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There has been controversy regarding the air pollutants emitted from sources closely related to the activities of daily life in China, such as cooking, setting off fireworks, sacrificial incense and joss paper burning, and barbecue (which have been named the five missing sources – FMSs), and the extent to which they impact the outdoor air quality. To date, due to the lack of both an activity dataset and emission factors, there have been no estimations of the emission of air pollutants from FMSs. In this work, we have attempted to combine questionnaire data, various statistical data, and data on points of interest to obtain a relatively complete set of activity data. The emission factors (EFs) of carbonaceous aerosols were tested in our lab. Emission inventories of carbonaceous aerosols with a high spatial–temporal resolution for FMSs were then established, and the spatial variation trend and driving forces were discussed. From 2000 to 2018, organic carbon (OC) emissions were in the range of 4268–4919 t. OC emissions from FMSs were between 1.5 ‰ and 2.2 ‰ of the total emissions in China. Black carbon (BC), elemental carbon (EC), and brown carbon (BrC) absorption cross-section (ACS BrC) emissions from FMSs were in the ranges of 22.6–43.9 t, 213–324 t, and 14.7–35.6 Gm 2 , respectively. Their emissions tended to be concentrated in certain periods and areas. The OC emission intensities in central urban areas were 3.85–50.5 times those in rural areas (due to the high density of human activity), while the ACS BrC emissions in rural regions accounted for 63.0–79.5 % of the total emissions (resulting from uncontrolled fireworks). A mass of fireworks led to much higher ACS BrC and EC emissions on Chinese New Year's Eve, with respective values that were 1444 and 262 times their corresponding yearly averages. Significant (p<0.01) correlations between the population's income and pollutant emissions were also found: these correlations were positive (r=0.94) and negative (r=-0.94) for urban and rural regions, respectively, indicating the necessity to regulate residents' lifestyles and increase residents' income in respective urban and rural regions. This study provides firsthand data to identify emissions, variation trends, and impacting factors from the FMSs. This is helpful for modeling works on air quality, the climate effect, and human health risks during specific periods or in specific regions as well as for modifying emission control policies. The data compiled in this work can found at 10.6084/m9.figshare.19999991.v2 (Cheng et al., 2022). [ABSTRACT FROM AUTHOR]

There has been controversy about the air pollutants emitted from sources closely related to people's daily life (such as cooking, fireworks burning, sacrificial incenses and joss paper burning, and barbecue, named as five missing sources, FMS) impacting the outdoor air quality to what extent. Till now, there is no emission estimation of air pollutants from FMS, as the missing of both activity dataset and emission factors. We attempted to combine the questionnaire data, various statistical data, and data of points of interest to obtain a relatively complete set of activity data. The emission factors (EFs) of carbonaceous aerosols were tested in our lab. Then, the emission inventories of carbonaceous aerosol with the high spatial-temporal resolution for FMS were established firstly, and the spatial-variation trend and driving forces were discussed. From 2000 to 2018, organic carbon (OC) increased from 4268 t to 4919 t. The OC emission from FMS was 1.48-2.18 - of its total emission in China. The emissions of black carbon, element carbon (EC), and brown carbon absorption cross-section (ACSBrC) emissions from FMS were in the ranges of 22.6-43.9 t, 213-324 t, and 14.7-35.6 Gm2, respectively. Their emissions tended to concentrate in special periods and areas. The OC emission intensities in central urban areas were 3.85-50.5 times that of rural areas due to the high density of human activities. While the ACSBrC emissions in rural regions accounted for 63.0-79.5% of the total emission result from uncontrolled fireworks burning. A mass of fireworks burning led to extremely higher ACSBrC and EC emissions on Chinese New Year's eve, as 1444 and 262 times their corresponding yearly average values. Interestingly, significant (p<0.01) correlations between human incomes and pollutant emissions were found, but they were positive (r = 0.94) and negative (r = -0.94) for urban and rural regions, indicating the necessity of regulating human lifestyle and increasing income for urban and rural peoples, respectively. This study provided the first-hand data for identifying the emissions, variation trends and impacting factors of FMS, which is helpful for modeling works on air quality, climate effect, and human health risks at specific periods or regions and for modifying their emission control policies. The data in this work could be found at https://doi.org/10.6084/m9.figshare.19999991.v1 (Cheng et al., 2022). [ABSTRACT FROM AUTHOR]

