Abstract
China and India are two of the fastest-growing developing economies covering about 35 % of the world’s population. Due to the extensive prevalence of air pollution across cities in China and India, contemporary assessment of atmospheric pollution through real-time and remote sensing observations is inadequate. The study aims to determine the spatial distribution and temporal variation of hazardous atmospheric pollutants across cities in China (Shanghai, Nanjing, Jinan, Zhengzhou and Beijing) and India (Kolkata, Asansol, Patna, Kanpur and Delhi). Ground observation data on CO, O3, PM2.5, PM10, NO2 and SO2 along with remote sensing data on AOD, CO, O3, BC, NO2, SO2 and dust surface mass concentrations are used to assess atmospheric pollution. This study examines daily, zonal and longitudinal pollutant distributions using Sentinel-5 P data and surface mass concentrations over the vertical column evaluated from NASA satellite data. The Mann-Kendall test and relative change methods have been implemented to assess pollutant trends while Sen’s Slope identifies the magnitude of change. The similarity test and data validation methods including NRMSE, PC and MBias have been employed to ensure consistency in analysing annual trends for each air pollutant in the datasets. Additionally, multiple correlation matrix analysis has been used to examine the associations among different pollutants from both datasets based on their annual averages. Remote sensing data reveals that eastern China and north-eastern India have the highest aerosol, BC, CO, NO2 and SO2 while western China and southern India lowest. Dust peaks in the west while O3 levels are highest in the northern part of China and India. Ground observation data indicates that Chinese cities have higher annual mean SO2 and O3 concentrations with yearly declines in PM2.5, PM10, NO2, SO2 and CO notably SO2. Indian cities witnessed overall increases in PM2.5, PM10, NO2 and SO2 from 2012 to 2019 with a slight decline in 2020 followed by a resurgence in 2023. The findings provide insights for implementing regional policy measures to reduce air pollution based on changes in pollutant behaviour. The study suggests that addressing atmospheric pollutants, particularly NO2, CO, PM2.5, PM10, and SO2 requires a comprehensive environmental policy framework involving central and state governments and enforcing stringent environmental protection laws.
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