Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)

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Statistical Analysis of Air Pollution with Artificial Intelligence

Authors
I. I. Nalband1, *, R. B. Magar2
1Research Scholar, A. I. Kalsekar Technical Campus, New Panvel , Navi Mumbai, Maharashtra, India
2Professor and Principal, A. I. Kalsekar Technical Campus, New Panvel, Navi Mumbai, Maharashtra, India
*Corresponding author.
Corresponding Author
I. I. Nalband
Available Online 7 October 2025.
DOI
10.2991/978-94-6463-852-3_20How to use a DOI?
Keywords
AQI (Air Quality Index); AI (Artificial Intelligence); PM (Particulate matters); NOx (Nitrogen oxides and dioxides)
Abstract

Anthropogenic activities have severely deteriorated the air quality and taken it to a critical level of hazard. There is a need to analyse the pattern and trend of pollutants with respect to the influence on AQI. To optimise the repercussions of air pollution it is very essential to quantify the pollutants statistically. A case study of Panvel from district Raigad (Maharashtra-India) is chosen and analysed using statistical, stochastical and AI predictive models. Inference of the research exhibits outnumber concentration of PM2.5 and lead to potential threat and spike in AQI. The maximum average annual concentration of SO2, NOx, PM2.5 and PM10 are 26 µg/m3, 63 µg/m3, 78.5 µg/m3 and 96 µg/m3. The study brings into notice that the concentration of SO2 in air is sparse. NOx, PM2.5 and PM10 concentration in the ambient air are at the level of menace. Yearly average level of AQI for year 2025 would be maximum 160, for year 2026 and 2027 up to 162 which indicates the Moderate AQI. This indicates AQI is at the level of menace which may cause breathing discomfort to the people with lung disease such as asthma and discomfort to people with heart disease, children and older adults.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
Series
Advances in Intelligent Systems Research
Publication Date
7 October 2025
ISBN
978-94-6463-852-3
ISSN
1951-6851
DOI
10.2991/978-94-6463-852-3_20How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - I. I. Nalband
AU  - R. B. Magar
PY  - 2025
DA  - 2025/10/07
TI  - Statistical Analysis of Air Pollution with Artificial Intelligence
BT  - Proceedings of the MULTINOVA: First International Conference on Artificial Intelligence in Engineering, Healthcare and Sciences (ICAIEHS- 2025)
PB  - Atlantis Press
SP  - 315
EP  - 330
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-852-3_20
DO  - 10.2991/978-94-6463-852-3_20
ID  - Nalband2025
ER  -