B i o t i c L i g a n d M o d e l - A t o o l f o r r i s k a s s e s s m e n t o f m e t a l s i n S c a n d i n a v i a n f r e s h w a t e r s?

B i o t i c L i g a n d M o d e l - A t o o l f o r r i s k a s s e s s m e n t o f m e t a l s i n S c a n d i n a v i a n f r e s h w a t e r s? Sabina Hoppe 1

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Biotic Ligand Model - A tool for risk assessment of metals in Scandinavian freshwaters? Sabina Hoppe 3

Sabina Hoppe, Stockholm University 2016 ISBN 978-91-7649-301-4 Cover illustration by Camilla Roman Bland Daphnior i underlandet Printed in Sweden by Holmbergs, Malmö 2016 Distributor: Department of Environmental Science and Analytical Chemistry (ACES) 4

Till Cassandra & Fanny You are never given a wish without also being given the power to make it true. You may have to work for it however. - Richard Bach 5

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Abstract Products from iron and copper mining are among Sweden s top exports. However, as the metals are excavated, they often end up in the aquatic environment where they can cause toxicity. To implement the Water Framework Directive (WFD) within the European Union, all member states must classify their waters and set local environmental quality standards (EQS). These EQS are used to set the maximum concentration of a metal allowed in water and can be set both by the Swedish EPA and EU. The WFD EQS are to be based on the bioavailable metal fraction, as compared to the total metal concentration as have been used previously. As a tool in setting EQS, bioavailability models, like the biotic ligand model (BLM), have been proposed. BLMs can calculate toxicity endpoints based on water chemistry and organismal response and these predictions can be used for regional EQS values. However, BLMs are often calibrated toward hard waters with neutral or high ph, containing low concentrations of natural organic material (NOM), a water chemistry most commonly found in the central and southern parts of Europe. The overarching aim of this doctoral thesis was, therefore, to assess the regulatory applicability of Cu BLMs for Swedish conditions. Results from Paper I and II show that for at least 50% of Fennoscandinavian (Sweden, Finland and Norway) freshwater bodies, the models are not fully applicable. This due to crucial model input parameters being outside of the current calibration range of the Cu BLM. Papers II and III further showed that BLM calculated results differed from measured Cu toxicity to daphnids and algae, indicating that model-based EQS will not be protective for these organisms. Moreover, Paper III showed that Al had an impact on Cu speciation and, hence, toxicity. In conclusion, the present thesis shows that several available Cu BLMs are not yet fully applicable for Swedish or Scandinavian freshwater bodies due to incomplete parameterisation of the models. To improve the applicability of the models, there is a need to calibrate the models for soft freshwater bodies and include Al and NOM properties as input parameters. 7

Sammanfattning Produkter från metallindustrin är bland Sveriges viktigaste exportprodukter. Metallutvinning leder dock till utsläpp som kan hamna i den akvatiska miljön och där orsaka toxicitet. Europeiska Unionens Vattendirektiv syftar till att alla medlemsstater ska klassificera sina vatten och ta fram miljökvalitetsnormer. Dessa normer ska baseras på den biotillgängliga fasen av metaller istället för den totala som tidigare. Biotiska ligand modeller (BLM) har förts fram som verktyg i denna process. BLM kan beräkna utsläppsnivåer för sötvatten baserat på rådande vattenkemi samt vattenorganismers känslighet och ger användaren en specifikt anpassad rekommendation (LC/EC 50, NOEC, PNEC o.s.v.). Dock är dessa modeller ofta kalibrerade för en vattenkemi gällande i de centrala och södra delarna av Europa. I Sverige är det vanligt med sura mjuka vatten, vilka har en högre koncentration av biotillgängliga metaller vilka kan orsaka toxicitet. Det övergripande syftet med denna doktorsavhandling var att undersöka hur dagens BLM för koppar (Cu) fungerar för svenska sötvatten. Resultaten från Artikel I och II visar att en stor del av de Fennoskandinaviska vatten som testats faller utanför kalibreringsintervallet för BLM. Vidare visar Artikel III och IV att de testade modellerna inte på ett korrekt sätt kunde uppskatta toxicitet för alger och vattenloppor, vilket innebär att de inte räknar ut skyddande rekommendationer för dessa arter. Det var även tydligt i Artikel IV att aluminium (Al) påverkar kopparspecieringen och genom detta även koppars toxicitet. Sammanfattningsvis visar denna avhandling att flera tillgängliga BLM inte är helt applicerbara i mjuka vatten, ofta p.g.a. opassande intervall för de kemiska parametrarna. För att förbättra tillämpbarheten av BLMs i Sverige krävs det att modellerna dels kalibreras för den rådande vattenkemin och dels att Al och NOMs egenskaper inkluderas. 8

List of papers I. Evaluation of current copper bioavailability tools for soft freshwaters in Sweden S. Hoppe, J-P. Gustafsson. H. Borg. M. Breitholtz (2015). Ecotoxicology & Environmental Safety, 114, 143 149. II. Soft and sour: The challenge of setting environmental quality standards for bioavailable metal concentration in Fennoscandinavian freshwaters S. Hoppe, Ø. Garmo, M. Leppanen, H. Borg, K. Ndungu (2015). Environmental Science & Policy, 54, 210 217. III. Predictions of Cu toxicity to three aquatic species using bioavailability tools in four Swedish soft freshwaters S. Hoppe, M. Sundbom, H. Borg, M. Breitholtz (2015). Environmental Sciences Europe, 27:25. IV. Can natural levels of Al influence Cu speciation and toxicity to Daphnia magna in a Swedish soft water lake? S. Hoppe. J-P. Gustafsson. H. Borg. M. Breitholtz (2015). Chemosphere 138, 205 210. Publications outside of this thesis: V. Does DOC origin influence binding potential for low concentrations of Cu when Al is present? S. Hoppe (2015). Manuscript VI. Effect of organic complexation on copper accumulation and toxicity to the estuarine red macroalga Ceramium tenuicorne: A test of the free ion activity model E. Ytreberg, J. Karlsson, S. Hoppe, B. Eklund, K. Ndungu (2011). Environmental Science & Technology, 45, 3145 3153. All published papers are reproduced with permission of the publisher. 9

