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.

Authors

Institutions

Publication Details

Journal
Environmental Toxicology and Chemistry
Published
2025-03-19
DOI
https://doi.org/10.1093/etojnl/vgaf069
Citations
2
Primary Topic
Fish Ecology and Management Studies
Type
article
Field-Weighted Citation Impact
4.63
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Stephanie L. Hummel, Laura DeCicco, S R Corsi, Matthew A. Pronschinske et al.
2 citations
Environmental Toxicology and Chemistry
Fish Ecology and Management Studies
4.63
article

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

Stephanie L. Hummel, Laura DeCicco, S R Corsi, Matthew A. Pronschinske, Daniel L. Villeneuve, A K Baldwin, Erin M. Maloney, Nicholas J. Fuller, Kimani L. Kimbrough, Natalia Vinas, M. A. Edwards, John R Frisch
article en
2 citations

Abstract

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.

Environmental Toxicology and ChemistryVol. 44(7)
United States Geological Survey (US), Environmental Protection Agency (US), NOAA National Ocean Service (US), United States Fish and Wildlife Service (US), General Dynamics (United States) (US), Environmental Protection Agency (GH), University of Minnesota, Duluth (US), Consolidated Safety Services-Dynamac (United States) (US), NOAA National Centers for Coastal Ocean Science (US), U.S. Army Engineer Research and Development Center (US)
Clean water and sanitation
Openalex Percentile: Top 7%
Fish Ecology and Management Studies
4.63
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.