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Air pollution poses a grave threat to human health and everyday life. Accurate air-quality prediction plays a crucial role in effectively preventing and controlling air pollution. A multi-graph spatial-temporal attention network is proposed to predict the air quality in a given area by analyzing the interconnections between stations and the individual characteristics of each station. Firstly, this paper constructs multi-scale spatial-temporal graphs from spatial and temporal perspectives to effectively capture the interconnections between stations, thereby comprehensively understanding the spatial-temporal relationships among them. Secondly, incorporating the nonlinear temporal correlation of station data, this paper proposes a temporal multi-graph attention-fusion module to integrate information from both neighboring stations and the station itself. Finally, predictions are made using a spatial-temporal graph network. The experiments in this paper were based on air-quality data from Beijing and Tianjin, and the following experimental results were obtained. (1) The proposed model outperforms the baselines significantly in both single-step and multi-step prediction tasks. (2) The ablation study confirms that the graph constructed in this paper contributes to improving the performance of the model. (3) Comparing the performance of attention components, the proposed attention mechanism exhibits better performance than other attention mechanisms. [ABSTRACT FROM AUTHOR]

Pollution havens create environmental inequality issues. China's recent policy of directing high-pollution firms to migrate out of its capital, Beijing, offers a case of a pollution haven that was mandated, rather than resulting from firms' responses to environmental regulation. More importantly, it leads to a question that has been less discussed in previous literature: does a pollution haven strike back? After the highly polluting enterprises moved out of Beijing to neighboring Hebei province, Hebei has been blamed for the continuing poor air quality in Beijing, based on a perception that the pollutants emitted in Hebei are being blown back to Beijing. This paper investigates the existence of a pollution haven in Hebei and quantifies the contribution of Hebei's emissions to Beijing's air quality. We find that the proportion of output values and the relative concentration of high-pollution industries in Hebei increased when the large-scale out-migration began, while the indicators in Beijing decreased. We also find higher emissions of enterprises in Hebei than their counterparts in Beijing. Together, these findings indicate the existence of a "manmade" pollution haven. Further, based on high-frequency spatial data, we combine an Autoregressive Distributed Lag model and a Spatial Autoregressive Model and decompose the factors that affect Beijing's air quality into local and regional transport of air-borne pollutants. We find a significant effect of the neighboring cities' air pollution on Beijing's air quality. This indicates that the pollution haven indeed struck back. These findings imply the importance of regional policies to control air pollution. [ABSTRACT FROM AUTHOR]

Persistent PM 2.5 pollution poses a serious threat to human health. Developing an accurate urban regional PM 2.5 forecasting is of practical significance for environmental protection. However, previous studies have mostly focused on individual monitoring stations, neglecting the influence of neighboring stations, which limits forecasting accuracy. Additionally, the PM 2.5 of a single monitoring station cannot reflect the overall situation of a region. Therefore, this paper develops a novel PM 2.5 spatiotemporal forecasting framework that combines graph convolutional module, temporal convolutional module, linear module. It enables the forecasting of PM 2.5 concentrations at multiple stations and multiple time steps in the future. Concretely, we utilize a mixed graph convolutional network to extract the spatial features of PM 2.5. Then, an improved temporal convolutional network, the second-order residual temporal convolutional network, is developed to capture complex temporal features. Following the classical "linear and non-linear" modeling strategy, a linear module is added to the forecasting framework. Experiments on the real air pollution dataset from Beijing demonstrate that our framework outperforms the state-of-the-art baselines. • Extracting and fusing spatiotemporal features of PM2.5 is crucial for prediction. • A novel spatiotemporal forecasting framework is proposed. • A second-order residual TCN is developed to extract complex temporal features. • Experiments on real air pollution dataset show our framework's superiority. [ABSTRACT FROM AUTHOR]

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]

