Handling concentration data below the analytical limit in environmental mixture risk assessment: A case-study on pesticide river monitoring.
Chemical water quality
Combined exposure
Cumulative toxicity
Detection/quantification limits
Risk metrics
Wide-scope pollution target screening
Journal
The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500
Informations de publication
Date de publication:
10 Jan 2024
10 Jan 2024
Historique:
received:
26
07
2023
revised:
17
09
2023
accepted:
06
10
2023
medline:
19
10
2023
pubmed:
19
10
2023
entrez:
18
10
2023
Statut:
ppublish
Résumé
Aquatic organisms are exposed to ever-changing complex mixtures of chemicals throughout their lifetime. Component-Based Mixture Risk Assessment (CBMRA) is a well-established methodology for water contaminant-mixture management, the use of which is growing due to improved access to reference ecotoxicity data and extensive monitoring datasets. It enables the translation of measured exposure concentrations of chemicals into biological effect values, and thus to quantitatively estimate the risk of the whole water sample (i.e., as a mixture). However, many factors can bias the final risk decision by impacting the risk metric components; thus, a careful design of the CBMRA is needed, taking into primary consideration the specific features of the dataset and mixture risk assessment assignments. This study systematically addressed the effects of the most common approaches used for handling the concentrations of chemicals below the limit of detection/quantification (LOD/LOQ) in CBMRA. The main results included: i) an informed CBMRA procedure that enables the tracking of the risk decisions triggered by substances below LOD/LOQ, ii) a conceptual map and guidance criteria to support the selection of the most suitable approach for specific scenarios and related interpretation; iii) a guided implementation of the informed CBMRA on dataset of pesticide concentrations in Italian rivers in 2020 (702,097 records).
Identifiants
pubmed: 37852501
pii: S0048-9697(23)06297-6
doi: 10.1016/j.scitotenv.2023.167670
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
167670Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.