Author contributions I, Sabina Hoppe, contributed to the papers presented in this theses according to the following: In Paper I, I planned the study and performed all the modelling as well as the interpretation of the results. I wrote the paper with helpful feedback from my co-authors. In Paper II, I planned the study with my co-authors and performed the modelling in collaboration with Øyvind Garmo. I wrote the paper with my coauthors. In Paper III, I planned and was responsible for the practical work with the exception for some of the chemical analyses. The biological tests were performed in collaboration with Karin Ek and Margareta Linde. I wrote the paper with helpful feedback from my co-authors. In Paper IV, I planned the study and was responsible for the practical work with the exception for some of the chemical analyses. The biological tests were performed in collaboration with Karin Ek. The modelling using Visual Minteq was done in collaboration with Jon-Petter Gustafsson and I wrote the paper with helpful feedback from my co-authors. 10

Table of content Abstract... 7 Sammanfattning... 8 List of papers... 9 Author contributions... 10 1. Introduction... 12 2. Aim and objective of the thesis... 13 3. Background... 13 3.1 Principles of the biotic ligand models... 13 3.2 Cu bioavailability and toxicity... 15 3.3 Natural organic matter (NOM)... 16 3.4 Fennoscandinavian water chemistry and Cu... 17 4. Assessment of the Cu BLM potential for Fennoscandinavian conditions (results from Papers I and II)... 18 4.1 Method... 18 4.2 Results and discussion... 19 5. Parameters influencing Cu toxicity in Swedish soft freshwater bodies (results from Papers III and IV)... 23 5.1 Method... 23 5.2 Results and discussion... 24 6. Conclusions... 28 7. Future perspectives... 29 8. Acknowledgement... 31 9. References... 36 11

1. Introduction The metal and mining industries played an active role during the Industrial Revolution, and continue to be an important part of Sweden s economic growth. However, as the mining activities and usage of metals increase, their concentrations in foremost the aquatic environment will likely increase as well, leading to increased toxicity in some cases. To handle these occurrences, both voluntary risk assessments, produced by the international metal industry (Zn, Cu a.s.o.), and international and national regulatory frameworks, such as the European Union Water Framework Directive (WFD), have been established over the last decades. Within the WFD, considerable responsibility has been placed on the member states; they are to categorise their waters and set local environmental quality standards (EQS). These EQS are used to set the maximum concentration of a metal allowed in the waters and can be set both by the national EPA and EU. Furthermore, the bioavailable metal species are to be measured instead of the total metal concentrations, as was done previously. The concept of observing and measuring the bioavailable species for assessing metal toxicity originates from biotic trace metal experiments. In those experiments, frogs, crustaceans, algae, and radio-labelled tracers were used, helping scientists to learn the mechanisms of metal toxicity and to reach a deeper understanding concerning metal-cellular interactions (Paquin et al., 2002). By using animals as ion selective organisms, a direct link between the concentrations/species of metal in the water and the organismal response was achieved. However, it soon became obvious that the laboratory experiments did not always correspond to experiments conducted out in the field (Paquin et. al. 2002). Due to these discrepancies, both chemical equilibrium and kinetic models (for toxic response) were created (Paquin et. al. 2002) that evolved into a combined bioavailability model: the biotic ligand model (BLM). That type of model that is now being recommended as a tool for setting local EQS under the WFD (European Parliament and of the council, 2013). BLMs are validated for chemical input parameters within a specific span (the models calibration range). Current BLMs are calibrated and validated for hard waters with neutral ph, low levels of natural organic matter (NOM) and metals, which are found in the central/southern parts of Europe. This has raised concern that currently available models may not be fully adequate for use in, e.g. Fennoscandinavian countries (Sweden, Norway and Finland), which have a very different water chemistry with soft waters, low ph, as well as higher levels of metals than the areas the models were calibrated for. 12

2. Aim and objective of the thesis Bioavailability tools have been proposed as instruments by the EU, under the WFD, to calculate local EQS. Due to the concerns about these models applicability outside of their intended calibration range, the main aim of this thesis was to assess the regulatory applicability of current Cu BLMs in Sweden for setting local EQS. To reach this aim, this thesis was divided into three main objectives: 1. To investigate the extent to which Fennoscandinavian water chemistry falls within the calibration range of Cu BLMs available today and to see if there would be any divergence between BLM calculated recommendations and established Swedish recommendations (Paper I and II). 2. To test BLM reliability in four representative soft- to ultra-soft Swedish freshwaters bodies by comparing Cu LC 50/EC 50 values predicted by the BLM to the corresponding bioassay data for Daphnia magna (D. magna), Daphnia pulex (D. pulex) and the freshwater alga Pseudokirchneriella subcapitata (P. subcapitata) (Papers III and IV). 3. To assess chemical confounding factors, such as: Al and Fe that could affect the Cu speciation and thereby toxicity of Cu in soft waters (Paper IV). 3. Background 3.1 Principles of the biotic ligand models Using reliable ecotoxicology data is very important in setting environmental recommendations for metals in aquatic environments. Because laboratory bioassays are often expensive and time-consuming (Bossuyt et al., 2004; Paquin et al., 2002), bioavailability models have generated great interest. One of the first models made was the free ion activity model (FIAM) by Morel (1983). This model was based on metal speciation experiments to determining free ion activities in freshwater bodies. Because FIAM was able to calculate free ions, it made it easier to estimate metal toxicity in specific waterbodies. The early version of FIAM calculated both the speciation of a metal and the competition between M n+ and cations for binding sites. However, FIAM did not integrate complexation by NOM, which is a very important factor when assessing metal toxicity (Di Toro et al., 2001). Another of the earlier models was the gill surface interaction model (GSIM) by Pagenkopf et al. (1983), which calculated metal toxicity for fish and also took into account the competition that occurs between M n+ and cations (Paquin 13

