Soil pollution is a critical global environmental issue affecting human health, food security, and ecosystem stability. Heavy metals and hydrocarbons are particularly pervasive in industrial and oil-producing regions, such as the Niger Delta region of Nigeria. Conventional detection methods, including GC–MS, AAS, ICP–MS, and XRF, provide accurate measurements but are costly, labour-intensive, and limited in scalability. Emerging artificial intelligence (AI)-based approaches, leveraging machine learning algorithms such as Support Vector Machines, Random Forests, Artificial Neural Networks, and Convolutional Neural Networks, enable real-time, predictive monitoring when integrated with spectral, remote sensing, or Internet of Things (IoT) data. Reported performance metrics from studies indicate that AI models can achieve R 2 values of 0.85–0.90, predict 88–92% of true pollutant occurrences, and reduce false positives to 10–15%, outperforming conventional methods. Despite their advantages, AI approaches face challenges including limited high-quality datasets, lack of transferability across soil types, and insufficient integration with risk assessment frameworks. This review emphasizes the potential of hybrid frameworks that combine the precision of laboratory analyses with the adaptability and speed of AI technologies. Adoption of standardized datasets and integrated monitoring strategies will enhance soil pollution management, support sustainable agriculture, and strengthen environmental governance and human health protection.
Briggs et al. (Thu,) studied this question.
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