• In Beijing, the lockdown has led to a sharp drop in road traffic. • Road traffic reduced by 46.9 %, while PM2.5 and NO 2 reduced by 5.6 % and 29.2 % respectively. • The Maximum Possible Contribution Rate of road traffic to PM2.5 and NO 2 are 11.9 % and 62.3 %, respectively. In Beijing, the lockdown imposed to curb the spread of COVID-2019 has led to a sharp drop in road traffic. This provides an opportunity to quantify the contribution rate of road traffic to PM2.5 and NO 2 concentrations. This paper creatively puts forward the concept of the Maximum Possible Contribution Rate (MPCR) and estimates the MPCR of road traffic to PM2.5 and NO 2 by analyzing the daily air pollution data and road traffic data in Beijing from January 24 to March 31, 2020 and the same period in 2019. The findings of this paper include: The decrease in SO 2 concentration during the lockdown indicates a reduction in pollutant emissions from industry and households. During the lockdown, road traffic in Beijing reduced by 46.9 %, while the concentrations of PM2.5 and NO 2 in the atmosphere reduced by 5.6 % and 29.2 % respectively. The MPCR of road traffic to PM2.5 and NO 2 concentrations are 11.9 % and 62.3 %, respectively. The concentration of O 3 did not increase significantly with the decrease of PM2.5 and NO 2 concentrations. The findings of this paper provide a reference for city managers to evaluate the contribution rate of Beijing's road traffic to air pollutants and to formulate reasonable emission reduction policies. [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]

Highlights • The concentration of PM10 and PM2.5 were monitored in real-time at subway stations in Beijing. • The characteristics were analysed using General linear model (GLM) and correlation approaches. • The average overall PM concentration ratio inside subway station is about 68.7%, lower than that in outdoor condition. • In the station hall and platform, the real-time PM concentrations varied periodically. • Outdoor environment is mutually correlated with PM concentrations. Abstract The particulate matters (PM10 and PM2.5) inside urban subway stations greatly influence indoor air quality and passenger comfort. This study aims to analyze and interpret the concentrations of PM10 and PM2.5, measured in several subway stations from October 9th to 22nd, 2016 in Beijing, China. The overall methodology was based on the Statistical Package for Social Science (SPSS) software while General linear model (GLM) and correlation analysis were further applied to examine the sensitivities of different variables to the particle concentrations. The data analysis showed the average overall mass ratio of PM concentrations inside subway station is about 68.7%, much lower than outdoor condition (79.6%). In the areas of the station hall and platform, the real-time PM10 and PM2.5 concentrations varied periodically. In working and operation offices, all rooms had much higher PM concentrations than the outdoor environment when its pollution level was level 3, in which the facility room reached the highest level, while the closed meeting room had the lowest. Correlation analysis results indicated that PM10 and PM2.5 concentrations were mutually correlated (average R2 = 0.854), and a strong linear correlation (R2 = 0.897) of the subway-station PM concentrations to the outdoor PM conditions, regardless of the outdoor atmospheric PM concentrations pollution level was. Nevertheless, the impact of passenger number and temperature & humidity on the station PM concentrations was less, when compared to the outdoor environment. This paper is expected to provide useful information for further research and design of effective prevention measures on PM in local subway stations, towards a more sustainable and healthier built environment in the city underground. [ABSTRACT FROM AUTHOR]

It is widely believed air pollution is an obstacle to cycling as it has negative effects on cyclists’ health outcomes and deteriorates their cycling experiences. However, the empirical studies investigating the impact of air pollution on cycling behaviour remains scarce. The aim of this paper is to fill the gap by looking at Beijing as a case study. The authors conducted a survey of 307 cyclists on the days with different levels of air quality in terms of concentration of PM 2.5 in 2015. The results show that in the polluted weather, those who persist in cycling are more likely to be male, over 30 years old, lower income or those who travel short distances. Specifically, female cyclists have a higher tendency to shift from cycling to public transit than the males and medium and high-income earners are more likely to shift to using a car than low income earners. The residents’ subjective perceptions of safety and comfort have major effects on their cycling behaviour. A higher perception of comfort and safety is related to a higher possibility of continuing cycling when air quality became polluted. Cycling for commuting trips is less likely to be replaced by other modes than cycling for non-commuting trips, such as shopping. Results of this study reveal that improving air quality in a metropolitan area such as Beijing has co-benefits of cycling renaissance. The huge investments into cycling infrastructure should be integrated with policies designed to create an attractive environment for cycling. [ABSTRACT FROM AUTHOR]

