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The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction's mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively. [ABSTRACT FROM AUTHOR]

During the "Parade Blue" period in 2015, Beijing and its surrounding areas implemented mandatory temporary source control strategies, which provided experimental conditions for studying the effects of source emission control measures on the sources of atmospheric PM2.5. Analyzing the source contribution rate of the main particulate matters via the source analysis method of the receptor model is an important method for studying the atmospheric pollution. Previous studies on the "Parade Blue" period only discussed the change in PM2.5 concentration during the source control period and the source non-control period, but did not select appropriate emission sources and acceptor components to analyze the contribution ratio of each emission source to fine particulate matter pollution. In this paper, based on the receptor component spectrum and emission source component spectrum, the chemical mass balance receptor model was used for source analysis. The results showed that outdoor PM2.5 concentration was 26.31 μg/m3 in the source control period, which was less than the 40.08 μg/m3 in the source non-control period. In the source control period, motor vehicle emissions, industrial combustion and urban fugitive dust contributed significantly to the fine particulate pollution, accounting for 76–81%, 8–9% and 11–16%, respectively. In the source non-control period, the contributions of motor vehicle emissions and industrial combustion accounted for 57–59% and 41–43%, respectively, significantly higher than that of urban fugitive dust, which accounted for less than 2%. The correlation between the contribution rate of emission sources and the source control strategy was studied and analyzed during the "Parade Blue" period, and the effectiveness of the source control strategy was proved. [ABSTRACT FROM AUTHOR]

In recent years, China has implemented several measures to improve air quality. The Beijing-Tianjin-Hebei (BTH) region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years. How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future. To evaluate the changes in major air pollutant emissions over this region, this paper employs ensemble Kalman filtering (EnKF) for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System (NAQPMS) simulation data to inversely estimate the emission rates of SO2, NOX, CO, and primary PM2.5 over BTH region in February from 2014 to 2019. The results show that SO2, NOX, CO, and primary PM2.5 emissions in the BTH region decreased in February from 2014 to 2019 by 83%, 37%, 41%, and 42%, while decreases in Beijing during this period were 86%, 67%, 59%, and 65%, respectively. Compared with the prior emission inventory, the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region, with simulated root mean square errors of the monthly average concentrations of SO2, NOX, PM2.5, and CO reduced by 41%, 30%, 31%, and 22%, respectively. The average uncertainties of SO2, NOX, PM2.5, and CO inversion emissions in 2014–19 are ±14.03% yr−1, ±28.91% yr−1, ±126.15% yr−1, and ±43.58% yr−1. Compared with the uncertainty of MEIC emission, the uncertainties of all species changed by +2% yr−1, −2% yr−1, −26% yr−1, and −4% yr−1, respectively. The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions. The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014. The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory. [ABSTRACT FROM AUTHOR]

With the accelerating urbanization process in China, the extensive economic growth model with high energy consumption and high pollution aggravates atmospheric environmental pollution and restricts the sustainable development of the social economy. The Beijing–Tianjin–Hebei region has the most severe haze pollution and the most acute contradiction between resources, environment and economic development. Based on the physical coupling theory, this paper constructs a coupling coordination degree model of the economy, environment and high haze pollution industries, such as the thermal power industry. Through literature analysis and expert investigation of the economic, industrial and environmental data of the Beijing–Tianjin–Hebei region from 2006 to 2018, the coordinated development of the economy, environment and high haze pollution industries, such as the thermal power industry, in the above region, was dynamically monitored. The monitoring results show that the development level of the Beijing, Tianjin and Hebei region is significantly different, and the three subregions have not entered the stage of coordinated development. Moreover, since 2015, they have been in a bottleneck period of development and have not achieved a higher level of progress. The three subregions have different advantages and restraining factors. For example, the backward level of Hebei's economy and high haze pollution industries, such as the thermal power industry, restrict the coordinated development of the region as a whole. Only by accelerating the high-quality development of the Hebei economy can we break through the bottleneck of the development of Beijing, Tianjin and Hebei. [ABSTRACT FROM AUTHOR]

