Impact of Land-Use Pattern on Particulate Matter Pollution in a Mega-City in India

Urbanization is an inevitable attribute associated with modern civilization and development. However, rapid urbanization has led to increase in particulate matter (PM_2.5) pollution in several Indian cities. Here, we study Bengaluru, where the city has expanded by 62% from 2001 (457 km²) to 2019 (741 km²), and consequently the PM_2.5 is mostly above the limit (40 ug/m³), except for the monsoon season. As the high ambient PM_2.5 concentration has an adverse effect on public health, monitoring PM_2.5 is key for air quality management. However, the sparse and non-uniform distribution of air quality monitoring stations (AQMSs) poses a hindrance in assessing the air quality in Bengaluru. Therefore, the present study uses satellite-derived aerosol optical depth (AOD), land use, and road density maps together with meteorological parameters to model spatio-temporal PM_2.5 levels in Bengaluru using general linear mixed effects regression (GLME) model. The chosen predictors explain about 62% variance in PM_2.5. When tenfold cross-validation was performed, relative root mean square error (rRMSE) and relative mean bias error (rMBE) are 37.42% and 19.174%, respectively.