the quality of water entering loskop dam, mpumalanga

THE QUALITY OF WATER ENTERING LOSKOP DAM,
MPUMALANGA, SOUTH AFRICA
by
Stanley Moyo
Submitted in partial fulfilment of the requirements for the degree
DOCTOR TECHNOLOGIAE
in the
Department of Chemistry
FACULTY OF SCIENCE
TSHWANE UNIVERSITY OF TECHNOLOGY
Supervisor: Prof RI McCrindle
Co-supervisors: Dr NS Mokgalaka
Dr JG Myburg
December 2012
DECLARATION BY CANDIDATE
“I hereby declare that the thesis submitted for the degree of D Tech: Chemistry, at
Tshwane University of Technology, is my own original work and has not previously been
submitted to any other institution of higher education. I further declare that all sources
cited or quoted are indicated and acknowledged by means of a comprehensive list of
references”.
Stanley Moyo
Copyright© Tshwane University of Technology 2012
ii
DEDICATION
This work is dedicated to my father, James Maserah Moyo (RIP)
iii
ACKNOWLEDGEMENTS
I would like to express my most profound and sincere gratitude to my supervisor Prof Rob
McCrindle, for his unwavering and valuable guidance, patience and support during the
course of this work.
I would also like to acknowledge Dr Ntebogeng Mokgalaka and Dr Jan Myburgh for their
constructive criticism. Many thanks also go to Prof Sandra Combrinck, Dr Munyaradzi
Mujuru and Dr Abayneh Ataro for their support and encouragement. I would also like to
thank Dr Comfort Nkambule (HOD Department of Chemistry) for all the support he has
given.
Special thanks also go to my family: my mother Susan Moyo, my sisters Thokozile and
Gertrude Moyo and my brothers Nkosilathi and Blessing Moyo. Thank you very much for
your support and for believing in me.
Many thanks also go to fellow postgraduate students in the Department of Chemistry:
Benias Nyamunda, Luke Gwatidzo, Nyamande Mapope, Haile Tekkie, Mathapelo
Seopela, Lynn Ziki, Johan Linde, Miranda Mafoko-Hlongwane, Christopher Nsaka, Jean
Oyourou, Akpa Omer Memel, Funzani Melato, Presely Lepule, Mariah Mashigo, Khuliso
Maphakela, Katlego Phala, Robert Mnisi and Tedlay Forsido.
iv
ABSTRACT
The increase in industrialisation in South Africa has led to an increase in coal mining and
coal utilisation in Mpumalanga. This has resulted in the deterioration in the quality of water
upstream of Loskop Dam. Little or no information is available on the occurrence or
fractionation of trace metals in sediments, which are reservoirs for pollutants.
Furthermore, no information is available concerning the occurrence of polycyclic aromatic
hydrocarbons (PAHs), known for their carcinogenic and mutagenic effects, in rivers in the
area. The possible impact of leaching of elements and organic compounds from South
African coals has not been exhaustively researched.
Samples of coal from a South African coal mine were leached under various pH
conditions and times, to compare leaching behaviour and to determine the potential
environmental impact of possibly hazardous elements and organic compounds. The
calculated leaching intensities, sequential extraction results and cumulative percentages,
demonstrated that the leaching behaviour of the elements was strongly influenced by the
pH, the leaching time and the properties and occurrences of the elements. The leached
concentrations of As, Cd, Co, Cr, Mn, Ni and Pb exceeded the maximum concentrations
recommended by the Environmental Protection Agency (EPA) for surface water. A wide
range of organic compounds were identified in coal leachates. These included some EPA
priority PAHs, benzene derivatives, biphenyls, and non-aromatic compounds. Major
groups of PAHs identified included: naphthalene and derivatives, fluorene and derivatives,
indene and derivatives, anthracene and derivatives, phenanthrene and derivatives, and
pyrene and derivatives.
Water and sediment samples were collected from selected sites located along the
Olifants, Klein Olifants, Wilge rivers and a tributary of the Olifants River. The fractionation
of the elements, Cd, Co, Cr, Fe, Mn, Pb, Ti and V, in the sediment samples, was
determined by employing a sequential extraction scheme. Most of the elements were
found to exist in the residual fraction. The non-residual fractions were analysed, since
elements in these fractions are more bioavailable. Correlation analysis and two
multivariate analysis techniques (factor and cluster analysis) were employed to shed light
on the associations between the non-residual phase of the trace metals and Fe-and Mnoxides within the sediments, since Fe-and Mn-oxides play a critical role in the adsorption
of trace metals within aquatic environments. The elements Co, V, Pb, Cr and Cd in the
oxidisable fraction were associated with Fe-oxides, while some V, Cr and Ti were
associated with Mn-oxides. Through cluster and factor analysis, three industrial activities
v
were found to be sources of metals, according to the following groups: Co, Cr and Pb; Cd
and Mn; and Fe and Ti.
In order to quantitatively apportion sources of PAHs, a combination of diagnostic ratios,
factor and cluster analysis, were employed. Possible sources identified as major
contributors for PAHs in the sediments include coal-fired power plants, coal/coke and
petroleum. Through diagnostic ratios most sites along the Wilge River were found to be
contaminated by PAHs emanating from petrogenic sources. The remaining sites along the
other rivers were affected by pyrogenic activities. The specific sources, resolved via factor
and cluster analysis, were: coal and biomass combustion, coal and tyre burning, coke
production and coal leaching, as well as petroleum.
vi
CONTENTS
PAGE
DECLARATION BY CANDIDATE
ii
DEDICATION
iii
ACKNOWLEDGEMENTS
iv
ABSTRACT
v
LIST OF FIGURE
xiii
LIST OF TABLES
xv
GLOSSARY
xvii
CHAPTER 1
INTRODUCTION
1.1
Background and motivation
1
1.2
Problem statement
8
1.3
Hypothesis
10
1.4
Objectives
10
1.4.1
General objectives
10
1.4.2
Specific objectives
10
CHAPTER 2
LITERATURE REVIEW
2.1
Introduction
12
2.2
Polycyclic aromatic hydrocarbons in the aquatic environment
12
2.2.1
Polycyclic aromatic hydrocarbons
12
2.2.2
Distribution and sources of PAHs
16
2.3
Source apportionment of polycyclic aromatic hydrocarbons
17
2.3.1
Chemical fingerprinting
17
2.3.2
Receptor modelling
19
2.3.2.1
The chemical mass balance model
20
2.3.2.2
Principal components analysis and factor analysis
20
2.3.2.3
Multivariate linear regression (MLR)
22
2.3.2.4
Cluster analysis
23
vii
2.3.3
Examples of source apportionment studies
24
2.4
Analysis of PAHs in the aquatic environment
29
2.4.1
Sampling plan
29
2.4.2
Surface water and sediment sampling
31
2.5
Analysis of polycyclic aromatic hydrocarbons
36
2.5.1
Extraction or seperation methods
36
2.5.2
Solid phase extraction
39
2.6
Methods of analysis
40
2.6.1
Gas chromatography mass spectrometry
41
2.6.2
Detection of PAHs
41
2.7
Pollution of sediments by metals
42
2.7.1
Methods for estimating metal pollutant impact
42
2.7.2
Enrichment factors
43
2.8
Determination of metal fractionation in sediments
44
2.8.1
Sequential extraction procedures
44
2.8.1.1
Exchangeable fraction
45
2.8.1.2
Acid soluble fraction
46
2.8.1.3
Reducible fraction
46
2.8.1.4
Oxidising fraction
47
2.8.1.5
Residual fraction
47
2.9
Review of sediment metal fractionation studies
48
2.10
Inductively coupled plasma spectroscopy for metal determination
51
CHAPTER 3
MATERIALS AND METHODS
3.1
Description of study area
54
3.2
Sampling sites
55
3.3
Sampling procedures
56
3.3.1
Water sampling for PAH analysis
56
3.3.2
Sediment sampling for PAH analysis
56
viii
3.3.3
Sediment sampling and preparation for trace metals analysis
56
3.4
Reagents
57
3.5
Total organic carbon analysis
58
3.6
Coal leaching procedures
58
3.6.1
Batch methods
58
3.6.2
Column methods
59
3.6.3
Monolithic methods
60
3.6.4
Particle size distribution of the leached coal
61
3.6.5
Metals limits of detection and limits of quantitation
61
3.6.6
Column leaching of trace metals
64
3.6.7
Sequential leaching of trace metals
64
3.6.8
Trace metals analysis
65
3.6.9
Leaching procedure for organics
65
3.6.10
Soxhlet extraction of the coal
66
3.6.11
Silica gel clean-up
66
3.6.12
Measurement of particle size distribution
66
3.7
Sediment and water preconcentration and extraction procedures
67
3.7.1
Sediment extraction procedure for PAH analysis
67
3.7.2
Water disc SPE procedure
68
3.8
Gas chromatography-mass spectrometry analysis of PAHs
69
3.8.1
Gas chromatography
69
3.8.2
Mass spectrometer tuning
69
3.8.3
Optimisation of GC-MS parameters
69
3.8.4
Calibration of GC-MS instrument
72
3.9
Sequential extraction procedure
76
3.9.1
The BCR sequential extraction procedure
76
3.9.2
Residual fraction and total digestion of sediments
76
3.9.3
Chemical analysis
77
3.10
Multivariate statistical analysis
77
CHAPTER 4
ENVIRONMENTAL IMPLICATIONS OF MATERIAL LEACHED FROM COAL
4.1
Results and discussion
79
4.2
Metals leaching
79
4.2.1
The effect of leaching time on the availability of metals in leachates
79
ix
4.2.2
Effect of pH of the leaching solution on leachability of the trace metals
82
4.2.3
Leaching behaviour of the metals
82
4.2.3.1
Leaching behaviour of Co
82
4.2.3.2
Leaching behaviour of Cr
86
4.2.3.3
Leaching behaviour of Cd
86
4.2.3.4
Leaching behaviour of As
87
4.2.3.5
Leaching behaviour of Ni
87
4.2.3.6
Leaching behaviour of Pb
88
4.2.3.7
Leaching behaviour of Th
88
4.2.3.8
Leaching behaviour of U
89
4.2.3.9
Leaching behaviour of Mn
89
4.3
Metal quantification in coal and certified reference material
90
4.3.1
Validation of digestion using SARM 18
90
4.3.2
Cobalt
90
4.3.3
Cadmium
91
4.3.4
Chromium
91
4.3.5
Arsenic
91
4.3.6
Nickel
92
4.3.7
Lead
92
4.3.8
Manganese
92
4.3.9
Uranium
92
4.3.10
Thorium
92
4.4
4.5
Estimation of amount of metal with potential to be leached into the
Mpumalanga ecosystem
Organics leaching: results and discussion
93
95
4.5.1
Physiochemical characterisation of coal leachates
95
4.5.2
EPA priority PAHs in coal leachates
98
4.5.3
Other organic compounds identified in the coal leachates
98
4.5.4
Environmental implications of leached PAHs
109
4.5.5
Summary
107
x
CHAPTER 5
DISTRIBUTION AND SOURCE APPORTIONMENT OF PAHs
5.1
Introduction
108
5.2
Results and discussion
108
5.3
PAH contents of water
108
5.4
PAH contents of suspended matter
117
5.5
PAH contents of sediments
121
5.6
5.7
Comparison of PAH levels with those in other rivers around the
world
Source apportionment of PAHs in sediment samples
128
130
5.7.1
Source apportionment of PAHs by diagnostic ratios
130
5.7.1.1
LMW to HMW ratios
130
5.7.1.2
FIA/FIA+Py, FIA/Py, AN/AN+PHA and PhA/AN ratios
131
5.7.1.3
IP/IP+BghiP, FI/FI+Py and BaA/BaA+Chy ratios
132
5.7.2
Source estimates from cluster analysis
142
5.7.3
Source estimates from principal component analysis
142
5.8
Summary
146
CHAPTER 6
DISTRIBUTION AND MOBILITY OF SELECTED METALS IN SEDIMENTS
6.1
Introduction
148
6.2
Results and discussion
148
6.2.1
Extent of sediment contamination
148
6.2.2
Fractionation pattern for metals
149
6.2.2.1
Partitioning of metals in studied sediments
149
6.2.2.2
Cadmium
152
6.2.2.3
Cobalt
153
6.2.2.4
Chromium
154
6.2.2.5
Iron
155
6.2.2.6
Manganese
155
6.2.2.7
Lead
156
6.2.2.8
Titanium
156
6.2.2.9
Vanadium
157
6.2.2.10
Summary
157
6.2.3
Statistical analysis
158
6.2.3.1
Univariate statistics
158
6.2.3.2
Correlation analysis
159
xi
6.2.4
Factor and cluster analysis
164
6.2.4.1
Factor 1
164
6.2.4.2
Factor 2
165
6.2.4.3
Factor 3
165
6.2.4.4
Summary
169
CHAPTER 7
CONCLUSIONS AND RECOMMENDATIONS
7.1
Introduction
170
7.2
Achievment of objectives
171
7.2.1
7.2.2
Environmental implications of leaching of trace elements and
organics in coal
Identification, quantification and source apportionment of PAHs
171
171
7.2.3
Distribution and mobility of trace elements in sediment samples
172
7.3
Contribution of the study
174
7.4
Recommendations and future research
175
REFERENCES
176
xii
LIST OF FIGURES
PAGE
Figure 1.1:
Figure 1.2:
Figure 1.3:
Figure 1.4:
Figure 1.5:
Figure 1.6:
Figure 2.1:
Figure 2.2:
Figure 3.1:
The spatial extent of the Olifants River Catchment
Coal emission sources reaching the aquatic environment
Decanting of Schoongezicht mine, where water decants into a
dam
Illustration of an unflooded mine, with one seam mined, decanting
into a dam
An illustration of an area where the seam in a shallow mine
determines whether the mine will flood or not
Map showing the Upper (study area), Middle and Lower sections
of the Olifants River Catchment
The chemical structures of sixteen US EPA priority polycyclic
aromatic hydrocarbons
Monooxygenase reaction involving benzo(a)pyrene hydroxylase
2
3
4
4
5
7
13
15
Figure 4.4:
Map of the Upper Olifants River catchment area (Sampling
locations are located along the Wilge (W), Olifants (OLI) and Klein
Olifants (KOL) rivers)
Location of the eMalahleni coal fields of South Africa
Representative particle size distribution in coal used for leaching
experiments
The column leaching design
Preconcentration and extraction techniques used for the
extraction of water and sediment samples
Total ion chromatogram obtained in selected ion monitoring mode
of 5 mg/L polycyclic aromatic hydrocarbons and internal
standards
Extracted ion chromatograms for pyrene and pyrene-d10
Extracted ion chromatograms for benz(a)anthracene, chrysene
and chrysene-d12
Cumulative percentages of Co and Cr leached from coa over a
period of 60 hours at different pHs
Variations of DOC, TOC, suspended solids and Eh values of
Seam 4 coal leachates
Variations of DOC, TOC, suspended solids and Eh values of
Seam 5 coal leachates
Representative chromatogram for Seam 4 coal leachate extract
105
Figure 4.5:
Representative chromatogram for Seam 5 coal leachate extract
105
Figure 5.1:
Representative chromatogram for Olifants River water sample
extract
Comparison of
total polycyclic aromatic hydrocarbon
concentrations in summer and winter water samples
Total organic carbon composition in suspended matter and
sediment samples collected in summer
Representative total ion chromatogram for Olifants River tributary
suspended matter extract
Total polycyclic aromatic hydrocarbon concentrations in
suspended particles samples from water sampled in summer and
winter 2010
111
Figure 3.2
Figure 3.3
Figure 3.4:
Figure 3.5:
Figure 3.6:
Figure 3.7:
Figure 3.8:
Figure 4.1:
Figure 4.2:
Figure 4.3:
Figure 5.2:
Figure 5.3:
Figure 5.4:
Figure 5.5:
xiii
55
60
61
65
67
73
74
75
83
95
96
114
117
118
119
Figure 5.6:
Figure 5.7:
Figure 5.8:
Figure 5.9:
Figure 5.10:
Figure 5.11
Figure 5.12:
Figure 5.13:
Figure 5.14:
Figure 5.15:
Figure 5.16:
Figure 5.17:
Figure 5.18:
Figure 5.19:
Figure 5.20:
Figure 5.21:
Figure 5.22:
Figure 5.23:
Figure 5.24:
Figure 5.25:
Figure 6.1:
Figure 6.2:
Figure 6.3:
Figure 6.4:
Correlation of total organic content with total polycyclic aromatic
hydrocarbon concentrations in suspended matter in water
sampled during winter
Correlation of total organic content with total five and six ring
polycyclic aromatic hydrocarbon concentrations in suspended
matter in water sampled during winter
Representative total ion chromatogram for summer sediment
sample extract from the Olifants River
Particle size composition of winter sediment samples from the
Olifants tributary, Olifants, Klein Olifants and Wilge rivers
Comparison of total polycyclic aromatic hydrocarbons in sediment
and suspended matter
Distribution of 2, 3, 4, 5 and 6 ring polycyclic aromatic
hydrocarbons in sediments from the Olifants River tributary,
Olifants, Klein Olifants and Wilge rivers
Correlation between total polycyclic aromatic hydrocarbon
concentrations and TOC in sediment samples collected in
summer
Bar chart indicating the ratios of AN and PhA, and FIA and Py in
summer water samples
Bar chart indicating the ratios of AN and PhA, and LMW and
HMW in summer water samples
Bar chart indicating the ratios of AN and PhA; FIA and Py; and
BaA and Chy in suspended matter samples
Bar chart indicating the ratios of FI and Py, and IP and BghiP in
suspended matter samples
Bar chart indicating the ratios of LMW and HMW hydrocarbons in
suspended matter samples
Bar chart indicating the ratios of various polycyclic aromatic
hydrocarbons in summer sediment samples (AN and PhA; FIA
and Py)
Bar chart indicating the ratios of various polycyclic aromatic
hydrocarbons in winter sediment samples (AN and PhA, FIA and
Py)
Bar chart indicating the ratios of FI and Py; BaA and Chy; IP and
BghiP; and LMW and HMW in summer sediment samples
Bar chart indicating the ratios of FI and Py; BaA and Chy; IP and
BghiP; and LMW and HMW in winter sediment samples
Hierarchial dendogram of the sixteen EPA priority polycyclic
aromatic hydrocarbons
Scores plot of principal components 1 and 3
Scores plot of principal components 3 and 2
Pie chart indicating the extent of the contributions of different PAH
sources
Fractionation pattern for metals in sediments from the Olifants
River
Fractionation pattern for metals in sediments from the Klein
Olifants River
Fractionation pattern for metals in sediments from Wilge River
Fractionation pattern for metals in sediments from the Olifants
River tributary
xiv
120
120
121
122
125
126
127
133
134
135
136
137
138
139
140
141
143
144
144
147
150
150
151
151
Figure 6.5:
Scatter plot of factor 3 and factor 2 loadings
166
Figure 6.6:
Figure 6.7:
Scatter plot of factor 2 and factor 1 loadings
Dendogram-cluster analysis of the data
167
168
xv
LIST OF TABLES
PAGE
Table 2.1:
Table 2.2:
Table 2.3:
Table 2.4:
Table 2.5:
Table 3.1:
Table 3.2:
Table 3.3:
Table 3.4:
Table 3.5:
Table 3.6:
Table 3.7:
Table 4.1:
Table 4.2:
Table 4.3:
Table 4.4:
Table 4.5:
Table 4.6:
Table 4.7:
Table 4.8:
Table 4.9:
Table 5.1:
Table 5.2:
Table 5.3:
Table 5.4:
Table 5.5:
Physical properties of, and abbreviations for selected PAHs
Parent PAH parameters and relative stabilities
Strengths and weaknesses of environmental sampling devices
for water
Strengths and weaknesses of environmental sampling devices
for sediment
Sampling devices for water and sediment samples and their
applications
Limits of detection (LOD) and quantitation (LOQ) (ug/L) for
potentially toxic metals in coal leachates and sequential
extraction phase
Operating conditions of the GC-MS instrument
Monitored ions for polycyclic aromatic hydrocarbons
Selected ion monitoring parameters for analysis of the
polycyclic aromatic hydrocarbons
European Commission for Standards, Measurement and
Testing sequential extraction procedure
Operating conditions of the ICP-OES
Operating conditions of the ICP-MS
Analytical concentrations of ten elements in the resulting
leachates compared to acidic water flowing from an abandoned
coal mine
Effect of pH on leaching intensities of the elements
Concentrations of metals (%) available after sequential
extraction of different fractions
Calculated t values and results from total digestion of coal and
SARM-18
Approximate amounts of metals leached from one tonne of coal
14
19
33
EPA priority PAH (µg/L) identified in leachates from acidified
water
EPA priority PAH (µg/L) identified in leachates from unacidified
water
Selected compounds (µg/L) identified in dichloromethane
extracts of Seam 4 coal leachates
Selected compounds (µg/L) identified in dichloromethane
extracts of Seam 4 coal leachates
List of analytes and corresponding internal standard/surrogate
compound
Average recoveries from CRM 1944 matrix using soxhlet
extraction, calibration statistics and method detection limits
(MDL) of polycyclic aromatic hydrocarbons in water, suspended
particles and sediments
Concentrations of polycyclic aromatic hydrocarbons (ng/L) in
water samples
Concentrations of polycyclic aromatic hydrocarbons (µg/kg, dry
weight) in suspended matter
99
Polycyclic aromatic hydrocarbon concenterations (µg/kg) in
sediment samples
xvi
34
35
63
70
71
71
76
77
78
81
84
85
91
94
100
101
103
109
110
112
116
124
Table 5.6:
Water and sediment quality guidelines compared with results
from this study
Concentrations of polycyclic aromatic hydrocarbons in river
sediments worldwide
Polycyclic aromatic hydrocarbon concentrations in water phase
of rivers of the world
127
Table 5.9:
The range of diagnostic ratios for PAH sources
131
Table 5.10:
Rotated component matrix for sixteen priority EPA polycyclic
aromatic hydrocarbons from sediments
Average enrichment factors for the metals in the sediments
146
Risk assessment code for metals according to Perin et al.
(1985)
Percentage of metals in the exchangeable and acid
soluble/carbonate phase
Descriptive statistics, from the analysis of the summer sediment
samples
Descriptive statistics, from the analysis of the winter sediment
samples
Correlation coefficients for extracted concentrations of Mn and
Fe in F1, F2, F3, F12 and F123
Correlation coefficients for F2 fractions of Fe and Mn, and F12
and F123 fractions of Co, V, Pb, Cr, Ti and Cd
Correlation coefficients for the non-residual fractions (F12 and
F123) and the residual (F4) fraction
Eigenvalues for the 8 factors and cumulative proportion of
variance
Sorted rotated factor loadings (Varimax rotated)
152
Table 5.7:
Table 5.8:
Table 6.1:
Table 6.2:
Table 6.3:
Table 6.4:
Table 6.5:
Table 6.6:
Table 6.7:
Table 6.8:
Table 6.9:
Table 6.10:
xvii
129
129
149
153
159
159
160
162
163
164
165
GLOSSARY
alkylBzs
alkylbenzene compounds
AMD
acid mine drainage
ANOVA
Analysis of Variance
BCR
Bureau Community of Reference
BTEX
benzene, toulene, ethylbenzene, xylenes
CA
cluster analysis
CI
chemical ionisation
CMB
chemical mass balance
CPE
cloud point extraction
CRM
certified reference material
dc
direct current
DDT
dichloro-diphenyl-trichloroethane
DNA
deoxyribonucleic acid
DO
dissolved oxygen
DWAF
Department of Water Affairs and Forestry
EC
European Commission
EF
enrichment factor
EI
electron impact
EIC
extracted ion chromatogram
Ɛo
eluotropic strength
EOP
emerging organic pollutants
ETAAS
electrothermal atomic absorption spectrometry
FA
factor analysis
FAAS
flame atomic absorption spectrometry
FBE
fluidised bed extraction
FID
flame ionisation detector
FLD
fluorescence detector
FRB
field reagent blank
FTIR
fourier transform infra red
GC
gas chromatography
GC-MS
gas chromatography-mass spectrometry
GF AAS
graphite furnace atomic absorption spectroscopy
HCB
hexachlorobenzene
HG
hydride generation
HMW
high molecular weight
xviii
HPLC
high performance liquid chromatography
HSME
head space microextraction
ICP-MS
inductively coupled plasma-mass spectrometry
ICP-OES
inductively coupled plasma-optical emission spectroscopy
ISO
International Organisation of Standards
KOL
Klein Olifants River
Kow
octanol-water partition coefficient
LC
liquid chromatography
LDR
linear dynamic range
LLE
liquid liquid extraction
LMW
low molecular weight
m/z
mass to charge ratio
MAE
microwave assisted extraction
MeOH
methanol
MLR
multivariate linear regression
MMC
melanomacrophage centres
MMW
medium molecular weight
MTBE
methyl-tert-butylether
NCI
negative chemical ionisation
NIPALS
non linear iterative partial least squares
NPS
non point sources
OC
organochlorinated compounds
OLI
Olifants River
OLT
Olifants River tributary
PAH
polycyclic aromatic hydrocarbon
PBDE
polybrominated diphenyl ethers
PCA
principal component analysis
PCB
polychlorinated biphenyls
PCDD
polychlorinated dibenzo-p-dioxin
PCDF
polychlorinated dibenzofuran
PCI
positive chemical ionisation
PCN
polychlorinated naphthalene
PDMS
polydimethylsiloxane
PE
polyethylene
PET
polyethylene tetraphthalate
PFOA
perfluorooctanoate
PFOS
perfluorooctane sulphonate
xix
PID
photo ionisation detector
PLE
pressurised liquid extraction
POPs
persistant organic pollutants
PTFBA
perfluorotributylamine
PTFE
polytetrafluoroethylene
RAC
risk assessment code
REEs
rare earth elements
RF
radio frequency
RNA
ribonucleic acid
RP-LC
reversed phase liquid chromatography
SDME
single drop microextraction
SE
soxhlet extraction
SFE
supercritical fluid extraction
SIM
selected ion monitoring
SN
signal to noise ratio
SPDE
solid phase disk extraction
SPE
solid phase extraction
SPMD
semi-permeable membrane device
SPME
solid phase microextraction
SS
stainless steel
TCD
thermal conductivity detector
THF
tetrahydrofuran
TIC
total ion chromatogram
TOC
total organic carbon
US EPA
United States Environmental Protection Agency
USN
ultrasonic nebuliser
VOCs
volatile organic compounds
WLG
Wilge River
WRC
Water Resources Commission
xx
CHAPTER 1
INTRODUCTION
1.1 Background and motivation
The Olifants River Catchment commences in the Highveld grasslands of South Africa. It
covers about 54 570 km2 and is divided into nine secondary catchments (Figure. 1.1). The
mean annual precipitation for the region is 683 mm, the mean annual runoff is about
10.8 billion cubic metres, and the mean annual evaporation is approximately 1 580 mm
(Midgley, Pitman & Middleton, 1994). Approximately 10% of South Africa's population
resides within this region. Furthermore, 90% of the country's saleable coal is mined in the
Olifants River basin1. The country's coal mining industry is the second largest mining
sector after gold, with sales contributing €1.98 billion to the annual export revenue by
December 2007. Together with the Highveld and Ermelo coalfields, the eMalahleni
coalfield represents the largest area of active coal mining in South Africa. These mines
produce coal for power generation and the region supports 55% of the country's total
power generating capacity for domestic consumption (Tshwete et al., 2006). The Highveld
is also home to a significant number of steel and petrochemical industries. Consequently,
most of the pollution of the Olifants River is likely to occur in the upper basin.
More than 200 dams have been constructed in the Olifants River basin. Thirty seven of
these can be considered as major dams with a capacity of more than two million cubic
metres. These include the Blyderivierspoort Dam, Loskop Dam, Middleburg Dam,
Origstad Dam and Witbank Dam. Of these dams the Loskop Dam is the largest. Most of
the water from this dam is utilised for crop irrigation.
A major concern is the deterioration of the quality of water in the Loskop Dam, which has
resulted in a precipitous drop in crocodile and fish populations (Ashton, 2010). The
Loskop Dam provides water to the second largest irrigation scheme (approximately
110 000 ha) in South Africa. Thus, the deteriorating water quality is of major concern to
the farmers who currently hold lucrative contracts for export of agricultural produce.
Besides the possible loss of these contracts, approximately 30 000 employment
opportunities are under threat. Pollutants originating from the upper part of the catchment
will migrate down the river and may influence the downstream water users. The upper
1
http://www.wrc.org.za (Water Wheel) [Accessed: 14-08-2010]
1
Olifants River drains to the middle Olifants catchment from which a number of irrigation
schemes and large towns, including Polokwane, are supplied with water. The Olifants
River then continues to the Lower Olifants catchment and flows, via Phalaborwa, to the
Kruger National Park. Mozambique, further downstream, is also dependant on the Olifants
River for diverse water uses, including agriculture and community water supply.
Figure 1.1: The spatial extent of the Olifants River Catchment (CSIR, 2001). [Ecoregions
7.02, 7.03, 7.04, 7.05 constitute the Upper Olifants Catchment]
According to Hodgson and Krantz (1998), water from the Olifants River is of major
concern since it contains effluent from coal mines in the eMalahleni area, municipal waste,
pollution from several sources, including power generation, sewage treatment, metal
industry/mineral processing (comprising mainly of steel, stainless steel, ferrosilicate and
vanadium-producing plants) as well as from agriculture. Effluent from these sources is
likely to contain material, which will subsequently pollute the Loskop Dam. Maree et al.
(2004) reported that approximately 50 mega litres of water is discharged into the Olifants
River Catchment per day. The Department of Water Affairs and Forestry (DWAF)
estimated that the post-closure decant from defunct coal mines in 2004 was
approximately 62 ML/d (DWAF, 2004). Abandoned coal mines fill up with water after
2
closure and this leads to hydraulic gradients, resulting in water flow between the mines or
percolation to the surface. This flow, referred to as intermine flow, may carry a wide range
of pollutants, including acid mine drainage (AMD). The AMD has the potential to
contaminate drinking water sources of nearby communities in eMalahleni (Gitari et al.,
2009).
Coal mining is likely to have the largest impact on the aquatic environment (Figure 1.2),
which, in turn, affects the socio-economic situation of areas downstream of the Olifants
River (DWAF, 2004). Environmental risks include surface and groundwater pollution, in
the form of potentially toxic metals and organic compounds taken up by the environment,
the degradation of soil quality and the harming of aquatic fauna (Bell et al., 2001; Pulles &
Banister, 2005; Adler & Rascher, 2007; Oelofse et al., 2007; Pone et al., 2007).
Figure 1.2: Coal emission sources reaching the aquatic environment (Ahrens & Morrisey,
2005)
Operational underground collieries also have the potential to contaminate/pollute surface
and ground water due to decanting. The decanting of mine water can occur when the coal
mine is flooded, or unflooded, depending upon the hydrological conditions of the mine.
When the mine fills, fractures caused by mining operations will serve as conduits from
where water will be forced out as a result of hydrological differences, usually at the lowest
interconnections between the surface and the mine (Vermeulen & Usher, 2006). The
various hydrological conditions under which decanting can occur are illustrated in Figures
1.3 to 1.5.
3
Figure 1.3: Decanting of Schoongezicht mine, where water decants into a dam
(Vermeulen & Usher, 2006)
Figure 1.4: Illustration of an unflooded mine, with one seam mined, decanting into a dam
(Vermeulen & Usher, 2006)
4
Figure 1.5: An illustration of an area where the seam in a shallow mine determines
whether the mine will flood or not (Vermeulen & Usher, 2006)
Of major concern in the study area, is the leaching of polycyclic aromatic hydrocarbons
(PAHs) and potentially toxic metals from coal and their existence in water and sediments.
Polycyclic aromatic hydrocarbons are problematic in the environment, having long halflives in soils, sediments, air or biota. There seems to be no consensus of opinion about
how long the half-life in a given medium should be for the term `persistent' to be
conferred. However, in practice, a PAH could have a half-life of years or decades in
soil/sediment and several days in the environment. Polycyclic aromatic hydrocarbons are
typically hydrophobic or lipophilic. In aquatic systems and soils, they partition strongly into
solids, notably organic matter, avoiding the aqueous phase. They also partition into lipids
within organisms, rather than entering the aqueous milieu of cells, and are stored in fatty
tissue. This confers persistence to the chemical in biota, since metabolism is slow and
PAHs may therefore accumulate in the food chain (Jones & Voogt, 1999).
Since PAHs are prone to bioaccumulate and magnify in the food chain, concerns about
their harmful effects have been centred on their impact on top predator species, including
humans. The PAHs are also amongst the many chemicals implicated in the current
concerns over hormone or endocrine disruption in humans and wildlife (Harrison et al.,
1995; Kelce et al., 1995; Kavlock, 1996). In addition to reproductive effects, many PAHs
are known or suspected carcinogens. Purported PAH effects also extend to damage of
the immune systems of top-predator species (Safe, 1994; Ross et al., 1995), enhancing
their susceptibility to disease and affecting patterns of behaviour (Swart et al., 1994;
Leonards, 1997).
5
Concerns over the adverse health effects of PAHs to human and wildlife, provide the
impetus for research on their concentration, distribution as well as their sources, in the
aquatic environment and its compartments. Since the bulk of PAHs reside in sediments
where they primarily partition into organic matter, the determination in these
environmental compartments is very important, particularly since small changes in the
mass of sediments can have major impacts on adjacent media, water or air (Fernandez et
al., 1999). Estimates of contemporary emission and or sources of PAHs are of
fundamental
importance
for
effective
source
reduction
measures
and
national/regional/global environmental inventories, budgets and models.
According to Roux et al. (1994), there is inadequate data on heavy metal concentrations
in South African waters. This has been attributed to the relatively limited number of
studies dealing with heavy metals that have been conducted in South Africa. There is a
particular lack of information on their fractionation in both water and sediments. Several
studies of pollution in South African river systems have been reported. Seymore et al.
(1994) investigated a number of water quality variables and determined the
concentrations of selected trace metals (Cr, Cu, Fe, Mn, Ni, Pb, Sr and Zn) in sediments
and water from the lower Olifants and Selati rivers, after the Loskop Dam (Figure 1.6).
They found the high concentrations of Cl, F, K, and Na ions in the water to be of major
concern. The trace metal concentrations, with the exception of Sr, were also found to be
high in both rivers, but decreased after confluence, due to dilution. Sediment analysis for
trace metals indicated that levels had increased. However, the authors did not attempt to
investigate fractionation or nature of these metals. To this end, the authors recommended
further studies on trace metals in sediments and water, as well as on their effects on
aquatic life. Binning and Baird (2001) determined the total concentrations of trace metals
(Cr, Cu, Mn, Pb, Sn, Sr, Ti and Zn) in sediments from Swartkops River Estuary in Port
Elizabeth, South Africa. In this study the high concentrations of the metals in both the river
and estuary were found at points where runoff from informal settlements and industry
enters the system. Although the ability of sediments to act as repositories of pollutants
was demonstrated, fractionation of the metals was not undertaken.
The physicochemical characteristics and pollution levels of heavy metals Cd, Cu, Pb and
Zn in water and sediment samples from the Dzindi, Madanzhe and Mvudi rivers in
Limpopo were studied by Okonkwo and Mothiba (2005). In the study, total, residual and
non-residual metal concentrations were determined. According to Christie (1995), residual
and non-residual metal concentrations provide information concerning the fate of the
metal in terms of its interaction with sediments, its bioavailability, and its resultant toxicity.
6
Figure 1.6: Map showing the Upper (study area), Middle and Lower sections of the
Olifants River Catchment (Ashton, 2010)
The concentration ranges of all the metals, with the exception of Cd and Pb, were found to
be below international guidelines for acceptable concentrations in drinking water. The
study revealed that Pb was found predominantly in the particulate fraction, while Cu and
Zn were dominant in the non-labile fraction. Although the authors determined the
distribution of the metals through residual and non-residual concentrations, the study
could have been complemented by also determining the distribution and fractionation of
these metals in sediments. Sediments are repositories of pollutants, including metals, and
changes in the physicochemical properties, including pH, oxidation and reduction
potentials of the water can mobilize metals, thus enhancing their bioavailability.
Greichus et al. (1977) studied the water, bottom sediment, aquatic plants, aquatic insects,
fish and fish-eating birds and their eggs from the Hartbeespoort and Voelvlei dams. They
determined insecticides, polychlorinated biphenyls (PCBs) using gas chromatography
mass spectrometry (GC-MS) and heavy metals (As, Cd, Mn, Pb, Zn and Hg). The levels
7
of heavy metals in the Hartbeespoort Dam were found to be higher than in the other
dams. This study did not, however, attempt to determine the occurrence/fractionation of
the metals in the sediment samples.
Grobler (1994) determined twenty metals in water, sediment and fish samples collected
from the lower and middle parts of the Olifants River (Figure 1.6). The area sampled
included the inflow into the Loskop Dam, Phalaborwa Barrage, Steelpoort and Selati
rivers. No As, Cd, Hg and Pb were detected in any of the aquatic compartments
investigated. Sediment analysis revealed that the Al concentration was comparable in all
samples. The concentrations of Be, Fe, Mn, V and Zn were comparable in the Loskop
Dam, Olifants River Oxford impoundment, Phalaborwa Barrage and Selati sediments.
Lower concentrations of these metals were, however, found in sediment from Olifants
River Zeekoegat impoundment.
1.2
Problem statement
With increasing use of coal, the growing impact on the environment and human health
from potentially hazardous elements and organic compounds has become of concern.
These substances have the potential to be released and transformed during the course of
coal exploitation, cleaning, transportation and combustion. Coal is subjected to continual
wetting during storage to prevent spontaneous combustion, thereby adding to the risk of
matter being leached. The impact of pollution occurs in areas where coal is used, resulting
in acid rain, arsenosis and fluorosis (Orem & Finkelman, 2004; Wagner & Hlatshwayo,
2005). Many countries, including South Africa, have established relevant standards for
water and air quality to limit the contents of Ag, As, Be, Cd, Cl, Co, Cr, Cu, F, Hg, Mn, Mo,
Ni, Pb, Sb, Se, Th, Tl, U, V and Zn in environmental compartments, including water. Coal
is one of the most important sources of these elements. However, little is known about the
metals that may be leached from coal. Trace element studies in South African coals have
focussed on the determination of total concentrations of trace elements. Wagner and
Hlatshwayo (2005) determined the amounts of fourteen trace elements (As, Cd, Co, Cr,
Cu, Hg, Mn, Mo, Ni, Pb, Sb, Se, V, and Zn) in five run-of mine (ROF) Highveld coals. The
leaching behaviour of trace elements and potentially toxic organic compounds from South
African coals by decanting, or ground water, has not been reported in literature.
Therefore, information on the concentration, distribution and leaching of these potentially
toxic elements from coals is urgently needed, prior to coal utilisation and environmental
assessment.
8
Increased use of coal globally, and in South Africa in particular, necessitates greater
outdoor storage at transportation depots and various industrial and electrical generation
sites. As a result, there are millions of metric tons of coal stock-piles, where the coal is
exposed to the elements. Given the complex organic content of coal and the increasing
amounts of coal in outdoor storage, the potential exists for the release of large amounts of
potentially toxic compounds into the environment. In South Africa, very little is known
about the substances that may be leached from coal during exposure to the environment
(Davis & Boegley, 1981; Lambor, Blowes & Ritchie, 1994; Wang et al., 2008). The organic
compounds may include PAHs, phenols, biphenyls, O-, N-, and S-containing heterocyclic
compounds, aromatic amines, various non-aromatic compounds and phthalates.
Compounds including PAHs are known to be carcinogenic.
Many studies have focussed on the measurement of both organic and inorganic pollutant
concentrations in water, and in some cases sediment, which are potential reservoirs of
pollutants. With respect to the Olifants River catchment, literature reports are available of
studies of samples collected from the middle to the lower portion of the Olifants River
catchment, but no studies on the Upper Olifants catchment have been reported.
Currently, very little data has been published on the occurrence of PAHs in the South
African environment, in particular, the study area. Fatoki, Ree and Nakhavhembe (2010)
investigated the levels of PAHs (azulene, pyrene, anthracene, dibenzothiophene and
fluoranthene) in water and sediment samples from rivers in the vicinity of Thohoyandou.
Run-off and run-off sediment samples were also collected for analysis. Although the study
confirmed the capability of sediments to act as reservoirs for PAHs, further studies on
source apportionment will assist in efforts to remediate the polluted rivers.
The distribution profiles of POPs (PCBs, OCPs and dioxin-like compounds) and PAHs in
sediments from the industrial, residential and agricultural regions of the Vaal Triangle
were investigated by Quinn et al. (2009). In that study, PAHs and polychlorinated paraffins
were found to be present in high concentrations in all the environmental media studied.
Through the use of principal component analysis (PCA), the researchers were able to
identify different pollution sources in industrial and agricultural areas. However, the source
apportionment could have been extended to include the quantitative contribution of each
identified source. The focus of previous studies has been on chlorinated pesticides, with
little attention being paid to other organic pollutants, including PAHs. In addition, no
attempt has been made to quantitatively assign sources of these organic pollutants within
9
the upper Olifants River system. Source apportionment information is critical in the
management of pollution and remediation of a river system.
Consequently, no information is available on the fractionation and bioavailability of the
trace metals in water and sediments in the study area. The fractionation of the metals
determines physicochemical conditions under which the metals become bioavailable.
1.3
Hypothesis
The impact of coal mining activities and the nature and extent of pollution of the Olifants,
Klein Olifants and Wilge rivers by PAHs and trace elements can be determined.
1.4
Objectives
1.4.1
General objectives
The general objectives of the study were to:

