ANNOUNCEMENTS
Today, the rapid urbanization world over has increasingly led to the deterioration of the environment. Environmental Pollution has become a matter of prime concern globally. The use of Geoinformatics tools has made data collection and further research in this field more simplified. In this study, Ordinary Least Squares (OLS) Regression method, and Spatial AutoCorrelation (Moran’s I) have been deployed to establish a causative linkage between the land use practices (particularly the built environment) of the Indian Capital City/Territory of Delhi and its impact on the ambient Air Quality, within 1km radius from the AQMS. These form the primary sources of pollutants. Mode of Transport also significantly impacts air quality. It is thus the secondary source of pollutants. Results from the two analyses show that: particulate matter PM 2.5 and PM 10 significantly deteriorate the air quality, followed by O3, whereas NO2 is near to insignificant in air quality deterioration. Data from the OLS regression for Poor AQI and Very Poor AQI are spatially correlated, thus, the Moran’s I value is closer to +1. Other alternatives, such as LUR, GWR, MultiNomial Logistic Regression and Spatial Lag Spatial Error Regression models are also suggested.
