Impact of Acid Mine Drainage (AMD) on Water Quality
Transcription
Impact of Acid Mine Drainage (AMD) on Water Quality
Water Air Soil Pollut DOI 10.1007/s11270-008-9900-z Impact of Acid Mine Drainage (AMD) on Water Quality, Stream Sediments and Periphytic Diatom Communities in the Surrounding Streams of Aljustrel Mining Area (Portugal) A. T. Luís & P. Teixeira & S. F. P. Almeida & L. Ector & J. X. Matos & E. A. Ferreira da Silva Received: 21 May 2008 / Accepted: 20 October 2008 # Springer Science + Business Media B.V. 2008 Abstract Aljustrel mining area is located in the Iberian Pyrite Belt, one of the greatest concentrations of massive sulphide deposits that extends from Lousal (Portugal) to Aznalcóllar (Spain). The surrounding streams, Roxo, Água Azeda and Água Forte, are influenced by the erosion of the tailing deposits and the input of acid mine drainage (AMD) from the abandoned Aljustrel pyrite mines, recently reopened in 2007. The purpose of this study was to understand how these adverse conditions influenced the stream sediments, water quality and periphytic diatom communities and establish the pre-restoration local conditions to judge the success of rehabilitation program now under way. For stream sediments, the highest metal concentration samples were found at sites F, G and H. Arsenic, Cu, Fe, Pb and Sb detected concentrations, generally exceeded the probable effect concentration values reaching level 4: the highest toxicity level. In surficial water samples of AMD affected sites (F, G and H), low pH values (1.5 to 3.5) and high metal concentrations of As (6,837 μg L−1), Cd (455 μg L −1 ), Cu (68,795 μg L −1 ), Fe (1,262,000 μg L −1), Mn (19,451 μg L −1), Pb (136 μg L−1), and Zn (264,377 μg L−1) were found. In these sites, the diversity index (H′) for diatoms was low (0.6 to 2.8) and the dominant taxa were Eunotia exigua (site F, 33.5%) and Pinnularia acoricola (abundances in sites: F, 86.8%; G, 88.5%; and H, 91.1%). In opposition, in less AMD impacted, H′ was high (1.5 to 4.6) and low metal concentrations and A. T. Luís : P. Teixeira : E. A. Ferreira da Silva (*) Geobiotec–Geobiosciences, Technologies and Engineering, Departamento de Geociências, Universidade de Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal e-mail: [email protected] L. Ector Department Environment and Agro-biotechnologies (EVA), Public Research Center–Gabriel Lippmann, Rue du Brill 41, 4422 Belvaux, Grand-duchy of Luxembourg e-mail: [email protected] A. T. Luís e-mail: [email protected] P. Teixeira e-mail: [email protected] S. F. P. Almeida Geobiotec-Geobiosciences, Technologies and Engineering, Departamento de Biologia, Universidade de Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal e-mail: [email protected] J. X. Matos Assessor do Centro de Estudos Geológicos e Mineiros de Beja, Rua Frei Amador Arrais No 39 r/c, Apartado 104, 7801-902 Beja, Portugal e-mail: [email protected] Water Air Soil Pollut high pH were found. Achnanthidium minutissimum was the dominant taxon in (abundances in sites: A, 76.1% and B, 24.39%). Canonical correspondence analysis showed that spatial variation due to mine influence was more important than seasonal variation, which did not show any pattern. Keywords Acid mine drainage (AMD) . Aljustrel mining area . Diatoms . Stream sediments . Surficial waters 1 Introduction Mining activity exerts a significant influence in energy and matter cycles of the natural environment, so it is important to analyse the dispersion and distribution of toxic elements mainly when their concentrations seem anomalous to natural biochemical background (Oliveira et al. 2002). Acid mine drainage (AMD) is formed when pyritic minerals are exposed to atmospheric, hydrological or biological weathering (oxygen, water and chemoautotrophic bacteria) becoming oxidised and resulting in sulphuric acid (low pH), dissolved metal ions, elevated sulphate contents (Skousen et al. 1994), low alkalinity and high conductivity. Arsenic, Cu, Fe, Mn, Pb, Zn and sulphates are frequently found in high concentrations, and their solubility increases with acidity (Dickson 1975; Stokes and Hutchinson 1975; Beamish and Vanloon 1977; Almer et al. 1978; Van Dam et al. 1981; Harding and Boothroyd 2004). Dilution may reduce metal concentrations while not markedly influencing pH. At higher pH (>4.0) precipitation of metal hydroxides can smother biota with precipitates [the most visible structures are red-orange ferric hydroxide (FeOH3) commonly referred to as yellow boy], whereas at lower pH, the dissolved metals toxicity can cross membranes (Van Ho et al. 2002). The AMD effects on aquatic ecosystems are twofold: (a) impacted communities experience lethal levels of pH and metals, which lead to a decrease in algal species richness and diversity (i.e. Mulholland et al. 1986; Planas 1996; Verb and Vis 2000a, b); (b) communities are restricted to tolerant organisms, which are able to survive in these conditions. Alterations in nutrient cycles and abiotic changes are supported for these communities with large impact in biotic relations, including extinction and succession of species and groups of sensitive taxa (Kwandrans 2007). Diatom’s metal response models are difficult to establish because metal contamination is frequently associated with acidic environments (Dixit et al. 1991). Several studies in metal-polluted rivers have shown that diatoms respond to perturbation not only at the community level through shifts in dominant taxa (Gustavson and Wängberg 1995; Hirst et al. 2004) as also with changes in diversity (Leland and Carter 1984; Medley and Clements 1998). Therefore, the aims of this work are (a) to characterise the water and stream sediments chemistry of the AMD impacted streams, identifying the sources and describing the dominant geochemical processes occurring in the area, in order to access their impact on diatom communities and (b) establish the prerestoration local conditions to judge the success of the rehabilitation program now under way. 2 The Study Area 2.1 Environmental Setting 2.1.1 Aljustrel Geology The Aljustrel region is characterised by a Palaeozoic basement of the South Portuguese Zone and by a modern sedimentary sequence of the Alto Sado Tertiary Basin. The active NE–SW Messejana fault defines the SE border of this basin, which is confined to the northwestern block of the fault (Fig. 1). The Messejana fault presents an Iberian dimension and a senestral strike-slip movement of 2.5 km. Along this major structure, a Jurassic dolerite is also observed (Schermerhorn et al. 1987). The Aljustrel’s Palaeozoic stratigraphic sequence is formed by the following main units (Andrade and Schermerhorn 1971; Schermerhorn et al. 1987; Silva et al. 1997; Leitão 1998; Matos 2005; Oliveira et al. 2006): Baixo Alentejo Flysch Group, Mértola Formation (Upper Visean)–shales and greywackes (flysch turbidites). The Iberian Pyrite Belt (IPB), volcano–sedimentary complex (VSC) (Upper Famennian–Upper Visean) is represented by the following units: Paraiso Fm.