areclearly needed.
couldalso
Discrepancies
betweenWHAMpredictions
free-metal
ion concentrations
and measured
Forexample,
ariseif ligands
otherthanfulvicand humicacidswereinvolvedin metalcomplexation.
surfacewatersand
Bakenet al. (2011)detectedamino-polycarboxylate
anionsin someurbanBelgian
for the greaterthan expected
suggested
that theseligandsof anthropogenic
originwere responsible
(asseenherefor Ni). However,
seems
metalcomplexation
in thesesamples
suchan explanation
of urban
unlikelyin the presentcase,giventhe remotelocationof our lakesandthe absence
wastewaterinputs.
r42
Table3.
(QC)and Sudbury
lakessampledfrom Rouyn-Noranda
Mean(t SD,n=3)calculated
%oaFAorrfor
( O N ) i n2 0 0 8 .
YoaFAop.
cd
Cu
Ni
Zn
(DS)
Dasserat
93141
80r12
140Oa
95r33
Dufault(DU)
61r 3
5618
NAO
65r21
(OP)
Opasatica
7 5! 5
1 6 13
NA
3L0t 33
Vaudray(VA)
92!1.4
32!7
NA
9s190
Bethel(be)
NA
9!2
1900r 130
1_90
t 36
Geneva(ge)
t40'
8r1
890r 170
290!120
Raft(ra)
2 5 0! 2 3
86r8
440!74
4LO!4
Whitson(wh)
140t 31
90 t 8
580t 75
180t 51
[ake (code]
(LOQ calculated
asthreetimesthe standarddeviationof
" indicatesonlyone sampleabovelimit of quantification
triplicateanalyses
of eachlake).
b
NA indicates
valuesnot available.
3.5
Relotionshipsbetween %aFAor,and DOM spectroscopicproperties
we exploredpossiblerelationships
in %aFAspe
between
Giventhe markedlake-to-lake
differences
of the DOMin the originallakewatersamples.lnitiallywe
thisvalueandthe opticalproperties
values,giventhe promising
resultsobtainedby soilscientists
lookedfor relationships
with SUVAzsq
in soilsolutions(Ameryet al.,2008;Cornuet al.,2011).However,
workingon CuandCdspeciation
(see
plotsof YoaFAool
indexfailedto yieldusefulrelationships
or the fluorescence
againstSUVAzs+
Figs.55 and56 in the Supplementary
Material).Ourinterestthenturnedto the fluorescence
spectrum,
to determineif it couldbe usedto estimatethe proportionof DOMthat is activein metal
fluorescence
of eachof the four PARAFAC
complexation.We plottedthe relativefluorescence
to
freemetalconcentration
againstthe %aFAneededto forcethe WHAM-calculated
components
(%aFAoor)
(seeFigs.57 and58 in the Supplementary
free metalionconcentration
equalthemeasured
L43
for the four metalsemerged
relationships
with %aFAopt
Material).Themoststatistically
significant
for CUC1andC3lCr(Figure3). In seeking
suchrelationships,
we are usingthe fluorescence
matrixas a "proxy"for the compositionof the DOM. Although
excitation-emission
probethe functionalgroupsor the exactsitesinvolvedin
spectrofluorimetry
doesnot necessarily
in the
metalcomplexation
by the DOM,it is nevertheless
a usefulandeasywayto trackchanges
qualityof DOMin naturalaquaticsamples
(Fellman
et al.,2010;Muelleret al.,2012b).
(y=380e(-6'4x))
(P<O.OOOL)
relationship
ForCd,the strongest(R2=0.79)
and moststatistically
significant
significant
emergedbetweenyoFAoptand
the relativeproportionof C3(Figure3e). A statistically
(P<O.OO1)
positivelinearrelationship
(y=1100x-260; R2=O.621was
found betweenCuand CL/CI
(y=-20 000x+6800)
(Figure3b). ForNi a strong(R2=0.96)
linearrelationship
and significant(P<0.001)
(P=0.01)
but statistically
significant
wasobservedwith Ct/Cr (Figure3c). Finally,a weak(R2=0.33),
(y=560e(-s'3x))
3h).
relationship
decreasing
exponential
wasfoundbetweenZn andC3/q (Figure
144
I
E
()
250
I
€ zoo
o
N
200
150
150
100
100
E
o
o
<
L
6
sso
010
030
r
ar(b
t
V
V
V
o80
(D
020
030
D
tr
t\
^^
N v w
c
=
o40
AA
I
o
S20
ot
"€
030
032
034
2000
z
"P
rsoo
()
N
E
Eo 1000
o
LL
o
s
s00
024
030
034
o
soo
015
020
025
a
o
N
E
6 200
010
t
400
tr
N
€
o
005
oo
I
I
s
F
- AV
100
'A
o24 0 26 028 0
C1/C1
Figure3.
G3/C1
Relationship
betweenthe WHAMcalculatedpercentfulvicacidactivein complexation
(%aFAopl;.;,"6)
for Cd, Cu,Ni andZn andthe relativecontributionof PARAFAC
fluorescence
(panels
(panels
components
1
a d) and3
e h) to the overallfluorescence
spectrumof lake
DOM.Pointsof the sameshapeandfill representreplicatesamples.Solidlinesrepresent
regression
equationsof statistical
significance
at the 0.05alphalevel.
145
ln termsof the ubiquitous
humic-like
DOMfluorescence
component(C1),the positivelinear
relationships
between%oaFAorl
and C1/Crfor Cdand Cu (Figures
3a and 3b) suggestthat with an
in the relativecontribution
increase
the proportionof
of thiscomponentin the lakewater
samples,
the DOMthat is involvedin CdandCucomplexation
In contrast,
a negativelinear
increases.
relationship
wasfoundfor Ni (Figure
in the bindingcapacity
of the DOMfor
3c),indicating
a decrease
Ni. With respectto the allochthonous-like
DOMcomponent(C3),the negative
exponential
relationships
between%aFAoo,
andC3/Crfor CdandZn (Figures
3e and3h)suggest
that with an
increase
in the allochthonous
signature
of the DOMin the lakewater
samples,
the proportionof the
DOMthat is involvedin CdandZn complexation
In contrast,a weakbut positive
decreases.
relationship
with the C3/q ratiowasfoundfor Ni - asthe allochthonous
signature
of the DOMin the
lakesamples
decreases,
the bindingactivityof the DOMfor Ni increases.
Thissingularbehaviour
of
plotsfor the
Ni,as demonstrated
by the contrastbetweenFigures
3c and39 andthe comparable
othermetals,suggests
that the DOMbindingsitesactivein Cd,CuandZn complexation
aredifferent
from thoseinvolvedin Ni complexation.
To our knowledge,
thisis the firsttime that sucha
hasbeenresolved
distinction
in naturalwatersamples.
Admittedly
the relationships
shownin Figure3 will haveto be exploredovera widerrangeof DOM
samples,
but theydo supportour originalideathat the proportionof DOMactivein metal
complexation
couldbe estimated
into
on the basisof itsfluorescence
andthen introduced
signature
chemical
equilibrium
modelssuchasWHAM.To testthisidea,we incorporated
eachlake'soptimized
%aFAvalue,ascalculated
fromthe relationship
between%aFA
optimized
andC1/Cror C3/Gfor each
metalandlake(i.e.,the regressions
shownin Figure3), intoWHAMandre-ranthe chemical
equilibrium
simulations
to predictthe free M2*concentrations;
thiswasdonefor eachmetal,since
the optimized
%aFAvalueis metal-specific.
Figure4 depictsthe comparison
betweenthese
"improved"WHAMcalculations
to
of free M2*andthe measured
free M2*concentrations.
Compared
the resultspresented
in Figure1, the greatestimprovement
usingthe CL/Crratiowasfoundfor
nickel,for whichthe modeledfree Ni2*decreased
from the
to withinat mosta factorof 1.3difference
measured
free Ni2*concentrations.
ForCd2*andZn2*,metalsfor whichthe originalWHAM
predictions
werealreadygood,no improvement
wasnoted(indeedthe agreement
wasslightlyworse
t46
thanwith the arbitraryvalueof 65%activefulvicacid).Althoughthe WHAMcalculation
of freeNi2*in
our lakeswasimprovedby estimating
the proportionof fulvicacidsactivein Ni complexation
from
qualityof DOM,the estimated%aFAwas
the spectroscopic
stillunrealistically
high,as mentioned
earlier.Nevertheless,
the ideaof usingthe spectroscopic
qualityof DOMas a proxyfor the
proportionof DoM that is activein metalcomplexation
remainspromising.
4
Conclusions
Themaingoalof thisstudywasto explorethe possibility
of incorporating
a measureof the qualityof
DOMinto chemical
speciation
models,suchasWHAM,to improvetheirpredictions
of tracemetal
speciation
in naturalaquaticsystems.ForCd,Cu,Ni andZn,we haveshownthat suchan approach
is
feasible,usingsimplefluorescence
measurements
on lakewaterDOMto estimatethe proportionof
DOMthat is activelyinvolvedin metalcomplexation
(aswasdonein generating
Figure4). However,
our resultshavealsoindicated
that in additionto adjustments
to the metal-binding
capacity
of the
DOM,it mayalsobe necessary
to adjustthe affinityconstantsthat are usedby WHAMto calculate
the speciationof somemetals,especially
Ni;the possibility
of usingthe spectroscopic
propertiesof
DOMto predictits metal-binding
affinityshouldbe explored.Thepossible
involvement
of ligands
otherthanfulvicand humicacidsin metalcomplexation
shouldalsobe considered,
particularly
for
samples
with low totaltracemetalconcentrations.
lmprovedtracemetalspeciation
predictions,
especially
at low,environmentally
significant
will be importantnot onlyfor
geochemical
studies,but alsofor the prediction
of chronictoxicityin naturalaquaticsystems
(e.g.,as
partof modelssuchasthe BioticLigandModelor BLM(Slaveykova
andWilkinson,
2005)).
t47
1e-8
(a)
^ 1e-9
(b)
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e
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./.
+
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oi
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.0e-8
c
o
1e-9
co
()
' 1.0e-9
1e-9
1e8
1e-7
MeasuredMt. (M)
Figure4.
1e-6
1.0e-8
1.0e-7
Measured M'. (M)
with WHAM
of the free metalion [Mz*]calculated
of mean(t SD,n=3)concentrations
Comparison
IET(Cd,Ni and
(withthe %aFAestimatedfrom Figure3) andthosemeasured(t SD,n=3)usingthe
the 1:1|ine'
Thedashedlinerepresents
Zn)or |SE(Cu)methods.
