Spatio-temporal Analysis of Urban Air Quality: A Comprehensive Approach Toward Building a Smart City

Observing and understanding atmospheric processes and impacts of air pollution in urban areas is essential for a sustainable and healthy city. Knowledge of temporal and spatial distribution of air quality at the landscape scale is essential for regional climate control and urban planning. For this purpose, spatial analysis of atmospheric aerosol concentrations and air pollution data by geo-statistical methods is carried out. The present study is focused on the megacity Delhi and its surrounding areas which are one of the most polluted urban regions in the world. The relationship between land-use structure and satellite retrieved AOD has been analyzed for the first time over Delhi, and the impacts of topography on the aerosol distribution have been discussed. It is observed that the major part of central and east Delhi showing built-up area depicted highest aerosol concentrations including high particulate matter (PM₁₀ and PM_2.5), whereas the surrounding areas around Delhi mostly having agricultural farmlands show lower values of the same. Further, application of stochastic modeling technique in analyzing the future trends of aerosol optical properties was performed using the Box–Jenkins autoregressive integrated moving average (ARIMA) model. After rigorous evaluation, the ARIMA (1, 0, 0) × (0, 1, 2)₁₂ was identified as the best predictive model for the time series under study, suggesting a simplistic modeling technique for determining the future values of AOD. This can provide a comprehensive approach that state and local authorities can use to deal with environmental protection issues.