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Varikuti Triveni is an Assistant Professor in the Department of Electronics and Communication Engineering at Gurunanak Institute of Technology, Hyderabad, Telangana, India. Her research interests include Artificial Intelligence, Internet of Things, embedded systems, environmental monitoring, and intelligent communication technologies. She actively contributes to research on AI-enabled smart environmental applications and sustainable technological solutions.
Air pollution has emerged as a critical environmental issue impacting human health, climate change, biodiversity, and sustainable development. Accelerated industrial growth, urban expansion, rising vehicle emissions, fossil fuel usage, and deforestation have led to the decline of global air quality. Traditional air quality assessment systems predominantly depend on stationary monitoring stations and intermittent data, which frequently exhibit insufficient geographical and temporal representation. The amalgamation of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), Cloud Computing, Edge Computing, satellite remote sensing, and Big Data Analytics has transformed air pollution forecasting and environmental surveillance through continuous monitoring, real-time predictions, anomaly identification, and smart decision-making assistance. AI-powered algorithms evaluate pollution levels, weather conditions, traffic trends, industrial discharges, and satellite data to effectively forecast air quality and facilitate proactive environmental stewardship. This chapter delineates the principles, framework, artificial intelligence methodologies, applications, obstacles, and prospective research avenues for AI-driven air pollution forecasting and environmental surveillance.