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WEBFRAM 5: A risk assessment module for soil invertebrates

WEBFRAM 5: A risk assessment module for soil invertebrates. Geoff Frampton University of Southampton (UK) Joerg Roembke ECT Oekotoxikologie (DE) Paul van den Brink Alterra Green World Research (NL) Janeck Scott-Fordsmand NERI (DK). Funded by. WEBFRAM-5 : Principal aim.

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WEBFRAM 5: A risk assessment module for soil invertebrates

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  1. WEBFRAM 5: A risk assessment module for soil invertebrates Geoff Frampton University of Southampton (UK) Joerg Roembke ECT Oekotoxikologie (DE) Paul van den Brink Alterra Green World Research (NL) Janeck Scott-Fordsmand NERI (DK) Funded by

  2. WEBFRAM-5 : Principal aim To investigate whether the pesticide risk assessment for below-ground invertebrates could be improved by explicitly incorporating variability and uncertainty into estimates of risk

  3. Soil invertebrates pesticide risk assessment ( 91 / 414 / EEC ) Standard higher-tier test ? Testing Earthworms routine yes optional Collembola no Enchytraeidae optional no

  4. WEBFRAM 5 : Background

  5. Deterministic risk assessment scheme toxicity Risk: based on × safety factor exposure Earthworms example Risk measure Safety factor Lower tier : acute TER 10 Lower tier : chronic TER 5 Higher tier : field effects none

  6. Deterministic risk assessment scheme toxicity Risk: based on × safety factor exposure Appropriate as a worst-case screening tool Simple to apply Harmonised calculations and interpretation Applicable to small data sets Safety factor represents uncertainty

  7. Deterministic risk assessment scheme Principal criticisms: Ecological relevance unclear Does not use all the available information Based on untested assumptions Risk estimates lack transparency Does not indicate: - likelihood of risk - degree of risk - certainty of the risk estimate

  8. Potential benefits of incorporating uncertainty in the risk assessment Clarify how conservative the risk estimate is Make better use of available information Improve realism (i.e. ecological relevance) Indicate certainty, likelihood, degree of risk Improve transparency of risk estimation Validate or refine assumptions Improve efficiency (reduce unnecessary testing)

  9. Potential criticisms of incorporating uncertainty in the risk assessment Requires more data than deterministic approach Statistical approaches more complex Could introduce more assumptions May not clarify risk if not communicated well

  10. output Deterministic risk assessment with supporting data and worked examples Risk assessment version(s) that include uncertainty where appropriate WEBFRAM 5 : Objectives 1. Acquire data (key step!) 2. Identify variables with adequately-supported distributions 3. Use data distributions to describe variability 4. Incorporate descriptions of variability in alternative version(s) of the risk assessment

  11. WEBFRAM 5 : Database summary

  12. Data sources Systematic search (literature, institutions, colleagues) > 1000 relevant publications screened > 400 selected for data extraction Data quality classes assigned after data extraction Lower tier Higher tier Research publications & reports Regulatory data in public domain Contract testing laboratory owned 82% 17% 1% 100% 0% 0%

  13. Below-ground invertebrates database Lower tier (laboratory) Higher tier (TME / field) Active substances (a. s.) Species / groups Effects data sets 257 70 1282 75 72 1029 a. s. with data for both tiers 45 (16%) a. s. with only one data set 108 (38%)

  14. Soil invertebrate effects data : pesticides with > 20 data sets Carbendazim Copper Benomyl Dimethoate Pentachlorophenol Parathion Carbofuran Diazinon Lindane Atrazine Chloroacetamide Lambda-cyhalothrin Imidacloprid Chlorpyrifos Carbaryl Halofenozide DNOC Bendiocarb Malathion Thiophanate-methyl Phorate Lower tier Higher tier Number of data sets 0 50 100 150 200 250 300 350

  15. Diflubenzuron Cypermethrin Methylacetophose Dicresyl Propxur 4-nitrophenol Parathion-methyl Chlordane Isofenphos Disulfoton DDT Phenmedipham Imazalil Flusilazole Cyfluthrin Chlorthal Boric acid Amidosulfuron Soil invertebrate effects data : pesticides with 9 - 20 data sets Lower tier Higher tier Number of data sets 0 2 4 6 8 10 12 14 16 18

  16. Distribution of pesticide effects data among soil invertebrate groups Lumbricidae Collembola Enchytraeidae Acari Coleoptera Nematoda Isopoda Formicidae Diptera Araneae Lower tier Higher tier Number of data sets 0 200 400 600 800 1000 1200 1400

