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Beijing, the capital of China, has experienced rapid industrialization, urbanization and motorization in recent decades. Consequently, air pollution in Beijing, especially fine particulate matter (PM 2.5 ) pollution, has gradually become a severe environmental issue, due to the continuing growth in energy consumption and the resulting multiple pollutant emissions. In response to the increasingly serious PM 2.5 pollution, Beijing's government implemented a series of policies, measures and regulations on air pollution prevention and control and took some concrete actions to improve air quality. In this paper, firstly, we summarize China's ambient air quality standards, China's policies and regulations on air pollution prevention and control. Secondly, we illustrate historical evolution and current status of air pollution in Beijing. Finally, we introduce control measures and actions in Beijing and its surrounding areas. The paper aims to help environmental scientists and policy makers around the world understand the past and current air pollution in Beijing and control strategies and actions taken by Beijing's government. [ABSTRACT FROM AUTHOR]

With the rapid development of China's industrialization, the air pollution is becoming more and more serious. It is vital for us to predict the air quality for determining the further prevention measures of avoiding the brought disasters. In this paper, we are going to propose an approach of predicting the air quality based on the multiple data features through fusing the multiple machine learning models. The approach takes the meteorological data and air quality data for the past six days as one batch of input (the whole data set is for 46 days) and employs a multi-model fusion to provide an improved 24-hour prediction of PM2.5 pollutant concentration all over Beijing. During the above process, two focal feature groups are composed. The first focal feature group contains the historical meteorological data, while the second group includes the statistical information, the date information and the polynomial variations. Besides the two groups, we complement one million more data items by employing the time sliding means. Among the supplementary data, we select the most critical 500 features with Light Gradient Boosting Machine (LightGBM) model and send the features as the input to Gradient Boosting Decision Tree (GBDT) and LightGBM models. Meanwhile, we screen the most critical 300 features with eXtreme Gradient Boosting (XGBoost) model and send them as the input to the three prediction models. Referring to each of the models, we respectively gain the optimal parameters through grid search methods and then fuse the models' contribution with the linear weighting. The experiments indicate that the proposed approach based on the weighting fusion is better than that provided by a single modeling scheme, and the loss value is 0.4158 under the SMAPE index. • An approach of predicting air quality with fusing the multiple machine learning models. • The machine learning solutions to model PM2.5 pollutant concentration distribution feature. • The time sliding means for developing one million more data items. • The fusion of multiple models under linear weighting strategy. The experiments indicate that the approach based on the weighting fusion is better than that provided by the single modeling scheme, and the loss value is 0.4158 under the SMAPE index. [ABSTRACT FROM AUTHOR]

From 1990 to 2017, the number of vehicles in China increased sharply from 5 million to 310 million, and vehicle emissions became one of the major sources of air pollution. Among the main sources of PM 2.5 in Beijing, the contributions from vehicle sources increased from 5.6% (2000) to 31.1% (2012) to 45% (2017). In 2010, the CO, hydrocarbon (HC), NO x , and particulate matter (PM) emissions of yellow-label vehicles (YLVs) accounted for 46.1%, 47.9%, 63.1% and 86.1% of the total emissions from on-road vehicles. In recent years, China has been implementing a YLV scrappage subsidy policy to control air pollution, and this policy has cost several hundred billion yuan nationwide. However, China has not evaluated the costs and benefits of the policy. Many people question whether the YLV scrappage subsidy policy is worthwhile, but all the stakeholders involved complicate the process of accurately evaluating the true value of the policy. In this study, we evaluated and compared the costs and benefits of the policy in the Beijing-Tianjin-Hebei (BTH) region based on 1.362 million YLVs from 2008 to 2015. Because of the implementation of the policy, the PM 2.5 , PM 10 , NO x , CO and HC emissions in the BTH region decreased by 16.46, 18.12, 174.44, 669.11, and 91.64 kt, respectively, in 2008–2015. The results show that the policy played an important role in reducing pollutant emissions and improving environmental quality in the BTH region. The costs, benefits, and net benefits of the policy were 13.69, 34.03, and 20.34 billion yuan , respectively. The cost-benefit ratio in the BTH region was 1:2.49; at the city scale, Beijing ranked first (1:7.27), and the cost-benefit ratios of Tianjin and Hebei Province were 1:2.06 and 1:1.14, respectively. Of the 13 cities in the BTH region, Beijing had the highest net benefits, amounting to 16.43 billion yuan , while Handan City has the lowest net benefits, amounting to −0.58 billion yuan. The results may provide scientific support for the promotion of older vehicle and diesel vehicle scrappage subsidy policies, the development of cost-benefit analysis (CBA), and effective environmental decision-making in China. • Based on net number of scrappage YLVs, net benefits of the policy are calculated. • This paper estimates costs and benefits of the policy based on 1.27 million YLVs. • The costs, benefits, and net benefits are 13.69, 34.03, and 20.34 billion yuan. • The results help China promote vehicle scrappage subsidy policy on a large scale. • The results promote the development of the cost-benefit analysis in China. [ABSTRACT FROM AUTHOR]

