For millions of years before human activity, the extent of heat radiated away from the earth remained largely unchanged, thus ensuring global climate patterns remained stable. Lately, the effects of anthropogenic climate change have become increasingly obvious in the form of increased incidence of forest fires, icecap melting, and hurricanes. The drastic increase in the levels of greenhouse gases (GHGs) in the atmosphere due to the Industrial Revolution has reduced the level of heat radiated away, thereby retaining more heat within the global climate system.

Emissions from the Indian transport sector currently account for almost 10 per cent of the country’s total GHG emissions, mandating significant sectoral interventions for attaining the ‘net zero’ goal.

Strategies to reduce emissions from the transport sector include electric vehicle (EV) adoption, shift to public transport (PT), and non-motorised transport (NMT). These typically require substantial behavioural change.

The mathematical solution to estimate surface fine particulate matter (PM2.5) from columnar aerosol optical depth (AOD) includes complex variables and involves a bunch of assumptions. Hence, researchers tend to use training-based models to predict PM2.5 from AOD. Here, we integrated regulatory composite PM2.5 measurements, high-resolution satellite AOD, reanalysis meteorological parameters, and a few other auxiliary parameters to train ten different regression models.