  17. Collembola species data : lower tier Folsomia candida Folsomia fimetaria Onychiurus folsomi Isotoma viridis Onychiurus armatus Proisotoma minuta Orchesella cincta Sinella communis Collembolans grouped Isotomidae Lepidocyrtus sp. Onychiurus apuanicus Sinella caeca Number of data sets

  18. Enchytraeidae species data : lower tier Enchytraeus albidus Cognettia sphagnetorum Enchytraeus crypticus Enchytraeus sp. indet. Enchytraeus coronatus Friderica ratzeli Enchytraeus buchholzi Number of data sets

  19. Lumbricidae species data : lower tier Eisenia fetida Earthworms grouped Eisenia andrei Lumbricus terrestris Aporrectodea caliginosa Lumbricus rubellus Aporrectodea tuberculata Allobophora chlorotica Dendrobaena rubida Apporectodea longa Aporrectodea rosea Octolasium lacteum Eisenia veneta Number of data sets 0 100 200 300 400 500

  20. 54 % 46 % Data reliability checks Following Klimisch et al. (1997) in Regulatory Toxicology & Pharmacology Number % (1) Reliable without restriction 114 9 586 45 (2) Reliable with restrictions 241 19 (3) Not reliable (4) Not assignable 351 27 1292 100 Total

  21. Cumulative sensitivity distributions for Cu based on data of varying quality EC50 Reliable without restriction 1.0 Reliable with restrictions Not reliable 0.8 LC50 Not assignable 0.6 Potentially Affected Fraction 0.4 0.2 0.0 10 100 1000 10000 EC50 and LC50 (mg/kg Cu)

  22. WEBFRAM 5 : Risk assessment approach

  23. Tiered risk assessment approach Earlier steps are more strict / conservative than later steps Later steps are more realistic than earlier steps Jumping to later steps is usually acceptable Earlier steps usually require less effort than later steps The same type of concentration applies to all steps

  24. Tiered risk assessment approach Exposure model

  25. Tiered risk assessment approach (Boesten, J.J.T.I.)

  26. 20 year period Annual carbendazim application 250 g.a.i. / ha on 15 May Annual ploughing to 15cm on 1 November Maximum carbendazim content in top 5cm soil (mg a.i. / kg)

  27. Tiered risk assessment approach Effects model

  28. Tiered risk assessment approach Effects model (top 5 cm soil) – earthworms example

  29. Assumptions: • even distribution • top 5cm soil • bulk density 1200 kg m • no loss -3 Tiered risk assessment approach earthworms Example: carbendazim Tier 1 (deterministic, acute) OECD 207 Lower limit TER acute trigger (safety factor) = 10

  30. Assumptions: • even distribution • top 5cm soil • bulk density 1200 kg m • no loss -3 Tiered risk assessment approach earthworms Example: carbendazim Tier 1 (deterministic, acute) OECD 207 Lowest LC50 acute = 3.9 mg a.i. / kg (EU SEEM project 2002) Typical application rate = 250 g a.i. / ha, equivalent to 0.418 mg a.i. / kg Lower limit TER acute trigger (safety factor) = 10

  31. Assumptions: • even distribution • top 5cm soil • bulk density 1200 kg m • no loss -3 Tiered risk assessment approach earthworms Example: carbendazim Tier 1 (deterministic, acute) OECD 207 Lowest LC50 acute = 3.9 mg a.i. / kg (EU SEEM project 2002) Typical application rate = 250 g a.i. / ha, equivalent to 0.418 mg a.i. / kg RISK indicated TER < 10 Lower limit TER acute trigger (safety factor) = 10

  32. Tiered risk assessment approach earthworms EU Terrestrial Guidance Document SANCO / 10329 / 2002 OECD 207 Requirement for chronic (reproduction) test if: • More than 6 applications (not fulfilled here) • DT90 field > 90 days (probably not fulfilled) • TER acute < 10 (fulfilled)

  33. Tiered risk assessment approach earthworms Example: carbendazim Tier 1 (deterministic, chronic) OECD 207 PEC chronic: Cumulative concentration In top 5cm over 20 years, assuming no loss Lower limit TER chronic trigger (safety factor) = 5

  34. Tiered risk assessment approach earthworms Example: carbendazim Tier 1 (deterministic, chronic) OECD 207 Lowest NOEC chronic = 0.6 mg a.i. / kg (van Gestel 1992) PEC chronic: Cumulative concentration In top 5cm over 20 years, assuming no loss PEC chronic = 8.36 mg a.i. / kg Lower limit TER chronic trigger (safety factor) = 5

