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

Autor: Rautela KS; Department of Civil Engineering, Indian Institute of Technology Indore, Simrol, Indore, 453552, Madhya Pradesh, India., Goyal MK; Department of Civil Engineering, Indian Institute of Technology Indore, Simrol, Indore, 453552, Madhya Pradesh, India. mkgoyal@iiti.ac.in.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Sep 02; Vol. 14 (1), pp. 20412. Date of Electronic Publication: 2024 Sep 02.
DOI: 10.1038/s41598-024-71269-7
Abstrakt: 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 PM 2.5 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 PM 2.5 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 PM 2.5 concentrations across India. The results reveal its exceptional precision in PM 2.5 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/m 3 . However, regulatory challenges persist, necessitating robust frameworks and consistent enforcement mechanisms, as evidenced by the complexities in predicting PM 2.5 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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