Quantifying climate variation and associated regional air pollution in southern India using Google Earth Engine

Abstract

Climate change and regional air pollution have had significant proportional coherence and are collectively hazardous for the regional ecosystem. To conduct this present investigation, we obtained high-resolution remotely sensed datasets from 2001 to 2022. To estimate climate variation, we utilized Climate Hazard Group InfraRed Precipitation with Station Data Version 2.0 (CHIRPS) and Moderate Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST). Additionally, we used Sentinel-5P datasets to collect spatio-temporal information for regional CO (Carbon Monoxide), NO2 (Nitrogen Dioxide), SO2 (Sulfur Dioxide), and UV Aerosol index for Coimbatore city. Numerous non-parametric and descriptive statistical applications were then employed to check the spatial integrity of satellite data products and spatio-temporal trends using Google Earth Engine algorithms. The study reveals most of the southern parts of Coimbatore city witnessed increased LST (0.10 °C/year) together with decreased rainfall (21.5 mm/year). Moreover, regional concentration of air pollutants exhibits spatio-temporal variability at annual and seasonal scales, where maximum engrossment is occupied by CO during the pre-monsoon and monsoon season. However, other pollutants are also dominant in the northern parts of the city, whereas NO2 and absorbing Aerosol during pre-monsoon season experienced significant increase throughout the years. Understanding the fluctuations in air pollution levels across different weather situations might help in developing targeted pollution reduction methods.