Abstract

China faces severe atmospheric problems due to the rapid increase in population and industrialization. Therefore, due to the insufficient number of monitoring stations in such a vast country, remote sensing of an ultraviolet aerosol index (UVAI) provides useful information about absorbing aerosols. This study used the OMI-retrieved UVAI data to investigate the spatial patterns, trends, and periodic nature of UVAI over China from October 2004 to December 2022. Additionally, the correlation and degree of coherence of UVAI with meteorological parameters (temperature & precipitation) and anthropogenic factors (Construction, population, CO2 emission, energy consumption, and primary industry) are also examined. Mean UVAI concentration with an increasing trend of 4.34% per year shows high UVAI values (0.70–1.41) in Xinjiang Uygar followed by Heilongjiang, Jilin, Liaoning, Shandong, Beijing, Hubei, and some northern regions i.e., Gansu, Qinghai, and western Nel Mongol. Seasonally, Xinjiang Uygur experiences high UVAI (<1.52) values during all four seasons with the dominance of both fine and coarse mode particles. Moreover, Hubei, Gansu, Jilin, Shandong, and Xinjiang Uygar experiences high UVAI values i.e. 2.55, 2.05, 1.87, 2.82, 2.27 during 2021 with an increasing trend of 5.50% year−1, 5.43% year−1, 3.82% year−1, 4.73% year−1, and 9.33% year−1 respectively. The HYSPLIT backward trajectories plotted at altitudes of 50m, 100m, and 500m indicate the dominance of desert dust over Jinan, Urumqi, and Lanzhou while anthropogenic pollutants over Wuhan and Changchun. Wavelet coherence (WTC) shows the positive correlation of UVAI with temperature and precipitation. Moreover, the co-variability of absorbing aerosols with anthropogenic and natural factors is also inculcated in this study. Considering the outcomes of this study it will help policymakers to analyze the variability of absorbing aerosols in China and different methods like plantation of more trees, efficient fuel consumption, and promotion of public transport, etc., can be used to reduce air pollution.

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