Urban air quality modeling and health impact analysis using geospatial methods and machine learning algorithms

Chetan Rathod, Aneesh Mathew () and Abhilash T. Nair
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Chetan Rathod: National Institute of Technology
Aneesh Mathew: National Institute of Technology
Abhilash T. Nair: National Institute of Advanced Manufacturing Technology (NIAMT)

Asia-Pacific Journal of Regional Science, 2025, vol. 9, issue 3, No 3, 693-731

Abstract: Abstract This study utilized geospatial techniques and machine learning (ML) algorithms, viz. Random Forest and XGBoost, for predicting the air quality and the AirQ+ model for assessing health risks in urban environments. We analyzed the annual variations in sulfur dioxide (SO2) and nitrogen dioxide (NO2) levels of five Indian metropolitan cities from 2019 to 2022. Preliminary analysis indicated the highest levels of NO2 and SO2 in Delhi and Kolkata as compared to other metropolises. Kolkata had an 11% increase in SO2 concentrations in 2022 compared to 2019, while Delhi had a 20% increase in NO2 concentrations in 2022 compared to 2019. The air pollutant levels predicted by ML algorithms were analyzed in the AirQ+ model for health risks. The health impact assessment conducted using the AirQ+ model revealed concerning trends. In 2023, particulate matter (PM2.5) was attributed to 20.26% of respiratory disease cases per 100,000 population in Delhi, followed by NO2, accounting for 11.01%. In Kolkata, SO2 was responsible for 3.21% of respiratory disease cases. By implementing this approach, policymakers can estimate the air pollution levels and potential respiratory disease health risks. This knowledge can help them formulate targeted interventions, such as implementing pollution control measures, managing health risks, and issuing health advisories, to protect public health and improve air quality in cities.

Keywords: Random forest; XGBoost; AirQ+; Air quality forecasting; Health impact assessment (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s41685-025-00387-5

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