– siliceous shales, phyllites, tuffites, purple shales, jaspers and cherts; felsitic/mine metavolcanics and megacryst/green metavolcanics sequences–sericitic Water Air Soil Pollut Fig. 1 Geological setting of the Aljustrel mining region (adapted from Matos 2005; Schermerhorn et al. 1987; Oliveira et al. 1992): 1 alluvium deposits; 2 undifferentiated Sado Tertiary Basin sediments; Palaeozoic Basement (South Portuguese Zone/ Iberian Pyrite Belt): 3 Messejana Dolerite Dike (Middle Jurassic); 4 Mértola Flysch Formation (Late Viséan); IPB Volcano Sedimentary Complex (Late Famennian-Late Viséan) 5 Sediment, 6 Volcanic, 7 Algares and S. João outcropping ore bodies; 8 Phyllite-Quartzite Group (Early Frasnian-Late Famennian). Mining waste: 9 brittle pyrite; 10 slag; 11 acid mine drainage/leached materials; 12 evaporation tanks; 13 host mineralization rocks; 14 urban areas; 15 acid water dams: BE Estéreis, BAF Água Forte; 16 clean waters dams: BAC Águas Claras, BAI Água Industrial; BMR Monte Ruas; 17 fault; 18 stream; 19 sample point. UTM coordinates in km felsic volcanics, felsites, felsophyres, volcanic breccias, massive sulphides, feldspar megacryst volcanics and lavas. The IPB lower unit, the sedimentary Phyllite–Quartzite Group (Frasnian–Upper Famennian), is not recognised in the Aljustrel area. The VSC Aljustrel Anticlinorium is represented by a NW–SE lineament (4.5 km length and 1.5 km across). Several NW–SE thrust are identified, mainly in the short limbs of the anticlines. The Aljustrel thrust is one of the main structures and materialises the SW contact between the Water Air Soil Pollut VSC and the Flysch Group. Late variscan faults with NNE–SSW direction produced important dextral strike-slip movements, probably conjugated faults of the Messejana fault. This system is represented from SE to NW by the Esteval, the Azinhal, the Feitais, the Represa, the Castelo and the Moinho faults. 2.1.2 The Aljustrel Mine Located in the Alentejo province in the south of Portugal, Aljustrel mine is one of the great IPB mining sites, a world class volcanic-hosted massive sulphides metalogenetic province (Barriga et al. 1997; Carvalho et al. 1999; Matos and Martins 2006; Oliveira et al. 2006; Relvas et al. 2006; Tornos 2006). In the Aljustrel mining site, six massive sulphide orebodies are recognised: Moinho, Feitais, Estação, Gavião, Algares and São João. The last two were mined since Roman times (Leitão 1998; Silva et al. 1997; Matos and Martins 2003, 2006). Moinho deposit was exploited by the public Pirites Alentejanas Company (PA) for copper until 1993. The gossans and the supergene zones of the Algares and São João orebodies were exploited during roman era to 100 m deep (Domergue 1983). rare chalcopyrite with sparse distribution in the iron silicate matrix. The Roman slag had high concentrations of Pb, Cu, Zn, Fe, As and Sb elements. Roman wastes are represented by in situ and reworked slags and are located in the upper stream sector of Água Forte stream. Some of the mining infrastructures are unsafe and potentially dangerous, like open pits, quarries, galleries and mining shafts very exposed to local urban areas (Matos and Martins 2006). The walls of São João open pit and Malpique and Moinho quarries present significant geotechnical instability. Other wastes are present in Aljustrel, with less contaminant potential like host rocks, represented by felsic well cleaved and coherent volcanics, siliceous, purple and black shales, jaspers and cherts (volcano– sedimentary complex host rocks). Roasted pyrite ore has a very low expression confined to spotty occurrences at Algares. Presently, the EDM public company (the owner of the Portuguese mines rehabilitation program) is developing a rehabilitation program at mine-activityaffected areas, before 1990 (Martins 2005; Nero 2005; Matos and Martins 2006). 2.1.4 Aljustrel Landscape 2.1.3 Environmental Impact in the Aljustrel Area Related with the Extractive Activities As a result of thousand of years of pyrite ores exploitation in Aljustrel, large areas are occupied by waste tailings composed by Roman slag, pyrite ore (blocks and brittle massive pyrite ore) and volcano–sedimentary complex host rocks. Algares industrial area and São João sector present the highest volumes of mine waste. The small Mn-Fe exploitations are represented by small open pits and associated ore tailings, usually with less than 1 m thickness. The environmental impact of these exploitations is not quantified but is certainly locally significant. Part of these small mines is used for illegal waste disposals. The petrographic study of ore waste samples allowed the identification of interstitial chalcopyrite, sphalerite, galena, arsenopyrite and minor sulfossalts in the massive pyrite ore. The pyrite ore present high concentrations of Fe, Cu, Pb, Zn, Ag, Sb, Hg, Se, Co, Au and Cd while the roasted pyrite ore shows high concentrations of Au, Pb, Ag, Fe, Sb, Bi, Se, Cu, Zn and Mo. Roman slag presents interstitial pyrite and In the SE block of the Messejana fault, welldeveloped hills occur, locally controlled by differential erosion of outcropping Palaeozoic VSC units, e.g. Algares and São João gossans, jasper and chert horizons. The NW block of the Messejana fault is characterised by a flat relief. Here, the Palaeozoic basement is covered by the sediments of the Alto Sado Tertiary Basin (up to 100 m thick). The hydrographic network is well developed and hierarchisied. From west to east, the local hydrographical basins are: (a) Barranco do Farrôbo (BF)–stream affected by the Sto. Antão new industrial area where the ore processing plant, the main acid water dam (Estéreis) and the Águas Claras clean water dam are located. The Estéreis dam was considered for the e-Ecorisk virtual model discharge, related with a virtual dam rupture. (b) Água Azeda (AA)–stream affected by the São João mining sector (tailings and open pit) and by the São João urban area. Water Air Soil Pollut (c) Água Forte (AF)–main damaged area, stream affected by the Algares large brittle pyrite and slag tailings. Extreme acid water dam (Água Forte) and a clean water dam (Barragem de Água Industrial) are present. Sado River is the final destiny of the downstream effluents of