FOCUS on Contamination: Hydrology-Informed Noise-Aware Learning for Geospatial PFAS Mapping

TL;DR

FOCUS is a geospatial deep learning framework that leverages environmental data and hydrological priors to map PFAS contamination effectively despite limited sampling, aiding environmental risk assessment.

Contribution

The paper introduces FOCUS, a novel noise-aware deep learning method that integrates hydrological and environmental priors for large-scale PFAS contamination mapping with sparse data.

Findings

FOCUS outperforms traditional methods like Kriging and pollutant transport models.

The framework maintains spatial coherence and scalability across large regions.

Real-world validation confirms robustness and effectiveness of FOCUS.

Abstract

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with significant public health impacts, yet large-scale monitoring remains severely limited due to the high cost and logistical challenges of field sampling. The lack of samples leads to difficulty simulating their spread with physical models and limited scientific understanding of PFAS transport in surface waters. Yet, rich geospatial and satellite-derived data describing land cover, hydrology, and industrial activity are widely available. We introduce FOCUS, a geospatial deep learning framework for PFAS contamination mapping that integrates sparse PFAS observations with large-scale environmental context, including priors derived from hydrological connectivity, land cover, source proximity, and sampling distance. These priors are integrated into a principled, noise-aware loss, yielding a robust training objective under sparse labels. Across extensive ablations, robustness analyses, and real-world validation, FOCUS consistently outperforms baselines including sparse segmentation, Kriging, and pollutant transport simulations, while preserving spatial coherence and scalability over large regions. Our results demonstrate how AI can support environmental science by providing screening-level risk maps that prioritize follow-up sampling and help connect potential sources to surface-water contamination patterns in the absence of complete physical models.