Abstract
Timely information concerning variations in the levels of air pollutants in urban regions is needed for implementing appropriate preventive actions. In this regard, an attempt has been made to develop statistical models using remote sensing data that can be beneficial in obtaining direct information on air quality using remotely sensed data easily and quickly. The present research is an integrated approach to attain the spatiotemporal attributes of air pollution index of particulate matter (PM10 and PM2.5) and traces gases (O3, NO2, and CO) pollutants in Mumbai city of India. Radiance values of different bands, vegetation indices, and urbanization index resulting from Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) images were employed to create regression-based models with air pollution indices (APIs), which were intended from field-based air pollution data. The spatial variation of API for different air pollutants was simulated using the inverse distance weighted (IDW) method of interpolation. It was observed that among all the image-based parameters, the highest correlation of pollutants was with near infrared (NIR) and normalized difference vegetation index (NDVI). The correlation between APIs, vegetation/urbanization based, and land surface temperature (LST) indices suggest that areas with low vegetation, dense urbanization, and high LST are consistently pertaining to elevated concentrations of air pollutants. The high correlation coefficient, 0.965 between overall API and API (PM10), indicates that PM10 has a significant effect on the overall air quality of the study area, followed by PM2.5. Correlation results also revealed a positive relation of air pollutants with urban settlement density and satellite-derived LST, respectively. The multivariate regression model with the two most correlated variables (NIR and NDVI) gave the most accurate air pollution image with R square (R2) value of 0.69, 0.55, and 0.52 for API (PM10), API (NO2), and overall API, respectively. Regression model for API (PM2.5) with a least R2 value of 0.34 seems inadequate for the prediction of API (PM2.5). On comparing among the APIs-modeled and APIs-interpolated images, 72.27% accuracy was obtained for API (PM10), 66.01% accuracy was obtained for API (NO2), and 79.89% accuracy was obtained in the case of overall API. The developed regression models were validated using February 12, 2019, in situ and satellite data.
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Kumari, M., Somvanshi, S.S., Zubair, S. (2021). Estimation of Air Pollution Using Regression Modelling Approach for Mumbai Region, Maharashtra, India. In: Kumar, P., Sajjad, H., Chaudhary, B.S., Rawat, J.S., Rani, M. (eds) Remote Sensing and GIScience . Springer, Cham. https://doi.org/10.1007/978-3-030-55092-9_13
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