Derivation and characterization of environmental hazard concentrations for chemical prioritization: a case study in the Great Lakes tributaries

作者：Erin M. Maloney, Steven R. Corsi, Matthew A. Pronschinske, Laura DeCicco, Michelle A. Nott, John R Frisch, Neil Fuller, Austin K. Baldwin, Kimani L. Kimbrough, M. A. Edwards, Stephanie L. Hummel, Natalia Vinas, Daniel L. Villeneuve · 发表于：Environmental Toxicology and Chemistry · 年份：2025 · DOI：10.1093/etojnl/vgae002 · 被引用次数：5 · 研究领域：Toxic Organic Pollutants Impact、Water Quality and Resources Studies

Ongoing anthropogenic activities and analytical advancements yield continuously expanding lists of environmental contaminants. This represents a challenge to environmental managers, who must prioritize chemicals for management actions (e.g., restriction, regulation, remediation) but are often hindered by resource limitations. To help facilitate prioritization efforts, this study presents several strategies for deriving environmental hazard concentrations using publicly accessible data and open-source computational tools. Using a Great Lakes tributaries aquatic monitoring dataset as a case study, environmental hazard concentrations were obtained or derived for 334 organic chemicals. These concentrations were based on (1) current water quality guidelines; (2) apical screening values; (3) apical and (4) nonapical effect concentrations from the ECOTOXicology Knowledgebase; (5) in vitro effect concentrations from the ToxCast database; (6) cytotoxic burst concentrations collated from the Comptox Dashboard; (7) "estimated screening values" derived from modeled or estimated data and available from various regulatory and nonregulatory agencies; (8) pharmaceutical potency estimates from the MaPPFAST database; and (9) quantitative structure-activity relationship (QSAR)-derived acute toxicity estimates. Environmental fate data included aquatic half-lives and bioconcentration factors collated from the Comptox Dashboard or estimated using QSARs. To identify patterns that could be used for ...