Air pollution is a growing problem and poses a challenge to people's healthy lives. Accurate prediction of air pollutant concentrations is considered the key to air pollution warning and management. In this paper, a novel PM2.5 concentration prediction model, CBAM-CNN-Bi LSTM, is constructed by deep learning techniques based on the principles related to spatial big data. This model consists of the convolutional block attention module (CBAM), the convolutional neural network (CNN), and the bi-directional long short-term memory neural network (Bi LSTM). CBAM is applied to the extraction of feature relationships between pollutant data and meteorological data and assists in deeply obtaining the spatial distribution characteristics of PM2.5 concentrations. As the output layer, Bi LSTM obtains the variation pattern of PM2.5 concentrations from spatial data, overcomes the problem of long-term dependence on PM2.5 concentrations, and achieves the task of accurately forecasting PM2.5 concentrations at multiple sites. Based on real datasets, we perform an experimental evaluation and the results show that, in comparison to other models, CBAM-CNN-Bi LSTM improves the accuracy of PM2.5 concentration prediction. For the prediction tasks from 1 to 12 h, our proposed prediction model performs well. For the 13 to 48 h prediction task, the CBAM-CNN-Bi LSTM also achieves satisfactory results. [ABSTRACT FROM AUTHOR]

In recent years, the Beijing–Tianjin–Hebei region has become one of the worst areas for haze pollution in China. Sun photometers are widely used for aerosol optical property monitoring due to the advantages of fully automatic acquisition, simple maintenance, standardization of data processing, and low uncertainty. Research sites are mostly concentrated in cities, while the long-term analysis of aerosol optical depth (AOD) for the pollution transmission channel in rural Beijing is still lacking. Here, we obtained an AOD monitoring dataset from August 2017 to March 2019 using the ground-based CE-318 sun photometer at the Gucheng meteorological observation site in southwest Beijing. These sun photometer AOD data were used for the ground-based validation of MODIS (Moderate Resolution Imaging Spectroradiometer) and AHI (Advanced Himawari Imager) AOD data. It was found that MODIS and AHI can reflect AOD variation trends by sun photometer on daily, monthly, and seasonal scales. The original AOD measurements of the sun photometer show good correlations with satellite observations by MODIS (R = 0.97), and AHI (R = 0.89), respectively, corresponding to their different optimal spatial and temporal windows for matching with collocated satellite ground pixels. However, MODIS is less stable for aerosols of different concentrations and particle sizes. Most of the linear regression intercepts between the satellite and the photometer are less than 0.1, indicating that the errors due to surface reflectance in the inversion are small, and the slope is least biased (AHI: slope = 0.91, MODIS: slope = 0.18) in the noon period (11 a.m.–2 p.m.) and most biased in summer (AHI: slope = 0.77, MODIS: slope = 1.31), probably due to errors in the aerosol model. The daily and seasonal variation trends between CE-318 AOD measurements in the Gucheng site and fine particulate observations from the national air quality site nearby were also compared and investigated. In addition, a typical haze–dust complex pollution event in North China was analyzed and the changes in AOD during the pollution event were quantified. In processing, we use sun photometer and satellite AOD data in combination with meteorological and PM data. Overall, this paper has implications for the study of AOD evolution patterns at different time scales, the association between PM2.5 concentrations and AOD changes, and pollution monitoring. [ABSTRACT FROM AUTHOR]

Air pollution caused by coal burning not only increases the cost of environmental pollution but also harms human health. It is urgent for China to change the practice of coal-fired central heating. Therefore, the effectiveness and sustainability of the Coal to Gas and Electricity policy have become the focus of all sectors of society. In this paper, eight cities in the Beijing–Tianjin–Hebei region were taken as the experimental groups and the other eleven cities as the control groups. Based on the PSM-DID model and the time-varying DID model, a quasi-natural experimental analysis was conducted to evaluate the effect of the policy of coal to gas and electricity to improve air quality in the Beijing–Tianjin–Hebei region from 2015 to 2020 and to test the sustainability of the policy. Three research conclusions are shown below: First, during the implementation of the policy, especially in 2019, the AQI index decreased significantly. Although there was a rebound thereafter, it was still lower than before. This shows that the Coal to Gas and Electricity policy has indeed improved the air quality in Beijing, Tianjin, and Hebei during its implementation. Second, the policy had a great impact on SO2 and PM10 but was relatively weak on PM2.5 and CO. Therefore, there is an urgent need to formulate scientific and accurate policies to control different air pollutants. Third, the time-varying DID model was used to identify the dynamic sustainability effect of the Coal to Gas and Electricity policy. The results showed that the policy had a strong impact in the initial stage, but its effect was greatly reduced at the end of the implementation or near the end, when it was far less obvious than in the initial stage of the policy. Therefore, in formulating relevant measures to reduce air pollution, it is necessary to fully consider the sustainability of the policy. [ABSTRACT FROM AUTHOR]

