Artificial Intelligence in Environmental Monitoring, Pollution Control, and Low-Carbon Management

Abstract

Air pollution, water contamination, soil degradation, solid waste accumulation, and carbon emissions are increasingly interconnected, posing common challenges to environmental engineering, including diverse monitoring targets, heterogeneous data sources, competing control objectives, and delayed management responses. This review examines the application progress of artificial intelligence in environmental fields through a three-level framework encompassing monitoring, control, and management. It systematically summarizes the roles of machine learning, deep learning, reinforcement learning, transfer learning, digital twins, and the Internet of Things in addressing multi-media environmental challenges. Current studies indicate that artificial intelligence has developed a relatively mature application basis in environmental monitoring as well as showing considerable potential for pollution-control processes. At the management level, the integration of machine learning with LCA, digital twins, and multi-objective optimization is gradually transforming environmental impact assessment from static accounting toward dynamic prediction, feedback, and decision-making. Despite these advances, key bottlenecks remain in data quality, model interpretability, field-scale validation, and the lack of cross-medium collaborative frameworks. Future research should therefore improve the reliability, generalizability, and interpretability of AI models while advancing AI from a task-specific modeling tool to a system-level decision-support technology for integrated environmental governance.