Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Goswami, Mausumi Title A name given to the resource Air quality index improvement through machine learning and quantum computing: a framework for advancing air quality prediction using quantum-inspired metaheuristics on climate change to achieve positive health Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Multilevel Quantum Metaheuristics: Applications in Data Exploration;pp.341-384 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-443-33136-7.00021-9" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-443-33136-7.00021-9</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032975207?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032975207?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Goswami M., AI, ML and Data Science Department, CHRIST (Deemed to be University), Karnataka, Bangalore, India Description An account of the resource Climate change significantly exacerbates air quality deterioration, intensifying health risks and environmental instability. Air pollution poses significant challenges to public health and environmental sustainability. Accurate prediction of the Air Quality Index (AQI) is crucial for timely interventions and policy-making. As urbanization and industrial activities intensify, there is an urgent need for accurate and real-time air quality monitoring systems. Advanced machine learning (ML) techniques have shown promise in air quality forecasting and classification. Recently, quantum-inspired computational paradigms have emerged as innovative tools to overcome the limitations of traditional models, particularly in areas like feature selection, optimization, and spatial-temporal pattern recognition. This study presents a comprehensive analysis of various machine learning and deep learning models for AQI prediction, utilizing pollutant concentration data. It also explores quantum computing-inspired approaches. We explore the efficacy of different algorithms, datasets, and preprocessing techniques. This paper critically reviews high-impact research that explores the intersection of climate-induced changes and air quality prediction using ML. It identifies trends, gaps, and emerging methodologies. We conduct a comparative analysis of datasets, prediction models, and performance metrics. The paper focuses on three case studies. The first case study focuses on the Indian aspect using an Indian dataset and the global aspects with different global datasets, and the second case study uses quantum-inspired approaches. We further evaluate the performance of 10 state-of-the-art ML models, offering a roadmap for future research and deployment. Effective air quality forecasting is vital in urban planning decisions. This also plays an essential role need in environmental management and the protection of public health. This issue directly deals with Sustainable Development Goal (SDG) 3 and SDG 13. SDG 3 is related to positive health and SDG 13 is related to climate action. Conventional predictive models in ML face challenges due to multiple reasons. Effective feature selection is one such challenge as well as effective hyperparameter tuning. These challenges limit the effectiveness of artificial intelligence models. In the proposed framework, searching is enhanced using quantum jump- and quantum mechanics-related principles. This approach leads to the development of a quantum-inspired particle swarm optimization called QPSO. QPSO is able to provide more promising results by bridging the gaps of traditional optimization techniques. Model convergence is accelerated by using quantum-inspired feature selection techniques. 2026 Elsevier Inc. All rights reserved. Subject The topic of the resource Climate change, machine learning, Sustainable Development Goals, quantum-inspired metaheuristics, climate action, artificial intelligence Publisher An entity responsible for making the resource available Elsevier Relation A related resource ISBN: 978-044333136-7; 978-044333137-4; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online