Measurements of speciated atmospheric mercury play a key role in identifying mercury behavior in the atmosphere. In this study, we measured speciated atmospheric mercury, including gaseous elemental mercury (GEM), reactive gaseous mercury (RGM), and particulate bound mercury (PBM) (<2.5 μm), in 2015 and 2016 at an urban site in Beijing, China. The mean concentrations of GEM, RGM, and PBM were 4.70 ± 3.53 ng m−3, 18.47 ± 22.27 pg m−3, and 85.18 ± 95.34 pg m−3, respectively. The concentration of PM 2.5 significantly affected the distribution of reactive mercury between the gaseous and particulate phases. With the raising of PM 2.5 levels, PBM concentrations increased, on the contrary, the concentrations of RGM decreased gradually. The mean concentration of PBM during air-pollution events was more than three times that during clear days. During days with air pollution, the relative humidity significantly affected the gas-particle partitioning of reactive mercury. The linear relationships between gas-particle partitioning coefficient and meteorological factors (air temperature and relative humidity) were obtained over the four seasons. The data also showed that the gas-particle partitioning coefficient of reactive mercury was related to particle composition (e.g., Cl−, BC). The data present in this paper suggested the influence of anthropogenic emissions on reactive mercury in Beijing urban. And the findings will contribute to understand the gas-particle partitioning of reactive mercury and its influencing factors with complex urban pollution. Image 1 • Three speciated atmospheric mercury exhibited obviously seasonal and diurnal variations. • Increasing PM 2.5 led to a decrease in RGM levels and an increase in PBM levels. • Temperature, relative humidity, and particle composition were important factors in determining the reactive mercury gas-particle partitioning. PM 2.5 levels influenced the RGM and PBM concentrations, and the important factors that affect the reactive mercury gas-particle partitioning were discussed. [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]

Abstract: To improve the air quality during the 2008 Beijing Olympic Games, the Beijing Municipal Government enforced a series of emission control measures. This provided a valuable case study to evaluate the effectiveness of these aggressive measures on mitigating air pollution and improving the atmospheric environment. In this paper, we report the first results of the chemical and Sr isotopic compositions of the rainwater in Beijing during the 2008 Olympic year. The ionic composition of samples show that Ca2+ and NH4 + were the dominant cations, accounting for about 76–97% of the total cations, and that SO4 2− and NO3 − were the major anions, accounting for 61–91% of the total anions measured. Using Na as the indicator of marine origin and Al as that of terrestrial inputs, the proportions of sea salt and terrestrial elements were estimated from elemental ratios. The calculated results indicate that the major chemical components were mainly of non-sea-salt origin. Good correlation between Ca2+ and Sr2+ (R 2 =0.85) in rainwater samples indicates the potential of Sr as a provenance tracer for Ca. Sr2+ concentrations in rainwater samples ranged from 0.01 to 2.87μmoll−1, with 87Sr/86Sr ratios from 0.7092 to 0.7109. All of the samples had a 87Sr/86Sr ratio higher than that of seawater. The 87Sr/86Sr isotopic and elemental ratio systems show that the data set of rainwater was mainly distributed between the seawater end-member (~0.70917) and the soil dust end-member (0.7111–0.7115), and the analysis indicate that the effect of anthropogenic inputs could decrease in 2008. Comparisons of the chemical composition of the rainwater in different periods show that all chemical components, except NH4 +, had the lowest concentration levels during the Olympic Game period. The VWM concentrations of major ions in the rainwater decreased significantly during the Olympic period by up to 29% for SO4 2−, 39% for NO3 −, 38% for Ca2+, 51% for Mg2+, 57% for K+, and 44% for Na+, compared with the pre-control period. The concentrations of almost all species decreased during the 2008 Olympics, most likely due to both strict emission controls and favorable meteorological conditions. [Copyright &y& Elsevier]

Abstract: Valuation of health effects of air pollution is becoming a critical component of the performance of cost–benefit analysis of pollution control measures, which provides a basis for setting priorities for action. Beijing has focused on control of transport emission as vehicular emissions have recently become an important source of air pollution, particularly during Olympic games and Post-games. In this paper, we conducted an estimation of health effects and economic cost caused by road transport-related air pollution using an integrated assessment approach which utilizes air quality model, engineering, epidemiology, and economics. The results show that the total economic cost of health impacts due to air pollution contributed from transport in Beijing during 2004–2008 was 272, 297, 310, 323, 298 million US$ (mean value), respectively. The economic costs of road transport accounted for 0.52, 0.57, 0.60, 0.62, and 0.58% of annual Beijing GDP from 2004 to 2008. Average cost per vehicle and per ton of PM10 emission from road transport can also be estimated as 106 US $/number and 3584 US $ t−1, respectively. These findings illustrate that the impact of road transport contributed particulate air pollution on human health could be substantial in Beijing, whether in physical and economic terms. Therefore, some control measures to reduce transport emissions could lead to considerable economic benefit. [Copyright &y& Elsevier]