Since 2006, Beijing lowered its public transit fares as a way to improve air quality. However, Beijing increased public transportation fare prices from December 28, 2014, and commuters pay for the distance they traveled rather than a flat fare. This paper explores the effect of Beijing public transit fares increase on air quality. We collect daily data of air pollution and weather variables and use synthetic control method of Abadie and Gardeazabal (2003) to select control units. We then estimate a difference-in-differences model and assess the effect of the policy on air quality index (AQI). We find a 16.28% increase in air pollution in short run. However, we find no longer-run effect on air quality. [ABSTRACT FROM AUTHOR]

Nowadays, air pollution has become a major challenge in urban management despite rapid economic development. Meanwhile, vehicle exhaust has gradually turned into the main source of air pollution in the city. To reduce air pollution, many measures have been taken including the odd-and-even license plate rule in some cities. However, it is difficult to evaluate the effectiveness of those measures. In view of this, based on the Davis method, this article has taken Beijing as its subject and built an odd-and-even license plate model by a probabilistic modelling method and the analysis of means, thus to quantify the pollution caused by vehicle exhaust emissions and the actual effect of the license plate limitation rule. This paper also examines the relationship between the license plate limitation rule and urban air pollution control and to see whether, or not, the rule exerts a positive influence on air pollution control. The results showed that the odd-and-even license plate rule has positive impacts on air pollution control in the short-term; however, the influence of the limitation policy gradually diminishes and disappears as the overall number of cars increases. Therefore, it is suggested to tackle air pollution in a broader and more effective way. [ABSTRACT FROM AUTHOR]

Over the past decades, China has been facing severe airborne pollution associated with atmospheric fine particulate matter (PM 2.5 ). Much attention has been paid to the physical transport of PM 2.5 emissions. However, the embodied emissions, namely the emissions transferred through economic activities, have seldom been investigated. In this paper, embodied emission of PM 2.5 from each sector of Beijing is quantified based on input–output analysis (IOA). Forty-two economic sectors from the input–output table are aggregated into fifteen components. Furthermore, the mutual interactions and control relationship within those sectors have been revealed by using ecological network analysis (ENA) to identify the dominant sectors. The results show that, in 2010, 34% of the total PM 2.5 emissions, or 59.4 kt PM 2.5 , were indirect emissions traded through economic sectors within Beijing. According to the results of ENA, we found that “Smelting & Pressing of Metals“, “Metal Products” and “Nonmetal Mineral Products“ are the top three sectors with the highest control levels while “Agriculture”, “Catering Services” and “Residential Services” are the lowest-ranking sectors among the system. The network confirms that sectors related to heavy industry are the dominant sectors driving the embodied PM 2.5 emissions in the whole system. Compared to the conventional approaches for tracking PM 2.5 emissions, ENA may provide a practical way to reveal the mechanisms of embodied PM 2.5 emission flows via socioeconomic activities from a holistic perspective. [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]

• A novel deep learning model is proposed for PM 2.5 prediction. • A new correlation calculation method is proposed to construct dynamic adjacency matrices. • Two real-world datasets are selected to verify the performance of models. Air pollution is a serious threat to both the ecological environment and the physical health of individuals. Therefore, accurate air quality prediction is urgent and necessary for pollution mitigation and residents' travel. However, few existing models are established based on the dynamic spatiotemporal correlation of air pollutants to predict air quality. In this paper, a novel deep learning model combining the dynamic graph convolutional network and the multi-channel temporal convolutional network (DGC-MTCN) is proposed for air quality prediction. To efficiently represent the time-varying spatial dependencies, a new spatiotemporal dynamic correlation calculation method based on gray relation analysis is proposed to construct dynamic adjacency matrices. Then, the spatiotemporal features are sufficiently extracted by the graph convolutional network and the multi-channel temporal convolutional network. Two real-world air quality datasets collected from Beijing and Fushun are applied to verify the performance of our proposed model. The experimental results show that compared with other baselines, the DGC-MTCN model has excellent prediction accuracy. Especially for the prediction of multi-step and different stations, our model performs better temporal stability and generalization ability. [ABSTRACT FROM AUTHOR]

