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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]

Currently, air pollution is primarily characterized by PM 2.5 and O 3. Therefore, the co-control of PM 2.5 and O 3 has become an important task of atmosphere pollution prevention and control in China. However, few studies have been conducted on the emissions from vapor recovery and processes, which is an important source of VOCs. This paper analyzed the VOC emissions of three vapor process technologies in service stations and first proposed key pollutants for priority control based on the coordinated reactivity of O 3 and SOA. The concentration of VOCs emitted from the vapor processor was 3.14–9.95 g m−3, compared to 631.2–717.8 g m−3 for uncontrolled vapor. Alkanes, alkenes, and halocarbons accounted for a high proportion of the vapor both before and after control. Among the emissions, i -pentane, n -butane, and i -butane were the most abundant species. Then, the species of OFP and SOAP were calculated through the maximum incremental reactivity (MIR) and fractional aerosol coefficient (FAC). The average source reactivity (SR) value of the VOC emissions from three service stations was 1.9 g g−1, while the OFP ranged from 8.2 to 13.9 g m−3 and SOAP ranged from 0.18 to 0.36 g m−3. By considering the coordinated chemical reactivity of O 3 and SOA, a comprehensive control index (CCI) was proposed for the control of key pollutant species that have multiplier effects on environment. For adsorption, trans -2-butene and p -xylene were the key co-control pollutants, while toluene and trans -2-butene were the most important for membrane and condensation + membrane control. A 50% emission reduction of the top two key species that emission account for 4.3% averagely will reduce O 3 by 18.4% and SOA by 17.9%. [Display omitted] • The organized emissions of VOCs from three vapor processors were studied in service stations. • The species composition of VOCs before and after control were analyzed. • The OFP and SOAP were calculated to evaluate the secondary pollution. • The co-control of key pollutants based on SOA and O 3 has great environmental benefits. [ABSTRACT FROM AUTHOR]

Self-reported life satisfaction of China's population has not improved as much as expected during the economic boom, which was accompanied by a significant decline in environmental performance. Is environmental pollution the culprit for the lagging subjective well-being? To explore this issue, this paper adopts sentiment analysis to construct a real-time daily subjective well-being metric at the city level based on the big data of online search traces. Using daily data from 13 Chinese cities centred on Beijing between August 2014 and December 2019, we look at the corelation between subjective well-being and air pollution and the heterogeneity in this relationship based on two separate identification strategies. We find that air pollutants are negatively correlated with subjective well-being, and well-being tends to decline more from pollution during hot seasons. In addition, residents in wealthier regions tend to be more sensitive to air pollution. This result may be explained by the differences in the subjective perception of air pollution and personal preferences at different levels of income. These findings provide information about concerns of the public, thereby helping the government to take appropriate actions to respond to the dynamics of subjective well-being. • Sentiment analysis and big data are adopted to construct a daily city-level subjective well-being metric. • Higher levels of airborne particulates lead to a decline in subjective well-being. • Peoples' well-being tends to decline more from pollution during hot seasons. • Residents in wealthier regions tend to be more sensitive to air pollution. [ABSTRACT FROM AUTHOR]