the Aljustrel mine. A virtual discharge of AMD could affect the very sensible Sado ecosystem characterised by marsh zones (Sado Natural Reserve). The downstream mine area is clearly affected by mining activity. Geochemical data are compatible with a contamination diffusion model of the mine site. 3 Methods 3.1 Collecting and Processing Samples One of the aims of this study was to provide data that accurately represent the stream sediments and surface water’s chemical contaminants concentrations in the sampling system. The selected sampling sites were located upstream and downstream of AMD input influence. Stream Sediment Samples These were sampled according to ASTM (1984) at eight sampling sites (Fig. 1): three sites in Roxo stream (sites B, Roxo; C, Porto Ferreira; and D, Jungeiros), one in Água Azeda stream (site E, Água Azeda) and four in Água Forte stream (site A, Águas Industriais Dam-upstream from the mine; F, Água Forte; G, Ponte do Curval; and H, Porto de Beja). For each sampling site, 3 kg of stream sediments was collected, which was then oven-dried at a constant temperature of 40°C to a constant weight. Samples were disaggregated, sieved to <2 mm, crushed and passed through a minus 80-mesh (177 μm). Surface Water Samples These samples were collected at the same sites of stream sediment samples (Fig. 1). Sampling was carried out seasonally [in May and December of 2005 (campaigns 1 and 2) and March and June of 2006 (campaigns 3 and 4)]. In order to assess the physical and chemical parameters of surface water, a volume of 1 L was collected from the surface, as close to the centre of the river as possible. Water samples were returned in a cooled box to the laboratory and stored at 4°C before the analyses were conducted, according to Scalf (in Coleto Fiano and Maestro Salmeron 1998) and according to the proposed norms by EPA-Environmental Protection Agency (1982), ASTM-American Society for Testing and Materials (1984) and by German Chemists Association (1980). In order to determine the concentrations of trace elements, a volume of 250 mL was taken from each sample and filtered through 0.45-μm Millipore membrane filters using an all-plastic pressurised filtration system. Diatom Samples The protocols proposed by Prygiel and Coste (2000) were used for sampling, identifying and quantifying diatoms. The epilithic samples were obtained by scraping five boulders using a toothbrush and the epiphytic by squeezing the plants from the margins of the streams; the epipsammic were removed from the sediment with a syringe. Following the sampling protocol, the pools of stagnant water and shaded sites were avoided. Each sample was split in two, one kept alive (without preservation) and the other preserved with formalin solution (5 %). From the first sub-sample, an aliquot was cleaned using HNO3 (65 %) and potassium dichromate (K2Cr2O7) at room temperature for 24 h, followed by several centrifugations (1500 rpm) to wash the excess of acid. Permanent slides were prepared using Naphrax®. 3.2 Analytical Methods Stream Sediment Samples A multi-element analysis using inductively coupled plasma with optical emission spectrometry (ICP-OES) was performed in an accredited Canadian lab (ACME Anal. ISO 9002 Accredited Lab–Canada). For this, a 0.5 g portion of each sample was loaded into hot (95°C) aqua regia (HCl–HNO3–H2O) for 1 h. After dilution to 10 mL with water, the solutions were analysed by ICP-OES for 14 chemical elements: Al, As, Ca, Cd, Cu, Fe, K, Mn, Na, Ni, Pb, S, Sb and Zn. Surface Water Samples Temperature (T, °C), pH and electrical conductivity (EC, μS cm−1, at 25°C) were measured directly in the field with a WTW® Multiline P4 SET. The chemical analysis of water samples was carried out using ICP mass spectrometry for the determination of 13 elements: As, Ca, Cd, Cl, Cu, Fe, K, Mg, Mn, Na, Pb, SO42− and Zn). The determination of NH4+, NO3− and NO2− took place at IDAD Laboratory at the University of Aveiro. A titrimetric Water Air Soil Pollut method 4500-NH3 (C) for NH4+ determination was used; the adapted colorimetric method 4500-NO3− (E) for NO3− and the colorimetric method 4500-NO2− (B) for NO2− [all methods adapted from Clescerl et al. (2000)]. HCO3− was determined by titration with H2SO4 at 0.16 N in the Geochemistry Lab of the Geosciences Department at University of Aveiro. Diatoms For diatom observation on scanning electron microscopy (JEOL-JSM 5400), a drop of the oxidised sample was placed on a metal stub previously covered with a thin carbon pellicle. The sample was dried at room temperature (Almeida 1998). The stub with the sample was then coated with a gold–palladium mixture. Diatoms were identified and semi-quantified under the light microscope (Leitz Biomed 20 EB) using a 100× objective (N.A. 1.32). A total of about 400 valves were counted in each sample. Taxonomy was based on Germain (1981), Krammer and Lange-Bertalot (1986, 1988, 1991a, b), Simonsen (1987a, b, c), Round et al. (1990) and Prygiel and Coste (2000). 3.3 Data Analysis Stream Sediments and Surface Waters The used methodology to ordinate the results of stream sediments and surface waters was principal component analysis (PCA). Arsenic, Cd, Cu, Fe, Mn, Ni, Pb, Sb and Zn potential toxicity in stream sediment samples was estimated based on “Consensus-Based Sediment Quality Guidelines”, developed by the Contaminated Sediment Standing Team-CSST-in 2003. These guidelines can be applied not only to individual contaminants but also to several metals and compounds to estimate their combined toxic effects. The environmental sample concentration is divided by the probable effect concentration (PEC), the concentration above which, according to the consensus-based sediment quality guidelines, adverse effects on benthic organisms are expected; the resulting value is called PEC quotient (Table 2). The guidelines list include threshold effect concentration (TEC), the concentrations below which adverse effects on benthic organisms are seldom observed, and PEC, the concentration above which adverse effects on benthic organisms are observed. The toxicity level increases from level 1 to 4. Diatoms The used methodology for species data analysis was canonical correspondence analysis (CCA), which is used to study diatom responses to the environmental variables. The main purpose of this method is treating the data as a whole, so that the diatom community structure is highlighted and the information is condensed in a simple and interpretable way. CCA was performed using the computer Canoco program (version 4.5; Ter Braak and Šmilauer 2002). The used CCA original matrix was composed of 142 diatom taxa and 31 environmental variables. Species data were square root transformed. One of the options of this program is the progressive selection of environmental variables, which chooses the set of variables that better explain the species dispersion. The statistical meaning of each variable is tested with a Monte Carlo permutation test. The power of this test increases with the number of permutations, so the maximum number possible (999) in the program was chosen. Only significant variables (P≤0.05) were included in the analysis (Ter Braak and Šmilauer 2002). Changes in diversity were checked by calculation of Shannon–Wiener (H′) Index, which is widely used in Almeida (1998), Butcher et al. (2003) and Reiss and Kröncke (2005). This index is defined byP s Shannon (1948) (in Washington 1984) as: H 0 ¼ nNi log2 nNi , i¼1 where s is the number of species, ni the number of specimens of i species and N total number of specimens. 