With a decrease in fine particulate matter (PM 2.5) due to the implementation of air pollution prevention-control measures, ozone (O 3) has risen in China especially in urban areas recently. It becomes more and more important to control air combined pollution with high concentration of PM 2.5 and O 3. We analyzed the characteristics of air combined pollution in Beijing from 2014 to 2019 in this paper. Results indicated that the trough O 3 showed an upward trend of 4.5 μg m−3 yr−1, while the peak decreased by −1.1 μg m−3 yr−1. There were three typical atmospheric chemical stages in the interaction between O 3 and PM 2.5 in summer through observations. The first was an intense photochemical stage with the synchronous and rapid growth of O 3 and PM 2.5. The O 3 concentration varied from 68 μg m−3 to 206 μg m−3 when PM 2.5 ≤ 35 μg m−3. While secondary inorganic ions (SNA) ranged from 2 to 21 μg m−3 especially SO 4 2− (0.8–12 μg m−3). The second was an aerosol chemical generation stage with the rapid increase of PM 2.5 and SNA under high O 3 concentration. O 3 was maintained at a high concentration between 198 μg m−3 and 241 μg m−3 when PM 2.5 was within the range of 35–125 μg m−3. SNA increased from 12 μg m−3 to 96 μg m−3. The third was a high PM 2.5 pollution stage suppressing the generation of O 3 when PM 2.5 ≥ 125 μg m−3. O 3 began to decline with the growth of PM 2.5 and decreased from 224 μg m−3 to 130 μg m−3. Ultraviolet radiation (UV) decreased from 13 MJ m−3 in the first to 8 MJ m−3 in this stage. [Display omitted] • The O 3 trough increased by 4.5 μg/m3 and the peak decreased by −1.1 μg/m3 per year in Beijing. • There were three typical processes in the interaction between O 3 and PM 2.5 in summer day. • The photochemical process was intense in dry-sunny days with the synchronous growth of O 3 and PM 2.5. • Aerosol chemical process intensified with a rapid growth of PM 2.5 and SNA in high O 3. • O 3 began to decline with the growth of PM 2.5 when PM 2.5 attenuated UV in a high level. [ABSTRACT FROM AUTHOR]

In the recent past Beijing has experienced rapid development. This growth has been accompanied by many problems including traffic congestion and air pollution. Understanding what stimulates urban growth is important for sustainable development in the coming years. In this paper, we first estimate a binary auto-logistic model of land use change, using physical and socioeconomic characteristics of the location and its access to major centers within the city as predictors. We find that variables determining regional access, like time distance to the city center, the Central Business District (CBD), industrial centers, employment centers, and the transportation system, significantly impact urban land conversion. By using measures of access to predict land use change we believe that we can better understand the planning implications of urban growth not only in Beijing but other rapidly developing cities. [ABSTRACT FROM AUTHOR]

China's most critically air polluted area, the Beijing-Tianjin-Hebei (BTH) region has attracted significant attention; the regional linkage of environmental indicators in BTH has been widely studied in recent years. The city-level air pollutants and economic benefits hidden in trade activities are still poorly understood. This paper examines air pollutants and economic benefits embodied in BTH's consumption based on a city-level input-output table. Our results show that transfer flows of air pollutants and value-added between different cities lead to the unequal exchange of air pollution and economic benefits in BTH. Beijing gains more value-added (38.40%) through trade compared with its atmospheric pollutant equivalents (APE, 1.75%) induced by consumption demand in BTH. Conversely, Tangshan, Shijiazhuang, and Handan emit more air pollutants compared with the benefits they gain through trade. The results provide evidence for the establishment of compensation mechanisms between cities. • Trade between cities lead to the unequal exchange of air pollution and economic benefits in BTH. • Beijing gains a higher proportion of value added compared with its air pollutants induced by trade. • Cities in Hebei province gain lower proportions of value added compared with their air pollutants induced by trade. [ABSTRACT FROM AUTHOR]

