Improving toxicity prediction of metal-contaminated sediments by incorporating sediment properties.


Journal

Environmental pollution (Barking, Essex : 1987)
ISSN: 1873-6424
Titre abrégé: Environ Pollut
Pays: England
ID NLM: 8804476

Informations de publication

Date de publication:
01 Dec 2023
Historique:
received: 22 06 2023
revised: 28 08 2023
accepted: 05 10 2023
medline: 6 11 2023
pubmed: 9 10 2023
entrez: 8 10 2023
Statut: ppublish

Résumé

For the purpose of sediment quality assessment, the prediction of toxicity risk-levels for aquatic organisms based on simple environmental measurements is desirable. One commonly used approach is the comparison of total contaminant concentrations with corresponding water and sediment quality guideline values, serving as a Line of Evidence (LoE) based on chemistry-toxicity effects relationships. However, the accuracy of toxicity predictions can be improved by considering the factors that modify contaminant bioavailability. In this study we used paired chemistry-ecotoxicity data sets for sediments to evaluate the improvement in toxicity risk predictions using bioavailability-modified guidelines. The sediments were predominantly contaminated with metals, and measurements of sediment particle size, total organic carbon (TOC) and acid volatile sulfide (AVS) were used to modify hazard quotients (HQ). To further assess the predictive efficacy of the bioavailability-modified guideline models, sediments with differing contamination levels were tested for toxicity to a benthic amphipod's reproduction. To account for differences between laboratory exposure and field exposure scenarios, where the latter creates greater dilution, both static-renewal and flow-through test procedures were employed, and flow-through resulted in lower dissolved metal concentrations in the overlying waters. We also investigated how lower AVS concentration by oxidation modified the toxicity. This study reaffirmed that consideration of factors that influence contaminant bioavailability improves toxicity risk predictions, however the improvements may be modest. The sediment particle size data had the greatest influence on the modified HQ, indicating that higher percentage of fine particle size (<63 μm) contributed most to a lower predicted toxicity. The comparison of the static-renewal and flow-through test results continue to raise important questions about the relevance of static or static-renewal toxicity test results for risk assessment decisions, as both these test designs may cause unrealistically high contributions of dissolved metals in overlying waters to toxicity. Overall, this study underscores the value of incorporating outcomes from simple and routine sediment analysis (e.g., particle size, TOC, and consideration of AVS) to enhance the predictive efficacy of toxicity risk assessments in the context of sediment quality risk assessment.

Identifiants

pubmed: 37806427
pii: S0269-7491(23)01710-4
doi: 10.1016/j.envpol.2023.122708
pii:
doi:

Substances chimiques

Water Pollutants, Chemical 0
Metals 0
Water 059QF0KO0R
Sulfides 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

122708

Informations de copyright

Copyright © 2023 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Yanfeng Zhang (Y)

CSIRO Environment, Lucas Heights, NSW, 2234, Australia; Tianjin Key Laboratory of Remediation & Pollution Control for Urban Ecological Environment, Key Laboratory of Pollution Processes and Environmental Criteria of Ministry of Education, College of Environmental Science and Engineering, Nankai University, Tianjin, 300350, China.

Minwei Xie (M)

State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian, 361102, China. Electronic address: minweixie@xmu.edu.cn.

David M Spadaro (DM)

CSIRO Environment, Lucas Heights, NSW, 2234, Australia.

Stuart L Simpson (SL)

CSIRO Environment, Lucas Heights, NSW, 2234, Australia; Hong Kong Branch of the Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.

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Classifications MeSH