Predicted Potential for Aquatic Exposure Effects of Per- and Polyfluorinated Alkyl Substances (PFAS) in Pennsylvania’s Statewide Network of Streams DOI Creative Commons
Sara E. Breitmeyer, Andrew Williams, Matthew D. Conlon

et al.

Toxics, Journal Year: 2024, Volume and Issue: 12(12), P. 921 - 921

Published: Dec. 19, 2024

Per- and polyfluoroalkyl substances (PFAS) are contaminants that can lead to adverse health effects in aquatic organisms, including reproductive toxicity developmental abnormalities. To assess the ecological risk of PFAS Pennsylvania stream surface water, we conducted a comprehensive analysis included both measured predicted estimates. The potential combined exposure 14 individual biota were estimated using sum exposure-activity ratios (ΣEARs) 280 streams. Additionally, machine learning techniques utilized predict unmonitored reaches, considering factors such as land use, climate, geology. Leveraging tailored convolutional neural network (CNN), validation accuracy 78% was achieved, directly outperforming traditional methods also used, logistic regression gradient boosting (accuracies ~65%). Feature importance highlighted key variables contributed CNN's predictive power. most influential features complex interplay anthropogenic environmental contributing contamination waters. Industrial urban cover, rainfall intensity, underlying geology, agricultural factors, their interactions emerged determinants. These findings may help inform biotic sampling strategies, water quality monitoring efforts, policy decisions aimed mitigate impacts

Language: Английский

High organofluorine concentrations in municipal wastewater affect downstream drinking water supplies for millions of Americans DOI Creative Commons
Bridger J. Ruyle, Emily H. Pennoyer, Šimon Vojta

et al.

Proceedings of the National Academy of Sciences, Journal Year: 2025, Volume and Issue: 122(3)

Published: Jan. 6, 2025

Wastewater receives per- and polyfluoroalkyl substances (PFAS) from diverse consumer industrial sources, discharges are known to be a concern for drinking water quality. The PFAS family includes thousands of potential chemical structures containing organofluorine moieties. Exposures few well-studied PFAS, mainly perfluoroalkyl acids (PFAA), have been associated with increased risk many adverse health outcomes, prompting federal regulations six compounds in 2024. Here, we find that the regulated (mean = 7 8%) 18 measured PFAA 11 21%) make up only small fraction extractable (EOF) influent effluent eight large municipal wastewater treatment facilities. Most EOF (75%) (62%) consists mono- polyfluorinated pharmaceuticals. technology sizes facilities this study similar those serving 70% US population. Despite advanced technologies, maximum removal efficiency among work was <25%. Extrapolating our measurements other across United States results nationwide discharge estimate 1.0 2.8 million moles F y-1. Using national model simulates connections between downstream intakes, sources 23 Americans could contaminated above regulatory thresholds by wastewater-derived alone. These emphasize importance further curbing ongoing additional evaluations fate toxicity fluorinated

Language: Английский

Citations

2
Predicted Potential for Aquatic Exposure Effects of Per- and Polyfluorinated Alkyl Substances (PFAS) in Pennsylvania’s Statewide Network of Streams DOI Creative Commons
Sara E. Breitmeyer, Andrew Williams, Matthew D. Conlon

et al.

Toxics, Journal Year: 2024, Volume and Issue: 12(12), P. 921 - 921

Published: Dec. 19, 2024

Per- and polyfluoroalkyl substances (PFAS) are contaminants that can lead to adverse health effects in aquatic organisms, including reproductive toxicity developmental abnormalities. To assess the ecological risk of PFAS Pennsylvania stream surface water, we conducted a comprehensive analysis included both measured predicted estimates. The potential combined exposure 14 individual biota were estimated using sum exposure-activity ratios (ΣEARs) 280 streams. Additionally, machine learning techniques utilized predict unmonitored reaches, considering factors such as land use, climate, geology. Leveraging tailored convolutional neural network (CNN), validation accuracy 78% was achieved, directly outperforming traditional methods also used, logistic regression gradient boosting (accuracies ~65%). Feature importance highlighted key variables contributed CNN's predictive power. most influential features complex interplay anthropogenic environmental contributing contamination waters. Industrial urban cover, rainfall intensity, underlying geology, agricultural factors, their interactions emerged determinants. These findings may help inform biotic sampling strategies, water quality monitoring efforts, policy decisions aimed mitigate impacts

Language: Английский

Citations

2