Multi-satellite sensors are capable of monitoring transport and characteristics of dust storms and changes in atmospheric parameters along their transport. The present paper discusses aerosol optical properties and meteorological parameters during major dust storm events occurred in the period 2005–2010 over Beijing, China. The back trajectory model shows that the dust is transported from the Inner Mongolia and Mongolia arid regions to Beijing. High aerosol optical depth (AOD) at the wavelength 675 nm and low Ångström exponent (AE) values in the wavelength 440–870 nm are observed during dusty days. The aerosol size distribution (ASD) in coarse mode shows a large increase in the volume during dusty days. The single scattering albedo (SSA) increases with higher wavelength on dusty days, and is generally found to be higher compared to the days prior to and after the dust events, indicating the presence of high concentrations of scattering particles due to dust storm events. The physico-chemical properties of aerosols during dusty and non dusty days show distinct characteristics as reflected from the changes in the real and imaginary parts of refractive index (RI). In addition, the CO volume mixing ratio (COVMR) from Atmospheric Infrared Sounder (AIRS) shows a pronounced decrease on dusty days, while the H 2 O mass mixing ratio (H 2 OMMR) shows enhanced signal. Furthermore, enhanced level of water vapor (WV) using Moderate Resolution Imaging Spectroradiometer (MODIS) data is also observed in and around Beijing over the dust storms track. [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]

In this paper, we apply the method of Granger causality which is more accurate than classical correlation analysis method to determine whether the main air pollutants — Nitrogen oxides (NO x ), SO2 (Sulfur Dioxide), CO (carbon monoxide), TSP (total suspended particulates), PM10 (particulate matter smaller than 10 microns) and the mortality of respiratory diseases of the residents in Beijing have causal relationship. After ensuring NO x , SO2 and CO as the responsible substances, we use the time series method to construct the autoregressive integrated moving average model (ARIMA) of the pollutants, so that we could predict the amount of the pollutants from 2005 to 2008. Then we use the predicted value of pollutants as the input of the neural network model and obtain the output as the change of the death rate of respiratory diseases from 2005 to 2008. In the end, reducing the amount of pollutants by 10% and inputting the data in the neural network model, we make the prediction to evaluate the level of the pollutants and concluded that NO x is the most important pollutant to control. [Copyright &y& Elsevier]

Particulate air pollution is a serious problem in Beijing. The annual concentration of particulate matter with aerodynamic diameter less than 10 μm (PM10), ranging from 141 to 166 μg m−3 in 2000–2004, could be very harmful to human health. In this paper, we presented the mortality and morbidity effects of PM10 pollution based on statistical data and the epidemiological exposure–response function. The economic costs to health during the 5 years were estimated to lie between US$1670 and $3655 million annually, accounting for about 6. 55% of Beijing''s gross domestic product each year. The total costs were apportioned into two parts caused by: the local emissions and long-range transported pollution. The contribution from local emissions dominated the total costs, accounting on average for 3. 60% of GDP. However, the contributions from transported pollution cannot be neglected, and the relative percentage to the total costs from the other regions could account for about 45%. An energy policy and effective measures should be proposed to reduce particulate matter, especially PM2. 5 pollution in Beijing to protect public health. The Beijing government also needs to cooperate with the other local governments to reduce high background level of particulate air pollution. [Copyright &y& Elsevier]

