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Record W1797018437 · doi:10.1002/ieam.1513

Deriving site-specific soil clean-up values for metals and metalloids: Rationale for including protection of soil microbial processes

2013· article· en· W1797018437 on OpenAlexafffund
Roman G. Kuperman, Steven D. Siciliano, Jörg Römbke, Koen Oorts

Bibliographic record

VenueIntegrated Environmental Assessment and Management · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Saskatchewan
FundersRio TintoInternational Zinc AssociationVale Canada LimitedCopper Development AssociationNickel Producers Environmental Research Association
KeywordsEnvironmental scienceSoil PollutantsSoil functionsEnvironmental chemistrySoil contaminationEnvironmental protectionSoil waterSoil fertilitySoil biodiversitySoil scienceChemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.261
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.258
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2013
Admission routes2
Has abstractyes

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