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  5. Transforming air pollution management in India with AI and machine learning technologies

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Article
English
2024

Transforming air pollution management in India with AI and machine learning technologies

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English
2024
Scientific Reports
Vol 14 (1)
DOI: 10.1038/s41598-024-71269-7

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Manish Kumar Goyal
Manish Kumar Goyal

Indian Institute Of Technology Indorethe Institution

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Kuldeep Singh Rautela
Manish Kumar Goyal

Abstract

A 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&ML) 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&ML 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&ML technologies, strengthening regulatory frameworks, promoting sustainable practices, and encouraging international collaboration are essential policy measures to mitigate air pollution in India.

How to cite this publication

Kuldeep Singh Rautela, Manish Kumar Goyal (2024). Transforming air pollution management in India with AI and machine learning technologies. Scientific Reports, 14(1), DOI: 10.1038/s41598-024-71269-7.

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Publication Details

Type

Article

Year

2024

Authors

2

Datasets

0

Total Files

0

Language

English

Journal

Scientific Reports

DOI

10.1038/s41598-024-71269-7

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