• A mathematic model is built to evaluate the air pollution of heating consumption. • The coal substitution policy can reduce the PM2.5 concentrations by about 20 μg/m3. • Effect of renewable energy and its development to air quality is considered. • PM2.5 concentration in 2030 will drop by about 27% in Northern China compared to 2016. The Air Pollution Prevention and Control Action Plan proposed in 2013 has contributed to the fossil fuel reduction in central heating, but the consumption of scattered coal in rural areas should not be overlooked as it accounts for nearly 50% pollutant emissions during the heating season. Air Pollution Prevention and Control Action Plan for Beijing-Tianjin-Hebei and Surrounding Areas was issued to solve the problem of scattered coal consumption and improve the heating energy structure in 2017. In this paper, an air quality-heating model is established to evaluate the air quality improvement potential and far-reaching influence of scattered coal consumption reduction in rural areas. Distinct from other air quality evaluation models, the proposed model is to evaluate the regional air quality related to the heating factors. The results indicate that the coal substitution policy can reduce PM2.5 concentrations in the heating season by about 20 μg/m3 in Beijing-Tianjin-Hebei and surrounding areas. Furthermore, predictions about the variation of PM2.5 concentration in the heating season are conducted, which indicate that the PM2.5 concentration of Beijing, Tianjin, Hebei and surrounding areas in 2030 will drop by 48%, 35%, 29% and 23% respectively compared to that in 2016. The reduction could be further enhanced to be 55%, 40%, 32% and 27% with the consideration of clean power development. This study is conducive to evaluate the clean heating technologies role in improving the environment and air quality. [ABSTRACT FROM AUTHOR]

The types of aerosols are essential for estimating radiative forcing effects, improving the lidar retrieval algorithms, and identifying aerosol sources. However, it is difficult to categorize the aerosols with similar optical properties based on lidar techniques. In this study, an approach was proposed to distinguish desert dust (DD), mineral dust (MD), air pollution aerosols (APA), and their mixtures (DD&MD, DD&APA, MD&APA, DD&MD&APA). We established the fluorescence–Mie​ polarization lidar (FMPL) system to categorize three types of main aerosols using the customized fluorescence-to-Mie ratio (FMR) and volume depolarization ratio (VDR) in the experiments from September 2017 to December 2018. Then, the mixture of the aerosols was classified by combining backward trajectory analysis. The feasibility of this method was verified through five typical cases in the paper, and it is proved that the method could be employed to study air pollution issues based on FMPL, which could provide references for the meteorological investigation. • We established a fluorescence–Mie polarization lidar system. • We defined Fluorescence-to-Mie ratio (FMR). • Aerosols were divided into seven types according to their FMR and VDR. [ABSTRACT FROM AUTHOR]

• Perceived pollution hazards exert the negative effect on life satisfaction. • Perceived pollution hazards provide the channel for mediating the greenness-satisfaction association. • Interaction effects between perceived pollution hazards and exposure to greenness on life satisfaction vary across social and spatial gradients. Urbanisation from the developing world has been phenomenal and renewed the interest of studying the connection between urban greenness and subjective wellbeing. This paper responds to this greenness-wellbeing connection by shifting its focus towards systematically exploring the influences of urban greenness, perceived pollution hazards, and their interaction terms on subjective wellbeing. Using a combination of green view data and individual survey data in Beijing, we find that perceived pollution hazards about the disposal of waste, polluted water, and air pollution have significant interaction effects with eye-sensored greenness exposures on subjective wellbeing. Findings of this study suggest that policies geared towards mitigating particular domains of pollution hazards and improving green landscape should work together for shaping people's quality of life. [ABSTRACT FROM AUTHOR]