Day by day pollution in cities is increasing due to urbanization. One of the biggest challenges posed by the rapid migration of inhabitants into cities is increased air pollution. Sustainable Development Goal 11 indicates that 99 percent of the world's urban population breathes polluted air. In such a trend of urbanization, predicting the concentrations of pollutants in advance is very important. Predictions of pollutants would help city administrations to take timely measures for ensuring Sustainable Development Goal 11. In data engineering, imputation and the removal of outliers are very important steps prior to forecasting the concentration of air pollutants. For pollution and meteorological data, missing values and outliers are critical problems that need to be addressed. This paper proposes a novel method called multiple iterative imputation using autoencoder-based long short-term memory (MIA-LSTM) which uses iterative imputation using an extra tree regressor as an estimator for the missing values in multivariate data followed by an LSTM autoencoder for the detection and removal of outliers present in the dataset. The preprocessed data were given to a multivariate LSTM for forecasting PM2.5 concentration. This paper also presents the effect of removing outliers and missing values from the dataset as well as the effect of imputing missing values in the process of forecasting the concentrations of air pollutants. The proposed method provides better results for forecasting with a root mean square error (RMSE) value of 9.8883. The obtained results were compared with the traditional gated recurrent unit (GRU), 1D convolutional neural network (CNN), and long short-term memory (LSTM) approaches for a dataset of the Aotizhonhxin area of Beijing in China. Similar results were observed for another two locations in China and one location in India. The results obtained show that imputation and outlier/anomaly removal improve the accuracy of air pollution forecasting. [ABSTRACT FROM AUTHOR]

The weekend effect, i.e., the concentration of air pollutants is different from weekends to weekdays, has been explored since the 1970 s. In most studies, the weekend effect is referred to the change of O 3 , i.e., lower emission of NO x on weekends leads to a higher concentration of O 3 . Determining whether it is true can provide insight into the strategy of air pollution controlling. In this study, we explore the weekly cycle of cities of China based on the weekly cycle anomaly (WCA), which is proposed in this paper. The advantage of using WCA is that we can avoid the influence of other change components such as the daily cycle and the seasonal cycle. The p values of the significant tests from all cities are analyzed for obtaining a whole picture of the weekly cycle of air pollution. The result shows that the concept of the weekend effect is not proper for cities in China, as many cities reach the valley phase of the emission on weekdays but not weekends. Thus, researches should not pre-assume that the weekend is the low emission scenario. We focus on the anomaly of O 3 on the peak and the valley of the emission scenario estimated by the NO 2 concentration. Through the analysis of the distribution of the p values of all cities, we show that almost all cities in China have weekly cycle of O 3 corresponding to the weekly cycle emission of NO x , i.e., O 3 of the peak time of NO 2 is less than valley time. The cities of the strong weekly cycle are located in four regions, i.e., the Beijing-Tianjing-Hebei region, the Shandong Peninsula Delta, the Yangtze River Delta, and the Pearl River Delta, and these regions are also the regions with relatively severe pollution levels. [ABSTRACT FROM AUTHOR]