assess the impact of potentially toxic metals and organic compounds leaching
from coal, on the quality of surface waters via simulated leaching experiments;

determine the extent of pollution by trace metals and PAHs of the Olifants, Klein
Olifants and Wilge rivers, at selected points in the upper catchment area; and

identify and apportion sources of PAHs in the sediment samples from the study
area.
1.4.2
Specific objectives
The specific objectives of the study were to:

estimate the potential environmental impact of leaching of trace metals and
organic compounds from coal on water quality in the upper Olifants River
catchment using column leaching experiments;

identify and quantify the levels of PAHs in surface water, suspended matter and
sediment samples from the Olifants, Klein Olifants and Wilge rivers, using gas
chromatography-mass spectrometry (GC-MS);

quantitatively apportion sources of PAHs in sediments using multivariate
techniques (factor, cluster, multivariate linear regression analysis);
10

determine the levels of trace metals in water and sediment samples from the
Olifants, Klein Olifants and Wilge rivers, using inductively coupled plasma optical
emission spectroscopy or inductively coupled plasma mass spectrometry (ICPOES or ICP-MS);

use sequential extraction techniques and ICP-OES or ICP-MS to determine the
fractionation and bioavailability of trace metals in sediment samples from the
Olifants, Klein Olifants and Wilge rivers; and

determine the association of trace metals with Fe-Mn-oxides in the non-residual
fraction of sediments using correlation analysis and two multivariate techniques
(factor and cluster analysis).
11
CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
The findings and arguments of a number of researchers with respect to metal and PAHs
pollution in water and sediments are discussed in this chapter. lncluded is the use of
various tools for source apportionment, as well as multivariate methods.
2.2
2.2.1
Polycyclic aromatic hydrocarbons in the aquatic environment
Polycyclic aromatic hydrocarbons
Polycyclic aromatic hydrocarbons are semi-volatile organic compounds containing two or
more fused benzene rings in a linear, angular or cluster arrangement (Maish & Taneja,
2006). Their presence in the environment has been linked to adverse effects to public
health (Grimmer et al., 1983). The lighter PAHs (2-3 rings) are generally not as
carcinogenic as the heavier PAHs with more than 3 rings. Some PAHs, including
benzo(a)pyrene and benzo(a)anthracene, are mutagenic and carcinogenic. In addition, a
few of them, including benzo(a)pyrene have been listed as endocrine disruptors (Zakaria
et al., 2002).
Polycyclic aromatic hydrocarbons are relatively stable neutral compounds. Although all
these compounds exhibit low solubilities in water, the solubilities tend to decrease as a
function of molecular weight. Polycyclic aromatic hydrocarbons are highly lipophilic, as
shown by their water-octanol partition coefficients (Kow). The physical properties of
selected PAHs are listed in Table 2.1.
The US Environmental Protection Agency (US EPA) lists sixteen PAHs as priority
pollutants (Mihelcic & Lithy, 1988). The structures of these compounds are illustrated in
Figure 2.1.
12
Naphthalene
Acenaphthylene
Acenaphthene
Fluorene
13
Anthracene
Chrysene
Benzo[a]pyrene
Phenanthrene
Benz[a]anthracene
Indeno[1,2,3-cd]pyrene
Pyrene
Fluoranthene
Benzo[k]fluoranthene
Benzo[b]fluoranthene
Benzo[ghi]perylene
Dibenzo[a,h]anthracene
Figure 2.1: The chemical structures of sixteen US EPA priority polycyclic aromatic hydrocarbons
Table 2.1: Physical properties of, and abbreviations for selected PAHs
Compound
a
Abbreviation
Formula
Molecular
Melting
Boiling
logKow
Point
a
(°C)
80.2
Point
a
(°C)
218
at
o
b
(25 C)
3.37
Naphthalene
NaP
C10H8
Weight
(g/mol)
128.18
Acenaphtylene
AcNPh
C12H8
152.2
92-93
265-280
4.00
Acenaphthene
AcN
C12H10
154.2
90-96
278-279
3.92
Fluorene
FI
C13H10
166.23
116-118
293-295
4.18
Anthracene
AN
C14H10
178.24
216-219
340
4.46
Phenanthrene
PhA
C14H10
178.24
96-101
339-340
4.49
Fluoranthene
FIA
C16H10
202.26
107-111
375-393
8.80
Pyrene
Py
C16H10
202.26
150-156
360-404
8.90
Benzo(a)anthracene
BaA
C18H12
228.3
157-167
435
5.73
Chrysene
Chy
C18H12
228.3
252-256
441-448
5.80
Benzo(b)fluoranthene
BkFIA
C20H12
252.32
167-168
481
6.35
Benzo(k)fluoranthene
BkFIA
C20H12
252.32
198-217
480-471
5.78
Benzo(a)pyrene
BaP
C20H12
252.32
177-179
493-496
6.50
Benzo(ghi)perylene
dBahA
C22H12
276.34
275-278
525
6.03
Indeno(1,2,3-cd)pyrene
IP
C22H12
276.34
162-163
Dibenzo(a,h)anthracene
BghiP
C22H12
278.35
266-270
6.70
524
6.50
b
Mackay et al., 2006; Tobiszewski & Namieśnik, 2012
According to Catoggio (1991), PAHs are the most toxic compounds in the hydrocarbon
family. They tend to manifest toxicity after biotransformation to metabolites (Varanasi &
Stein, 1991; Stein et al., 1992) through metabolic activation (one-or-two-electron
oxidation) in the organism (Cavalieri & Rogan, 1985). Polycyclic aromatic hydrocarbons
are toxic to aquatic organisms at concentrations of about 0.2 to 10 mg/L. Neff (1985)
reported deleterious sub-lethal responses in aquatic organisms at PAH concentrations in
the range of 5-100 µg/L.
Research has shown that PAHs exert toxicity by interfering with cellular membrane
function and membrane associated enzyme systems. This is particularly true for the lower
molecular weight (LMW) PAHs. In addition, these LMW PAHs physically interact with
hydrophobic sites on the cell membrane, leading to molecular deformation and
perturbation in surface organisation that result in increased membrane permeability
(Anderson & Part, 1989).
Investigations by Schnitz and O'Connor (1992) found that small quantities of PAHs in the
environment can be bound to cellular macromolecules of young fish. Neff (1985) found
14
that four-, five-, and six-ring, high molecular weight (HMW) PAHs, appear to exhibit
increased carcinogenic properties compared to PAHs with smaller ring systems.
Carcinogenic effects result from the formation of PAH metabolites, spontaneously or
through enzyme mediated chemical reactions, including monooxygenase activity, as well
as reactions that are catalysed by cytochrome P450. These chemical reactions result in
the formation of highly electrophilic intermediate arene oxides, some which are capable of
binding to cellular macromolecules, including DNA, RNA, and protein.
An increase in monooxygenase activity, due to PAHs, results in an increase in the amount
of potentially reactive intermediates or metabolites that can bind to DNA (Tuvikene, 1995).
Experiments conducted by Stegeman and Kloepper-Sams (1987) and Jimenez and
Stegeman (1990) revealed that dihydrodiols (Figure 2.2), which are precursors of the main
BaP metabolite that can bind to DNA, make up as much as 75% of the metabolites formed
by cytrochrome P450E.
OH
HO
OH
monooxygenase
NADPH, O2
HO
OH
OH
OH
Figure 2.2: Monooxygenase reaction involving benzo(a)pyrene hydroxylase (Jimenez &
Stegeman 1990)
Photo-induced toxicity of HMW PAHs in juvenile fish has also been reported in the
literature (Tilghman Hall & Oris, 1991). The presence of PAHs, originating from petroleum,
in sediments has been linked to immune suppression in fish. Payne and Fancey (1989)
reported a decreased number of melanomacrophage centres (MMC) in the liver of
flounder that had been exposed to sediments contaminated with petrogenic PAHs. A
study by Faisal and Huggett (1993) on Leiostomus xanthurus, which had been exposed to
PAHs, revealed the suppression of proliferative responses of T-lymphocytes. In another
study, Faisal et al. (1991) demonstrated the depression of the tumorilytic activity of
anterior kidney and splenic leukocytes of mummichog.
15
2.2.2
Distribution and sources of polycyclic aromatic hydrocarbons
Under environmental temperatures, PAHs have the propensity to volatilise from water
bodies and enter the atmosphere. As a consequence of their resistance to breakdown
reactions in air, PAHs are capable of traversing long distances before being re-deposited,
so called trans boundary/''grasshopper effect'' (Wania & Mackay, 1996; Koziol &
Pudykiewicz, 2001). They can be transported through the atmosphere over long
distances, entering the aquatic environment by wet and dry deposition and/or gas water
interchange. Once in aquatic systems, most of the PAHs are associated with the
particulate phase, due to their hydrophobic properties. This gives rise to accumulation in
sediments, which are good environmental compartments for the recording of long-range
distribution of these compounds (Fernandez, Vilanova & Grimalt, 1999). The repetition of
the cycle of volatilisation and atmospheric cycling in warmer climates, and condensation
and deposition in colder climates, results in the accumulation of PAHs in areas far away
from where they were used or first emitted into the environment.
Pyrogenic sources of PAHs include incomplete combustion of fossil fuels (Costa & Sauer,
2005) by internal combustion engines, power generation from fossil fuels (including coal),
coke production, wood burning, and incineration of industrial and domestic waste
(Albaiges, Morales-Nin & Vilas, 2006), or more generally, incineration of materials
containing C and H. During the formation of PAHs, reactive free radicals are formed by
pyrolysis of hydrocarbon containing fuels, in the chemically reducing zone of a flame,
burning at temperatures of 500 to 800°C and in a limited supply of oxygen. The fragments
combine in the reducing atmosphere to form partially condensed aromatic molecules
(pyrosynthesis).
Once formed, PAHs can be widely dispersed into the environment by atmospheric
transport or through stream pathways, and eventually accumulate in soils and aquatic
sediments (Liu et al., 2009). Point sources may emanate from oil spills (Short et al., 2007),
used motor oil (Stout & Emsbo-Mattingly, 2008), contaminated industrial sites, Al
production (Booth & Gribben, 2005), or steel works (Almaula, 2005).
Unburnt coal has also been identified as a major source of PAHs in soils and sediment
(Ahrens & Morrisey, 2005). In addition to sorbed PAH from exposure of the coal to the
environment, original hard coals can contain up to hundreds, and in some cases,
thousands of µg PAHs per g of coal (Willsch & Radke, 1995; Stout & Emsbo-Mattingly,
2008). Coal particles can be released by open cast mining, spills during coal loading,
16
transport or accidents releasing coal into fresh water or marine systems (French, 1998;
Johnson & Bustin, 2006; Pies, Yang & Hofmann, 2007). In addition, coal stored in stock
piles is subject to erosion and can therefore be introduced into river systems. South Africa
is among the top eleven hard coal producing countries. Most of these coal reserves, coal
mining activities and utilisation for thermal power generation, occur in eMalahleni,
Mpumalanga Province, where the study area was located.
2.3
2.3.1
Source apportionment of polycyclic aromatic hydrocarbons
Chemical fingerprinting
Recognising and unravelling the relative contributions of PAHs derived from various
sources may enable the control or management of their introduction into the aquatic
environment. Parent PAHs and their alkyl derivatives have both natural and anthropogenic
sources. However, it is difficult to resolve sources on the basis of PAH parent data alone. 2
In order to circumvent these problems, a wide range of hydrocarbon indicators are used.
These include, but are not limited to, the use of tools such as multivariate pattern
recognition techniques. Current approaches to source apportionment of PAHs are
generally divided into two categories, chemical fingerprinting and receptor modelling
(Stout & Graan, 2010).
Chemical fingerprinting methods are based upon quantitative and qualitative comparison
of PAH concentration profiles, or ''fingerprints'', with those of candidate source materials
or reference materials (Stout, Uhler & McCarthy, 2001). Although the sixteen priority
PAHs have been widely used, the benefits of including data beyond these regulated
priority PAHs, including total hydrocarbon ''fingerprints'', alkylated derivatives, sulfur
containing aromatics or petroleum biomarkers, has been reported (Stout, Uhler &
McCarthy, 2001; Emsbo-Mattingly et al., 2006).
Qualitative chemical fingerprinting enables recognition of major sources of PAHs and thus
roughly provides a source apportionment (Stout & Graan, 2010). However, when
sediments contain hybrid fingerprints as a result of mixtures originating from more than
one source, the method becomes confounded.
2
http://research.rem.sfu.ca/downloads/frap/anah.pdf [Accessed: 07-05-2010]
17
An objective decision regarding sources of PAH can be reached by employing quantitative
chemical fingerprinting. Quantitative chemical fingerprinting is premised on the
comparison of diagnostic ratios based on the concentration of individual parent PAHs,
alkylated PAHs or PAH groups (Budzinski et al., 1997; Dickhut et al., 2000; Stark et al.,
2003), the stable isotopic composition of individual PAHs (Walker et al., 2005; Yan et al.,
2006), or the concentrations of source specific tracers. The contribution of each source in
mixed samples is then computed by employing absolute concentrations rather than ratios.
A disadvantage with this approach is that it becomes increasingly complex when more
than two sources are involved (Yan et al., 2005).
The application of diagnostic ratios requires an understanding of the relative
thermodynamic stabilitities of different PAHs, the characteristics of PAH sources and the
changes in the PAH composition that occurs between source and sediment, i.e., the
relative stabilities of different PAH isomers and PAHs from different sources (Table 2.2).
Combustion and/or anthropogenic sources are usually deduced from an increase in the
proportion of the less stable (kinetic) PAH isomer compared to the more stable
(thermodynamic) isomer (Yunker & Macdonald, 1995). The relative stabilities of parent
PAHs have been computed from the relative heat of formations (Hf) (Yunker et al., 2002)
(Table 2.2). For the two calculations in Table 2.2, the PAH masses (mean Hf range) follow
the order: 276 (33.2 kcal/mol); 202 (24.2 kcal/mol); 252 (20.6 kcal/mol); 178
(5.9 kcal/mol); 278 (4.6 kcal/mol); 228 (2.4 kcal/mol).
Thus, the isomers 276 and 202 have the greatest potential as indicators of kinetic and
thermodynamic stability, i.e., petroleum vs combustion sources. In order to minimise
confounding effects including variations in volatility, water solubility, adsorption, etc, the
choice of PAHs for calculation of ratios is restricted to PAHs within a given molecular
mass (McVeety & Hites, 1988). The PAHs with molecular mass 178 and 202 g/mol are
widely used in order to resolve petroleum and combustion sources of PAHs.
The ratios of methylphenanthrenes/phenanthrene (MP/PhA) and fluoranthene and
pyrene/sum of C2 and C3 alkylphenanthrenes (FlA+Py/C2P+C3P) have previously been
used to resolve PAHs of petrogenic and pyrogenic origins (Page et al., 1999; Zakaria et
al., 2002; Savinov et al., 2003). The MP/PhA ratios in combustion residues are generally
<1, whereas they vary from 2 to 6 for petrogenic sources or unburnt fossil fuels. The
(FlA+Py)/(C2P+C3P) ratio reflects the relative abundance of pyrogenic PAHs and
increases with increasing pyrogenic character (Page et al., 1999).
18
Table 2.2: Parent PAH parameters and relative stabilities (Yunker et al., 2002)
Molecular
mass
(g/mol)
128
Compound
152
Acenaphtylene
154
Acenaphthene
166
Fluorene
178
202
228
252
276
278
2.3.2
Hf difference 1
Hf difference 2
(kcal/mol)
(kcal/mol)
Phenanthrene
0
0
Anthracene
5.48
6.4
Pyrene
0
0
Fluoranthene
20.58
13.2
Acephenanthrylene
28.55
19.8
Triphenylene
0
1.1
Chrysene
0.72
0
Benzo(a)anthracene
2.75
2
Benzo(e)pyrene
0
0
Benzo(a)pyrene
3.52
2.8
Perylene
5.31
6.5
Benzo(b)fluoranthene
19.25
10.2
Benzo(j)fluoranthene
21.08
13.1
Benzo(k)fluoranthene
25.08
16.2
Benzo(ghi)perylene
0
0
Anthanthrene
8.1
9.1
Indeno(1,2,3-cd)pyrene
25.02
16.6
Indeno[7,1,2,3-cdef]chrysene
40.28
26.2
Dibenz(a,h)anthracene
0
0
Dibenz(a,j)anthracene
0.19
0.4
Dibenz(a,c)anthracene
1.37
4.3
Benzo(b)Chrysene
3.5
4.6
Pentaphene
4.16
5
Naphthalene
Receptor modelling
Receptor models assess contributions from a number of sources on the basis of
observations at sampling sites, the ''receptors'' (Gordon, 1988). Two types of receptor
models are used for source apportionment. These are chemical mass balance (CMB) and
multivariate models. Within each class, there are specific models. These include tracer
element, linear programming, ordinary least-squares and ridge analysis (all solutions of
19
the chemical mass balance equation), and factor analysis, multiple linear regression and
extended Q-mode factor analysis, all types of multivariate models (Henry et al., 1984).
2.3.2.1 The chemical mass balance model
The CMB assumes that the profile of a marker chemical species determined at a specific
receptor site is a linear combination of concentration profiles of the chemical species
emitted from an independent contributing source (Li, Jang & Scheff, 2003). To be
effective, the model relies upon input of PAH concentration profiles from a predetermined
set of candidate PAH sources within the study area (Hidy & Venkataraman, 1996). The
linear combination of the candidate source profile is then used to understand the profiles
at each receptor. One drawback of the CMB model for source apportionment is the
requirement for input of source emission profiles in order to calculate the source
contributions (Li et al., 2001; Li et al., 2003).
2.3.2.2 Principal components analysis and factor analysis
In contrast to the CMB, multivariate source apportionment methods require no a priori
estimates of the number and compositions of components. Instead, these methods
identify data set for groups of species (including principal components, factors or clusters)
from which the collective variations account for most of the fluctuation of the species
measured (Gordon, 1988).
The central point of principal component analysis (PCA) and factor analysis (FA) is to
reduce the original (m, n)-data matrix X, with m features and n objects, to factor loadings
(A) and factor scores (F) (Einax, Zwanziger & Geiss, 1997) with minimum loss of
information expressed by the matrix of residuals E:
X = AF + E
Eq. 2.1
This reduction is achieved by the linear combination of different factors in matrix A with
factor scores in matrix F, as shown in Eq. 2.1. The factors that result from the reduction
are new synthetic variables and they represent a certain quantity of features of the data
set. These factors explain the total variance of all features in a descending order and are
themselves non-correlated.
20
The reduction, according to Eq. 2.1, is performed by a principal axes transformation of the
correlation matrix R, obtained from the raw data. The mathematical algorithm is the
solution of the eigenvalue problem:
R.e1 = λ1.e1
Eq. 2.2
where, e and λ are eigenvectors and eigenvalues respectively.
The following properties of these mathematical operations are important:

eigenvalues are a measure of the extracted variance from the total feature
variance, expressed by the correlation matrix R;

eigenvalues are arranged in descending order:
λ1 > λ2 > λ3 > .....> λm;

the sum of all eigenvalues is equal to the number of features m;

eigenvalues and eigenvectors are coupled pairs;

eigenvectors are orthogonal/un-correlated to each other; and

eigenvectors contain the non-normalised coefficients in the matrix of factor
loadings A.
When the eigenvalues are normalised by the square root of λj, they attain a variance of
unity, giving rise to a matrix of factor loadings A. The factor loadings are the weights of
original features in the new variables, the factors. Features with low loadings have only
slight influence on the factor; features with high positive or negative loadings essentially
determine the factor. The new synthetic factors are non-correlated with each other; they
have themselves a variance of one, and contain a certain part of the total variance of the
data set expressed by their eigenvalues (Einax, Zwanziger & Geiss, 1997). All factors with
an eigenvalues λ > 1 are significant.
The total number of factors from PCA, m, describes the full variance of the full data set. If
the redundancy of factors is accepted it is possible to reduce the number of factors by
factor extraction in factor analysis. Two guidelines are commonly used for factor extraction
and seem to yield the best results in practice. These are the ''Kaiser criterion'' and the
''scree test''. They are both based on inspection of the correlation matrix eigenvalues
(Wold, 1978).
According to the Kaiser criterion (Kaiser, 1960), only factors with eigenvalues greater than
1 can be retained. In essence, unless a factor extracts at least as much as the equivalent
21
of one original variable, it has to be rejected. This criterion is probably the one most widely
used. The scree test, first proposed by Cattell (1966) recommends the retention of only
those components above the point of inflection, on a plot of eigenvalues ordered by
diminishing size.
Both criteria have been studied extensively in order to determine which model accurately
predicts the number of factors (Cattell, 1966; Browne, 1968; Linn, 1968; Tucker, Koopman
& Linn, 1969; Hakstian, Rogers & Cattell, 1982). In all cases, each criterion was evaluated
by generating random databases based on a particular number of factors. Using this
general technique, the Kaiser criterion was found to sometimes retain too many factors,
while the scree test sometimes retained too few. However, both techniques perform well
under normal conditions, that is, when there are relatively few factors and many
observations.
After extraction of the number of factors, a process known as factor rotation is followed.
Rotation of the factor solution is a tool to aid the interpretation of factor loadings. For any
given set of correlations and number of factors, there exists an infinite number of ways in
which the factors can be defined and still account for the same degree of covariance in
measurements (Einax et al., 1997). Some of these definitions are theoretically easier to
interpret than others. In factor rotation, an attempt is made to retrieve a factor solution that
is equal to the initial factor extraction, but provides the simplest and most reliable (i.e.,
easier to replicate with different data samples) interpretation of the data set.3
There are two major classes of rotations: orthogonal rotations, which yield uncorrelated,
factors, and oblique rotations, which give rise to correlated factors. Typical examples of
orthogonal rotations include Varimax, Quartimax, and Equimax methods with Varimax
widely regarded as the best. Oblique rotations are less distinguishable with the three most
commonly used being Direct Quartimin, Promax, and Harris-Kaiser Orthoblique.
2.3.2.3 Multivariate linear regression (MLR)
The ultimate goal of source apportionment in environmental analysis is to determine the
percentage contribution of different sources of pollutants, for a given set of samples (Liu et
al., 2009). In order to achieve this goal, the percentage contribution of the major sources
of pollutants are calculated using MLR from the PCA factor scores and the standardised
3
http://www.stat-help.com/notes.html [Accessed: 25-11-2009]
22
normal deviation of total concentrations of pollutants, as independent and dependent
variables, respectively. The linear regression model developed by Larsen and Baker
(2003) is:
Z = ∑BiXi
Eq. 2.4
where, Z is the standardised normal deviate of total [PAH], B i is the partial regression
coefficient, and Xi is the PCA factor score. By expanding Z and rearranging terms, the
multiple regression equation is obtained:
∑[PAH] = ∑Biσ∑[PAH]FSi + ∑[PAH]mean
Eq. 2.5
where, ∑[PAH] is the total PAH concentration, σ∑[PAH] is the standard deviation for the total
PAH concentration, FSi is the score factor of i, and ∑[PAH]mean is the mean total PAH
concentration. The calculated mean percentage contribution becomes:
mean contribution of source i (%) = 100 x B i/∑Bi
Eq. 2.6
2.3.2.4 Cluster analysis
Cluster analysis (CA) encompasses a family of methods that are primarily useful for
finding and making visible structures. It is a pattern recognition technique in which the
view of the data is optimised, rather than the data being manipulated. During the analysis,
a priori knowledge is neither needed nor included, even if it is available, hence cluster
analysis is specified as unsupervised pattern recognition (Einax, Zwanziger & Geiss,
1997).
There are numerous ways in which clusters can be formed. A hierarchical clustering
algorithm is one of the most widely used methods. It can be either agglomerative or
divisive. Agglomerative hierarchical clustering begins with every case being a cluster unto
itself. Once a cluster is formed, it cannot be split; it can only be combined with other
clusters. At successive steps, similar clusters are merged. The algorithm ends with
everything in one cluster. In contrast, divisive clustering starts with one cluster and end up
with individual clusters.
23
In order to unravel structure in the data set or to reveal similarities of objects, a similarity
measure in the form of separation by distance is employed (Einax, Zwanziger & Geiss,
1997)
2.3.3
Examples of source apportionment studies
Source apportionment of PAHs in sediments employing the CMB model has been
reported by several researchers. Xue et al. (2010) used the model developed by the US
EPA to apportion sources of PAHs in coastal sediments from the Rizhao, an off-shore
area in China. The concentrations of the PAHs in the sediments ranged from 76.4 to
27 512.0 µg/g, with an average value of 2 622.6 µg/g. The profiles of the chosen seven
possible sources were obtained from the literature. The CMB model relies on the
assumption that no change in source profile occurs between source and receptor.
However, the researchers suggested that this assumption does not always hold, since
differential loss of PAHs can be incurred from source to receptor as a result of
photooxidation in the atmosphere, or photolysis, or biodegradation in the sediment.
Consequently, the authors proposed a modification of the EPA CMB model by including
degradation factors. After modifying the model for degradation factors, the model results
indicated diesel oil leaks (9.25%); diesel engines exhaust (15.05%) and coal burning
(75.70%), as the major sources of PAHs in sediments.
In a study carried out by Su, Christensen and Karls (1998) on sediments from Green Bay
in Wisconsin, coke burning, highway dust and wood burning were found to be likely
sources of PAHs. The contribution of coke oven emissions to the Green Bay sediments,
obtained after application of the CMB model, was in the range of 5 to 90%, while the
overall highway dust contribution ranged between 5 and 70%.
In another study, Su et al. (2000) determined the sources of PAHs in sediments from the
lower Fox River, Wisconsin, USA, by applying the CMB model. The sediment cores
exhibited total PAH concentrations between 0.34 and 19.3 µg/g. In that study, the
researchers went a step further to determine historical trends of PAH inputs by employing
210
Pb and137Cs dating techniques. Source fingerprints for CMB model input were obtained
from the literature. The results from the study indicated that coke oven emissions,
highway dust, coal gasification, and wood burning were the most likely sources of PAHs.
Coke oven emissions were 40 to 90% of total PAHs. Historical trend results revealed that
this contribution decreased from 1930 to 1990. The overall highway dust contribution was
24
between 10 and 75%, and this fraction increased from 1930 to 2000. The contribution of
wood burning was less than 7% in the cores.
Li et al. (2003) used the CMB model to apportion sources of PAHs found in sediments
from Lake Calumet and the surrounding wetlands in southeast Chicago. In order to
establish the fingerprints of the PAH sources, 28 source profiles were obtained from the
literature. After taking gas/particle partitioning of some of the PAHs into consideration,
some of the source profiles were modified accordingly. Modelling results indicated that
coke ovens and traffic were the major sources of PAHs in the area. The average
contribution from coke oven emissions ranged from 21 to 53% of all sources, and that
from traffic ranged from 27 to 63% (Li et al., 2003). As a subsequent study, a FA model
with non-negative constraints was used to apportion the sources of PAHs found in
sediments. The source profiles and contributions, with uncertainties, were determined with
no prior knowledge of sources. This is opposed to the CMB model where a priori
knowledge of sources is a requirement. The FA results for a two-source solution indicated
coke oven (45%) and traffic (55%) as the primary PAH sources in Lake Calumet
sediment. A six-source FA solution indicated that coke oven burning (47%) and trafficrelated sources (45%) were major PAH sources, while wood burning-coal residential
(2.3%) was a minor PAH source. From the six-source solution, two coke oven profiles
were observed, a standard coke oven profile (33%), and a degraded or second coke oven
profile (14%), which was low in phenanthrene and pyrene. Traffic-related sources
identified included petrol engine exhaust (36%) and traffic tunnel air (9.3%). These results
supported findings made using the CMB model (Li et al., 2003).
The composition and distribution of aliphatic (n-alkanes) and PAHs were measured for
surface sediments collected at 25 sites from Jiaozhou Bay, Qingdao, China (Wang et al.,
2006). The total n-alkanes and PAH concentrations ranged from 0.5 to 8.2 μg/g-dw and
0.02 to 2.2 μg/g-dw, respectively. Large spatial variations were observed in the distribution
of both n-alkanes and PAHs in the bay. The distribution of PAHs in the sediments was
dominated by three or more ring compounds. High hydrocarbon levels were generally
found in areas associated with high anthropogenic impact and port activities in the bay. A
detailed source apportionment study was, however, not done in the study (Wang et al.,
2006).
The ratios of ∑LMW/∑HMW (low moecular weight to high molecular weight PAHs) and
phenanthrene to anthracene (PhA/AN) were used to assess the main source of PAHs to
Jiaozhou Bay sediments (Lang & Cao, 2010). The same ratios have also been used as
25
useful tools to identify petrogenic and pyrolytic sources of PAHs in marine sediments
(Budzinski et al., 1997; Soclo et al., 2000). High ∑LMW/∑HMW ratios (>1) often indicate
PAHs of petrogenic origin predominate, while low ∑LMW/∑HMW ratios suggest PAHs of
pyrolytic origin. As for PhA/AN ratios, PAHs from petrogenic sources usually have values
larger than 15 and are lower than 10 when they are of pyrolytic origin. The calculated
ratios for the Jiaozhou Bay sediments suggested that petroleum contamination was the
main source of n-alkanes, while both pyrolytic and petrogenic sources contributed PAHs
to the surface sediments of Jiaozhou Bay. The researchers compared the results to other
polluted coastal sediments and found that the level of contamination from both aliphatic
hydrocarbons and PAHs in Jiaozhou Bay sediments was relatively low (Lang & Cao,
2010).
Li et al. (2006) investigated the concentrations and distribution of PAHs between water,
suspended particles and sediments from the middle and lower reaches of the Yellow
River, China. The concentrations of the PAHs ranged from, 179 to 369 ng/L, 54 to
155 µg/kg and 31 to 133 µg/kg, in water, suspended matter and sediments, respectively.
Concentration sums of thirteen PAHs in suspended particles were positively correlated
using the content of total organic carbon, while in surface sediments they varied
significantly among sampling locations. The concentration sums of the thirteen PAHs were
mainly correlated with particles with grain size less than 0.01 mm, instead of with total
organic carbon. Source analysis using diagnostic ratios, Fl/(Fl+Py) and AN/(AN+PhA),
revealed that the PAHs mainly originated from coal burning, although in some tributaries
the sources could be attributed to combustion of petroleum.
The concentrations and spatial distributions of seventeen PAHs and methylnaphthalene in
sediments of a river and estuary from Shanghai China were investigated by Liu et al.
(2008). Total PAH concentrations, excluding those of perylene, ranged from 107 to
1707 ng/g-dw. Sedimentary PAH concentrations of the Huangpu River were found to be
higher than those of the Yangtze Estuary. However, the average concentration in the
Suzhou River sediments was close to the average concentration in the Huangpu River.
The PAHs source analysis was accomplished using diagnostic ratios LMW/HMW and
AN/(AN+PhA). These diagnostic ratios revealed that, in the Yangtze Estuary, PAHs at
locations far away from cities were mainly from petrogenic sources. At other locations,
both petrogenic and pyrogenic inputs were significant. In the Huangpu and Suzhou rivers,
pyrogenic input outweighed other sources. The pyrogenic PAHs in the upper reaches of
the Huangpu River were mainly from the incomplete combustion of grass, wood and coal,
while those in the middle and lower reaches originated from vehicle and vessel exhaust.
26
Soclo, Garrigues and Ewald (2000) identified and quantified PAHs in sediments from the
Cotonou coastal zones (Benin) and Aquitaine (France). The measured total PAH
concentrations were in the range 25 to 1450 ng/g and 14 to 855 ng/g for Cotonou and
Aquitaine sediments, respectively. The highest contents of PAHs were found in the
Cotonou harbour. However, the PAH concentrations were comparable with those of
slightly contaminated zones. The researchers used PhA/AN, FIA/Py, Chry/BaA,
LMW/HMW, Per/(tot. PAH), and Per/(penta-aromatics) diagnostic ratios to identify the
PAH contamination sources in the studied sampling stations. In general, the Cotonou
lagoon sampling sites were contaminated mainly by petrogenic PAHs, due to petroleum
trade on an individual scale along the lagoon, and also by waste oils from mechanics
workshops. The Aquitaine samples were found to be polluted by pyrolytic PAHs. A
combination of both petrogenic and pyrolytic PAH contaminations were observed in the
harbours, and was attributed to petroleum product deliveries and fuel combustion
emissions from ships alongside the quays.
Polycyclic aromatic hydrocarbons were measured in 59 surface sediments from rivers in
the Pearl River Delta and the northern continental shelf of the South China Sea (Luo et
al., 2008). Total PAH concentrations varied from 138 to 6 793 ng/g-dw. The sources of
PAH input to sediments in the Pearl River Delta were qualitatively and quantitatively
determined by diagnostic ratios and PCA with MLR. The following diagnostic ratios were
used (Luo et al., 2008): methylphenanthrenes/phenanthrene (MP/PhA), sum of
fluoranthene and pyrene/sum of C2 and C3 alkylphenanthrenes (FlA+Py)/(C2P+C3P),
fluoranthene/sum of fluoranthene and pyrene (FlA/(FlA+Py)), and indeno(1,2,3cd)perylene/sum of indeno(1,2,3-cd)perylene and benzo(ghi)perylene (IP/(IP+BghiP)).
The PCA with MLR results indicated that, on average, coal and wood combustion,
petroleum spills, vehicle emissions, and natural sources contributed 36, 27, 25, and 12%
of total PAHs, respectively. Coal and biomass combustion was the main source of PAHs
in sediments of the South China Sea, whereas petroleum combustion was the main
source of pyrolytic PAHs in river and estuarine sediments of the Pearl River Delta (Luo et
al., 2008).
Twenty-nine Malaysian river and coastal sediments samples were analysed for PAHs by
GC-MS (Zakaria et al., 2002). The total PAHs concentrations in the sediment ranged from
4 to 924 ng/g. Alkylated homologues were found to be abundant in all sediment samples.
The researchers used ratio of the sum of methylphenanthrenes to phenanthrene
(MP/PhA), as an indicator of petrogenic PAH input. The MP/PhA ratio was > 1 for 26
27
sediment samples and > 3 for seven samples from urban rivers covering a broad range of
locations, indicating petrogenic PAH input. This finding is in contrast to other studies
reported in many industrialized countries, where PAHs are mostly of pyrogenic origin. The
MP/PhA ratio was also significantly correlated with HMW PAHs, including benzo[a]pyrene,
suggesting a unique PAH source in Malaysia that contains both petrogenic PAHs and
pyrogenic PAHs.
The sources of PAHs that enter ambient air in Baltimore (Maryland, USA) were
determined by using three source apportionment methods, PCA with MLR, EPA Unmix
model and PMF (Larsen & Baker, 2003). In the study, vehicles, both diesel and petrol,
were found to contribute on average 16−26%, coal 28−36%, oil 15−23%, and wood/other
having the greatest disparity of 23−35% of the total (gas-plus particle-phase) PAHs.
Seasonal trends were evident for both coal and oil. Coal was the dominant PAH source
during the summer, while oil dominated during the winter. Positive matrix factorization was
the only method able to separate diesel from petrol sources. By determining the source
apportionment through multiple techniques, weaknesses in individual methods were
mitigated and overlapping conclusions were strengthened.
Zuo et al. (2007) applied PCA and MLR to apportion sources of PAHs in surface
sediments in Tianjin River, China. Four principal components were extracted representing
coal combustion, petrol, coke and biomass burning and industry discharge as sources.
The contributions of major sources were quantified using MLR as 41% coal, 20%
petroleum and 39% from coking and biomass. The researchers further divided the study
area into three distinctive areas with different PAH concentrations and applied PCA and
MLR to quantify contributions from major sources in these areas. The three zones were
found to have distinct differences in PAH concentrations and profiles. Different source
features were also unveiled. For the industrialized Tanggu-Hangu zone, the major
contributors were coking (43%), coal (37%) and vehicle exhaust (20%). In rural areas,
however, in addition to the three main sources, biomass burning was also important
(13%). However, in the urban-suburban zone, incineration accounted for one fourth of the
total.
Liu et al. (2009) determined the concentrations of 18 PAHs in 32 samples collected from
the Huangpu River in Shangai, China. Cluster analysis diffrentiated the individual PAHs
into three major groups. Two of the groups represented pyrogenic and petrogenic
sources, while the third cluster represented an unknown source. The results of diagnostic
ratios indicated that pyrogenic sources were the major sources of the PAHs. The
28
PCA/MLR analysis of the data revealed that contributions from coal combustion, trafficrelated pollution and spills of petroleum products (petrogenic) were 40, 36 and 24%,
respectively. Furthermore, the investigators were able to show that sedimentary PAH
pollution was significantly higher in spring than in other seasons. They attributed the
higher concentrations to contributions from coal combustion and petrogenic sources.
The PAHs in 350 sediments from a 1.5-mile portion of the Little Menomonee River
(Milwaukee, USA) were determined using PCA, chemical fingerprinting and positive matrix
factorization (PMF) (Stout & Graan, 2010). In total, creosote and urban background
contributed 27 and 73% of eight carcinogenic PAHs (CPAHs), respectively, in that part of
the river. The concentrations of CPAHs derived from urban background were highest in
surface sediments (20 mg/kg), particularly near major roadway crossings. It increased in
the downstream direction, and (on average) exceeded the 15 mg/kg regulatory cleanup
threshold. Weathered creosote derived CPAHs were widespread at low concentrations
(4.8 mg/kg), although some discrete sediment, mostly at depths below 15.24 cm,
contained elevated CPAHs derived from creosote. The investigation demonstrated the
value of combining multiple techniques in source apportionment studies of PAHs in
sediments. Furthermore, it indicated that PMF can be used as a means to determine the
concentrations of PAHs attributable to background in sediments, without the need to
identify, collect, and analyze background samples, which may not even exist in
heterogeneous aquatic environments.
2.4
2.4.1
Analysis of PAHs in the aquatic environment
Sampling plan
Sampling is a critical part in the analysis of organic compounds in the environment
(Hildebrandt, Lacorte & Barceló, 2006). If sampling is not performed using adequate
techniques and according to specific objectives the result of the analysis can be biased.
The sampling plan should reflect the composition of the environment compartment under
study. In addition, it should preserve the sample so that the exact concentration of
compound can be determined.
According to Einax et al. (1997), the design of a sampling plan is inextricably linked to the
purposes of the sampling and to the specific information to be gathered from the samples.
The distribution of organic compounds can be random, uniform, patchy, stratified or
following a gradient. As a result, Hildebrandt, Larcorte and Barcelo (2006) recommend
29
that preliminary studies should be performed to establish the existing distribution in the
river. This assists in deciding whether systematic, random or judgemental sampling
should be used. The purpose of the study also determines if sampling should be
performed in a quantitative or qualitative way. If the purpose of the study is to gather
quantitative data, Namieśnik et al. (2005) advised that the sampling equipment must be
accurately calibrated to avoid uncertainties in the final results.
According to (Green, 1979), planning of representative sampling is based on the following
methodology:

samples must be replicated within each combination of time, location, or other
variables of interest;

an equal number of randomly allocated samples should be taken;

the samples should be collected in the presence and absence of conditions of
interest in order to test the possible effect of the conditions;

preliminary sampling provides the basis for the evaluation of sampling design and
options for statistical analysis;

the efficiency and adequacy of the sampling device and or method over the range
of conditions must be verified;

proportional focussing of homogeneous sub-areas or sub-spaces is necessary if
the whole sampling area has high variability as a result of the environment within
the features of interest;

the sample unit size (representative of the size of the population), densities, and
spatial distribution of the objects being sampled must be verified; and

the data must be tested in order to establish the nature of error variation to enable
decision on whether the transformation of the data, the utilisation of distribution
free statistical analysis procedures, or the test against simulated zero hypothesis
data is necessary.
The sampling plan should include, inter alia, (Hoffmann, 1992):

place, location, and position of sampling;

size, quantity, and volume of the sample;

number of samples;

date, duration and frequency of sampling;

homogeneity, contamination and decontamination of the sample;

sample conservation and storage; and
30

the type of sample [discrete, composite, active (grab or multilevel) or passive
(diffusion)].
2.4.2
Surface water and sediment sampling
Adsorption of organic pollutants to the sampling containers and possible contamination of
the sample results in biased result. The degree of contamination depends on the type of
sampling material, the hydrophobicity and concentrations of the contaminants and the
contact time between them (Hildebrandt, Larcorte & Barcelo, 2006). To avoid sample
contamination, sampling devices should be carefully selected according to sample matrix
and target compounds (Tables 2.3 to 2.5).
When sampling for PAHs, the US EPA (1982)4 recommends avoiding plastic materials
since they contain phthalates and other additives that might migrate to the sample, thus
contaminating it. In addition, plastics are porous, and lead to losses of volatile
compounds, and the surface facilitates the growth of microorganisms, leading to
degradation of target compounds. When glass containers are used for sample collection,
it is recommended that 10% methanol be added to the sample, to prevent highly lipophilic
compounds from being adsorbed. However, the addition of methanol is strongly
dependent on the organic compounds and the analytical extraction procedure to be
employed. It is not recommended for very polar/water soluble compounds or if liquid-liquid
extraction is to be employed (Hildebrandt, Larcorte & Barcelo, 2006).
According to the US EPA (1982), water samples containing organic compounds should be
analysed within 15 days. After sampling, the samples should be stored at 4°C and
acidification might be necessary (ascorbic, hydrochloric, phosphoric acid) to inhibit
bacterial growth. With regards to the sampling of surface water, the sample can be
collected from several points, depending on availability. Streams that are normally shallow
are more subject to bank sampling, while river water should be collected from boats,
bridges or from a shore platform (US EPA, 1983)2. The US EPA (1983) recommends that
sampling should be performed either upstream or downstream of agricultural, industrial
areas, urban discharge, inflows, and outflows of lakes, dams, waste water treatment
plants (WWTPs) or water supply units. It also recommends sampling in main streams of
rivers, lakes or dams, or at the mouths of significant tributaries.
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31
Sediments are unconsolidated material transported by surface water or deposited under it.
They can have different textures ranging from thin dust to thick gravel. The composition
percentage, organic matter content, pH, permeability and cation exchange capacity will
modify the characteristics of the sediment, thus influencing its content of pollutants.
Sediments have a tendency to retain and accumulate the most hydrophobic compounds
carried by the surface water body.
32
Table 2.3: Strengths and weaknesses of environmental sampling devices for water (Hildebrandt, Larcorte & Barcelo, 2006)
Construction
Strengths
Weaknesses
Use
Glass (bottle)
Simple, inexpensive, easy to clean
Fixed capacity, breakable, no depth
All water types
control
Glass (syringe)
Simple, inexpensive
Little sample volume, breakable, no
All water types
depth control
SS(bottle)
Simple, easy to clean, unbreakable
Fixed capacity, breakable, no depth
All water types
control
33
Teflon and/SS (bottle)
Vertical sampling profile
Fixed capacity
Ground water, lakes and dams
Teflon and/SS (bottle)
Horizontal sampling profile
Fixed capacity, heavy
rivers
SS + tubing material (pumps)
Continous sampling, large volume
Possible loss of VOCs
All water types
Hydrophobic membrane,
Commercially available, well tested
Susceptible to biofouling
All water types, passive sampling
Commercially available, well tested
Scarcely used in in situ sampling
All water types, passive sampling
No biofouling
Poor sensitivity for strong
All water types, passive sampling
hydrophobic filling SPMD
C-18 Empore disks
or other sorbents
(Chemcatcher)
Hydrophilic membrane,
hydrophobic filling
hydrophobic compounds
SS Stainless steel, SPMD semi-permeable membrane device, VOCs volatile organic compounds.
Table 2.4: Strengths and weaknesses of environmental sampling devices for sediments (Hildebrandt, Larcorte & Barcelo, 2006)
Sampling device
Strengths
Weaknesses
Use
Spoon (SS)
Simple, inexpensive, easy to
Fine material may wash out, little
Streams, shallow rivers
clean
sample volume
Simple, easy to clean
Poor jaw shape, stones and branches
Van Veen, Ponar, Petersen
(SS)
Ekman (SS)
Calm waters
may interfere with jaw closure
Simple, low sample disturbance
Small sample
Calm waters
Heavy, may need mechanical
Shipek (SS)
Low sample disturbance
help/boat
All sediment types
Heavy, may need mechanical
Box (SS)
Low sample disturbance
help/boat
All sediment types
Difficulties in sampling sands
Shallow waters
Heavy, difficulties when sampling
All sediment types
34
large sample size
Corer (SS/Teflon)
Simple, inexpensive, easy to
clean, undisturbed profiles
Gravity corer (SS)
Undisturbed profiles
sands
Vibro corer (SS)
Undisturbed profiles
Heavy, difficulties when sampling
sands, needs power supply
SS Stainless steel
All sediment types
Table 2.5: Sampling devices for water and sediment samples and their applications
Sampling device
Construction
Sample matrix
Target compounds
References
Sub-surface grab device
Glass
River
Pesticides and chlorophenols
(De Almeida et al., 2000)
compounds,
Sub-surface grab device
Polypropylene
River (15 cm below surface)
PFOS and PFOA (polyF)
(Hansen et al., 2002)
Sub-surface grab device
Teflon and glass
River (1 m below surface)
Pharmaceuticals
(Wiegel et al., 2004)
Sub-surface grab device
SS
River
Pharmaceuticals and pesticides
(Comoretto & Chiron, 2005)
Niskins bottle
SS
River
VOCs
( art ne et al., 2002)
Syringe (and tubing)
Glass (and nylon)
Ground water
PAHs, BTEX and OC
(Martin et al., 2003)
PAHs, BTEX, OC, alkylBzs and
35
Pump
SS and PVC
Ground water
others
(Imbrigiotta et al., 1998)
Multilevel well
Polyethylene
Ground water
MTBE, BTEX and other VOCs
(Rosell et al., 2005)
Gravity corer
Unspecified
Lake sediment
OC
(Imbrigiotta et al., 1998)
(Ricking, Schwarzbauer &
Ekman grab
SS
River sediment (0-20 cm)
Different organic compounds
Franke, 2003)
Ekman grab
SS
River sediment (0-10 cm)
Phthalates
(Chang, Liao & Yuan, 2005)
Core sampler
Unspecified
River sediment (0-2 m)
Different organic compounds
(Ricking,
Schwarzbauer
Franke, 2003)
Gravity corer
Unspecified
River sediment (0-50 cm)
PCBs, DDTs and PBDEs
(Punning et al., 2004)
SS stainless steel, PET polyethylene terephthalate, PE polyethylene, PFOS perfluorooctane sulphonate, PFOA perfluorooctanoate, VOCs volatile organic
compounds, PAH polycyclic aromatic hydrocarbons, BTEX benzene, toulene, ethylbenzene, xylenes, OC organochlorinated compounds, alkylBzs
alkylbenzene compounds, MTBE methyl-tert-butylether, SPMD semi-permeable membrane device, PCB polychlorinated biphenyls, HCB
hexachlorobenzene, PCDD polychlorinated dibenzo-p-dioxin, PCDF polychlorinated dibenzofuran, PCN polychlorinated naphthalene, DDT dichlorodiphenyl-trichloroethane, PBDE polybrominated diphenyl ethers
&
The criteria for selection of appropriate sediment monitoring points are different from the
sampling of other environmental samples (Hildebrandt, Larcorte & Barcelo, 2006). Firstly,
sediment samples are not always present within a river basin and secondly, they must fulfil
some physical specifications including grain size and clay content. Jones (2001)
recommended that the fine material content (< 0.06 mm) must be above 30% to obtain an
equilibrated sample with enough surface exchange with water. As with water, sediment
samples are collected from boats, platforms, bridges or by wading in streams (US EPA,
1983). Special precautions should be taken to avoid disturbance of the sediment layer. The
sampling equipment must be selected according to the sampling site and sample type
(discrete or continuous).
2.5
Analysis of polycyclic aromatic hydrocarbons
2.5.1
Extraction or seperation methods
To enable the determination of very low concentrations/trace or ultra trace amounts of
pollutants in the environment, the following series of operations are recommended (Lanças,
2003):

isolation (extraction and separation) of target chemicals from the sample matrix (e.g.
water and sediment);

separation and purification of the target chemicals from co-extracted, non-target
chemicals (sample clean-up);

sample pre-concentration; and

measurement by GC-MS, a highly selective and sensitive analytical instrument.
According to Fritz and Masso (2001), 60% of the time involved in an analytical process is
spent on sample preparation, while a mere 7% is spent on sample analysis using an
analytical instrument. As a result, faster and more efficient sample pre-treatment methods
are gaining recognition.
Trace or ultra-trace determination of organic pollutants, specifically PAHs, is hindered by two
major obstacles. The first is that the concentration of the organic pollutant is, in most cases,
too low for determination by direct injection into the GC-MS, thus necessitating preconcentration of the sample. Secondly, water is not compatible with most GC column
stationary phases, therefore its injection into the GC column should be avoided. To
circumvent these obstacles, a number of different methods for transferring the analytes from
36
a large volume of water to a small volume of an organic solvent (phase switching) have been
developed.
The determination of semi-volatile organic compounds, including PAHs, in water, traditionally
involves the use of conventional techniques, including liquid-liquid extraction (LLE) and solid
phase extraction (SPE).
Liquid-liquid extraction makes use of non-polar solvents, which are immiscible with water, to
extract the target compound from water by using the greater solubility of the target
compound in the organic solvent, rather than in water. The target compound is selectively
extracted by employing a solvent with a polarity similar to that of the target compound.
A wide range of volatile solvents, including hexane, benzene, ether, ethyl acetate, and
dichloromethane, are usually employed for the extraction of semi-volatile compounds,
including PAHs (Junk, Avery & Richard, 1988; Tan, 1992). Hexane is suitable for extraction
of non-polar compounds such as aliphatic hydrocarbons, while benzene is suitable for
aromatic compounds, and ether and ethyl acetate are suitable for relatively polar compounds
containing oxygen. Dichloromethane has high extraction efficiency for a wide range of nonpolar to polar compounds. In addition, its boiling point is low and hence is easy to remove
after extraction. The solvent separates readily from water, because of its higher specific
gravity. Although dichloromethane has the advantage of being non-flammable, it is
carcinogenic as is benzene. Recent trends have been to avoid using these solvents in LLEs
(Chen & Pawliszyn, 1995; Powell, 1995; King, Readman & Zhou, 2003; Hawthorne et al.,
2005).
Liquid-liquid extraction is a time-consuming method and requires large amounts of toxic
solvents (Tavakoli et al., 2008). In addition, it is not amenable to automation. This is an
important consideration, since automation reduces labour costs and improves turnaround
time. The technique also generates a large volume of waste solvent, the disposal of which
adds significantly to the cost of analysis (Powell, 1995).
An ideal solution to the problems associated with LLE is the use of solid-phase extraction
(SPE), a technique that is easily automated and requires the use of relatively low volumes of
organic solvents. One of the principal characteristics of SPE is its selectivity. By a judicious
choice of bonded phase, sample pH, ionic strength and elution conditions, the analyst can
control the classes of compounds that are isolated from the matrix (Newman, 1992).
However, in order to replace dichloromethane extraction, the SPE sorbent would have to be
37
capable of retaining (and subsequently releasing to the elution solvent) a wide range of
different compounds.
The SPE technique is limited to semi-volatile compounds, because the boiling points of the
analytes must be substantially above that of the solvents (Eisert & Levsen, 1996; Manoli &
Samara, 1999; Santos & Galceran, 2002). Furthermore, the surface chemistry, and therefore
sorption properties of solid phases, is not as reproducible as solvent properties, thus
interfering with the recovery of analytes. This is as a consequence of mixed retention
mechanisms involving low selective sorption by the bonded phase and ion exchange
interactions with accessible dissociated silanol groups of the silica substrate (Poole, 2003).
Solid phases tend to have a greater degree of contamination from manufacturing and
packaging materials than the solvents in LLE. The impurities provide a chemical background
that may interfere with the subsequent determination of the analytes. Sample processing
problems associated with SPE are related to the limited sorption capacity of sorbents and
analyte displacement, including plugging of sorbent pores by matrix components.
Over the years, several other extraction methods have been developed and adopted. These
methods include homogeneous liquid-liquid extraction (HLLE) (Tavakoli et al., 2008), solidphase microextraction (SPME) (Djozan, Assadi & Haddadi, 2001; Djozan, Pournaghi-Azar &
Bahar, 2004), headspace solvent microextraction (HSME) (Arthur & Pawliszyn, 1990), single
drop microextraction (SDME) (Ahmadi et al., 2006), supercritical fluid extraction (SFE)
(Heemken, Theobald & Wenclawiak, 1997), pressurised liquid extraction (PLE) (Heemken et
al., 1997), microwave assisted extraction (MAE) (Heemken et al., 1997) and cloud point
extraction (CPE) (Tavakoli et al., 2008). Another well studied extracion method for PAHs in
water is strir bar sorptive extraction (SBSE) (Popp, Bauer & Wennrich, 2001).
The first instrumental technique to be introduced was SFE. It is based on gas-like and liquidlike properties of a supercritical fluid, usually carbon dioxide. Initially, the technique was
limited to the extraction of non-polar compounds. However, the use of organic modifiers has
enabled the technique to be used for the extraction of compounds with a wide range of
polarities. The technique is environmentally friendly, since it uses carbon dioxide as a
solvent.
A number of methods have been developed and standardized for the extraction of PAHs
from solids, including sediments, soils, sludge, and waste, applying soxhlet extraction,
38
automated soxhlet extraction, SFE, fluidized bed extraction (FBE) and PLE (US EPA,
2006)5.
2.5.2
Solid phase extraction
Solid phase extraction is a sample treatment technique in which a liquid sample is passed
through a sorbent. Both the analytes to be determined and compounds that may interfere
with the determination of the analytes are retained on the sorbent by different mechanisms.
The analytes are eluted in a small volume of a solvent and hence, the analytes are
concentrated; secondly, the function of the solid-phase extraction is to clean the sample. The
first case is mainly used for liquid samples and the second for solids, gases or liquids,
usually after another sample-treatment technique. Therefore, SPE is extremely versatile
since it can be applied to a wide range of samples (Marce & Borrull, 2000).
The types of SPE can be classified similarly to those of liquid chromatography (LC), that is,
normal phase, reversed phase and ion exchange. Reversed phase separations involve a
polar (usually aqueous) or moderately polar sample matrix (mobile phase) and a non-polar
stationary phase. The analyte of interest is typically mid- to non-polar. The retention of
hydrophobic analytes during the percolation step is through London forces between the
carbon and hydrogen bonds in analyte molecule and the hydrophobic functional groups
bonded to the silica surface (Pichon, 2000). Several SPE materials, including the alkyl- or
aryl-bonded silicates, fall under the reversed phase category.
Cartridge and disc SPE devices are most widely applied for the extraction of PAHs from
various environmental liquid matrices. These devices use the same sorbent technology, with
the only distinguishing feature being their difference in shape.
Sample clean-up is introduced in the sample preparation procedure as a way of purifying or
enriching the sample and thus improving on sensitivity. It is achieved by the removal of nontarget bulk matrix interferences. The matrix interferences can damage the analytical system
and suppress, or mask, the analyte signal.
The required sample clean-up depends on the selectivity of the applied extraction technique
and on the chromatographic method to be applied (Wenzl et al., 2006). The extraction of
PAHs from sediment samples makes use of non-polar solvents. Consequently, co-
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39
extractives, humic acid and aliphatic hydrocarbons, are also dissolved. However, these
interfering compounds are frequently removed by clean-up procedures, including adsorption
chromatography with silica gel, alumina or florisil. These procedures are described in a US
EPA method (US EPA, 2006).
2.6
2.6.1
Methods of analysis
Gas chromatography mass spectrometry
The chromatographic separation and detection of the PAHs is performed by GC-MS,
operated in selected ion monitoring (SIM) mode, and by GC coupled to a flame ionisation
detector (FID), or by HPLC with fluorescence detection (FLD). Although methods devised by
the US EPA are widely used, standard procedures have also been published by the
International Organisation for Standardisation (ISO). The ISO standard, 13877:1998,
describes a method for the determination of PAHs in soil by HPLC, whereas the recently
published standard, ISO 18287:2006, specifies a GC-MS method for the determination of
PAHs in soil.6
Tandem mass spectrometry (MS/MS) has
recently become more important for
environmental analysis. The MS fragmentation pattern obtained from MS-MS allows for
compound verification with great confidence, even when chromatographic separation is not
good, hence short chromatographic times can be used (Fernandez et al., 2001). Frenich, et
al. (2010) developed a GC/MS/MS method for the determination of 24 PAHs in airborne
particulate matter after ultrasonic extraction (USE) and pressurized liquid extraction (PLE).
The popularity of capillary GC for the determination of PAHs is based on a favourable
combination of greater selectivity, resolution, and sensitivity, compared to that achieved with
HPLC (Fetzer, 1989; Santos & Galceran, 2002). In analysis of complex samples including
carbon black, coal tars, and shale oils by capillary GC, it is not uncommon to resolve
hundreds of components (Fetzer, 1989), whereas in LC a practical limit is a few dozen
components, because of the limited peak capacity of LC columns. The easy use and
compatibility of GC with mass spectrometers (GC-MS) are additional reasons for the
selection of GC in preference to LC for the determination of PAHs in environmental samples.
Other advantages are that PAHs tend to have thermal properties amenable to GC, and MS
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40
techniques are fairly sensitive due to the projection of large molecular ion peaks or little
fragmentation in common MS sources.
Methyl and phenyl substituted polysiloxanes are the most popular capillary column stationary
phases for separation of PAHs in environmental samples. These columns possess relatively
low background from column bleed, even at high temperatures above 300°C even when
using non-selective detectors such as the FID (Poster et al., 2006). Columns containing
liquid crystalline stationary phases have been specifically developed for the seperation of
PAHs. These columns are selective to molecular shape thus enabling the separation of PAH
isomers such as chrysene and triphenylene; benzo(b)-and benzo(j)fluoranthene; dibenz(a,c)and dibenz(a,h)anthracene that typically coelute on traditional 5% phenyl methylpolysiloxane
columns (Poster, Sander & Wise, 1998; Wise et al., 2000; Poster et al., 2004; Wise et al.,
2004).
Furthermore,
higher-molecular-weight
PAHs,
including
dibenz(a,c)-and
dibenz(a,h)anthracene, coronene, and the six-ring C24H14 (molecular mass 302) can be
determined by use of these liquid-crystalline phase GC columns (Schubert et al., 2003).
2.6.2
Detection of PAHs
Electron impact (EI) ionisation is the most widely used method for the production of ions. It
produces both molecular and fragment ions. The resulting ions are scanned in a mass
analyser giving rise to a mass spectrum. Compound identification is accomplished by
comparing an unknown electron impact ionisation mass spectrum with reference spectra
found in spectral libraries or spectra obtained with authentic standards. The National Institute
of Science and Technology (NIST) library contains 275 000 spectra that can be used for
compound identification (Santos & Galceran, 2003).
As a result of the enhanced sensitivity in GC-MS, environmental chemists are in a position to
quantify and identify trace to ultra trace quantities of PAHs in various environmental
compartments. The improved sensitivity of GC-MS emanates from its operation in the SIM
mode by limiting the mass of the ions detected to one or more specific fragment ions of
known mass. This eliminates a large portion of noise that exists in the full scan mode. It also
allows overlapping peaks to be ''resolved'' resulting in unambiguous identification.
Numerous GC-MS applications make use of capillary GC with quadrupole MS detection and
electron impact ionisation (EI). Nevertheless, there are number of applications utilising other
types of mass spectrometers. The figures of merit of a method for the separation and
determination of PAHs using an ion-trap MS were compared with those of GC-MS using a
41
quadrupole and SIM techniques, and LC–fluorimetric methods. Ion trap detection gave lower
detection limits than GC-MS or LC–fluorimetry (Castello & Gerbino, 1993). In contrast to a
quadrupole, there is no sensitivity advantage obtained by using SIM in the ion-trap.
Consequently, all samples can be scanned in full-scan mode with little or no additional effort.
Time of flight (TOF-MS) with GC and LC sample introduction has become increasingly
common in PAH analysis (Koester & Moulik, 2005). The novel aspect of this detection mode
is the accuracy in mass measurements and fast full-scan data acquisition. In addition,
quantitative data can also be obtained. The high mass resolving power of TOF-MS enables
better structural elucidations and enhancd signal-to-noise ratios than single quadrupole
analyses. This is especially so for complex environmental samples samples.
Source apportonments of PAHs to the environment have been accomplished by using
carbon isotope measurements of individual PAHs using GC-isotope ratio mass spectrometry
(IRMS) (O'Malley, Abrajano & Hellou, 1996). Compound-specific carbon-isotope (13C/12C)
ratios enables the quantitative assessment of PAH sources in natural environments such as
soils and sediments from marine, lacustrine, and terrestrial environments (O'Malley,
Abrajano & Hellou, 1996; Kim, Kennicutt & Qian, 2005). These specialized measurements
were conducted by use of GC, coupled to an isotope-ratio magnetic sector MS.
2.7
2.7.1
Pollution of sediments by metals
Methods for estimating metal pollutant impact
A crucial first step in evaluating the impact of sediment pollution and the level of
contamination affecting a given area is to establish a reference background or baseline
sample of known metal composition (Abrahim & Parker, 2008). Methods for establishing
background levels include the use of average crustal values as reference concentrations,
and the analysis of comparable local sediment unaffected by anthropogenic activity.
In earlier environmental studies, a common method for comparing sediment metal
concentrations with background levels, involved the determination of contemporary metal
levels by measuring their concentrations in standard earth materials including average shale
(Turekian & Wedepohl, 1961), or average crustal values (Taylor, 1964). The main
disadvantage of using average earth or crustal levels is that they are not affected by natural
geochemical variability. This may lead to false anomalies being recognised or anomalous
concentrations above the pristine local background may not be recognised at all (Covelli &
42
Fontolan, 1997). Furthermore, crustal values represent bulk material thus undermining
comparison with “fine fraction” sediment concentrations (Abrahim & Parker, 2008).
Another realistic approach to establish reference values has been suggested. This approach
involves comparison of concentrations of the target elements in contaminated and
uncontaminated sediments that are mineralogically and texturally similar (Salomons &
Förstner, 1984; Hornung, Krom & Cohen, 1989; Williamson et al., 1992; Abrahim & Parker,
2008). This can best be achieved in sediments, by comparing the pollutant concentrations in
the upper sediment layers with concentration in the deeper layers of the same core
(Faganeli et al., 1991; Siegel, Slaboda & Stanley, 1994).
2.7.2
Enrichment factors (EFs)
A number of calculation methods have been proposed for quantifying the degree of metal
enrichment in sediments (Ridgway & Shimmield, 2002). Three calculation methods, each
with its merits and demerits, have found widespread application in environmental analysis.
These are: enrichment factors (EF), geoaccumulation index, l geo, (Müller, 1969) and degree
of contamination, Cd, (Hakanson, 1980). However, the Hakanson (1980) formula for C d is
restricted to the seven metals (As, Cd, Cu, Cr, Hg, Pb, Zn) plus PCBs, specified in
Hakanson's study. Furthermore, all eight species must be analysed in order to calculate the
correct Cd for the range of classes defined by Hakanson. Abrahim (2005) proposed a
modified and more general form of the Hakanson (1980) equation for the calculation of the
overall degree of contamination at a given sampling or coring site, the modified degree of
contamination, mCd. The mCd allows the incorporation of as many metals as the study may
analyse with no upper limit.
The most popular approach of estimating the anthropogenic impact on sediments is the
calculation of normalised enrichment factors (EF) for metal concentrations above the
background level. By normalisation, the EF calculation seeks to reduce the metal variability
associated with mud/sand ratios and is a convenient tool for plotting geochemical trends
across large geographic areas, which may have substantial variations in the mud (i.e. clay
rich) to sand ratios (Salomons & Forstner, 1984; Hornung, Krom & Cohen, 1989; Dickinson,
Dunbar & McLeod, 1996).
The measured heavy metal content is normalised with respect to a sample reference metal
such as Fe or Al (Ravichandran et al., 1995). In this approach, the Fe or Al is considered to
43
act as a “proxy” for the clay content (Windom, Smith & Rawlinson, 1989; Din, 1992). The EF
is calculated according to the following equation:
EF = MxFeb/MbFe x
Eq. 2.7
where, Mx and Fex are the sediment sample concentrations of the heavy metal and Fe (or
other normalising element), while Mb and Feb are their concentrations in a suitable
background or baseline reference material.
2.8
2.8.1
Determination of metal fractionation in sediments
Sequential extraction procedures
Trace amounts of metals are common in water and these are normally not harmful to health.
In fact, some metals are essential to sustain life. Cobalt, Cu, Fe, Mn, Mo, Se, and Zn are
needed at low levels as catalysts for enzyme activities. However, consumption of water
containing high levels of these essential metals, including Ag, Al, As, Ba, Cd, Cr, Hg, Pb and
Se, may cause acute or chronic toxicity (Ferner, 2001).
Anthropogenic trace element contamination of various environments is a persistent problem
in industrial societies like South Africa. These pollutants are non-biodegradable and they
tend to accumulate in the upper layers of stream or river beds in chemical forms that are
often more reactive, depending on the physicochemical conditions prevailing in the aquatic
system, than native the ones (Gleyzes, Tellier & Astruc, 2002). Therefore, sediments
constitute reservoirs of bioavailable trace elements that can lead to a bioaccumulation of
toxic elements in the food chain, and induce perturbation of the ecosystem with adverse
health effects.
It is widely known that the toxicity and the mobility of trace elements in sediments is a
function of their specific chemical forms and on their binding state (Gleyzes, Tellier & Astruc,
2002). Consequently, changes in environmental conditions, including pH, changes in the
redox potential conditions or increases in organic ligand concentrations, can cause traceelement mobilisation from the sediment to the liquid phase and thus contaminating the
surrounding waters. Sediments are composed of a wide range of geochemical phases to
which these trace elements can bind (Mantei & Foster, 1991). Hence, the elucidation of the
geochemical phases associated with trace elements in sediments assists in understanding
44
geochemical processes in order to evaluate the remobilisation potential and the risks
involved (Buckley & Winters, 1992).
The fractionation of trace elements in various geochemical phases can be studied using
sequential selective extractions. The sequential extraction schemes involve the use of a
series of more or less selective reagents. These reagents successively mobilise metals
associated with the different mineralogical fractions. They are intended to simulate the
various possible natural and anthropogenic modifications of environmental conditions.
Trace metals in sediments can be (Relic et al., 2005):

adsorbed on the particle surface of clays, iron-and manganese-oxyhdroxides or
organic matter;

present in the lattice of secondary minerals, including carbonates or sulfides;

occluded in amorphous iron and manganese oxyhdroxides; sulfides or remains of
biological organisms; and

occluded in the lattice of primary minerals.
Trace metals in sediments can be classified on the basis of primary mechanism of
accumulation (Salomons & Forstner, 1980). These are:

adsorptive and exchangeable;

bound to carbonate phases (acid soluble fraction);

bound to Fe and Mn-oxides (reducible fraction); and

bound to organic matter and sulfides (oxidisable fraction), or

bound to detrital or lattice metals.
2.8.1.1 Exchangeable fraction
Sequential extraction, which is one of the most widely-applied procedures, was first
proposed by Tessier, Campbell and Bisson (1979). It enables the partitioning of elements
into five operationally defined geochemical fractions including:

exchangeable; carbonates (acid-soluble);

Fe and Mn-oxides (reducible);

organic matter (oxidisable); and

a residual fraction.
45
In the exchangeable fraction, metal species are weakly sorbed to the sediment surface by
relatively weak electrostatic forces. These metals can be released into solution by ion
exchange processes. The extraction of metals in the exchangeable fraction is usually
accomplished by employing electrolytes. These may be salts of strong acids and bases or
salts of weak acids and bases at pH 7, with MgCl2 and ethanoate salts being the most widely
used (Tessier, Campbell & Bisson, 1979; Rauret, 1998). The Mg2+ ion has a strong ion
exchange capacity, while the Cl- ion has low propensity to form complexes (Krishnamurti et
al., 1995).
The use of acetate salts (particularly NH4OAc) in metal partitioning studies has also been
reported. Although divalent cations are generally more effective than monovalent cations as
extractants of metals in the exchangeable phase, the NH4+ ion has been shown to enhance
the extraction of ions in the interlayer exchange sites of minerals including vermiculite. Metal
ions complexed to the acetate ions are slightly more stable than chloro-complexes from
MgCl2; this promotes the exchange process and reduces re-adsorption or precipitation of the
extracted metals (Gleyzes, Tellier & Astruc, 2002).
2.8.1.2 Acid soluble fraction
The metals that are extracted from the acid soluble fraction are typically co-precipitated with
carbonates and those sorbed to some sites of the surface of clays, organic matter and
Fe/Mn oxyhydroxides. This fraction is sensitive to pH changes, and metal mobilisation is
accomplished at a pH close to 5. A buffered acetic acid/sodium acetate solution is generally
employed as an extractant. Furthermore, by lowering the pH from 7 (pH of the extracting
solution used in the first step) trace metals ions that escaped extraction in the extraction of
exchangeable metals are released (Gleyzes, Tellier & Astruc, 2002).
In order to obtain a relatively high percentage of recovery (90%) in this step, Sheppard and
Stephenson (1997) recommended the use of a minimal solid/liquid ratio (1:25). Ma and Uren
(1995) preferred to use a more concentrated sodium acetate solution (0.5 M) at pH 4.74 in
order to achieve a relatively high dissolution (>90%) of the carbonate phase.
2.8.1.3 Reducible fraction
Iron and Mn-oxides occur in various physical forms and their presence in sediment strongly
influences the levels of trace metals due to their tendency to adsorb or co-precipitate metals
from solution. The interaction of trace metals with precipitated Fe- and Mn-oxides varies
46
from relatively strongly bound (e.g. occluded) in crystalline oxides through moderately fixed
(e.g. amorphous oxides) forms to loosely adsorbed forms (Pickering, 1986). By carefully
adjusting the pH and Eh of reagents, extraction of some or all metal-oxide phases can be
achieved. The ideal reagent for extracting the total amount of metal ion in the reducible
phase consists of a reducing agent and a ligand able to retain released ions in a soluble
form. Gleyzes, Tellier and Astruc (2002) recommend a combination of hydroxylamine and
acetic acid.
2.8.1.4 Oxidising fraction
Trace metals may also be incorporated into many forms of organic matter. In sediments, the
organic matter consists mainly of humic substances, while carbohydrates, proteins, peptides,
amino acids, fats, waxes and resins constitute a minor portion. Under oxidising conditions,
this organic material has a propensity to be degraded via oxidation, resulting in the release
of sorbed metals. Hydrogen peroxide and NaOCI are widely used reagents for extracting
metals in this phase. However, some oxidising agents, like H2O2, may not completely attack
organic matter and they tend to simultaneously oxidise any sulfides present.
2.8.1.5 Residual fraction
The residual fraction consists mostly of primary and secondary minerals containing metals in
the crystalline lattice. The destruction of this fraction is achieved by digestion with strong
acids including HF, HClO4, HCI and HNO3.
The application of sequential extraction is still subject to much controversy. One of the
problems associated with sequential extraction schemes is the non-selectivity of the
reagents and the redistribution of trace elements among the different geochemical phases
during the extraction process (Tack & Verloo, 1995). An alternative approach to sequential
extraction employs ion exchangers (Gleyzes, Tellier & Astruc, 2002). The ion exchangers
are brought into contact with sediment suspensions. Trace metals are then adsorbed, eluted
from the resin and subsequently quantified. This approach enables the differentiation of
metals associated with non-labile and labile fractions in the sediments. By applying resins
with different properties, the labile fraction can further be differentiated into a low-pH labile, a
weak-acid labile, and a readily desorbed fraction. In contrast to sequential extraction, the ion
exchange approah tends to be more selective towards metal species that are of biological
importance. A draw back of the procedure is that it is inherently cumbersome (Akcay, Oguz
& Karapire, 2003).
47
2.9
Review of sediment metal fractionation studies
Few studies have been reported on metal fractionation in sediments in conjunction with
statistical analysis. Most reported studies focussed on soils. Since sediments are, for all
intents and purposes, similar to soils, such fractionation studies with multivariate tools to
decipher associations and sources of metals can be extended to sediments. However, a
number of studies describing sediment contamination by heavy metals have been published.
Abrahim and Parker (2008) collected eight sediment cores from the Tamaki estuary in
Auckland, New Zealand, and analysed them for the presence of Cd, Cu, Pb and Zn. In the
study, the use of depth profiling, enrichment factors, geoaccumulation index and the degree
of contamination to determine the extent of sediment contamination, was reviewed. The
assessment of heavy metal pollution in sediments requires knowledge of pre-anthropogenic
metal concentrations to act as a reference against which the measured values are
compared. By using depth profiling, the authors were able to determine that there was a
significant upward enrichment of the metals, with the highest concentrations occurring in the
upper-most (0-10 cm) layer.
Sediment samples were collected in rivers and harbours around Lake Balaton, in Hungary,
and its catchment area (Weisz, Polyák & Hlavay, 2000). A modified four-step sequential
leaching procedure was applied for determination of the distribution of elements, As, Cd, Cu,
Cr, Mn, Ni, Pb and Zn. The concentrations of the elements were mostly well below the
Hungarian standards set for soils and geochemical background values. Most of the elements
were reported to occur in the acid-soluble residue and bound to organic matter/sulfide
fractions. Generally, the researchers found elemental concentrations in sediments inside of
the lake to be less than in the catchment area. The incorporation of multivariate statistical
analysis with correlation analysis could have aided in deciphering the associations and
sources of the metals in the sediments.
A study was conducted to examine total metal contents in bed sediments from a 5.8 km
stretch of Manoa Stream, Hawaii (Sutherland, 2000). A total of 123 samples were analysed
for 18 elements and 14 samples for 21 elements. All trace metal data, calculations of
enrichment ratios and the modified index of geoaccumulation pointed to mineralogical
control for Cr and Ni; minor anthropogenic contamination for Ba, Cd, Cu, Hg and Zn; and a
very strong contamination by Pb. Lead had the highest non-residual component of elements
examined, dominantly in the reducible phase associated with Mn and amorphous Fe
48
oxyhydroxides. Statistical analysis of the generated data could have shed some light on the
sources and associations of the various metal phases.
In a study carried out by Borovec (1996), a sequential extraction procedure was applied and
FA was employed to identify dominant types of components of sediments from the Elbe
River, Czech Republic, containing high levels of Ag, As, Be, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb
and Zn. The sequential extraction procedure partitioned the metals to those in the
exchangeable phase. Those adsorbed to carbonates, bonded in reducible non-or poorly
crystalline MnO/hydrous oxide phases, bonded in reducible non-or poorly crystalline
FeyOx/hydrous oxide phases, bonded in organics/oxidizable sulfides and bonded in
lithogenic material. Factor analysis revealed three factors accounting for 78% of the total
variance. These three factors had significant loadings from pollutants in (a) kaolanite, fine
grained lithogenic material and non- or partially crystalline iron oxide/hydrous oxide phases
(reducible), (b) detrius with organics (oxidisable), and (c) non, or partially crystalline
MnO/hydrous oxide phases (reducible). Results from the sequential extraction revealed that
more than 70% of the total metals in the sediments had the potential to be mobilised with a
decrease in pH or under oxidising or reducing conditions. Two main elemental associations
were apparent from the cluster analysis. The first group of association consisted of Cd, Fe,
Mn, Ni, Pb and Zn, while the second group consisted of As, Ag, Be, Co, Cr and Cu.
A five steps sequential extraction procedure was applied on alluvial sediments from the
Danube River, Serbia (Relic et al., 2005). The sequential extraction procedure partitioned
metals (Ni, Zn, Pb and Cu with Fe- and Mn-oxides) into:

extractable;

carbonate extractable and easily reducible;

moderately reducible;