L48
Acknowledgements
assistance
the technical
S.Duval,P.Fournier
andJ. Perreault
in the laboratory
in the field. Sampling
and P.GirardandA. Cr6mazy
wasgreatly
facilitatedby the personnelof the LaurentianUniversityCooperative
Freshwater
EcologyUnit,led by
J.Gunn,in the Sudburyareaand by L.Jourdainof the Ministiredes Ressources
naturelleset de lo
Faunedu Qudbecin the Rouyn-Noranda
datafor the Sudburyarealakes
area. Accessto limnological
was providedby StephenMonetof the Cityof GreaterSudbury.Financial
supportwas providedby
Research
Councilof Canada
and by the Metalsin the Human
the NaturalSciences
and Engineering
Environment
Research
P.G.C.
Campbell
andC.Fortinaresupportedby
Network(www.mithe-rn.ore).
the CanadaResearch
ChairProgram.S.Loftsacknowledges
the supportof the UKNatural
Research
Environment
Council.
149
5
5.1
SupplementaryMaterial
(IET)calibrationresults
lon ExchangeTechnique
Thecalibrationof the IETfor Cd2*,Ni2*andZn2*wasperformedwith standardsolutionsin a pH range
of 5.29to 8.86andwith knownconcentrations
of freecd2*(0.24to 405 nM),Ni2*(12.8to L750nM)
(I)for Cd,Ni
in the distribution
coefficient
andZnz*(4.86to L310nM). FigureSl depictsthe changes
andZn asa functionof the free metalconcentration.
stable.Thelt
weregenerally
Acrossthis pH andconcentration
range,the distribution
coefficients
at the highestpH valueof 8.86(closedsquares
in Figure5L),however,aremuch
valuescalculated
l, values.At highpHvalues,metalhydroxocomplexes
tendto interact
higherthanthe remaining
andincreasing
the h valuesfor all
with the resin(Fortin
et al.,2010),creatinga positiveinterference
pH range(5.29to 7.88)the l, valuescanbe considered
to be
threemetals.Forthe remaining
range(N=56,
constant.ForNi,the hvalueswerefoundto be stableacrossthe wholeconcentration
(0.24-O.26
ForCdand Zn,the lowestconcentration
nM for Cdand4.86-5.42nM for Zn)
CV<L8%1.
introduced
a considerable
amountof variability
in the I values.At the lowestfreemetal
(strong)ligandmayhavecomplexed
the metal,
traceamountsof an unidentified
concentrations,
yieldingan artificiallylow valuefor 1,.Theh valuesfor Cdand Zn were,however,foundto be stable
and 14.6to
acrossthe free metalconcentration
of rangeof 4.08to 405 nM Cd2*(N=46,Cv<23%)
of the free-metalion
1310nM Zn2*1N=46,CV<94%)
and thesevalueswere usedfor the calculation
concentrations.
150
14
tz
'o) 1 0
T
8
E
O
o
4
oo
z
s
0
oP
6gu9 \gu$
\Fu$
?gos
abrs tgu$
o5u$ rgu$
tcd'z.l(M)
14
12
10
o)
2
8
b
e e{oae
4
2
o
0
sS
ugu$
os.$
sgu$
nlux
nb"x
n9"x
l.\ux
tNi'z.l(M)
25
20
'g)
E
N
15
10
u g %"e@
5
0
g9
zguo
lgu$
ogu$
ugu$
ngux
ny/
n.o"x
1zn2.11tvt;
Figure51.
(l\)for Cd,Ni andZn asa functionof free metalconcentration.
IETdistributioncoefficients
Open
circlesrepresenttr calculated
at pH 5.29to 7.88,whereasclosedsquareswere calculated
at pH
8.86,
151
5.2
lon ExchangeTechnique(lET)calibrationresults(Rachouet al., 2007)
freeCu2*
basedon measured
Althoughthe calibration
curve(Figure
52)wascalculated
as low as 4.5x 10-1s
M, a methoddetectionlimit of 2.6x LO-llM Cuz*wascalculated
concentrations
methodblankmeasurements.
of sixreplicate
basedon the threetimesthe standarddeviation
pcu
15
13
11
a
10
-40
-90
g
-140
lu -190
-240
y=-29x+112
R2= 0.99
-290
-340
Figure52.
ISEpotentialresponseasa functionof Cuactivity(pCu).N=66.
L52
Relationships
betweenpercentfree metal ond pH and DOCconcentrations
5.3
Forallfour metals(Cd,Cu,Ni andZn),andin boththe Rouyn-Noranda
andSudburyregions,
the
percentfreemetalasdetermined
by the IETand ISEmethodstendedto decrease
asthe lakepH
pointingto complexation
increased,
competitionby protonsasan importantfactoraffectingmetal
speciation.
T
+
N
o
o
gl
Ir
N
T-
60
E
l!
s
o3
o'
I
IL
x- 2
tr..i
20
80
120
T
+
qL
100
*80
I
z
o,^
E*'
u-
s
C
N
90u
IL
s
i
40
TT
a
6.0
6.4
6.8
1-L
7.2
pH
Figure53.
/.b
6.4
I.b
8.0
pH
Measured%free metal(tSD,n=3)asa functionof lakepH.
153
percentfree metalwasalsoobserved
for Cd,Cuand Ni asthe DOC
A generaldeclinein the measured
presumably
metalbindingsiteson the
in available
increased,
reflecting
an increase
concentration
ratio.
DOMwith an increasein the ligand-to-metal
I
I.T
+
N
E
o
R
60
g
o
o?
o-
{I T
l-
s
1
N
1
tL
TI
LL
t{
>s
-2
t^
i
20
rG.I
80
120
100
*80
sz
N
o,^
g*t
o^^
EbU
u-
tL
s
s
I
40
TTT
I
I
I t
I't-t
I'
l.
-T1T1
a
a
54JbI
DOC(m9 C L-1)
IT
{l
3456
DOC (mg C L-1)
Figure
54. Measured
n=3)asa functionof lakeDOCconcentration.
%freemetal(+SD,
5.4
Relationships
betweenthe optimized%oFAoos
and DOMopticalproperties
below
Themeasured
opticalqualityof the DOMin the lakessampledfor thisstudyis presented
(Table51)andis described
(Muelleret al.,201-2b).
in detailelsewhere
L54
Table51.
Lake
Measured(+SD,n=3)DOMopticalquality(SUVA2'4,
Fluorescence
Index(Fl)and the relative
proportionof PARAFAC
fluorescence
components1 to 4)for lakessampledfrom Rouyn-Noranda
(QC)andSudbury(ON)in 2008.
SUVAzsa
Fl
ctlcr
czlcr
c3lcr
c4lcl
(L m-t mg c-1)
Dasserat
Dufault
7 . 8t 0 . 3
7.3! O.4
Opasatica 7.5!0.4
Vaudray
L 0 .0r 0 .3
Bethel
Geneva
Raft
Whitson
5 .0r 0 .3
5.3r 0.2
5.8r 0.8
7 .31 0 .5
0.384r
0.34r 0.0L
0.325r
0 . 2 8 1 0 . 0 2 0.254!
4.246!
0.380r
0.30'
0.39'
0.32L!
0.384r
0.3L0r
0.385r
1 . 3 3 1 0 . 0 2 0.287!
1.301r
0.28r 0.01
1.3611
0.27Lt
L .2 7IO.OL
1 .5 1 r0 .0 4
1.30'
1.30t 0.09
1 -.3r60 .0 3
0.2L6r
0.25r 0.03
0.29r 0.0L
0.40r 0.02
0.L67t
o.Lla
0.08r 0.0L
0.161!
0.114r
0.133t
0.116r
0.068r
0.2061
O.r7^
0.218t
0.1.44!
" indicatesonlyone replicatesamplemeasured.
We exploredpossible
relationships
betweenthe optimalpercentof FAactivelyinvolvedin metal
(%aFAool)
complexation
of the DOMin the lakewatersamples,
in orderto
andthe opticalproperties
estimatethe proportionof DOMthat is activein metalcomplexation.The moststatistically
for the four metalsemergedfor CL/G and C3/Cr(seeFigure3 in
significantrelationships
with %aFA6p1
the manuscript).
bivariate
relationships
Thefollowingfigures(S5to 58)showthe lackof significant
between%oaFAopl
and the other opticalproperties.
155
120
t
I
t'
lC
O
V
\9 2oo
o
OAn
150
Vn
Y
A
AA
V
tr^
L
LL
o
s
%
q)
.N
E60
.N
E
lY
r
AA
o
s
ooal
AA
50
20
a^
âo
ovv
10
"t800
z
x 1600
!
11
c
N
400
!
soo
a
raoo
.N
E
o
o
><
-
a
.N
.E 12oo
t
a
1000
8oo
ogvv
A
LL
600
+v
400
200
V
Y
"
100
5^
A
An
'4.
200
4
56789456789't0
suvA2s4
Figure55.
11
suvA2s4
(%aFAoo,)
Relationship
(SUVA254)
for Cd,Cu,Ni andZn andthe specificUVabsorbance
of lakeDOM.Pointsof the same
shapeandfill representreplicatesamples.
156
120
300
100
250
o
()
o
1t
o
.N
200
980
v
E 150
V
o.
o
II
o
s
Vv
tr
100
A^
o
o
,s
B
AA
20
a
aa
0
U
1;15
tr
A
s
4$
Y
I
E
c)
.N
'at b u
LL
(!
't
50
lV
o
V
I
1.20
1.30
1.25
1.35
1.40
1.1
1.45
1.2
1.3
1.4
1.5
1.6
2000
z
I 800
o
1600
E
N
o
E
1l
400
o 1400
.N
E 1200
oo 1000
$ soo
t!
LL
C'
s
o
a
o
.N
.E
o
o
800
v
600
_tr
100
Y
t
400
^'
I
o
o
'|1
200
I
A
A
200
1.2
1.3
1.4
FI
Figure56.
t.c
1.6
1.1
1.2
1?