4 Results 4.1 Stream Sediments Descriptive statistics were used to characterise a group of data and research the presence of anomalous structures. The data matrix included 20 samples of stream sediments and 14 active variables: Al, As, Ca, Cd, Cu, Fe, K, Mn, Na, Ni, Pb, S, Sb and Zn. The explained variance % and the cumulative variance % of PCA axes are shown in Table 1. The first three axes explained 83.55% of the total inertia. The three axes retention was based on an empirical criterion of eigenvalues >1 (Davis 1973, 1986): axis 1 explained S, Pb, Sb, Cu, As, Fe, Zn and Cd in opposition to Ni and Mn; axis 2 explained Al, K and Na; and axis 3 explained Ca (Cd and Zn are already explained by axis 1). The variable projection on PCA’s first factorial plane (had 71.62% of the matrix total information) established groups of chemical elements according to their affinity (Fig. 2). Water Air Soil Pollut Al As Ca Cd Cu Fe K Mn Na Ni Pb S Sb Zn Eigenvalues % Explained variance % Cumulative variance Axis 1 Axis 2 Axis 3 0.03 −0.86 0.36 −0.63 −0.88 −0.80 −0.07 0.71 −0.34 0.76 −0.91 −0.92 −0.89 −0.69 6.83 48.79 48.79 −0.95 −0.02 −0.24 0.08 0.25 −0.03 −0.94 −0.38 −0.87 −0.43 −0.25 −0.22 −0.26 0.13 3.20 22.83 71.62 −0.02 −0.23 0.70 0.71 0.19 −0.26 −0.12 0.33 0.04 0.07 −0.06 −0.03 −0.03 0.63 1.67 11.93 83.55 In axis 1, Cu–Zn–Cd–As–Fe–S–Pb–Sb association reflected the massive sulphide signature [pyrite (FeS2), chalcopyrite (CuFeS2), esfalerite (ZnS), galena (PbS), tetrahedrite (Cu12As4S13) and tenantite (Cu12 As4 S13) and other less abundant minerals: bournonite (CuPbSbS3), stanite (Cu2PbSbS4) and marcassite (FeS2)] in opposition to manganese mineralisations [hematite (Fe2O3) and pirolusite(MnO2)]. The samples that contribute for this association were G1, G3, G4, H1 and H4 (Água Forte stream). Axis 2 represents the element–mineral association of the source rocks (Na– K–Al) in the studied area and discriminates also three groups of variables: Cu–Zn–Cd, As–Fe and S–Pb–Sb. The consensus-based guidelines application, using the PEC values is a new approach that estimates the sediment potential toxicity for invertebrate test organisms at sites where multiple contaminants are present. Table 2 shows the obtained results in the studied sites. The trace elements were selected according to PCA results. Many of the sediment samples collected in Aljustrel streams contained metal concentrations that exceeded criteria for benthic organisms. The highest metal concentration samples were found at sites F, G and H. Additional samples with high metal concentrations were found at sites C and D. Arsenic, Cu, Fe, Pb and Sb detected concentrations generally exceeded the PEC values reaching level 4, which is the highest toxicity level. Cadmium concentrations were lower than the TEC value (0.99 mg kg−1). Only samples E1, G1, H1, D4 and H4 exceeded the TEC value and fall in level 2. None exceeded the PEC value (5 mg kg−1). For Ni, all samples were between levels 1 and 2 (except D1 and B4), but none exceeded PEC. The examination of the results for potential toxicity showed that samples collected at sites F, G and H stood out in terms of potential toxicity. Sites A and B showed the lowest concentrations for all the trace metals. Sample A4 is an exception (As and Fe concentrations exceeded the PEC values). Site B is located in Roxo stream, upstream from Aljustrel mining contaminated area, and the concentrations of Mn and Ni revealed the proximity of Mn mineralisations. 4.2 Surface Waters Surface waters results (Fig. 3) showed large variation in metal concentrations (sample H4 was excluded due to presence of extremely high values): As, 1.7 to 6,837 μg L−1; Cd, 0.025 to 455 μg L−1; Cu, 1.4 to 68,795 μg L−1; Fe, 5 to 1,262,000 μg L−1: Mn, 0.025 to 19,451 μg L−1; Pb, 0.05 to 136 μg L−1; and Zn, 0.25 to 264377 μg L−1. The highest values occurred at site H. The analysis of Fig. 3 revealed the presence of outlier values, especially of Cd, Cu, Fe, Mn and 0,5 Factor 2 : 22,83% Table 1 Correlations between variables and PCA axes (selected variables with values > |0.5|), eigenvalues, percentage of explained variance and percentage of cumulative variance for stream sediments Cu Zn Cd As Fe 0,0 S Pb Sb Ca Mn Ni -0,5 Na K Al S, Sb; Pb -1,0 -1,0 -0,5 0,0 0,5 1,0 Factor 1 : 48,79% Fig. 2 Principal component analysis of stream sediments: projection of the variables on the first factorial plane (axis 1/axis 2) Water Air Soil Pollut June 2006 March 2006 May 2005 Table 2 Seasonal and spatial variation of As, Cd, Cu, Fe, Mn, Ni, Pb, Sb and Zn in stream sediments of the studied sites and comparison with TEC, MEC and PEC guideline values used for stream sediments evaluation Stream Site Background values As Cd Cu Fe Mn Ni Pb Sb Zn 26 0.3 65 38000 759 29 65 3.9 118 TEC 9.8 a) 0.99 a) 32 a) 20 000 b) 460 b) 23a) 36 a) 2 c) 120 a) MEC 21.4 a) 3 a) 91 a) 30 000 b) 780 b) 36 a) 83 a) 13.5 c) 290 a) PEC 33 a) 5 a) 150 a) 40 000 b) 1100b) 49 a) 130 a) 25 c) 460 a) B1 RX C1 D1 AZ E1 F1 AF G1 H1 B3 RX C3 D3 AZ E3 F3 AF G3 AF (U) A4 B4 RX C4 D4 F4 AF G4 H4 17 92 21 51 168 1182 2490 18 295 42 110 704 1171 56 20 323 46 734 1045 1779 0.2 0.7 0.1 1.1 0.9 1.2 2.6 0.2 0.7 0.9 0.9 0.3 0.2 0.1 0.2 0.8 3 0.2 0.3 1.1 32 30 45 23 33 12 12 30 29 17 22 21 19 23 42 22 22 24 21 21 39 306 25 80 475 2339 2627 18 244 56 98 260 537 83 25 167 52 339 499 1176 2.5 17.8 2.0 6.2 30.1 270 311 0.9 8.3 1.9 5.0 17.2 36.2 3.6 1.7 9.3 3.2 36.0 44.0 97.0 82 390 81 765 393 580 937 91 460 386 392 216 227 128 108 536 1009 251 217 585 51 241 55 245 298 685 843 31 618 459 214 361 400 78 43 564 575 376 280 584 35600 56100 41500 28200 48600 142900 213300 33800 65500 26300 41400 109400 123600 44600 41800 