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Communication Dans Un Congrès Année : 2010

What can we learn from an ecotoxicity database in a pre-reach context ?

Résumé

According to the former dangerous substances directive (Directive 67/548/EEC), labelling of chemicals was performed using three endpoints, namely algae EC50 72h, daphnia EC50 48h and fish LC50 96h. Nowadays, the new chemicals legislation REACH applies in all of EU and special emphasis is placed on the reduction of systematic toxicity testing. It is therefore crucial to extract a maximum of information from existing ecotoxicological databases. For this reason, we analysed a database composed by new chemicals registered in France over the last years. Before carrying out our analysis, ecotoxicological data were pruned to identify pertinent information, on the basis of their quality (preciseness of test results, information on concentrations measurements, purity, focus on single compounds, robustness of the statistical methodology, data available on the three trophic levels). At the end of the selection, only 5% of the initial number of chemicals (164 substances) was deemed appropriate for subsequent analysis. Our work was particularly focused on the differences of sensitivities between species, between trophic levels (fish, alga, daphnia) and between experimental methods. An analysis of putative relationships between the physico-chemical properties of the chemicals and the sensitiveness of species with respect to test results was also attempted. Our results show that tests on fish are significantly less sensitive that the two other tests. Moreover, applying a security ratio of 10 would permit to extrapolate results from 2 to 3 species in 95% of the cases.
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Dates et versions

ineris-00970615 , version 1 (02-04-2014)

Identifiants

  • HAL Id : ineris-00970615 , version 1
  • INERIS : EN-2010-129

Citer

Alexandre R.R. Pery, Adina Henegar, Pascal Pandard, Enrico Mombelli. What can we learn from an ecotoxicity database in a pre-reach context ?. 20. SETAC Europe Annual Meeting, May 2010, Séville, Spain. ⟨ineris-00970615⟩

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