This paper examines whether mega-events-initiated planning regulations improved air quality in Chinese cities and explores the driving forces of the enforcement of such regulations. Using the 2008 Beijing Olympics as an example, we find that mega-events serve as an opportunity for cities to combat air pollution. The 2008 Olympics prompted a handful of Chinese cities to cut pollution and sustain a blue sky: Cities with air quality regulated for the Olympics cut their Air Pollution Index by about 16 points during the Games, compared to non-regulated cities, and 60% of that effect remained four years after the event. These achievements are obtained through effective mobilization of city leaders by associating air quality with their political careers. This study reveals that 1) a mega-event may improve urban environmental quality beyond the host cities and the event period, and 2) successful implementation of environmental regulations hinges on incentivizing local leaders. [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]

Limited evidence was seen as the association between fine particulate matter (PM2.5) and physician visits for allergic rhinitis (AR), especially in countries with extreme air pollution exposure. This paper addressed the issues about the association between PM2.5 and daily outpatient visits for AR among individuals residing in Beijing, China. Data on daily outpatient visits for AR obtained from Beijing Medical Claim Data for Employees and daily PM2.5 concentrations available from US embassy reports were linked by date from January 1, 2010, to June 30, 2012. A time-series analysis was conducted with a generalized additive Poisson model to assess the association between PM2.5 and AR, adjusting for daily average temperature, relative humidity, day of the week, calendar time, and public holiday. Totally, 229,685 outpatient visits for AR were included in the analysis. The daily mean (SD) concentration of PM2.5 was 99.5 (75.3) μg/m3 during the study period. We found that a 10-μg/m3 increase in PM2.5 content was associated with a 0.47% (95% CI: 0.39% to 0.55%) increase in the number of outpatient visits on the same day. Furthermore, results from subgroup analyses suggested that the association was consistently significant among the groups of different ages (< 65 years and ≥ 65 years) and gender. However, this study failed to find a statistically significant association in the autumn season but found significant positive associations during the spring and summer seasons (P for interaction < 0.001). This study indicated a possible association between PM2.5 and AR outpatients, which may benefit further researches in studying PM2.5 and its influence on diseases in a real and seriously air-polluted context. [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]

The study and control of air pollution involves measuring the structure of the atmospheric boundary layer (ABL) to understand the mechanisms of the interactions occurring between the atmospheric boundary layer and air pollution. Beijing, the capital of China, experienced heavy haze pollution in December 2016, and the city issued its first red-alert air pollution warning of the year (the highest PM 2.5 concentrations were later found to exceed 450 µgm-3). In this paper, the vertical profiles of wind, temperature, humidity and the extinction coefficient (reflecting aerosol concentrations), as well as ABL heights and turbulence quantities under heavy haze pollution conditions, are analyzed, with data collected from lidar, wind profile radar (WPR), radiosondes, a 325 m meteorological tower (equipped with a 7-layer ultrasonic anemometer and 15-layer low-frequency wind, temperature, and humidity sensors) and ground observations. The ABL heights obtained by three different methods based on lidar extinction coefficient data (Hc) are compared with the heights calculated from radiosonde temperature data (Hθ), and their correlation coefficient can reach 72 %. Our results show that Hθ measured on heavy haze pollution days was generally lower than that measured on clean days without pollution, but Hc increased from clean to heavy pollution days. The time changes in friction velocity (u*) and turbulent kinetic energy (TKE) were clearly inversely correlated with PM 2.5 concentration. Momentum and heat fluxes varied very little with altitude. The nocturnal sensible heat fluxes close to the Earth surface always stay positive. In the daytime of the haze pollution period, sensible heat fluxes were greatly reduced within 300 m of the ground. These findings will deepen our understanding of the boundary layer structure under heavy pollution conditions and improve the boundary layer parameterization in numerical models. [ABSTRACT FROM AUTHOR]

