Recommendations to improve wildlife exposure estimation for development of soil screening and cleanup values
Notice bibliographique
Résumé
Abstract An integral component in the development of media-specific values for the ecological risk assessment of chemicals is the derivation of safe levels of exposure for wildlife. Although the derivation and subsequent application of these values can be used for screening purposes, there is a need to identify the threshold for effects when making remedial decisions during site-specific assessments. Methods for evaluation of wildlife exposure are included in the US Environmental Protection Agency (USEPA) ecological soil screening levels (Eco-SSLs), registration, evaluation, authorization, and restriction of chemicals (REACH), and other risk-based soil assessment approaches. The goal of these approaches is to ensure that soil-associated contaminants do not pose a risk to wildlife that directly ingest soil, or to species that may be exposed to contaminants that persist in the food chain. These approaches incorporate broad assumptions in the exposure and effects assessments and in the risk characterization process. Consequently, thresholds for concluding risk are frequently very low with conclusions of risk possible when soil metal concentrations fall in the range of natural background. A workshop held in September, 2012 evaluated existing methods and explored recent science about factors to consider when establishing appropriate remedial goals for concentrations of metals in soils. A Foodweb Exposure Workgroup was organized to evaluate methods for quantifying exposure of wildlife to soil-associated metals through soil and food consumption and to provide recommendations for the development of ecological soil cleanup values (Eco-SCVs) that are both practical and scientifically defensible. The specific goals of this article are to review the current practices for quantifying exposure of wildlife to soil-associated contaminants via bioaccumulation and trophic transfer, to identify potential opportunities for refining and improving these exposure estimates, and finally, to make recommendations for application of these improved models to the development of site-specific remedial goals protective of wildlife. Although the focus is on metals contamination, many of the methods and tools discussed are also applicable to organic contaminants. The conclusion of this workgroup was that existing exposure estimation models are generally appropriate when fully expanded and that methods are generally available to develop more robust site-specific exposure estimates. Improved realism in site-specific wildlife Eco-SCVs could be achieved by obtaining more realistic estimates for diet composition, bioaccumulation, bioavailability and/or bioaccessibility, soil ingestion, spatial aspects of exposure, and target organ exposure. These components of wildlife exposure estimation should be developed on a site-, species-, and analyte-specific basis to the extent that the expense for their derivation is justified by the value they add to Eco-SCV development. Integr Environ Assess Manag 2014;10:372–387. © 2013 The Authors. Integrated Environmental Assessment and Management Published by SETAC Key Points Evaluation of wildlife exposure for the USEPA Eco-SSLs and REACH incorporate broad exposure and effects assumptions to ensure that soil contaminants do not pose risks to wildlife, resulting in thresholds that may be low, with conclusions of risk possible at background soil concentrations. Methods for quantifying exposure of wildlife to soil-associated metals through soil and food consumption were evaluated to provide recommendations for the development of ecological soil cleanup values (Eco-SCVs) that are both practical and scientifically defensible. Existing exposure estimation models are generally appropriate when fully expanded, with improved realism possible through integration of more realistic and site-specific diet composition estimates, bioaccumulation, bioavailability, soil ingestion, habitat use, and target organ exposure. These components of wildlife exposure estimation should be developed on a site-, species-, and analyte-specific basis to the extent that the expense for their derivation is justified by the value they add to Eco-SCV development.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».