Karel De Schamphelaere

1. Bioavailability of metals seminar – 18 October 2007 Karel.Deschamphelaere@Ugent.be European perspective on metals´ bioavailability research and implementationof the Biotic Ligand Model (BLM) into regulatory frameworks Karel De Schamphelaere

2. Bioavailability of metals seminar – 18 October 2007 Karel.Deschamphelaere@Ugent.be BLM IN THE REAL WORLD Karel De Schamphelaere

3. Scientific EQS approach for metals • EQS = HC5 based on Species Sensitivity Distribution (SSD) • Metals (Cu, Zn, Ni, Cd) very data rich • NOEC/EC10 available for 19-32 species • Potential pitfall: • NOEC/EC10 obtained in test media with widely varying chemistry (= very different bioavailability) • Generic/uncorrected SSD does not represent ‘intrinsic sensitivity’ alone but rather a mix of ‘intrinsic sensitivity’ + bioavailability effects • Need models to perform bioavailability normalization of NOEC/EC10 to site/region specific water chemistry before SSD and HC5 estimation • e.g., Biotic Ligand models (BLM)

4. Ca2+ Log KCaBL Water Organism Log KMgBL Log KNaBL Mg2+ Log KHBL H+ pH Me-DOC Log KHBL [Me] on ‘biotic ligand’ Toxic effect Me2+ Na+ pH MeCO3 MeOH+ Intrinsic sensitivity Speciation (WHAM) Competition (log K’s) ‘biotic ligand’ e.g. gill, cell surface

5. Overview of available models • Cu, Zn, Ni: BLM models or similar taking into account the effects of DOC, pH, hardness (Ca+Mg), Na, alkalinity • Cd: Bioavailability correction based on hardness-toxicity relation for 3 species and 7 datapoints (applied to all species) • HC5 (µg Cd/L) = 0.09 x (Hardness/50)0.7409

6. BLM’s are validated in field waters • Factor 10 to 30 variability of toxicity • > 90% of prediction errors < factor 2

7. BLM NOECspecies A [Me-BL] NOECspecies A (µg/L) For site water Y BLM What is normalization with BLM? NOECspecies A (µg/L) Test waterX (pHx,DOCx, Cax) Intrinsic sensitivity Site waterY (pHY,DOCY, Cay) • Principle = NOECalg with algae-BLM, NOECinvertebrate with Daphnia-BLM, NOECfish/vertebrate with fish-BLM • =Refinement compared to hardness-Cd toxicity correction

8. Plot normalized NOEC’s according to increasing probability SSD and HC5

9. Plot normalized NOEC’s according to increasing probability • Fit statistical distribution (SSD) SSD and HC5

10. Plot normalized NOEC’s according to increasing probability • Fit statistical distribution (SSD) • Calculate HC5(50%) SSD and HC5 HC5 = 25 µg Zn/L

11. Plot normalized NOEC’s according to increasing probability • Fit statistical distribution (SSD) • Calculate HC5(50%) SSD and HC5 HC5 = 25 µg Zn/L • HC5 increases substantially with increasing pH, DOC and hardness • Bioavailability matters! HC5 = 168 µg Zn/L

12. REAL WORLD ISSUES

13. Limited number of BLM’s (for standard species) • Extrapolation to other species? (“non-BLM species”) • Lab to field extrapolation? • Species vs. communities? • Conservatism? • Models have boundaries • What to do outside boundaries? Extrapolate BLM’s? • How to implement in regulation? • Consequences + practicalities Real world issues

14. ISSUE 1Limited number of BLM’sExtrapolation to other (non-BLM) species?A few examples

15. Same effect of pH on chronic toxiity of Cu2+ for 4 species of algae (slope ~ 1.4)… • …and 3 different endpoints (growth, biomass, P-uptake) • Extrapolatable! • Natural waters? BLM Cu algae De Schamphelaere & Janssen (2006) ES&T 40, 4514-4522

16. BLM Cu algae in natural waters • Typically: factor 10 to 30 variability in toxicity • > 90% of prediction errors < factor 2

17. Reduction of variability in NOEC data from literature Fish-BLM Daphnia BLM Alga-BLM • From Cu VRAR report (2007) • Supports extrapolation of BLM’s across species

18. Ni-BLM fish • A single BLM can be used to effects of pH, hardness, and DOC on acute and chronic Ni toxicity to rainbow trout and fathead minnow •  Extrapolation possible! Deleebeeck et al. (2007) Ecotoxicology and Environmental Safety 67: 1–13

19. Zn BLM algae De Schamphelaere et al. (2005) Environ Toxicol Chem 24:1190-1197 Wilde et al. (2006) Arch. Environ. Contam. Toxicol. 51: 174–185 • Very similar pH slope for Zn among two algae species •  Can be extrapolated!