1.4
t.c
FI
(%aFAoo,)
Relationship
betweenthe WHAMcalculated
index(Fl)of lakeDOM.Pointsof the sameshapeand
for Cd,Cu,Ni andZn andthe fluorescence
fill representreplicatesamples.
r57
120
I
I
io
o
b
I
r rv
L
e
200
Oan
o
.N
E 150
o
o
V
V
o
VV
tr
IL
(5
A
'l
(!
s
tr
E
q)
.N
EOU
.E
AA
s
tr
O
aa
N
vv
0
0.24
0.28
0.32
0.36
0.40
0
0.24
0.44
0.28
0.32
0.36
0.40
500
z
E
1800
c
N
o
1600
8 r+oo
V
o
a
a
$ eoo
.N
.N
.E 12oo
E
o
o 200
CL
o looo
k
s-
1l
400
8oo
€v
LL
G'
-
600
E
100
vl
400
200
0.37 0.38
I
0.38
0.39
C2ten
0.39
0.40
0.40
0
0.24
0.28
0.32
0.36
0.40
0.44
c2tcr
(%aFA"o1)
Figure57. Relationship
betweenthe WHAMcalculated
for Cd,Cu,Ni andZn andthe relativecontributionof PARAFAC
fluorescence
component2 to the
overallfluorescence
spectrumof lakewaterDOM.Pointsof the sameshapeandfill represent
replicatesamples.
158
120
I
250
o
I
B 2oo
E
€80
o
o
.N
.N
o
.E 150
tE
s
-o
.F
0.04
A
u
A
A,
LL
(U
s
A
ft-
50
0.08
0.12
v
v
o
oo
!
tr
^. t
IL
Y
I
a
a
0.16
0.20
0.24
0.04
0.08
0.12
0.16
0.20
0.24
2200
2000
1800
c
N
o
z
.E 1600
E i4oo
.N
.E 12oo
o
TL
(s
s'
a
a
.N
E
o
voo
200
Ll-
n
o
OUU
Y
Y
V
f,LY
100
A
lr
400
I
200
0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24
c4tcr
Figure58.
a
$ soo
1000
600
lg
400
0.04
0.08
0.12
0.16
0.20
0.24
c4lcr
(%aFAool)
Relationship
for Cd,Cu,Ni andZn and the relativecontributionof PARAFAC
fluorescence
component4 to the
overallfluorescence
spectrumof lakewaterDOM.Pointsof the sameshapeandfill represent
replicatesamples.
L59
Article3
Nickeland coppercomplexation
by naturaldissolved
organicmatterin lake
KristinK.Mueller,ClaudeFortinand PeterG.C.Campbell
du Qu6bec,
Universit6
490 de la Couronne,
Qu6bec
(Qu6bec),
GlK 9A9
Canada
Abstract
TheWindermere
HumicAqueousModel(WHAM),usedto estimatethe bindingof cationsby
usingthe resultsof
is calibrated
dissolved
organicmatter(DOM)in naturalaquaticsystems,
titrationexperiments
on isolatedandpurifiedhumicandfulvicacids,at DOMandtotalmetal
concentrations
that areoftenunrealistic
for naturalaquaticsystems.Usingwell-characterized
conditions,
that the
naturalwatersamples,
we demonstrate,
for realistic
environmental
conditional
bindingparameters
of Ni andCuwith naturalDOMvaryasa
qualityof the DOM. Watersfrom two lakeswith contrasting
functionof the spectroscopic
bindingparameters
typesof DOMweretitratedwith CuandNi andthe calculated
conditional
were comparedbetweenlakesand betweenmetals,andthe titrationcurvesfor eachlakewere
comparedto thosepredictedby the off-the-shelf
versionof WHAM6.1-.Bindingaffinitiesand
capacities
of DOMfor Ni and Cuwerefoundto be significantly
differentnot only between
the amountof DOMlakes,but alsobetweenmetals.WHAMwasfoundto underestimate
the titrationresults
boundNi,whilethe oppositetrendwasfoundfor DOM-bound
Cu. Overall,
sourcesmayhavea
suggest
that the morearomatichumic-like
DOM
highercomplexation
significantly
affinityandcapacity
for Cuthanthe moreprotein-like
that Ni
from autochthonous
whereasthe oppositetrendwasfoundfor Ni,indicating
sources,
andCuare bindingto differenttypesof bindingsiteswithinthe DOMmatrix.
L62
1
lntroduction
organicmatter(DOM),an important
Thecomplexation
of tracemetalsby naturaldissolved
chemicalreactivity,
organiccomplexing
agentin mostnaturalwaters,affectstheir speciation,
HumicAqueous
models,suchasthe Windermere
and bioavailability.
Chemical
equilibrium
of metalsby DOMin natural
Model(WHAM),areoftenusedto predictthe complexation
equilibrium
reactions.
The
thermodynamic
aquaticsystems
basedon established
heterogeneous
natureof DOM,however,makesit difficultto assignspecificthermodynamic
assumptions
basedon
with the useof somereasonable
datato its complexes.
Nevertheless,
of cationswith DOMhas
currentknowledgeof the characteristics
of DOM,the complexation
(Tipping,2002;Tippinget al.,ZO11-1.
beenmodeledwith somesuccess
gainedfrom laboratory
studieson
TheWHAMmodelis parameterised
basedon information
of metalsto organic
isolatedhumicsubstances,
but it is often usedto predictthe complexation
havebeenfoundbetweenthe
matterin naturalwatersamples.Unfortunately,
discrepancies
for Cuand Ni,andthat measured
WHAM-predicted
in
speciation
of somemetals,especially
(Fortinet al.,2010;Muelleret al.,2OL2c;
Unsworthet al.,2006;Van
naturalfreshwater
samples
andmodeledfreemetal
Laeret al.,2006).Thediscrepancies
betweenmeasured
in metal-binding
concentrations
in naturalfreshwater
systemsmaybe dueto differences
usedto
character
betweenthe DOMin naturalwatersandthe isolatedhumicsubstances
parameterise
the model(LoftsandTipping,zOtL).
with Cuand Ni andcompared
ln the presentstudy,we havetitratedtwo lakewatersamples
the resultingtitrationcurveswith thosepredictedby WHAM. Thetwo lakeswere chosen
and because
their
because
earlierwork hadshownthat their DOMdifferedspectroscopically,
ratios).
ambientmetallevelswerelow (thusallowingusto explorea wide rangeof metal:DOM
propertiesof two contrasting
Theobjectiveof the work wasto comparethe metal-binding
of Cuand Ni with the
typesof DOM. Conditional
bindingparameters
naturalDOMhavebeencalculated,
comparedbetweenlakesand betweenmetals,and related
to the opticalqualityof the lakeDOM.
163
2
2.1
Materialsand Methods
Sitedescriptionand sompling
TheCanadian
areasof Rouyn-Noranda,
Ontariohavebeenimpactedby
Qu6becandSudbury,
pastand presentcopper/nickel
mining,smeltingand refiningactivities.A detaileddescription
of thesestudyareascanbe foundin Muelleret al. (2012b).Two lakeswith low total metal
(48"04'32"N;
were selectedfor the titrationexperiments:
concentrations
LakeOpasatica
79"L7'49"W) from the Rouyn-Noranda
regionand LakeGeneva(79"17'49"W;8L"32'46"W)
from the Sudburyregion.Basedon the findingsreportedby Muelleret al. (2012b;2012c),Lake
pH (7.47),
hasa moderateconcentration
Opasatica
of DOC(6.0t 0.4 mg.L-1),
a near-neutral
very low total dissolved
concentrations
of Ni and low concentrations
of Cd,Cuand Zn (see
pH
Table1). LakeGenevahasa low concentration
a near-neutral
of DOC(3.4t 0.4mg.L-1),
(6.89),andthe totaldissolved
concentrations
of Cd,Cu,Ni andZn areallverylow (seeTable1).
qualityof DOMbetweenthe lakeswere alsoreported(Mueller
Differences
et al.,2012b).
Grabsamplesweretakenfrom the littoralzonesof LakesOpasatica
and Genevausingtwo 4-L
polypropylene
(l-CHEM
amberglassjugswith Teflon@-lined,
Brand)for eachlakein
closures
JuneandJulyof 2009,respectively.
Thesamples
werethentransported
backto the laboratory
within24 h andvacuumfilteredundera cleanlaminarflow hoodusingpolycarbonate
filter
holders(Advantec
MFS,Inc.)anda seriesof 47-mmpolycarbonate
filters{.5,2,O.4
and0.2 pm;
MilliporeandAMD Manufacturing
Inc.).Beforefilteringthe lakewatersamples,
1 L of
ultrapurewateranda smallvolumeof samplewerefilteredthroughthe filtrationapparatus
Following
anddiscarded.
filtration,the samples
werestoredin the darkandat 4"Cuntil
(storage
analysis
time < 72 d).
2.2
Analyses
(v/v)
All plasticware
usedfor sampling
andsampleanalyses
wassoakedin a solutionof 1:O%
nitricacidfor at least24 h, rinsedseveraltimes
with ultrapurewater(>18Mohms.cm)
and
driedundera laminarflow hood. Polystyrene
for anion
vialsusedfor storingthe subsamples
1.64
analyses
were,however,rinsedthreetimeswith ultrapurewateronly.All glassware
wassoaked
in a solutionof 2 N HCIand rinsedwith ultrapurewater.
Following
samplefiltration,subsamples
weretakenundera laminarflow hoodto measure
the
ambientwaterchemistry
for LakesOpasatica
andGeneva.Subsamples
(10-mL)
were
transferredto polypropylene
vialsand acidifiedto 2o/o
(v/v) nitricacid(ultra-tracenitricacid,
BDHAristar)foranalysis
of majorcations(Ca,Mg, Na,K)by atomicemission
spectroscopy
(VistaAXCCDSimultaneous
lcP-AEs,
Varian)andtotaltracemetals(Cd,Cu,Ni,Zn)by
inductively
coupledplasmamassspectrometry
(lCP-MS,
ThermoElementX Series).Similar
10-mLsubsamples
were alsotransferredto polystyrene
vials(VWRlnternational)formajor
anions(cl, N03,Soa)analysisby ion chromatography
(lcS-2000lon chromatography
system,
Dionex),
and20-mLsubsamples
weretransferred
to amberglassvials(l-cHEMBrand)for
total
organiccarbonanalysis
(TOC-VCpH,
Shimadzu).
Finally,
L-mLsubsamples
weretransferred
to
3-mLglassvacutainers
(KendalMonojet)for total inorganiccarbonanalysisby gas
chromatography
(3800Varian).
