Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/14757
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dc.contributor.authorRautela, Kuldeep Singhen_US
dc.contributor.authorGoyal, Manish Kumaren_US
dc.date.accessioned2024-10-25T05:51:01Z-
dc.date.available2024-10-25T05:51:01Z-
dc.date.issued2024-
dc.identifier.citationRautela, K. S., & Goyal, M. K. (2024). Transforming air pollution management in India with AI and machine learning technologies. Scientific Reports. Scopus. https://doi.org/10.1038/s41598-024-71269-7en_US
dc.identifier.issn2045-2322-
dc.identifier.otherEID(2-s2.0-85202921239)-
dc.identifier.urihttps://doi.org/10.1038/s41598-024-71269-7-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/14757-
dc.description.abstractA comprehensive approach is essential in India's ongoing battle against air pollution, combining technological advancements, regulatory reinforcement, and widespread societal engagement. Bridging technological gaps involves deploying sophisticated pollution control technologies and addressing the rural–urban disparity through innovative solutions. The review found that integrating Artificial Intelligence and Machine Learning (AI&ampen_US
dc.description.abstractML) in air quality forecasting demonstrates promising results with a remarkable model efficiency. In this study, initially, we compute the PM2.5 concentration over India using a surface mass concentration of 5 key aerosols such as black carbon (BC), dust (DU), organic carbon (OC), sea salt (SS) and sulphates (SU), respectively. The study identifies several regions highly vulnerable to PM2.5 pollution due to specific sources. The Indo-Gangetic Plains are notably impacted by high concentrations of BC, OC, and SU resulting from anthropogenic activities. Western India experiences higher DU concentrations due to its proximity to the Sahara Desert. Additionally, certain areas in northeast India show significant contributions of OC from biogenic activities. Moreover, an AI&ampen_US
dc.description.abstractML model based on convolutional autoencoder architecture underwent rigorous training, testing, and validation to forecast PM2.5 concentrations across India. The results reveal its exceptional precision in PM2.5 prediction, as demonstrated by model evaluation metrics, including a Structural Similarity Index exceeding 0.60, Peak Signal-to-Noise Ratio ranging from 28–30 dB and Mean Square Error below 10 μg/m3. However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM2.5 concentrations. Implementing tailored regional pollution control strategies, integrating AI&ampen_US
dc.description.abstractML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India. © The Author(s) 2024.en_US
dc.language.isoenen_US
dc.publisherNature Researchen_US
dc.sourceScientific Reportsen_US
dc.subjectAir pollutionen_US
dc.subjectGlobal collaborationen_US
dc.subjectRegulatory frameworksen_US
dc.subjectSocietal engagementen_US
dc.subjectTechnological innovationen_US
dc.titleTransforming air pollution management in India with AI and machine learning technologiesen_US
dc.typeJournal Articleen_US
dc.rights.licenseAll Open Access, Gold-
Appears in Collections:Department of Civil Engineering

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