Predicting air quality index using machine learning: a case study of the Himalayan city of Dehradun
Ishaan Dawar (),
Maanas Singal (),
Vijayant Singh (),
Sumita Lamba () and
Shreyal Jain ()
Additional contact information
Ishaan Dawar: DIT University
Maanas Singal: DIT University
Vijayant Singh: DIT University
Sumita Lamba: DIT University
Shreyal Jain: DIT University
Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 5, No 28, 5847 pages
Abstract:
Abstract Air quality is a crucial concern for urban environmental health, affecting human well-being and ecological equilibrium. Improving air quality by reducing air quality index (AQI) levels directly contributes to achieving sustainable development goals (SDGs), particularly SDG 3 (good health and well-being) and SDG 11 (sustainable cities and communities). Dehradun, situated in the foothills of the Himalayas, faces the challenge of deteriorating air quality due to geographical and climatic factors. This study introduces machine learning (ML) models to forecast the AQI in Dehradun City, addressing the local need for effective air quality management in Himalayan towns. The research utilizes data collected over 2 years from the city, encompassing parameters such as nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), particulate matter (PM2.5 and PM10), and ozone (O3). Performance metrics such as R-squared (R2), root mean square error (RMSE), and mean absolute error (MAE) are used to assess the prediction accuracy of these ML models. Lasso regressor performs the best with MAPE: 0.0269, MAE: 0.0185, RMSE: 0.0272 and R2 score of 0.9999. The results illustrate the effectiveness of these techniques in forecasting AQI levels in Dehradun, facilitating pre-emptive measures to overcome air pollution and protect public health. This study contributes to advancing air quality prediction methodologies. It provides insights for policymakers and urban planners to develop effective plans tailored to Himalayan towns like Dehradun, where air quality degradation remains a pressing issue often overlooked.
Keywords: Air pollution; Environmental impact assessment; Public health; Data-driven approaches; Artificial intelligence (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-07027-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:121:y:2025:i:5:d:10.1007_s11069-024-07027-9
Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069
DOI: 10.1007/s11069-024-07027-9
Access Statistics for this article
Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk
More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().