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Integrated AI-Driven Assessment and Sustainable Remediation of Contaminated Soils: Innovations, Challenges and Future Directions
Summary
Contaminated soil from farming, industry, and mining can carry harmful substances like heavy metals, PFAS "forever chemicals," and microplastics into our food and water, posing real risks to human health. This review paper explains how combining artificial intelligence with cleanup methods like using plants and microbes to absorb toxins could make identifying and removing these contaminants faster, cheaper, and less disruptive to soil health than traditional dig-and-dispose approaches. While promising, the authors note this smarter, tech-assisted approach to soil cleanup still faces hurdles like unclear regulations and the need for community buy-in
Soil contamination driven by industrialization, intensive agriculture, mining, urbanization, and poor waste management is a major global challenge affecting food security, ecosystem integrity, climate resilience, and human health. Agricultural and industrial lands are increasingly burdened with heavy metals, persistent organic pollutants, hydrocarbons, emerging contaminants such as PFAS, and microplastics, many of which exhibit persistence, bioaccumulation, and chronic toxicity. Conventional remediation approaches—primarily excavation, containment, and disposal—offer rapid risk reduction but are economically costly, carbon-intensive, and disruptive to soil ecological functions. This chapter develops an integrated framework for AI-driven sustainable soil remediation, combining environmental science, engineering, and digital technologies. It reviews contaminant types, sources, transport pathways, and ecological and health impacts to inform remediation strategies. Sustainable remediation technologies, including bioremediation, phytoremediation, soil washing, electrokinetic methods, and nanotechnology-based approaches, are evaluated in terms of efficiency, scalability, and life-cycle sustainability. A central focus is the transformative role of artificial intelligence, machine learning, geospatial analytics, digital twins, and IoT-enabled monitoring systems. These tools enable high-resolution contamination mapping, optimization of remediation pathways, adaptive control, and long-term performance prediction. Integration of AI with sensor networks supports a transition from reactive remediation to predictive, precision-based, and risk-informed management, reducing uncertainty, costs, and remediation time. Case studies across mining, agriculture, and industrial sites demonstrate improved efficiency and environmental outcomes. However, regulatory frameworks, socioeconomic constraints, data governance, and community participation remain critical. Future directions include AI–synthetic biology integration, low-carbon hybrid systems, and interoperable soil data infrastructures.