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Erin M. Maloney

Publications and source records attributed to Erin M. Maloney.

2 recordsLinked to original sources

Retrospective stepwise prioritization of chemicals detected in Great Lakes tributaries (2008–2018)

Through the U.S. Great Lakes Restoration Initiative, a 10-year, multiagency chemical monitoring effort was undertaken across the Great Lakes. In this effort, 586 chemicals were monitored and 334 were detected in grab/composite water samples. To help inform potential future actions, a stepwise prioritization framework was used to identify compounds for which publicly accessible water quality guidelines or effects information suggested there was potential aquatic ecotoxicity. Because water quality guidelines were only available for some chemicals, this framework used apical toxicity data collated from publicly accessible databases (e.g., the ECOTOXicology Knowledgebase) and alternative data, including literature-derived non-apical effect concentrations, in vitro bioactivities from high-throughput screening, and modeled ecotoxicity. To account for the diverse levels of confidence in these data, chemicals were prioritized within specific action categories, which suggested potential management or experimental activities that may be considered based on the types of data available for each compound. Overall, 11 detected chemicals were identified as high priority in different action categories. This included four chemicals prioritized for environmental management or targeted risk assessment, three chemicals prioritized for effects-based monitoring, one chemical prioritized for apical effects assessment, and three chemicals targeted for non-apical effects evaluation. This framework also identified 164 low-priority chemicals, among which more than 50% were prioritized based on water quality guidelines or apical effect concentrations (thus could be considered low priority for future risk assessment or management activities). Results aim to help regulatory agencies, environmental managers, and other stakeholders focus available resources on carrying out monitoring, experimental, and risk assessments for the chemicals that display the greatest potential to adversely impact Great Lakes ecosystems.

Great Lakes

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

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 characterization, availability of ecotoxicological concentrations and environmental fate data were evaluated. Furthermore, exceedances of hazard concentrations were evaluated and compared across diverse ecotoxicological data types. Altogether, by providing detailed methodology and practical examples generated with real monitoring data, this study demonstrated that these hazard concentration derivation strategies can be efficiently and effectively used with large, complex datasets and identified critical considerations for future prioritization efforts.

Great Lakes region