The impact of PFAS on the public health and safety of future food supply in Europe: Challenges and AI technologies solutions of environmental sustainability

Per- and polyfluoroalkyl substances (PFAS) are persistent organic pollutants extensively used in industrial and consumer applications. Their accumulation in European agricultural soils through industrial discharges, biosolid applications, and contaminated irrigation water poses an unprecedented threat to food security, soil health, and water quality. Despite extensive laboratory research, no full-scale, long-term validated PFAS soil remediation study exists, leaving critical gaps in mitigation strategies. Existing approaches–including mobilization, immobilization, and degradation techniques–have demonstrated effectiveness in controlled environments but lack real-world validation in dynamic agricultural settings. This study proposes an artificial intelligence (AI)-driven remediation framework that integrates real-time detection tools, predictive modeling, and adaptive remediation technologies to overcome these challenges. Unlike static remediation strategies, the proposed AI-assisted system dynamically optimizes remediation interventions based on contamination patterns, soil composition, and environmental conditions. Machine learning algorithms and statistical models enable precise contamination tracking, predictive PFAS migration modeling, and automated remediation decision-making, offering a scalable and responsive solution for sustainable agricultural management. This study underscores the urgent need for large-scale, policy-backed field trials to validate AI-driven PFAS remediation technologies, bridging the gap between scientific advancements and real-world implementation. By transitioning AI-assisted mitigation from theory to an adaptive, field-deployable framework, this research ensures scalable solutions for sustainable food security, environmental resilience, and long-term public health protection.