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Integrated AI-Driven Assessment and Sustainable Remediation of Contaminated Soils: Innovations, Challenges and Future Directions
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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.
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Integrated Monitoring, Remediation and Management of Soil Pollution: A Comprehensive Review
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This research review summarizes the latest methods for finding, cleaning up, and managing polluted soil that can harm human health through contaminated food and water. The study highlights that newer pollutants like microplastics and "forever chemicals" (PFAS) are especially hard to remove from soil using traditional cleanup methods. The authors argue that we need better, combined approaches to monitor and clean up soil pollution to protect our food supply and health.
Soil Biofilms in Pollutant Dynamics and Detoxification
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This review pulls together what scientists know about tiny communities of soil microbes (called biofilms) that can trap and break down harmful pollutants like pesticides, "forever chemicals" (PFAS), microplastics, and heavy metals before they spread further into soil, water, or crops. The authors argue that combining biology with new tools like AI could help researchers better predict and boost how well these microbial communities clean up contamination naturally, which matters because reducing pollutant buildup in soil ultimately means less exposure to these harmful substances through our food and water. It's worth noting this is a review paper summarizing existing research and proposing a new
Python-Powered Environmental Intelligence: Computational Workflows for Soil Pollution Assessment Using ML Methods
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Scientists have built a set of computer tools (using AI/machine learning) that can help detect harmful soil contaminants like heavy metals, pesticides, microplastics, and "forever chemicals" (PFAS) — all substances that can end up in our food and water. Importantly, this study is a proof-of-concept using test data, not real-world soil samples, so the tools still need to be validated on actual contaminated sites before they can reliably protect public health. Still, this kind of technology could eventually speed up pollution detection, helping identify risks in farmland and food supplies faster and more affordably.
Green technologies for soil remediation: a systematic review
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Soil pollution from things like heavy metals, oil residues, microplastics, and pesticides doesn't just hurt the environment, it can work its way into the food we grow and eat, potentially raising risks of cancer and other health problems. This review rounds up existing research on eco-friendly cleanup methods, like using special plants, charcoal-like soil additives, and natural minerals to soak up or break down these toxins without harsh chemicals. The takeaway: safer, greener ways to clean contaminated soil already exist and could help protect both farmland and the safety of our food supply.
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This perspective reviews emerging challenges in soil pollution, including contamination from heavy metals, pesticides, PFAS, and microplastics, all worsened by urbanization and climate change. Researchers highlight innovative monitoring and remediation technologies, such as advanced sensors, bioremediation, and machine learning approaches for predicting contamination patterns. The study emphasizes the need for sustainable, integrated soil management strategies to protect environmental health.
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