Apply AI and computational methods to real-world pollution challenges

Monitoring and mitigating pollution across air, water, and soil systems demands computational approaches that can handle complex, uncertain environmental data.

AI for Pollution Management presents interdisciplinary frameworks combining machine learning, deep learning, fuzzy logic, soft computing, and multi-criteria decision-making approaches. This edited volume delivers both conceptual models and case studies that connect theoretical methods with actionable pollution management strategies.

The book details the integration of IoT, smart sensor networks, remote sensing, edge and cloud computing, explainable AI, and emerging computational approaches for intelligent environmental monitoring and decision support. Contributors demonstrate practical approaches for implementing smart pollution monitoring systems and provide decision-making frameworks to support sustainable environmental management and policy. Across the volume, conceptual frameworks, technical methodologies, and applied case studies address air-quality prediction, water-quality assessment and contamination detection, soil-pollution monitoring, industrial pollution control, emerging contaminants, and sustainable waste management.

The book also covers:

• AI and machine-learning approaches for pollution detection, prediction, forecasting, and environmental decision support
• MCDM frameworks applied to prioritize pollution interventions and evaluate environmental remediation strategies across competing criteria
• IoT-enabled smart sensor networks, edge computing, and AI-based systems for real-time environmental monitoring
• Hybrid quantum-classical algorithms and emerging quantum-computing approaches for addressing carbon-emission problems
• Soft computing techniques including fuzzy logic for handling imprecise and uncertain environmental information
• AI applications in circular economy, industrial pollution control, waste recycling, and sustainable environmental management

AI for Pollution Management serves environmental scientists, computational researchers, academics, and sustainability professionals who need rigorous and practically relevant computational approaches for pollution monitoring and mitigation.

Industry decision-makers seeking to strengthen environmental performance through data-driven strategies will also find directly applicable frameworks and case studies throughout the volume.

About the Editors

Nandini Gupta, PhD, is Associate Professor and Head of the Department of Environmental Science at Bir Bikram Memorial College, Tripura, India. Her research focuses on environmental monitoring, climate and resource management, biodiversity conservation, circular economy, and the application of artificial intelligence, fuzzy logic, and multi-criteria decision-making techniques to environmental problems. She has led multiple funded research projects involving sustainability, traditional ecological knowledge, water-resource management, and waste valorization. She has published extensively in SCIE- and Scopus-indexed journals on environmental assessment, AI-assisted decision-making, and sustainable resource management.

Ajoy Kanti Das, PhD, is Associate Professor in the Department of Mathematics at Tripura University, India. His research is primarily centred on fuzzy set theory, soft computing, multi-criteria decision making, uncertainty modelling, artificial intelligence, and mathematical approaches to intelligent decision systems. His work develops and applies mathematical and computational methods for addressing uncertainty in environmental, engineering, and decision-making problems. He serves on editorial boards of academic journals and has published extensively in SCIE- and Scopus-indexed journals.