Fordetailson the calibration
and measurement
of the free Ni2*concentrations
usingan ion
exchange
technique(lET)andthe freecu2*concentrations
usinga cupricion selective
electrode
(lSE),
the readeris referredto a previouspaperby Muelleret al. (2012c).Briefly,when
using
the lET,the free Niz*ion in a lakewatersamplewasequilibratedin a flow-through
systemwith
a cationexchange
resin(Dowex50w-X8,50-100mesh)thathadbeencalibrated
usingstandard
solutionswith a pH rangeof 5.3to 7.9and with a knownconcentration
of free Ni2*ranging
from L3 nM to 1.8pM (seesupplementary
materialin Muelleret al. (2012c)).
The
concentration
of Cain the calibration
andnaturallakesamples
wasadjustedusinga stock
solutionof 25 mM Ca(Nog)z
(99%,Sigma-Aldrich)
to a finalconcentration
of 0.46mM in order
to attaina constantionicstrengthamongsamples
andto ensurethat the time neededto reach
equilibrium
betweenthe resinandthe samplewassimilarfor all samples.Following
the elution
of the resinwith an acidicsolutionanddetermination
of the amountof Ni that had
been
retainedby the resin,the concentration
of freeNi2*in the originalunknownsamplewasthen
165
Forthe
(r) for Ni (Fortinandcampbell'1998)'
coefficient
usingthe resindistribution
calculated
potential
in the lakewater samples'the electrode
Cu2*
of
concentration
the
of
analysis
rangingfrom the method
overa free cu2*concentration
calibrated
was
lsE,
cu
the
of
response
of 0'03
deviationof sixrepricatebrankmeasurements)
detectionrimit(threetimesthe standard
material
of Rachouet al' (2007) seethe supplementary
method
the
pM
following
63
to
nM
wereamendedwith KNOg
andlakesamples
Thecalibration
for Muelleret al. (2012c)).
wasaddedto achievea final
amountof a L.23M stocksolut'ton
known
a
lgg.gg9%,Fluka);
and a simirarelectrode
thusensurea constantionicstrength
and
KNOg
mM
r.0
of
concentration
analysed'
amongall samples
response
2.3
Titrationexperimentolset-up
and Ni' Forthe cu
were intrividuatytitratedwith cu
Geneva
and
opasatica
Lakes
from
sampres
Plasmacal)
of 0'2 mM coppernitrate(cu(NOr)z'
titrations,knownamountsof a stocksolution
which
BDFalcon)'
container'
the sample(polypropylene
wereaddedto eight200-mLaliquotsof
to the rraturallake
pH of the titrationaliquotswasadjusted
The
KNog.
with
amended
been
had
wereleftto equilibrate
of eitherHNOaor NaOH,andthe solutions
pH usingsmallvolumes
the freeCu2*
in the darkbeforemeasuring
overnightundera laminarflow hoodand
24
time of approximately
An equilibration
usinga cupricion selectiveelectrode'
concentration
hourswasconsideredaconservativeestimateofthetimeneededfortheaddedmetaltoreach
by Tipping(2002)'ThepH of
presented
values
literature
on
based
DoM,
the
with
equilibrium
to the
and re-adjusted
the free Cu2*concentration
the aliquotswascheckedbeforemeasuring
analysed'
aliquotwith no addedcu wasalso
sample
initial
An
pH
necessary.
if
lake
natural
After
of 27 cutitrationariquotsanarysed'
cu titrationswere carriedout for a totar
Tripricate
of eachtitrationaliquotwere
subsamples
of the free Cu2*concentration,
the measurement
takenforthemeasurementoftotaldissolvedcu(5mLina13-mLpolypropylenevial'sarstedt)
Brand)'
(10mLin a 40-mLamberglassvial,l-cHEM
and Doc concentrations
(Ni(NO3)2'
a stocksolutionof 1 mM nickelnitrate
Forthe Ni titrations,knownamountsof
container'BD
sample(polypropylene
plasmacal)
wereaddedto seven220-mLaliquotsof
166
Falcon),
whichhadbeenamendedwith Ca(NO3)2.
Again,the pH of the titrationaliquotswas
adjustedto the naturallakepH andthe solutions
wereleftto equilibrate
overnightbefore
measuring
usingthe IET(thepH wasre-adjusted
to the naturallake
just priorto IETanalysis).
pH if necessary
An initialsamplealiquotwith no addedNi wasalso
analysed.
Triplicate
Ni titrationswerecarriedout for a totalof 24 Ni titrationaliquotsanalysed.
In the samemannerasfor the Cutitrations,
subsamples
of eachtitrationaliquotweretakenfor
the measurement
of total dissolved
Ni (5 mLin a 13-mLpolypropylene
vial,Sarstedt)
and DOC
(10mLin a 40-mLamberglassvial,l-CHEM
concentrations
Brand).
2.4
Calculations
Triplicatetitrationscurvesof measuredfree Ni2*and Cu2*asa functionof addedtotal metal
weregenerated
for eachlakeandfor eachmetal.Bindingparameters
werecalculated
from
eachreplicatetitrationcurveand then the averages
of thesetriplicatebindingparameters
were
calculated
alongwith theirassociated
variability.
Theequilibrium
formationof a 1:1complexes
betweenmetal(tvlz*)
anda two-siteligand(Li),with an associated
conditionalstability
(K;)wasassumed.
constants
M2++Li=MLi xi=ffi
(1)
The bindingparameters,
Lt, K7,Lzand K2,w€r€ calculated
from titrationcurvesusingthe
followingtwo-siteLangmuir-type
modelequation:
IMu=ffi*m
el
where[ML]isthe molarconcentration
(version11.0),nonof boundmetal. UsingSigmaPlot
linearregressions
wereperformedon triplicatetitrationsfor eachlakeandeachmetalfor a
total of 12 titrationcurves.
Forcomparison
with the measured
concentrations
of boundmetal,[ML],in the sampledlakes,
usingthe off-the-shelf
versionof WHAM6.1and measured
valuesfor
[ML]wasalsocalculated
pH,DOCandthetotalconcentrationsof
Na,Mg, K,Ca,Ni, Cu,Zn,Cd,Cl,NO:,SOq,COg
(measured
astotal inorganic
carbon)and F. We assumed
that the DOC:DOM
ratiowas2
t67
of metals(Bryanet al',
(Buffle,1988),that 65%of the DoM wasactivein the complexation
that
that this activefractionwascomposedof fulvicacidsonly. we alsoassumed
2OO2l,and
ascalculated
of their hydroxides,
by the solubility
arecontrolled
activities
both Fe(lll)andAl(il1)
givenby Loftset al. (2008)andTipping(2005),respectively'
equations
usingthe empirical
(version1L.0)at the alphaequals0'05
in sigmaPlot
wereall conducted
analyses
statistical
level.
confidence
Resultsand Discussion
3
3.7
Lakewater and DOMchorocteristics
andSudburyhavebeenimpactedby pastandpresentmetal
Theregionsof Rouyn-Noranda
givingriseto lakeswith a wide rangeof metalconcentrations
miningand smeltingactivities,
to theirlocationup-or down-windof the smelters.LakesOpasatica
and pH valuesaccording
andwerechosenfor theirnaturallylow total
andGenevaare locatedupwindfrom the smelters
(Table1). Thespectroscopic
and differingDOMconcentrations
dissolvedmetalconcentrations
lake
qualityof the DOMwasalsofoundto differbetweenthe two lakes(Muelleret al.,2012b);
whereasthe
sources,
of allochthonous
wasfoundto haveDOMlargelycharacteristic
Opasatica
a markedcontributionfrom autochthonous
of LakeGenevaDOMsuggested
characteristics
with low ambienttotaldissolved
sources.Bytitratingnaturalsamples
by metalsandthe strongbindingsites,of low
fewerDOMbindingsitesarealreadyoccupied
pHvalues,LakeGenevais
canbe probed.Althoughbdthlakeshavecircumneutral
abundance,
of major
LakeGenevaalsohasa lowerconcentration
slightlymoreacidicthanLakeOpasatica.
metal(Cd'Cu'Ni
cations(Ca,Mg, NaandK),majoranions(Cl,NOa,SOqand F),totaldissolved
(Table1)'
andZn)andDOCthan LakeOpasatica
3.2
Titrotion curves
and
with naturalligandsin LakesOpasatica
Thetitrationcurvesfor Ni andCucomplexation
ligandsin the
of Ni andCuby inorganic
Genevaare plottedin Figure1. Thecomplexation
presence
of DOMfor the lakessampledwasestimatedusingWHAM' ForLakeOpasatica'
Ni and t.7-3.9%of Cuwasboundin carbonate'
totaldissolved
ely4.8-5.Z%of
approximat
168
sulfateandchloridespecies.Althougha similarL.t-4.3%of totaldissolved
Cuwasboundby
theseinorganicligandsfor LakeGeneva,44-47%of Ni wascalculated
to be presentas inorganic
with theseligands.Therefore
complexes
the concentration
of boundmetalto DOM,[ML],was
to be equalto the total dissolved
metalconcentration
minusthe concentration
assumed
of free
metalandthat boundto inorganic
complexes.
Fora giventotal dissolvedmetalconcentration,
LakeGenevahada higherfree metal
(Figure1). Because
LakeGenevahasa lowerDOC
concentration
than LakeOpasatica
presumably
concentration,
lessDOMis available
to bindNi andCu. ln addition,at the (slightly)
lower pH in LakeGeneva,the protonwould be expectedto competesomewhatmore
for cationbindingsiteson the DOM.As Ni is knownto be moreweaklyboundto
successfully
DOMthan Cu(Buffle,1988),the effectof protoncompetitionwould be expectedto be greater
for Ni than for Cu. Thiswas in fact observedfor both lakeswherethe concentration
of free Ni
wasgenerallyhigherthan free Cufor a giventotal metalconcentration.The presenceof strong
Cu-binding
siteson the DOMis suggested
by Figure18,wherewith the first few additionsof
total Curesultin littleincrease
in the freeCu2*concentration.
Thisbehaviour
is characteristic
of Cufillingup the strongDOMbindingsitesfirst,followedby the weakersites(Sundaand
Hanson,19791.
