Intelligent Monitoring and Online Early Warning Systems for Groundwater Contamination in Chemical Industrial Park: Challenges and Perspectives
Abstract
Groundwater contamination in chemical industrial parks (CIPs) poses a significant global threat due to complex pollutant compositions, high source intensity, and accidental release risks. Traditional manual monitoring methods often fail to capture transient contamination pulses or incipient leakages, necessitating a transition toward intelligent, real-time surveillance. This review comprehensively synthesized the state-of-the-art in intelligent online monitoring and early warning systems for CIP groundwater. First, integrated sensing architectures for high-frequency data acquisition were evaluated, and their capacity to resolve spatiotemporal data gaps within highly heterogeneous industrial environments was critically assessed. Analysis of online early warning platforms demonstrated that integrating fundamental hydrogeological principles and advanced data analytics within Digital Twin platforms significantly enhanced predictive reliability and enabled real-time risk quantification. Furthermore, this integration effectively overcame the inherent limitations of purely data-driven black-box models. Field-scale case studies demonstrated the practical efficacy of these systems in improving early warning precision and source tracking across diverse industrial sites. Finally, the persistent technical bottlenecks of sensor fouling and data silos were identified as primary drivers for proposed future research advancing autonomous self-healing hardware and federated learning protocols. This study provided a comprehensive scientific framework to support proactive, data-driven groundwater protection in high-risk CIPs.