organic extractable; and

residual fractions.
Extracted concentrations (mg/kg) of trace metals, analysed after all five steps, were found to
be for Mn: 656, Fe: 26734, Ni: 32.3, Zn: 72.8, Pb: 13.4 and Cu: 27.0. Relic et al. (2005)
found most of the elements to exist in the residual fraction. Non-residual fractions of trace
metals (sum of the first four fractions) were subjected to statistical analysis (correlation
analysis, FA and cluster analysis) since they pose more environmental risks than the
residual amount. The use of these statistical tools enabled the researchers to understand
and visualize the associations between the non-residual fractions of trace metals and Fe-and
49
Mn-oxides within the analysed sediments. Iron-and Mn-oxides are known to play an
important role in trace metal sorption within aquatic systems. The main substrates for the
non-residual fraction of Ni and Cu were Mn-oxides, while the remainder of trace metals
within these non-residual fractions was associated with the Fe-oxides. From the high positive
correlations between Ni and Cu found within non-residual fractions and those in the residual
fraction, the authors concluded that Ni and Cu originated from the geochemical background.
In contrast, negative correlations of Zn and Pb between non-residual fractions and
corresponding residual fractions, indicated an anthropogenic source for these metals.
The mobility of the elements, As, Cd, Cu, Ni, Pb and Zn, in contaminated soils from an
abandoned mining area, has been evaluated by means of a sequential extraction scheme
and multivariate statistical analysis (Perez & Valiente, 2005). Partitioning of elements in each
sample was determined using the BCR sequential extraction procedure. Statistical analysis
of the ontained data by PCA, cluster analysis and FA enabled identification of groups of
samples with similar characteristics and observations of correlations between variables,
determining the pollution trends and distribution of metals within the study area. Analysis
revealed a high level of As, Cu, Cd, Pb and Cu contamination as a result of mining activity.
Three groups of samples, reflecting a greater degree of pollution were distinguished. From
partitioning data, the authors were able to conclude that Cd and Zn were the most mobile.
Sequential extraction data from the study also indicated that the risk of other hazadous
elements, including As and Pb, was related to their availability under reducing conditions.
Thirty five bottom sediment samples were collected from Wadi Al-Arab Dam, Jordan
(Ghrefat & Yusuf, 2006). The amounts of Cd, Cu, Fe, Mn, Zn, total organic matter (TOM)
and carbonate in the sediments were determined so as to assess the extent of
environmental pollution and to apportion sources of these pollutants. Data obtained was
processed using correlation analysis and FA. The low positive and negative correlations
among Cd, Cu, Fe, Mn, Zn, TOM and carbonate from correlation analysis and FA revealed
that there were different anthropogenic and natural sources. Enrichment factors and the
geoaccumulation indices were employed to assess sediment contamination. The calculation
of EFs indicated that Mn and Cu were depleted by factors of 0.76, and 1.33, respectively,
whereas Zn and Cd were enriched by 3.6, and 30, respectively. The results from
geoaccumulation index calculations revealed that sediments of Wadi Al-Arab were not
contaminated with Mn, Fe, and Cu, moderately contaminated with Zn, and strongly, to
extremely strongly, contaminated with Cd. Some of the elevated concentrations of Zn and
Cd were attributed to anthropogenic sources (agriculture and effluent treatment) within the
50
proximity of the dam. However, the researchers did not attempt to determine the
fractionation or bioavailability of these elements.
A similar approach was employed by Rubio, Nombela and Vilas (2000) while investigating
66 sediment samples from the Ria de Vigo, Spain. The samples were subjected to a total
digestion technique and analysed for major and trace elements (Al, As, Cd, Co, Cr, Fe, Mn,
Ni, Pb, Sr, Ti and Zn). Geoaccumulation indices and enrichment factors were computed to
assess
whether
the
concentrations
observed
represent
pre-civilisation
levels
or
contamination. It was concluded that the Ria de Vigo was slightly to moderately polluted by
some of the metals studied, Cr, Cu, Fe, Pb and Zn. Principal component analysis proved to
be a very useful tool to define background values for metals in the area. Three principal
components (PC) were identified, explaining 70% of the total variance. The first group
included a correlation of sand, calcium carbonate, gravel and Sr. This association was
assumed to be strongly controlled by the biogenic carbonates. A second group consisting of
Cu, Fe, Pb, Zn and organic matter, was accounted for by anthropogenic inputs and was
thought to reflect the complexing nature of the organic matter in the sediments. The
remaining elements, excluding Cr, constituted a third group that represents the geochemical
or background input in the area.
2.10 Inductively coupled plasma spectroscopy for metal determination
Various instruments are employed for the analysis of metals. Each instrument has
advantages and disadvantages. Most important for metal determination are atomic
absorption spectroscopy (AAS), X-ray fluorescence spectroscopy (XRF), inductively coupled
plasma-optical emission spectroscopy (ICP-OES), inductively coupled plasma-mass
spectrometry (ICP-MS) and neutron activation analysis (NAA). In this study ICP-OES and
MS were employed for elemental analysis.
Before the conception of ICP spectroscopy, metal analysis was mostly conducted by using
AAS. However, most of the applications fields assigned to AAS, using both flame and
graphite furnace atomic absorption spectrometry (GFAAS), have been relinquished to ICP
spectroscopy.
The main analytical advantages of the ICP over other excitation sources originate from its
capability for efficient and reproducible vaporization, atomisation, excitation, and ionisation,
for over a wide range of elements, in various sample matrices. This is mainly due to the high
temperature, 6000–7000 K, in the observation zones of the ICP. This temperature is much
51
higher than the maximum temperatures of flames or furnaces (2800 K) (Butcher, 2010). The
high temperature of the ICP also makes it capable of exciting refractory elements, and
renders it less prone to matrix interferences. Other electrical-discharge-based sources,
including alternating current and direct current arcs and sparks as well as microwave
induced plasma (MIP), also generate high temperatures for excitation and ionisation.
However, the ICP is typically less noisy and better able to handle liquid samples. In addition,
the ICP is an electrodeless source, so there is no contamination from the impurities present
in an electrode material (Tatro & Amarasiriwardena, 2006). Furthermore, it is relatively easy
to build an ICP assembly and it is relatively inexpensive, compared to some other sources,
including laser induced plasma (LIP). The most beneficial characteristics of the ICP source
include:

high electron density (1014–1016 cm-3);

appreciable degree of ionisation for many elements;

simultaneous multielement capability (over 70 elements including P and S);

low background emission, and relatively low chemical interference;

high stability leading to excellent accuracy and precision;

excellent detection limits for most elements (0.1–100 ng/mL);

wide linear dynamic range (LDR) (four to six orders of magnitude);

applicable to the refractory elements; and

cost-effective analyses (Hou & Jones, 2000).
Inductively coupled optical emission spectroscopic techniques have found wide-spread
applications in environmental analysis. Applications include the analysis of sediments, coal,
minerals, fossils, fossil fuel, ore, rocks, sediments, soils and water (Hou & Jones, 2000).
An instrument that requires a small sample, while still offering very low detection limits, a
wide linear dynamic range (to minimize the need for sample dilution), good precision and a
high sample throughput, would go a long way to satisfy many of the requirements of
environmental metal analysis. The ICP-MS appears to possess these attributes
(Beauchemin, 2010). The majority of ICP-MS applications involve the analysis of aqueous
samples, directly or following sample pretreatment, because of the advantages of working
with samples in solution.
Hall et al. (1996) evaluated the capability of the ICP-MS to be used in determining trace and
ultra-trace elements, including rare earth elements (REE) at their natural concentrations, in
stream and lake waters. The need to preconcentrate trace elements such as the first-row
52
transition elements and REEs was demonstrated and figures of merit given for the chelation
preconcentration method employing iminodiacetate resin. Method detection limits ranged
from 0.1 to1 ng/L for Cd, U and REEs, while detection limits of 2 to 3 ng/L were achieved for
Bi, Sb, Se and Te.
The incorporation of a dynamic reaction cell (DRC) has greatly improved figures of merit for
the analysis of trace elements by eliminating polyatomic or atomic interferences generated
by Ar or the sample matrix. Vais and Cornett (2004) used oxygen and ammonia as reaction
gases for the chemical separation between uranium and plutonium in the DRC in water
samples. The DRC has also enabled speciation of Cr in environmental samples to be
determined (Ambushe and McCrindle, 2008).
The hyphenation of ICP-MS to chromatographic instruments has enabled the speciation of
trace metals to be determined. Kuo, Jiang and Sahayam (2007) determined the speciation of
Cr(lll), Cr(Vl), V(V) and V(IV) in plant and sediment samples by interfacing HPLC to DRC
ICP-MS. They found the concentration of Cr(lll) and Cr(Vl) to be 45.2 and 44.9 µg/g,
respectively, while V(V) and V(IV) were available at concentrations of 26.0 and 46.8 µg/g,
respectively.
The application of laser ablation (LA) as a sample introduction technique into the ICP-MS
has been reviewed. Laser ablation has been shown to remove cumbersome sample
preparation such as acid digestion for sample analysis by ICP-MS. Other merits of LA-ICPMS are: improved detection limits, depth and spatial profiling of solid samples such as
sediments (Mokgalaka and Gardea‐Torresdey, 2006).
Experimental details including methods and procedures used in this study are discussed in
the next chapter. Attention is paid to the sampling of water and sediments. The extraction of
PAHs, sequential extraction of metals and the subsequent instrumental analyses are also
described.
53
CHAPTER 3
Materials and methods
3.1 Description of study area
The area of study is located in the upper catchment of the Olifants River, in Mpumalanga
Province, South Africa. This area consists of three main contributing rivers, that is, the
Olifants, Klein Olifants and the Wilge rivers (Figure 3.1), which feed the Loskop Dam
downstream. These rivers were chosen because they receive effluent from various
industries and run-offs from, inter alia, residential areas, coal mines, steel manufacture
plants, coal-fired power stations and vanadium mines.
The Wilge River (Mpumalanga), which drains the eastern part of the region, has its origin
near Leandra, and is a tributary of the Bronkhorstspruit River, just downstream from the town
of that name. The main reservoir is the Bronkhorspruit Dam that is situated along the river,
near the town. Populated areas along the Wilge River in Mpumalanga include Botleng,
Delmas, Kendal, Leandra, Leslie and Ogies. Agriculture is a major activity in the irrigated
and rain-fed areas along the Wilge River with the clay-loamy soils highly suitable for
commercial growing of crops of maize, Alpha-alpha (lucerne), sunflower and potatoes.
Further from the main river channels, most of the land use has been given over to small- and
medium-scale livestock farming. Light and heavy industries are also situated in the area with
many industries geared specifically towards satisfying the extensive mining sector of the
region7.
The Olifants River and some of its tributaries, notably the Klein Olifants, Elands, Wilge and
Bronkhorstspruit rivers, arise in the Highveld grasslands. The upper reaches of the Olifants
River are characterised by mining, agricultural and conservation activities. Over-grazing and
highly erodable soils result in severe erosion in parts of the middle section. The
consequence of heavy rains is that the Olifants River has a red-brown colour from all the
suspended sediment. There are thirty large dams in the Olifants River catchment. These
include the Witbank Dam, Loskop Dam, Middelburg Dam, Arabie Dam and the Phalaborwa
Barrage. In addition, many smaller dams in this catchment have a considerable combined
capacity.
7
http://www.dwaf.gov.za/iwqs/rhp/state_of_rivers/ [Accessed: 17-02-2009]
54
3.2
Sampling sites
Water and sediment samples were collected from sixteen sampling sites over a two year
period in summer (March 2009 and October 2010) and winter (May 2009 and June 2010)
along the Kranspoort, Olitants, Wilge and Klein Olifants rivers (Figure 3.1).
Figure 3.1: Map of the Upper Olifants River catchment area (Sampling locations are located
along the Wilge (W), Olifants (OLI) and Klein Olifants (KOL) rivers and the
Olifants River tributary (OLT))8
Two categories of sampling sites were selected. Primary sites most likely to be contaminated
were sampled and screened for compounds likely to be of environmental concern in the
study area. Secondary sites were then selected after primary samples indicated that a
problem existed.9 Three samples were collected randomly at each sampling site (two from
the two banks and the other one from the middle of the river). The samples were composited
in order to obtain a sample representative of the site. This was carried out in triplicate.
8
9
www.googlemaps.com [Accessed: 05-07-2009]
nepis.epa.gov [Accessed: 15-01-2009, no longer available]
55
3.3
3.3.1
Sampling procedures
Water sampling for PAH analysis
Water samples were collected in precleaned 2.5 L amber glass bottles, rinsed three times
with pesticide grade dichloromethane. The collection bottles were rinsed three times with
water from the sampling site, prior to collecting the sample.
A field reagent blank (FRB) was processed along with each sample set. A FRB was obtained
by filling a sample container in the laboratory with reagent water. The FRB was sealed, and
shipped to the sampling site along with the empty sample containers. In the field the FRB
was subjected to the same treatment, including sample preservation with hydrochloric acid,
as the samples.
All samples were iced or refrigerated at 4°C from the time of collection until extraction. The
microbiological chemical degradation of the samples of unchlorinated water was prevented
by adjusting the pH of the samples to less than 2 with 6 M HCI.
3.3.2
Sediment sampling for PAH analysis
According to the EPA9, the finer and more polluted sediments tend to deposit along the
edges of a navigational channel and on the inside edge of a curve in a river. Consequently,
sediment samples were generally collected from these areas rather than mid-channel. Initial
investigations of the sampling sites revealed the absence of stratification of the sediment
with respect to pollutants. A stainless steel Auger corer, of length 50 cm, and 10 cm
diameter was used to collect the sediment. Sediment samples were collected from the 20 cm
layer of the sediment. The samples were transferred into pre-cleaned glass jars with Teflonlined lids using a metal spoon.
3.3.3
Sediment sampling and preparation for trace metals analysis
The sediments were stored in 500 mL polyethene bottles at a temperature of 2°C. Pollutants
tend to be associated with the fine particles of marine sediments due to the relative higher
surface area and the compositional characteristics of the fine particles. Both phyllosilicates
and organic matter, which have chemical affinity for trace elements and organic pollutants,
are concentrated in the clay (< 2 μm) and fine silt (2-20 μm) fractions.
ost other minerals,
including feldspars and heavy minerals, are found in the fine and coarse (20-63 μm) silt
56
fractions, whereas the sand fraction (63 μm-2 mm) mainly consists of carbonate (calcite,
aragonite, dolomite) and/or silica (quartz, opal) minerals (Salomons & Forstner, 1984). The
two groups of carbonates and silica minerals naturally contain negligible amounts of trace
metals and therefore serve as diluents of the marine sediments. Removal of much of those
diluents enables: a) enhanced analytical capability of detecting low-concentration pollutants
and b) comparison between samples on compositional basis of improved homogeneity.
Consequently, the samples were separated with a nylon sieve to obtain the ≤ 63 μm grain
size fraction (Herut & Sandler, 2006).
The samples were transported to the laboratory in a cooler box. In the laboratory the
samples were kept frozen at -18°C before subsequent extraction. Samples were air-dried
and divided into two parts. One part was subjected to particle size and total organic carbon
analysis, while the other part was extracted for determination of organic pollutants.
3.4
Reagents
Seam 4 and 5 coal samples were obtained from eMalahleni, South Africa, and the reference
material bituminous coal, SARM 18, was supplied by the South African Bureau of Standards,
Pretoria, South Africa. Concentrated sulfuric acid (Merck, Saarchem, RSA) was used for the
preparation of the three leaching solutions of different pH, whilst the sequential leaching of
the coals was achieved using sodium acetate, acetic acid, ammonium hydroxide,
oxammonium hydrochloride, sodium hydroxide, sodium pyrophosphate and hydrogen
peroxide (all available from Merck, Saarchem, RSA). Calibration standards were prepared
by serial dilutions of 1000 mg/L stock solutions (Fluka, Switzerland) using 0.5% HNO3. All
solutions were prepared using double deionised water passed through a water purification
system (Millipore Simplicity UV, France).
For organic PAH and leaching studies, HPLC solvents and reagents were used. These
included dichloromethane, pentane, cyclohexane, hexane, ethyl acetate, acetone, silica gel
and methanol (SMM Instruments, RSA). Anhydrous sodium sulfate (Saarchem, RSA), was
used as drying agent and was Soxhlet extracted for 24 h prior to use. Millipore water was
used as a laboratory reagent blank (LRB) and for the preparation of leaching water. The LRB
was used to determine if method analytes or other interferences were present in the
laboratory environment, the reagents, or the apparatus.
The following internal standards were spiked into the extracts and calibration standards.
phenanthrene-d10, acenaphthene-d10, pyrene-d10, chrysene-d12 (SUPELCO, USA). The
57
EPA mix stock solution containing the sixteen EPA priority PAHs was used for the
preparation of calibration standards. The recovery of PAHs from sediments was assessed by
subjecting the certified reference material CRM 1944 (New York/New Jersey waterway
sediment sample), obtained from Industrial Analytical, Midrand (RSA), to the same Soxhlet
extraction procedure.
Sequential extraction was achieved using acetic acid (SMM Instruments, RSA), hydroxyl
amine hydrochloride, 30%(w/v) hydrogen peroxide, Suprapur nitric acid (65%) and
ammonium acetate (Merck, RSA). Hydrofluoric acid used (40%) for the pseudo total
digestion was obtained from Merck, RSA. Extractant solutions were prepared with deionised
water (MilliporeTM, 18.2 MΩ cm resistivity). Dry residues were dissolved in 13 M HNO3.
Calibration standards were prepared by serial dilutions of 1000 mg/L stock solutions (Fluka,
Switzerland) using deionised water. Validation of sequential extraction and sediment
digestion was accomplished using sediment reference materials C73-11 and RTC CRM016
(Industrial Analytical, RSA)
3.5
Total organic carbon analysis
The TOC in the sediments was determined using a Shimadzu SSM-5000A TOC analyser
equipped with a FTIR detector (Tokyo, Japan). A 1.00 g portion of sediment sample was
subjected to total carbon (TC) and inorganic carbon (IC) analysis by catalytically-aided
combustion oxidation at 900°C and preacidification at 250°C, respectively. The TOC was
computed by subtracting IC value from the corresponding TC value. The carrier gas used
was 99.9% O2 (Afrox, SA) at a flow rate of 500 mL/min.
3.6
3.6.1
Coal leaching procedures
Batch methods
Leaching methods can be categorised, depending on whether the leaching fluid is a single
addition (static) or is renewed (dynamic).10 Static methods are further sub-divided into batch
leaching, flow through (column) systems and flow around systems for monolithic samples
(Kim, Kazomih & Pahlberg, 2003).
10
http://www.osti.gov/bridge/purl.cover [Accessed: 14-05-2009, no longer available]
58
Batch leaching methods are those in which a sample is placed in a given volume of leachant
solution for a set period of time. Most of these methods require some type of agitation to
ensure continous contact between the sample and the leachant. At the end of the leaching
period, the liquid is removed and analysed. Batch methods can be divided into serial and
sequential batch leaching.
With the serial batch leaching approach, a sample is leached successively with fresh
aliquots of the same leaching fluid. This method is intended to eliminate the effect of
concentration on solubility and to simulate long-term exposure to the leachant solution.
Sequential batch leaching tests, on the other hand, use a single sample that is leached by a
series of different leaching fluids. Palmer et al. (1999) developed a sequential leaching
method as a rapid indirect method of determining the forms of occurrence of trace elements
in coal.
3.6.2
Column methods
Column leaching tests are designed to simulate the flow of percolating groundwater through
a porous bed of granular material. The flow of the leaching solution may be in either the
down-flow or the up-flow direction, and continuous or intermittent. The flow rate is generally
accelerated when compared to natural flow conditions. However, it should be sufficiently
slow to allow leaching to occur. A basic assumption in column leaching is that the distribution
of the leaching solution is uniform and that all particles are exposed equally to the leachant.
Precipitation or sorption within the column may affect the results (Kim, Kazomih & Pahlberg,
2003).
Column experiments can be conducted in both saturated and unsaturated modes.
Unsaturated conditions are usually intended to mimic vadose zone placement. Intermittent
addition of a given volume of leachant solution at the top of the column can provide uniform
distribution of the fluid and approximate a constant fluid front moving through the
unsaturated column. Saturated columns are obtained by a constant fluid flux, and by
allowing the fluid to pond at the top of the column. Variables, including leachate collection,
sampling frequency, leachant flow rate, and duration of the experiment, are determined by
the experimental objectives (Kim, Kazomih & Pahlberg, 2003).
59
3.6.3
Monolithic methods
Monolithic leaching methods are used to evaluate the release of elements from a material
that normally exists as a massive solid, cement for example, and are frequently used to
characterise the release of pollutants from stabilised waste materials. The release of an
element is a function of the exposed surface area as opposed to the mass. Flow-through
systems relate solubility to the surface area. Flow-through systems also consider the internal
pore surface. Some systems also take the rate of diffusion of the leachant solution into the
pores into account (Kim, Kazomih & Pahlberg, 2003).
The merits and demerits of column and batch leaching methods have been discussed in
detail elsewhere (Jones, 1990). Column leaching initially strips the coal of the most mobile
elements, but does not leave these in solution for continued interaction with other elements,
as is observed in batch leaching. According to Jones (1990), the composition of leachate
produced as a function of time by the slow percolation of water through a mass of ash is
likely to be better predicted by a column test. This is because dynamic leaching procedures
better simulate natural conditions. Therefore, to accurately predict the potential
environmental impacts of hazardous elements leached from coal under natural conditions,
column leaching studies should be performed. In addition, column experiments more closely
approximate the particle size distribution and pore structure, leachant flow, and solute
transport occuring in the field (Zachara & Streile, 1990). In this study, samples of of
bituminous coal from eMalahleni (Figure 3.2) were column leached with water of pHs of 2.0,
4.0 and 6.0 to model the washing of metals and organics from coal and their migration into
surface water in the eMalahleni Coalfields.
Figure 3.2: Location of the eMalahleni coal fields of South Africa
60
3.6.4
Particle size distribution of the leached coal
The particle size distribution of the ground coal sample is illustrated in Figure 3.3. The
samples, which were leached at the different pHs, exhibited the same particle size
distribution. This implies that the different leaching behaviour of the trace metals cannot be
accounted for by particle size distribution, but by pH, leaching time and the mode of
occurrence of the elements.
14
Volume of particles(%)
12
10
8
6
4
2
0
Particle size(μm)
Figure 3.3: Representative particle size distribution in coal used for leaching experiments
3.6.5
Metals limits of detection and limits of quantitation
The limits of detection (LOD) and limits of quantitation (LOQ) for the leached elements
calculated using equations 3.1 and 3.2 are given in Table 3.1.
LOD = 3.143 s, n = 7
Eqn. 3.1
LOQ = 10 s, n = 7
Eqn. 3.2
where s is the standard deviation for n replicate blank measurements.
Elements with concentrations below the detection limit were considered not detected.
Although the reported concentrations of the elements were above the detection limit for
61
some, many of the values were below the limit of quantitation. This was true for Cr (at pH 2,
5-10 h; pH 6, 10-30 h), As (at pH 4, 10-30 and 30-60 h) and Cr, Cd, Ni, U and Mn (at pH 6,
30-60 h).
62
Table 3.1: Limits of detection (LOD) and quantitation (LOQ) (ug/L) for potentially toxic metals in coal leachates and sequential extraction
phases
Element
Leachates
Coal sequential extraction phases
F1
63
F2
F3
F4
F5
LOD
LOQ
LOD
LOQ
LOD
LOQ
LOD
LOQ
LOD
LOQ
LOD
LOQ
Co
1.32
4.20
1.17
3.72
0.96
3.05
1.57
5.00
0.83
2.64
1.71
5.44
Cr
0.67
2.13
0.86
2.74
0.35
1.11
1.38
4.39
0.64
2.04
1.37
4.36
Cd
0.41
1.30
0.91
2.90
0.23
0.73
0.65
2.07
0.32
1.02
0.86
2.74
As
0.68
2.16
0.31
0.99
0.47
1.50
0.35
1.11
0.84
2.67
0.36
1.15
Ni
0.44
1.40
0.73
2.32
0.41
1.30
0.82
2.61
0.48
1.53
0.32
1.02
Pb
0.27
0.86
0.31
0.99
0.27
0.86
0.49
1.56
0.33
1.05
0.48
1.53
Th
0.61
1.94
0.72
2.29
0.65
2.07
0.63
2.00
0.42
1.34
0.52
1.65
U
1.18
3.75
1.17
3.72
0.97
3.09
1.03
3.28
0.89
2.83
1.37
4.36
Mn
1.32
4.20
1.02
3.25
0.81
2.58
1.31
4.17
1.09
3.47
0.92
2.93
3.6.6
Column leaching of trace metals
Seam 4 and 5 coal samples from eMalahleni were ground to -40 mesh (450 μm), blended
and divided into four equal portions. For leaching experiments, a 20 g sub-sample was
transferred into a fixed glass column. A small amount of glass wool was packed at the
bottom of the column to prevent loss of fine particles during leaching. The three pH
solutions, prepared using sulfuric acid, were allowed to flow into each of the columns, at
room temperature, so that approximately 15 cm of solution was maintained above the
sample in each column. The outlet flow rate of the resulting leachates was set at 4.0 mL/h
and the leaching was allowed to occur for up to 60 h. At each pH five parallel columns
were run. After the exhausting the leaching the coal material was changed and leaching
commenced. The resulting leachates were collected after 5, 10, 30 and 60 h and then
vacuum filtered using a 0.45 μm membrane filter. Approximately 20 mL, 40 mL, 120 mL
and 240 mL of leachate were collected at 5, 10, 30 and 60 h intervals, respectively. The
concentrations of elements As, Cd, Co, Cr, Mn, Ni, Pb, Th and U in the leaching solutions
and leachates were determined using an ELAN DRC-e inductively coupled plasma-mass
spectrometer (ICP-MS) (Perkin Elmer, Ontario, Canada).
3.6.7
Sequential leaching of trace metals
The forms in which elements occur in coal samples have an important impact on their
leaching behaviour. Therefore, a sequential extraction experiment was used to
characterise the occurrence or phases of the trace metals. The five steps of the sequential
extraction procedure are as follows:

the samples were treated with 1 M sodium acetate to extract elements that were in
a water soluble and ion exchangeable state;

the remaining material was treated with a 1:1 mixture of acetic acid (25%) and
oxammonium hydrochloride (0.04 M) to remove elements bound to carbonates,
oxides, of ferric and/or sulfides;

the remaining material was treated with sodium hydrate (10%) and sodium
pyrophosphate (0.1 M) to extract elements bound by humic and fluvic acid;

the remaining material were ashed for 48 h, then reacted with 0.2 M HNO3 and
8.8 M H2O2 to remove elements bound by the organic matter; and