Background: Air pollution is an important factor affecting residents' health. Methods: Based on the health data of 9959 residents from 28 provinces in China in the 2017 Chinese General Social Survey (CGSS), this paper uses a hierarchical linear model (HLM) to scientifically analyze the impact of air pollution on residents' health and to explore the moderating role of air pollution in various factors influencing residents' health. Results: Air pollution has significant negative effects on residents' health (coef. −0.103, p < 0.01), while frequency of exercise (coef. 0.070, p < 0.01), education level (coef. 0.012, p < 0.1) and income level (coef. 0.383, p < 0.01) have positive effects on residents' health. Air pollution has a negative moderating effect on the positive impact of exercise frequency on residents' health (coef. −0.015, p = 0.004). Conclusions: Air pollution causes serious damage to residents' health and it is necessary to control air pollution. Increasing residents' income, education level and exercise frequency can help improve residents' health level. Air pollution significantly reduce the positive impact of exercise frequency on residents' health. In order to effectively improve their own health, it is a better choice for residents to exercise when the air quality is good. [ABSTRACT FROM AUTHOR]

Air pollution control policies in China have been experiencing profound changes, highlighting a strategic transformation from total pollutant emission control to air quality improvement, along with the shifting targets starting from acid rain and NO x emissions to PM 2.5 pollution, and then the emerging O 3 challenges. The marvelous achievements have been made with the dramatic decrease of SO 2 emission and fundamental improvement of PM 2.5 concentration. Despite these achievements, China has proposed Beautiful China target through 2035 and the goal of 2030 carbon peak and 2060 carbon neutrality, which impose stricter requirements on air quality and synergistic mitigation with Greenhouse Gas (GHG) emissions. Against this background, an integrated multi-objective and multi-benefit roadmap is required to provide decision support for China's long-term air quality improvement strategy. This paper systematically reviews the technical system for developing the air quality improvement roadmap, which was integrated from the research output of China's National Key R&D Program for Research on Atmospheric Pollution Factors and Control Technologies (hereafter Special NKP), covering mid- and long-term air quality target setting techniques, quantitative analysis techniques for emission reduction targets corresponding to air quality targets, and pathway optimization techniques for realizing reduction targets. The experience and lessons derived from the reviews have implications for the reformation of China's air quality improvement roadmap in facing challenges of synergistic mitigation of PM 2.5 and O 3 , and the coupling with climate change mitigation. [Display omitted] [ABSTRACT FROM AUTHOR]

Abrupt air pollution accidents can endanger people's health and destroy the local ecological environment. The appropriate emergency response can minimize the harmful effects of accidents and protect people's lives and property. This paper provides an overview of the key emergency response technologies for abrupt air pollution accidents around the globe with emphasis on the major achievements that China has obtained in recent years. With decades of effort, China has made significant progress in emergency monitoring technologies and equipment, source estimation technologies, pollutant dispersion simulation technologies and others. Many effective domestic emergency monitoring instruments (e.g., portable DOAS/FT-IR systems, portable FID/PID systems, portable GC-MS systems, scanning imaging remote sensing systems, and emergency monitoring vehicles) had been developed which can meet the demands for routine emergency response activities. A monitoring layout technique combining air dispersion simulation, fuzzy comprehensive evaluation, and a post-optimality analysis was proposed to identify the optimal monitoring layout scheme under the constraints of limited monitoring resources. Multiple source estimation technologies, including the forward method and the inversion method, have been established and evaluated under various scenarios. Multi-scale dynamic pollution dispersion simulation systems with high temporal and spatial resolution were further developed. A comprehensive emergency response platform integrating database support, source estimation, monitoring schemes, fast monitoring of pollutants, pollution predictions and risk assessment was developed based on the technical idea of "source identification - model simulation - environmental monitoring" dynamic interactive feedback. It is expected that the emergency response capability for abrupt air pollution accidents will gradually improve in China. [Display omitted] [ABSTRACT FROM AUTHOR]