et al., 2002). Both the GSIM and FIAM were calibrated using single metals (Cu, Cd, Pb or Zn) and integrated hardness ions (Ca 2+ and Mg 2+ ) to reduce toxicity at the active gill site (Santore et al., 2001). One of the latest models for assessing metal toxicity is the BLM. The BLM is a fusion between a chemical equilibrium model and a toxicology model (Paquin et. al. 2002; Bell et al. 2002; Bryan et. al. 2002), integrating NOM speciation to the calculations. The Metal-NOM speciation model used in the BLM is often WHAM model V or VI (Bryan et. al., 2002) created by Tipping & Hurley (1992) and further developed by Tipping (1998). The BLM calculates metal speciation and toxicity, focusing on the M n+ and -OH fractions because they ameliorate the toxic response (Bell et al., 2002; Paquin et al., 2002). The model combines water chemical properties with organismal biology to calculate metal speciation and the toxic response, based on the specific metal accumulation on the biotic ligands (BL). Ca 2+ NOM Cu Biotic ligand Inorganic complexes Figure 1: the basic complexing functions of the biotic ligand model (BLM). Similar to FIAM and GSIM, BLM takes into consideration the competition between the Mn+ and other cations, anions as well as some protons (Di Toro et al., 2001; Santore et al., 2001; Bell et al., 2002; Paquin et al., 2002). Within the BLM concept, water chemical properties that affect bioavailability, e.g. DOC and hardness, are taken into account when calculating both the speciation of the metal and the expected organism response (Fig. 1) (Di Toro et al., 2001). Fundamentally, the model can be divided into three different compartments (Fig. 2); the first calculating the metal speciation of the water, the second calculating the binding of metal ions taking place at the BL, and the third calculating the organism response to the metal (Paquin et al., 2002). 14

Metal speciation Water Biotic ligand Competition on the biotic ligand Organism reaction to metal Organism Figure 2: three components included in toxicity calculations by BLM. The basic assumption of the model is that all aquatic systems are at equilibrium and that every organism has a BL, where the metal complexation takes place, triggering a response (Bell et al., 2002; Bianchini & Bowles, 2002; Paquin et al., 2002). For every organism, there is a critical gill concentration, at which the metal concentration becomes sufficiently high to cause adverse effects (Bell et al., 2002; Paquin et al., 2002, Di Toro et. al., 2001). For instance, considering Cu toxicity for the crustacean D. magna, this level is generally reached when Cu occupies ~39% of the crustacean s BL. Currently, BLMs are available for Cu, Ni, Pb, and Zn and more are being developed. 3.2 Cu bioavailability and toxicity Cu is an essential metal of great importance to aquatic organisms. However, its usefulness is restricted to a very narrow window, outside which it becomes harmful, causing severe toxic responses from organisms, often by disturbing their Na + fluxes (Chen et. al., 2013). Early studies in the 1970s (Zitko et al., 1973) showed that the speciation of metals in freshwater influences the toxic response. For Cu toxicity it is the free metal ion (M n+ ) and labile complexes, such as CuOH, that are considered to be the toxic bioavailable species (Bell, 2002; Campbell et al., 2002; De Schamphelaere et al., 2002a). The metal speciation depends on physico-chemical parameters such as ph, hardness and NOM (Bianchini & Bowles, 2002; Chapman, 2008; Deleebeeck et al., 2007; Di Toro et al., 2001; Paquin et al., 2002; Rozan & Benoit, 1999). At higher ph, strong metal complexes, both inorganic complexes and complexes with NOM, are formed with fewer bioavailable species. A high concentration of cations (Ca 2+ and Mg 2+ ) and anions (e.g. Cl - ) results in competition for complexing ligand sites, both on NOM and BL (Di Toro et al., 2001), thereby reducing the toxicity. For NOM, it is the humic fractions that have a particularly high affinity for complexing metal ions, thereby inhibiting them from binding to BL (e.g. gills), causing toxicity 15

(De Schamphelaere & Janssen, 2002a). However, at lower ph, fewer strong complexes are formed and the M n+ does not bind as strongly to the NOM (Di Toro et al., 2001), resulting in a higher concentration of labile metal species able to cause toxicity. As the speciation of the metal in the water is important for assessing the toxicity, sensitive speciation techniques for analysing very low concentrations of trace metals, like voltammetry, have become important because they are able to measure the actual bioavailable phase. There are several different kinds of voltammetric methods but one of the most powerful ones is the stripping voltammetry (e.g. differential pulse anodic stripping voltammetry (DP-ASV)) used for trace- and speciation analysis (Thomas & Henze, 2001; Rozan & Benoit, 1999). By using this method, many different metals can be measured simultaneously, (Rozan & Benoit, 1999), thereby making it easier to assess additive toxicity. 3.3 Natural organic matter (NOM) As mentioned above, the presence of NOM in aquatic environments constrains metal toxicity through the formation of humic-m n+ complexes. There are various types of NOM, as they are comprised of different organic molecules, both large, such as proteins and humic substances, and small, such as amino acids (Chappaz & Curtis, 2013). The properties of aquatic NOM depend on its origin; generally, dark high-molecular terrestrial (allochtonous) NOM form stronger bonds with M n+, as compared to light low-molecular primary productive aquatic (autochthonous) NOM (Fellman et al., 2010). However, the main parts of NOM often consist of carboxyl and phenolic groups (Kinniburgh et. al., 1999), which bind metals weakly as compared to the strong bonds formed with e.g. N or S groups. When calculating metal toxicity, the entity used for NOM is often dissolved organic carbon (DOC) or total organic carbon (TOC), which consist of humic and fulvic acids. Recent studies have shown that DOC of different origin can complex Cu in different ways when Al is present (Chappaz & Curtis, 2013), indicating a need to characterise DOC to assess metal binding complexing correctly when modelling freshwater bodies. Concerning the integration and complexation between metals and NOM, there are advanced models able to calculate the M n+ -DOC complexation, taking the competition from cations into account (Paquin et al., 2002). Two of the most commonly used models are the NICA-Donnan (NICA stands for non-ideal competitive adsorption) model and WHAM (the Windermere humic aqueous model). Larger geochemical models such as Visual MINTEQ include the NICA-Donnan model, but WHAM has been selected for use with Cu-BLMs (Di Toro et al., 2001). 16

