A Study on Artificial Intelligence Applications in Environmental Pollution Monitoring and Control

Abstract

This study explores the use of artificial intelligence (AI) technologies in the field of environmental pollution monitoring and control. It offers a systematic discussion of the limitations of the existing monitoring approaches against the background of rising environmental pollution around the world, and the techniques behind deep learning and reinforcement learning in terms of monitoring, prediction, anomaly detection, pollution source tracking, and intelligent control. In addition, it offers insights into the development trends of multiple data source fusion technology, model generalization, and interpretability of AI applications in governance decision-making and ecology restoration. It is revealed that the introduction of AI greatly improves the timeliness of environmental monitoring and scientific, economical decision-making in governance solutions, thus paving the way for preventive governance. However, some limitations such as insufficient cross-region generalization ability, data leakage, and ethical bias still exist. Future studies should focus more on the combination of physics-based information neural network and digital twin as well as improving the mechanism of ethical evaluation.