Alternatively,
maybe a resultof
the observedasymptoteat low total Cuconcentrations
reaching
the detectionlimitof the CuISEmethod.ln four of the eightlakessampledin 2008by
Muelleret al. (20L2b),
i.e.in thosewith the lowesttotalambientdissolved
Cuconcentrations
(Opasatica,
Vaudray,
freeCu2*concentrations
werehigher
BethelandGeneva),
the measured
that the measuredconcentrations
were
than thosepredictedby WHAM,possiblysuggesting
affectedby dissolution
of Cufrom the lSE.However,whenthe free Cu2*activityin solutionis
M canbe achieved
well bufferedwith organicligands,an ISEdetectionlimit as low as l-O-14
(Rachou
et al.,2007).In the presentstudy,aswellasin the Muelleret al.(2012c)study,the
in the calibration
werebufferedwith iminodiacetic
acid(lDA)and
free Cu2*activities
solutions
with DOMin the naturallakewatersamples.
159
Table1.
concentrations
of majorcations,anionsandtrace metalsfor Lakes
Totaldissolved
conditions
Opasatica
andGenevaunderIETandISEexperimental
LakeOpasatica
LakeGeneva
IET
rsE
IET
ISE
pH
7.69
7.77
7.02
6.97
Doc (melL)
6.92
7.47
3.36
3.54
N a( m M )
0.13
0.13
0.027
0.027
Mg(mM)
0.11
0.11
0.025
0.025
K (mM)
0.023
1.0
0.0070
10
Ca(mM)
0.46
O.zt
0.46
0.055
Ni(nM)
16
16
22
23
Cu(nM)
39
38
tr
13
Zn (nM)
33
20
31
L2
cd (nM)
0.062
0.0s0
o.oL2
0.0s3
cl(pM)
8s
89
7.0
7.0
NOg(mM)
0.93
10
0.93
10
SO+(mM)
0.058
0.058
0.044
0.044
CO3(mM)
0.s2
0.52
0.05
0.05
2.6
2.6
2.8
2.8
F (pM)
to the
with WHAMVl andcompared
Thenickelandcoppertitrationcurvesweresimulated
i.e.,for low Ni-DOMratios,WHAM
2). Earlyin the Nititrations,
observed
values(Figure
2A and 2C),but for total
in both lakes(Figures
overestimated
and
greaterthanabout10-6M the agreement
betweenobserved
dissolved
Ni concentrations
predictedvaluesimproved.In the caseof Cu,the two lakewatersbehaveddifferently.ForLake
throughoutthe titration(Figure
the free Cu2*concentrations
Geneva,WHAMunderestimated
andthe ISE
betweenWHAMpredictions
the agreement
2B),whereasfor LakeOpasatica
M Cu);for higher
goodearlyin the titration(upto about10-6'7
wasreasonably
measurements
the free Cu2*
Cuconcentrations,
however,WHAMagainunderestimated
total dissolved
(Figure2D).
concentrations
170
-5.5
-6.0
.
I
A
Lake Opasatica
Lake Geneva
l a'
l
.
r
-6.5
I
l.'
+
qt
-7.0
lo
z -7.5
I
q,
-9
a
-8.0
I
T
-8.5
-9.0
t
-9.5
-8.0
-7.5
-7.0
-6.0
-6.5
-5.5
log Ni1e1
I
+
I
N
J
O
(')
o
tr
a
a
-7.5
-7.0
-6.5
-6.0
-5.5
logCu1s1
Figure1.
(circles)
Comparison
of titrationcurvesfor Ni (A)and Cu (B),for LakeOpasatica
and Lake
Geneva(squares).
L7t
-5.5
r
-6.0 v
s!z
E
E
-6.5
+
B
LakeGeneva
WHAM modeled
-7.0
I
TT
I
t
N
f
vl
I
o) -7.5
-9
rr'
-7
+
o-8
I
o)
o
-9
-8.0
I
-8.5
-10
-9.0
-8.0
-11
-7.5
-7.0
-6.5
-6.0
v
I
4a
oee
a
+
o
o)
o)
o
9 -s.o
oA
-9.0
-10.5
-9.5
-7.0
-6.5
-6.0
log Ni1s1
Figure2.
-5.5
L
I
-9.5
-10.0
-11.0
-8.0
A
6
-9.0
-8.5
D
t
a
-8.5
N
5
-7.5
0
-8.0
+
qt
z -7.5
3.3
-7.5
c
LakeOpasatica
WHAM modeled
-7'o
-5.5
log Cu1e1
-5.5
-6.5
-6.0
-6.5
-7.0
-7.5
log Ni1q1
.
-6.0 a
v
v
-8.0
-5.5
rl
v
9v
v
ae3
-7.5
-7.0
-6.5
-6.0
-5.5
log Cu1e1
(AandB)andLake
for LakeGeneva
Nickel(AandC)andcopper(BandD)titrationcurves
withWHAM
(CandD). Measured
calculated
Opasatica
values
areshownin colour;values
gray
Vl areshownin
triangles.
Metolbindingporometers
are
Thecomplexation
curvesfor Ni andCuwith naturalligandsin LakeGenevaandOpasatica
presented
canbe foundin Table
in Figure3 andtheirassociated
bindingparameters
calculated
model(Tipping,2OO2l.
were derivedusinga two-siteLangmuir-type
2. The bindingparameters
It shouldbe notedthat a successful
fit usingtwo or more discretesitesdoesnot constitute
772
for the true existence
of suchsites.As pointedout by Tipping,it simplymeansthat
evidence
([DOM],pH,pM),the effectsof siteheterogeneity
canbe
withina givenrangeof conditions
degree.
accountedfor to someacceptable
the concentrations
of boundNi (M)
in the titrations,
Forthe rangeof free Ni concentrations
of DOC(e/L)areslightlyhigherin LakeGenevathan in Lake
normalized
to the concentration
(Figure34). Thecalculated
reflectthe resultsof the titration
bindingparameters
Opasatica
= 6.98)on DOMfrom LakeOpasatica
is
curves;the Ni bindingaffinityof the weaksites(LogK1,p;
the
higher(P= 0.009)thanfor LakeGeneva(LogKr,u= 6.30).In contrast,
significantly
= 34.2nmole.mgC t) is nearlyfourtimeslower(P= 0.01)
of theseweaksites(Lr.rui
concentration
= 137nmole.mgC-1)
(Table2). TheNi bindingaffinities
for the strong
thanin LakeGeneva(Lr,ni
= L1.6and 11.7for LakesOpasatica
different(LogK2,x;
sitesin both lakesare not significantly
respectively),
andGeneva,
althoughthereare nearlysixtimesless(P= 0.025)of thesebinding
= 0.70nmole'mgC-1),
(Lz,r..r
to the DOMfrom
compared
sitesper unit DOCin LakeOpasatica
betweenthe
LakeGeneva(Lz,r,ri=
3.98nmole.mgC-1).Theseresultspointto the difference
discussed
with respectto their Ni-binding
characteristics,
DOMin LakesOpasatica
andGeneva,
furtherin section3.4.
L73
LakeOpasatica
LakeOpasaticamodeled
-4.0
o
o
o
-4.5
fz_ -uo
o)
9 -s.s
-6.0
-6.5
- t1 1 I
-10
-9
-8
log Ni2+
-3.5
B
-4,0
o
6
:I
-4.5
f
O
6o - s o
a.
C'
a
o
-5.5
-11
-10
-9
-8
-7
-6
log Cu2+
Figure3.
Comparison
of complexation
(circles)
curvesfor Ni (A)andCu (B),for LakeOpasatica
and
LakeGeneva(squares).
Themodelledcurveswere calculated
with the two-siteLangmuir
bindingmodel(seetext for details).
174
sitedensities(L;,pmole/gC)
Conditional
stabilityconstants(logK) and carbonnormalized
and Genevamodeledusinga 2-site
by DOMfor LakesOpasatica
of Ni and Cucomplexation
(P)valuesfor the non-linearregressions
ranged
significance
Langmuirmodel.Thestatistical
from 0.001to0.024.
Table2.
Lake
Lake
Opasatica
Geneva
6.98r 0.12
6.30r 0.23
Lr,rui(nmole.mg
C-t)
34.2!3.2
L37!24
Log Kz,r.1
1 1 . 6t 0 . 6
1 1 . 7t 0 . 5
0.7010.40
3 . 9 8r 0 . 1 1
7 . 5 9! 0 . L 2
7 . 6 3! 0 . 4 6
2 4 4! L O
75.6!19.4
9.46t 0.13
8.90r 0.04
4 4 . 5! 7 . 4
2 7 . O! 8 . 1
IET
Log Kr,rui
L2,x;(nmole.mg C-1)
rsE
Log K1,6,
( nmole.mgC-1)
11,6,
Log K2,6,
12,6,(nmole.mgCt)
than in Lake
A higherconcentration
of Cuwasboundpergramof DOCin LakeOpasatica
(Figure3B). Furthermore,
the
for LakeGeneva,
at allfreeCu2*concentrations
Geneva,
in the
with an increase
of boundCuappearsto tendtowardsan asymptote
concentration
indicating
bindingsitesaturation.Althoughthe strengthof the
of freeCu2*,
concentration
differentfor both lakes(Log
bindingof Cuto weaksiteson the DOMwasnot significantly
= 7.63for LakeGeneva),
the numberof Lrsitesper
and LogK1,6,
Kr,c,= 7.59for LakeOpasatica
(h,cu=244
threetimeshigher(P< 0.001)in LakeOpasatica
unit DOCwasapproximately
t).
in LakeGeneva(Lr,c,= 75.6nmole.mgC Thereweresignificantly
nmole.mgC-1)than
= 9.46)andtwiceas many(P= 0.003)strongbindingCusitesper
(P= 0.014)stronger(LogKz,cu
= 44.5nmole.mgC-1)
comparedto Lake
on the DOMfrom LakeOpasatica
unit DOC(Lz,cu
= 27.0nmole.mgC-1).Clearly
= 8.90dhd 12,6u
theseresultsfor Ni andCu
Geneva(LogK2,6u
L75
demonstrate
complexation
that the DOMin LakeOpasatica
differsfrom that in LakeGeneva,
with respectto theiraffinityfor thesemetalsandtheircomplexation
capacity.These
differences
are exploredin section3.4.
We comparedour calculated
free
concentrations
of boundmetalasa functionof the measured
metalconcentration
to thosepredictedby WHAM.Throughout
the entireNi2*concentration
range,the measured
amountof boundNi in bothlakesis muchhigherthanthat predictedby
WHAM(Figures
44 and4C).A contrasting
for Cu. Theconcentration
of
trendwasobserved
boundCupredictedby WHAMis verysimilarto that measured
for LakesOpasatica
andGeneva,
overnearlythe entirefreeCu2*concentration
48 and4D). Onlyat freeCu2*
range(Figures
concentrations
above1O-7
M do the WHAMpredictions
divergefrom the measuredvalues,in
LakeGeneva,
wherethe amountof boundCupredictedby WHAMis higherthanthat
measured.
