Special Issue on Air Pollution and its Impact in South and Southeast Asia

V.P. Lavanyaa  1,2, S. Varshini1, Souvik Sankar Mitra1, Kiran M. Hungund1, Rudrodip Majumdar1, R. Srikanth This email address is being protected from spambots. You need JavaScript enabled to view it.1 

1 National Institute of Advanced Studies, Indian Institute of Science, Bengaluru, Karnataka 560012, India
2 Manipal Academy of Higher Education, Manipal, Karnataka 576104, India

Received: March 1, 2022
Revised: April 26, 2022
Accepted: May 5, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.220110  

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Cite this article:

Lavanyaa, V.P., Varshini, S., Mitra, S.S., Hungund, K.M., Majumdar, R., Srikanth, R. (2022). Geospatial Modelling for Estimation of PM2.5 Concentrations in Two Megacities in Peninsular India. Aerosol Air Qual. Res. https://doi.org/10.4209/aaqr.220110


  • PM2.5 levels are modelled at a 1 km2 resolution in two cities in peninsular India.
  • Ten-fold cross-validated R2 of the Linear Mixed Effects model is between 62%–66%
  • Meteorological parameters and LULC changes play a key role in PM2.5 levels.
  • These models are useful to conduct exposure-response studies in these cities.


Airborne particles finer than 2.5 microns (PM2.5) constitute a major public health risk in India. Therefore, extensive scientific studies must be conducted to assess the PM2.5 exposures of Indians and determine the “exposure-response function” specific to India. While Peninsular India includes three megacities with populations exceeding 10 million each, there are very few studies on air quality modelling in this region compared to North India. In this paper, the authors describe a Linear Mixed Effects (LME) model to estimate monthly-average PM2.5 concentrations at a spatial resolution of 1 km2 between 2016 and 2019 in the megacities of Bengaluru and Hyderabad with a total population of 23 million. This model is based on covariates such as aerosol optical depth (AOD), meteorological parameters, and Land-use-Land-cover (LULC) variables and is validated with extensive datasets from continuous and manual air quality monitoring stations through a 10-fold cross-validation process. The final LME model can explain more than 60 percent of the variation in the PM2.5 concentrations in Bengaluru and Hyderabad. This model is then used to predict the monthly-average grid-wise PM2.5 concentrations in more than 800 grids in each of these two cities to study the spatial and temporal patterns in PM2.5 concentrations between 2016 and 2019. These spatiotemporal maps of PM2.5 concentration are critical to overcoming the misclassification of exposure and will form a crucial input to much-needed PM exposure-response studies in these two megacities. This paper can serve as a useful framework for similar studies by showing the way to bridge the gaps in the current air quality monitoring network in Peninsular India.

Keywords: Aerosol Optical Depth (AOD), Linear Mixed Effects (LME) Model, LULC classification, Exposure-Response function, Spatiotemporal maps

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