Specific aspects for PNEC derivation for metals. P. Van Sprang, F. Verdonck, M. Vangheluwe

Transcription

1 Specific aspects for PNEC derivation for metals P. Van Sprang, F. Verdonck, M. Vangheluwe 1

2 2

3 Outline PNEC derivation Uncertainty management 3

4 PNEC DERIVATION 4

5 Effects assessment general framework PNEC derivation Algae/invertebrat es /fish Bacteria/ protozoa Sediment dwelling organisms Bacteria/plants/ invertebrates Micro-organisms in STP Terrestrial ecosystem Aquatic ecosystem Sediment ecosystem 5

6 Effects assessment general framework Reference Value derivation Algae/invertebrat es /fish Aquatic ecosystem 6

7 Effects assessment general framework 1. Data compilation 2. Data selection (reliability & relevance criteria) 3. Data normalization 4. Data aggregation 5. Reference Value & PNEC derivation 7

8 Data compilation & selection Reliability 1. Type of test (standard and non-standard tests for RA; standard tests (e.g. OECD, ASTM) preferred for classification) 2. Description of test material & methods (life stage of organisms, experimental design, ) 3. Description of physico-chemical test conditions (tolerance limits, bioavailability parameters, culture conditions) 4. Chemical analysis (measured are preferred) 5. Concentration-effect relationship (control requirements, statistics, test concentration interval) 8

9 Data compilation & selection 6. Derivation of toxicity values Reliability - NOEC/L(E)C 50 values should be estimated using appropriate statistics - NOEC = L(E)C x (x=10%?, within range of tested conc.?) - LOEC - >10 & <20% effect NOEC = LOEC/2 - >20 % effect: NOEC could not be estimated - MATC: NOEC = MATC/ 2 - Use of unbounded NOEC/LOEC could be considered in specific cases! 9

10 Data compilation & selection Relevance 1. Biological relevance (endpoints) 2. Relevancy of test substance (impurities) 3. Relevancy of test medium (natural & artificial media, conditions related to the environment for RA or transformation/dissolution media for classification ) 4. Relevancy of species (endemic and non-endemic, origin important when they drive the PNEC, species assemblage; focus to standard species in classification) 5. Relevancy of exposure duration (exposure time & life cycle of test organisms) focus on chronic exposure for RA/ acute & chronic for classification 6. Relevancy of culture medium (metal background) 10

11 Data aggregation 1. Geometric mean (# > 2 dp per species and endpoint) 2. Lowest value based on different endpoints 3. Most sensitive life stage 4. Bioavailability normalisation of toxicity data To specific regional/local environment for RA To transformation/dissolution media for classification (e.g. ph 6-8.5) 11

12 Reference Value derivation acute & chronic exposures Geometric mean values Limited dataset ( 3 species) Large dataset (# species??) Lowest L(E)C 50 /NOEC All available toxicity data Reference Value = Lowest value Statistical extrapolation (SSD) Reference Value = HC 5 12

13 PNEC derivation chronic exposure Geometric mean values Limited dataset ( 3 species) Large dataset (# species??) Lowest L(E)C 50 /NOEC All available toxicity data Assessment factor (10-1,000) Statistical extrapolation (SSD) PNEC = Lowest value/af PNEC = HC 5 13

14 PNEC/Reference Value derivation specific issues 1. Number of dp needed for statistical extrapolation 2. Taxonomic group requirements 3. Use of integrated SSD (species & processes for terrestrial compartment) 4. Bioavailability normalisation of toxicity data 5. Statisical issues 14

15 PNEC/Reference Value derivation specific issues 5. Statistical issues Long-term toxicity data are log-transformed Selection of SSD distribution model: Lognormal distribution is a pragmatic/historical choice, others better fitting distributions are possible Goodness-of-fit statistics such as Anderson/Darling test Mechanistic reasons e.g. threshold models for essential elements, truncation of SSD for toxicity values above solubility product Avoid overfitting (2-3 parameter functions are preferred) More believe in ecotoxicity data or in underlying model? 15

16 SSD Example: Cu freshwater (1) Log normal vs best fitting Log normal versus best fitting (e.g. Beta distribution) HC 5 50 = 11.5 µg/l HC 5 50 = 11.1 µg/l 16

17 SSD Example: Ni freshwater (1) Log normal vs best fitting Log normal versus best fitting (e.g. Log logistic distribution) Log logistic Log normal 17

18 SSD Example: Pb freshwater (1) Log normal vs best fitting Log normal versus best fitting (e.g. Uniform distribution) 18