The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep learning network. The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality. We evaluate our approach with extensive experiments based on real data sources obtained in Beijing, China. Experiments show that DAL is superior to the peer models from the recent literature when solving the topics of interpolation, prediction, and feature analysis of fine-gained air quality. [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]

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]

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]

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]

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]

The heavy fog and smog in Beijing lets citizens and policy makers reflect on the moral and political ramifications of the rapid economic growth, and determine a strong policy towards a green future. This paper first investigates the serious impact of the fog and smog, and the response of citizens and municipality, then discusses the possible causes of this disastrous fog and smog, and reflects on why and how developing countries like China should accommodate a green future. In discussing the case of heavy fog and smog in Beijing, I aim to show that disastrous environmental incidents related to the rapid economic growth are beneficial to the sustainable development of developing countries like China, and the urge to survive and to live a good life will drive people to take the common requirements and interests of the social community, including future generations, into moral consideration, and guarantee the possibility of a green future, regardless of the possible huge costs. [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]

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]

Many studies have shown that air pollutants have complex impacts on urban precipitation. Meteorological weather station and satellite Aerosol Optical Depth (AOD) product data from the last 20 years, combined with simulation results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), this paper focuses on the effects of air pollutants on summer precipitation in different regions of Beijing. These results showed that air pollution intensity during the summer affected the precipitation contribution rate (PCR) of plains and mountainous regions in the Beijing area, especially in the plains. Over the past 20 years, plains PCR increased by ~10% when the AOD augmented by 0.15, whereas it decreased with lower pollution levels. In contrast, PCR in mountainous areas decreased with higher pollution levels and increased with lower pollution levels. Our analysis from model results indicated that aerosol increases reduce the effective particle size of cloud droplets and raindrops. Smaller cloud raindrops more readily transport to high air layers and participate in the generation of ice-phase substances in the clouds, increasing the total amount of cloud water in the air in a certain time, which ultimately enhanced precipitation intensity on the plains. The removal of pollutants caused by increased precipitation in the plains decreased rainfall levels in mountainous areas. [ABSTRACT FROM AUTHOR]

In Beijing, the probability of snowfall is decreasing as a result of global warming. At the same time, Beijing has suffered severe air pollution. In this paper, the concentration and particle size characteristics of BC (Black Carbon) in snow during the winter of 2015 in Beijing were analyzed by the SP2 method. The average concentration of BC in snow meltwater in Beijing is 82 ng/mL, with a minimum value of 62.9 ng/mL and a maximum of 210.6 ng/mL. The BC particle size in snow and ice in the Beijing area is mostly concentrated in the range of 70–400 nm. After log-normal, the BC particle size above 600 nm is still small, which should be closely related to the nature of the local BC emission source. The concentration of BC in snow is highly susceptible to meteorological conditions and local pollution levels. When Beijing is under the control of the east wind or the southeast wind, aerosols in the urban areas can easily accumulate in the northwestern mountains and then settle or participate in the snowfall process, resulting in an increase in BC aerosol accumulation in the snow, thus further changing the optical properties of snow in the Beijing area. [ABSTRACT FROM AUTHOR]

