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Methods to derive benchmarks (reminder)

Numerical Benchmarks for protecting biota against radiation in the environment Methodology to derive benchmarks, selected methods used in PROTECT and Results. Methods to derive benchmarks (reminder). 3 main methodologies :

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Methods to derive benchmarks (reminder)

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  1. Numerical Benchmarks for protecting biota against radiation in the environment Methodology to derive benchmarks, selected methods used in PROTECT and Results

  2. Methods to derive benchmarks (reminder) 3 main methodologies : (1) Deterministic : based on application of Assessment Factor to critical ecotoxicity data, or (2) Probabilistic : based on Species Sensitivity Distribution (SSD) approach associated with an arbitrary cut-off value, usually set at a protection level of 95% of the species, and the use of an additional AF, (3) a weight of evidence approach using effect data from field exposures • & (2) based on critical ecotoxicity values from tests in lab • e.g., EC10 (preferred to NOEC) : stressor level in a given medium giving 10% effect in the exposed group in comparison to the control group for chronic exposure. • to (3) usually applied for chemical substances to derive PNEC (e.g., TGD, 2003) • Concerning RAS: • (1) and (2) applied and compared within ERICA to derive PNEDR (Garnier-laplace et al., 2006) • (1) used by Environment Canada (2003) for radionuclides • (3) used by Thompson et al. (2005) for multipollution context (radionuclides + metals)

  3. Methods used within PROTECT • Following PROTECT Consultation and availability of various EDR10 values (from ERICA and follow up), SSD method adopted when data sets were adequate (quality and quantity) (1) allow to make the best use of the available most relevant knowledge in a transparent way, (2) allow to quantify the associated uncertainty, (3) easily allow to revise the outcoming values when new knowledge becomes available • Need to incorporate documented expert judgement at various stages (1) when selecting ecotoxicity data (EDR10 in our case), (2) when selecting a cut-off value to estimate the Hazardous Dose Rate of interest (HDR5), (3) when applying an AF to obtain the final benchmark (PNEDR).

  4. STEP 2 – Building dose rate-effect relationships to estimate critical ecotoxicity values EDR10 Dose-effect relationship was built for each accepted test. The quality of the fitted model was judged before accepted EDR10. Two models tested (logistic/hormetic) Effect (%) 100 % 50 % PAF (% of Affected Species) STEP 3 – Deriving benchmarcks Application of SSD on the estimated toxicity values accepted after step 2. Application or not of the taxonomic weighted options Application of an AF to the HDR5 to obtain the final benchmark 10 % 100 80 EDR50 Dose Rate (µGy/h) EDR10 60 40 Dose Rate (µGy/h) 20 0 1 10 100 1000 10000 The 3-step method used STEP 1 – Compiling quality assessed exposure-effect data Data sorted per ecosystem, per exposure condition, per bibliographic reference and per test . Quality of data describing each test was assessed against 3 criteria (dosimetry, exp. Design, stats). Multi-rules applied to accept or reject each data set FREDERICA Radiation Effect Database

  5. Data set for one test (a test is defined as a consistent group of (dose or dose rate, effect) couples from a given species and a given effect, examined under defined exposure conditions (duration, irradiation pathway) The data set is made of:  at least 3 different couples (dose or dose rate, effect) including one for the control group (no dose(rate))  at least two different couples if the effect is analysed relatively to the control. data set is rejected YES NO  The variation of effect with dose (or dose rate) is monotonous.  The pattern is consistent with the state-of-the-art on the tested effect data set is rejected YES NO  The maximum effect value in case it was not reached during the test can be fixed theoretically if knowledge on such effect is sufficient. The difference between the maximum effect value observed y(MaxObs) during the test and the theoretical one y(MaxTheo) are used to calculate the extrapolation percentage needed to model dose-effect relationship as follows : %Extrapol = 100 *(y(MaxObs) – y(MaxTheo))/(y(ControlObs) – y(Maxtheo)) where y(ControlObs) is the effect value observed for the control group. data set is rejected YES NO  At least one couple is located within the 10 to 90% of the variation of effect observed. This latter is defined as (y(ControlObs)-y(MaxObs)) data set is rejected YES NO  The Estimated ED50 or EDR10 are between two experimental couples. data set is rejected YES NO The Estimated ED50 or EDR10 can be used within a SSD analysis. EDR10 from logistic models Logistic model Control (response at dose rate 0) Effect EDR10 Dose rate (µGy/h) EDR10

  6. « Hormetic » model Control (response at dose rate 0) Effect EDR10 Dose rate (µGy/h) EDR10 from hormetic models Dose-response exhibiting an initial response stimulation or an effect minimisation