3.4 Fennoscandinavian water chemistry and Cu Sweden is the biggest producer of iron in Europe and a substantial producer of copper and Finland has a substantial mining of nickel, copper and zinc. Large underground metal deposits, foremost in the northern and north-eastern parts of the countries, have had a significant influence on both the economy and society. This influence can be observed in the small rural industrial communities that developed around the mines and still are there today. However, in some cases the environmental regulations have not been able to amend to the increased addition of metals, especially in the aquatic environments, leading to the set regulations not being conservative enough. Fennoscandinavian freshwater chemistry is often characterised by low levels of Ca 2+ (e.g. soft waters) and low ph combined with high levels of Al and Fe (Fig. 3). Figure 3: Ca 2+ levels for stream water in Europe (FOREGS, 2011). These characteristics make Fennoscandinavian freshwater bodies very sensitive to metal discharges because the low ph makes Cu and other metals transform to their bioavailable phases. The low ph in these countries is often joined by low concentrations of cations and for Norway also low concentrations of TOC. Cations and TOC often prevent metal toxicity through competition at the ligand sites (cations) and by forming inorganic non-bioavailable complexes (TOC) with free metal ions (De Schamphelaere & Jansen, 2002b). Because the concentration of Ca ions is lower in soft freshwater, their protective effect is less pronounced as compared to hard waters (Kozlova et al. 2009). In Fennoscandinavia, high concentrations of Al is also often present, originating from the bedrock (Geochemical Atlas of Europe, 2011). In neutral/alkaline freshwater, Al is often bound tightly as Al-humic complexes (Urban et. al., 17

1990). However, in freshwater with low ph, toxic, labile, inorganic Al complexes can be formed (Alstad et al., 2005; Campbell & Stokes, 1985). These Al complexes are bioavailable and therefore able to cause a toxic response. When considering water chemistry of Swedish freshwater bodies, when taking the water chemistry into account, the current recommendation set by the Swedish EPA for Cu in surface waters is 4 µg Cu/L. This recommendation is based on BLM calculations, using a water chemistry from the industry voluntary risk assessment (European copper institute, 2007, 2008) worst case scenario, and by applying the precautionary principle of dividing the calculated BLM recommendation by 2 (Swedish EPA, 2008). 4. Assessment of the Cu BLM potential for Fennoscandinavian conditions (results from Papers I and II) In the two first papers, we assessed the extent to which currently available Cu bioavailability models are applicable and relevant to Fennoscandinavia. Paper I focused on how many of Sweden s freshwater bodies fall within the calibration range of the Cu bioavailability models and how the calculated BLM results compared to established Swedish limits regarding Cu. For this purpose, three different BLMs were used (see section 4.1.3). Paper II used a broader perspective and focused how large part of the Fennoscandinavian countries (Sweden, Finland and Norway) waterbodies that would fall outside of the Cu, Zn and Ni BLMs. 4.1 Method 4.1.1 The lakes and rivers Lakes and rivers from the Swedish national monitoring program was used for Paper I (926). For Paper II ~2500 freshwater bodies from the joint survey of fall 1995 was used (Henriksen et al., 1996; 1997; 1998; Mannio et al., 2000; Skjelkvåle et al., 2001). 18

4.1.2 Biotic ligand models Three different BLMs were used in Papers I and II: (1) HQ-BLM, an acute BLM (HydroQual, 2005) (Papers I and II). This model calculates acute values and uses an acute-to-chronic ratio (3.2 for Cu) to produce recommended values for chronic toxicity, so-called CCC (criteria continuous concentration) values, which are commonly used by the US EPA (2011). (2) Full BLM is a chronic BLM used in the EU voluntary risk assessments, V.0.0.0.17 (European Copper Institute, 2007; 2008) (Papers I). This model includes literature-based ecotoxicological data for a large variety of species and uses a 5% hazardous curve (HC5), which is based on species sensitivity distributions (SSDs), where 95% of the species included in the database have NOECs above the HC5 value (Wheeler et al., 2002). (3) Bio-met tool (www.bio-met.net; Peters et al., 2011) is a chronic bioavailability model (Paper I and II). This is a downscaled simplified version of the Full BLM, requiring fewer chemical variables (ph, Ca 2+ and DOC). The HQ- BLM as well as the Full BLM use the WHAM model by Tipping (1994, 1998) for calculating the free metal ion distribution, whereas the Bio-met tool uses equations based on multiple regressions to reproduce the results given by the Full BLM to derive effect concentrations (Bio-met, 2011, David et al., 2012, Peters et al., 2011). In Paper I and II all the models mentioned above was used and the results compared to each other. Furthermore, in Paper II, calculated EQS values were compared with calculated LC 50 values for D. magna and Rainbow trout. 4.2 Results and discussion 4.2.1 BLM versus. Swedish water chemistry In Paper I, we looked at freshwater bodies included in the Swedish national survey programs for lakes and rivers; 926 lakes and 51 rivers. Results from the data analyses showed that 750 of the 1530 data entries were outside of the BLM calibration range. Extrapolating these results to include all Swedish lakes (approximately 90.000-100.000) would mean that ~45.000-50.000 freshwater bodies would end up outside the calibration range of current BLMs. Furthermore, this divergence was found all over the country, indicating that the models in their current state are often not applicable to use in Sweden. The chemical parameters most frequently falling outside of the calibration range were Ca, alkalinity and ph. As Al and Fe are currently not among the BLM input parameters, we chose not to include any comparison to the models upper calibration limit of ~300 µg/l. However, if we did, the percentage of waters 19

