Environmental Studies and Forestry

A data-driven approach to identifying PFAS water sampling priorities in Colorado, United States

K. E. Barton, P. J. Anthamatten, et al.

This innovative research conducted by Kelsey E. Barton and colleagues employs a data-driven random forest classification to predict groundwater contamination risks from PFOS and PFOA in Colorado. It identifies key sampling locations, focusing on vulnerable populations and highlighting critical data gaps.

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Abstract

This research uses a data-driven approach, specifically random forest classification, to predict groundwater contamination risk from PFOS and PFOA in Colorado. The model incorporated known and potential PFAS sources, environmental characteristics, and existing sampling data. Results showed good sensitivity and precision in predicting high and low-risk areas, identifying private wells, schools, mobile home parks, and public water systems as priority sampling locations. The study also highlighted data gaps and recommended focusing limited resources on vulnerable populations.

Publisher

Journal of Exposure Science & Environmental Epidemiology

Published On

Aug 01, 2024

Authors

Kelsey E. Barton, Peter J. Anthamatten, John L. Adgate, Lisa M. McKenzie, Anne P. Starling, Kevin Berg, Robert C. Murphy, Kristy Richardson

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