  35. Tiered risk assessment approach earthworms Example: carbendazim Tier 1 (deterministic, chronic) OECD 207 Lowest NOEC chronic = 0.6 mg a.i. / kg (van Gestel 1992) PEC chronic: Cumulative concentration In top 5cm over 20 years, assuming no loss PEC chronic = 8.36 mg a.i. / kg RISK indicated TER < 5 Lower limit TER chronic trigger (safety factor) = 5

  36. Tiered risk assessment approach earthworms & enchytraeids Example: carbendazim Tier 2 (probabilistic) An effect estimate based on the median HC5, in this example derived from an array of individual toxicity (NOEC) data for earthworms and enchytraeids

  37. NOECs for various species & experiments 5 species (1 – 14 NOEC values per species) Frequency HC (50%) 5 HC5 (50%) = 0.53 mg / kg (95% CL 0.059 – 1.30) Soil concentrations (log) Tiered risk assessment approach earthworms & enchytraeids Example: carbendazim Tier 2 (probabilistic)

  38. 5 species (1 – 14 NOEC values per species) HC5 (50%) = 0.53 mg / kg (95% CL 0.059 – 1.30) Tiered risk assessment approach earthworms & enchytraeids Example: carbendazim Tier 2 (probabilistic) Lowest PEC from step 2 of exposure model = 0.6 mg / kg TER refined = 0.53 / 0.6 = 0.88

  39. 5 species (1 – 14 NOEC values per species) Safety factor ? Assume = 5 (conservative, from Tier 1) HC5 (50%) = 0.53 mg / kg RISK indicated TER < 5 (95% CL 0.059 – 1.30) Tiered risk assessment approach earthworms & enchytraeids Example: carbendazim Tier 2 (probabilistic) Lowest PEC from step 2 of exposure model = 0.6 mg / kg TER refined = 0.53 / 0.6 = 0.88

  40. Tiered risk assessment approach multiple species Example: carbendazim Tier 3 (semi-field / field) Higher-tier studies did not yield data suitable for constructing distributions of sensitivities An HC5 type approach therefore could not be applied to the higher-tier data to estimate risk Instead, the effect estimate (NOEC field) may be determined from TME and field experiments that simulate or represent realistic agroecological conditions

  41. Higher-tier effects classes (based on Brock et al. (2000) Alterra Report 88) Class 1 No effect demonstrable Class 2 Slight effect, transient Class 3 Slight effect, long term; Pronounced effect, transient or long term

  42. The effect estimate (NOEC field) is determined from TME and field experiments that simulate or represent realistic agroecological conditions Effects class 3 2 1 Tiered risk assessment approach multiple species Example: carbendazim Tier 3 (semi-field / field) 61 data entries for Lumbricidae

  43. The effect estimate (NOEC field) is determined from TME and field experiments that simulate or represent realistic agroecological conditions Effects class 3 2 1 Tiered risk assessment approach multiple species Example: carbendazim Tier 3 (semi-field / field) 61 data entries for Lumbricidae NOEC field = 1.0 mg / kg

  44. The effect estimate (NOEC field) is determined from TME and field experiments that simulate or represent realistic agroecological conditions Effects class 3 2 1 Tiered risk assessment approach multiple species Example: carbendazim Tier 3 (semi-field / field) 61 data entries for Lumbricidae NOEC field = 1.0 mg / kg Step 2 PECs

  45. The effect estimate (NOEC field) is determined from TME and field experiments that simulate or represent realistic agroecological conditions Effects class 3 2 NOEC > PEC NO RISK 1 Tiered risk assessment approach multiple species Example: carbendazim Tier 3 (semi-field / field) 61 data entries for Lumbricidae NOEC field = 1.0 mg / kg Step 2 PECs Step 3 PECs

  46. Project outputs An internet-based risk assessment tool that would enable stakeholders to input their own data or use default examples to explore the impact on risk estimates of incorporating uncertainty, using: a species sensitivity distribution model to calculate HC5 (or HCx) values for lower-tier data a tiered exposure model an interface to enable exposure and effects estimates to be combined and plotted (where appropriate) to indicate probability and certainty of risk estimates online guidance and links to other relevant risk assessment resources

  47. Purpose of the internet resource Optimise opportunities for interested parties to explore alternative ways of estimating risk Assist decision making at each risk assessment tier Provide feedback Raise awareness of data availability issues and limitations Could be used as an educational and training resource

  48. Conclusions Opportunities to explicitly incorporate uncertainty in the risk assessment are limited, even for standard test species, due to a lack of appropriate empirical data However, the feasibility of incorporating uncertainty can be illustrated for components of the risk assessment scheme where data shortage is least problematic Data from the independent literature is biased strongly towards standard test species, meaning that few data are available to support extrapolation to non-standard species Further development of the database is imperative, to enable advances in these research areas

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