Photochemical air quality models (AQMs) are a vital tool for atmospheric pollution research and have been widely used in various applications, such as air quality prediction and evaluation of pollution control strategies. Before using these models for further studies, it is essential to thoroughly evaluate their reliability and accuracy. While previous guidelines and benchmarks have primarily focused on fine particulate matter (PM 2.5) and ozone (O 3), there is still a lack of benchmarks for evaluating the model performance on primary criteria pollutants such as sulfur dioxide (SO 2), nitrogen dioxide (NO 2), carbon monoxide (CO), and particulate matter with aerodynamic diameter less than or equal to 10 μm (PM 10). The use of air quality models in China has increased significantly in the past decades. However, there is still a lack of standardized benchmarks for the performance evaluation of these models. Building upon our previous work on PM 2.5 and its chemical species, we propose a set of benchmarks for evaluating the performance of the aforementioned four air pollutants. Initially, we identified a total of 475 papers published during 2007–2019 that utilized at least one of the five commonly used AQMs in China. From these papers, we selected 164 articles that provided model performance evaluation (MPE) results of the four primary air pollutants. The three most frequently used Model Performance Evaluation (MPE) metrics were selected to analyse the impact of different model configurations on the reported statistics, including modelling region, season, and emission inventory. Lastly, three commonly used statistical indicators, including normalized mean bias (NMB), normalized mean error (NME), and correlation coefficient (R), were proposed for the validation of simulated NO 2 , SO 2 , CO, and PM 10. Two sets of benchmarks are given, including the "goal" and "criteria". The "goal" represents the best range of performance that a model can be expected to achieve, and the "criteria" represents performance that the majority of studies have achieved. We recommend R values above 0.50, 0.35, 0.45, and 0.40 for NO 2 , SO 2 , PM 10 , and CO, respectively, in order to meet the "criteria" benchmark. If the "goal" benchmark is to be achieved, the corresponding R values are 0.60, 0.55, 0.60, and 0.60. The "goal" benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are within ±20%, ±25%, ±20%, and ±25%, respectively; while the "goal" benchmarks of NME for the four pollutants are less than 40%, 45%, 45%, and 60%, respectively. These benchmarks supplement our previous benchmarks for PM 2.5 and its components and provide a more comprehensive guideline for the air quality modelling community in China. • A set of benchmarks for evaluation of the NO 2 , SO 2 , CO and PM 10 simulation over China are proposed. • Three commonly used statistical indicators (NMB, NME, and R) were proposed for the validation of the four pollutants. • The goal benchmarks of NMB for NO 2 , SO 2 , PM 10 , and CO are <20%, <25%, <20% and <25%, respectively. [ABSTRACT FROM AUTHOR]

The concept and theory of ecological compensation (EC) have been widely studied in the past 20 years, but its practical effects have not yet been agreed upon. Based on a panel data from 281 prefecture-level cities in China from 2009 to 2018, this paper examines the impact of air ecological compensation (AEC) policy on air pollutant emissions and its mechanism with time-varying DID model. The study found that AEC will reduce the emissions of sulfur dioxide, PM2.5, industrial smoke and dust, which remained significant after a series of robustness test. Mechanism analysis concluded that AEC can reduce air pollutant emissions mainly by promoting green technology innovation, strengthening pollution treatment intensity and improving energy efficiency. The effect of AEC varied with city characteristics, in cities with high proportion of Industrial, low environmental regulation intensity and high fiscal pressure, AEC have significant or better policy effects, while, AEC does not have significant policy effects in cities with high environmental regulations intensity and low financial pressure. China should consider further promoting the pilot scope of AEC, but it also needs to improve the compensation standard and compensation mechanism to achieve better policy effects. [ABSTRACT FROM AUTHOR]

Most conventional air pollutants and carbon dioxide (CO2) are simultaneously emitted from the same sources, providing a sound theoretical basis for the synergistic governance of these emissions. At present, China faces severe challenges in protecting the eco-environment, such as reducing conventional pollutants, improving environmental quality, and peaking CO2 emissions. Internationally, the control of greenhouse gas (GHG) emissions has been incorporated into the comprehensive environmental management system in major developed economies such as the United States (US) and the European Union (EU), where, based on GHG emissions monitoring and statistical data, decision-making support is provided for the national governments in the form of policy evaluation, thus forming a management mode that enables overall planning, coordination, and unified regulatory at the national level and participation of multiple departments under the national government. The synergistic control of conventional air pollutants and GHGs is becoming an important measure to strengthen the environmental management and achieve low-carbon development. This paper summarizes the synergistic effects of reducing air pollution and carbon emissions from the aspects of synergistic policies, strategic planning, and institutional systems, and conducts an analysis based on existing synergistic practices. [ABSTRACT FROM AUTHOR]

