Hazardous waste risk detection powered by artificial intelligence and machine learning for resilient monitoring and mitigation of potential environmental threats

Abstract

• Systematic synthesis of 280 studies into a sensor-to-governance blueprint • Eight innovation classes unify sensing, AI models, validation, and governance • Decision-grade gains require calibrated uncertainty, drift audits, and cost maps • Validation ladder specifies leakage-safe splits, external holdouts, and replay tests • FAIR/KG and federated pathways enable auditable, privacy-preserving collaboration This study synthesizes 280 peer-reviewed studies (2002–2025) into a deployment-directed “sensor-to-governance” blueprint for AI/ML-enabled hazardous-waste risk detection. Publication activity shifted from early exploratory work (2002–2014) to sustained scaling after 2017, with stable output since 2022. China and the United States dominate publication volume, while wider Global South participation strengthens the case for privacy-preserving collaboration. Evidence clusters into eight innovation streams spanning multimodal sensing, geospatial intelligence, edge analytics, knowledge-graph governance, smart sensing materials, exposure analytics, circularity intelligence, and digital-twin integration. Performance gains become decision-relevant only when uncertainty calibration , drift auditing , and cost-aware risk mapping are co-optimized. Transformative potential lies in converting fragmented detections into auditable, privacy-preserving risk intelligence linked to intervention choices and equity-relevant exposure endpoints. However, sparse ground truth, domain shift, cybersecurity barriers, and the sustainability cost of continuous monitoring remain binding constraints. Practical deployment remains limited by sparse labels and external validation. Priority gaps include leakage-resistant splits, ground truth scarcity, sensor interoperability, and cybersecurity safeguards for deployed monitoring networks.