unsuitable to model would increase greatly as compared to the current analyse. As available BLMs have been calibrated to include mostly central and southern European freshwater conditions, Scandinavian waters with low levels of Ca 2+ and high levels of TOC, Al and Fe, are not the targeted waterbodies. Furthermore, results from Paper I showed that BLM-calculated EQS values exceeded the Swedish Cu criteria for freshwaters (4 µg Cu/L) in 99% of the rivers and 98% of the lakes. In Sweden, according to the precautionary principle, it has been suggested that BLM-calculated PNEC values should be used with an assessment factor of 2. This value comes from the Swedish EPA recommendation after calculating a worst case scenario, from the industry voluntary risk assessment, using a BLM and dividing the recommendation by 2 (Swedish EPA, 2007). However, despite this precautionary action, a majority of Swedish water bodies tested still would have BLM calculated EQS values above the current limit, indicating that if the BLM approach is accepted the new Swedish Cu criteria would be vastly increased. 4.2.2 BLM versus. Fennoscandinavian water chemistry In Paper II, we looked at freshwater bodies included in the joint Fennoscandinavian survey conducted in the fall 1995. Approximately 2500 freshwater bodies were included. The lakes included in this study was selected thru a stratified random sampling according to the following criteria: 1) a minimum of 1% of the lakes within any county/region were included; 2) the percentage of lakes sampled in size classes 0.04-0.1, 0.1-1, 1-10 and 10-100 km 2 were 1: 1:4:8; and 3) all lakes > 100 km 2 were included. A total of 1035 freshwater bodies were sampled in Sweden, 988 in Norway and 463 in Finland. The samples were collected between September 1995 and January 1996, during or shortly after autumn overturn, using harmonized procedures, making the data comparable between countries. All analytical work was carried out by national laboratories with quality control routines including inter-laboratory calibrations. A more detailed description of lake selection, sampling, analytical methods, inter-calibration and results can be found elsewhere (Henriksen et al., 1996; 1997; 1998; Mannio et al., 2000; Skjelkvåle et al., 2001). Alkalinity (CaCO 3), for BLM calculations were calculated from the meq alkalinity, measured in the lakes. The chemical data used for Paper II is from water sampled 1995. However, the core BLM input parameters of ph, Ca 2+ and DOC have not significantly changed since and therefore, the arguments and conclusions reached would be similar even if data from a more recent sampling had been used (Garmo et al., 2014). The results from Paper II gave a significant number of water bodies, in all three Fennoscandinavian countries, outside of the model s intended range (Figure 4). 20

Figure 4: distribution of ph, Ca concentration and DOC in Finland (n=463), Sweden (n=1035) and Norway (n=988). Boxes and whiskers cover the 25 th to 75 th percentile and the 5 th to 95 th percentiles, respectively. The alkalinity in nearly 60% of Norwegian water bodies fell outside the HQ- BLM`s calibration range (compared to ~10% for Finland and Sweden). As the HQ-BLM has a wider calibration span compared to the Bio-met model a higher fraction of water bodies is within its calibration. However, the HQ- BLM requires more input parameters (cf. Bio-met). Moreover, the Bio-met model calculated EQS values for Cu above the calculated acute LC 50 values for both D. magna and Rainbow trout (Figure 5). 21

Figure 5: annual average EQS and LC50 Cu concentrations versus ph for Fennoscandinavian freshwaters. The green dots represent Cu EQS values calculated with the Bio-met model while the blue and red dots are Cu LC50 values for rainbow trout and D. magna respectively, calculated with the HQ-BLM. The panels are grouped according to DOC (horizontal) and Ca (vertical) concentrations. The vertical dotted lines indicate the ph range where the Bio-met model is validated. The bottom three group of curves are within the model`s validation range for Ca concentration. The green EQS values on top and over the blue and red LC50 values suggests that the EQS values will not be protective to the D. magna and Rainbow trout population. The highest fraction of all water bodies sampled, with calculated EQS higher than the corresponding Cu LC 50, had acidic waters with low Ca and DOC concentrations. The number of water bodies outside the Bio-met-calibrated ph range increases with decreasing Ca concentration (Figure 5). Calculated Zn and Ni EQS showed little sensitivity to ph for both D. magna and Rainbow trout. The calculated Ni and Zn LC 50 concentrations are much higher than the EQS, even outside the Bio-met calibrated ph range. 22