20. Extrapolation: conclusions & outlook • Much evidence that Cu-BLM’s for all trophic levels can be accurately extrapolated (see also additional evidence in Cu-VRAR documents) • Clear evidence that Ni-BLM for fish may be extrapolated to non-BLM fish • Results of a comprehensive “spot-check” study indicate that BLM’s for other trophic levels may also be extrapolated (this issue is still under discussion at TC-NES) • Clear evidence that Zn-BLMfor algae may be extrapolated to other algae • Although there is no toxicity-based evidence for invertebrate and fish Zn-BLM’s, extrapolation may possibly be justified on the basis of: • Very similar mode of action (disruption of Ca-balance) • Ca is most important protective cation • BLM-constants (log K’s) of fish and Daphnia are very similar • Clear need for toxicity-based research to test applicability of extrapolation

21. ISSUE 2LAB TO FIELD EXTRAPOLATION

22. Example 1: Cu mesocosm data • Three high quality mesocosm studies • Estimate HC5 based on NOEC values for the species within the mesocosm experiment = observed HC5 • Estimate HC5 based on SSD with single-species literature toxicity data normalized to mesocosm chemistry (pH, DOC, Ca, …) = predicted HC5 • Compare observed vs. predicted HC5

23. Example 1: Cu mesocosm data • From Cu VRAR (2007) – arrow reflects uncertainty due to non-equilibrium • HC5(observed) from 3.4 to 19.6 µg/L • Good agreement between observed and predicted HC5 • SSD+BLM methodology for Cu seems appropriate for accurate protection in the field

24. Example 2: UK EQS project • Conducted for the UK Environment Agency • Research consortium of Centre for Ecology and Hydrology (UK), UGent (B), Univ. Antwerp (B), Univ. Wageningen (NL) • Monitoring of full chemistry, invertebrate and diatom community composition, metal bioaccumulation in invertebrates, Toxicity Identification Evaluation for reference and metal contaminated streams (n=35) • Aims: • To investigate if water chemistry and bioavailability should be taken into account when looking at ecological, community-level effects in the field • To investigate if current and proposed EQS methodologies are adequate for protecting field communities

25. UK EQS project - concept Chemical analyses (dissolved metals, DOC, pH, major ions, alkalinity, etc.) Physical site characterization (width, depth, stream velocity, etc.) Ecological analyses (invertebrates, diatoms) RIVPACS MODEL Observed No. of TAXA present in stream Expected No. of TAXA present in stream BLM+SSD Agreement? Conservatism? Predicted HC5 and % affected species Observed/Expected No. of TAXA

26. UK EQS project – Main Results • Chemistry clearly influenced how metals affect community composition • Both speciation and competition effects seemed important • The importance of metal mixtures in the field could not be dismissed • Regression analysis suggested that ecological effects in non-acidic sites (pH>6) could best be explained in terms of contamination by Zn and/or Al and/or Cu and/or a mixture of these elements, although Cd could not be excluded either due to its correlation with Zn • Under these circumstances: predictive capacity of Zn-BLM + SSD approach for effects observed in the field?

27. Mean (Zn/HC5) vs. field effectsPreliminary calculations – do not quote ≥ 0.79 RIVPACS Class A quality • Ecological effects are significantly correlated to exceedence of HC5(Zn) • 7 sites correctly classified as non-impacted, 12 sites correctly classified as impacted, 6 false-negatives, 4 false-positives

28. Interpretation • In general: ecological effects in the field can be related to exceedence of thresholds (HC5) based on laboratory-based ecotoxicity data, normalized for bioavailability • False-positives can be due to: • Over-conservative HC5 • Tolerance acquisition of local communities • False-negatives can be due to: • Under-conservative HC5 • Temporary exceedence of HC5 (in two out of six cases) • Toxicity contribution from other metals, including Al (mixture effects) • In order to understand better the ecological effects of metal contamination in the field: mixture toxicity needs to be understood

29. Preliminary approach for metal mixtures toxicity in the field • Assume that organisms consist of a set of binding sites • relevant for accumulation and toxicity • with which all metals and competing cations react (cf. BLM) • in a similar way as humic acid reacts with all metals and cations • Then: the total amount of all metal calculated with WHAM VI to be bound to HA (mol/g) could potentially be related to accumulation and effects • The Toxicity Binding Model (TBM) • Two examples: • Metal accumulation in bryophytes • Toxicity to P. subcapitata in the field samples

30. Metal accumulation in bryophytes • Metals in bryophytes agrees fairly well with WHAM VI calculated metal binding to HA  proof of principle that mixture-BLM is possible

31. Metal toxicity to algae in field samples • Ftox = S [metal bound to HA]/[metal’s specific toxicity] • TBM approach is also promising for predicting metal mixture toxicity