19 SSD Example: Ni marine (1) Weight of evidence approach Log normal distribution rejected by both the Anderson Darling and Kolmogorov Smirnov Goodness of Fit tests Weight of evidence approach: only the statistically significant parametric distributions and the semi parametric flexible kernel density estimation were selected for the final PNEC derivation 19

20 SSD Example: Ni marine (2) Weight of evidence approach Statistically significant best fitting (maximal 2 3 parameter functions) parametric distribution functions median HC 5 values between 5.3 and 25.4 μg/l (mean value of 19.9 μg/l) Non parametric flexible kernel density estimation Final median HC5 value = 17.2 μg/l 1 Kernel density fit, Ni marine: bandwidth 0.4 probability median HC 5 value of 14.5 μg/l log10 Ni mug Per L 20

21 HC5 derivation (Percent of species affected) 95% uncertainty interval 5 th - percentile = HC 5 21

22 PNEC derivation AF = assessment factor between 1 and 5 Overal quality of database and endpoints covered Diversity and representativity of taxonomic groups Knowledge on presumed mode of action Statistical uncertainties around HC5 Comparison field and mesocosm studies 22

23 Assessment factor: weight of evidence. Cu example AF = 1 Thorough consideration of: large amount of high quality single species chronic NOEC values for a wide variety of taxonomic groups knowledge on the mechanism of action of copper robustness of the copper BLM conservative factors build into the NOECs, the BLM and HC5 derivations small statistical uncertainty around the HC5 50 validation of the BLM predicted HC5 50 values for mesocosms threshold values, protective to the structure and functioning of the ecosystems and representing lotic and lentic systems of varying sensitivity. EU natural background levels essentiality of copper and the homeostatic capacity of aquatic organisms the assessment factors used in other RAs 23

24 Assessment factor: weight of evidence. Mo example AF = 3 Thorough consideration of amount of high quality single species chronic NOEC/EC 10 values for a wide variety of taxonomic groups, covering the requirements with regard to number of species and taxonomic groups low toxicity of Mo; no need for BLM development, and artificial test media reflect natural variation of relevant environmental conditions (ph, hardness) and maximize bioavailability (low DOC levels) statistical uncertainty around the HC5 50 which is covered by the AF of 3 AF of 3 that covers available unbounded NOEC data for fish species not included in the SSD long term bioaccumulation study with the most sensitive species of the SSD (O. mykiss) revealing regulation and no effects at PNEC level of 12.7 mg Mo/L. no field data/mesocosm data the assessment factors used in other RAs 24

25 UNCERTAINTY MANAGEMENT 25

26 The uncertainty paradigm Here we are where we EXACTLY have to be according to our model Policy maker SCIENTIST 26

27 The uncertainty paradigm Classical AF approach SSD + AF approach SSD + AF + bioavailability intra- and inter-laboratory intra- and inter-species short-term to long-term toxicity extrapolation; lab to field extrapolation (+ additive, synergistic and antagonistic effects) intra- and inter-laboratory More information intra- and inter-species collected/available for data rich natural substances short-term to long-term toxicity extrapolation; lab to field extrapolation knowledge on presumed mode of action Additional, new sources of uncertainty assessed overall quality of the database and the endpoints covered statistical uncertainties around HC5, e.g., goodness of fit or size of confidence interval diversity and representativity of Proportional consideration taxonomy, life forms, feeding of strategies uncertainty and trophic important levels intra- and inter-laboratory intra- and inter-species short-term to long-term toxicity extrapolation; lab to field extrapolation knowledge on presumed mode of action overall quality of the database and the endpoints covered statistical uncertainties around HC5, e.g., goodness of fit or size of confidence interval diversity and representativity of taxonomy, life forms, feeding strategies and trophic levels conservatism in use BLM models conservatism in phys-chem parameters 27

28 The uncertainty paradigm Guidelines on Assessment Factors (AF) for sample sizes Guidance Sample size AF TGD (1996): 1,2,3 and >3 species London workshop (2001): >8 species SSD Lacking guidance: >15 20 species SSD +? ECHA R.19: AF modification only possible based on the same TGD principles regulating the assessment factors derivation 28

29 Uncertainty paradigm shift Historical issues Only quantifiable/known uncertainty is assessed, unquantifiable/ignorance is ignored Risk and uncertainty is mixed together PEC RCR = HC5 <> 1 if AF, RCR Hidden and concealed in numbers that appear to be certain and therefore create a false sense of certainty and protectiveness AF Solutions Differentiate and report all uncertainties transparently Assess uncertainty separately from risk characterisation Use uncertainty for: sensitivity analysis search for alternative solutions 29

30 References ECHA Guidance R.10: Characterisation of dose(concentration) response for environment ECHA Guidance R.19: Uncertainty analysis 30