5. Parameters influencing Cu toxicity in Swedish soft freshwater bodies (results from Papers III and IV) In these two papers, the aim was to investigate the cause of the discrepancies between modelled results from Paper I and II. Because there were indications in the two previous papers that Al/Fe and NOM origin could be responsible for the discrepancies, we chose to focus on the impact of Al on Cu speciation. In Paper III, Cu toxicity were estimated by BLM, using two different models, one calculating the Daphnia LC 50 values and one the alga EC 50 values. The calculated values were then compared to experimental data using D. magna, D. pulex and the algae P. subcapitata. In Paper IV, we studied the influence of Al on Cu speciation and whether Al could cause increased Cu toxicity to D. magna. 5.1 Method 5.1.1 Freshwater bodies For Paper III, four lakes representing the most common lake types in Sweden were selected. The four lakes used (Table 1) were: Lake Abiskojaure is a clear, nutrient-poor lake with low primary production, located in Norrbotten, northern Sweden (maximal depth 35 m, surface area 2.82 km 2 ). The inflow consists mainly of meltwater from the surrounding mountains. Lake Fiolen is located in Kronoberg, southern Sweden (maximal depth 10.5 m, surface area 1.63 km 2 ). The surrounding area consists mostly of agricultural land as well as coniferous forest. This lake has a high trophic level and a relatively high TOC value (~6.4 mg C/L). The only inflow to the lake comes from the Vångsnäs bog in the south. Lake Älgsjön is located in Södermanland, south-eastern Sweden (maximal depth 7.7 m, surface area 0.35 km 2 ). It has brown humus-rich water with high TOC (sometimes >30 mg C/L). The lake trophic level is moderate to high and there is substantial vegetation coverage. Lake St. Envättern is located in a natural reservation area in Stockholm (maximal depth 11 m, surface area 3.70 km 2 ). Its surrounding environment consists mainly of coniferous forest. The lake is oligotrophic and considered to be metal-contaminant free. The TOC concentrations are relatively high and can reach 10 mg C/L during the summer months. Water from the freshwater Lake St. Envättern (Table 1) was used for D. magna bioassays in Paper IV. 23

Table 1: Water chemistry in the four major lakes used for studies in this thesis (samples filtered thru 0.22 µm). Major ion content is multi-annual means (2000-2009) from the Swedish national monitoring program Abiskojaure Fiolen St. Envättern Älgsjön ph 7.6 6.5 6.5 6.5 DOC (mg/l) 0.88 7.0 10 17 Ca (mg/l) 4.5 2.9 3.4 5.6 Mg (mg/l) 0.70 1.0 0.85 1.9 Na (mg/l) 1.0 3.9 2.2 3.2 K (mg/l) 0.59 1.5 0.29 0.85 SO 4 (mg/l) 4.3 6.2 5.9 5.6 Cl (mg/l) 1.1 6.0 2.8 2.8 Alkalinity (mg 10 3.0 3.0 13 CaCO 3/L) Hardness (mg CaCO 3/L) 13 9.8 10 18 5.1.2 Bioassays and BLMs In Paper III, bioassays with D. magna (48 h), D. pulex (48 h) and P. subcapitata (72 h) were conducted. These test organisms are well known and commonly used for investigating metal toxicity. The tests were chosen as they are standardised (OECD, 2004) and the endpoints are available in the BLM models. To calculate Cu toxicity, both the HQ-BLM and the Bio-met tool were used. The Full BLM calculated algae EC 50 data (Paper III) and the HQ-BLM calculated LC 50 values for D. magna as well as speciation data (Papers III and IV). 5.1.3 Chemical analyses The concentration of labile Cu in the samples (Papers IV) was determined using electrochemical detection: DP-ASV. 5.2 Results and discussion 5.2.1 Cu BLM performance in Swedish soft waterbodies In Paper III, a discrepancy between the calculated and measured toxicity (EC/LC 50 for daphniids and algae), was observed, indicating that current BLMs are not accurately predicting Cu toxicity in tested Swedish soft freshwaters. The BLM underestimated Cu toxicity to D. magna, D. pulex and P. 24

subcapitata at factors ranging from 1.1 to 9. However, in Paper I, the BLM slightly overestimated the toxicity (factor of 0.7) of Lake Älgsjön (mean TOC 18.5 mg/l). This lake differed from the other three when the ratio between water colour (absorbance of filtered water at 420 nm), carbon stable isotope signature δ 13 C in fish (Perca fluviatilis) muscle, spectrofluorometric and the fluorescence index (FI) are considered (Fig. 6). Figure 6: the carbon specific properties of the four lakes used in Paper III. All these parameters combined indicates a higher portion of allochtonous carbon (see section 4.4) in Lake Älgsjön as compared to the other three lakes. Considering Lake Älgsjön and the other 3 lakes in this study, there is a clear indication that NOM properties influence Cu speciation (de Schamphelaere et al., 2003; de Schamphelaere & Janssen, 2004a; Ghreghori et al., 2010) enough to increase the acute toxicity to D. magna. Furthermore, the model seems to best predict Cu toxicity in freshwater bodies with an allochtonous carbon source. Because allochtonous NOM has a higher ability to complex metals 25

(Chappaz & Curtis, 2013), this may explain previous underestimations calculating Cu toxicity in soft waters (De Schamphelaere & Janssen, 2004b). 5.2.2 Interference by Al on Cu speciation Comparing bioassays (D. magna) with and without Al additions (Paper IV), gave a significant difference (p<0.05) between the mortality when Al was added into the treatments as compared to only Cu (Fig. 7). Figure 7: mortality in D. magna exposed to Cu in the different Al treatments. The LC50 value for Cu decreases when Al is added to the test waters e.g. the mortality increases Furthermore, the concentrations of free Cu 2+ (ASV) also differed significantly (p:<0.05), between the treatments with and without added Al (Fig. 8). 26

Figure 8: ASV labile Cu fractions in the different Al treatments. The ASV labile Cu increased with the addition of Al into the bioassays indicating a loss in free ligands able to complex the Cu. The concentrations of Al used in this study were low enough not to cause any direct toxic response of their own to the Daphnia (Al LC 50: 480 µg/l). However, the addition of Al changed the Cu speciation, resulting in a higher concentration of free Cu 2+ ions in solution, able to cause a significantly (p<0.05), higher mortality to the Daphnia. This could be caused by the Al complexing to the NOM in the treatments, thereby reducing the amount of possible binding sites for the Cu too complex to. The increased mortality in the Al treatments caused a difference between BLM (HydroQual, 2007) calculated and measured LC 50 values to a factor of 2.3. The results from Paper IV clearly showed that there is an effect on Cu speciation and toxicity from Al, whereas Fe did not seem to interact in any substantial way. As Fe can form very strong complexes with the NOM present (Tipping et al., 2002), these results were expected. As neither Al nor Fe are included as input parameters in currently available BLMs, the models would miss this effect leading to a potential discrepancy between calculated and measured results. 27