32. Main conclusions UK EQS project • Chemistry (both speciation and competition) seemed to be important for ecological effects of metals in the field • As shown for Zn, ecological effects in the field can be related to exceedence of thresholds (HC5) based on laboratory-based ecotoxicity data, if normalized for bioavailability • Metal mixtures in the field are a reality • The TBM shows that BLM-like approaches might be valuable for taking mixture effects into account • Final report expected soon (end 2007) • Further information: UK Environment Agency (Paul Whitehouse)

33. ISSUE 3MODELS HAVE BOUNDARIES

34. Boundaries within which bioavailability models for three trophic levels have been developed and/or validated * Before Ni research with soft waters (lower hardness boundary was reduced to 6 mg CaCO3/L based on Ni-SOFT research (see further)

35. Three examples Cu toxicity to cladocerans in acidic waters Ni toxicity to cladocerans in soft waters Cd toxicity to Daphnia longispina in soft waters

36. Cu toxicity to field cladocerans • Collected field waters and their inhabiting field cladocerans (water fleas) populations • Toxicity test results in standard medium with these species were used to calibrate Cu-BLM Daphnia to sensitivity of field-species • Predicted toxicity in natural waters with varying composition was compared with observed toxicity in natural waters

37. Cu toxicity to field cladocera • For normal sites (pH > 5.5): 27/28 LC50’s accurately predicted • =further evidence in support of extrapolation Bossuyt et al. (2004) Environ Sci Technol 38: 5030-5037

38. Cu toxicity to field cladocera • For normal sites (pH > 5.5): 27/28 LC50’s accurately predicted • =further evidence in support of extrapolation • For acidic sites (pH < 5.5): general overestimation of toxicity • Further research required Bossuyt et al. (2004) Environ Sci Technol 38: 5030-5037

39. Ni SOFT project • Selected region for sampling: • Both soft and hard close to each other • Low anthropogenic input (N,P) • Same climate • Calcareous deposits for ‘hard’ region • Collect cladocerans and algae from soft (H~6) and hard water (H~42) and test for chronic Ni toxicity in soft, moderately hard and hard water • Deleebeeck et al. (2007) Aquat. Toxicol 84:223-235

40. Ni SOFT project – hypotheses • Cladocerans originating from soft water would be inherently more sensitive to Ni than those originating from hard water • Cladocerans from soft water would be more protected against Ni toxicity by hardness than those from hard water

41. Ni SOFT project – design • Chronic toxicity testing (reproduction, 10d to 21d) • Species from soft water tested in Soft (S, hardness 6 mg/L) and Moderately Hard (MH, hardness 16 mg/L) water • Species from hard water tested in Moderately Hard (MH) and Hard water (H, hardness 42 mg/L) • Allows comparison of • species sensitivity (comparison of EC50 in MH water) • hardness effect (comparison of KCaBL and KMgBL estimated for soft and hard water species)

42. Chronic Ni toxicity to cladocerans • No significant difference in intrinsic sensitivity • No significant difference in protective hardness effect • Ni-BLM can be extrapolated down to hardness 6 mg/L

43. Cd SOFT project • Hardness correction equation proposed in Cd RAR was only derived for hardness > 40 mg CaCO3/L • Can equation be extrapolated to hardness as low as 5 mg CaCO3/L? • Chronic toxicity testing (reproduction, 21d) with D. longispina • In two Swedish soft waters with manipulated hardness

44. Cd SOFT project • RAR hardness slope (0.7409 – dashed line) cannot be extrapolated to hardness < 40 mg CaCO3/L • Hardness effect at hardness <50 is much lower (slope=0.1562=n.s.)

45. Conclusion extrapolation outside model boundaries • Based on the given examples, any type of outcome may be expected from extrapolation outside model boundaries (accurate, overconservative, underconservative) • Thus, extrapolation outside model boundaries will usually not be recommended without additional investigation for the specific local or regional abiotic conditions

46. IMPLEMENTATIONDemonstration project in NL

47. Cu and Zn were considered nation-wide problematic substances • Yellow, orange and red dots are sites where [Me] > EQS • Baseline EQS not corrected for bioavailability (1.5 µg Cu/L, 9.4 µg Zn/L) • Additional metal removal step from WWTP was planned nationally • Large investments required while local water agencies wanted to invest mainly in ‘more important’ problems (eutrofication, habitat restoration) • Thus: how large are true ecological risks if bioavailability is considered? NL issue Cu Zn

48. Monitoring campaignJune 2006 – January 2007 • Total and dissolved metal (Cu, Zn, Ni) • TOC, DOC, pH, Ca, Mg, Na, K, Cl, SO4, alkalinity

49. Chemistry summary(percentiles)

50. Metal measurements • Despite careful discussions with and protocol transfer to people from local water agencies some difficulties noted • Some laboratories acidified samples before filtration • In many samples dissolved metal > total metal (varies among agencies) • Cu (7-34%), Ni (2-50%), Zn (5-21%)