FOCUS on Contamination: A Geospatial Deep Learning Framework with a Noise-Aware Loss for Surface Water PFAS Prediction
This work addresses the challenge of scalable PFAS monitoring for environmental and public health protection, though it appears incremental as it builds on existing geospatial and deep learning methods.
The authors tackled the problem of predicting PFAS contamination in surface water over large regions by introducing FOCUS, a geospatial deep learning framework with a noise-aware loss function, which improved prediction accuracy through integration of hydrological, land cover, and source proximity data, as validated against baselines like Kriging and simulations.
Per- and polyfluoroalkyl substances (PFAS), chemicals found in products like non-stick cookware, are unfortunately persistent environmental pollutants with severe health risks. Accurately mapping PFAS contamination is crucial for guiding targeted remediation efforts and protecting public and environmental health, yet detection across large regions remains challenging due to the cost of testing and the difficulty of simulating their spread. In this work, we introduce FOCUS, a geospatial deep learning framework with a label noise-aware loss function, to predict PFAS contamination in surface water over large regions. By integrating hydrological flow data, land cover information, and proximity to known PFAS sources, our approach leverages both spatial and environmental context to improve prediction accuracy. We evaluate the performance of our approach through extensive ablation studies, robustness analysis, real-world validation, and comparative analyses against baselines like sparse segmentation, as well as existing scientific methods, including Kriging and pollutant transport simulations. Results and expert feedback highlight our framework's potential for scalable PFAS monitoring.