Development of land use regression (LUR) models for criteria air pollutants in Delhi: Use of regulatory monitoring data

High-resolution spatial maps of air pollution can be useful for air quality management. In low- and middle-income countries, regulatory measurements of criteria pollutants are typically insufficient to generate spatial maps, due to the sparsely located monitoring stations. Alternatively, high-resolution spatial maps of air pollution can be achieved by dispersion (physics-based) and statistical regression (training-based) modelling. Resolutions of up to ~25 meters can be achieved by Land Use Regression (LUR) modelling based spatial predictions. In this study, we leveraged the high density of regulatory monitors located in New Delhi, India, and developed LUR models for all the major criteria pollutants (PM₁₀, PM_2.5, SO₂, NO₂, and Ozone). New Delhi is one of the most heavily polluted cities in the world. We used data from 40 continuous ambient air quality monitoring stations’ (CAAQMS) for the year 2019 to develop seasonal and annual LUR models, following the ESCAPE (European Study of Cohorts for Air Pollution Effects) stepwise supervised regression method. Model predictors included land use parameters, road lengths, rail track lengths, population, satellite pollution, and NDVI data, along with air pollution point source location data and reanalysis meteorology. The models were validated using leave one station out (LOSO) and 10-fold cross validations (CV). The model adjusted R² values varied between 0.08 and 0.64. Particle pollutant models (PM_2.5 and PM₁₀) performed better than those of gaseous pollutants. Further, ozone models performed the least. Across seasons, summer models performed the best (least) for PM (gaseous pollutants). Models with adjusted R² were used for spatial predictions at 50-m resolution for the Delhi National Capital Territory region. Spatio-seasonal characteristics of air pollution were studied using the generated high-resolution maps.