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Ms. Swati Chaitandas Hadke is affiliated with the Department of Electronics and Telecommunications Engineering at G H Raisoni College of Engineering and Management, Nagpur, Maharashtra, India. Her academic interests include electronics, telecommunications, and emerging intelligent technologies. She contributes to research on AI-enabled environmental monitoring and air pollution prediction.
Air pollution constitutes a multifaceted environmental issue shaped by factors such as traffic, industrial operations, energy generation, construction, agricultural methods, weather patterns, and urban expansion.This chapter introduces a cohesive structure for utilising Agentic AI in the real-time observation and forecasting of air pollution. It addresses environmental sensing, IoT-driven data collection, sensor calibration, data quality evaluation, pollutant prediction, spatiotemporal modelling, meteorological integration, anomaly detection, pollution source analysis, adaptive monitoring, intelligent alert generation, edge-cloud intelligence, UAV-based monitoring, satellite data integration, and multi-agent environmental systems. The section additionally explores elucidation, uncertainty assessment, cybersecurity measures, data management, human-in-the-loop decision processes, and evaluative techniques. A conceptual agentic framework is established wherein specialised agents cooperate with sensors, predictive models, databases, geographic information systems, meteorological services, and decision-support systems. The chapter wraps up by exploring prospective avenues related to multimodal environmental foundation models, digital twins, federated learning, autonomous environmental observatories, and collaborative multi-agent systems. The primary aim is to develop Agentic AI as a regulated and comprehensible computational system for ongoing environmental intelligence instead of an unbounded autonomous decision-maker.