Deriving site-specific soil clean-up values for metals and metalloids: Rationale for including protection of soil microbial processes
Notice bibliographique
Résumé
Abstract Although it is widely recognized that microorganisms are essential for sustaining soil fertility, structure, nutrient cycling, groundwater purification, and other soil functions, soil microbial toxicity data were excluded from the derivation of Ecological Soil Screening Levels (Eco-SSL) in the United States. Among the reasons for such exclusion were claims that microbial toxicity tests were too difficult to interpret because of the high variability of microbial responses, uncertainty regarding the relevance of the various endpoints, and functional redundancy. Since the release of the first draft of the Eco-SSL Guidance document by the US Environmental Protection Agency in 2003, soil microbial toxicity testing and its use in ecological risk assessments have substantially improved. A wide range of standardized and nonstandardized methods became available for testing chemical toxicity to microbial functions in soil. Regulatory frameworks in the European Union and Australia have successfully incorporated microbial toxicity data into the derivation of soil threshold concentrations for ecological risk assessments. This article provides the 3-part rationale for including soil microbial processes in the development of soil clean-up values (SCVs): 1) presenting a brief overview of relevant test methods for assessing microbial functions in soil, 2) examining data sets for Cu, Ni, Zn, and Mo that incorporated soil microbial toxicity data into regulatory frameworks, and 3) offering recommendations on how to integrate the best available science into the method development for deriving site-specific SCVs that account for bioavailability of metals and metalloids in soil. Although the primary focus of this article is on the development of the approach for deriving SCVs for metals and metalloids in the United States, the recommendations provided in this article may also be applicable in other jurisdictions that aim at developing ecological soil threshold values for protection of microbial processes in contaminated soils. Integr Environ Assess Manag 2014;10:388–400. © 2014 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of SETAC. Key Points This article presents an overview of relevant test methods for assessing microbial functions in soil, and examines soil microbial toxicity data sets for Cu, Ni, Zn, and Mo that have been incorporated into regulatory frameworks High natural spatial and temporal variation of microbial biomass, and its potential for functional redundancy and adaptation to elevated contaminant concentrations, did not compromise the relevance and utility of functional endpoints for ecosystem protection. Incorporating microbial endpoints into regulatory frameworks resulted in calculated hazardous concentration values similar to those calculated without, increased the confidence associated with these values, and provided a statistically more robust determination of an ecological soil standard. Recommendations are provided on how to integrate the best available science into method development for deriving site-specific soil clean-up values that account for bioavailability of metals and metalloids in soil for protection of critical soil microbial processes.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,026 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,004 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».