Notethat sincethe bindingof Cuto DOMis muchmoreimportantthanis the bindingof Ni,the
plotsof boundmetal(IM-DOMI)
versus[M2.]for Cuappearto be verycloseto the WHAM
predictions.
Theearlierplotsof IlVt2.]
versus[M]oissorueo,
are more sensitiveand moreusefulfor
predicted
comparing
and measured
values(e.g.,compareFigures
34 and5A). In addition,from
perspective,
an ecotoxicological
is muchmoreimportantthan
the freemetalion concentration
isthe concentration
of boundmetal.Fromthispointof view,the typeof DOMinvolvedin
metalcomplexation,
i.e.,the contrastbetweenLakesGenevaandOpasatica,
is importantfor
b o t hC ua n dN i .
776
r
v
-3.5
LakeGeneva
WHAM modeled
..|f
o-5
o
o
lv
o
o
tJo
w
-4.0
slrf
Q -4.5
-J
J
2-o
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o
9 -s.o
o)
o
-7
g
-5.5
-6.0
-10
-8
-11
-10
-9
-8
-7
-8
C
LakeOpasatica
WHAM modeled
a.tt'
C)-5
o
ooF
o
J
a
I
z-o
-7
-6
log Cu2+
tog Ni2+
o
a
Y1t.
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utrl
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a
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-7
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4
A
-5.0
A
-8
-11
-10
-9
-8
tog Ni2+
Figure
4.
-7
-5.5
41
-10
-9
-8
-7
log Cu2+
(N|-DOM/DOC)for
Lake
curves
Ni(AandC)andCu(BandD)complexation
Normalized
(CandD). Measured
(AandB)andLakeOpasatica
values
areshownin colour;
Geneva
values
withWHAMVl areshownin graytriangles,
calculated
areconditional
andapplyonlyto the
Ni andCubindingparameters
Althoughthe calculated
it is usefulto comparethemto
underwhichtheyweredetermined,
experimental
conditions
therearefew studiesthat reportthe
foundin the literature.Unfortunately
otherparameters
measured
of metalsto naturalorganicmatter,asopposedto isolatedhumic
bindingproperties
(e.9.(Anteloet al.,1.998;
Sundaand Hanson
in freshwater
systems
substances,
, 1979;Xueand
L77
Sunda,1997)).In naturallakewatersandat low,environmentally
Ni
significant
concentrations,
wasfoundto bindto strongligandswith logK valuesfrom L2to 14 (Xueet al.,2001).Xueand
Sigg(1999)reportedsimilarly
strongbindingof Cuto naturalligandsin lakewater(logK values
from 10 to 15). lt has,however,beensuggested
from competitive
that thesevaluescalculated
(CLE-AdSV)
ligandexchange-adsorptive
stripping
voltammetry
measurements
under-estimated
the freemetalconcentrations
andthereforeover-estimated
the complexation
of metalswith
naturalligands(VanLeeuwen
andTown,2005).Nevertheless,
it is apparentthat overa wide
rangeof Ni concentrations
in naturalwatersWHAMoverestimates
(Figures
2A and2C;seealsoMuelleret al. (2OL2c)
andVanLaeret al. (2006)).Theopposite
trendis observed
for Cu,i.e.,an underestimate
but the absolute
of the freeCu2*concentration,
differences
betweenmeasured
andpredicted
valuesaresmallerthanfor Ni2*,and in addition
(Figures
the differences
aremoreimportantin LakeGenevathanin LakeOpasatica
28 and2D),
suggesting
that the bindingof Cuto naturalligandsmaydependof the typeof DOMpresent.
3.4
Relotionship
betweenmetal bindingand DOM charocteristics
prevailing
Forthe ambientconditions
in the two lakes,LakeOpasatica
DOMwasfoundto bind
moreNi andCuthan LakeGenevaDOM(Figure1). Whenthe concentration
of metalbinding
siteswasnormalized
to the totalorganiccarbonconcentration,
LakeOpasatica
DOMhada
higherapparentnumberof metalbindingsitesfor Cubut a lowernumberof sitesfor Ni (Table
2). Theconditional
stabilityconstants
alsodifferedbetweenthe two lakes.Basedon the
qualityof the DOMsampledfrom the samelakesin 2OO7and2008,Muelleret al.
spectroscopic
(2012b)foundLakeOpasatica
to haveDOMlargelycharacteristic
of allochthonous
sources,
whereasthe characteristics
of LakeGenevaDOMsuggested
a markedcontribution
from
autochthonous
sources.Thiswasevidenced
UVabsorbance
at 254nm
by a higherspecific
(SUVA254
values),
a higherrelativeproportionin LakeOpasatica
DOMof the PARAFAC
fluorescence
component
attributedto reduced-quinone,
fluorophores
humic-like
of
allochthonous
origin,anda lowerproportionof the component
attributedto tryptophan-like
proteinfluorescence
of autochthonous
origin.Theauthorssupported
theirfindingwith
watershed
characteristic
datathat showedthat LakeOpasatica
liesin a regionwith a thicker
L78
soillayerand hasa muchlargerwatershed-to-lake
surfacearearatiothan LakeGeneva,
contributing
to higherinputsof DOMfrom the lake'swatershed
asopposedto beingproduced
withinthe lakeitself.Theseresultssuggest
that the morearomatichumic-like
DOMfrom
allochthonous
sourcesmayhavea highercomplexation
capacity
for Cuthandoesthe proteinlikeDOMfrom autochthonous
whereasfor Nithe oppositetrendis apparent.A
sources,
positiverelationship
betweenthe Ni andCubindingaffinityandthe opticalquality(SUVA254)
wasalsofoundfor naturalsurfacewatersby Bakenet al. (2011).Similarpositiverelationships
for Cuhavebeenreportedby Al-Reasi
et al. (20L2).Thisis,however,the firsttime that the
parallelfactoranalysis
(PARAFAC)
of the fluorescence
character
of DOMhasbeencomparedto
its metalbindingaffinityandcapacity.
3.5
Environmentolimplications
Currently,
chemical
equilibrium
models,suchasWHAM,areusedto estimatethe bindingof
cationsby DOMin naturalaquaticsystems(Tipping,2OO2l.Thesemodelsare,however,
calibrated
usingthe resultsof titrationexperiments
on isolatedandpurifiedhumicandfulvic
acidat highDOCandtotalmetalconcentrations
that areoftenenvironmentally
unrealistic
for
naturalaquaticsystems.Herewe demonstrate,
for low,environmentally
realistic
concentrations,
that the conditional
bindingparameters
of Ni andCuwith
naturalDOMvaryasa functionof the qualityof the DOM,as revealed
by previous
fluorescence
measurements.
Thisresulthasimportantconsequences
for thoseinterested
in estimating
the speciation
of
thesetracemetalsin naturalaquaticsystems.Forexample,
the BioticLigandModel(BLM),
usedto predictthe toxicityof tracemetalsto aquaticorganisms,
incorporates
WHAMto
metalspeciation
calculate
in the exposure
medium(Paquin
et al.,2002).lmprovedbinding
parameters
for Ni andCuwouldimprovethe prediction
of the bioavailability
andtoxicityof
thesemetalsat low,chroniclevelexposures.
179
Acknowledgments
S.Duval,P.
the technical
assistance
in the laboratory.Financial
supportwasprovidedby the Natural
FournierandJ. Perreault
andby the Metalsin the Human
Research
Council
of Canada
Sciences
andEngineering
andC.Fortinare
P.G.C.
Campbell
Research
Network(www.mithe-rn.org).
Environment
Research
ChairProgram.
supportedby the Canada
180
Troisidmepartie: Annexes
AppendixA: Effectof samplestorageon metalspeciationand DOM quatity/ AnnexeA : Effet
de l'entreposage
des6chantillonssur la sp6ciationde m6tauxet la qualit6de la MOD
1
Introduction
Oncea naturalaqueoussampleistakenfrom the environment,
possible
changes
in tracemetal
speciation
mayoccurdueto abioticchanges
(e.g.pH,adsorption
andchemicalreactions)
or
(e.g.microbial
bioticchanges
activity)occurring
in the sampleduringstorage.Althoughvery
few studieson the stabilityof samples
duringstoragecanbe foundin the literature,
filtration
through0.2-pmfilterscoupledwith storageat 4"Cand in the darkisgenerally
accepted
asa
meansto minimizethesechanges
(Batleyet al.,2004)s.Herewe testedthe effectof sample
storageon tracemetalspeciation
and DOMspectroscopic
quality.Forthisexperiment
we
identified
two lakes(LakeGenevaand LakeVaudray)
that exhibitmarkeddifferences
in DOM
(quantityandquality)and in totalandfreemetalconcentrations.
2
Methods
Largevolume(10L,polypropylene,
Nalgene)
surfacewatersamples
weretakenfrom lakes
GenevaandVaudrayin July2009,returnedto the labandsequentially
filteredthrougha series
of acid-cleaned
polycarbonate
filters(5 Um,2 pm,0.4prmand0.2 pm; AMD Manufacturing
Inc.).Subsamples
weretakenfor analyses
of pH,majorion concentrations,
DOMoptical
properties(absorbance
and fluorescence)
aswell astotal andfree Cd2*,Cuz*,Ni2*and Zn2*
concentrations
immediately
afterfiltrationandat regularintervals
(approximately
3 months)
overan 8-monthperiod.Betweensampleanalyses,
samples
werestoredin the darkat 4'C,as
wasthe casefor the lakewatersamples
collected
with the in situdiffusionsamplers.
Fora moredetaileddescription
of the sampleanalyses,
the readeris referredto Muelleret al.
(Muelleret al.,20L2a;
Muelleret al.,2O!2b;Muelleret al.,2O].2c)
foundin partll of the thesis.
Briefly,subsamples
weretakenfor determination
of total metalconcentrations.
The
concentrations
of cd2*,Ni2*andznz*in the lakesamples
werealsomeasured
usingan ion
'All
references
canbe found on page205of the thesis.
