Evaluating within-city variability in air pollution is important for sustainable urban planning and policy forming. Land use regression (LUR) model are capable of explaining such small-scale variation due to its high validity in prediction. To advance city level exposure estimates, we performed land use regression with ordinary kriging to predict NO2 concentration on ground level over two global megacities of India, Delhi and Mumbai. The predictors are estimated using the Google Earth and OpenStreet Maps. The model found traffic intensity on the secondary road to be the primary predictor for both Mumbai (0.0421) and Delhi (0.0069) for NO2. Kriged Map for both Mumbai and Delhi found centre part of the city to be most affected compared to the periphery. Regions with high concentration are filled with industries and higher traffic percolation. The robustness of the methodology is established through congruency between predicted and observed values using scatterplot.
Presented in Session 3. Population, Development, & the Environment; Data & Methods; Applied Demography