Search

Risk-related information regarding air pollution can help people understand the risk involved and take preventive measures to reduce health loss. However, the health benefits through these protective behaviors and the health threat of information inequality have not been systematically measured. This article reports the health gains and losses caused by the interaction of "air pollution—air pollution information—human", and studies the heterogeneity and impact of this interaction. Based on field investigations and transfer learning algorism, this study compiled the first nationwide city-level risk-related information (ERI) response parameter set in China. Then, we developed a Information-Behavioral Equivalent PM2.5 Exposure Model (I-BEPEM) model to project the health benefits caused by the impact of environmental risk-related information on residents' protective behaviors under different scenarios. The protective behavior led by air pollution risk information reduces 5.7% PM2.5-related premature deaths per year. With a 1% increase in regional ERI reception, PM2.5-related premature mortality decreases by 0.1% on average; If the level of information perception and behavioral protection in all cities is the same as that in Beijing, PM2.5-related premature deaths will decrease by 6.9% annually in China. Further, changing the air quality standard issued by China to the American standard can reduce the overall PM2.5-related premature deaths by 9.9%. Meanwhile, compared with men, other age groups and rural residents, women, older persons, and urban residents are more likely to conceive risk information and adopt protective behaviors to reduce the risk of premature death from air pollution. Air pollution risk information can significantly reduce people's health loss. Changing the real-time air quality monitoring information indicator standard to a more stringent level can quickly and effectively enhance this effect. However, the uneven distribution of this information in regions and populations has resulted in the inequality of health gains and losses. [ABSTRACT FROM AUTHOR]

Air pollution has become an increasingly serious environmental problem in China. Especially in winter, the air pollution in northern China becomes even worse due to winter heating. The "coal to gas" policy, which uses natural gas to replace coal in the heating system in winter, was implemented in Beijing in the year 2013. However, the effects of this policy reform have not been examined. Using a panel dataset of 16 districts in Beijing, this paper employs a first difference model to examine the impact of the "coal to gas" policy on air quality. Strong evidence shows that the "coal to gas" policy has significantly improved the air quality in Beijing. On average, the "coal to gas" policy reduced sulfur dioxide ( SO 2 ), nitrogen dioxide ( NO 2 ), particulate matter smaller than 10 µm (PM10), particulate matter smaller than 2.5 µm (PM2.5) and carbon monoxide (CO) by 12.08%, 4.89%, 13.07%, 11.94% and 11.10% per year, respectively. We find that the "coal to gas" policy is more effective in areas with less energy use efficiency. The finding of this paper suggests that the government should continue to implement the "coal to gas" policy, so as to alleviate the air pollution in Beijing, China. [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]

Air pollution is a major global problem, closely related to economic and social development and ecological environment construction. Air pollution data for most regions of China have a close correlation with time and seasons and are affected by multidimensional factors such as meteorology and air quality. In contrast with classical peaks-over-threshold modeling approaches, we use a deep learning technique and three new dynamic conditional generalized Pareto distribution (DCP) models with weather and air quality factors for fitting the time-dependence of the air pollutant concentration and make statistical inferences about their application in air quality analysis. Specifically, in the proposed three DCP models, a dynamic autoregressive exponential function mechanism is applied for the time-varying scale parameter and tail index of the conditional generalized Pareto distribution, and a sufficiently high threshold is chosen using two threshold selection procedures. The probabilistic properties of the DCP model and the statistical properties of the maximum likelihood estimation (MLE) are investigated, simulating and showing the stability and sensitivity of the MLE estimations. The three proposed models are applied to fit the PM 2.5 time series in Beijing from 2015 to 2021. Real data are used to illustrate the advantages of the DCP, especially compared to the estimation volatility of GARCH and AIC or BIC criteria. The DCP model involving both the mixed weather and air quality factors performs better than the other two models with weather factors or air quality factors alone. Finally, a prediction model based on long short-term memory (LSTM) is used to predict PM 2.5 concentration, achieving ideal results. [ABSTRACT FROM AUTHOR]

Although much effort has been put into studying air pollution, our knowledge of the mechanisms of frequently occurring intense haze episodes in China is still limited. In this study, using 3 years of measurements of air pollutants at three different height levels on a 325 m Beijing meteorology tower, we found that a positive aerosol–boundary layer feedback mechanism existed at three vertical observation heights during intense haze polluted periods within the mixing layer. This feedback was characterized by a higher loading of PM 2.5 with a shallower mixing layer. Modelling results indicated that the presence of PM 2.5 within the boundary layer led to reduced surface temperature, relative humidity and mixing layer height during an intensive haze episode. Measurements showed that the aerosol–boundary layer feedback was related to the decrease in solar radiation, turbulent kinetic energy and thereby suppression of the mixing layer. The feedback mechanism can explain the rapid formation of intense haze episodes to some extent, and we suggest that the detailed feedback mechanism warrants further investigation from both model simulations and field observations. [ABSTRACT FROM AUTHOR]

Currently, Haze is one of the greatest environmental problems with serious impacts on human health in China, especially in capital region (Beijing-Tianjin-Hebei region). To alleviate this problem, the Chinese government introduced a National Air Pollution Control Action Plan (NAPCAP) with air pollutants reduction targets by 2017. However, there is doubt whether these targets can be achieved once the plan is implemented. In this work, the effectiveness of NAPCAP is analyzed by developing models of the statistical relationship between PM2.5 concentrations and air pollutant emissions (SO2, NOx, smoke and dust), while taking into account wind and neighboring transfer impacts. The model can also identify ways of calculating the intended emission levels in the Beijing–Tianjin–Hebei area. The results indicate that haze concentration control targets will not be attained by following the NAPCAP, and that the amount of progress needed to meet the targets is unrealistic. A more appropriate approach to reducing air emissions is proposed, which addresses joint regional efforts. [ABSTRACT FROM AUTHOR]