the remaining material was digested by hydrogen peroxide and hydrofluoric and
nitric acids to remove elements bound by silicates.
64
3.6.8
Trace metals analysis
Elemental analysis for the coal samples was achieved by further grinding -40 mesh coal to
less than 74 μm (-200 mesh). A 0.25 g of the coal sample was then submitted for total
digestion using hydrogen peroxide, hydrofluoric and nitric acid. The resultant extract was
evaporated to dryness and re-dissolved in 10.00 mL 0.5% HNO3. The concentrations of
As, Cd, Co, Cr, Mn, Ni, Pb, Th and U were determined by aspirating the solution into an
ICP-MS instrument. The accuracy of the digestion procedure was tested by subjecting the
coal reference material, SARM, 18 to the same digestion and analytical procedure as
outlined above.
3.6.9
Leaching procedure for organics
A 500 g portion of ground coal was loaded into a glass column (Figure 3.4). Millipore
water, adjusted to pH of 2, 4 and 6, mimicking that of water in disused coal mines and
acid rain, was applied to the coal. Leaching events were allowed to occur at 5 days
intervals for 30 days. After 5, 10, 15, 20, 25 and 30 days a total of 480 mL, 960 mL,
1440 mL, 1920 mL, 2400 mL and 2880 mL of leachate was collected respectively, for
analysis. Temperature, pH and conductivity were measured after each leaching interval.
After filtration, the leachate filtrates were then transferred into amber bottles to prevent
deterioration and stored in a cool dry place until extraction.
Figure 3.4: The column leaching design (not to scale) (Fendinger et al., 1989)
65
3.6.10 Soxhlet extraction of the coal
About 30 g of an accurately weighed coal sample was blended with 10.00 g of anhydrous
sodium sulfate and placed into an extraction thimble. Glass wool was used as a plug
above and below the sample in the thimble. The leachates were extracted with 200 mL of
1:1 dichloromethane: acetone in the boiling flask. Boiling chips were also added to the
flask. Extraction was allowed to proceed for 24 h. The extracted leachates were
concentrated using a rotory evaporator (Buchi, Switzerland).
3.6.11 Silica gel clean-up
Silica gel was first activated in an oven for 16 h at 130°C. About 10 g of activated silica gel
was added to a beaker with dichloromethane and prepared as a slurry. The slurry was
placed in a chromatographic column. A 2.00 cm portion of anhydrous sodium sulfate was
added after eluting dichloromethane from the column. Pentane (40 mL) was used to clean
the silica gel and was discarded after its elution just prior to exposure of the sodium
sulfate layer to air. Leachate extracts were transferred onto the column using an additional
5 mL aliquot of cyclohexane to complete the transfer. Pentane (25 mL) was added and the
clean-up continued, after which the pentane eluate was discarded. The column was then
eluted with 25 mL of 2:3 (v/v) dichloromethane: pentane into a flask for pre-concentration.
The final eluate was concentrated using the rotory evaporator/nitrovap and analysed by
GC-MS.
The identification and quantification of organics was based on three criteria: the
compound must have the same molecular weight as the standard; the retention time of
the compound must correspond to that of the standard; and, the compound must have a
similar mass spectrum (i.e. the seven largest fragments must correspond) to that of the
standard (Ragunathan et al., 1999).
3.6.12 Measurement of particle size distribution
Grinding of the coal samples was done using a micronising mill mounted with agate
grinding elements (McCrone Instruments, UK). After grinding, the particle size distribution
of each ground portion was determined with a Malvern Mastersizer 2000 instrument
(Malvern Instruments, Malvern UK).
66
3.7
Sediment and water preconcentration and extraction procedures
Analytical procedures applied for sediment and water samples are outlined in Figure 3.5.
3.7.1
Sediment extraction procedure for PAH analysis
Sediment samples were thawed at room temperature and mixed thoroughly to ensure a
composite sample. The wet samples were air-dried and then sieved through a 0.15 mm
sieve. Prior to extraction, samples were spiked with an internal standard and surrogate
solution containing four deuterated surrogate standards (acenaphthene-d10, chrysened12, phenanthrene-d10 and pyrene-d12) to monitor recovery.
SAMPLES
Water
samples
Sediment
samples
Glass
fibre
filtration
Glass
fibre
filtration
SPE
Soxhlet
extraction
of suspend
material
Soxhlet
extraction
Figure 3.5: Preconcentration and extraction techniques used for the extraction of water
and sediment samples
Approximately 20 g of air-dried sediment was Soxhlet extracted with 150 mL of HPLC
grade dichloromethane for 24 h according to the modified EPA method 3540 (1996) 11. The
Soxhlet apparatus consisted of a 250 mL round bottomed flask, Soxhlet extractor,
condenser and a thermostated heating mantle. The volume of the extract was reduced to
4 mL using a rotary evaporator (Buchi, Switzerland). Sample clean-up was accomplished
by passing 4 mL of the concentrated dichloromethane extract through a column of silica
gel mesh (activated for 16 h at 130°C). The sample was then eluted with 25 mL of
11
www.epa.gov/epahome/index [Accessed: 10-02-2009]
67
dichloromethane. The resulting eluate was then concentrated to 1 mL under a stream of
nitrogen and the internal standards were added. About 5 g of suspended material were
subjected to the same extraction procedure with a corresponding scale down in solvent
volumes.
3.7.2
Water disc SPE procedure
The extraction was carried out according to the US EPA 525.2 method (1995)7. One litre
of sample was passed through a 47 mm Empore C18 SPE disk supported on a Millipore
filtering apparatus. The disk was then washed with 5 mL dichloromethane by adding the
dichloromethane to the disk, drawing about half through the disk, allowing it to soak the
disk for about a minute, then drawing the remaining dichloromethane through the disk.
The disk was pre-wetted with 5 mL methanol and allowed to soak for about a minute, after
which the remaining methanol was drawn through. A layer of methanol was left on the
surface of the disk, which was not allowed to dry until the end of the extraction. Finally, the
disk was rinsed with 5 mL reagent water by adding the water to the disk and drawing most
through, again leaving a layer on the surface of the disk.
The samples and laboratory blank were fortified with an internal standard and surrogate
solution containing four deuterated surrogates, acenaphthene-d10, chrysene-d12,
phenanthrene-d10 and pyrene-d10, prior to extraction of samples. This was done to
monitor the recovery of analytes from samples. A 5 mL volume of methanol was added to
each litre of sample. The water sample was added to the reservoir and the extraction
commenced by applying vacuum. The extracted compounds were eluted from the disk
using two portions of 5 mL dichloromethane and collected using a test tube. The
combined eluates were then passed through 6 g of sodium sulphate in a column. The test
tube and sodium sulphate were rinsed with two 5 mL portions of dichloromethane. All the
extracts and washings were collected in a concentrator tube. The extracts were
concentrated to less than 0.5 mL under a gentle stream of nitrogen. The exact volume of
the extract was measured using a glass syringe and appropriate amounts of the internal
standards were added. Thereafter, the solutions were diluted to 0.5 -1 mL in a 2 mL
amber glass vial by solvent for analysis by GC-MS. The same procedure was used for the
extraction of organics from coal leachates.
68
3.8
Gas chromatography-mass spectrometry analysis of PAHs
3.8.1
Gas chromatography
A GC (7890 series) coupled to a inert triple axis quadrupole mass spectrometer (Triple
Axis 5975C model) fitted with an electron impact (EI) ioniser (Agilent Technologies, USA)
was used for the analysis of PAHs. A 30 mx 0.25 mm id x 0.25 µm film thickness, cross
linked 5% phenyl methyl siloxane, HP 5MS, capillary column (Agilent Technologies) was
used for the separation of PAHs throughout the study.
3.8.2
Mass spectrometer tuning
Perfluorotributylamine (PTFBA) is predominantly used for the tuning of mass
spectrometers in GC-MS. This can be attributed to the mass range of its ionisation
fragments. The major fragments are evenly spaced, and, in addition PTFBA is volatile
under vacuum conditions. When the instrument requires calibration, the valve is opened
and the calibration gas is allowed to vaporise into the source chamber. The calibration gas
is ionised in the mass spectrometer’s source by an electron beam from the filament and
passes into the analyzer where its fragments are separated and detected according to
their mass-to-charge ratio (m/z). The major fragments for calibration are m/z 69, 131, 219,
264, 414, 464, 502, 614. In a well-tuned MS, the 69 mass is the base mass, and
fragments 219 and 502 have relative abundances greater than 30% and 1%, respectively
(Agilent, 2011).
3.8.3
Optimisation of GC-MS parameters
Gas chromatography-MS parameters were optimised prior to the analysis of samples. A
splitless glass liner with glass wool was chosen to prevent the contamination of the
column since glass wool prevents the entrance of small particles to the column. The
injection port temperature was set at 310°C. Several temperature programs were studied
to obtain the optimal resolution of PAHs. The one given in Table 3.2 was found to be
optimum and was used for determination of PAHs.
The SIM mode improves sensitivity by limiting the mass of the ions detected to one or
more specific fragment ions of known mass (Santos & Galceran, 2002). Consequently, it
is highly selective and eliminates a large portion of the noise inherent in full scan detection
mode.
69
The monitored ions and SIM parameters are listed in Tables 3.3 and 3.4. An EPA 610
mixed standard was purchased from Industrial Analytical, South Africa. The surrogate
internal standard mixture, used for the analyses, contained acenapthene-d10,
phenanthrene-d10, chrysene-d12, and pyrene-d10, was purchased from Supelco, USA.
External calibration and internal standardisation were used for the quantification of PAHs
in all samples.
Table 3.2: Operating conditions of the GC-MS instrument
GC column
30 m x 0.25 mm id, 0.25 µm film thickness
5% Phenyl methyl siloxane, HP 5MS, capillary column
Liner
Splitless glass liner with glass wool,
deactivated (Agilent Technologies), 310°C
Carrier gas
Ultra purified helium, 99.999% (Affrox), 1 mL/min
Oven temperature
70°C (2 min), 30°C/min to 200°C (5 min),
5°C/min to 300°C (2 min)
Injection volume
1 µL
Mass spectrometer
Electron impact,70 eV
Mass spect. quadropole temp.
150°C
Mass spect. source temp.
230°C
The total ion chromatogram (TIC) of PAHs in SIM mode, obtained by optimised
conditions, is illustrated in Figure 3.6. All the compounds were well separated with the
exception of pyrene and pyrene-d10, benzo(a)anthracene, chrysene and chrysene-d12.
However, in the extracted ion chromatograms of pyrene and pyrene-d10 (Figure 3.7), and
benzo(a)anthracene, chrysene and chrysene-d12 (Figure 3.8), the compounds are well
resolved. Since the ions used for quantification of benzo(a)anthracene and chrysene was
m/z 228 and m/z 240 for chrysene d12, there was inherent selectivity. This demonstrates
the advantage of mass spectrometry since it is not always possible to separate all peaks
using a GC column.
The entire chromatogram was divided into five time intervals in which specific ions were
monitored. This increased the sensitivity of the measurements by decreasing the
background in the entire chromatogram.
70
Table 3.3: Monitored ions for polycyclic aromatic hydrocarbons (target ions in bold and
underlined)
PAH
Abbreviation
R.T.
(min)
NaP
Target and
identifier
fragments
(m/z) (amu)
128, 129, 127
1
Naphthalene
2
Acenaphthylene
AcN
152, 151, 153
6.64
3
Acenaphthene-d10
AcNPh-d10
164, 162, 165
6.78
4
Acenaphthene
AcNPh
154, 153, 152
6.81
5
Fluorene
FI
166, 165, 167
7.36
6
Anthracene-d10
PhA-d10
188, 94, 189
8.81
7
Phenanthrene
PhA
178, 179, 176
8.95
8
Anthracene
AN
178, 179, 176
8.86
9
Fluoranthene
FIA
202, 101, 203
12.71
10
Pyrene
Py
202, 101, 203
13.62
11
Pyrene-d10
Py-10
212, 106, 213
13.55
12
Benz(a)anthracene
BaA
228, 229, 226
19.27
13
Chysene d12
Chy-d12
240, 120, 241
19.34
14
Chrysene
Chy
228, 229, 226
19.44
15
Benzo(b)fluoranthene
BbFIA
252, 253, 126
24.10
16
Benzo(k)fluoranthene
BkFIA
252, 253, 126
24.20
17
Benz(a)pyrene
BaP
252, 253, 126
25.34
18
Indeno(1,2,3-cd)pyrene
IP
276, 138, 277
29.52
19
Dibenzo(a,h)anthracene
dBahA
278, 139, 279
29.69
20
Benzo(ghi)perylene
BghiP
276, 138, 277
30.32
5.250
Quantitation ion is bold and underlined
R.T. Retention time
Table 3.4: Selected ion monitoring parameters for analysis of the polycyclic aromatic
hydrocarbons
Window
Time period (min)
Ions monitored
1
5 to 9
128, 129, 127, 152, 151, 153, 164, 162, 165
154, 166, 167, 188, 94, 189, 178, 179, 176
2
9 to 14
202, 101, 203, 202, 101, 203, 212, 106, 213,
3
14 to 20
228, 229, 226, 240, 120, 241
4
20 to 26
252, 253, 126
5
26 to 31
276, 138, 277, 278, 139, 279
71
3.8.4
Calibration of GC-MS instrument
A calibration curve was prepared by determining at least four standards prior to the
analysis of a sample. There are two approaches commonly employed for calibration in
chromatography. These are: external and internal standard calibration methods. External
standard method involves preparation of a calibration curve by plotting area or height
response as a function of concentrations of analyte(s) in the standards.
Internal standard calibration entails equal amounts of one or more internal standards are
added into equal volumes to sample extracts and calibration standards. The response
factor (RF) is then calculated as follows:
RF = As.Cis/Ais.Cs
Eq. 3.3
where As and Ais are the areas (or heights) responses for the analyte and the internal
standard, respectively; while Cs and Cis are their concentrations.
72
Figure 3.6: Total ion chromatogram obtained in selected ion monitoring mode of 5 mg/L
polycyclic aromatic hydrocarbons and internal standards
73
Abundance
I o n
2 1 2 . 0 0
(2 1 1 . 7 0
t o
2 1 2 . 7 0 ):
p a h
m
ix
1 2 5 0
m
u lt iv
a r ia . D
\
d a t a . m
\
d a t a . m
160000
140000
120000
100000
80000
60000
40000
20000
0
6.00
8.00 10.0012.0014.0016.0018.0020.0022.0024.0026.0028.0030.0032.00
Time-->
Abundance
I o n
2 0 2 . 0 0
(2 0 1 . 7 0
t o
2 0 2 . 7 0 ):
p a h
m
ix
1 2 5 0
m
u lt iv
a r ia . D
160000
140000
120000
100000
80000
60000
40000
20000
0
6.00
8.00 10.0012.0014.0016.0018.0020.0022.0024.0026.0028.0030.0032.00
Time-->
Figure 3.7: Extracted ion chromatograms for pyrene and pyrene-d10
74
Abundance
I o
n
2
2
6
. 0
0
( 2
2
5
. 7
0
t o
2
2
6
. 7
0
) :
p
a
h
m
i x
1
2
5
0
m
u
l t i v
a
r i a
. D
\
d
a
t
r i a
. D
\
d
a
t
r i a
. D
\
d
a
t
120000
100000
80000
60000
40000
20000
0
6.00
8.00 10.00
12.00
14.00
16.00
18.00
20.00
22.00
24.00
26.00
28.00
30.00
32.00
Time- - >
Abundance
I o
n
2
4
0
. 0
0
( 2
3
9
. 7
0
t o
2
4
0
. 7
0
) :
p
a
h
m
i x
1
2
5
0
m
u
l t i v
a
120000
100000
80000
60000
40000
20000
0
6.00
8.00 10.00
12.00
14.00
16.00
18.00
20.00
22.00
24.00
26.00
28.00
30.00
32.00
Time- - >
Abundance
I o
n
2
2
8
. 0
0
( 2
2
7
. 7
0
t o
2
2
8
. 7
0
) :
p
a
h
m
i x
1
2
5
0
m
u
l t i v
a
120000
100000
80000
60000
40000
20000
0
6.00
8.00 10.00
12.00
14.00
16.00
18.00
20.00
22.00
24.00
26.00
28.00
30.00
32.00
Time- - >
Figure 3.8: Extracted ion chromatograms for benz(a)anthracene, chrysene and chrysened12
Thus, the RF for analytes may be determined by analysing standard solutions of the
analytes containing internal standards. The concentration of the analyte in the sample is
then computed from:
Cs = AsCis/AisRF
Eq. 3.4
Rearranging Eq. 3.2:
Cs.RF/Cis = As/Ais
Eq. 3.5
Thus, a plot of As/Ais as a function of Cs/Cis yields a calibration curve with a gradient given
by the RF.
The internal standard method is more reliable than the external standard method. It is
most suitable when variations in analytical sample volume and matrix, limit the precision.
Furthermore, this method may correct for certain types of noise (Santos & Galceran,
2002).
75
3.9
3.9.1
Sequential extraction procedure
The BCR sequential extraction procedure
For this study, the sequential extraction scheme employed was developed by the
European Commission (EC) of Standards, Measurement and Testing Programme,
formerly the Bureau Community of Reference (BCR) (Rauret, 1998). The procedure is
schematised in Table 3.5. Five grams of sediment sample, with a grain size of < 63 μm,
were subjected to a sequential extraction scheme. The reliability of the metal
determination was tested by comparing the concentration sums throughout the procedure
using stream sediment C73-11.
Table 3.5: European Commission for Standards, Measurement and Testing sequential
extraction procedure
Step
Conditions
1
0.11 M CH3COOH, V/m = 40 mL/g, temp. 20°C, shaking overnight (exchangeable and
acid soluble metals fraction)
2
0.11 M, NH2OH HCI, V/m = 40 mL/g, temp. 20°C, shaking overnight (reducible fraction)
3
8.8 M H2O2 (pH = 2-3 with HNO3), V/m = 10 mL/g, room temp. 1 h
New addition 10 mL/g, 85°C for 1 h, reduce volume to a few mL, 1 M NH4OAc (pH = 2
with HNO3), V/m = 50 mL/g, temp. 20°C, shake overnight (oxidisable fraction)
3.9.2
Residual fraction and total digestion of sediments
Total digestion was accomplished by subjecting the samples to the following procedure:
1 g of dry sediment sample was weighed into a polytetrafluoroethylene (PTFE) beaker. To
this, 20 mL of concentrated HNO 3 and 20 mL concentrated HF were added. After
effervescence, the beaker was heated to 100°C and held at that temperature for 12 h.
Then, 15 mL of H2O2 was added and the vessels were maintained at 70°C for 2 h. A
second step, with 10 mL HNO 3, 10 mL HF and 10 mL H2O2 was executed. The digestion
was completed by adding 10 mL HNO3 twice. The residue from the third step was
submitted for HNO3-HF-H2O2 digestion using the same procedure, with the quantities of
the digesting reagents adjusted for changes in the mass of sample. Every step was
followed by evaporation to dryness. The accuracy of the total digestion procedure was
tested using sediment reference material, RTC CRM016.
76
3.9.3
Chemical analysis
The resultant extracts and digests were analysed for the following elements: Cd, Co, Cr,
Pb, Ti, and V using an Elan DRC-e ICP-MS (Perkin Elmer, Canada) and for Fe and Mn
using a Spectro Arcos ICPE-OES instrument (Spectro, Kleive, Germany). Three internal
standards were selected to cover the mass range of the analytes. For the light elements,
59
Co,
52
Cr,
48
Ti and
standard was
51
V, the internal standard
69
Ga was used. For
115
In, while the measured intensities for
normalised to those of
206
Pb,
207
112
Cd, the internal
Pb, and
208
Pb were
205
Tl. Triplicate analyses were done for each element to assess the
precision of the sequential extraction and digestion procedures. Iron and Mn were
analysed via external calibration. The instrument conditions of the ICP-OES and MS used
in the analyses of the samples are summarised in Tables 3.6 and 3.7.
3.10 Multivariate statistical analysis
Prior to performing multivariate analysis, (using Statistica 8®) the metal and its
concentrations were subjected to descriptive statics/univariate statistics to check the
variability in the sampling area and to detect anomalies that could distort the final results
(Gallego, Ordonez & Loredo, 2002). The distribution of environmental data sets tends to
be skewed as a result of outliers causing deviation from normal distribution. To this end,
the following univariate parameters were determined: relative standard deviations,
minimum and maximum values. Data not reflecting normal distribution were then
logarithmically transformed and checked for normality before further processing by
factorial and cluster analysis (Reimann & Filzmoser, 2000).
Table 3.6: Operating conditions for the ICP-OES
Parameter
Setting
RF Generator Power (W)
1500
Plasma gas flow rate (L/min)
15.00
Auxillary gas flow rate (L/min)
1.00
Nebuliser gas flow rate (L/min)
0.80
Sample uptake (mL/min)
1.60
Type of nebuliser
Cross flow
Type of spray chamber
Scott double pass
77
Table 3.7: Operating conditions for the ICP-MS
Parameter
Setting
RF Generator Power (W)
1150
Analogue stage voltage (V)
-1850
Pulse stage voltage (V)
800
Lens voltage (V)
6.75
Plasma gas flow rate (L/min)
15.00
Auxillary gas flow rate (L/min)
1.20
Nebuliser gas flow rate (L/min)
0.89-1.05
Sample uptake (mL/min)
2.00
Type of nebuliser
Cross flow
Type of spray chamber
Ryton®, double pass
Ni Sampler cone i.d. (mm)
1.1
Ni Skimmer cone i.d. (mm)
1.0
Number of replicates
3
78
CHAPTER 4
ENVIRONMENTAL IMPLICATIONS OF MATERIAL LEACHED FROM COAL
4.1 Results and discussion
This chapter presents findings from simulated leaching of metals and organics in coall
under various pH conditions found in water from coal mines.
4.2
4.2.1
Metals leaching
The effect of leaching time on the availability of metals in leachates
Leaching time has been shown to be an important factor that influences the amounts of
metals leaching out of coal. The analytical concentrations of metals (Table 4.1) leached
out of coal were obtained by calculating the differences between the blank value (obtained
from analysis of the leaching solutions) and the concentration values of the leachate.
Four types of leaching trends, namely, V-shaped, gradually descending (\), parabolic-like,
(∩/U), and wave-like were discerned. At pH 2.0, the most pronounced trend is the Vshaped (for Cr, Ni, Pb and Th), followed by wave-like (for Cd and As) and gradually
descending (\) (for Co, Mn and U). At pH 4.0, all elements except
n, Cr (U) and As (∩)
followed a gradually descending trend (\). At pH 6.0, only the gradually descending (Co,
Cr, Cd, Ni, Pb, U and Mn) trend was observed. The concentrations of the remaining
elements at pH 6.0 were below the detection limit. With the V-shaped trend, the leached
concentration decreased gradually for sometime, after which it started to increase. The ∩
shaped trend is characterised by a gradual increase, followed by a decrease in the
leached concentration, with time. These trends can be rationalised by considering the
mechanism of the leaching process and the occurrence of the elements in the coal.
During the leaching process, elements may dissolve or undergo physical and chemical
reactions, after which they are transferred into the leaching media.
A high extractable concentration of an element during the initial stages of leaching can be
ascribed to the element being adsorbed onto the surface of the coal particles or when an
element occurs in the water soluble phase of the coal. However, as the leaching
progresses, the concentrations of elements adsorbed on the surface and in the form of
water solution decreased gradually, hence the observed gradually descending trend in
79
concentration of the metals in the leachate. In addition, if the rate at which an element,
existing in an unstable or metastable state, migrates from the interior of the coal particles
to the surface, is lower than the rate at which the element was extracted into the leaching
media, the leached concentration decreased (Wang et al., 2008).
80
Table 4.1: Analytical concentrations (µg/L) of ten elements in the resulting leachates compared to acidic water flowing from an abandoned
coal mine
Element
pH =2.0
pH=4.0
water from
pH=6.0
coal mine pH
Co
Cr
Cd
As
81
Ni
Pb
Th
U
Mn
0-5
5-10
10-30
30-60
0-5
5-10
10-30
30-60
0-5
5-10
10-30
30-60
(h)
(h)
(h)
(h)
(h)
(h)
(h)
(h)
(h)
(h)
(h)
(h)
999
231
140
70.3
64.3
40.7
10.6
6.4
1431
374
80.6
38.6
420
±1.0
±5.0
±8.0
±3.8
±2.3
±1.3
±1.0
±0.8
±5.0
±5.0
±2.6
±1.9
±1.3
60.8
2.11
6.41
40.6
30.6
6.4
2.38
7.4
5.8
3.64
1.06
0.93
232
±0.9
±0.14
±0.5
±5
±2.5
±0.6
±0.09
±0.5
±0.6
±0.05
±0.01
±0.01
±0.8
30.4
10.6
13.3
7.33
70.3
26.4
11.6
8.66
38.5
8.64
4.74
0.63
2.91
±0.7
±0.2
±0.9
±0.73
±3.5
±0.8
±0.9
±0.05
±0.9
±0.06
±0.05
±0.02
±0.4
46.7
22.7
30.3
28.6
3.21
8.68
0.68
1.83
nd
nd
nd
nd
87.9
±0.8
±0.6
±3.5
±1.4
±0.24
±0.06
±0.02
±0.01
nd
nd
nd
nd
±0.4
621.6
384
186
600
428
384
60.7
13.9
352
47.6
11.2
1.39
1828
±2.6
±2.0
±3.0
±5.0
±6.0
±5.0
±2.5
±1.0
±3.0
±0.8
±0.5
±0.03
±1.1
80.3
15.6
30.6
60.7
34.8
8.32
4.26
1.68
15.8
nd
nd
nd
133
±0.6
±0.9
±1.7
±2.5
±3.6
±0.28
±0.08
±0.01
±1
nd
nd
nd
±0.9
46.7
20.6
16.5
30.3
64.8
33.8
14.6
8.36
nd
nd
nd
nd
38.7
±0.5
±2.5
±2.3
±0.6
±2
±2.3
±1
±0.06
nd
nd
nd
nd
1.1
80.7
60.7
15.3
6.43
63.8
56.1
48.2
36.1
16.9
7.28
3.86
1.47
5.91
±0.4
±3.8
±2.7
±0.16
±3.5
±2.3
±3
±2
±0.8
±0.6
±0.02
±0.05
0.3
1473
410
202
34.7
459
259
396
18.5
345.5
140
38.8
3.16
728
±1.0
±10
±6.0
±1.3
±6.0
±5.0
±5.0
±0.6
±3.5
±6.0
±1.3
±0.02
0.5
nd= Not detected, values in bold and italic below LOQ
4.8
4.2.2
Effect of pH of the leaching solution on leachability of the trace metals
The ease, with which an element is leached (leachability) per unit time, can be described
using its leaching intensity. Leachability, and therefore the leaching intensity, are expected
to be closely related to the pH of the leaching solution. Results from this study show that
there is a close association between the leaching intensity and the pH of the solution. The
leaching intensity (h-1), (Table 4.2) for each element was calculated using an equation
derived by Wang, Ren and Zhao (1999):
I = CxV103/Axmt
Eq. 4.1
where Cx is the concentration of the element in the leachate (μg/L), V the total volume of
the leached solution, Ax is the concentration of the element in the original coal sample
(μg/g), m is the total mass of the sample (g) and t is the leaching time (h).
Generally, the leaching intensity decreased with increasing leaching time and pH. This
trend is clear for the elements Co, Cr, Cd, As, Ni and Pb (Table 4.2). For Th, the increase
in pH from 2.0 to 4.0 was accompanied by an increase in leaching intensity. No
observable leaching of this element was recorded at pH 6.0. In contrast to the mentioned
elements, the metals U and Mn (Table 4.2) exhibited a decrease in leaching intensities
with increasing pH and leaching time. The high leaching intensity, at low pH,
corresponded to the leaching of less stable or water soluble elements existing as sulfides,
clays, sulfites, clays, carbonates, ion exchangeables and those in the Fe-Mn oxide
phases. Alternatively, the low leaching intensity can be ascribed to the leaching of
elements existing as aluminosilicates and associated with organic matter (Querol et al.,
1996; Zhao et al., 2006).
4.2.3
Leaching behaviour of the metals
The cumulative percentage leached was calculated using the formula for all the metals:
Cumulative % = (Total amount leached at time t (µg)/Total amount in 20 g) x100
Eq. 4.2
4.2.3.1 Leaching behaviour of Co
Cobalt exhibits similar leaching trends at different pHs of leaching media. The
concentrations of Co approach equilibrium as the leaching experiment progresses (Figure
82
4.1). When the pHs of the solution was 2.0, 4.0 and 6.0 the cumulative percentage of
leached Co was 93.2%, 64.7% and 5.9%, respectively. This implies that acidic conditions
promote the leaching of Co. Sequential extraction results indicated that 96.4±2.0% of Co
in the coal was potentially leachable (Table 4.3).
Co
100.0
Cumulative percent leached(%)
90.0
80.0
70.0
60.0
50.0
pH=2.00
40.0
pH=4.00
30.0
pH=6.00
20.0
10.0
0.0
0
5
10
30
60
Time (hour)
Cr
70.0
Cumulative percent leached(%)
60.0
50.0
40.0
30.0
pH=2.00
pH=4.00
20.0
pH=6.00
10.0
0.0
0
5
10
30
60
Time (hour)
Figure 4.1: Cumulative percentages of Co and Cr leached from coal over a period of
60 hours at different pHs
83
Table 4.2: Effect of pH on average leaching intensities (h-1) of the elements
Element
pH =2.0
pH=4.0
pH=6.0
84
0-5(h)
5-10(h)
10-30(h)
30-60(h)
0-5(h)
5-10(h)
10-30(h)
30-60(h)
0-5(h)
5-10(h)
10-30(h)
30-60(h)
Co
77.3
8.97
7.26
2.72
4.98
1.58
0.55
0.25
111
14.5
4.16
1.49
Cr
56.3
0.98
0.99
18.82
28.37
2.97
0.37
3.44
5.41
1.69
0.16
0.43
Cd
26.3
4.61
7.73
3.17
60.94
11.4
6.71
3.75
33.3
3.74
2.73
0.27
As
14.6
3.53
6.30
4.46
1.00
1.35
0.14
0.29
nd
nd
nd
nd
Ni
2.71
0.84
0.54
1.31
1.87
0.84
0.18
0.03
1.54
0.1
0.03
nd
Pb
7.59
0.74
1.93
2.87
3.29
0.39
0.27
0.08
1.5
nd
nd
nd
Th
0.49
0.11
0.11
0.16
0.67
0.18
0.10
0.04
nd
nd
nd
nd
U
3.08
1.16
0.39
0.12
2.44
1.07
1.23
0.69
0.65
0.14
0.10
0.03
Mn
5.82
0.81
0.53
0.07
1.82
0.51
1.04
0.04
1.36
0.28
0.10
0.01
nd not detected
Table 4.3: Concentrations of metals (%) available after sequential extraction of different fractions
85
Phase
Co
Cr
Cd
As
Ni
Pb
Th
U
Mn
Acid soluble and exchangeable (F1)
32.6±0.8
25.8±1.4
9.6±0.8
5.6±1.3
6.0±0.9
0
0
1.9±0.6
7.0±1.3
Fe-Mn oxide and sulfide (F2)
63.8±1.2
46.3±0.8
65.2±0.9
34.7±1.5
15.6±1.8
34±1.2
7.0±1.4
12.4±1.6
30.8±0.9
Humic and fluvic acid (F3)
0
0
3.4±0.6
5.3±0.9
0
5.6±1.1
3.6±0.8
4.6±1.2
0
Organic matter (F4)
6.3±0.9
0
6.8±1.5
22.3±1.3
30.6±2.1
7.3±1.2
11.2±2.0
16.3±1.9
11.0±2.1
Silicate (F5)
4.9± 1.9
26.9±2.0
23.1±0.9
41.7±1.6
61.2±1.4
66.4±0.8
88.4±2.3
76.0±0.9
74.1±0.8
Total potentially leachable (%) (F1+F2)
96.4±2.0
72.1±2.2
74.8±1.7
40.3±2.8
21.6±2.7
34.0±1.2
7.6±1.4
14.3±2.2
37.8±2.2
Extracting reagent: F1: 1 M NaAc, F2: HAc/NH2OH-HCI, F3: NaOH/Na2H2P2O7, F4: HNO3/concH2O2, F4: HF/HNO3
High concentrations of Co were leached at pH 2.0; the cumulative percentage leached
was 93.2%. The large decrease in the cumulative percentage leached when a leaching
solution of higher pH was used indicated that pH plays a critical role in controlling the
leaching behaviour of Co. This implies that the environmental impact of Co in coal
samples would be small when exposed to an environment of pH > 6.0. These results
proved that Co is easily leached in a more acidic solution, and that Co was mainly leached
from the surface. Furthermore, the cumulative percentage of Co leached from coal was
proportional to the percentage of potentially leachable Co. The maximum concentration of
Co in the leachate (1430 µg/L) was higher than the EPA standard (1000 µg/L) for surface
water12.
4.2.3.2 Leaching behaviour of Cr
The leachabilty of Cr in the coal sample was found to be dependent on the pH of the
leaching media. From Figure 4.1, it can be seen that the cumulative percentage of Cr
leached increased as the pH decreased. At pH 6.0, the concentration of Cr attained
equilibrium with the surface of the coal. There was a sharp and slight increase in the
cumulative percentage leached at pH 2.0 and 4.0. From Table 4.3, it can be deduced that
72.1±2.2% of Cr in the coal sample was potentially leachable. This value compares
favourably with the cumulative percentage Cr leached (63.7%) when the pH of the
leaching solution was 2.0. Generally, the leaching behaviour of Co and Cr, as illustrated
by the cumulative percentage curves (Figure 4.1), was representative of the rest of the
metals.
This indicates that the pH of the leaching solution plays an important role in the leaching
behaviour of Cr in coal. Therefore, the environmental impact of Cr in the coal is potentially
considerable at low pH, such as those encountered in AMD. The maximum concentration
of Cr in the leachate (60.8 µg/L) was found to be less than the EPA standard of 100 µg/L
for surface water (US EPA, 2011).
4.2.3.3 Leaching behaviour of Cd
Cadmium exhibited similar leaching behaviour at different pHs of leaching solution, i.e. the
leached concentration attained equilibrium with the surface of coal particles as the
leaching progressed. However, the cumulative Cd percentage leached was highest at pH
12
www.epa.gov/safewater/contaminants/index.html#primary [Accessed: 17-11-2009]
86
4.0 (63.9%), followed by pH 2.0 (33.4%) and finally by pH 6.0 (28.4%). The experimental
results from sequential extraction (Table 4.3) indicated that 78.4±1.7% of Cd in the coal
was potentially leachable. This value compared well with the cumulative percentage of
64.1% at pH 4.0 after 60 h. This suggests that the cumulative percentage of Cd leached
from coal at pH 4.0 is proportional to the potentially leachable Cd. The maximum
concentration of Cd in the leachate (70.4 µg/L) exceeded the EPA standard of 5.00 µg/L
for surface water (US EPA, 2011).
4.2.3.4 Leaching behaviour of As
The leachability of As from coal varied at different pHs of the leachant. At pH 4.0, the
concentration of As approached equilibrium, whilst at pH 2.0, the period of the leaching
experiment had to be extended to observe equilibrium. The cumulative percentage of As
leached increased as the pH decreased. No As was leached when the coal was exposed
to a leaching solution of pH 6.0. The cumulative percentage of As leached at pH 2.0 and
4.0 was 25.0% and 2.8%, respectively. This implies that, although the percentage of
potentially leachable As was 40.3±2.8% (Table 4.3) most of it did not occur on the surface
of the coal particle. The mobilization and transfer of As from coal to water depends on the