Air pollution, such as PM 2.5 (particulate matter with an aerodynamic equivalent diameter of less than 2.5 μm), PM 10 (particulate matter with an aerodynamic equivalent diameter of less than 10 μm), NO x , and SO x , is a global concern because it may cause many chronic and fatal diseases, especially in developing countries. To better address air pollution problems, an important step is the timely and accurate prediction of air quality. Traditional methods are mainly based on meteorological data, regression model data, remote sensing data and different retrieval methods. Numerous studies on deep learning methods have suggested that these approaches may be able to perform accurate predictions for complex systems. In this paper, a long short-term memory (LSTM) approach for predicting air quality is proposed; moreover, meteorological data are used and Chinese social media is investigated as a proxy for public perceptions and responses for air quality prediction. We gathered daily air quality data, meteorological data and Weibo check-in data for Beijing, China from January 1, 2015 to December 31, 2016. The average sentiment of the related Weibo posts was selected as the public response proxy. The performance of our proposed model is evaluated based on real data. The root-mean-square error (RMSE) and the mean absolute error (MAE) indicated that our method presented better prediction results than traditional methods in terms of the PM 2.5 , PM 10 , O 3 , NO 2 , SO 2 and CO concentrations. We focused on the prediction performance during the 2015 China Victory Day Parade period, during which social and political factors played an important role in air quality predictions. The results indicated that the proposed method, which incorporates public response data, was especially suitable for predicting the air quality in extreme short-term social events and provides a timely social measurement and feedback for environmental problems. • Social media were selected as a proxy for public perceptions for air quality. • LSTM model was proposed for air quality prediction. • Weibo data was suitable for air quality prediction during short-term social events. [ABSTRACT FROM AUTHOR]

Pollutant gases and particulate matters (PM) from livestock facilities can affect the health of animals and farm workers and lead to great social environmental risks. This paper presents a comprehensive study on the characteristics of ammonia (NH 3), nitrogen oxides (NO x), sulfur dioxide (SO 2) and PM (including PM 2.5 and PM 10) in a 100,000-bird manure-belt layer house in suburb Beijing for three typical seasons of summer, autumn and winter. Indoor air was sampled at an exhaust fan of the mechanically ventilated commercial house. The monitored indoor concentrations of NH 3 , NO x , SO 2 , PM 2.5 and PM 10 were 3.7–5.0 mg m−3, 17–58 μg m−3, 0–11 μg m−3, 100–149 μg m−3 and 354–828 μg m−3, respectively. The indoor NH 3 concentrations were largely influenced by the manure removal frequency. The NO x and SO 2 were mainly sourced from the ambient air, and the NO x was also partly sourced from manure decomposition in summer. The indoor PM 2.5 and PM 10 were largely sourced from the ambient air and the indoor manure, respectively. The abundant indoor NH 3 caused significantly higher NH 4 + concentration in the indoor PM 10 (7.98 ± 9.04 μg m−3) than that in the ambient PM 10 (3.48 ± 3.52 μg m−3). Secondary inorganic ions (SO 4 2−, NO 3 − and NH 4 +) totally contributed 5.7% and 14.6% to the indoor and ambient PM 2.5 , respectively; they contributed 2.8% and 8.9% to the indoor and ambient PM 10 , respectively. Organic carbon was the main component of the PM and accounted for 26.6% and 41.5% of the indoor PM 2.5 and PM 10 , respectively. Heavy metal elements (Zn, Cu and Cr) were likely transported from feed to manure and finally accumulated in the PM. Given the high emission potential, the air pollutants from animal production suggested potential risks for human health. Image 1 • Indoor NO x , SO 2 and PM 2.5 were mainly sourced from the ambient air. • Indoor NO x was also partly sourced from manure decomposition in summer. • Indoor PM 10 had higher NH 4 + concentration than ambient PM 10 (7.98 vs. 3.48 μg m−3). • Secondary inorganic ions contributed 5.7% (14.6%) to the indoor (ambient) PM 2.5. • Zn, Cu, Cr likely transported from feed to manure and finally accumulated in PM. Sources for aerial pollutants of NH3, NOx, SO2, PM2.5 and PM10 in layer house were identified. The characteristics of indoor PM components including heavy metal elements and secondary inorganic ions were revealed. [ABSTRACT FROM AUTHOR]