  7. STEP 1 – EDR10 selection (1/4) • ERICA : 82 EDR10 obtained for chronic g external exposure meeting the previous criteria, covering 18 species (5 plants, 9 invertebrates, 10 vertebrates) and various effect endpoints grouped as one geometric mean per effect category (mortality, morbidity, reproduction) and per species (ie. for a given species, a single value per category of effect (the GM)-> this allows to ignore intra-species variation for the same effect category) • SSD on 24 GM -> HDR5=82 µGy/h [24-336] • PROTECT : 104 EDR10 obtained for chronic g external exposure meeting the previous criteria, covering 23 species (6 plants, 8 invertebrates, 9 vertebrates) and various effect endpoints

  8. STEP 1 – EDR10 selection (2/4) • Among those 104 EDR10, • One single endpoint per species (the lowest value) • Relevance for population sustainability but expert discussion on ecological relevancy of some of them • To illustrate the sensitivity of the SSD method and the derived screening level dose rate, two sets of EDR10 selected: • « repro » list: most sensitive reproduction endpoint per species (at the population level -i.e. include juvenile survival) • « ecol » list: most relevant with regard to « direct » ecological interpretation

  9. X X X X X X X X STEP 1 – EDR10 selection (3/4) « repro » list : 19 EDR10 µGy/h

  10. STEP 1 – EDR10 selection (4/4) « ecol » list : 14 EDR10 Omission of endpoints with difficult ecological interpretation (no direct link with reproductive success) e.g., bud production, gonad weight, sperm cell or spermagonia count µGy/h

  11. STEP 2 - SSD-fit to the selected list (1/3) • Repro-list HDR5 = 8.4 µGy/h CI 95% = [1.4;99] Dose rate (µGy/h)

  12. STEP 2 - SSD-fit to the selected list (2/3) • Ecol-list HDR5 = 91 µGy/h CI 95% = [11;710] Dose rate (µGy/h)

  13. Taxonomic weight options to fit SSD STEP 2 - SSD-fit to the selected list (3/3) Influence of the proportion of tested species from different taxonomic groups (trophic levels) on the HDR5 while giving the same weight to each trophic/taxonomic group For the repro-list, applying the same weight for each TG means to decrease the proportion of the most radiosensitive group from 37% (7/19) to 33% (equiprobability) -> HDR5 increases slightly Ecol-case : V from 28% to 33% -> HDR5 decreases

  14. Rounded to the nearest 10 Rounded to the nearest 10 STEP 3 - Screening generic dose rates(in µGy/h) • AF= from 1 to 5 according to the TGD • - taxonomic coverage • coverage of the inter-species sensitivity • ecological relevancy of endpoints • line of evidence from field • final value compared to background • e.g., 0.1 to 6 µGy/h for marine organisms (Brown et al., 2004) • 0.4 to 4 ________freshwater______________________ • 0.07 to 0.6______terrestrial________(Beresford et al. in press)

  15. Rounded to the nearest 10 Rounded to the nearest 10 STEP 3 - Screening generic dose rates(in µGy/h) • For illustration purpose: AF=1 for the repro-list and 5 for the ecol-list generic screening level recommended by PROTECT : 10 µGy/h

  16. STEP 3’ – Taxonomic screening dose rates(in µGy/h) • Data were insufficient to create an SSD for the 3 basic taxonomic groups (plants(5), invertebrates(7), vertebrates(7)) • A limited sensitivity analyses of the overall SSD suggested that the only statistically justifiable separation was to remove vertebrates leaving an SSD for plants and invertebrates (with a total of 12 data points) • We suggest that a taxonomic screening level of 450 µGy/h be used for plants and invertebrates in refined assessments if the results of the screening tier assessment suggest this is required. For vertebrates, a taxonomic screening level of 10 µGy/h (i.e. the generic screening level) is recommended.

  17. Different percentiles of an unweighted SSD derived using EDRx values from the repro-list as input and following the same derivation methodology as used for the HDR5 values Regulatory action level • The statistical extrapolation approach could be used to help in the management decision making process by generating different levels of potential impact (e.g. by taking the 50th percentile of the EDR10 distribution or 20th percentile of the EDR50 distribution etc). • PROTECT will thus provide a range of tabulated effect levels and percentiles which may prove to be useful to decision-makers

  18. Summary of the proposed approach and associated benchmarks (1/2) • First tier of an ERA-type approach (simple – conservative) The use of the proposed generic screening level should enable assessed sites for which incremental dose rates are estimated as being below 10 µGy/h to be confidently excluded from further assessment. • Second tier of an ERA-type approach (less conservative) The application of the 10 µGy/h screening dose rate for vertebrates and 450 µGy/h for plants and invertebrates is likely to identify those sites which pose an insignificant risk when used in such assessments. • The generic and taxonomic screening values we have derived are within the range of values suggested as being appropriate for population level protection by other organisations using expert judgement. • Suitable data we have identified are currently insufficient to enable the derivation of more refined levels of taxonomic screening values (if these are required) using the SSD approach.

  19. Summary of the proposed approach and associated benchmarks (2/2) • Regulatory action level We have presented a SSD-based scientific analysis of the available data which may help decision makers select a value appropriate to either planned or existing situations in consultation with appropriate stakeholders.

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