hydro-chemical characteristics (e.g. pH) of the water and the As species in the coal
(Evans, 1989; Driehaus, Jekel & Hildebrandt, 2002; Saxena & Singh, 2004). Arsenic is
mainly found in the trivalent and pentavalent form. From the leaching experiments, it could
be observed that As was predominantly leached under acidic conditions, while no As was
leached at pH 6.0. The difference in the leaching behaviour at these pHs can be ascribed
to the existence of As in the pentavalent form (H 2AsO4-) in the coal. This species is
dominant under oxidising and acidic conditions (pH < 6.9) (Saxena & Singh, 2004). The
lack of As leaching at pH 6.0, and above, can be attributed to the absence of H 3AsO3 and
HAsO42- in the coal. Zhao et al. (2006) also reported that As could not be leached at pH
6.0. For As, the maximum concentration leached was 46.8 μg/L, which exceeds the EPA
standard (10.0 µg/L) for As in surface water (US EPA, 2011).
4.2.3.5 Leaching behaviour of Ni
The observed high concentration of Ni at pH 2.0 indicated that Ni is susceptible to
leaching under acidic conditions. The concentration of Ni attained equilibrium after 5 h and
10 h, when the pH of the leaching environment was 6.0 and 4.0, respectively. Sequential
extraction data (Table 4.3) indicated that 21.6±2.7% of Ni in coal was potentially leachable
providing the duration of leaching was long enough. The highest cumulative percentage
87
leached (4.9%), proved that most of the Ni was not leached. This could be attributed to a
low occurrence of the leachable forms of Ni on the surface of the coal particles. Although
the cumulative percentage leached was lower than the potentially leachable percentage,
the maximum concentration of Ni in the leachate (622 µg/L) was higher than the EPA
standard (20.0 µg/L) for surface water (US EPA, 2011). It could hence be concluded that
the environmental impact of Ni in coal samples is potentially high when coal is exposed to
low pHs.
4.2.3.6 Leaching behaviour of Pb
The leaching trend of Pb was different under the various pH conditions of the leaching
solution. When the pH was 2.0, the cumulative percentage leached rose sharply with time
and did not approach equilibrium during the the leaching experiment.
On the other hand, the cumulative percentage leached, when the pH of the leaching
solution was 4.0 and 6.0, rose slightly and approached a plateau/equilibrium after 10 h of
leaching. Although 34±1.2% of Pb was potentially leachable (Table 4.3), in practice, only a
maximum of 11.1% was leached. This indicates that most of the Pb amenable to leaching
did not occur on the surface, but within the inner matrix of the coal particles. Thus, in order
for extensive leaching to occur, the coal must be exposed to acidic conditions for a longer
leaching period. The highest concentration of Pb in the leachate (80.3 μg/L) was found to
be higher than the EPA standard of 15 μg/L (US EPA, 2011).
4.2.3.7 Leaching behaviour of Th
The leaching behaviour of Th was similar to that of the other elements when the pH of the
leaching solution was 2.0 and 4.0. The cumulative percentage of Th leached increased to
0.7% and 0.8%, at pH 2.0 and 4.0, respectively. Equilibrium was not reached between the
concentration of Th in the leachate and the surface of the coal during the leaching
experiment. This implies that a much longer duration is necessary to further evaluate the
leachability of Th from the coal samples. A higher proportion of Th was leached at pH 4.0
than at pH 2.0, while no Th was leached when the pH of the leaching solution was 6.0.
Based on the results from sequential extraction experiments (Table 4.3), 7.6±1.4% of Th
in the coal samples was theoretically leachable. However, only 0.8% was leached in
practice, indicating that most of the potentially leachable Th occurs within the inner matrix
of the coal sample and is therefore not easily leachable. The maximum concentration of
Th in the leachate (64.75 µg/L) after 60 h of leaching, under the designed pH condition of
88
the leaching medium, exceeded the 2.0 μg/L EPA standard for surface water (US EPA,
2011).
4.2.3.8 Leaching behaviour of U
The leaching behaviour of U at pH 4.0 was different from that at pH 2.0 and 6.0. At pH 2.0
and 6.0, the amount of U in the leachate approached equilibrium with the surface of the
coal particles. At pH 4.0, the leaching had to be carried out for an extended period in order
for equilibrium to be established. The cumulative percentages of leached U were 3.9, 4.9
and 0.7%, at pH 2.0, 4.0 and 6.0, respectively. This indicates that the leaching of U is
favoured by acidic conditions.
The experimental results of sequential extraction are reported in Table 4.3. The
percentage of U theoretically susceptible to leaching was 14.3±2.2%. The low cumulative
percentages imply that most of the leachable U occurs in the inner matrix of the coal
particles. The possibility of competing processes, including adsorption and precipitation,
which would limit the concentration of U in the leachate, cannot be overruled. For U, the
maximum concentration in the leachate (80.7 µg/L) was higher than the corresponding
EPA standard (30.0 µg/L) for surface water (US EPA, 2011).
4.2.3.9 Leaching behaviour of Mn
The leaching behaviour of Mn was similar under different pH conditions. The amount of
Mn in the leaching solution approached a plateau, with time, in all cases. At the plateau,
the amount of Mn in the leaching solution was in equilibrium with the surface of the solid
sample. After 60 h, the cumulative percentage of Mn leached was 5.2, 2.8 and 1.3%,
when the pH of the leaching solution was 2.0, 4.0 and 6.0, respectively. This indicates that
Mn is easily leachable under acidic conditions. From data on the occurrence of the
elements in the fractions it was calculated that 37.8±2.2% of Mn in coal is potentially
leachable (Table 4.3). The low cumulative percentage can be a result of the leaching of
only small amounts of the element occurring on the surface of the coal particle. The
results demonstrate that most of the potentially leachable Mn does not occur on the
surface of the coal particle, but is present within the matrix of the coal, hence the low
leachability of Mn from coal. The low concentrations of Mn in the leaching solutions could
be attributed to limiting processes, which include adsorption on the surfaces of the silicate,
or metal hydroxide and oxide, and precipitation of hydroxide/oxide and carbonate
compounds (Evans, 1989). The maximum concentration of Mn in the leachate (1470 µg/L)
89
was higher than the EPA surface water standard (50.0 µg/L) for the element (US EPA,
2011).
Also included in Table 4.1 are the concentrations of the metals in AMD water sampled
from a stream emanating from an abandoned coal mine in eMalahleni. It was observed
that the concentrations of Pb, As, Ni, Cr and Mn in the water were all above the
concentrations set out in the EPA guideline for surface water. Throughout the leaching
experiments, all elements (including Cd and Co) but with the exception of Cr, were
leached in concentrations exceeding the EPA guideline. The low value of Co in the AMD
can be accounted for by the low pH of the medium. The leaching experiments revealed
that leaching of Co from coal is favoured by a pH value of 6.
4.3
4.3.1
Metal quantification in coal and certified reference material
Validation of digestion using SARM 18
The metal composition of the coal samples and reference material SARM 18, together
with Student t-test values, are presented in Table 4.4. Experimentals t values were
calculated using the formula:
Eq. 4.3
It must be noted that all the experimental t-values were less than the critical t-value at the
95% confidence level. Consequently, the null hypothesis that the experimental value was
not significantly different from the certified value was retained. The concentrations of
elements in coal are influenced by a number of factors including depositional environment
and volcanic activity (Mujuru et al., 2009). Evaluating the elemental composition of the
coal is important in assessing the potential environmental impact.
4.3.2
Cobalt
The amount of Co in the coal was determined as 2.58 µg/g. This value lies within the
global range of 0.50-30.00 µg/g (Goodarzi, 1995). Most of the Co occurred in association
with Fe-Mn-oxides and sulfides. The second largest Co-containing fraction was found in
the acid soluble and exchangeable form (Table 4.3).
90
4.3.3
Cadmium
The Cd content of the coal was found to be 0.23 µg/g (Table 4.6). Although the value is
low, it lies within the same range as that reported globally for coal (0.1-3.0 µg/g)
(Goodarzi, 1995). Most of the Cd in the coal occurred in association with Fe-Mn oxide and
sulfide (Table 4.3), but also with clay, carbonate, and the organic fraction coal (Swaine,
1990; Finkelman, 1995; Goodarzi, 1995).
Table 4.4: Calculated t values and results from total digestion of coal and SARM-18
Element
Coal
SARM 18
SARM 18
Calculated
Co
certified
(µg/g)
6.7(5.5-7.2)
Experimental
(µg/g)
6.30±0.40
t value
(µg/g)
2.58±0.18
Cr
0.22±0.09
16(14-18)
14.0±0.7
-6.389
Cd
0.23±0.08
na
As
0.64±0.11
na
Ni
45.9±3.74
10.8(10.1-11.5)
12.2±1.4
0.639
Pb
2.11±0.97
5*
4.4±0.04
-33.541
Th
19.2±5.18
3.4(3.0-4.3)
3.60±0.70
0.639
U
5.24±1.41
1.5(1.5-2.0)
1.13±0.03
-27.578
Mn
50.6±7.92
22(21-23)
19.3±0.9
-6.708
-2.236
na: not available. Confidence limit in parenthesis, *confidence limit not available and concentration value is not
certified.
Null hypothesis: the experimental value is not significantly different from the certified value.
t4;0.05 = 2.132
4.3.4
Chromium
The Cr content in the coal was determined as 0.22 µg/g. This is low compared to global
figures for coal (0.5-60 µg/g). Most of the Cr in the coal occurred in association with FeMn-oxides and sulfides (Table 4.3). The remaining Cr appeared to be bound to silicates
and to exist in an acid soluble and exchangeable form. Chromium has been observed to
occur in association with most mineral groups, including clay minerals, carbonates, oxides
and sulfides (Querol et al., 1996; Spear, Booth & Statoug, 1999).
4.3.5
Arsenic
The As content of the coal samples was 0.64 µg/g. This is low compared to global levels
for coal (0.5-80.0 µg/g) (Driehaus et al., 2002). Arsenic in the coal sample was associated
with silicates, organic matter and Fe-Mn-oxides and sulfides (Table 4.3). According to
91
Finkelman (1995), Davidson (2000) and Goodarzi (2002), As is generally present in pyrite
in solid solution, or in the organic fraction of coal (Swaine, 1990; Galbreath, DeWall &
Zygarlicke, 1999).
4.3.6
Nickel
The Ni content of the coal is 45.9 µg/g, which is within global levels 0.5-50 µg/g)
(Goodarzi, 1995). Most of the Ni was associated with silicates, organic matter and Fe-Mnoxides and sulfides (Table 4.3). According to literature, Ni, in coal, is probably associated
with sulfides [millerite (NiS)] and also within organic matter (Goodarzi, 1995).
4.3.7
Lead
Lead is generally associated with the mineral matter in coal, including sulfides and
selenides, clay materials and carbonates (Swaine, 1990; Finkelman, 1995; Goodarzi,
1995). The concentration of Pb in the coal was 2.11 µg/g, which lies within global levels
(2.0-80 µg/g) (Goodarzi, 1995).
4.3.8
Manganese
The Mn content of the coal was 50.6 µg/g, which is slightly above the global average (50.0
µg/g) (Pollock, Goodarzi & Riediger, 2000). Most of the Mn in the coal was associated
with silicates, organic matter and sulfides (Table 4.3). Manganese in coal is generally
associated with pyrites, clays and organic material (Goodarzi, 1995).
4.3.9
Uranium
The U content in the coal was found to be 5.24 µg/g. Most of this U was bound to silicates,
Fe-Mn-oxides and sulfides and organic matter (Table 4.4). Generally, U in coal occurs in
association with organic matter, zircon and phosphates (Goodarzi, 1995).
4.3.10 Thorium
Thorium in the coal samples was mainly associated with silicates and organic matter
(Table 4.4). The total thorium was determined as 19.23 µg/g. Generally, Th in coal occurs
in association with rare earth phosphates and clays (Goodarzi, 1995).
92
4.4
Estimation of amount of metal with potential to be leached into the
Mpumalanga ecosystem
The approximate amounts of metals that can be leached from a tonne of coal were
calculated from the amount leached from 20 g of coal. This was undertaken in order to
ascertain the potential environmental impact of leaching of metals from outdoor coal
stored outdoors at mines, powerstations and coal depots. Generally, large amounts of
metals have the potental to be leached at pH 2 within the first 5 h (Table 4.5). The largest
amount was obtained for Mn (1800 g), Co (1200 g) and Ni (800 g) whereas the lowest
amount was obtained for Cr (2.6 g). At pH 4, large amounts of Ni (536 g) and Mn (575 g)
were found to have the potential to be leached into the aquatic system. Nickel (441 g) and
Co (1790 g) were also found to have the potential to be leached in large amounts at pH 6.
The Mpumalanga region produces approximately 240 million tonnes of coal per annum 13.
This translates to approximately 442 000 tonnes (Mn), 30 000 tonnes (Co), 42 500 tonnes
(Ni) and 624 tonnes (Cr) that potentially may be leached from the mined coal at pH 2.
With leaching solutions at pH 4 and 6, 138 000 tonnes (Mn) and 129 000 tonnes (Co), and
106 000 tonnes (Ni) and 429 000 tonnes (Cr), respectively, can potentially be leached
from 240 million tonnes of mined coal. Of the 240 million tonnes of coal mined in
Mpumalanga about half of it is used by the power utility ESKOM for generation of
electricity, while 45 million tonnes is used by SASOL and approximately 65 million tonnes
is exported. After taking into consideration coal usage in the studied area viz a viz
environmental impact it can be extrapolated that approximately 221 000 tonnes (Mn),
150 000 tonnes (Co), 21 200 tonnes (Ni) and 312 tonnes (Cr) can potentially be leached
from the mined coal at pH 2, while at pH 4 and 6, 69 000 tonnes (Mn) and 64 300 tonnes
(Ni), and 52 900 tonnes (Ni) and 215 000 tonnes (Cr) can potentially be leached into the
Mpumalanga ecosystem that includes the sampled rivers.
13
http://www.mpumalangacompanies.co.za/pls/cms/ [Accessed: 21-09-2012]
93
Table 4.5: Approximate amounts (g) of metals leached from one tonne of coal
Element
Co
Cr
94
Cd
As
Ni
Pb
Th
U
Mn
pH =2.0
pH=4.0
pH=6.0
0-5h
5-10h
10-30h
30-60h
0-5h
5-10h
10-30h
30-60h
0-5h
5-10h
10-30h
30-60h
1248.8
76.0
38.0
289.6
2.6
13.3
175.8
8.0
16.7
87.9
50.8
9.2
80.5
38.3
88.0
50.9
8.0
33.0
13.4
3.0
14.5
8.0
9.3
10.8
1789.5
7.3
48.1
467.9
4.6
10.8
100.8
1.3
5.9
48.2
1.2
0.8
58.4
777.0
28.3
480.6
37.9
232.9
35.8
751.1
4.0
535.5
10.9
481.0
0.9
75.9
2.3
17.3
0.0
441.0
0.0
59.5
0.0
14.0
0.0
1.7
100.4
58.4
100.8
1841.7
19.5
25.8
75.9
513.6
38.3
20.6
19.2
253.0
75.9
38.0
8.0
43.4
43.5
80.9
79.8
574.8
10.4
42.3
70.2
323.6
5.3
18.4
60.3
495.6
2.1
10.5
45.2
23.1
19.8
0.0
21.2
431.9
0.1
0.0
9.1
176.0
0.0
0.0
4.8
48.6
0.0
0.0
1.8
4.0
4.5
4.5.1
Organics leaching: results and discussion
Physiochemical characterisation of coal leachates
The variations of the physicochemical properties of coal leachates with time and pH
conditions are presented in Figures 4.2 and 4.3. The physicochemical properties
monitored include dissolved organic carbon (DOC), total organic carbon (TOC),
suspended solids and reduction potential (Eh).
Seam 4
60.00
DOC/TOC (mg/l)
50.00
DOC (mg/l) AW
40.00
30.00
DOC (mg/l) UAW
20.00
TOC (mg/l) AW
10.00
TOC (mg/l) UAW
0.00
1
2
3
4
5
6
Leaching event
Seam 4
900
Eh/Susp. solids
800
Susp. Solids (mg/l) AW
700
600
Susp. Solids (mg/l) UAW
500
Eh (mV) AW
400
Eh (mV) UAW
300
1
2
3
4
5
6
Leaching event
Figure 4.2: Variations of DOC, TOC, suspended solids and Eh values of Seam 4 coal
leachates. AW=acidified water, UAW=unacidified water
95
Seam 5
90
80
DOC (mg/l) AW
DOC/TOC (mg/l)
70
60
DOC (mg/l) UAW
50
TOC (mg/l) AW
40
30
TOC (mg/l) UAW
20
10
0
1
2
3
4
5
6
Leaching event
Seam 5
1200
1100
Susp. Solids (mg/l) AW
Eh/Susp. solids
1000
Susp. Solids (mg/l) UAW
900
800
Eh (mV) AW
700
600
Eh (mV) UAW
500
400
1
2
3
4
5
6
Leaching event
Figure 4.3: Variations of DOC, TOC, suspended solids and Eh values of Seam 5 coal
leachates. AW=acidified water, UAW=unacidified water.
For Seam 4 coal, DOC and TOC exhibited the same trend. Both decreased with time, until
the fourth leaching event or 25 days, after which they remained almost constant. There
was no significant difference in the values obtained using acidified water or unacidified
water. However, the TOC was greater than the DOC. Dissolved organic carbon is the
organic carbon present in water in the dissolved form, able to pass through a 0.45 μm
membrane filter. The TOC is a measure of the organic carbon found in solution and in
suspended matter.
For Seam 4 coal, the suspended solids in acidified and unacidified water leachates
increased until the fourth leaching event, after which it reached a plateau. The
96
concentration was the same during the initial stages of the leaching, but differed after the
fourth leaching event, with acidified water giving slightly higher suspended solids. The Eh
values for both acidified and neutral water gradually decreased as the leaching
proceeded. Leachates form acidified water had slightly higher Eh values than unacidified
water leachates.
The leachates obtained from leaching Seam 5 coal with acidic water and neutral water
exhibited the same trend for DOC. There was a decrease in the concentration of DOC in
the leachates, with time. When compared to Seam 4 coal, the DOC concentrations in
Seam 5 coal leachates were slightly higher. The TOC in Seam 5 coal leachates, from both
pHs, generally had the same trend. However, slightly higher concentrations were obtained
from the second and fourth leaching event and fifth and sixth leaching events, for acidified
water and unacidified water, respectively.
The suspended solid concentrations from Seam 5 coal increased with leaching time for
both environmental conditions. However, no significant differences were recorded for the
suspended solids when applying the two pH conditions of leaching water. The Eh values
decreased slightly during the initial stages of the leaching experiment, but remained
almost constant from the fourth leaching event onwards.
From the foregoing, it can be concluded that the TOC and DOC concentrations decreased
with leaching time. This decrease can be explained by the decrease in acid production
and therefore decreased chemical weathering of the coal matrix during the leaching
experiment. Acid production occurs when the pyrites in the coal is exposed to water and
oxygen. The oxidation reaction is catalysed in the presence of autotrophic bacteria.
Generally, low oxygen concentrations and temperatures tend to slow down the bacteriamediated oxidation of pyrite.
The Eh values are a measure of the tendency of a chemical species to acquire electrons
and thereby be reduced. In aqueous solutions, the reduction potential is a measure of the
tendency of the solution to either gain or lose electrons when it is subject to change
resulting from introduction of a new species. A solution with a higher (more positive)
reduction potential will have a tendency to be reduced by oxidation of the new species,
while a solution with a lower (more negative) reduction potential will have a tendency to
lose electrons to the new species (i.e. to be oxidized by reducing the new species).
97
From the leaching experiments, it was observed that there was a general decrease in the
Eh values as a function of time. This implies the leachate solutions tend to be reducing as
the leaching process proceeds. This can be related to the decrease in the amount of
oxygen as it is consumed during the initial stages of the leaching by the oxidation of sulfur
in the coal.
4.5.2
EPA priority PAHs in coal leachates
The identified EPA priority PAHs in coal leachates obtained from acidified leachates are
presented
in
Tables
4.6
and
4.7.
The
PAHs
naphthalene,
acenaphthalene,
acenaphthylene, fluorene, phenanthrene and pyrene were identified in Seam 5 coal
leachates from both acidified and neutral water. However, the PAHs benzo(a)anthracene
and chrysene were obtained only when acidic water was used for leaching. Seam 4 coal
leachates contained the PAHs naphthalene, acenaphthalene, acenaphthylene and pyrene
for acidic water, and naphthalene, acenaphthene, fluorene, phenanthrene and pyrene
when neutral water was used for leaching.
4.5.3
Other organic compounds identified in the coal leachates
Besides the EPA priority PAHs, a wide range of organic compounds were leached from
the coal samples (Tables 4.8 and 4.9). The GC-MS TIC chromatograms of the
dichloromethane extracts (Figures 4.4 and 4.5) of produced leachates did not differ much
in the number of peaks and compounds present. Despite the complexity of the
chromatograms quantification was possible through SIM, whereby only an ion perculiar to
a given compound is detected and used for quantification, thus reducing interferences
from co-eluting inteferences. Only a fraction of the total peaks were identified with some
degree of confidence. Tables 4.8 and 4.9 list the identified compounds according to their
class and approximate concentrations. These values are only appproximate, since no
calibration standards were available.
98
Table 4.6: Average concentrations (n=3) of EPA priority PAHs (µg/L) identified in leachates from acidified water
EPA PAH
Seam 4
Seam 5
Leaching days
Naphthalene
Acenaphthylene
Acenaphthene
Fluorene
99
Phenanthrene
Pyrene
Benz(a)anthracene
Chrysene
Leaching days
5
10
15
20
25
30
5
10
15
20
25
30
0.56
0.68
0.84
1.07
0.88
0.88
0.56
0.32
0.44
0.16
3.88
2.12
±0.03
±0.05
±0.11
±0.15
±0.09
±0.05
±0.14
±0.07
±0.09
±0.05
±0.87
±0.95
0.76
0.52
0.64
0.72
0.76
0.76
0.76
0.24
0.56
3.64
5.52
3.00
±0.15
±0.17
±0.08
±0.05
±0.09
±0.07
±0.08
±0.02
±0.09
±1.08
±1.15
±1.08
1.25
1.24
1.16
1.16
1.04
1.04
1.29
nd
nd
nd
0.56
1.76
±0.15
±0.11
±0.15
±0.17
±0.12
±0.09
±0.19
±0.17
±0.42
nd
nd
nd
nd
nd
nd
nd
0.32
1.24
±0.08
±0.15
1.12
1.12
±0.18
±0.15
nd
nd
nd
nd
nd
nd
0.68
0.44
0.68
0.84
0.96
0.96
±0.17
±0.09
±0.17
±0.09
±0.18
±0.12
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
0.32
0.32
0.32
1.64
0.32
±0.09
±0.07
±0.05
±0.51
±0.14
0.32
0.32
0.44
0.16
3.88
2.12
±0.09
±0.05
±0.07
±0.04
±1.05
±0.69
0.24
0.24
0.56
3.64
5.52
3.00
±0.02
±0.05
±0.14
±1.05
±1.15
±1.08
Table 4.7: Average concentrations (n=3) of EPA priority PAHs (µg/L) identified in leachates from unacidified water
EPA PAH
Seam 4
Seam 5
Leaching days
Naphthalene
Acenaphthylene
100
Acenaphthene
Fluorene
Phenanthrene
Pyrene
Leaching days
5
10
15
20
25
30
5
10
15
20
25
30
0.84
1.52
2.56
2.68
3.68
3.88
0.32
0.32
0.44
0.16
3.88
2.12
±0.17
±0.41
±0.59
±0.71
±0.39
±0.58
±0.95
±0.07
±0.09
±0.05
±0.79
±0.51
0.36
0.64
2.44
1.12
0.76
0.76
0.24
0.24
0.56
3.64
5.52
3.00
±0.09
±0.17
±0.91
±0.52
±0.15
±0.17
±0.08
±0.05
±0.15
±0.57
±1.05
±1.24
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
0.56
1.76
0.15
0.95
0.32
1.24
±0.07
±0.19
1.12
1.12
±0.09
±0.09
0.32
0.64
1.32
0.72
1.16
1.16
±0.17
±0.15
±0.18
±0.08
±0.19
±0.14
nd
nd
nd
1.04
0.72
0.72
±0.09
±0.14
±0.15
nd
nd
0.84
0.24
0.32
0.32
0.32
1.64
0.32
±0.14
±0.05
±0.04
±0.05
±0.08
±0.51
±0.08
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
Table 4.8: Selected compounds (µg/L) identified in dichloromethane extracts of Seam 5 coal leachates
Polycyclic aromatic hydrocarbons*
Seam 4 coal
Seam 5 coal
Leaching days
101
15
20
25
30
15
20
25
30
Methylnaphthalene
0.08
0.09
0.14
0.18
0.11
0.15
0.18
0.21
Dimethylnaphthalene
0.04
0.06
0.21
0.15
0.09
0.11
0.38
0.41
Trimethylnaphthalene
0.04
0.11
0.18
0.26
0.08
0.17
0.21
0.38
Tetramethylnaphthalene
0.38
0.41
0.72
0.79
0.53
0.71
0.97
1.64
Tetrahydro-dimethylnaphthalene
0.12
0.19
0.85
1.38
0.21
0.28
1.08
2.11
Tetrahydro-trimethylnaphthalene
0.17
0.25
0.38
0.51
0.18
0.31
0.47
0.83
Ethyl-tetrahydronaphthalene
0.28
0.31
0.41
0.48
0.38
0.59
0.74
1.62
Ethyl dimethyl azulene
0.17
0.19
0.24
0.32
0.13
0.27
0.35
0.53
Methylphenanthrene
0.06
0.18
0.19
0.22
0.13
0.18
0.26
0.29
Dihyro-1-methylphenanthrene
0.09
0.15
0.17
0.31
0.06
0.21
0.28
0.31
1-Methyl-7-(1-methylethyl)phenanthrene
0.07
0.14
0.19
0.24
0.08
0.19
0.21
0.31
Methylanthracene
0.04
0.12
0.19
0.09
0.09
0.18
0.44
0.38
1-Methyl-9H-fluorene
0.14
0.83
0.92
1.07
0.19
1.61
1.89
2.01
9H-Fluoren-9-ol
nd
nd
0.09
0.16
nd
0.09
1.18
0.85
Methylpyrene
nd
nd
0.05
0.14
nd
0.21
0.18
0.31
2,3-Dihydro-1,1,2,3,3-pentamethyl-1H-indene
nd
nd
0.03
0.09
nd
nd
0.07
0.14
*concentrations estimated
Table 4.8 (continued)
Compound by class
Seam 4 coal
Seam 5 coal
Leaching days
15
20
25
30
15
20
25
30
Di-n-octyl phthalate
nd
0.11
0.31
0.46
nd
0.15
0.28
0.36
Methyl-biphenyl
nd
0.15
0.21
0.81
nd
0.19
0.24
1.31
Dimethyl-biphenyl
nd
0.09
0.13
0.52
nd
0.14
0.25
0.71
Tetradecane
nd
nd
0.31
0.54
nd
nd
0.31
0.54
1,7,11-Trimethylcyclotetradecane
0.09
0.13
0.28
0.57
0.11
0.23
0.35
0.47
2,6,10,14-Tetramethyl-hexadecane
0.15
0.18
0.41
0.74
0.21
0.38
0.48
0.95
1-Chloro-octadecane
0.13
0.09
0.83
1.01
0.13
0.09
0.83
1.01
Heptacosane
0.08
0.12
0.09
0.74
0.19
0.17
0.29
0.57
Pentacosane
0.05
0.09
0.12
0.86
0.11
0.13
0.16
0.28
Tridecanedial
0.05
0.08
0.08
0.15
0.09
0.12
0.09
0.17
Other aromatics*
102
Non-aromatic compounds*
*concentrations estimated
Table 4.9: Selected compounds (µg/L) identified in dichloromethane extracts of Seam 4 coal leachates
Polycyclic aromatic hydrocarbons*
Seam 4 coal
Seam 5 coal
Leaching days
103
15
20
25
30
15
20
25
30
Methylnaphthalene
0.03
0.08
0.07
0.12
0.09
0.17
0.21
0.27
Dimethylnaphthalene
0.08
0.06
0.18
0.21
0.11
0.14
0.31
0.42
Trimethylnaphthalene
0.07
0.11
0.09
0.18
0.05
0.13
0.21
0.35
Tetramethylnaphthalene
0.22
0.37
0.51
0.59
0.49
0.53
0.68
0.91
Tetrahydro-dimethylnaphthalene
0.09
0.14
0.81
0.98
0.25
0.33
0.73
1.16
Tetrahydro-trimethylnaphthalene
0.12
0.37
0.23
0.58
0.12
0.18
0.29
0.47
Ethyl-tetrahydronaphthalene
0.32
0.38
0.51
0.58
0.09
0.17
0.21
0.58
Ethyl dimethyl azulene
0.21
0.37
0.49
0.32
0.08
0.26
0.37
0.69
methylphenanthrene
0.09
0.21
0.17
0.24
0.15
0.17
0.21
0.35
Dihyro-1-methylphenanthrene
0.13
0.28
0.31
0.31
0.08
0.14
0.26
0.38
1-Methyl-7-(1-methylethyl)phenanthrene
0.11
0.17
0.24
0.29
0.09
0.21
0.24
0.35
Methylanthracene
0.08
0.09
0.18
0.21
0.11
0.25
0.31
0.38
1-Methyl-9H-fluorene
0.13
0.71
0.83
0.95
0.21
0.97
1.51
2.58
9H-Fluoren-9-ol
nd
nd
0.07
0.19
nd
0.13
0.82
1.01
Methylpyrene
nd
nd
0.09
0.14
nd
0.31
0.26
0.47
2,3-Dihydro-1,1,2,3,3-pentamethyl-1H-indene
nd
nd
0.07
0.12
nd
nd
0.09
0.18
*concentrations estimated
Table 4.9 (continued)
Compound by class
Seam 4 coal
Seam 5 coal
Leaching days
15
20
25
30
15
20
25
30
Di-n-octyl phthalate
nd
0.17
0.35
0.51
nd
0.18
0.31
0.43
Methyl-biphenyl
nd
0.12
0.25
0.29
nd
0.21
0.28
0.95
Dimethyl-biphenyl
nd
0.11
0.15
0.34
nd
0.17
0.37
0.91
Tetradecane
nd
nd
0.27
0.39
nd
nd
0.38
0.45
1,7,11-Trimethylcyclotetradecane
0.09
0.15
0.31
0.48
0.11
0.24
0.41
0.59
2,6,10,14-Tetramethyl-hexadecane
0.18
0.31
0.45
0.74
0.29
0.35
0.55
0.71
1-Chloro-octadecane
0.17
0.11
0.47
0.52
0.18
0.11
0.97
1.09
Heptacosane
0.11
0.19
0.25
0.28
0.24
0.27
0.35
0.69
Pentacosane
0.11
0.17
0.23
0.37
0.17
0.26
0.43
0.52
Tridecanedial
0.08
0.18
0.17
0.25
0.11
0.15
0.09
0.26
Other aromatics*
104
Non-aromatic compounds*
*concentrations estimated
Figure 4.4: Representative chromatogram for Seam 4 coal leachate extract (1naphthalene, 2-acenaphthylene, 3-acenaphthene and 4-pyrene)
Figure 4.5: Representative chromatogram for Seam 5 coal leachate extract (1naphthalene,
2-acenaphthylene,
3-acenaphthene,
4-fluorene,
5phenanthrene and 6-pyrene)
Compounds identified include functional derivatives of PAHs, benzene derivatives,
biphenyls and non-aromatic compounds (n-alkanes and cyclic aliphatic compounds).
Generally, PAHs and their functional derivatives were the compounds most frequently
identified in these leachate extracts. Major groups of PAHs identified included
naphthalene and derivatives, fluorene and derivatives, indene and derivatives, anthracene
105
and derivatives, phenanthrene and derivatives, and pyrene and derivatives. The PAHs are
widely distributed in coal, with the spectrum of leached compounds depending on the
type, or rank, of the coal, among other factors (Filho et al., 2002; Van Kooten, Short &
Kolak, 2002; Donahue, Allen & Schindler, 2006).
4.5.4
Environmental implications of leached PAHs
In surface waters, PAHs have a propensity to adsorb onto particulate material due to their
generally low solubility in aqueous solutions (de Maagd et al., 1998). The solubilities of
PAHs in aqueous solutions are temperature dependent, but may be enhanced by the
presence of natural organic matter, including humic substances (Maxin & Kögel-Knabner,
1995). It is possible that PAHs in surface water are stabilised in solution by humic-like
substances or suspended particles derived from the coal. Polycyclic aromatic
hydrocarbons discharged into the environment in surface waters will tend to adsorb on
soils or sediments (Jafvert et al., 2006). Biodegradation of PAHs by soil microorganisms
occurs, with half lives ranging from days to years, depending on the compound, the nature
of the microbial community present, and the biogeochemical conditions present
(Shuttleworth
&
Cerniglia,
1995).
Polycyclic
aromatic
hydrocarbons
may
also
bioconcentrate, with bioconcentration factors ranging from <1 to 1000 (Jonsson et al.,
2004).
Higher molecular weight PAHs (four to six rings), in particular those with structural
embayments (e.g. benzo[a]pyrene, dibenzo[a,l]pyrene), have been associated with the
development of cancer in humans through enzymatic conversion to epoxides in the body
and subsequent adduct formation with cellular DNA. The PAHs leached from coal in this
study were low molecular weight (three-rings) with some high molecular weight (four
rings) PAHs and alkyl derivatives. None of the well known potentially cancer-causing
PAHs were observed. It should be noted, however, that the health and environmental
impacts of long-term, chronic exposure to many of the PAHs observed in these leachates
is not known.
Derivatives of biphenyls were also observed in dichloromethane extracts of coal
leachates. These compounds are widely distributed in coal and are most likely produced
by the alteration of the lignin biopolymer during coalification (Orem & Finkelman, 2004).
Biphenyls and their derivatives may have toxic effects, particularly on the liver and kidney
(Orem et al., 2007).
106
4.5.5
Summary
Leaching behaviour was found to be influenced by pH, time and the nature of the
elements in eMalahleni coal. When the pH of the leaching solution was 2.0, most of the
elements where successfully leached. The leachability of the elements decreased with
time and increased pH. High leaching concentrations were obtained during the initial
stages (5 h) of the leaching process. Generally, the leaching decreased gradually over
time, however, for some elements, it increased. The maximum leached concentrations of
Pb, Cd, Ni, Mn, As and Cr exceeded their recommended maximum limits. For some
elements, the leached concentrations reached equilibrium with the surface of the coal
samples after 60 h.
The suspended particle concentrations, TOC and DOC in the coal leachates were found
to vary with leaching time. Generally, the TOC and DOC decreased, while suspended
particle concentrations increased, with leaching time, for both acidic and unacidicied
water. Some of the sixteen priority EPA PAHs and other organic compounds in coal
leachates were identified in this study. The other organic compounds identified include
methyl derivatives of naphthalene, biphenyls, and non-aromatic compounds (n-alkanes
and cyclic aliphatic compounds). Generally, PAHs and their functional derivatives were
the compounds most frequently encountered in the leachate extracts.
107
CHAPTER 5
DISTRIBUTION AND SOURCE APPORTIONMENT OF PAHs
5.1 Introduction
In this chapter the findings of the study with respect to PAHs are presented. Results from
the analysis of water, suspended matter and sediments are also given and statistical data
processing results are presented and discussed.
5.2
Results and discussion
Final concentrations of PAHs were obtained after recovery correction of the PAHs, using
surrogates for water samples and certified reference material for sediments. The
surrogates and their representative PAHs for recovery calculations for the samples are
shown in Table 5.1. Recoveries ranged from 77% (PhA-d10) to 110%. Implying that PAHs
phenanthrene, anthracene, fluoranthene and pyrene had lower recoveries from the water
samples. On the other hand, PAHs represented by chy-d12, including benz(a)pyrene,
exihibited higher recoveries from the water samples.
Percentage recoveries from sediment reference material (CRM 1944) and calibration
statistics for the PAHs are presented in Table 5.2.
5.3
PAH contents of water
The nomenclature of the sample sites is as follows:

OLI-Olifants River

OLT-Olifants River tributary

WLG-Wilge River

KOL-Klein Olifants River

S1 first summer sample, S2 second summer sample, W1 first winter sample, W2
second winter sample
For example, OLI1 S1 refers to the first sample collected from the first site along the
Olifants River in summer.
108
The detection limits of the studied PAHs in water, sediment, and suspended matter are
recorded in Table 5.2.
Table 5.1: List of analytes and corresponding internal standard/surrogate compound
PAH
Internal standard/surrogate
Naphthalene
Acenaphthene-d10(AcNPh-d10)
Acenaphthylene
AcNPh-d10
Acenaphthene
AcNPh-d10
Fluorene
AcNPh-d10
Phenanthrene
Phenanthrene- d10(PhA-d10)
Anthracene
PhA-d10
Fluoranthene
PhA-d10
Pyrene
PhA-d10/Chy-d12
Benz(a)anthracene
Chysene d12(Chy-d12)
Chrysene
Chy-d12
Benzo(b)fluoranthene
Chy-d12
Benzo(k)fluoranthene
Chy-d12
Benz(a)pyrene
Chy-d12
Indeno(1,2,3-cd)pyrene
Chy-d12
Dibenzo(a,h)anthracene
Chy-d12
Benzo(ghi)perylene
Chy-d12
A typical chromatogram obtained from analysis of water sample extract is depicted in
Figure 5.1. The total concentrations of PAHs in summer and winter samples from the
Olifants River were in the range of 192 to 263 and 195 to 267 ng/L, respectively (Table
5.3). Generally, the concentration of the PAHs decreased along the length of the river,
with highest concentrations recorded at sampling points within the coal mining and power
station areas.
109
Table 5.2: Average recoveries from CRM 1944 matrix using soxhlet extraction, calibration statistics and method detection limits (MDL) of
polycyclic aromatic hydrocarbons in water, suspended particles and sediments
PAH
Average
recoveries
2
R
Sediments
Suspended particles
Water
MLD
(µg/kg)
LOQ
(µg/kg)
MDL
(µg/kg)
LOQ
(µg/kg)
MLD
(ng/L)
110
NaP
AcN
AcNPh
74.5±3.5
85.3±2.9
71.9±4.1
0.999256
0.997634
0.998455
0.52
0.25
0.28
1.65
0.80
0.89
1.17
1.09
0.57
3.72
3.47
1.81
1.04
2.61
1.15
FI
PhA
94.5±2.7
87.1±4.1
0.998301
0.998023
0.28
0.13
0.89
0.41
0.51
0.48
1.62
1.53
1.08
1.04
AN
FIA
102.5±4.5
88.5±3.2
0.998895
0.998105
0.11
0.24
0.35
0.76
0.48
1.16
1.53
3.69
1.08
2.72
Py
BaA
Chy
BbFIA
91.7±5.1
75.2±1.6
82.1±11.4
79.2±9.5
0.998286
0.994894
0.996479
0.991749
0.11
0.11
0.11
0.25
0.35
0.35
0.35
0.80
0.48
0.42
0.42
0.38
1.53
1.34
1.34
1.27
1.13
1.06
1.04
2.07
BkFIA
BaP
IP
dBahA
84.7±1.8
91.5±2.7
72.5±5.8
81.2±7.1
0.993109
0.99177
0.996168
0.996466
0.21
0.12
0.09
0.25
0.67
0.38
0.29
0.80
0.37
0.38
2.38
2.58
1.18
1.27
7.58
8.61
0.97
0.85
5.8
4.45
BghiP
97.5±3.5
R2 is regression value
0.990873
0.21
0.67
2.14
7.14
6.79
LOQ
(ng/L)
3.31
8.30
3.66
3.44
3.31
3.44
8.65
3.60
3.37
3.31
6.59
3.09
2.70
18.45
14.16
21.60
Figure 5.1: Representative chromatogram for Olifants River water sample extract (1naphthalene, 2-acenaphthene, 3-acenaphthylene, 4-fluorene, 5-anthracene,
6-fluoranthene,
7-pyrene,
8-benzo(a)anthracene,
9-chrysene,
10benzo(a)pyrene
The relative proportions of PAHs in the Olifants River samples were found to be in the
order of NaP > PhA > FIA > Py > FI > AcN > Chy > BaA > AN > BaP > AcNPh. Of all the
PAHs detected in water samples, the concentration of NaP was the highest, possibly due
to its extensive use in industries and households. With regards to the PAHs found in the
Olifants River, only FIA was not detected in coal leachates. This could be an indication
that these PAHs were derived from leaching of coal. The compound, BaP, a known
carcinogen, was only detected in the Olifants River. The Olifants River tributary emanating
from an abandoned coal mine had lower total PAH concentrations (40.9-118 ng/L and
43.4-106 ng/L in summer and winter, respectively) than those in the Olifants River itself,
perhaps because of other sources of PAHs upstream of the Olifants River.
The following PAHs were not detected in any of the samples: B(k)FIA, B(b)FIA, dBahA, IP
and BghiP. The PAHs, BaA, Chy and BaP, were not detected in samples from the Olifants
River tributary although they were detected in coal leachates. The variation in the
distribution of the PAHs in the water samples can be attributed to differences in input
sources and chemical processes, including degradation, which determine the residence
time and fate of the PAHs once they enter the aquatic environment.
111
Table 5.3: Concentrations of polycyclic aromatic hydrocarbons (ng/L) in water samples
River
PAH
Total
NaP
PhA
AcN
AcNPh
FI
AN
FIA
Py
BaA
Chy
BaP
PAH
OLIS
113.9-152.1
19.1-57.2
3.16-9.66
1.81-5.81
7.9-26.1
2.12-3.71
14.1-24.8
11.5-29.1
3.51-6.17
4.06-6.07
2.54-4.71
192-263
OLIW
114.2-127.9
19.6-57.8
2.95-6.84
1.46-3.17
11.8-21.2
1.81-4.57
11.3-28.7
10.6-27.1
2.38-5.91
3.52-6.17
3.09-4.73
195-267
OLTS
32.4-87.1
3.05-9.51
1.51-2.19
2.08-3.52
1.85-3.78
nd
2.95-6.29
1.94-5.29
1.86-2.14
2.14-2.17
nd
40.9-118
OLTW
35.2-82.3
2.86-5.81
1.63-2.27
1.84-3.89
2.06-4.52
nd
2.84-4.91
2.31-4.81
1.57-2.51
1.83-3.03
nd
43.4-106
KOLS
15.4-62.3
2.95-27.1
1.68-2.89
2.16-5.97
1.83-7.46
1.47-5.21
2.69-17.6
1.97-6.15
1.85-9.54
2.09-6.18
nd
31.0-145
KOLW
17.9-57.4
3.07-20.4
1.76-3.86
2.65-4.58
2.61-8.27
1.82-5.11
3.17-14.5
2.59-5.62
2.93-10.6
3.71-8.34
nd
17.9-134
WLGS
2.79-4.81
2.72-11.5
nd
nd
nd
nd
nd
nd
nd
nd
nd
3.71-15.4
WLGW 2.53-6.17
2.85-13.7
bold values: < LOQ; > MDL
nd
nd
nd
nd
nd
nd
nd
nd
nd
2.85-19.9
112
The concentrations of PAHs in the Wilge and Klein Olifants River water samples are
presented in Table 5.3. The total PAH concentrations showed a decreasing trend
downstream of the Klein Olifants. This can be attributed to a decrease in mining and
industrial activities along the river and dilution of the water. None of the following PAHs
were detected in any of the water samples: BaP, B(k and b)FIA, dBhA, IP and BghiP. The
PAHs, BaA, Chy and BaP, were not detected in samples from the Klein Olifants River,
although they were leached from coal. The Wilge River exhibited the lowest
concentrations of PAHs compared to the rest of the studied rivers. However, total
concentrations of the PAHs increased downstream of the river. Only NaP and PhA were
detected in the water samples from the Wilge River. Industrial, mining and domestic
activities, which are likely to be sources of PAHs, are mainly found downstream of the
studied section of the river. Agricultural activities predominate in the upper section of the
river, hence the low concentrations. Comparisons of the total PAH concentrations in
summer and winter for the sampled sites are presented in Figure 5.2. The decrease in
total concentration was generally in the order Olifants River > Klein Olifants > Olifants
River tributary > Wilge River. The differences in total concentrations for summer and
winter were found to be statistically insignificant (p<0.05) for most of the samples, with the
exception of KOL 1 and OLT 4, where the summer and winter total concentrations,
respectively, were found to be significantly higher. This can possibly be attributed to
discreet point sources of PAHs during the sampling period.
113
114
Tota PAH concentration in water (ng/L)
250
200
150
Summer
Winter
100
50
0
OLI OLI OLI OLI OLI OLI OLI OLI OLT OLT OLT OLT OLT OLT OLT OLT KOL KOL KOL KOL KOL KOL KOL KOL WLG WLG WLG WLG WLG WLG WLG WLG
1.1 1.2 2.1 2.2 3.1 3.2 4.1 4.2 1.1 1.2 2.1 2.2 3.1 3.2 4.1 4.2 1.1 1.2 2.1 2.2 3.1 3.2 4.1 4.2 1.1 1.2 2.1 2.2 3.1 3.2 4.1 4.2
Sample
Figure 5.2: Comparison of total polycyclic aromatic hydrocarbon concentrations in summer and winter samples
5.4
PAH contents of suspended matter
The concentrations of the PAHs extracted from the suspended materials in water samples
are presented in Table 5.4. The PAHs, NaP, BaA and Chy were not detected in
suspended particles from OLI and OLT samples, while PhA, AcN, AcNPh and FIA, were
not detected in either summer or winter OLI samples. However, the contributions of the
low and higher molecular weight (LMW and HMW) PAHs, to the total concentration, was
higher for the OLI samples. In addition, fewer PAHs were detected in the suspended
matter from this river. Water samples from OLI displayed a wider range of PAHs, with the
major contribution coming from HMW and MMW aromatics. This corresponded to the low
TOC in suspended material from these samples (Figure 5.3). Suspended particles from
the OLT contained a wide range of PAHs, with the MMW and HMW having the highest
concentrations of 8.65 to 18.6 µg/kg, dw (dry weight). This corresponded to a high TOC
content which was found for the suspended matter from OLT. The TOC content could be
derived from organic material, including humic material and algae attached to the
suspended particles (Li et al., 2006). A representative chromatogram of the PAHs in an
extract from the Olifants River tributary is illustrated in Figure 5.4.
The high proportion of MMW and HMW PAHs in suspended matter can be explained by
their low solubility in water and high octanol-water partition coefficient (K oc) values
determined for these PAHs (Maish & Taneja, 2006). The K oc values are used as an
indication of how hydrophobic or lipophilic molecules are, with higher values indicating
high lipophicity. The suspended material from the KOL (Table 5.4) sites displayed more
types of PAHs compared to water samples, which contained only high proportions of LMW
and MMW PAHs.
The total PAH concentrations in winter were generally not significantly greater than those
in summer, with the exception of the two sites along the Olifants River tributary, where
higher concentrations were obtained in the second sampling excursion (Figure 5.5). There
was a strong link between TOC and total PAH concentrations in the suspended matter as
illustrated in Figure 5.6. This can be taken as an indication that the total PAH
concentration in suspended matter contributed a significant component to the TOC (Zhou
et al., 1998; Chiou, 2002).
In addition, a link was observed between TOC and the total concentrations of HMW (5
and 6 rings) PAHs (Figure 5.7).
115
Table 5.4: Concentrations of polycyclic aromatic hydrocarbons (µg/kg, dry weight) in suspended matter
PAH
OLIS
OLIW
OLTS
nd
nd
nd
3.81-22.7
2.83-19.2
AcNPh
nd
nd
nd
FI
0.97-7.51
PhA
AcN
River
OLTW
KOLS
KOLW
WLGS
WLGW
2.65-18.3
6.41-8.47
5.32-9.27
4.13-13.7
1.47-8.74
1.99-7.49
nd
nd
nd
nd
1.52-19.2
2.18-17.5
2.47-6.28
1.91-8.42
0.93-2.72
1.44-2.53
0.62-8.14
1.95-9.83
2.45-8.35
1.17-5.85
0.97-8.53
0.85-1.65
0.61-1.76
116
AN
1.27-14.2
0.92-18.5
2.62-13.7
3.57-11.9
1.06-4.61
0.72-6.26
0.79-2.98
0.94-2.75
FIA
nd
nd
1.43-4.97
1.64-4.64
1.37-1.80
1.62-2.15
nd
nd
Py
0.77-4.82
0.83-5.71
1.48-6.53
1.94-7.22
0.71-1.15
1.28-1.95
0.76-1.14
0.81-1.51
BaA
nd
nd
nd
nd
nd
nd
0.73-2.75
0.85-1.91
0.53-0.62
0.71-0.82
Chy
nd
nd
0.81-1.73
0.91-1.35
0.51-0.83
0.65-1.74
BaP
1.17-2.13
1.45-2.87
1.65-25.3
1.91-26.7
1.14-2.83
1.42-1.99
BkFIA
0.52-2.74
0.75-1.85
nd
nd
1.25-1.85
1.57-2.35
BbFIA
0.41-1.73
0.82-2.91
4.51-8.65
3.72-9.71
3.72-7.92
3.65-8.25
dBahA
nd
nd
4.81-11.2
5.81-9.55
nd
nd
nd
nd
nd
nd
nd
nd
nd
nd
IP
3.83-9.75
3.95-11.3
3.47-5.82
4.91-8.17
3.72-7.92
3.65-8.31
3.97-4.74
3.84-5.26
BghiP
Total PAH
4.83-7.82
7.76-37.2
5.47-9.15
14.5-48.3
5.51-18.6
51.0-107
5.75-19.2
51.0-125
4.77-7.41
14.5-47.15
14.8-56.6
4.55-5.38
7.83-17.1
4.82-5.27
7.60-16.7
bold values: < LOQ; > MDL
3.5
3
2.5
TOC (%)
Sediment
2
Suspended
matter
1.5
117
1
0.5
0
OLI OLI OLI OLI OLI OLI OLI OLI OLT OLT OLT OLT OLT OLT OLT OLT KOL KOL KOL KOL KOL KOL KOL KOL WLG WLG WLG WLG WLG WLG WLG WLG
1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1 S1 1 S2 2S1 2S2 3S1 3S2 4S1 4S2
Sample
Figure 5.3: Total organic carbon composition in suspended matter and sediment samples collected in summer
Figure 5.4: Representative total ion chromatogram for Olifants River tributary suspended
matter extract (1-acenaphthylene, 2-acenaphthene, 3-fluorene, 4phenanthrene,
5-anthracene,
6-fluoranthene,
7-pyrene,
8benzo(b)fluoranthene, 9-benzo(a)pyrene, 10-indeno(1,2,3-cd)pyrene, 11dibenzo(a,h)anthracene and 12-benzo(ghi)perylene
118
119
Total PAH conc. in suspended particles (µg/kg)
120
100
80
Summer
Winter
60
40
20
0
OLI
1.1
OLI
1.2
OLI
2.1
OLI
2.2
OLI
3.1
OLI
3.2
OLI
4.1
OLI OLT OLT OLT OLT OLT OLT OLT OLT KOL KOL KOL KOL KOL KOL KOL KOL WLG WLG WLG WLG WLG
4.2 1.1 1.2 2.1 2.2 3.1 3.2 4.1 4.2 1.1 1.2 2.1 2.2 3.1 3.2 4.1 4.2 1.1 1.2 2.1 2.2 3.1
Sample
Figure 5.5: Total polycyclic aromatic hydrocarbon concentrations in suspended particles from samples collected in summer and winter 2010
(OLI 1.1 means first sample collected during summer or winter season on site one along the Olifants River)
Scatterplot of TOC (%) against Total [PAH] winter
TOC AND TOT PAH suspended material winter 64 spread sheet.sta 2v*64c
TOC (%) = 0.2784+0.0095*x; 0.95 Conf.Int.
1.8
Total [PAH] winter:TOC (%): r2 = 0.8954
1.6
1.4
TOC (%)
1.2
1.0
0.8
0.6
0.4
0.2
0
20
40
60
80
100
120
140
Total [PAH] winter
Figure 5.6: Correlation of total organic content with total polycyclic aromatic hydrocarbon
concentrations in suspended matter in water sampled during winter (N=32;
p<0.001)
Scatterplot of TOC (%) against Tot. 5 & 6 rings winter susp. matter
Spreadsheet2 2v*64c
TOC (%) = 0.4315+0.0125*x; 0.95 Conf.Int.
1.2
1.1
Tot. 5 & 6 rings winter susp. matter:TOC (%): r 2 = 0.9066
1.0
TOC (%)
0.9
0.8
0.7
0.6
0.5
0.4
0.3
-10
0
10
20
30
40
50
60
70
Tot. 5 & 6 rings winter susp. matter
Figure 5.7: Correlation of total organic content with total five and six ring polycyclic
aromatic hydrocarbon concentrations in suspended matter in water sampled
during winter (N=32; p<0.05)
120
5.5
PAH contents of sediments
A representative TIC of PAHs extracted from the Olifants River sediment is illustrated in
Figure 5.8. A complex chromatogram was obtained due to coextraction of other organic
compuonds in the sediment. As a result, selectivity during quantification was achieved
through SIM. The sediments were characterised in terms of their TOC content (Figure 5.3)
and particle size distribution (Figure 5.9). Generally, the TOC content of sediments was
greater than that in suspended matter. The highest TOC content was recorded in
sediments from the tributary passing through an abandoned coal mine, close to the site
having the highest TOC content. All of the sediment samples were classified as silt and
clay. Particle size distribution is important since particle size tends to influence the
adsorption of PAHs.
The total PAHs in sediments ranged from 24.7 µg/kg, dw (Wilge River) to 926 µg/kg
(Olifants River tributary) and 38.1 µg/kg (Wilge River) to 1035 µg/kg (Olifants River
tributary) in summer and winter samples, respectively (Table 5.5 and Figure 5.10). The
distribution characteristics of the sixteen EPA priority PAHs at the sampling sites were
similar, with the exception of Site WLG 2.
Figure 5.8: Representative total ion chromatogram for summer sediment sample extract
from Olifants River (1-naphthalene, 2-acenaphthylene, 3-acenaphthene, 4fluorene, 5-phenanthrene, 6-anthracene, 7-fluoranthene, 8-pyrene, 9benzo(a)anthracene,
10-chrysene,
11-benzo(b)fluoranthene,
12benzo(b)fluoranthene, 13-benzo(a)pyrene, 14-indeno(1,2,3-cd)pyrene, 15dibenzo(a,h)anthracene and 16-benzo(ghi)perylene
121
100.00
Particle size distribution (%)
90.00
80.00
70.00
60.00
50.00
(<0.004 mm)clay
40.00
(0.004-0.062 mm)silt
30.00
(0.062-2 mm)sand
20.00
10.00
OLI 1S1
OLI 1S2
OLI 2S1
OLI 2S2
OLI 3S1
OLI 3S2
OLI 4S1
OLI 4S2
OLT 1S1
OLT 1S2
OLT 2S1
OLT 2S2
OLT 3S1
OLT 3S2
OLT 4S1
OLT 4S2
KOL 1S1
KOL 1S2
KOL 2S1
KOL 2S2
KOL 3S1
KOL 3S2
KOL 4S1
KOL 4S2
WLG 1 S1
WLG 1 S2
WLG 2S1
WLG 2S2
WLG 3S1
WLG 3S2
WLG 4S1
WLG 4S2
0.00
Sample
Figure 5.9: Particle size composition of winter sediment samples from the Olifants River
tributary, Olitants, Klein Olifants and Wilge rivers
Low molecular weight PAHs accounted for the largest proportion (45 to 92%) of PAHs in all
sediment samples (Figure 5.11). The two, four and five rings PAHs contributed small
amounts to the total PAH concentration. Overall, the LMW, PAHs, NaP, PhA, AcN, AcNPh,
FI and AN, accounted for a large proportion of the total PAHs in all the sediment samples.
This can be perceived as an indication of a common source(s), possibly leaching of coal and
coal fines incorporated into the sediments, since these PAHs were detected in coal
leachates. Pyrolytic activities at high temperatures, incomplete combustion of fossil fuels and
biomass mainly yield LMW, MMW and HMW species. Therefore, the presence of LMW,
MMW and HMW PAHs in sediments can be attributed to pyrolytic sources.
The concentrations of PAHs in water and sediment samples were compared to Canadian
water quality guidelines.14 Pyrene detected in the Olifants River and also found in coal
leachates, exceeds the water quality standard (Table 5.6) and therefore poses a health
hazard to both aquatic life and humans (Nielsen et al., 1996). Sediment concentrations were
compared to the threshold effect level (TEL) and probable effect level (PEL). The lower
value, referred to as the TEL or interim sediment quality guideline (ISQG), represents the
concentration below which adverse biological effects are expected to occur rarely. The upper
14
www.ccme.ca/publications/ceqg_rcqe.html/ [Accessed: 09-07-2012]
122
value, referred to as the PEL, defines the level above which adverse effects are expected to
occur frequently.
Most of the LMW and MMW PAHs were found to have the potential to cause pollution of the
rivers. These problematic PAHs investigated were also observed in coal leachates. Two
HMW PAHs (BaP) and dBahA were also found to have the potential to pollute the Olifants
River tributary. High molecular weight PAHs are likely to result mainly from pyrolytic activities
such as coal combustion at nearby power stations.
The TOC content of sediment samples and the total PAH concentrations were found to be
positively correlated, as represented in Figure 5.12. Higher concentrations of PAHs were
found in sediment samples from the Olifants River tributary impling a higher component of
PAHs in the TOC contents of the sediment.
Very low concentrations of PAHs were detected in the Wilge River. This could be correlated
to the relatively lower TOC content of the sediments and the lower concentration of PAHs in
the aqueous phase, due to few major polluting activities.
The findings of the study revealed the presence of PAHs in the sections of the upper Olifants
River catchment studied. Some stations had particularly high loads of PAHs that could have
eco-toxicological consequences, as indicated by the threshold effect level (TEL) and
probable effect level (PEL) values set out for these PAHs.
123
Table 5.5: Polycyclic aromatic hydrocarbon concenterations (µg/kg) in sediment samples
River
OLIS
OLIW
OLTS
124
OLTW
KOLS
KOLW
WLGS
WLGW
PAH
NaP
PhA
AcN
AcNPh
FI
AN
FIA
Py
BaA
Chy
BaP
BkFIA
BbFIA
dBahA
IP
BghiP
Total
2.16-
2.84-
0.94-
3.86-
2.07-
11.5-
0.55-
0.16-
0.91-
0.79-
0.41-
0.51-
0.29-
0.35-
2.17-
0.65-
31.5-
8.71
21.7
28.6
11.5
14.2
21.3
1.76
4.81
1.87
1.52
2.15
1.92
1.74
1.61
9.73
7.82
129
3.74-
5.31-
1.57-
5.21-
2.73-
19.4-
0.11-
0.75-
0.14-
0.14-
0.62-
0.21-
0.21-
0.31-
3.94-
2.07-
44.7-
11.6
24.1
33.1
15.3
11.6
26.4
3.14
5.68
2.41
1.61
2.81
2.73
2.91
2.68
11.3
9.13
144.5
7.92-
25.3-
69.7-
21.5-
10.2-
28.6-
0.85-
3.74-
0.94-
0.88-
0.71-
0.69-
0.82-
2.28-
0.81-
2.28-
205-
34.2
409
154
113.8
37.2
215.5
17.8
19.6
4.62
3.61
21.8
3.85
3.81
12.4
6.97
27.5
926
13.5-
28.7-
73.5-
17.9-
13.2-
31.6-
1.41-
4.11-
1.33-
1.96-
0.83-
0.11-
0.81-
3.05-
1.73-
2.74-
224-
41.3
417.7
161.5
125.7
46.2
217.2
19.3
23.1
5.94
5.84
22.7
4.69
4.13
14.7
11.8
34.1
1035
1.52-
29.2-
25.3-
9.83-
6.53-
12.4-
0.18-
1.21-
0.24-
0.26-
0.74-
0.62-
0.25-
0.37-
1.82-
6.15-
140-
9.86
69.2
49.7
25.3
14.3
45.9
3.17
4.65
3.14
1.78
3.11
1.95
0.95
4.81
15.2
37.5
252.3
1.65-
37.8-
29.4-
10.6-
7.41-
19.3-
0.64-
1.68-
0.31-
0.13-
0.17-
0.71-
0.14-
0.71-
2.13-
7.71-
198-
11.6
93.1
51.8
28.1
19.4
53.1
3.75
5.59
2.75
1.84
0.41
2.53
1.41
5.83
18.7
38.9
291
1.15-
1.31-
0.91-
2.64-
0.18-
7.46-
0.25-
0.51-
0.15-
0.18-
0.51-
0.35-
0.95-
0.85-
2.81-
0.90-
27.4-
3.84
21.6
13.2
15.2
3.09
19.9
1.77
1.64
1.24
1.14
1.17
0.91
1.38
2.82
12.2
14.2
95.2
1.05-
2.74-
0.91-
3.84-
0.91-
11.3-
0.28-
0.52-
0.28-
0.17-
0.11-
0.13-
0.13-
0.76-
3.53-
1.74-
38.1-
4.24
22.5
18.5
19.5
3.75
28.7
2.86
1.83
0.86
1.13
1.93
1.81
1.17
3.94
15.3
17.5
118
bold values: < LOQ; > MDL
1000
Total [PAH]/ (µg/kg)
800
600
sediment
400
SPM
125
200
0
OLI OLI OLI OLI OLI OLI OLI OLI OLT OLT OLT OLT OLT OLT OLT OLT KOL KOL KOL KOL KOL KOL KOL KOL WLG WLG WLG WLG WLG WLG WLG WLG
1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1 S1 1 S2 2S1 2S2 3S1 3S2 4S1 4S2
Sample
Figure 5.10: Comparison of total polycyclic aromatic hydrocarbons in sediment and suspended matter
100.0
90.0
80.0
70.0
126
Fraction (%)
60.0
% 6 rings
50.0
% 5 rings
40.0
% 4 rings
% 3 rings
30.0
% 2 rings
20.0
10.0
0.0
OLI OLI OLI OLI OLI OLI OLI OLI OLT OLT OLT OLT OLT OLT OLT OLT KOL KOL KOL KOL KOL KOL KOL KOL WLG WLG WLG WLG WLG WLG WLG WLG
1W2 1W2 2W1 2W2 3W1 3W2 4W1 4W2 1W1 1W2 2W1 2W2 3W1 3W2 4W1 4W2 1W1 1W2 2W1 2W2 3W1 3W2 4W1 4W2 1
1 2W1 2W2 3W1 3W2 4W1 4W2
W1 W2
Sample
Figure 5.11: Distribution of 2, 3, 4, 5 and 6 ring polycyclic aromatic hydrocarbons in the sediments from the Olifants River tributary, Olifants,
Klein Olifants and Wilge rivers
Table 5.6: Water and sediment quality guidelines compared with results from this study
CWQG
PAH
Water
ng/L
Sediment
(µg/kg)
Sediment samples
PAHs
from
coal
OLT 1&2S/W
√
OLT 1, 2&3S; OLT 1, 2, 3&4W; KOL 1, 2,
3&4S/W
OLI 1, 3&4S/W; OLT 1, 2, 3&4S/W; KOL 1, 2, 3
&4S/W; WLG 1, 3 &4S/W
OLI 1, 3&4S, 2W; OLT 1, 2, 3&4S; KOL 1, 2, 3
&4S; WLG 1&4S
OLT 1, 2, 3&4S/W; KOL 3W
√
OLT 1, 2, 3&4S/W; KOL 2S/W; KOL 4S/W
X
X
√
NaP
1100
ISQG
34.6
PEL
391
PhA
400
41.9
515
AcN
5900
6.71
88.9
AcNPh
na
5.87
128
FI
3000
21.2
144
AN
FIA
Py
12
40
25
46.9
111
53.0
245
2355
875
BaA
18
31.7
385
√
Chy
na
57.1
862
√
BaP
BkFIA
BbFIA
dBahA
IP
BghiP
15
na
na
na
na
na
31.9
na
na
6.22
na
na
782
na
na
135
na
na
OLT 1&2W
√
√
√
X
X
X
X
X
X
OLT 1,3 &4W
ISQG: Interim sediment quality guideline
PEL: Probable effect level
Water samples OLI 4S/W>Canadian water quality guideline (CWQG)
na not available
Scatterplot of Tot. [PAH]summer SED against TOC (%)
TOC AND tot PAHSpreadshee summer SEDIMENT).sta 2v*64c
Tot. [PAH]summer SED = -342.183+478.5829*x; 0.95 Conf.Int.
1200 TOC (%):Tot. [PAH]summer SED: r2 = 0.9507
Tot. [PAH]summer SED
1000
800
600
400
200
0
-200
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
TOC (%)
Figure 5.12: Correlation between total polycyclic aromatic hydrocarbon concentrations
and TOC in sediment samples collected in summer
127
5.6
Comparison of PAH levels with those in other rivers around the world
The total PAH concentrations were compared to available data from sediments of other
rivers in the world (Table 5.7 The total concentrations obtained by researchers from the
Niger Delta region, Al-Arab, Kishon, Luan, and Malaysia rivers, are comparable to the
values from the Olifants, Wilge, Klein Olifants rivers and the Olifants River tributary,
obtained in this study. The area studied in this work is mainly affected by coal mining and
related activities, while the Niger Delta region, Al-Arab, Kishon, Luan, and Malaysia rivers
are most likely to receive PAHs from petroleum sources. This suggests that coal mining
and coal utilisation activities can have the same environmental impact on the environment
as petroleum related activities when total PAHs concentrations are considered. With the
economic development and increasing demands for electricity in South Africa, pollution by
PAHs is anticipated to become more serious.
The concentration range for the sixteen EPA priority PAHs determined in water samples in
this study were also compared to reported data from different rivers in the world (Table
5.7). The results demonstrated that the levels of PAHs reported in this study are higher
than for most of the listed rivers in other countries. However, the contamination levels of
PAHs in rivers in China were more severe, because of the extensive use of fossil fuels in
urban vehicle traffic (Li et al., 2006).
128
Table 5.7: Concentrations of polycyclic aromatic hydrocarbons in river sediments
worldwide
Location
N
Concentration References
(ng/g)
Mississippi River, USA
16
62.9-144.7
Zhang et al., 2007
Hyeongsan River, Korea
11
5.3-7680
Koh et al., 2004
Malaysia River, Malaysia
16
4.0-924
Zakaria et al., 2002
Haihe River, China
16
787-1943.0
Jiang et al., 2007
Luan River, China
16
nd-478
Cao et al., 2010
Kishon River, Israel
16
59.5-299
Oren, Aizenshtat & Shefetz,
2006
Shatt Al-Arab River, Arab Gulf
16
0.2-68
Al-Saad, 1987
Brisbane River, Australia
11
3940-16110
Kayal & Connell, 1989
George River, Australia
13
400-21400
Brown & Maher, 1992
Niger Delta, Nigeria
16
3.2-144.9
Anyakora et al., 2005
Vaal River, Klip River, Orange
NA
44-2799
Quinn et al., 2009
Olifants River, RSA
16
44.8-106
this study
Klein Olifants River, RSA
16
152-206
this study
Olifants River Tributary, RSA
16
225-1040
this study
Wilge River, RSA
16
38.2-87.6
this study
River,RSA
N= number of PAHs
Table 5.8: Polycyclic aromatic hydrocarbon concentrations in water phase of rivers of the
world
River
N
Range (ng/L)
References
Lower Mississippi River, USA
13
5.6-68.9
Mitra & Bianci, 2003
Elbe River, Germany
16
107-124
Gotz et al, 1998
Lower Siene River, France
11
4-36
Fernandes et al., 1997
Lower Brisbane River, Australia
15
5-12.0
Shaw et al., 2004
Gaoping River, China
16
10-9400
Doong and Lin, 2004
Tonghui River, China
16
193-2651
Zhang et al. 2004
Olifants River, RSA
16
193-268
this study
Klein Olifants River, RSA
16
17.9-145
this study
Wilge River, RSA
16
2.85-19.9
this study
N= number of PAHs
129
5.7
Source apportionment of PAHs in sediment samples
Since sediments were shown to contain all the sixteen EPA priority PAHs studied, the
concentrations of these PAHs were used to apportion sources. This was achieved using
chemical fingerprinting (diagnostic ratios) and receptor modelling (multivariate statistical
approaches).
5.7.1
Source apportionment of PAHs by diagnostic ratios
5.7.1.1 LMW to HMW ratios
Polycyclic aromatic hydrocarbons are produced from three main sources, from incomplete
combustion of organic materials such as plants and fossil fuels (pyrolytic origin), from the
discharge of petroleum and its associated products (petroleum origin/petrogenic), or from
post-depositional transformation of biogenic precursors (biogenic/natural origin). Leaching
of coal is also a potential source of PAHs as indicated by coal leaching studies.
Diagnostic ratios or relative abundances of selected PAHs (isomers) are useful indicators
of PAH sources. This is because isomer pairs are diluted to the same extent when they
interact with particulate matter. Consequently, these isomers are partitioned similarly to
the other phases since they have comparable thermodynamic partitioning and kinetic
mass transfer coefficients.
As demonstrated in Table 5.9, the ratios of LMW to HMW, AN to AN+PhA, FIA to FIA+Py,
FIA/Py, PhA/AN, IP/IP+BghiP, FI/FI+Py and BaA/BaA+Chy can be used to tentatively
apportion sources of PAHs. Plots of diagnostic ratios are illustrated in Figures 5.13 to
5.21.
The ratios of LMW/HMW in water (Figure 5.14), suspended matter (Figure 5.17) and
sediment (Figure 5.21) samples were mostly greater than 1 with the exception of two sites
(OLT and KOL). For pyrogenic PAHs, gas to particle partition coefficients of HMW PAHs
are high, due to their low vapour pressure, when compared to the LMW PAHs. As a result,
these species have a propensity to be adsorbed on soot and particulate matter. These
HMW PAHs are classified in the low mobility group of persistent organic pollutants
(POPs). They are subject to rapid deposition and retention close to the source and are
also transported into river systems by runoff. The LMW PAHs, on the other hand, exhibit
low temperatures of condensation and are found predominantly in the gas phase. These
130
PAHs have high to moderate mobility and undergo global atmospheric dispersion and
preferentially accumulate far removed from where they originate. Thus the low LMW/HMW
ratio for the KOL and OLT sites can be attributed to their proximity to the coal fired power
stations where depositon of particulate matter containing HMW PAHs is likely to occur.
The high LMW concentrations, hence high LMW/HMW ratios can also be attributed to coal
leaching, since LMW PAHs were detected in coal leachates.
Table 5.9: The range of diagnostic ratios for PAH sources
Diagnostic ratio
Petrogenic
Pyrogenic
References
LMW/HMW
>1
<1
(Wang et al., 2006)
(Rocher et al., 2004)
AN/(AN+PhA)
<0.1
(Li et al., 2006)
>0.1
(Zhang et al., 2004)
FIA/(FIA+Py)
<0.4
(Li et al., 2006)
>0.4
(Zhang et al., 2004)
FIA/Py
<1
>1
(Yinhai & Zhengmei, 2010)
PhA/AN
>10
<10
(Yinhai & Zhengmei, 2010)
IP/IP+BghiP
<0.2
>0.2
(Guo et al. 2006)
FI/FI+Py
<0.5
>0.5
(Ravindra Wantes & van Grieken, 2008)
BaA/BaA+Chy
<0.2
>0.35
(Guo et al. 2006)
5.7.1.2 FIA/FIA+Py, FIA/Py, AN/AN+PHA and PhA/AN ratios
Isomeric ratios FIA/FIA+Py, FIA/Py, AN/AN+PhA and PhA/AN plots can be found in
Figures 5.13 and 5.14 (water), Figure 5.15 (suspended matter) and in Figures 5.18 and
5.19 (sediment). The ratios FIA/FIA+Py and FIA/Py were greater than 0.4 and 1,
respectively, for most of the samples except for two sampling sites along the KOL and
OLT. This can be percieved as an indication of a pyrogenic source of the PAHs. Possible
pyrogenic sources include coal biomass combustion at a nearby power station. The ratios
AN/AN+PhA and PhA/AN were less and greater than 0.1 and 10 for most of the water
samples, indicating a source other than pyrogenic. However, the reverse was observed
for the ratios in suspended and sediment material, indicating the presence of a pyrogenic
source of the PAHs.
131
5.7.1.3 IP/IP+BghiP, FI/FI+Py and BaA/BaA+Chy ratios
The isomeric ratios plot involving the PAHs IP and BghiP; FI and Py, and BaA and Chy
are presented in Figures 5.15 and 5.16 (suspended matter) and Figures 5.20 and 5.21
(sediments). These ratios can be used to tentatively infer the presence of pyrogenic
sources. These PAHs were all detected in suspended material and sediment samples.
The computed IP/IP+BghiP ratios for the PAHs in sediments were greater than 0.2 thus
meeting the criterion for pyrogenic source for all samples with the exception of two sites
along OLT and KOL rivers. Along the WLG and OLT rivers, two sites did not meet the
criterion for pyrogenic sources using the ratio FI/FI+Py for PAHs in sediments. According
to Guo et al (2006), a ratio of IP/IP+BghiP greater than 0.5 can be used to infer the
presence of PAHs derived from coal combustion. Most of the samples exhibited a ratio
greater than 0.5 thus indicating coal combustion as one of the pyrogenic activities
contributing PAHs.
The results obtained by comparing the results obtained in this study with the diagnostic
ratios indicate the presence of both petrogenic and pyrolytic sources, with pyrolytic
sources predominating. The predominance of LMW PAHs, as evidenced by the high
LMW/HMW ratios from most samples, could be an indication of a non-pyrogenic source
possibly coal, since these PAHs were the only ones observed in coal leachates. Although
other high temperature processes, including the carbonisation of bituminous coal to form
creosote, have ratios similar to coal tar or coal combustion and are generally
indistinguishable from combustion sources, these cannot be considered in the present
study since they are not found within the study area. Thus, the results indicate that the
main source of PAHs in the studied sediments were from combustion activities, while nonpyrogenic activities such as coal leaching/coal particles deposited in water were also
possible sources.
The diagnostic ratios calculated for KOL and OLT samples indicated the presence of other
sources other than pyrogenic sources. These two sites are located close to coal mines
and coal storage sites. Consequently coal leaching and coal particulates are likely to be
the main sources of PAHs in sediements from these two sites.
132
1.7
1.6
1.5
1.4
1.3
1.2
1.1
133
Ratio
1
0.9
AN/PhA+AN
0.8
FIA/FIA+Py
0.7
FIA/Py
0.6
0.5
0.4
0.3
0.2
0.1
0
OLI 1S1
OLI 1S2
OLI 2S1
OLI 2S2
OLI 3S1
OLI 3S2
OLI 4S1
OLI 4S2
OLI 4S2
OLT 1S1 OLT 2S1 OLT 3S1 KOL 1S1 KOL 1S2 KOL 2S1 KOL 2S2 KOL 4S1
Figure 5.13: Bar chart indicating the ratios of anthracene and phenanthrene, and fluoranthene and pyrene in summer water samples
50
Ratio
40
30
LMW/HMW
PhA/AN
20
10
0
OLI
1S1
OLI
1S2
OLI
2S1
OLI
2S2
OLI
3S1
OLI
3S2
OLI
4S1
OLI
4S2
OLI
4S2
OLT
1S1
OLT
1S2
KOL
1S1
KOL
1S2
KOL
2S1
KOL
2S2
KOL
4S1
Figure 5.14: Bar chart indicating the ratios of anthracene and phenanthrene, and low
molecular weight and high molecular weight in summer water samples
134
2.4
2.2
PhA/AN
2
FIA/Py
Ratio
1.8
1.6
AN/AN+PhA
1.4
FIA/FIA+Py
1.2
BaA/BaA+Chy
1
0.8
0.6
135
0.4
0.2
0
KOL KOL KOL KOL KOL KOL KOL KOL KOL KOL OLT OLT OLT OLT OLT OLT OLT OLT OLT OLT OLT OLT OLT OLT OLT OLT WLG WLG WLG WLG WLG
1S2 2S2 3S1 3S2 4S2 1W2 2W1 2W2 3W2 4W2 1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1W1 1W2 2W1 2W2 3W1 3W2 4W1 4W2 2S2 3S1 4S1 2W2 4W1
Figure 5.15: Bar chart indicating the ratios of anthracene and phenanthrene; fluoranthene and pyrene; and benzo(a)anthracene and
chrysene in suspended matter samples
WLG 4W2
WLG 4W1
WLG 3W2
WLG 3W1
WLG 2W2
WLG 2W1
WLG 1 W2
WLG 1 W1
WLG 4S2
WLG 4S1
WLG 3S2
WLG 3S1
WLG 2S2
WLG 2S1
WLG 1 S2
WLG 1 S1
KOL 4W1
KOL 3W2
KOL 3W1
KOL 2W2
KOL 2W1
KOL 1W2
KOL 1W1
KOL 4S2
KOL 4S1
KOL 3S2
KOL 3S1
KOL 2S2
KOL 2S1
KOL 1S2
KOL 1S1
OLI 4W2
OLI 4W1
OLI 3W2
OLI 3W1
OLI 2W2
OLI 2W1
OLI 1W2
OLI 1W2
OLI 4S2
OLI 4S1
OLI 3S2
OLI 3S1
OLI 2S2
OLI 2S1
OLI 1S2
136
OLI 1S1
Ratio
1.2
1.1
1
0.9
0.8
FI/FI+Py
0.7
0.6
IP/IP+BghiP
0.5
0.4
0.3
0.2
0.1
0
Figure 5.16: Bar chart indicating the ratios of fluorene and pyrene, and indeno(1,2,3-cd)pyrene and benzo(ghi)perylene in suspended matter
samples
4.5
4
3.5
LMW/HMW
137
3
Ratio
2.5
2
1.5
1
0.5
0
OLT
1S1
OLT
1S2
OLT
2S1
OLT
2S2
OLT
3S1
OLT
3S2
OLT
4S1
OLT OLT OLT OLT OLT OLT OLT OLT OLT KOL
4S2 1W1 1W2 2W1 2W2 3W1 3W2 4W1 4W2 1S2
KOL
2S2
KOL
3S1
KOL
3S2
KOL KOL KOL KOL KOL KOL
4S2 1W2 2W1 2W2 3W2 4W2
Figure 5.17: Bar chart indicating the ratios of low molecular weight and high molecular weight hydrocarbons in suspended matter samples
1.00
0.90
0.80
An/(An+PhA)
0.70
FIA/(FIA+Py)
Ratio
0.60
FIA/Py
0.50
138
0.40
PhA/AN
0.30
0.20
0.10
0.00
OLI OLI OLI OLI OLI OLI OLI OLI OLT OLT OLT OLT OLT OLT OLT OLT KOL KOL KOL KOL KOL KOL KOL KOL WLG WLG WLG WLG WLG WLG WLG WLG
1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1 S1 1 S2 2S1 2S2 3S1 3S2 4S1 4S2
Figure 5.18: Bar chart indicating the ratios of various polycyclic aromatic hydrocarbons in summer sediment samples (anthracene and
phenanthrene; fluoranthene and pyrene)
2
1.8
PhA/AN
1.6
FIA/Py
1.4
An/(An+PhA)
Ratio
1.2
FIA/(FIA+Py)
1
0.8
139
0.6
0.4
0.2
0
OLI OLI OLI OLI OLI OLI OLI OLI OLT OLT OLT OLT OLT OLT OLT OLT KOL KOL KOL KOL KOL KOL KOL KOL WLG WLG WLG WLG WLG WLG WLG WLG
1W2 1W2 2W1 2W2 3W1 3W2 4W1 4W2 1W1 1W2 2W1 2W2 3W1 3W2 4W1 4W2 1W1 1W2 2W1 2W2 3W1 3W2 4W1 4W2 1 W1 1 W2 2W1 2W2 3W1 3W2 4W1 4W2
Figure 5.19: Bar chart indicating the ratios of various polycyclic aromatic hydrocarbons in winter sediment samples (anthracene and
phenanthrene; fluoranthene and pyrene)
1
0.9
0.8
0.7
0.6
Ratio
FI/FI+Py
0.5
BaA/BaA+Chy
IP/IP+BghiP
0.4
LMW/HMW
0.3
140
0.2
0.1
0
OLI OLI OLI OLI OLI OLI OLI OLI OLT OLT OLT OLT OLT OLT OLT OLT KOL KOL KOL KOL KOL KOL KOL KOL WLG WLG WLG WLG WLG WLG WLG WLG
1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1S1 1S2 2S1 2S2 3S1 3S2 4S1 4S2 1 S1 1 S2 2S1 2S2 3S1 3S2 4S1 4S2
Figure 5.20: Bar chart indicating the ratios of fluorene and pyrene; benzo(a)anthracene and chrysene; indeno(1,2,3-cd)pyrene and
benzo(ghi)perylene; and low molecular weight and high molecular weight in summer sediment samples
2
1.8
1.6
1.4
FI/FI+Py
Ratio
1.2
BaA/BaA+Chy
1
141
0.8
IP/IP+BghiP
0.6
LMW/HMW
0.4
0.2
0
OLI OLI OLI OLI OLI OLI OLI OLI OLT OLT OLT OLT OLT OLT OLT OLT KOL KOL KOL KOL KOL KOL KOL KOL WLG WLG WLG WLG WLG WLG WLG WLG
1W2 1W2 2W1 2W2 3W1 3W2 4W1 4W2 1W1 1W2 2W1 2W2 3W1 3W2 4W1 4W2 1W1 1W2 2W1 2W2 3W1 3W2 4W1 4W2 1 W1 1 W2 2W1 2W2 3W1 3W2 4W1 4W2
Figure 5.21: Bar chart indicating the ratios of fluorene and pyrene; benzo(a)anthracene and chrysene; indeno(1,2,3-cd)pyrene and
benzo(ghi)perylene; and low molecular weight and high molecular weight winter sediment samples
5.7.2
Source estimates from cluster analysis
Cluster analysis was conducted to identify groups of individual PAHs with similar
chemical/physical properties in the sediments. The result of the cluster analysis is shown
in the hierarchical dendogram (Figure 5.22), which divides the sixteen EPA priority
individual PAHs into three main groups. The first group consists of NaP, FI and AcNPh, all
LMW PAHs with 2-3 rings, which are abundant in petrogenic sources including petroleum
oil spills (Marr et al., 1999; Gonzalez et al., 2006). These LMW PAHs were also found to
be leached from coal. Impling that coal is a plausable source of these PAHs. The second
major group is sub-divided into two sub-groups. The first sub-group consists of BaP, FIA
and Py, which are MMW-HMW PAHs with 4-5 rings. The second sub-group consists of
BaA, Chy, BbFIA, BkFIA, dBahA and IP, which are also MMW-HMW PAHs, with 4-6 rings.
The short linkage distances within these clusters are an indication that the PAHs in these
clusters are similar. Both of these sub-groups are an indication of pyrogenic sources,
including combustion of coal and biomass (Zakaria et al., 2002; Liu et al., 2009). The subclustering of the second major cluster could be an indication of the existence of two
different kinds of pyrogenic sources. The third group consists of AcN, BghiP, PhA and AN,
which are LMW PAHs with 3 rings, perhaps resulting from one or more other nonpyrogenic source. This cluster has long linkage distance when compared to the rest of the
clusters, an indication that there is no correlation between PAHs in this group and the
PAHs in the first two clusters.
5.7.3
Source estimates from principal component analysis
The use of diagnostic ratios provides only qualitative information about the contribution of
various sources of the PAHs in the sediments. Quantitative apportionment of sources can
be achieved with FA and multivariate linear regression (FA/MLR) (Liu et al., 2009).
Principal component analysis is used in FA to represent the total variability of the original
PAH data with a minimum number of factors. An estimate of the chemical source
responsible for each factor can be obtained by evaluating the factor loadings (Larsen &
Baker, 2003). The rotated factors of the sixteen EPA priority PAHs from the studied rivers
are presented in Table 5.11. Factor rotation reduces the number of extracted or initial
factors to a minimum number, which adequately explains the variations in the data.
142
Tree Diagram for 16 Variables
Weighted pair-group average
City-block (Manhattan) distances
NaP
FI
AcNPh
FIA
BaP
Py
BaA
Chy
BbFIA
BkFIA
dBahA
IP
PhA
AcN
BghiP
AN
0
100
200
300
400
500
600
700
800
Linkage Distance
Figure 5.22: Hierarchial dendogram of the sixteen EPA priority polycyclic aromatic
hydrocarbons
The three extracted factors accounted for 87.8% of the variability of the data. Factor 1
accounts for 65.2% of the variance due to the prominence of FIA, Py, BaA, Chy, BaP,
BkFIA, BbFIA, dBahA and IP.
Factor 2, contributing 14.2% of the total variance, is highly weighted by NaP, FI and
AcNPh. Factor 3, which explains 8.3% of the variance, is dominated by PhA, AcN, BghiP
and AN. The result of PCA is similar to that obtained from cluster analysis (Figure 5.22). A
scatter plot of factor loadings is illustrated in Figure 5.23 and Figure 5.24. The three
factors are clearly resolved; however, the sub-cluster in the dendogram could not be
resolved. Factor 1, corresponding to the second group, represents a pyrogenic source. It
is predominantly loaded by FIA, Py, BaP, dBahA, Chy, BkFIA, BbFIA, IP and BaA. This
factor, which corresponds to the second sub-clustered group in cluster analysis, could not
be resolved by PCA.
143
Scatterplot of factor 3 against factor 1
Spreadsheet19 3v*16c
0.9
AN
BghiP
PhA
AcN
0.8
0.7
factor 3
0.6
0.5
FI
AcNPh
0.4
0.3
FIA
dBahA
BaAChy Py
BbFIA BkFIA BaP
IP
0.2
NaP
0.1
0.0
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
factor 1
Figure 5.23: Scores plot of principal components 1 and 3
Scatter plot of factor 3 against factor 2
factor 2 and 3 loadings spread sheet.sta 2v*16c
1.0
NaP
AcNPh
0.9
0.8
FI
0.7
Factor 3
0.6
0.5
0.4
Py
BaP
BbFIA
Chy
BkFIA
FIA
dBahA
BaA
IP
BghiP
0.3
0.2
PhA
AcN
0.1
AN
0.0
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Factor 2
Figure 5.24: Scores plot of principal components 3 and 2
144
0.7
0.8
0.9
According to Zuo et al. (2007), high loadings resulting from contributions by FIA, Py, BaP,
dBahA, Chy, BkFIA, BbFIA and BaA are typical of coal and biomass combustion.
Levendis, Atal and Carlson (1998) reported that the burning of coal and tyres at 1000°C
(close to the temperature of coal combustion) yields FIA and Py as the major products
and low concentrations of the 5 and 6 ring PAHs detected in furnace effluents. Other
researchers have considered FIA, Py, BaA and Chy as predominant in coal combustion
profiles (Duval & Friedlander, 1981; Harrison, Smith & Luhana, 1996).
Generally, in South Africa and in particular the area of study, coal is a primary energy
source and is used widely for domestic and industrial purposes. Electricity in South Africa
is mainly derived from coal-fired power stations, strategically located in close proximity to
the studied rivers. It is therefore reasonable to assign this factor to coal combustion.
Factor 3, corresponding to the third group in the cluster analysis, is loaded by PhA, AcN
and AN, represents a typical petroleum source.
Factor 2, corresponding to the first group, is loaded by NaP, AcNPh and FI, yields the
same result as that obtained from cluster analysis. According to Zuo et al. (2007), AcNPh
is the dominant PAH in profiles reflecting coke production, while NaP was attributed to
unburned fossil fuel coal (Yunker et al., 1996; Page et al., 1999). Consequently, this factor
is attributed to PAHs from fossil fuels (possibly coal leaching) and coke production.
Coal combustion is the main contributor to PAH pollution. Most of the coal in Mpumalanga
is used to generate electric power. The consumption of coal is anticipated to increase as
the demand for electricity increases due to an expanding economy, causing a higher
percentage contribution of coal combustion to the environment. Several large-scale coal
burning power stations are located along the rivers studied, leading to an easy input of
coal combustion products including PAHs, into the rivers.
Unburnt fossil fuels, including coal and coke, contribute to the total PAHs in the study
area. Coal mining and transportation activities are likely to result in the introduction of
coals and therefore PAHs into the water bodies. According to Walker et al. (2005) and
Achten and Hofmann (2009), coal is a potential source of PAHs in sediments. In addition
to the sorbed PAHs, original hard coals can contain high concentrations of PAHs ranging
from hundreds to thoudands µg/g. Coal dust particles from coal mining and transportation
are deposited on surfaces and are transferred to rivers via run-off. In addition, the
145
leaching of coals in abandoned and operational coal mines by decanting water, results in
the introduction of PAHs to nearby rivers.
Table 5.10: Rotated component matrix for sixteen EPA priority polycyclic aromatic
hydrocarbons from sediments
No.
PAH
Factor
Factor
Factor
1
2
3
1
NaP
0.157
0.907
0.121
2
PhA
0.197
0.157
0.742
3
AcN
0.152
0.131
0.739
4
AcNPh
0.096
0.906
0.371
5
FI
0.181
0.702
0.418
6
AN
0.157
0.026
0.778
7
FIA
0.965
0.481
0.181
8
Py
0.964
0.558
0.114
9
BaA
0.842
0.375
0.094
10
Chy
0.885
0.514
0.097
11
BaP
0.986
0.531
0.047
12
BkFIA
0.881
0.497
0.058
13
BbFIA
0.783
0.518
0.061
14
dBahA
0.911
0.472
0.151
15
IP
0.751
0.385
0.179
16
BghiP
0.152
0.351
0.775
Coal combustion
Unburnt fossil
Petrogenic
Estimated
source
Var.(%)
fuels
65.23
14.21
8.34
Rotation method: Varimax with Kaiser normalisation
Bold loadings > 0.700
5.8
Summary
In this study, the distribution and sources of PAHs in water, suspended matter and
sediments sampled from three rivers in eMalahleni, South Africa, were investigated. The
total PAH concentration in sediments ranged from 38.3 to 1030 ng/g. The most PAH
enriched samples were from the Olifants River tributary flowing from an abandoned coal
mine and located within the proximity of a coal-fired power station. The overall levels of
PAHs were substantially lower than from other relevant parts of the world. They were,
however, comparable to some rivers in Africa, Middle East and Asia.
146
The repeatedly calculated one way ANOVA results indicated that there was no distinct
seasonal variation, that is, the total PAHs in summer and winter were not significantly
different at the 95% confidence level.
The PAH diagnostic ratios indicated the presence of both pyrogenic and non-combustion
activities as sources and pointed out pyrolytic sources as the main contributers to PAH
pollution. Multivariate techniques, including cluster and factor analysis, can be used to
assign sources of PAHs. Factor analysis revealed that coal combustion is the dominant
source of PAHs in the sediments. Other sources included a petrogenic source and
coal/coke production. The contributions of the different sources are illustrated in Figure
5.24. Conributions were obtained using multivariate linear regression analysis with PCA
factor scores and the standrdised normal deviation of total PAH concentrations as
independent and dependent variables respectively. The coefficients in the regression
model represented the contribution of each source.
Petrogenic, 6%
Unburnt coal,
27%
Coal
combustion,
67%
Figure 5.25: Pie chart indicating the extent of the contributions of different PAH sources
147
CHAPTER 6
DISTRIBUTION AND MOBILITY OF SELECTED METALS IN
SEDIMENTS
6.1 Introduction
In this chapter, the findings of the study with respect to metals, are presented. The results
from the analysis of contaminated sediments are given. Results from statistical data
processing are also presented and discussed.
6.2
6.2.1
Results and discussion
Extent of sediment contamination
In the present study, enrichment factors were used to assess the levels of contamination
and the possible anthropogenic impact of sediments from the Olifants River (OLI), Klein
Olifants River (KO), Wilge River and a tributary of the Olifants River (OLT). To identify
anomalous metal concentrations, geochemical normalization of the heavy metals data to a
conservative element, Al, was employed. Many authors prefer to express the metal
contamination with respect to average shale, to quantify the extent and degree of metal
pollution (Muller, 1969; Förstner & Müller, 1973; Ghrefat & Yusuf, 2006). The background
concentrations of the elements Cd, Co, Cr, Fe, Mn, Pb, Ti and V were obtained by depth
profiling of the sediments.
The enrichment factor values for the studied metals are listed in Table 6.1. According to
Zhang and Liu (2002), EF values between 0.5 and 1.5 indicate that the metal originates
entirely from crustal materials or natural processes; whereas EF values greater than 1.5
suggest that the sources are more likely to be anthropogenic.
The results from the present study suggest that the sediment samples were enriched with
all the metals except Fe, which was enriched in sediments from the Olifants tributary only.
In all samples, the highest EFs were obtained for Cd. The highest EF is observed, for Cd
in sediment from the Klein Olifants River, with a factor of 11.9. The rest of the metals
followed a trend of decreasing enrichment in the order Pb>Ti>Co>Cr>Mn>V. The
difference in EF values may be due to the differences in the magnitude of input for each
148
metal in the sediment and/or the differences in the removal rate of each metal from the
sediment.
Table 6.1: Average enrichment factors for the metals in the sediments
Site
Mn
Fe
Co
V
Pb
Cr
Ti
Cd
OLI 1
1.41
1.36
1.47
1.53
4.79
3.65
2.52
11.8
OLI 2
2.55
1.33
1.18
2.14
3.28
1.49
2.24
11.3
OLI 3
2.58
1.26
1.72
1.78
5.67
2.66
4.45
8.82
OLI 4
2.81
1.24
2.60
1.91
5.26
1.42
4.63
9.38
KO 1
2.90
1.49
2.75
2.68
3.25
2.93
3.01
11.3
KO 2
2.98
1.41
3.86
2.53
2.33
2.27
2.41
9.96
KO 3
1.43
1.34
2.51
2.71
2.78
2.55
2.98
11.9
KO 4
2.55
1.38
3.59
1.76
9.58
2.01
2.57
11.3
WILGE 1
1.37
1.28
3.58
2.02
1.06
2.54
1.93
9.49
WILGE 2
2.54
1.33
3.62
2.54
0.92
2.51
2.67
8.19
WILGE 3
2.75
1.28
1.44
2.52
1.27
1.42
2.45
10.7
WILGE 4
2.91
1.38
1.39
2.61
1.44
1.43
1.43
10.7
OLT 1
2.49
2.65
3.42
2.85
2.69
2.78
2.66
9.91
OLT 2
2.05
2.56
3.17
3.07
3.12
2.22
3.19
10.1
Average
2.38
1.52
2.59
2.33
3.39
2.35
2.80
10.3
The elevated levels of these metals in sediments can be attributed to anthropogenic
sources within the vicinities of the rivers. These may include coal mining, V processing,
steel production and electricity generation by coal-fired power stations.
6.2.2
Fractionation pattern for metals
6.2.2.1 Partitioning of metals in studied sediments
The following designation is used to represent the various fractions throughout the
discussion:

F1 exchangeable acid soluble metal fraction;

F2 reducible metal fraction;

F3 oxidisable metal fraction;

F4 residual fraction;