The experiments in Paper IV show that the presence of Al could at least be partly responsible for the discrepancies found for Cu toxicity estimates (De Schamphelaere & Janssen, 2004a). In Paper IV, the speciation of Cu could be changed by the addition of Al: the concentration of ASV labile Cu and the toxicity of Cu to D. magna was increased (Fig. 6-8), while BLM-calculated recommendations was not affected, as Al is not among the model input parameters. The affinity for both Cu and Al is greater (higher complexing (K) value), for binding to active NOM humic sites than to BL, therefore a greater competition between the metals for these binding sites occurs. As the levels of Al in Lake St. Envättern (Papers III and IV) were higher than those of Cu, it likely complexed with the NOM (Tipping et al., 2002) and in some cases, even precipitated (Visual Minteq calculations in Paper IV), resulting in fewer available binding sites and a higher concentration of free Cu ions (ASV measurements). As the free Cu ions were prevented from binding onto NOM complexes, they instead bound to the other strong ligand present, the animal BL, thereby causing an increased acute toxicity (lower measured LC 50 values). The complexing by Al to both NOM and BL makes it somewhat hard to calculate binding constants, as there is a competition occurring at both binding sites (Tipping et al., 2002). Because the waters used in these studies were soft (hardness: 10 mg CaCO 3/l), there was a very low protective effect on the BL from Ca 2+ against Cu 2+. As a result, the effect from competition between the Al and Cu for all ligand binding sites was not interfered with and was more direct. While the data analyses in Paper III suggest a strong correlation between Cu-Al-Fe and DOC and toxicity, no correlation between levels of Fe and toxicity was found in Paper IV. However, as Al additions in Paper IV clearly affected Cu toxicity (p :< 0.05), there seems to be important interactions between Al-Cu and DOC. 6. Conclusions 1. Analysis of 1530 data entries from Swedish lakes and rivers showed that a majority of the data from the Swedish national survey program concerning lakes and rivers did not fit into the calibration range of the models tested (Paper I). Furthermore, it was shown that a vast majority of BLM-calculated PNECs (98-99%) were above Swedish EPA recommended limits for Cu in surface waters (4µg/L). 2. Paper II further illustrated that Current Cu, Zn and Ni BLMs are not applicable in Fennoscandinavian soft waterbodies. Analysis of 2500 data entries from Sweden, Finland and Norway showed that a majority of the data from the joint survey in 1995 did not fit into the calibration range of the models tested (Paper I). Furthermore, it was shown that for Norway especially the calibration range of ph and Ca was an issue. 28

3. Three currently used BLM versions (v.0.0.0.17, Bio-met BLM, and v.2.2.3) are not fully capable of adequately predicting Cu toxicity to D. magna, D. pulex and the alga P. subcapitata in investigated Swedish soft waters (Paper III). The primary confounding factors were: alkalinity, NOM properties and Al. Comparing calculated and measured Cu toxicity for daphnids at varying levels of Fe and Al, yielded important conclusions. Competitive binding by Al to humic substances (Paper IV) seems to have a strong effect on Cu speciation resulting in higher abundances of free Cu that are able to cause a toxic response. This will increase the uncertainty of Cu toxicity estimates by current BLMs. 4. Paper IV showed that Al can affect acute Cu toxicity in D. magna bioassays by causing changes in the Cu speciation, thus, further increasing the discrepancy between calculated and measured acute toxicity. Speciation modelling using Visual Minteq showed that Al formed complexes with NOM and precipitated, thereby removing binding sites for Cu. As there were less binding sites available for the Cu, it complexed to the BL of the daphnids instead, causing an increased toxic response. This thesis has identified areas where current BLMs need to be modified to operate successfully in Fennoscandinavia and other countries with soft freshwater bodies. It is clear that focusing on the bioavailable metal phase when assessing metal toxicity is scientifically justified. However, this has to be done in a scientifically sound way that yields reliable estimates. In this thesis, it has been shown that there are uncertainties that need to be addressed before BLMs are considered safe for application in soft, Aland NOM rich, freshwater bodies. There is no doubt that the BLM is a tool with great promise in metal risk assessment, and is, in fact, already applicable for use in many areas of Europe. However, for use in Sweden and other countries with soft- to ultra-soft freshwater bodies, there is still a need to include certain important parameters and to include a broader calibration range before it can be accurately applied. 7. Future perspectives The approaches in assessing metal toxicity have gradually been refined during the last decades. With the help of ion selective organisms, sensitive chemical instruments and models, the regulatory practice has changed, from considering the total metal concentrations to focusing on the bioavailable fractions and chemical variables able to influence metal toxicity. Now, the implementation of EQS bioavailable for metals under the WFD demands fast and reliable assessment methods that can be easily implemented in all of the EU member countries. However, as the water chemistry in Europe vary widely, it is important 29