183
usinga cupricion
of Cuz*wasmeasured
whereasthe concentration
technique(lET),
exchange
aswellas
organiccarbon(Doc)concentration'
dissolved
(lsE).Finally,
electrode
selective
werealso
measurements
CompOnentS)
(Fland PARAFAC
fluorescence
(suvA2s4)and
absorbance
made.
levelusing
confidence
at the alpha=0'05
werecalculated
analyses
Thefollowingstatistical
sigmaPlotlt.O(systatsoftwarelnc.,chicago,lL).Linearregressionsandone-wayANOVA',s
methodwereusedwhen
usingthe Holm-sidak
procedures
thenormaIityandhomogeneityofvariancesamongdatasetswereconfirmed;otherwise,the
one-wayANOVAon Ranks
testswereemployed(Kruskal-Wallis
non-parametric
appropriate
usingDunn'smethod'insteadof a parametric
procedures
ANoVA).A||proportionaIdatawerearcsinesquareroottransformedbeforeperformingthese
statisticaltests'Dateswereconvertedto Juliandays'
3
Resultsand discussion
and relative
in thetotal metalconcentrations
(P<0.05)
differences
significant
statistically
as a functionof time werefound
proportionthe free metalion to the total metalconcentration
a|thoughsma||,
significant,
A].andA2). Statistica||y
for somemeta|sin both|akes(Figures
over8
the total metalconcentration
in the relativeproportionof the free metalion to
increases
for Zn in LakeVaudrayandCuin Lake
monthswerefoundfor all metalsin bothlakesexcept
for Lake
andfreeCdconcentrations
Geneva(TableA1). lt shouldbe notedthat the total
Genevaatthetwofina|timepointswerebe|owthedetection|imitsofthemeasurements.
or any DOMspectroscopic
(D0.05) in DOCconcentrations
differences
significant
No statisticaily
(P<0'05)in DOC
differences
parameterswerefoundfor LakeGeneva.However,significant
LakeVaudray(Figure
andC3/Crasa functionof time werenotedfor
SUVA25a
concentration,
trendsin the
significant
no statistically
in theseparameters,
A3). Despitethe variability
of
from LakesVaudrayandGenevaasa function
qualityof the DOMsampled
spectroscopic
time were observed(TableA1)'
184
Althoughsomevariability
in the totaldissolved
the free metalion
qualitywasobserved
concentrations
and DOMspectroscopic
overthe 8-monthsamplestorage
time,the temporaltrends(whenobserved)
weremodest.TheNiz*concentration
appeared
to
changemoreovertime thandid the otherfree metalion concentrations
(Figure
A2),which
againmaypointto the bindingof Ni to differentsitesor differentligands
withinthe DOM
mosaic.We speculate
that theseNi-binding
sitesmaybe morelabileor susceptible
to change
overtime thanthe ligandsinvolvedin complexing
the othermetals.Despite
the possible
liberation
of Ni2*ionsas ligands
aredegraded
overtime,our measured
free Ni2*concentrations
were,nevertheless,
muchlowerthan thosevaluespredictedby WHAM. We thereforehave
confidence
that therewasno significant
bias,dueto sampledegradation,
in the tracemetal
speciation
and DOMqualityresultsreportedin the thesis.
185
5.0e-8
a
o
8 0e-10
LakeVaudray
LakeGeneva
a
I
4.0e-8
qp
b?
T
?
6.0e-10
3.0e-8
F
C)
20e-8
2.0e-10
1 0e-8
0.0
00
D
o
bb
OG
7.0e-8
3.0e-8
TI
6 0e-8
2.5e-8
I
5 0e-8
2.0e-8
1.5e-8
z
F
=
qI
F
I
C
N
1.0e-8
I
4.0e-8
b
3 0e-8
o
2.0e-8
5.0e-9
T
t_
I
I
1.0e-8
00
0.0
Nov
Jan
Month
May
Nov
Jan
May
Month
in filteredsamplesof lakesVaudray(filled
in total metal(Mr)concentrations
FigureA1. Changes
Pointswith similarletters(a-d)
circles)and Geneva(opencircles)overtime (2009-2010).
points.
An asteriskindicatesonly
(P>0.05)
the
between
difference
indicateno significant
totalesde
les
dans concentrations
one replicateabovethe detectionlimit/ Changement
pleins)
et Geneva(cercles
filtr6sdeslacsVaudray(cercles
m6taux(Mr) dansles6chantillons
Leslettresidentiques(a-d)indiquentqu'il n'y a
ouverts)en fonctiondu temps(2009-2010).
(P>0.05)
pasde diff6rencesignificative
entrelespoints.Lesymbole* d6noteun r6pliqua
sont au-delAde la limited6tection.
dont lesconcentrations
186
1.4
.
o
1.2
Lakevaudray
LakeGeneva
ab
1.0
F
F
P
IJ
nn
()
vva
-
+
b
N
0.6
I
002
a
00
000
0.5
1.4
b
rI
1.2
b
1.0
c
N
z
+
z
I
o)
L
N
na
0.6
T
t
0.4
Ia
0.2
00
0.0
sep
Nov
Jan
Month
May
sep
Nov
Jan
May
Month
FigureA2. Changes
in the ratioof the free metalCd2*,
cu2*,Ni2*andZn2*to their total metal(M1)
concentrations
in filteredsamplesof lakesVaudray(filledcircles)and Geneva(opencircles),
Pointswith similarletters(a-c)indicateno significant
overtime (2009-2010).
difference
(P>0.05)
betweenthe points.An asteriskindicatesonlyone replicateabovethe detection
limit / Evolutiondu rapportentrelesconcentrations
de m6tauxlibresCd2*,Cu2*,Ni2*et Zn2*
filtr6sdeslacsVaudray(cercles
et lesconcentrations
de m6taltotale(M1)dans6chantillons
pleins)et Geneva(cerclesouverts)au coursdu temps(2009-2010).
Leslettresidentiques
(a-c)indiquentqu'il n'y a pasde diff6rencesignificative
(P>0.05)
entrelespoints.Le
symbole* d6noteun r6pliquadont lesconcentrations
sontau-delide la limited6tection.
t87
12
14
o
o
LakeVaudray
LakeGeneva
ito
o
ab
I
J
11
1
q
()
I
g)
E9
a
5
I
g8
I
,tn
E^
-J
()
o
ob
d
d
lA
xI
a
I
I
1.45
T
b
a
4
I
1.35
tr 130
C)
(r)
()
t
1
1.25
d
o
rI
0.1
120
rI
1.15
sep
Nov
Jan
Month
May
Nov
Jan
May
Month
Fl and
properties,
SUVAzsc,
andthe DOMspectroscopic
in the DOCconcentration
FigureA3. Changes
C3/Cr,in filteredsamplesof lakesVaudray(filledcircles)and Geneva(opencircles)over
difference(P>0.05)
Pointswith similarletters(a-d)indicateno significant
time (2009-2010).
propri6t6s
et
des
COD
de
points
betweenthe
/ Evolutiondesconcentrations
filtr6sdeslacs
Fl et C3/Cr)dansdes6chantillons
de la MOD(SUVA2'4,
spectroscopiques
(2009-2010)'
Les
ouverts)au coursdu temps
Vaudray(cerclespleins)et Geneva(cercles
(P>0.05)
entreles
lettresidentiques(a-d)indiquentqu'il n'y a pasde diff6rencesignificative
points.
188
TableA1.
coefficientR'; F statistic,P value)for changesin metal
Regression
statistics(regression
speciationand DOMqualityin filteredsamplesof lakesVaudrayand Genevaoverthe 8monthsamplestoragetime.P valuelessthan0.05indicates
a slopenot equalto zero,NA
indicatesnot enoughdatato performthe test / R6gression
statistique(coefficient
de
de la sp6ciationdes
r6gression
R2,statistiqueF et valeursde P) pour leschangements
filtr6sdeslacsVaudrayet Geneva
m6tauxet de la qualit6de la MODdansdes6chantillons
plus
petite
que0.05indiquentune pente
pendant8 moisd'entreposage.
Lesvaleursde P
non6galed z6ro,tandisque le symboleNAindiquequ'iln'y avaitpasassezde donn6es
poureffectuerle test statistique.
disponibles
189
qualityresults/ AnnexeB : R6sultatsde l'analyse
AppendixB: LakeDOMspectroscopic
spectroscopique
de la MODlacustre
resultsfor the eighteenlakes
Thefollowingsectionis a compilation
of the spectroscopic
ON. Figure81 consists
of the
sampledfrom the regionsof Rouyn-Noranda,
QCandSudbury,
excitation-emission
spectrafor the lakessampledin 2007(presented
on the rightsideof
on the left sideof the page)andfor the lakessampledin 2008(presented
resultscalculated
for the lakessampled.
the page).Table81 consists
of all of the spectroscopic
L91
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480
500
520
320
340
360
380
400
420
140
460
480
500
520
spectraof lakeDOMfrom
Figure81. Average(N=3)3Dexcitation-emission
(left)andthe eightlakessampledin 2008(right).Each
the eighteenlakessampledin2OOT
to Ramanunits
spectrumwascorrectedfor instrumentand innerfilter effects,normalised
was
Raman
scatter
where
the
regions
Jagged
regions
represent
and blanksubtracted.
en
d'excitation-6mission3D de la
removed./ Moyenne(N=3)desspectresde fluorescence
et leshuit lacs
en 2007(i gauche)
MODprovenant
desdix-huitlacs6chantillonn6s
en 2008(d droite).Chaquespectrea 6t6 corrig6pourdeseffets
6chantillonn6s
d'instruments
et deseffetsli6si l'att6nuationde l'6missionpar la MODelle-mAme(< inner
d'un
filter effects>),normalis6par rapportauxunit6sde Raman,et corrig6parsoustraction
a 6t6 enlev6e.
repr6sentent
desr6gionsoir la dispersion
blanc.Lesr6gionsembrouill6es
198
Table81.
(SUVAzas),
specificabsorbance
Measuredmean(+ SD,N=3)dissolvedorganicmatter(DOM)concentration,
specificUV-absorbance
fluorescence
index(Fl),and relativeproportionof the PARAFAC
components(C1,C2,C3and C4)for eachof the
coefficient(SACa4o),
moyennes(t SD,N=3)de matidreorganique
eighteenlakessampledin2OOT
andthe eightlakessampledin 2008/ Concentrations
(Fl)
l'indicede fluorescence
le coefficientd'absorbance
sp6cifique(SACa4o),
dissoute(MOD),l'absorbance
UV sp6cifique(SUVA245),
(CL,C2,C3et C4)pour chacundes 18 lacs6chantillonn6s
en 2007et les8
de fluorescence
et la proportionrelativedescomposantes
lacs6chantillonn6s
en 2008.