205400 32300 124300 97100 149300 762 1182 1087 451 991 155 226 795 309 564 938 475 392 718 2113 340 738 806 508 360 Values in mg kg−1 (adapted from Consensus-Based Sediment Quality Guidelines Recommendations for Use & Application, developed by the CSST 2003) TEC threshold effect concentration, MEC midpoint effect concentration, PEC probable effect concentration), RX Roxo stream, AZ Água Azeda stream, AF Água Forte stream, AF (U) Forte stream (upstream) a CBSQG (2000a) in CSST (2003) b Ontário (1993) in CSST (2003) c NOAA (1991) in CSST (2003) ≤ TEC Level 1 > TEC ≤ MEC Level 2 > MEC ≤ PEC Zn. All variables showed positive asymmetry, except Cl and pH (negative asymmetry). Principal component analysis was applied to a matrix of 28 individuals and 16 variables [Ca, Cl, K, Mg, Na, HCO3−, SO42− (major elements), As, Cd, Cu, Fe, Mn, Pb, Zn (trace elements), pH and conductiv- Level 3 > PEC Level 4 ity]. Table 3 shows the 16 coordinates for the four axes that explained 88.89% of total variance. The first factorial plane contains 49.88% of matrix total information. In axis 1, with an explained variance of 49.88%, 12 of the 16 active variables (conductivity, Mn, Cd, Water Air Soil Pollut Fig. 3 Box plots of As, Ca, Cd, Cl, Cu, Fe, K, Mg, Mn, Na, Pb, SO42−, Zn, pH and conductivity (major elements and trace metals expressed in μg L−1;conductivity values expressed in μS cm−1) of surface waters 10000000 1000000 100000 10000 1000 100 10 1 0.1 0.01 0.001 As Ca Cd SO42−, Cu, Zn, Fe, Pb, As, Mg, pH and HCO3−) were well represented. However, three of these variables were also explained by two other axes: pH and HCO3−, (also explained by axis 3) and Mg (by axis 2). The variables Mn, Cd, SO42−, Cu, Zn, Fe, conductivity, Pb, As and Mg were positively explained by axis 1 in opposition to pH and HCO3−, negatively explained showing that the high concentrations of trace metals are related to acidic waters. Axis 2 explained Cl, Ca, Na and Mg variables, while axis 3 explained HCO3− and pH (already explained by axis 1). Variable K was explained by axis 4. Figure 4 shows the variable projection on the first factorial plane: 1/2. 4.3 Biological variables 4.3.1 Canonical Correspondence Analysis Non-impacted sites (A and B) were removed from the statistical analysis because their physical, chemical and biological characteristics were very different from other sites, which resulted in the clumping of the other sites in the ordination diagram. Cl Cu Fe K Mg Mn Na Pb SO42- Zn pH Cond. The cumulative percentage variance of environment–species data for each axis was 17.6% (axis 1), 28.8% (axis 2), 39.8% (axis 3) and 48.8% (axis 4). Axes 1 and 2 explained about 20% (19.8%) of the variance in the species data and 28.8 % of the environment–species relation. The total inertia was 3.32. Axis 1 had a strong positive correlation with pH and a negative correlation with Pb and Si. Axis 2 had positive correlation with Mg and negative correlation with HCO3− and K. Ca, Cl and Sr showed positive correlation with axis 3. Monte Carlo’s test with 999 permutations revealed as significant variables: pH, Cd (correlated with axis 1), Ba, Ca, K, Mg, HCO3− and conductivity (correlated with axis 2). Axis 1 is related to aquatic acidity (Fig. 5a). Quadrants 1 and 4 show the sites with higher pH (sites D and E) in contrast with quadrant 2 where sites with lower pH (C, F and H) and high metal concentrations are represented. Conductivity, SO42−, Li and Ni variables are correlated with axis 2, which represents the mineralisation gradient. Water Air Soil Pollut pH Conductivity HCO3− SO42− Ca Cl K Mg Na As Cd Cu Fe Mn Pb Zn Eigenvalues Explained variance % Cumulative variance % Axis 1 Axis 2 Axis 3 Axis 4 −0.73 0.86 −0.51 0.94 0.19 −0.21 −0.22 0.52 −0.07 0.64 0.94 0.92 0.87 0.96 0.71 0.91 7.98 49.88 49.88 −0.15 0.45 −0.13 0.25 0.89 0.91 0.28 0.76 0.85 −0.33 −0.21 −0.35 −0.37 0.14 0.07 −0.21 3.77 23.54 73.41 0.51 0.11 0.57 0.01 0.24 0.27 −0.15 0.31 −0.25 0.47 0.00 0.15 0.29 −0.02 −0.40 −0.04 1.39 8.68 82.09 0.12 0.09 0.48 −0.09 −0.23 0.06 0.78 −0.11 0.19 −0.08 0.13 0.09 0.09 0.05 0.25 0.18 1.10 6.90 88.89 According to Fig. 5a sampling sites are well discriminated despite substrate or seasonal variation. This means that spatial variation is more important than seasonal or substrate variation, making possible to analyse diatom communities from different substrates altogether because diatom communities are similar. The relationship between environmental variables and diatoms is shown in Fig. 5b. For interpretation of diatom labels, see Tables 4, 5, 6 and 7 in the Appendix. Figure 5b shows that all taxa in quadrant 2 are correlated with low pH, high conductivity and high concentrations of trace metals: Al, Cd, Co, Cu, Fe, Mn, Ni, Sb and Zn. Most of this quadrant taxa belong to campaign 3 (Table 6 in the Appendix). In opposition, on quadrant 4 (mostly of the taxa belonging to campaign 1), the represented taxa prefer the pH and HCO3− highest values and the lowest metal concentrations (Table 7 in the Appendix). 4.3.2 Species Diversity and Patterns between Dominant Taxa, pH and Trace Metals Seasonal and spatial variations of H′ for epiphytic, epilithic and epipsammic communities are shown in Fig. 6. No epipsammic or epiphytic samples were found at site A, so measures are absent in the graph (Fig 6a,c). Other sites did not have the three substrate types, so they are missing as shown in Fig. 6 (e.g. sites B and E in Fig. 6c). The diversity index (H′) of epiphytic and epilithic diatom samples decreased with proximity to the mine (Fig. 6a,b). H′ of sites A (1.5 to 4.6) and B (2.6 to 4.6) were higher than H′ of sites D (0.8 to 2), F (0.7 to 2.2) and H (0.6 to 2.8). The epipsammic diatom samples behaved inversely: H′ increased with increasing proximity to the mine (Fig. 6c). Considering the three substrates together it’s clear that (a) campaign 1 Shannon–Wiener values (May of 2005) were generally the lowest; (b) the highest H′ values were found at sites A and B, the farthest from the mines; (c) sites E and F had the lowest H′ values; and (d) campaign 3 (March of 2006) showed the highest H′ values. Eunotia exigua and Pinnularia acoricola (Fig. 7) were the dominant taxa in AMD most affected sites (F and G). Achnanthidium minutissimum (Fig. 7) was the dominant taxon in AMD less affected sites (A and B). 