A statistical approach for identifying private wells susceptible to perfluoroalkyl substances (PFAS) contamination
Open accessAbstract
Drinking water concentrations of per- and polyfluoroalkyl substances (PFAS) exceed provisional guidelines for millions of Americans. Data on private well PFAS concentrations are limited in many regions, and monitoring initiatives are costly and time-consuming. Here, we examine modeling approaches for predicting private wells likely to have detectable PFAS concentrations that could be used to prioritize monitoring initiatives. We used nationally available data on PFAS sources, and geologic, hydrologic and soil properties that affect PFAS transport as predictors, and trained and evaluated models using PFAS data ( n ∼ 2300 wells) collected by the state of New Hampshire between 2014 and 2017. Models were developed for the five most frequently detected PFAS: perfluoropentanoate, perfluorohexanoate, perfluoroheptanoate, perfluorooctanoate, and perfluorooctanesulfonate. Classification random forest models that allow nonlinearity in interactions among predictors performed the best (area under the receiver operating characteristics curve: 0.74–0.86). Point sources such as the plastics/rubber and textile industries accounted for the highest contribution to accuracy. Groundwater recharge, precipitation, soil sand content, and hydraulic conductivity were secondary predictors. Our study demonstrates the utility of machine learning models for predicting PFAS in private wells, and the classification random forest model based on nationally available predictors is readily extendable to other regions.
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Data on hydrology and PFAS sources can be predictive of PFAS occurrence in groundwater.
“A state-wide study in New Hampshire showed that data on hydrology and PFAS sources can also be predictive of PFAS occurrence in groundwater.22”
From Perfluoroalkyl and Polyfluoroalkyl Substances in Groundwater Used as a Source of Drinking Water in the Eastern United States (2022) · cites this paper as [22]
The model developed here indicates that it may be possible to predict PFAS detections in drinking-water wells using data sources already available.
“The model developed here indicates that it may be possible to predict PFAS detections in drinking-water wells using data sources already available, consistent with another recent study that used statistical models to predict susceptibility of private wells to PFAS contamination.22”
From Perfluoroalkyl and Polyfluoroalkyl Substances in Groundwater Used as a Source of Drinking Water in the Eastern United States (2022) · cites this paper as [22]
Another recent study used statistical models to predict susceptibility of private wells to PFAS contamination.
“The model developed here indicates that it may be possible to predict PFAS detections in drinking-water wells using data sources already available, consistent with another recent study that used statistical models to predict susceptibility of private wells to PFAS contamination.22”
From Perfluoroalkyl and Polyfluoroalkyl Substances in Groundwater Used as a Source of Drinking Water in the Eastern United States (2022) · cites this paper as [22]
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Perfluoroalkyl and Polyfluoroalkyl Substances
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- Authors
- Hu, X. C., Ge, B., Ruyle, B. J., Sun, J., & Sunderland, E. M.
- Affiliations
- Harvard University (US)
- Corresponding author
- Hu, Xindi C.
- Venue
- Environmental Science & Technology Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2021-05-11
- Volume
- 8
- Issue
- 7
- Pages
- 596-602
- ISSN
- 2328-8930 (ISSN-L)
- PMC
- PMC10312616
- OpenAlex
- W3161489899
- Cited by
- 63
- References
- 44
- Citations by year
- 2026: 13 · 2025: 22 · 2024: 12 · 2023: 9 · 2022: 6 · 2021: 1
- FWCI
- 2.64
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Environmental Science & Technology Letters (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institute of Environmental Health Sciences (P42ES02770)
- Faculty of Arts and Sciences
- Topics
- Physical Sciences › Environmental Science › Environmental Chemistry › Per- and polyfluoroalkyl substances research
- Physical Sciences › Environmental Science › Water Science and Technology › Fluoride Effects and Removal
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality and Pollution Assessment
- Keywords
- per- and polyfluoroalkyl substances, PFAS contamination, private wells, groundwater contamination, drinking water quality, random forest classification, groundwater recharge, soil properties, hydraulic conductivity, New Hampshire
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