Adeline
Bousquet
D'Alembert
Dasserat
Dufault
Dufay
Joannds
Opasatica
Vaudray
Bethel
Crowley
Geneva
Hannah
Middle
Nelson
Raft
Ramsey
Whitson
DOC
SUVA254
sAc340
(mg Ltl
(L m-l mgc-l)
(L m-1mgc'l)
4 . 6 0! 0 . 7 9
L2.58t 0.69
6 . 9 6! 7 . 2 7
11.03t 0.62
8.00t
5.14r
5.10r
8.25r
8.67r
8.00r
6.85r
8.36r
1.46r 0.25
3 . 5 2r 0 . 2 0
2.20!0.22
1.98t 0.18
1.48r 0.25
2 . 1 1r 0 . 1 3
2.90t 0.06
1.55t 0.14
2 . 8 8! O . 2 4
0.84r 0.25
L.O2!0.r2
1.09r0.25
1.03t 0.08
1 . 0 1 r0 . 0 8
0.83t 0.42
1 . 3 5r 0 . 7 1
0.74t 0.09
7 . 7 3! 0 . 3 4
0.80
0.58
0.35
0.37
70.26!0.26
6.00r 0.40
8.07r 0.09
5.22!0.46
2.59t 0.28
3.2510.39
3 . 6 1 r0 . L 2
3.32r 0.40
1.36r 0.46
2 . 2 6! 0 . 3 r
3.55r 0.53
3 . 8 2t 0 . 7 2
0.90
0.40
0.70
0.44
9.98! 0.25
7.4r!0.34
9.83t 0.43
4.64!O.47
4.82t0.44
5 . 2 3r 0 . 2 8
5.05r 0.25
5.17tO.57
4 . 5 3! 2 . 0 2
5 . 9 9! 2 . 3 2
4.32r 0.60
6.87r 0.55
1.33r 0.01
1.29r 0.0L
1.30r 0.01
L . 3 51 0 . 0 3
1.32r 0.03
L.30r 0.02
1.28r 0.01
1.35r 0.02
1 . 2 5r 0 . 0 1
1.49t 0.06
1.33! 0.08
1.33r 0.05
1.31r 0.04
1.36r 0.07
1.32r 0.03
1.28r 0.07
7.4I!0.02
1.34r 0.04
ctlc'r
c2lcr
c3lcr
c4lcr
0 . 2 7! 0 . 0 2
0.15r 0.02
0 . 2 1 r0 . 0 3
0.26r 0.19
0 . 2 7! O . L 2
0.22r 0.03
0.18r 0.04
0.25r 0.05
0.27r 0.08
0.24r 0.08
0.29r 0.07
0.29r 0.09
0.28t 0.04
0.25r 0.03
0.28t 0.08
0.29t 0.07
0.24r 0.05
0.30r 0.06
0.33r 0.02
0.07t 0.01
0.24r 0.03
0.34r 0.25
0.33r 0.14
0.24r 0.03
0.15t 0.03
0.32r 0.05
o . 2 4! 0 . 0 7
0.37! 0.13
0.36r 0.08
0.37r 0.10
0.34r 0.04
0.34t 0.04
0.35r 0.09
0.34r 0.08
0.33r 0.06
0.36r 0.08
0.26!0.02
0.75r 0.05
0.48r 0.07
0.30r 0.32
o . 2 7! 0 . 1 0
0.45r 0.05
0 . 5 1 r0 . 1 6
0.29r 0.05
0.43r 0.10
0.15t 0.06
0.15i 0.05
0.18r 0.07
0 . 1 8 10 . 0 3
0.20r 0.03
0.14r 0.03
0 . 1 5r 0 . 1 6
0.17t 0.04
0.19r 0.04
0.15t 0.01
0.02r 0.00
0.08t 0.01
0.10r 0.07
0.14r 0.05
0.09r 0.01
0.05r 0.01
o.t2!0.o2
0.07r 0.02
0.23r 0.09
0.19r 0.04
0.L7r 0.05
0.20r 0.03
0.20r 0.03
0.23r 0.07
o.22!0.06
0.25r 0.05
0.15r 0.03
200
AppendixC: Effectsof aluminum(Al)and iron (Fe)on WHAM Vl predictionsof Cd,Cu,Ni and
Zn speciation/ AnnexeC : Effetsde l'aluminium(Al)et du fer (FE)surles
pr6dictionsde la sp6ciationdu Cd,du Cu,du Ni et du Zn
for bindingsites
with traceelements
suchasAl3*and Fe3*,
Thecompetition
of trivalentcations,
how we
on DOMis accountedfor in WHAMVl (Tipping2OO2l.Thefollowingsectiondescribes
of Cd,Cu,Ni andZn
choseto dealwith the effectsof Al and Feon WHAMVl predictions
in the sampledlakes.
speciation
in the lakessampled
of Al and Feweremeasured
concentrations
Althoughthe total dissolved
Al and Fe
(seeTableC1,it is unknownwhatfractionof the measured
totaldissolved
andavailable
to
is colloidal
Al and Feandwhatfractionis trulydissolved
concentrations
by Ed
competewith tracemetalsfor bindingsiteson the DOM. First,followingsuggestions
using
the activityof Al3*and Fe3*in the samples
TippingandSteveLofts,we calculated
of Al3*
presented
in Tipping(2005)and Loftset al. (2008)wherethe concentrations
equations
(s),
(s)andFe(OH)3
of AI(OH)s
asa functionof pH andthe solubility
and Fe3*arecalculated
ThesevaluesweretheninputteddirectlyintoWHAM. In the secondapproach,
respectively.
form,andthat all
Al and Fewasin the trulydissolved
we assumed
that all of the total measured
the ironwaspresentas Fe(lll).In thiscase,WHAMVl allowsthe userto inputthe total
the Al3*and Fe3*concentrations
andthencalculates
Al(lll)and Fe(lll)concentrations,
dissolved
usedby WHAMareslightlydifferentfromthe empirical
internally.Thesolubility
equations
that the
in Tipping(2005)and Loftset al. (2008).FigureC1,demonstrates
relations
suggested
by
aresimilarto thosecalculated
WHAMcalculated
Cd,Cu,Ni andZnfree metalconcentrations
equations.
WHAMwhenusingthe externalpH-solubility
20L
TableC1.
Lake
Comparison
of measuredtotal dissolved
Al3*and Fe3*activities
Al and Feconcentrations,
calculated
usingWHAMVl assuming
Al andFeistrulydissolved,
all measured
andAl3*and
Fe3*activitiescalculatedusingthe externalpH-solubility
relationsfor Al and Fehydroxides
/
Comparaison
de concentrations
totalesdissoutes
mesur6espour l'Al et le Fe,lesactivit6s
d'Al3*et de Fe3*calcul6es
que tout l'Alet Femesur6
en utilisantWHAMVl et en consid6rant
est dissous,
et lesactivit6sd'Al3*et de Fe3*calcul6es
d partirdesrelationspH-solubilitd
externespour leshydroxydes
d'Al et de Fe.
W.HA,M
v1
wHAIvlv1
Measured
calculated'Measured
calculated^
Bethel
Dasserat
Dufault
Geneva
Opasatica
Raft
Vaudray
Whitson
(M) "i:liii,'J" Ar(r)a rFelr
(M) 'i:l,f,Tt Fe(u)a
tArtr
7.6E-08 7.3E-1.8
4.9E-O7 4 . 1 E - L 3
8.1E-08 6 . 0 E - 1 6
1.6E-08 1 . 1 E - L 5
3.7E-O7 1 . 2 E - 1 5
NA
NA
3.4E-06 1 . 9 E - 1 1
1.5E-07 7.5E-14
L.2E-L6
6.7E-L2
1.8E-14
7.9E-r2
6.5E-15
3.3E-10
1.6E-11
7.9E-I2
NA
2.6E-O7
L.2E-07
3.9E-08
1,.5E-07
1.6E-08
s.0E-07
2.2E-O7
NA
4.4E-L7
2.6E-L9
2.tE-Lg
9.4E-20
4.6E-18
3.5E-16
2.7E-r7
5.7E-20
7.4E-77
1.5E-18
6.3E-r7
7.OE-rg
L.7E.Ls
t.2E-L6
9.6E-L7
*indicatesAl activitycalculated
usingthe equationoutlinedin Tippinget al. (2005).
îndicatedFeactivitycalculated
usingthe equationoutlinedin LoftsandTipping(2008).
202
4e-9
1.2e-9
T
1.0e-9
I
E
J
I
*,
I
3
6.0e-
E
&
()
2e-9
f
()
4.0e-
T
2.0e-
o
tIl
I
n
3.5e-7
0.0
1e-6
3.0e-7
8e-7
2.5e-7
6e-7
s-
z
2.0e-7
+
N
4e-7
c
N
1.5e-7
1.0e-7
2e-7
5.0e-8
0
0.0
rI
t-L
"$*'%";1))"*$"""),."t..od€"u$+-%.q))'"$*'")".'1'"".'d"'.
FigureC1. Comparison
betweenmeasuredconcentrations
of free Cd2*,Cu2*,Ni2*and Zn2*(circles)
and
predictedby WHAMVl (i) assuming
free M2*concentrations
all measuredAl and Fe istruly
dissolved(squares),
or (ii) usingthe Al3*and Fe3'activities
calculated
from empiricalpH(triangles).
solubilityrelationsfor Al and Fehydroxides
Results
for eightlakessampledfrom
the regionsof Rouyn-Noranda,
entre les
QCand Sudbury,ON./ Comparaison
(cercles)
concentrations
de Cd2*,Cu2*,Ni2*etZn2*libresmesur6es
et lesconcentrations
des
que tout l'Al et Femesur6est
m6tauxlibresM2*pr6ditespar WHAMVl (i) en consid6rant
dissous(carr6s),
ou (ii)en utilisantlesactivit6sd'Al3*et de Fe3*calcul6es
i partirde
relationspH-solubilit6
empiriquespour leshydroxydes
d'Al et de Fe(triangles).
R6sultats
pour leshuit lacs6chantillonn6s
danslesr6gionsde Rouyn-Noranda,
QCet Sudbury,ON.
203
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2t4

exploration des liens entre les propriétés optiques de

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