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Predicting PFAS Exposure Risks from Rural Private Wells Using Integrated Mechanistic and Machine-Learned Bayesian
Hana Chmielewski Long1, Rohit Warrier2, Krishna Ganta1
1Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina27695-7908, United States.
Environmental Science & Technology
|July 21, 2026
Summary
Bayesian networks and hybrid models offer efficient tools for assessing per- and polyfluoroalkyl substances (PFAS) risks in private wells. These data-driven approaches outperform traditional models, aiding rural communities in managing emerging contaminants like GenX.
Area of Science:
- Environmental Science
- Hydrogeology
- Data Science
Background:
- Per- and polyfluoroalkyl substances (PFAS) present significant drinking water challenges, especially for private wells lacking regulatory oversight.
- Resource limitations and evolving standards necessitate efficient risk assessment models for PFAS exposure.
Purpose of the Study:
- To compare the performance of mechanistic groundwater models, Bayesian networks, and hybrid models for assessing PFAS exposure risks.
- To evaluate models based on their ability to identify contaminated wells, robustness to data gaps, and resource efficiency.
Main Methods:
- Case study of GenX contamination near North Carolina's Fayetteville Works Plant.
- Comparison of traditional mechanistic groundwater models with machine-learned Bayesian networks and hybrid approaches.
- Evaluation of model classification performance, data robustness, and calibration time.
Main Results:
- Bayesian networks demonstrated comparable or superior classification performance to mechanistic models.
- Bayesian networks showed enhanced robustness with incomplete data and varying thresholds, requiring fewer resources.
- Hybrid models, integrating mechanistic outputs into Bayesian networks, achieved the highest performance.
Conclusions:
- Machine-learned models, particularly Bayesian networks and hybrid approaches, provide scalable and resource-efficient tools for PFAS risk management.
- Mechanistic insights can enhance the predictive power of data-driven models.
- A flexible modeling framework is crucial for adapting to changing regulations and emerging contaminants in drinking water management.
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