1,0 Cl Na Ca Mg Impacted sites 0,5 Factor 2 : 23,54% Table 3 Correlations between variables and PCA axes (selected variables with values > |0.5|); eigenvalues, percentage of explained variance and percentage of cumulative variance for surface waters Cond K SO4 Mn Pb 0,0 pH HCO3 Cd Zn As Non Impacted sites -0,5 Cu Fe -1,0 -1,0 -0,5 0,0 0,5 1,0 Factor 1 : 49,88% Diatom diversity was calculated using the Shannon– Wiener Index (H′) (Shannon 1948 in Washington 1984). Fig. 4 Principal component analysis of surface waters: projection of the variables on the first factorial plane (axis 1/ axis 2) -1.0 Fig. 5 a CCA ordination of the first two axes showing scores for samples and environmental variables (sites A and B were excluded). Each sample is represented by two letters and a number, which represent the site (from C to H)/type of sample (E epiphytic, R epilithic and S epipsammic)/number of campaign (from 1 to 4) and environmental variables are represented by vectors; b CCA ordination of the first two axes showing scores for taxa and environmental variables. Taxa labels consist of a four-letter code (for further information see Appendix): the species closest to the tip of the arrow of an environmental variable were the most correlated to it 1.0 Water Air Soil Pollut 1.0 -1.0 1.0 -1.0 -1.0 Although less dominant than the previous, several other taxa were also numerically important in the diatom communities: Brachysira vitrea, Nitzschia hantzschiana, Navicula veneta, Nitzschia 1.0 fonticola and Nitzschia capitellata (Fig. 7) in sites C and D. It is important to know if there is no pattern between these dominant taxa, pH and trace metals, so Water Air Soil Pollut Fig. 6 Shannon–Wiener (H′) values for epiphytic (a), epilithic (b) and epipsammic (c) samples during the four sampling campaigns at the studied sites (decreasing mine distance from site A to H) (a) 5 4.5 Campaign 1 4 Campaign 2 3.5 Campaign 3 3 H' 2.5 Campaign 4 2 1.5 1 0.5 0 A B C D E F G H Sites (b) 4.5 Campaign 1 4 Campaign 2 3.5 Campaign 3 3 H' Campaign 4 2.5 2 1.5 1 0.5 0 A B C D E F G H E F G H Sites (c) 4 Campaign 1 3.5 H' Campaign 2 3 Campaign 3 2.5 Campaign 4 2 1.5 1 0.5 0 A B C D Sites Pearson’s correlations were done, and they were significant (P < 0.05) for H′/Cd (−0.40), H′/Pb (−0.42) and H′/Zn (−0.41). The following diagrams (Fig. 8a,b) show the relation between the dominant taxa E. exigua and P. acoricola and pH (only samples, where each taxon appears, were projected in the graph). E. exigua reached a 33.5% maximum at site F2. Sites C, D and H showed over 5% relative abundance (Fig. 8a). E. exigua is clearly acidophilous, reaching its maximum abundance at pH 2 and revealed unimodel-skewed curve to the left of the graph (Fig. 8a). E. exigua increased its relative abundance with decreasing pH. Samples with low pH [F2 (2.3), Water Air Soil Pollut F3 (2.6) and F4 (3.0) and C4 (3.4)] had higher metal and sulphate concentrations and were those with higher E. exigua relative abundances. P. acoricola was abundant (>20 %) in a large number of samples from metal-contaminated sites (C3, F1, F2, F3, F4, G2, G3, G4, H2, H3 and H4) reaching 90% maximum relative abundance at site H2 (Fig. 8b). P. acoricola is more abundant at sites with pH around 2 to 3 (H2, H3 and H4), which are those with high metals and sulphate concentrations. 5 Discussion In this study, PCA showed that chemical characteristics of Aljustrel stream systems were influenced by geology and AMD: (a) trace elements, pH, conductivity and sulphate concentrations were very high in stream sediments and surface waters; (b) there was an increase in drainage waters acidity and trace metal concentrations when dissolution periods were coincident with strong rainfall during winter, spring and autumn, which can be explained by metal lixiviation processes; (c) the concentrations of trace elements were higher at sites F, G and H (Água Forte stream); and (d) sites A and B had the lowest metal concentrations (the most upstream sites). Surficial water of the three sub-basins show similar global behaviour: at non-metal contaminated sites (A and B), the water’s hydrochemical facies is mainly chloride or bicarbonated, while at sites F, G and H, the water is mainly sulphated and enriched in Mg. In general, most of the surface water samples showed sulphated facies characteristic of waters affected by AMD as result of pyrite oxidation in tailings and waste rocks. A simplified representation of this chemical process is given below: reaction of pyrite with air and water, 2 þ FeS2ðsÞ þ 7=2O2ðgÞ þ H2 Oð1Þ ! Fe2þ ðaqÞ þ 2SO4ðaqÞ þ 2HðaqÞ ð1Þ in which the product is a ferrous sulphate and sulphuric acid solution. The dissolved ferrous iron continues to oxidise and hydrolyse when mine water is no longer in contact with pyrite surfaces. þ Fe2þ ðaqÞ þ 1=4O2ðgÞÞ þ 5=2H2ð1Þ ! FeðOHÞ3S þ 2HðaqÞ ð2Þ producing additional acidity (Nordstrom and Alpers 1999). The severity of AMD (extreme acidity and high amounts of sulphates and dissolved metals like Fe, Al, Mn, Cu, Zn, Cd, Pb and As) is related with the tailings and waste rocks mineralogy. Some of the AMD elements may be transported downstream as dissolved free ions, but others, especially Fe and Al, can be quickly removed from the water by precipitation as solid phases depending on the physicochemical conditions along their migration path. These precipitates play an important role in heavy metals removal by adsorption and coprecipitation. In this study, surface waters and stream sediments analyses showed a geochemical gradient from high metal concentrations and low pH at the sites nearest to the mining area (sites F, G and H), the most impacted by AMD, to the sites furthest away from AMD impacted areas (sites A and B), with low metal concentrations and circumneutral environment. A lack of seasonal variation was noted for surface water results, probably due to the atypical climatic years of the sampling, with registered high temperatures and weak rainfall. The spatial gradient was the main driving force for diatom community behaviour. The conjugation of geochemical and diatomic data highlights the fact that diatoms were strongly influenced by pH and metal concentrations variation (Fig. 6b). Whitton and Satake (1996) mention that diatoms of AMD environments (pH<3) belong mostly to Pinnularia, Eunotia, Navicula and Nitzschia genera. These results are also in accordance with those found in Gerhardt et al. (2008). In this study, substantial reduction on diatom diversity and species richness was found in acidic environments with high metal concentrations (sites F, G and H; Fig. 6b), according to Hargreaves et al. 1975; Yan and Stokes 1978; Van Dam 1981; Mulholland et al. 1986; Stokes 1986; Eloranta 1988, 1990; Kwandrans 1993; Verb and Vis 2000a, b, 2001, 2005; Sabater et al. 2003). Other researches combined information of geochemical indicators and diatoms to assess pollution [Lac Dufault (Canada)], where