Precipitation chemistry and atmospheric nitrogen (N) deposition are of great concern worldwide due to their close relationships with air quality and impacts on ecosystems. However, evaluation of the chemical composition of precipitation and N deposition flux in rural areas of Beijing has received little attention to date. This paper presents the chemical constituents, possible sources and wet deposition fluxes of water-soluble ions (NH 4 +, NO 3 −, SO 4 2−, Cl−, Na+, K+, Ca2+, Mg2+) in precipitation samples collected during 2017–2018 at a rural site located at northwest of Beijing city; meanwhile, dry deposition of reactive N species (gaseous ammonia, nitrogen dioxide, nitric acid, and particulate (p) NH 4 + and NO 3 −) were also quantified. During this 2-year period, the volume-weighted mean (VWM) pH of precipitation was 6.73, and all samples had pH values above 6.0. The VWM electric conductivity of precipitation and the mean sum of all measured ions was 43.8 μS cm−1 and 591.9 μeq L−1, respectively, indicating a significant impact of atmospheric pollution. Ca2+ and NH 4 + were the dominant neutralizing species for precipitation acidity. Positive matrix factorization analysis further confirmed five sources for water-soluble ions, including sea salt aging, secondary formation, agriculture, crust, and biomass burning. The annual mean wet N deposition was 4.6, 3.4 and 8.0 kg N ha−1 yr−1 for NH 4 +, NO 3 − and total inorganic N, respectively. The total dry N deposition was dominated by gaseous ammonia (11.5 kg N ha−1 yr−1). The total N deposition (wet plus dry) was 27.7 kg N ha−1 yr−1, where dry deposition contributed to 75% and wet deposition 25% of the total. The simulations from the GEOS-Chem model indicate that agricultural (fertilizer use and livestock) and nonagricultural sources (industry, power plant, and transportation) are both important contributors to total N deposition. These results could be useful in evaluating/developing emission control policies to protect the eco-environment in Beijing. • Precipitation chemistry and nitrogen (N) deposition were investigated in Beijing. • The pH values of precipitation samples were all above 6.0 and averaged 6.7. • Ion source was sea salt, secondary source, agriculture, crust, and biomass burning. • Total N deposition was 27.7 kg N ha−1 yr−1, of which 75% was from dry deposition. • Agricultural and nonagricultural sources contributed equally to total N deposition. [ABSTRACT FROM AUTHOR]

Recently, air pollution has been substantially decreased in China, including particulate matter (PM) pollution in Beijing. However, PM pollution still has a great impact on human health, as stated in several epidemiological studies. In this paper, we used a generalized additive model (GAM) to evaluate the associations between PM 2.5 and cardiovascular diseases from 2009 to 2012 in the Beijing metropolitan area. In GAM, we controlled the confounding factors, such as day of the week and long-term trends. We used the number of daily hospital emergency room (ER) visits to represent the incidence of the studied diseases. We divided the entire study group by gender (i.e., male and female) and age (i.e., 15–60 yr, 60–75 yr, and ≥75 yr). The results showed strong links were observed between increase in PM 2.5 concentration and the number of hospital ER visits for cardiovascular diseases. For each interquartile range (IQR) increase in PM 2.5 , the relative risks (RRs) of the incidence of ischemic heart disease, arrhythmia, high blood pressure, and cerebrovascular disease were 1.023 (95% CI: 1.007–1.040), 1.019 (95% CI: 0.992–1.047), 1.082 (95% CI: 0.985–1.188), 0.976 (95%CI: 0.957–0.996), and 1.038 (95% CI: 1.005–1.072), respectively. Overall, the short-term impact of PM 2.5 on the incidence of cardiovascular diseases was the strongest for the 60–75 yr subgroup. For the two different gender subgroups, the short-term impact of PM 2.5 on ischemic heart disease and high blood pressure was relatively stronger for the females than for the males, while the opposite was true for arrhythmia and cerebrovascular disease. • Particulate matter pollution has worsened recently in Beijing, China. • Short-term impacts of PM 2.5 on different cardiovascular diseases were evaluated by a generalized additive model. • Obvious lag effects of PM 2.5 were observed for different subgroups. • Relative risks of cardiovascular diseases showed the shortest lag effect for the 60–75 yr subgroup. [ABSTRACT FROM AUTHOR]

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]