Multiyear inventories of vehicular emissions in Beijing, Shanghai and Guangzhou from 1995 through 2005 have been developed in this paper to study the vehicle emissions trends in China''s mega cities during the past decade. The results show that the vehicular emissions of CO, HC, NO x and PM10 have begun to slow their growth rates and perhaps even to decline in recent years due to the implementation of measures to control vehicular emissions in these cities. However, vehicular CO2 emissions have substantially increased and still continue to grow due to little fuel economy improvement. Passenger cars and large vehicles (including heavy duty trucks and buses) are the major sources of vehicular CO2 and CO emissions while large vehicles were responsible for nearly 70% and 80% of the vehicular NO x and PM10 emissions in these mega cities. Motorcycles are also important contributors to vehicular emissions in Guangzhou and Shanghai. [Copyright &y& Elsevier]

Abstract: The aggressive traffic interventions and emission control measures implemented to improve air quality during the 2008 Beijing Olympic Games created a valuable case study to evaluate the effectiveness of these measures on mitigating air pollution and protecting public health. In this paper, we report the results from our field campaign in summer 2008 on the on-road emission factors of carbon monoxide, black carbon (BC) and ultrafine particles (UFP) as well as the ambient BC concentrations. The fleet average emission factors for light-duty gasoline vehicles (LDGV) showed considerable reduction in the Olympic year (2008) compared to the pre-Olympic year (2007). Our measurement of Black Carbon (BC), a primary pollutant, at different elevations at the ambient site suggests consistent decrease in BC concentrations as the height increased near the ground level, which indicates that the nearby ground level sources, probably dominated by traffic, contributed to a large portion of BC concentrations in the lower atmospheric layer in Beijing during summertime. These observations indicate that people living in near ground levels experience higher exposures than those living in higher floors in Beijing. The BC diurnal patterns on days when traffic control were in place during the Olympic Games were compared to those on non-traffic-control days in both 2007 and in 2008. These patterns strongly suggest that diesel trucks are a major source of summertime BC in Beijing. The median BC concentration on Olympic days was 3.7 μg m−3, which was dramatically lower than the value on non-traffic-control days, indicating the effectiveness of traffic control regulations in BC reduction in Beijing. [Copyright &y& Elsevier]

In this paper, we report the results and analysis of a recent field campaign in August 2007 investigating the impacts of emissions from transportation on air quality and community concentrations in Beijing, China. We conducted measurements in three different environments, on-road, roadside and ambient. The carbon monoxide, black carbon and ultrafine particle number emission factors for on-road light-duty vehicles are derived to be 95gkg−1-fuel, 0.3gkg−1-fuel and 1.8×1015 particleskg−1-fuel, respectively. The emission factors for on-road heavy-duty vehicles are 50gkg−1-fuel, 1.3gkg−1-fuel and 1.1×1016 particleskg−1-fuel, respectively. The carbon monoxide emission factors from this study agree with those derived from remote sensing and on-board vehicle emission testing systems in China. The on-road black carbon and particle number emission factors for Chinese vehicles are reported for the first time in the literature. Strong traffic impacts can be observed from the concentrations measured in these different environments. Most clear is a reflection of diesel truck traffic activity in black carbon concentrations. The comparison of the particle size distributions measured at the three environments suggests that the traffic is a major source of ultrafine particles. A four-day traffic control experiment conducted by the Beijing Government as a pilot to test the effectiveness of proposed controls was found to be effective in reducing extreme concentrations that occurred at both on-road and ambient environments. [Copyright &y& Elsevier]

This paper presents a novel method for quantification of the total emission of gases from area sources using the mobile mini-DOAS (Differential Optical Absorption Spectroscopy) instrument, a ground based optical remote sensing technique. The presented method has been applied to measure the emission of SO2 and NO2 from the city of Beijing, P.R. China, on a time scale of approximately 2h. The measurements have been performed during two field campaigns, in April and in August 2005. The estimated emission of NO2 is roughly the same during the two field campaigns with an average emission of 189tonsd−1 during April and 174tonsd−1 during August. The estimated emission of SO2 varies greatly between the two periods, with an average emission of 293tonsd−1 during April and 52tonsd−1 during August. [Copyright &y& Elsevier]

Air pollution has become an emerging environmental issue in developing countries like China in the last two decades. Sulphur dioxide (SO 2 ) is one of the major air pollutants that poses significant risks in many areas undergoing a process of industrialization such as Beijing. Realizing the main factor causing environmental quality changes is the key to solving this problem. By using an extended version of IPAT model, this paper aims to identify the main contributors of air pollution in Beijing from 1989 to 2012. The result shows that the most influential factors affecting air pollution in Beijing are affluence and emission intensity. From analyzing the historical background, we conclude that the air pollution change in Beijing is heavily policy-driven. [ABSTRACT FROM AUTHOR]