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The mechanism of aerosol pollution transport remains highly elusive owing to the myriad of influential factors. In this study, ground station data, satellite data, ground-based LiDAR remote sensing data, sounding data, ERA5 reanalysis and a backward trajectory model were combined to investigate the formation process and optical properties of winter aerosol pollution in Beijing and surrounding areas. The analysis of ground station data shows that compared to 2019 and 2021, the pandemic lockdown policy resulted in a decrease in the total number of pollution days and a decrease in the average concentration of particulate matter in the Beijing area in 2020. The terrain characteristics of the Beijing-Tianjin-Hebei (BTH) made it prone to northeast and southwest winds. The highest incidence of aerosol pollution in Beijing occurs in February and March during the spring and winter seasons. Analysis of a typical heavy aerosol pollution process in the Beijing area from 28 February to 5 March 2019 shows that dust and fine particulate matter contributed to the primary pollution; surface air temperature inversion and an average wind speed of less than 3 m/s were conducive to the continuous accumulation of pollutants, which was accompanied by the oxidation reaction of NO2 and O3, forming photochemical pollution. The heavy aerosol pollution was transmitted and diffused towards the southeast, gradually eliminating the pollution. Our results provide relevant research support for the prevention and control of aerosol pollution. [ABSTRACT FROM AUTHOR]

This paper investigated the dynamic relationship between ife expectancy (LE) and its inflencing factors including, health care, socioeconomic, and environment factors in Beijing City of China. Time-series data from 2000 to 2018 were collected and vector autoregression (VAR) modeling was performed. This study quantified the lagged effect of air pollution and socioeconomic factors on increased LE over the 19 years of the study period in Beijing. The results showed that a VAR model with optimum lag 3 was constructed between LE and three explanatory variables including per capita area of green land (AGL), the average wage of employed staff and workers (WAGE), and PM2.5. In addition, LE showed a decrease from its present value once the impulse of AGL and WAGE are given. In contrast, LE increase from its present value after the impulse of PM2.5 given in the ten-year period analysis. In sum, environment factors such as air pollution and area of green land are considered to be highly effective in influencing LE than socioeconomic factors in Beijing City. [ABSTRACT FROM AUTHOR]