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.

翻译：全氟和多氟烷基物质（PFAS）是一类具有显著公共卫生影响的持久性环境污染物，然而，由于现场采样的高成本和后勤挑战，大规模监测仍然受到严重限制。样本的缺乏导致难以用物理模型模拟其扩散，并且对PFAS在地表水中迁移的科学认识有限。然而，描述土地覆盖、水文和工业活动的丰富地理空间和卫星衍生数据却广泛可用。我们提出了FOCUS，一个用于PFAS污染制图的地理空间深度学习框架，它将稀疏的PFAS观测数据与大规模环境背景信息相结合，这些背景信息包括源自水文连通性、土地覆盖、污染源邻近度和采样距离的先验知识。这些先验知识被整合到一个有理论依据的、噪声感知的损失函数中，从而在稀疏标签下产生一个鲁棒的训练目标。在广泛的消融实验、鲁棒性分析和实际验证中，FOCUS始终优于基线方法，包括稀疏分割、克里金法和污染物迁移模拟，同时在大区域上保持了空间一致性和可扩展性。我们的研究结果表明，在缺乏完整物理模型的情况下，人工智能如何通过提供筛查级风险地图来支持环境科学，这些地图可以优先安排后续采样，并有助于将潜在污染源与地表水污染模式联系起来。