to find a method that can be implemented over a broad geographical range with varying water chemistry. In this thesis, the focus has been on evaluating the applicability of today s BLMs for metal toxicity assessment in Swedish soft freshwater bodies. These waters will often differ from the majority of European freshwaters with regard to ph, alkalinity, Fe, Al and NOM content (FORGES, 2011). We have seen in our four studies (Papers I-IV) discrepancies between modelled results and ecotoxicological bioassays. Furthermore, the validity of the model as compared to measured results depends on which model is used and the current water chemistry in the waters tested/modelled. As there were such large discrepancies between the models ability to calculate Cu toxicity in Papers I-IV, a new BLM, applicable in soft freshwater bodies need to be constructed. As shown here, there are issues that need to be investigated further; the effect of Al on Cu speciation is one example. The Al influence on Cu speciation could be connected to the NOM origin (Chappaz & Curtis, 2013). Furthermore, it would be interesting to observe if this can occur with other metals and if the pattern will be the same? The complexation by NOM to metals is well known. However, the complexing capacity differs depending on the origin of NOM (e.g. Chappaz & Curtis, 2013). It would be of great interest to investigate weather, and to what extent, this would affect the metal toxicity. A deeper understanding of the complex mechanisms of metal toxicity could be gained by exploring these issues. To this end, an extensive ecotoxicological testing should be done in concert with modelling and taking into account various water properties (ph, alkalinity, NOM origin, Al and Fe content). Furthermore, because the NOM properties seemed to influence the accuracy of the model calculations (Paper III), it would be exciting to consider the possibility of including information on NOM quality into the models. Finally, an additional interesting as well as an important matter would be to construct a model able to calculate metal toxicity in brackish waters and to provide this type of assessment tool for the Baltic Sea waters. 30

8. Acknowledgement Magnus, tack för din eviga optimism! Det känns som att du ofta trott mer på mig än vad jag själv gjort och även om saker inte alltid gått som jag velat så har jag har nog lärt mig något av det ändå, som du alltid sagt. Men jag tror att det viktigaste jag har lärt mig under den här resan är att ha tålamod och att lita på att saker kommer hända då de ska. Ett stort tack även för att du fick mig att upptäcka en av mina stora passioner, att undervisa. Du har låtit mig få all den frihet som jag behövt och låtit mig vara självständig och det uppskattar jag verkligen! Jon-Petter, tack för all din hjälp och för att du alltid har funnits där och svarat på alla mina funderingar när jag behövt dig. Jag tror att du är en av de smartaste, mest ödmjuka människor jag träffat och jag är oerhört tacksam att jag har fått ha dig som min handledare! Hasse, jag hade verkligen hoppats att du skulle få vara med och se att allt löste sig för mig till slut, trots alla problem med bråkiga Daphnior och kruxiga modeller. Jag saknar dig fortfarande så mycket och att kunna gå till dig med mina frågor som du alltid hade bra svar på. Jag kommer aldrig att glömma hur orolig du var då jag skulle läsa Jon-Petters kurs i Uppsala, för hur skulle jag ta mig dit och hem?! Nu fick jag som tur var åka med Jon-Petter hela kursen så det löste sig. Men det visade mig tidigt hur omtänksam du var och det var något som hängde kvar oavsett om det gällde mina Uppsalaresor, konferenser i Canada eller något annat. Jag kommer alltid vara tacksam för att du gav mig den här chansen, du förändrade mitt liv och min syn på vad som är viktigt. Sov gott Marcus, du har varit mitt bollplank, medförfattare och oofficiella handledare under de senaste åren. Jag hade gärna velat att du officiellt skulle få mer cred för vad du faktiskt har betytt för mig och för all hjälp du har gett mig, men du får det här istället. Tack för allt du gjort och för att du har varit där då jag haft mina 1000 stressiga frågor som du alltid svarat på, lugnt och sansat. Karin Ek, jag vet inte var jag ska börja, du har betytt så mycket för mig från första dagen och under hela den här perioden. Du har alltid funnits där när det har varit tufft och peppat och påmint mig om vem jag egentligen är då jag varit less på allt. Du är en av de bästa, mest omtänksamma fantastiska människor jag någonsin mött och jag är så tacksam för att jag har fått lära känna dig. Elena, tack för att du tagit med mig i projekt, läst mina grejer då mina handledare inte kunnat, kommit med tips och nu sist hjälp mig med både kappan, 31

statistik och manus. Du är tuff, smart, oerhört omtänksam och en av mina förebilder och jag har lärt mig massor av dig trots att du inte varit min handledare. Ann-Kristin & Frida, tack för era värdefulla kommentarer på min avhandling, jag uppskattar verkligen den tid och möda som ni lagt ned. Sofia, jag tror att människor kommer in i ens liv då man mest behöver dem eller för att man behöver lära sig något av dem. Med dig har det nog varit båda, du kom verkligen in i mitt liv då jag verkligen behövde det och du har varit och är väldigt viktig för mig. Du är en helt underbar människa och det har blivit en hel del skratt och tårar det sista året. Jag är så tacksam att jag fått lära känna dig och du är bäst! Carro, du är så härligt galen och en av de människor som jag trivs sjukt bra med! Du är så oerhört lätt att umgås med och även om jag inte alltid riktigt tror på dina idéer, typ att utrota svamp med napalm, så har du en hel del kloka saker att säga. Per, min bråkiga, irriterande och helt fantastiska vän. Du har gjort min tid här så kul och jag är glad att jag har haft någon här från mitt tidigare liv i Linköping. Det är inte många som kan reta upp mig så mycket som du kan, oavsett om du låst/kapat min dator eller bara har helt knäppa åsikter, men du gör det alltid med glimten i ögat. Jag längtar tills du får dina trillingar för du kommer att klara av det galant Sara F, tack för alla tips och råd nu i slutet med avhandlingen. Det känns skönt att ha haft dig att rådfråga om alla konstiga detaljer och hur man ska göra med allt. Lisa, du är precis som Carro en sjukt skön människa att umgås med och jag lovar att jag ska ta och fixa fram lite svart salt snart så kan vi testa våra teorier Karin S, tack för din hjälp med CV, lamineringsmaskinen och annat, du är grym. Då man varit här så länge som jag så har man hunnit med en del rumskompisar. Vissa har delat ens mer flummiga intressen (Ellen) och vissa hotar att utrota ens fiskar då man inte är där (Sara S). Men oavsett om de varit snälla mot ens husdjur och adopterat dem (Henrik) eller inte gillat fiskar för att pumpen låter (Erik), så har jag haft kul med er alla. Trots sköldpaddshot MMS (Jon). 32