Predicting PFAS Exposure Risks from Rural Private Wells

Predicting PFAS Exposure Risks from Rural Private Wells

Presented at the 2024 BayesiaLab Conference in Cincinnati on April 11, 2024.


To better manage emerging public health challenges, communities, water utilities, and regulators must improve methods to assess and prioritize environmental health risks. PFAS exposure in North Carolina is a prime example where methodologies for predicting and mitigating risks are developing concurrently with contaminant regulations and the allocation of mitigation funding. This project aims to predict the risk of private wells exceeding the provisional health goal for the PFAS GenX. PFAS compounds are notoriously difficult to model with mechanistic groundwater flow and fate and transport models. The sheer number of different PFAS chemicals and uncertainty in their individual and interacting characteristics all make them complex to model in the environment. Mechanistic models are also resource-intensive to develop and calibrate. This project builds upon previous work that developed a Machine-Learned Bayesian Network (MLBN) classification model to predict at-risk wells; current work integrates outputs from a mechanistic groundwater fate and transport model as input variables to new MLBNs, classified as low-, medium-, and high-effort models in terms of mechanistic modeling resources required. The performance of each model is compared to the mechanistic model predictions of at-risk wells using several performance metrics, including accuracy, area under the receiver operating characteristic curve (AU-ROC), and F-score curves, and the importance of each metric and model performance is discussed in the context of environmental health risks. Results show that MLBNs perform as well as the mechanistic models in accuracy and AU-ROC performance metrics while being more robust in terms of the range of decision thresholds selected for risk classification. High-effort models make slight improvements in AU-ROC metrics while more easily incorporating insights from mechanistic model performance without the need to recalibrate the mechanistic model. The project aims to assist regulators in advancing public health and methodologies to integrate traditional engineering models with machine-learning approaches.

Presentation Video

Presentation Slides

About the Authors

Krishna Ganta1^1, Rohit Warrier2^2, Riley Mulhern3^3, Ted Lillys2^2, Jennifer Hoponick Redmon2^2, Jacqueline MacDonald Gibson1^1

1^1Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC, USA

2^2Research Triangle Institute International, Research Triangle Park, NC, USA

3^3Brown & Caldwell, Englewood, CO, US

About the Presenter

Hana C. Long is a postdoctoral researcher in the Department of Civil, Construction, and Environmental Engineering at NC State University (NCSU). Her research uses mathematical optimization and statistical modeling to help communities make sustainable and resilient infrastructure decisions. Hana holds a PhD in Operations Research from NCSU. She previously worked as a project engineer in the Wastewater Research Group at the Los Angeles County Sanitation Districts and with the Community Resilience Group at the National Institute of Standards and Technology. She holds a Master's in Civil Engineering (NCSU) and a Bachelor's degree in Mathematics and Russian Language (Vanderbilt University).

Hana C. Long, Ph.D.

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