The concentration of PM2.5 is an important index to measure the degree of air pollution. When it exceeds the standard value, it is considered to cause pollution and lower the air quality, which is harmful to human health and can cause a variety of diseases, i.e., asthma, chronic bronchitis, etc. Therefore, the prediction of PM2.5 concentration is helpful to reduce its harm. In this paper, a hybrid model called CNN-BiLSTM-Attention is proposed to predict the PM2.5 concentration over the next two days. First, we select the PM2.5 concentration data in hours from January 2013 to February 2017 of Shunyi District, Beijing. The auxiliary data includes air quality data and meteorological data. We use the sliding window method for preprocessing and dividing the corresponding data into a training set, a validation set, and a test set. Second, CNN-BiLSTM-Attention is composed of the convolutional neural network, bidirectional long short-term memory neural network, and attention mechanism. The parameters of this network structure are determined by the minimum error in the training process, including the size of the convolution kernel, activation function, batch size, dropout rate, learning rate, etc. We determine the feature size of the input and output by evaluating the performance of the model, finding out the best output for the next 48 h. Third, in the experimental part, we use the test set to check the performance of the proposed CNN-BiLSTM-Attention on PM2.5 prediction, which is compared by other comparison models, i.e., lasso regression, ridge regression, XGBOOST, SVR, CNN-LSTM, and CNN-BiLSTM. We conduct short-term prediction (48 h) and long-term prediction (72 h, 96 h, 120 h, 144 h), respectively. The results demonstrate that even the predictions of the next 144 h with CNN-BiLSTM-Attention is better than the predictions of the next 48 h with the comparison models in terms of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). [ABSTRACT FROM AUTHOR]

• 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]

Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between the PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations and explanatory variables. Spatial correlation has not been effectively incorporated into prediction models, therefore exhibiting poor performance in PM2.5 prediction tasks. Additionally, determining the manner by which to expand longer-term prediction tasks is still challenging. In this paper, to allow for spatiotemporal correlations, a spatiotemporal convolutional recursive long short-term memory (CR-LSTM) neural network model is proposed for predicting the PM2.5 concentrations in long-term prediction tasks by combining a convolutional long short-term memory (ConvLSTM) neural network and a recursive strategy. Herein, the ConvLSTM network was used to capture the complex spatiotemporal correlations and to predict the future PM2.5 concentrations; the recursive strategy was used for expanding the long-term prediction tasks. The CR-LSTM model was used to realize the prediction of the future 24 h of PM2.5 concentrations for 12 air quality monitoring stations in Beijing by configuring both the appropriate time lag derived from the temporal correlations and the spatial neighborhood, including the hourly historical PM2.5 concentrations, the daily mean meteorological data, and the annual nighttime light and normalized difference vegetation index (NDVI). The results showed that the proposed CR-LSTM model achieved better performance (coefficient of determination (R2) = 0.74; root mean square error (RMSE) = 18.96 μg/m3) than other common models, such as multiple linear regression (MLR), support vector regression (SVR), the conventional LSTM model, the LSTM extended (LSTME) model, and the temporal sliding LSTM extended (TS-LSTME) model. The proposed CR-LSTM model, implementing a combination of geographical rules, recursive strategy, and deep learning, shows improved performance in longer-term prediction tasks. [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]

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]

Abstract: Several days of heavy pollution weather occurred in Beijing, Tianjin and Hebei province during December 16 to 21, 2016, many places issued red warnings of heavy air pollution. In this paper, the characteristics of the pollution process, the relationship between the pollution process and meteorological conditions, and the structure of the boundary layer are analyzed in detail by using the ground routine observation data of China Meteorological Administration, the MINI-MPL data, the wind profiler radar data and the atmospheric composition data of the Ministry of Ecology and Environment. The results show that the atmosphere is in a static and stable state, and the lower atmosphere is prevalent in the southerly airflow. The continuous increase of atmospheric humidity and the special terrain around the mountains on three sides of Beijing which is not conducive to the diffusion of pollutants are the important factors causing the serious air pollution in Beijing. During the period of heavy pollution, the pollutants mainly accumulate below 800 meters. When the pollution is serious, the height of pollutants is only about 400 meters. During the pollution weather process, the depolarization ratio is less than 0.25, reflecting that the main pollution is the aerosol generated by human activities. In the early stage, primary particulate matter is the main pollutant, and in the later stage, secondary transformation particulate matter is the main pollutant. The depolarization ratio showed obvious diurnal variation in the early stage of pollution process, and the depolarization ratio was higher in daytime than at night..Wind field retrieved wind profile radar shows that the two rapid rising stages of PM2.5 concentration during the polluted weather process are accompanied by persistent southerly wind. When the southerly wind speed reaches 6-8m/s at 800-1200m altitude, it is conducive to the continuous transport of regional pollution to Beijing. When the wind speed exceeds 12m/s, the vertical diffusion of the atmosphere increases significantly, while the pollutant concentration shows a fluctuating change. [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]