F12 sum of F1 and F2; and

F123 sum of F1, F2 and F3 (total non-residual fraction).
149
Fractionation pattern results for Cd, Co, Cr, Fe, Mn, Pb, Ti and V are represented in
Figures 6.1 to 6.4 as the percentage of individual fractions with respect to the total amount
extracted from the sediment sample.
100%
90%
80%
70%
%
60%
F4
50%
F3
40%
F2
30%
F1
20%
10%
0%
Mn
Fe
Co
V
Pb
Metal
Cr
Ti
Cd
Figure 6.1: Fractionation pattern for metals in sediments from the Olifants River
100%
90%
80%
70%
%
60%
F4
50%
F3
40%
F2
30%
F1
20%
10%
0%
Mn
Fe
Co
V
Pb
Metal
Cr
Ti
Cd
Figure 6.2: Fractionation pattern for metals in sediments from the Klein Olifants River
150
100%
90%
80%
70%
%
60%
F4
50%
F3
40%
F2
30%
F1
20%
10%
0%
Mn
Fe
Co
V
Pb
Metal
Cr
Ti
Cd
Figure 6.3: Fractionation pattern for metals in sediments from Wilge River
100%
90%
80%
70%
%
60%
F4
50%
F3
40%
F2
30%
F1
20%
10%
0%
Mn
Fe
Co
V
Pb
Metal
Cr
Ti
Cd
Figure 6.4: Fractionation pattern for metals in sediments from the Olifants River tributary
Metal fractionation is of critical importance to the potential toxicity and mobility of
contaminant metals released into the aquatic environment through either natural or
anthropogenic processes. Environmental risks of the enriched metals were evaluated
using the risk assessment code (RAC) (Table 6.2). The RAC assesses the availability of
metals in solution by applying a scale to the percentage metal in the adsorptive and
exchangeable fractions, which are bound to carbonate fractions (Perin et al., 1985). These
151
fractions are considered to contain weakly bonded metals that may equilibrate with the
aqueous phase and, thus, become more rapidly bioavailabile (Pardo et al., 1990).
Table 6.2: Risk assessment code for metals according to Perin et al. (1985)
Risk
Metal in carbonate and exchangeable fraction
(%)
No
<1
Low
1 to 10
Medium
11 to 30
High
31 to 50
Very high
75
The code has been applied by Singh et al. (2005), when they studied the distribution and
fractionation of metals in sediments from the Gomati River in India. Their study revealed
that most of the trace elements in the sediments were associated with the carbonate and
exchangeable fractions (11–30% and occasionally more than 50%). Thus, according to
the RAC, these sediments posed a medium risk. Ghrefat and Yusuf (2006) studied the
pollution of sediments from Wadi Al-Arab Dam, Jordan, by measuring Mn, Fe, Cu, Zn, and
Cd in the different fractions. The results from the study suggested that Zn posed a low risk
since only 3% of it occured in exchangeable and carbonate fractions. The percentage of
Cd in exchangeable and carbonate fractions was 24.5%, thus posing a medium
environmental risk. The remaining elements were not enriched in the sediments.
6.2.2.2 Cadmium
Most of the Cd in the sediments from the Olifants and Klein Olifants can be accounted for
by the residual fraction of the sediment (Figures 6.1 and 6.2). A small amount of Cd was,
however, extracted from the oxidisable and exchangeable metal phases of the sediment
sample from the upper section of the Klein Olifants River. Although the residual fraction in
the Wilge River sediment had the greatest concentration of Cd, the rest was
approximately equally divided between the oxidisable and reducible phases (Figure 6.3).
Almost equal amounts of Cd were fractionated into the residual and exchangeable metal
phases of the sediment from the Olifants River tributary (Figure 6.4). Cadmium existing in
the residual is derived from geochemical sources and is not likely to pose a risk. However,
Cd in the oxidisable or reducible phase has the potential to cause problems at points
downstream, including the Loskop Dam, if the conditions in the water become oxic or
anoxic.
152
In the present study, the Cd in the exchangeable carbonate fraction (Table 6.3) was found
to be of no risk and of low risk for sediments collected from the Klein Olifants and Olifants
rivers, respectively. Sediment samples collected downstream of the Wilge River (WILGE
4) and from the Olifants River tributary were found to pose a medium risk. The Cd in the
exchangeable and carbonate phase includes Cd sorbed to sediments through
electrostatic forces and Cd exisiting as carbonates. When the pH of the water is lowered,
for example by the introduction of acid mine drainage from coal mining, the Cd existing as
carbonates is mobilised posing a risk to animals and humans using the water at the
contaminated site and downstream.
Table 6.3: Percentage of metals in the exchangeable and acid soluble/carbonate phase
Station
% in exchangeable and carbonate fraction of sediment
Mn
Fe
Co
V
Pb
Cr
Ti
Cd
OLI 1
51.5
2.31
45.0
5.15
26.8
2.07
0.95
0.00
OLI 2
45.8
3.15
36.8
5.67
30.1
3.01
1.21
0.00
OLI 3
44.0
3.61
41.3
4.15
26.8
4.75
2.14
0.00
OLI 4
34.0
2.86
1.5
43.1
29.5
6.51
2.75
3.81
K OLI 1
14.0
4.17
23.1
12.3
29.7
3.49
1.82
0.00
K OLI 2
16.6
3.64
28.6
12.4
31.7
3.04
1.36
0.00
K OLI 3
15.5
3.01
32.1
0.00
22.3
4.73
1.75
0.00
K OLI 4
22.7
12.7
22.8
27.7
53.4
16.0
2.51
0.00
WILGE 1
17.7
9.06
18.4
23.5
46.1
11.0
3.83
0.00
WILGE 2
14.9
9.65
22.3
33.6
64.3
22.3
1.41
0.00
WILGE 3
15.3
10.8
16.2
37.8
52.5
28.4
1.37
0.00
WILGE 4
21.6
10.5
4.78
4.61
1.65
2.19
0.00
25.0
OLIT 3
25.5
10.0
12.6
23.4
5.92
21.8
4.91
12.3
OLI T 4
23.5
10.3
11.0
35.4
1.27
5.97
2.17
10.2
6.2.2.3 Cobalt
For the Olifants and Klein Olifants rivers, approximately equal amounts of Co were
partitioned into the exchangeable/bound to carbonates, reducible/bound to Fe-or Mnoxides and the residual fractions (Figure 6.1 and 6.2). Most of the Co extracted from the
Wilge River sediment was in the reducible fraction. This implies that if the conditions in
water become anoxic most of the Co is mobilised into water, thus affecting the quality of
water in the Wilge River. The exchangeable carbonate phases and residual phase
contributed almost equal and significant proportions to the total Co in the sediments
(Figure 6.3). However, the Co in the residual phase is not of concern compared to that in
153
the
exchangeable
crabonate
phase,
since
it
is
not
bioavailable.
The
exchangeable/carbonate Co is easily mobilised into water since it is held to the sediment
surface by weak electrostatic forces. In addition, the Co in the exchangeable and
carbonate phase includes Co bound to carbonates. Thus the existence of cations with a
higher affinity for the adsorption sites on the sediments and the acidification of the water
are likely to facilitate solubilisation of Co ions through ion exchange and acid base
processes. Almost all the Co in the sediments can be accounted for by these three
phases, while the oxidasable fraction contributed a small amount to the overall Co.
The Co in the Olifants River tributary sediments can be accounted for almost entirely by
the reducible fraction (Figures 6.4). The order in terms of decreasing amounts for the rest
of the fractions in the sediment samples is reducible > exchangeable and carbonate >
oxidisable> residual. Thus, a depletion of oxygen or the existence of electron donating
species in the water along this tributary will create an environment conducive for the
mobilisation of Co into water thus affecting the quality of water by making Co bioavailable
at the sampled point, and downstream along the Olifants River.
According to the RAC, the sediments collected upstream of the Olifants River (OLI 1 to 3)
contain Co in the high risk category, while the sediments collected downstream (OLI 4 and
WILGE 4) contain Co in the low risk category. Although Co in sediments downstream of
these rivers is likely to pose a low risk to biota, mobilisation of the Co in the exhangeable
phase upstream by ion exchange and reduction processes can cause health problems.
The rest of the sediment samples from the Klein Olifants River, Wilge River, and the
Olifants River tributary exhibited Co in the medium risk category. The high amounts of Co
can be attributed to coal mining, coal powered power stations and steel processing plants
upstream of the rivers. Coal power plants, with their generating capacity (MW), located
within the study area include Amot (2100), Camden (1600), Duvha (3600), Grootvlei
(1200), Hendrina (2000), Kendal (4116), Komati (1000), Kriel (3000), Majuba (4100),
Matla (3600) and Tutuka (3654) power stations15.
6.2.2.4 Chromium
A substantial amount of Cr was extracted from the residual phase of the sediments from
all the rivers (Figures 6.1 to 6.3), with the notable exception of sediment from the tributary
(Figure 6.4). In the tributary, the reducible fraction almost completely accounted for the
15
http://en.wikipedia.org/wiki/List_of_power_stations_in_South_Africa [Accessed: 17-10-2012]
154
total amount of Cr. Thus, like Co reducing conditions will mobilise Cr into the water with
the concomintant deterioration of the quality of water.
Most of the sediment samples contained Cr in the low risk category, with the exception of
the KOLI 4 (downstream), WILGE 1 to 3 (upstream) and all OLT samples, which
potentially posed a medium level risk to plants and animals.
6.2.2.5 Iron
All the river sediments exhibited a similar distribution pattern, with the exception of the
sediments from the Olifants River tributary emanating from an abandoned coal mine
(Figures 6.1 to 6.3). For all the river sediment samples, a negligible amount of Fe was
fractionated into the first three stages of the sequential extraction scheme. Most of the Fe
was found to be concentrated in the residual fraction, most likely existing as crystalline
oxides in the sediment. Similar observations were made by Tuzen (2003) and Relic et al.
(2005). In contrast, McAlister and Smith (1999) obtained high concentrations of Fe in the
reducible phase using an oxalate reagent at pH 3. This reagent is a specific extracting
reagent for FeOX (Pickering, 1986). However, the sediment from a tributary originating
from a closed coal mine and flowing into the Olifants River, exhibited a different
distribution pattern, whereby almost all the Fe was equally distributed between the
reducible and residual phases of the sediments (Figure 6.4). The low pH (4.27-5.31) of the
sampling site can be attributed to the acid mine drainage (AMD) from the abandoned
mine. Acid mine drainage is known to contain high levels of iron oxide. Iron occuring in the
reducible phase has the potential to cause problems if the conditions in the tributary
become reducing. The Fe in the exchangeable and carbonate fraction was found to be in
the low risk category of the RAC code.
6.2.2.6 Manganese
A small percentage of Mn was partitioned into the residual fraction, while a large portion
was almost equally divided between the exchangeable and carbonate phases and
reducible phase in the sediments from the Olifants River (Figure 6.1). This implies that
most of the Mn exists in the reduced state of +2. These Mn 2+ ions are most probably
adsorbed or partially available for ion exchange on the surface of MnO 2, possibly as a
result of the high thermodynamic affinity of Mn2+ for MnO2. The surface of MnO2 is
negatively charged at pH above 3 and the sorption of metal ions as free hydrated ions by
the displacement of protons is preferred (Weisz, Polykak & Hlavay, 2000). According to
155
Tessier, Campbell and Bisson (1979) this can be explained by Mn 2+ oxidation being a very
slow process in natural waters. Relic et al. (2005) also observed the same distribution of
Mn when they studied alluvial sediments within an oil refinery. Weisz, Polykak and Hlavay,
(2000) made a similar observation when they studied sediments collected from rivers and
harbours in Hungary. A relatively high amount of Mn was also found in the reducible
fraction of the sediments, suggesting that compounds of Mn are easily mobilised into
solution under reducing conditions (Salomons & Forstner, 1980; Pickering, 1986). All the
sediment samples from the Klein Olifants, Wilge and the Olifants River tributary exhibited
a similar distribution pattern for Mn in the various sediment fractions, i.e., a large
proportion of the Mn occurred in the reducible phase (Figures 6.2 to 6.4). The residual and
exchangeable and carbonate metal fractions all contained approximately equal amounts
of Mn. Only a small amount of Mn was extracted from the oxidisable fraction.
The Mn fractionated into the exchangeable and carbonate phase of the sediment samples
from the Olifants River can be classified as potentially high risk, while the rest of the
sediment samples exhibited Mn in the medium risk category of the RAC.
6.2.2.7 Lead
For sediments from the Olifants and Klein Olifants rivers (Figures 6.1 and 6.2). The
oxidisable fraction contributed a negligible amount to the Pb concentration. This was
unforeseen, considering that Pb forms stable organocomplexes and tends to bind to
sulfides (Weisz, Polykak & Hlavay, 2000). Relic et al. (2005) observed high extraction
rates for Pb from sediment samples, from both the reducible and residual phases.
Although the exchangeable and carbonate metal phases and the residual fraction
contributed to most of the Pb in the sediments from the Wilge River, Klein Olifants and
Olifants rivers (Figures 6.1 to 6.3), there was no noteworthy contribution from the
oxidisable fraction.
6.2.2.8 Titanium
Most of the Ti was fractionated into the residual fraction of the sediments from all the
rivers, including the tributary. Negligible amounts were extracted from the remaining
phases (Figures 6.1 to 6.4). The Ti in the exchangeable and carbonate phase was found
to range in the no risk, to low risk, category of the RAC code.
156
6.2.2.9 Vanadium
Vanadium was found to occur mostly in the residual phase of sediment samples from all
the rivers (Figures 6.1 to 6.4). The lowest amount was found in the oxidisable fraction, i.e.
the small amount, bound to organic material and sulfur, is easily oxidised. This explains
the tendency for V to become unavailable once it is in sediment. The Olifants River
tributary recorded a high proportion of V in the reducible fraction. This implies that most of
the V is available under reducing environmental conditions. Reducing conditionds are
likely to arise when processes which use oxygen are dominant. These include, among
others, aerobic respiration by microorganisms. In addition, changes in the temperature of
the water are likely to result in varying concentrations of dissolved oxygen in water, thus
influencing the reduction potentials of the water. Cold water dissolves more oxygen than
warm water.
The percentage of V in the exchangeable and carbonate fraction was in the high, low, no
and medium risk category for sediments from WILGE 2,3 and OLI 4; OLI 1 to 3 and
WILGE 4; KOL 3; and KOL 1, 2, 4 and WILGE 1, respectively.
6.2.2.10
Summary
From the foregoing discussion it is apparent that some trace metals were present in the
residual phase of the sediments from different rivers. For each river these were the metals
found to occur mostly in the residual fraction and are thus likely to be less bioavailable:

Olifants River: Fe, Co, V, Pb, Cr, Ti and Cd;

Klein Olifants River: Fe, V, Pb, Cr, Ti and Cd;

Wilge River: Fe, V, Cr, Ti and Cd and

Olifants River tributary: Fe, Cd and Ti.
It can be assumed that these elements are primarily derived from a geochemical
background, rather than from anthropogenic inputs (Qian et al., 1996). It is well known
that the metals associated with the residual fraction are less bioavailable than those
bound to the non-residual fraction (Qian et al., 1996; Maiz et al., 2000).
However, when the exchangeable and carbonate phase was considered through the use
of the RAC some of the trace metals were found have the potential to cause problems at
the sampled sites as summarised below:
157

Cd: poses a medium risk (Wilge and Olifants River tributary);

Co: poses a medium risk (Olifants River tributary, Klein Olifants and Wilge rivers
upstream);

Cr: poses a medium risk (Olifants River tributary, Klein Olifants and Wilge rivers);

Pb: high risk (Wilge River) and medium risk (Klein Olifants and Olifants rivers);

Mn: medium risk (Olifants River tributary, Wilge and Klein Olifants rivers) and high
risk (Olifants River); and

V: medium risk.
Although most of the elements were in the medium risk category for some sites, with the
exception of Pb and Mn, the risk of the elements is likely to increase with time as more
accumulation of the elements in the exchangeable and carbonate phases takes place.
Further consideration of the effect of redox conditions in the water on the mobilisation of
trace elements revealed that the most elements are likely to pose health problems when
conditions in the water become reducing:

Olifants River tributary: Mn, Fe, Co, V, Pb, Cr and Cd;

Wilge River: Mn, Co and Cd;

Klein Olifants: Mn, Co, Pb and Cd and

Olifants River: Mn, Co; and Pb.
6.2.3
Statistical analysis
6.2.3.1 Univariate statistics
The main descriptive statistical results for the elemental concentrations in summer and
winter sediments from the sampling area are presented in Tables 6.4 and 6.5. There was
no significant difference between summer and winter sediment samples. However, during
summer, there was pronounced flow of water at the sampled sites. Flow of water
generally assists in the movement and deposition of sediment material from one point to
another with the concomintant changes in the chemical compositon, trace metals
included, of the sediments. This large flow in water did not have an effect on the chemical
composition of the sediments, as far as trace metals were concerned. In addition,
physicochemical properties, including pH, conductivity and Eh values, likely to determine
the residence time of metals in sediemnts, did not differ much between the two seasons.
Results from summer sediments were arbitrarily chosen to represent statistical treatment,
since samples from other sampling excursions yielded similar interpretations. The high
158
standard deviations are a strong indication of the spatial variability, perhaps due to the
presence of different anthropogenic sources and differences in the chemical processes
determining the fate of metals in the sediments.
6.2.3.2 Correlation analysis
The Pearson correlation coefficients between the extracted concentrations of Mn and Fe
in the exchangeable and carbonate (F1), reducible (F2) and oxidisable (F3) fractions, as
well as their non-residual totals (F12 and F123), are presented in Table 6.6.
Table 6.4: Descriptive statistics, from the analysis of the summer sediment samples
Metal
Mean
Minimum
Maximum
(µg/g))
(µg/g))
(µg/g))
Mn
1061
507.4
1511
Fe
5689
4014
10441
Co
51.87
0.21
443.2
V
53.64
11.98
93.61
Pb
42.21
9.04
124.6
Cr
124.4
3.04
461.4
Ti
275.3
16.73
487.3
Cd
33.36
3.81
86.69
Table 6.5: Descriptive statistics, from the analysis of the winter sediment samples
Metal
Mean
Minimum
Maximum
(µg/g))
(µg/g))
(µg/g))
Mn
1047
519.3
1725
Fe
5671
4025
10581
Co
47.35
0.18
457.2
V
55.83
12.05
94.18
Pb
45.19
8.51
129.5
Cr
121.4
2.74
458.1
Ti
281.9
17.09
499.7
Cd
36.57
3.52
89.53
159
Table 6.6: Correlation coefficients for extracted concentrations of Mn and Fe in F1, F2,
F3, F12 and F123
F1
Mn
F3
F12
F123
F1
F2
0.150
F3
0.117
F12
0.542
F123
Fe
F2
a
0.519
0.286
a
0.322
a
0.507
0.749
0.743
0.979a
F1
F2
0.795a
F3
0.814
0.904
F12
0.854a
0.994a
0.916a
F123
0.856a
0.994a
0.923a
a
a
0.820a
a
correlation significant at p < 0.05
The high degree of correlation between the Mn extracted from the exchangeable and
carbonate, and reducible phases (F12), versus the total non-residual fraction (F123), may
be an indication that the Mn extracted in the first two steps determined the overall
availability of Mn in the investigated sediments. Unlike Mn, there was a high degree of
correlation between the Fe extracted from the exchangeable and carbonate (F1),
reducible (F2) and oxidisable (F3) phases of the procedure, together with the non-residual
fractions totals (F12 and F123). This is confirmation that the concentration of this metal
extracted from the exchangeable and carbonate, reducible and oxidisable phases
determined the overall availability of Fe in the studied sediments.
The Pearson correlation coefficients between the extracted amounts of Mn and Fe in the
reducible phase (F2) and the amounts of Co, V, Pb, Cr, Ti and Cd in the non-residual
fractions, F12 and F123, are presented in Table 6.7.
There were positive correlations between V, Cr and Ti in the F12 and F123 non-residual
fractions and the amount of Mn extracted in the reducible phase (F2).
This could be an indication that these elements are associated with the reducible fraction
(MnO x) of the sediment samples. The non-residual fractions (F12 and F123) of Cr, were
also associated with the non-residual fractions of Ti (F12 and F123). Positive linear
correlation was also observed between Co, V, Pb, Cr and Cd in the F12 and F123 nonresidual fractions and the amount of Fe extracted in the reducible phase. This may be an
160
indication that iron oxides (FeyOx) in the reducible phases determine the availability Co, V,
Pb, Cr and Cd.
The association between Co, V, Cr and Ti with Mn could be an indication of a common
source for these elements. These metals are widely used in the manufacture of steel.
Steel/ferro alloy manufacturing concerns are mainly located within the studied area. These
include, among others, Columbus stainless and Middleburg steel. In addition 80%, 73%
and 45% of Mn, Cr and V mining respectively in South Africa is undertaken in the
Mpumalanga region where the studied rivers are located. Iron-chrome manufacture is
undertaken in Machadosdorp while Cr mining and production is carried out at mines such
as Dwarsrivier. The inclusion of Cd and Pb in the association matrix may be viewed as an
indication of a common anthropogenic source. The very low correlation coefficients (Table
6.8) between the non-residual fractions and residual fractions for Co, V, Pb and Ti, could
be an indication of anthropogenic sources of these elements.
161
Table 6.7: Correlation coefficients for F2 fractions of Fe and Mn, and F12 and F123 fractions of Co, V, Pb, Cr, Ti and Cd
Mn F2
Fe F2
Co F12
CoF123
V F12
V F123
Pb F12
PbF123
Cr F12
Cr F123
Ti F12
Ti F123
Cd F12
Mn F2
Fe F2
0.297
Co F12
0.944
162
a
-0.006
CoF123
a
0.920
-0.003
1.000
V F12
0.951a
0.742a
0.524a
0.528a
V F123
0.980a
0.741a
0.524a
0.528a
1.000a
Pb F12
0.877a
-0.122
0.683a
0.642a
-0.677a
-0.687a
PbF123
0.849a
-0.096
0.787a
0.686a
-0.633a
-0.632a
0.993a
Cr F12
0.850a
0.791a
-0.609a
-0.816a
0.786a
0.787a
0.891a
0.782a
Cr F123
0.875a
0.755a
-0.635a
-0.832a
0.746a
0.747a
0.855a
0.714a
0.989a
Ti F12
0.422
0.967a
0.033
0.036
0.778a
0.778a
0.055
0.076
0.882a
Ti F123
0.405
0.975
a
0.052
0.056
0.773
0.773
a
0.019
0.043
0.851
a
0.835
0.995
Cd F12
0.618a
-0.209
-0.305
-0.309
-0.346
-0.343
0.500
0.500
-0.018
0.083
-0.065
-0.116
a
-0.326
-0.319
-0.324
-0.412
-0.411
0.380
0.356
-0.085
0.012
-0.164
-0.214
CdF123
a
0.678
correlation significant at p < 0.05
a
a
0.871a
a
a
0.912a
CdF123
Table 6.8: Correlation coefficients for the non-residual fractions (F12 and F123) and the residual (F4) fraction
CoF123
Co F4
V F123
V F4
Pb F123
Pb F4
Cr F123
Cr F4
Ti F123
Ti F4
Cd F123
Co F123
Co F4
0.191
V F123
0.533
-0.148
V F4
0.038
0.624a
-0.361
Pb F123
0.085
-0.165
-0.018
0.323
0.437
a
0.012
0.552a
0.020
a
-0.214
0.204
-0.312
a
0.733
0.772
0.039
0.022
-0.405
0.044
-0.293
0.835a
-0.191
-0.410
0.102
-0.222
Pb F4
163
Cr F123
Cr F4
Ti F123
Ti F4
Cd F 123
Cd F4
a
a
-0.033
-0.110
0.056
-0.165
-0.323
-0.231
0.908
-0.309
0.027
-0.364
-0.229
-0.454
-0.172
correlation significant at p < 0.05
0.745
a
0.777
0.055
-0.431
-0.394
-0.062
0.268
a
0.534
-0.285
a
a
-0.621
0.355
0.437
a
-0.541
-0.304
-0.006
-0.019
0.258
a
-0.230
0.217
a
-0.269
0.133
0.568
0.758
a
0.853
Cd F4
6.2.4
Factor and cluster analysis
6.2.4.1 Factor 1
To obtain more reliable information concerning the relationships among the variables, FA
was applied (Kelepertsis, Karamanou & Polizoins, 1982; Alexakis & Kelepertsis, 1998;
Bartolomeo et al., 2004). A total of eight factors (Table 6.9) were obtained by the principal
component method for the eight elements, ordered according to their significance, and the
eight eigenvalues. Trivial factors were discarded, by normalizing, using rotation factor
loadings. This resulted in three factors being selected by the software. The first factor
accounted for 43% of the total variance, the second for 22%, while the third factor
accounted for 15% (Table 6.10).
Table 6.9: Eigenvalues for the 8 factors and cumulative proportion of variance
Factor
Eigenvalue
% Total
variance
1
6.070
43.358
2
3.059
3
Cumulative
Eigen value
Cumulative%
variance
In data space
Cumulative%
variance
In factor space
6.070
43.358
48.544
21.848
9.129
65.205
73.008
2.041
14.581
11.170
79.786
89.331
4
1.334
9.525
12.504
89.312
5
0.740
5.288
13.244
94.600
6
0.482
3.441
13.726
98.040
7
0.178
1.270
13.903
99.310
8
0.071
0.509
13.975
99.819
Only small loadings were assigned for the extracted Mn in the second step of the
procedure as reflected by Factor 3 (Table 6.10). Small loadings were also obtained for
extracted Co in the non-residual fractions (F12 and F123) in Factor 1. The lowest
communality (which determines for each variable the total variance of that variable
accounted for by the three factors) was obtained for Pb (F123) of 72%, while for the other
variables, the determined factors account for 83-96% of their variance.
Approximately 49% of the variance among the elements or variables was accounted for
by this factor. For this factor, loadings were obtained for the F12 and F123 fractions of
most metals, with Co, Cr, Pb and V yielding the highest loadings and Mn and Ti having
the lowest contributions (Table 6.10 and Figure 6.5). These fractions were clustered within
164
the same group (Figure 6.6). This factor is characterised by low negative loadings from Cd
in the non-residual fractions.
Table 6.10: Sorted rotated factor loadings (Varimax rotated)
Variable
Factor
1
Factor
2
Factor
3
Communality of
a variable
Mn F2
-0.481
-0.397
0.581
0.854
Fe F2
0.289
0.917
-0.051
0.9393
Co F12
0.553
0.173
0.215
0.8396
Co F123
0.571
0.091
0.207
0.8434
V F12
0.718
-0.157
0.191
0.9093
V F123
0.753
-0.173
0.125
0.9238
Pb F12
0.942
0.371
0.115
0.9603
Pb F123
0.746
0.412
0.202
0.7163
Cr F12
0.858
0.215
0.171
0.872
Cr F123
0.814
0.196
0.124
0.8758
Ti F12
0.489
0.848
-0.075
0.9636
Ti F123
0.471
0.911
-0.083
0.9571
Cd F12
-0.381
-0.218
0.951
0.9394
Cd F123
-0.427
-0.346
0.847
0.9098
6.2.4.2 Factor 2
This factor expresses almost 24% of the variance among the elements. Based on the
rotated PCA, Factor 2 displays a high loading of Fe (F2) and Ti in its non-residual
fractions, while a small loading was recorded for Mn (Table 6.10 and Figure 6.5). This is
indicated as one cluster of the first group of clusters on the dendogram (Figure 6.6). The
high correlation between the elements in this factor is an indication that Ti is mainly
associated with iron oxide or hydrous oxide phases.
6.2.4.3 Factor 3
Factor 3 is characterised by high positive loadings from the F2 Mn fraction and the nonresidual fractions of Cd. This is indicated as a cluster on the dendogram (Figure 6.6).
These observations suggest that there is some association between Co, Cr and Pb in the
non-residual fraction and oxides of Mn.
165
Scatterplot of Factor 3 against Factor 2
metals factor loadings spreadsheet.sta 3v*14c
1.0
Cd F12
Cd F123
0.8
Factor 3
0.6
Mn F2
0.4
Co F12
Co F123
Pb F123
V F12
Cr F12
V F123
Cr F123 Pb F12
0.2
0.0
-0.2
-0.6
Fe F2
Ti F12
Ti F123
-0.4
-0.2
0.0
0.2
Factor 2
Figure 6.5: Scatter plot of factor 3 and factor 2 loadings
166
0.4
0.6
0.8
1.0
Scatterplot of Factor 2 against Factor 1
metals factor loadings spreadsheet.sta 3v*14c
1.0
Fe F2
Ti F123
Ti F12
0.8
0.6
Pb F123
Factor 2
0.4
Co F12
Co F123
0.2
0.0
-0.2
-0.4
-0.6
-0.6
Pb F12
Cr F12
Cr F123
V F123
V F12
Cd F12
Cd F123
Mn F2
-0.4
-0.2
0.0
0.2
Factor 1
Figure 6.6: Scatter plot of factor 2 and factor 1 loadings
167
0.4
0.6
0.8
1.0
Tree Diagram for 14 Variables
Single Linkage
Euclidean distances
Mn F2
Cd F12
Cd F123
Fe F2
Ti F12
Ti F123
Co F12
Co F123
Pb F12
Pb F123
Cr F12
Cr F123
V F12
V F123
0.0
0.2
0.4
0.6
0.8
Linkage Distance
Figure 6.7: Dendogram-cluster analysis of the data
168
1.0
1.2
1.4
1.6
6.2.4.4 Summary
Identification of heavy metal sources, as well as the fractionation of the heavy metals, are
important environmental issues. The present study presents useful tools, methods, and
indices for the evaluation of sediment contamination. This study also provides a powerful
tool for processing, analyzing and conveying raw environmental information for decisionmaking processes and management involving aquatic environment.
Multivariate analyses, including the correlation matrix analysis and FA used in this study
provide an important tool for better understanding the complex dynamics of pollutants.
The sequential extraction scheme was used to fractionate Cd, Co, Cr, Fe, Mn, Pb, Ti and
V in the investigated sediments. Some significant associations among selected chemical
fractions of the metals were established, via statistical interpretation.
The trace metals were mostly extracted from the residual fraction of the sediments,
suggesting that they are derived more from geochemical sources rather than
anthropogenic sources. The main substrate for the non-residual fraction of V, Cr, Co and
Pb were Mn-oxides while Ti may be associated with Fe-oxides. The low (and in some
cases negative) correlation coefficients of the non-residual fractions of Co, V, Pb and Ti,
and the corresponding residual fractions, indicate an anthropogenic source for these
metals. The use of statistical tools also confirms some of the findings.
169
CHAPTER 7
CONCLUSIONS AND RECOMMENDATIONS
7.1 Introduction
The increase in industrialisation in South Africa has lead to an increase in coal mining and
coal utilisation in Mpumalanga. These activities are located mainly within the upper
catchment of the Olifants River, which consists of the Olifants, Klein Olifants and Wilge
rivers. These rivers, in turn, flow into the Loskop Dam, where deteriorating water quality
has been reported. The Loskop Dam supports aquatic life and various irrigation schemes.
Effluents from operational and abandoned mines are likely to contain potentially toxic
metals and organic compounds, including PAHs, leached from coals, which end up in the
various environmental compartments of the aquatic system, particularly in sediments. In
addition to coal mining and its use, other anthropogenic activities are likely to introduce
pollutants into the rivers. Thus, the importance of identifying organics and inorganics likely
to be leached from South African coals and the elucidation of their leaching behavior is of
fundamental importance. The existence of other sources of both organics and inorganics
makes it imperative to have information on the various contributors of pollutants. Such
information is vital for the implementation of effective remedial or risk control activities to
ameliorate the impact on fauna, flora and people who depend on the water. Chemical
fingerprinting and multivariate statistical techniques are indispensible in this regard.
The general objective of the study was to determine the extent of pollution of the Olifants,
Klein Olifants and Wilge rivers, by trace metals and sixteen EPA priority PAHs at selected
points in the upper catchment area. Furthermore, the study sought to identify and
apportion sources of PAHs in the sediment samples from the study area. The impact of
coal mining activities on potentially toxic metal and PAH pollution of surface waters was
also investigated via leaching experiments.
The study has demonstrated that sediments in the Olifants River, Klein Olifants and Wilge
rivers, flowing into the Loskop Dam, were contaminated with trace elements, but not yet at
levels that suggests cause for crocodile and fish mortalities. Some EPA priority PAHs
were detected in water and suspended matter, while all sixteen EPA priority PAHs were
detected in sediment samples from the studied rivers. Chemical fingerprinting and
170
multivariate approaches were employed to decipher sources of the pollutants. Model
experiments to simulate leaching of trace elements and organics were also conducted.
7.2
7.2.1
Achievment of objectives
Environmental implications of leaching of trace elements and organics in coal
The leaching of trace elements from coals, under different environmental pH conditions to
which the coals were exposed, was studied via column leaching and sequential extraction
experiments. Selected elements (As, Cd, Co, Cr, Mn, Ni and Pb) were found to be
leached at concentrations above EPA standards for surface water. The concentrations of
elements in the non-residual fractions were used as an indicator. Four types of leaching
trends, namely, V shaped, gradually descending (\), and parabolic-like (∩/U), were
discerned after the coals were exposed to leachants of pH 2.0, 4.0 and 6.0, as a function
of time.
Leaching of trace elements was found to depend on the pH, time and mode of
occurrence, Adsorption and desorption kinetics, co-precipitation of the metals, and
different rates of migration of elements to the coal surface were proposed as phenomena
influencing their concentrations in leachates.
Possible leaching of organic compounds from coal was also investigated. In contrast to
trace metals, an extended period of leaching (days) was required for organic compounds
to be detected in the leachates. A wide range of compounds with the potential to cause
health problems were detected in the coal leachates. These included some of the sixteen
EPA priority PAHs, functional derivatives of PAHs, benzene derivatives, biphenyls, and
non-aromatic compounds (n-alkanes and cyclic aliphatic compounds).
7.2.2
Identification, quantification and source apportionment of PAHs
The analysis of samples revealed the presence of some of the sixteen EPA priority PAHs
in water, suspended matter and sediments. Low molecular weight PAHs were mostly
detected in water samples, while certain high molecular weight (HMW) and LMW PAHs
were detected in suspended matter. The distribution of PAHs in suspended matter was
found to vary between the rivers. Fewer PAHs (mostly HMW and LMW), were measured
in suspended matter from the Olifants River. High molecular weight and MMW PAHs were
found in higher concentrations than LMW PAHs in suspended matter from the Olifants
171
River tributary. Samples from the Klein Olifants River suspended matter revealed only
MMW and LMW PAHs. The Wilge River was polluted by LMW, MMW and HMW PAHs,
while naphthalene and phenanthrene were only detected in water samples. All of the
sixteen EPA priority PAHs were detected in the sediment samples. However, some of the
PAH concentrations were found to be below the corresponding limits of quantitation. The
total concentrations of the PAHs ranged from 24.7 (Wilge River) to 926 µg/kg dry weight
(Olifants River tributary) and from 38.1 to 1040 µg/kg dry weight for summer and winter
samples, respectively.
Some of the PAHs detected in water samples were also detected in coal leachates
obtained by simulated leaching of coal. These PAHs are naphthalene, phenanthrene,
acenaphthene, acenaphthylene, fluorene, anthracene, pyrene, benz(a)anthracene and
chrysene. This finding implies that coal leaching from coal stock piles and coal seams
might be contributing to the pollution of surface waters by PAHs.
The variation in the distribution of the PAHs in the three aquatic compartments (sediment,
water and suspended matter) was attributed to differences in sources and various
processes (chemical and biological), which determine the fate and residence time of the
PAHs. The concentrations of the PAHs decreased downstream along the lengths of the
rivers, with the exception of the Wilge River. The Wilge River also contained the lowest
concentrations
compared
to
the
other
rivers
(Wilge<Olifants
Tributary<Klein
Olifants<Olifants River). This means that interms of potential health problems associated
with PAHs the Wilge River is likely to have the least impact. Sediment samples from the
Olifants River Tributary exhibited the highest individual and total concentrations of PAHs.
Through the use of diagnostic ratios, FIA/FIA+Py, FIA/Py, AN/AN+PhA, PhA/AN,
IP/IP+BghiP, FI/FI+Py and BaA/BaA+Chy, the inputs of PAHs were found to be a
combination of pyrogenic and petrogenic sources. By employing a combination factor and
cluster analysis, sources of PAHs were found to be coal and biomass combustion, coal
and tyre burning, and unburnt fossil fuels (possibly leaching of coal or deposits of coal
fines/particles onto sediments by run-off). The major contributors of PAHs were found to
be coal combustion (67%), unburnt coal (27%) and petrogenic (6%) sources.
7.2.3
Distribution and mobility of trace elements in sediment samples
Sediment samples were found to contain trace elements (Cd, Co, Cr, Mn, Pb, Ti and V) at
concentrations above the background levels, as indicated by enrichment factors,
172
suggesting anthropogenic input. The occurence of the elements was studied by applying a
sequential extraction procedure. High mobility of trace elements was indicated by the high
percentages of elements found in the non-residual fractions, especially in the
exchangeable and carbonate phases. The percentages of a trace element in the
exchangeable and carbonate phases were used to determine the possible risk of
mobilisation of trace elements. According to the risk assessment code employed, most
elements were in the medium risk category, with the exception of Mn and Pb, which were
classified as high risk:

Cd: poses a medium risk (Wilge and Olifants River tributary);

Co: poses a medium risk (Olifants River tributary, Klein Olifants and Wilge rivers
upstream);

Cr: poses a medium risk (Olifants River tributary, Klein Olifants and Wilge rivers);

Pb: high risk (Wilge River) and medium risk (Klein Olifants and Olifants rivers);

Mn: medium risk (Olifants River tributary, Wilge and Klein Olifants rivers) and high
risk (Olifants River); and

V: medium risk.
These metals are likely to be mobilised in the presence of ions with a higher affinity for
adsorption sites on the sediments or in the event of the diminution of the pH of water. This
will also affect sites downstream of the contaminated sites. Although most of the metals
pose a medium risk, it is anticipated that the risk will increase with an increase in
anthropogenic activities.
In addition to metals in the exchangeable and carbonate phases, metals in the reducible
fraction were found to have the potential to cause health problems for the studied sites:

Olifants River tributary: Mn, Fe, Co, V, Pb, Cr and Cd;

Wilge River: Mn, Co and Cd;

Klein Olifants: Mn, Co, Pb and Cd; and

Olifants River: Mn, Co and Pb.
The water at these sites, and those downstream, is likely to be contamianted by these
metals in the event of the conditions in the water becoming anoxic.
The association of trace elements in the reducible fraction with Fe and Mn-oxides was
investigated via correlation analysis. Iron and Mn-oxides are known to play important roles
in the accumulation of metals in sediments. The association of Fe-oxides with Co, V, Pb,
Cr and Cd and of Mn-oxides with V, Ti and Cr was deduced.
173
Results from the three non-residual fractions were subjected to factor and cluster analysis.
The elements were found to form three groups consisting of:

Cd and Mn;

Co, Pb and Cr; and

Fe and Ti.
These three groupings were taken to represent possible sources of these elements.
7.3
Contribution of the study
Coal leaching studies were undertaken to determine potentially toxic compounds that
would be leached from the coal by runoff. Data from coal leaching studies will assist in
understanding and identifying the leached material, together with the mode of occurrence
of the elements in coal, as drainage occurring as water filters through disused mines and
coal storage sites. This can assist in directing responsible parties towards possible
solutions. The identification of potentially toxic organic compounds in the coal leachates
will assist in source apportionment and directing liability in the event of health ailments,
such as Balkan endemic nephropathy (BEN), in people consuming or exposed to water
from the studied rivers. Thus, data from coal leaching studies can assist in formulation of
solutions to ameliorate the impact of pollution on the environment. Similar or modified
leaching studies can be extended to coals from different areas.
As far as can be determined, this study presents the first such assessment of pollutants in
sediments in South Africa. The study revealed the presence of pollutants in various
environmental compartments of the three main rivers contributing water to the Loskop
Dam. Presence of the pollutants was found to be influenced by sources in proximity of the
rivers. These sources include mainly coal mining and coal utilisation activities. This
research provided information to compliment other multidisciplinary studies being carried
out to find a solution to the deteriorating water quality in the Olifants River catchment. The
identification of the specific sources of pollutants detected in the sampled sites will provide
assistance to efforts directed at mitigating the deteriorating quality of water in these three
rivers.
A risk assessment, by way of sequential extraction studies on the sediments, provided
information concerning the bioavailability of trace elements accumulating in sediments.
This information is vital, since the toxicity of elements will depend on the form in which
they exist. Environmental conditions under which mobilisation of trace elements present in
174
sediments is favoured, are now known from the present study. The study also
demonstrated the use of chemical fingerprinting (isomer diagnostic ratios) and receptor
modelling (multivariate statistics) for apportioning sources of PAHs in the sediments.
Fractionation studies were found to provide valuable information regarding the mobility
and bioavailability of the elements in sediments.
The results obtained in the study can be of use for local authorities to decide on
reclamation or remediation of the polluted sites and the level of priority of the intervention.
Moreover, the data is of interest to the Department of Water and Forestry (DWAF) and the
Water Research Commission (WRC).
contamination
and
metal
Results regarding the source of
bioavailability
provide
the
necessary
metal
information
for
recommending proper management of metal contaminated-areas.
7.4
Recommendations and future research
Further work is required to obtain a better understanding and to enable risk
determinations of polluted water to communities and the environment. This can be
achieved by an intensive programme involving the monitoring of water quality on a regular
basis, instead of seasonally. Toxicity studies to evaluate the impact of pollutants on
aquatic organisms are also recommended. Future research should also focus on a
comprehensive sampling of the Loskop Dam itself and analysis of sediment and water
samples for the presence of pollutants.
175
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