shifts in species composition with metal pollution as well as morphological changes were noted (Cattaneo et al. 2004; Couillard et al. 2004). The most AMD-affected sites were dominated by E. exigua (F, 33.5 %) and P. acoricola (F, 86.8%; G, Water Air Soil Pollut 88.5%; H, 91.1%). This is in opposition to less AMDimpacted sites where A. minutissimum was the dominant species (A, 76.1% and B, 24.39%). Eunotia genus is well distributed in acidic waters (Patrick 1977; Verb and Vis 2000b) as shown in this study. The morphological variations of some species of Eunotia are substantial (i.e. Steinman 1987), which makes the taxonomy of this group very difficult. Many of the typical taxa of acidic environments are included in this genus: E. exigua, Eunotia tenella, Eunotia levistriata and some varieties of Eunotia septentrionalis. E. exigua is acidobiont (Patrick 1977; Van Dam et al. 1994) and is one of the common species in rivers and lakes influenced by AMD, with pH<5 (Kwandrans 1993) in North America and Europe. Lessmann et al. (1999) found this taxon in lignite-influenced lakes with pH of 2 and 3 in Germany. E. exigua is α-mesosaprobiont and acidobiont according to Van Dam et al. (1994) and shows high tolerance to contamination (Baffico et al. 2004) and to large spectra of chemical pollutants (Guasch et al. 1998) as seen in this study. In the present study, samples with low pH and high metal concentration had high relative abundances in E. exigua, as in other studies (Warner 1971; DeNicola 2000; Passy 2006). P. acoricola, also an abundant taxon in AMDaffected sites, was described by Hustedt (1935) as a common taxon in acidic environments (pH<3.5), extended all over the world: in Java, sulphuric streams with pH of 2.8 to 3.0 (Carter 1972) and in New Zealand in waters with pH below 1 (Cassie and Cooper 1989). Negoro (1985) found this species well distributed in acidic habitats in Japan, with pH variation between 2 and 4 and temperatures of 20.5°C to 46.8°C; Watanabe and Asai (1995) quoted this taxon also in Japan, in sites of pH below 1.1; and Sabater et al. (2003) found this taxon in an acid stream of southwestern Spain, the Rio Tinto (same geological unit: IPB, as Aljustrel). This taxon has a variable morphology and resembles Pinnularia obscura Krasske (Carter 1972), both recorded in other sites: England (Hargreaves et al. 1975), North America (Whitton and Diaz 1981) and South Africa (DeNicola 2000). The dominant diatom, A. minutissimum, is abundant in a wide variety of habitats and conditions (Beaver 1981; Verb and Vis 2000b). It has a much wider range of tolerance to many environmental Fig. 7 Scanning electron microscopy photographs (JEOL-JSM 5400) of dominant taxa. 1 External valvar view of Achnanthidium minutissimum (Kützing) Czarnecki; 2 internal valvar view of Brachysira vitrea (Grunow) Ross in Hartley; 3 external valvar view of Nitzschia capitellata Hustedt in A. Schmidt et al.; 4 girdle view of Nitzschia hantzschiana Rabenhorst; 5 external valvar view of Navicula veneta Kützing; 6a external valvar view of Pinnularia acoricola Hustedt; 6b internal valvar view of Pinnularia acoricola Hustedt; 7 internal valvar view of Eunotia exigua (Brébisson ex Kützing) Rabenhorst; internal valvar view of Nitzschia fonticola Grunow in Cleve et Möller parameters than other species of Achnanthidium and is usually the only reported Achnanthidium species in AMD-polluted streams (Ponader and Potapova 2007). Some authors consider it a pioneer coloniser and characteristic of disturbed conditions (Sabater et al. 1998; Sabater 2000), capable of invading open areas due to changes in environmental conditions (Peterson and Stevenson 1992; DeNicola 2000). It is very common in pH below 5 according to Van Dam et al. (1994) but may exist in media with widely variable pH. Based on Young (1976) and Verb and Vis (2000a), it can survive in sites of very low pH (3.0 to 3.5). Other authors refer it as cosmopolitan being present in waters with pH around 7 (neutrophilous) but being able to tolerate waters of low pH and high metal concentrations (e.g. Takamura et al. 1990; Ruggiu et al. 1998; Ivorra et al. 1999; Cattaneo et al. 2004). Yoshiaki et al. (2004) showed that relative abundance of A. minutissimum had a raising tendency when Cu, Zn and Pb concentrations were high. Therefore, they concluded that A. minutissimum was tolerant to heavy metals, probably due to its small size (also concluded in Cattaneo et al. 1998), and it could be used as heavy metal pollution bioindicator. In the present study, we can consider A. minutissimum as neutrophilous and avoiding high metal concentrations. In AMD moderately influenced sites (C, D and E), the dominant species were B. vitrea, N. hantzschiana, N. veneta, N. fonticola and N. capitellata. B. vitrea is common in oligosaprobic media (Van Dam et al. 1994). It is considered an acidophilic taxon (Monteith and Evans 2005; Van Dam et al. 1994) and can stand pH oscillations in AMD-impacted waters (Verb and Vis 2000a, b; Cattaneo et al. 2007), which justify its dominance in this study in some AMD impacted sites. Concerning this taxon’s optimum pH, references are contradictory: for Dixit et al. (1991) and Charles et al. (1989), this taxon is neutrophilic. b Water Air Soil Pollut Water Air Soil Pollut a) 40 b) 100 H2 F1 F2 High metal content 80 30 G3 G4 % PACO % EEXI High metal content 20 F3 F4 60 G2 H4 F2 F3 H3 40 C3 10 F4 20 C4 C2 H4 C3 C2 Low metal content Low metal content F1 H3 D2 C4 D3 0 D2 G1 D1 D3 E2 E3 0 0 2 4 6 8 0 2 pH 4 6 8 pH Fig. 8 Variation of Eunotia exigua (EEXI) (a) and Pinnularia acoricola (PACO) (b) relative abundances (%) with pH N. hantzschiana is very rare in mine-impacted sites (Hofmann 1994), but it was sometimes identified as dominant in sites just after the mine. Therefore, it is capable of surviving in acidic sites. Based on Van Dam et al. (1994), this taxon is found in mesotrophic and oligosaprobic media. This author defines it as neutrophilic taxon. N. veneta, based on Van Dam et al. (1994), is an alcaliphilic taxon. Related with trophic state, it is eutrophic, and related with saprobic level, it is αmesosaprobic to polisaprobic. In this study, it is very abundant in the least AMD-impacted sites (A and B), which are those with the highest nutrient levels and are closest to agricultural areas. N. fonticola and N. capitellata are alcaliphilic taxa, and based on Van Dam et al. (1994), the first one is pollution sensitive while N. capitellata is pollution tolerant. In the present study, these taxa are abundant in moderately metal contaminated to very contaminated sites (C, F, G and H), which have low pH. The H′ diversity index showed that epipsammic samples had lower H′ values than epilithic and epiphytic, which showed similar H′ values. However, epipsamic samples were scarce, and we cannot consider them as conclusive. Canonical correspondence analysis showed axis 1 related with acidity and Pb (see also Verb and Vis 2000b, 2001), and it separated AMD-impacted sites (quadrant 2) from non-impacted ones (quadrant 1). Axis 2 represented the mineralisation due to dissolved metallic ions, acidic runoff and mechanical dispersion and divided chemical water facies: sulphated facies (quadrant 3) from bicarbonated facies (quadrant 4). 