AI-Powered Environmental Surveillance: Enhancing Air and Water Quality Monitoring through Real-Time Predictive Analytics

Abstract

Rapid urbanization, industrial escalation, and climate-driven ecological shifts have intensified the degradation of air and water quality, resulting in substantial public health burdens worldwide. Traditional environmental monitoring systems—largely dependent on manual sampling, periodic laboratory analysis, and retrospective reporting—are often inadequate for timely risk mitigation and early warning at population scale. Recent advances in artificial intelligence (AI), combined with IoT sensing, satellite-based remote sensing, and real-time data assimilation, have transformed environmental surveillance into a proactive, predictive, and health-oriented intelligence system. This review examines AI-powered environmental surveillance frameworks that integrate air and water quality data streams to forecast contamination patterns, detect anomalies, and generate risk alerts with direct implications for cardiovascular, respiratory, gastrointestinal, and neuro-immune health outcomes.

The paper consolidates state-of-the-art predictive models—including deep learning architectures (CNNs, RNNs, LSTMs, Transformers), hybrid spatio-temporal frameworks, anomaly detection engines, and decision-support systems—that fuse multi-modal signals from ground sensors, UAVs, satellites, wastewater IoT probes, and epidemiological feeds. Real-time predictive analytics have shown measurable success in forecasting PM2.5 exceedance events, detecting pathogenic bursts in municipal water lines, quantifying source contributions, and estimating likely health burden using AI-driven exposure-response models. In particular, the integration of WHO AirQ+-style health-risk modules with machine learning pipelines enables early identification of at-risk zones, informing policy, industrial compliance, and community advisories.

Despite their promise, AI-enabled surveillance faces critical challenges in model generalizability, sensor drift, data sparsity, explainability, ethical governance, and computational equity in low-resource regions. The review concludes that AI-powered environmental surveillance is not merely a diagnostic instrument but a prevention-aligned public health infrastructure that bridges environmental forensics with health intelligence. Moving forward, regulatory harmonization, interpretable models, federated learning, and digital-epidemiology integration are essential to institutionalize predictive environmental intelligence for safeguarding planetary and human health.