Principal component regression approach for measuring the impact of built environment variables on multiple air pollutants in Delhi

Abstract Air pollution will likely increase as cities continue to intensify urban activities and expand their infrastructure. Delhi is one of the most polluted cities in the world. Yet, there is a lack of understanding of how the built environment (BE) strategies can address local air quality levels for the city. Additionally, the existing studies use the land use regression (LUR) technique, assuming independence between BE variables. We assessed the impact of BE variables measured at various spatial scales on Delhi's air pollutants (PM2.5, PM10, CO, NO2 and O3). This study used the Principal Component Regression (PCR) approach to account for the multicollinearity between BE variables. As per the analysis, PCR provided better estimates for PM10, PM2.5, and CO concentrations. LUR was found better for modelling NO2 and O3. The findings show that as built-up percentage and the metro station density increases the PM10 and CO levels are also likely to increase, while increasing green percentage is likely to result in decreasing pollutant concentrations. We also identify BE variables that affect a particular pollutant. Percentage institutional within 700 m buffer radii affects PM10, distance to CBD affects CO levels, and distance to the bus depot is affects both CO and NO2 levels. The PCR helped measure the joint effect of BE variables on pollutant concentrations in Delhi. Simultaneously modelling multiple air pollutants can help develop a better urban development strategy for addressing air pollution.