6 Conclusions Acid mine drainage is affecting, in several ways, the Aljustrel mining area: extremely high concentrations of trace elements in stream sediments and waters, very different from background values; decrease of diversity in diatom communities; elimination of sensitive taxa; and shifts in taxonomic composition. Decreasing AMD would minimise environmental impact in this region. Water pH influenced significantly not only the composition of diatom assemblages but also other environmental variables such as As, Cu, Fe, SO42−, Mn, Pb, conductivity, etc; high acidity is mainly correlated with high Fe and Al concentrations and less with other metal concentrations (Hargreaves et al. 1975; Gross et al. 1998; Gross 2000) that leads to low concentrations of total inorganic carbon and low phosphorus concentrations, both affecting primary production (Lessmann et al. 1999, 2000; Niyogi et al. 1999). Diatom communities of contaminated habitats can differ from a community of a non-polluted habitat not Water Air Soil Pollut only in species composition but also on the tolerance degree developed by species. Therefore, the relation between metal pollution and diatom community composition is not simple (Foster 1982). Canonical correspondence analysis is a powerful method for examining the multiple environmental variables influence on diatom species distribution. Understanding the ecology of diatoms is not straightforward. In this study, some trends were noted considering pH and some metals, but we must be careful with hasty decisions. The media under study have a very complex geochemistry, making difficult the interpretation of the species’ ecological behaviour and the multiple parameters isolation. Parallel laboratory studies can help to elucidate these problems performing toxicity tests of single species with a single varying factor. The metal uptake of acidophilic microalgae in the lab, accomplished of the maintenance of the same conditions in the field (mesocosm test), can also aid in the understanding of this problem. Appendix Table 4 Diatom taxa from Quadrant 1 of CCA Code Name CRAC EALA EMON FPYG LCOH GTER NAUR NCLA NDUB NLIN NNAN PGIB PSCA SANG Craticula accomoda (Hustedt) D.G. Mann Entomoneis alata Ehrenberg Eunotia monodon Ehrenberg Fallacia pygmaea (Kützing) Stickle et D.G: Mann Luticola cohnii (Hilse) D.G. Mann Gomphonema tergestinum (Grunow) Fricke Nitzschia aurariae Cholnoky Nitzschia clausii Hantzsch Nitzschia dubia W. M. Smith Nitzschia linearis (Agardh) W.M. Smith Nitzschia nana Grunow in Van Heurck Pinnularia gibba Ehrenberg Pinnularia subcapitata Gregory Surirella angusta Kützing Correspondence between each four-letter code and scientific name Table 5 Diatom taxa from Quadrant 2 of CCA Code Name AEXG Achnanthes exigua Grunow ALAN=PTLA Achnanthes lanceolata (Bréb.) Grunow var. lanceolata Grunow = Planothidum lanceolatum (Brébisson ex Kützing) Lange-Bertalot APED Amphora pediculus (Kützing) Grunow BPAR Bacillaria paradoxa Gmelin CMOL Caloneis molaris (Grunow) Krammer COCE Cyclotella ocellata Pantocsek EEXI Eunotia exigua (Brébisson in Kützing) Rabenhorst FSAP Fistulifera saprophila (Lange-Bertalot et Bonik) Lange-Bertalot FCVA Fragilaria capucina var. vaucheriae (Kützing) Lange- Bertalot FFAS Fragilaria fasciculata (C.A. Agardh) Lange-Bertalot FSAX Frustulia saxonica Rabenhorst HAMP Hantzschia amphioxys (Ehrenberg) Grunow in Cleve et Grunow LMUT Luticola mutica (Kützing) D.G. Mann LVEN Luticola ventricosa (Kützing) D.G. Mann NCPR Navicula capitatoradiata Germain NDIG Navicula digitatoradiata (Gregory) Ralfs NERI Navicula erifuga Lange-Bertalot NGRE Navicula gregaria Donkin NRCH Navicula reichardtiana Lange-Bertalot NVEN Navicula veneta Kützing NVRO=NROS Navicula viridula (Kütz.) Ehr. var. rostellata (Kütz.) Cleve = Navicula rostellata Kützing NACI Nitzschia acicularis (Kützing) W. M. Smith NCPL Nitzschia capitellata Hustedt in A. Schmidt et al. NHAN Nitzschia hantzschiana Rabenhorst NINC Nitzschia inconspicua Grunow NMIC Nitzschia microcephala Grunow in Cleve et Möller NIPM Nitzschia perminuta (Grunow) M. Peragallo NPRP Nitzschia perspicua Cholnoky NTHE Nitzschia thermaloides Hustedt PALP Pinnularia alpina W. Smith RSIN Reimeria sinuata (Gregory) Kociolek et Stoermer STAN Stauroneis anceps Ehrenberg STKR Stauroneis kriegeri Patrick Correspondence between each four-letter code and scientific name Water Air Soil Pollut Table 6 Diatom taxa from Quadrant 3 of CCA Code Name ADEL Achnanthes delicatula (Kützing) Grunow in Cleve & Grunow Cocconeis placentula Ehrenberg Diploneis oblongella (Naegeli) Cleve-Euler Encyonema minutum (Hilse in Rabenhorst) D. G. Mann Fragilaria capucina Desmazières Fragilaria tenera (W. Smith) Lange-Bertalot Gomphonema angustum Agardh Gomphonema gracile Ehrenberg Gomphonema parvulum Kützing Mayamaea atomus var. permitis (Hustedt) Lange-Bertalot Meridion circulare (Greville) C.A. Agardh Navicula gibbula Cleve Navicula insociabilis Krasske Navicula subminuscula Manguin Navicula tenelloides Hustedt Neidium affine (Ehrenberg) Pfitzer Nitzschia constricta (Kützing) Ralfs Nitzschia dissipata (Kützing) Grunow Nitzschia paleacea (Grunow) Grunow in van Heurck Pinnularia acoricola Hustedt Pinnularia borealis Ehrenberg Pinnularia viridis (Nitzsch) Ehrenberg Surirella minuta Brébisson CPLA DOBL ENMI FCAP FTEN GANT GGRA GPAR MAPE MCIR NGBL NINS NSBM NTEN NEAF NCOT NDIS NPAE PACO PBOR PVIR SUMI Correspondence between each four-letter code and scientific name Table 7 Diatom taxa from Quadrant 4 of CCA Code Name AHIN ADMI Achnanthes hintzii Lange-Bertalot et Krammer Achnanthidium minutissimum (Kützing) Czarnecki AVEN Amphora veneta Kützing BVIT Brachysira vitrea Ross in Hartley CMEN Cyclotella meneghiniana Kützing CAPH=CBAM Cymbella amphicephala Naegeli = Cymbopleura amphicephala Krammer FULN=UULN Fragilaria ulna (Nitzsch) Lange-Bertalot var. ulna = Ulnaria ulna (Nitzsch) Compère NPHY Navicula phyllepta Kützing NBIL Nitzschia bilobata W. 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