Haze is the phenomenon of visibility degradation caused by extinction effects related to the physicochemical properties of atmospheric particulate matter (APM). Atmosphere heterogeneous reactions can alter the physicochemical properties of APM. Therefore, it is important to understand the atmospheric heterogeneous reactions of APM in order to reveal the cause of haze. Herein, the current situation, developmental trend, source, and composition of APM pollution in China are reviewed. Additionally, we introduce the reaction characteristics and key chemical processes of common inorganic, organic, and mixed pollutant gases on the surface of mineral particles. The effects of mineral particulate matter on aggregation, regulation, and catalysis in the formation of atmospheric aerosols and the synergistic reaction mechanism of SO2, NO2, O3, and VOCs on the surfaces of different mineral particles are summarized. The problems existing in the current research on heterogeneous reactions on the surfaces of mineral particles are also evaluated. This paper aims to gain a deep understanding of the mechanism of mineral particulate matter promoting the formation of secondary aerosols and attempts to provide theoretical support for effective haze control. [ABSTRACT FROM AUTHOR]

Air pollution has been one of the biggest environmental challenges for cities in their pursuit of sustainability. The removal of air pollutants usually entails a cost that can negatively impact economic activities. Assessing urban environmental performance can offer valuable insights for cities to balance their economic production and environmental protection. This paper develops a novel approach to measuring environmental performance by using the Euclidean distance function. An appealing feature of this approach is its ability in endogenously allocating the optimization pathway to each emitter, thereby avoiding arbitrary estimation results and distorted managerial implications. We apply this approach to study the environmental performance of Chinese key environmental protection cities. We find the heterogeneity in performance estimates and endogenous optimization pathways. Prioritizing the reduction of a specific type of emissions while simultaneously increasing industrial output value seems to be the most appropriate objective for the majority of cities. Our study can serve as a basis for urban governments to pinpoint the underlying factors contributing to low urban environmental performance and establish diverse objectives for enhancing environmental performance. • Environmental performance index is constructed based on endogenous directions. • A Euclidean distance function is used to identify endogenous directions. • Optimization pathways and environmental performance show heterogeneity among cities. • Mitigating NO x emissions can improve environmental performance significantly. [ABSTRACT FROM AUTHOR]

[Display omitted] Particulate matter (PM) is an important air pollutant in subway systems. Exposure to high PM concentrations poses a threat to human health and raises concerns. Piston effect can provide the outdoor fresh air to subway stations through station exits, but it also brings PM from tunnels to platforms when the train enters the station. In this paper, a transient numerical model is developed according to an actual subway station and validated by field tests. The model is used to investigate PM 10 concentration at platform induced by train movement. The cumulative PM 10 concentration at platform in one day is calculated according to the train schedule. Platform screen door (PSD) with adjustable vents is studied to control the PM 10 level at platform and energy saving in 27 cities of five climatic zones in China are discussed. The results show that adjustable vent height should be less than 0.70 m to ensure that PM 10 concentrations at platform can be maintained at an acceptable level and the requirement of fresh air can be met. Moreover, energy consumption can be reduced by more than 70 % in different cities compared with the conventional mechanical ventilation system. The optimized adjustable vent can provide an important reference for improving indoor air quality (IAQ) and saving energy consumption of ventilation systems in underground facilities. [ABSTRACT FROM AUTHOR]

Air pollution is an inevitable negative environmental externality in the process of industrialization and urbanization in countries around the world. Land misallocation in the process of urbanization and industrialization and the resulting urban sprawl exacerbates the above problems. As a major country in the stage of rapid urbanization and industrialization, China also faces severe air pollution problems. Therefore, this paper will systematically study the impact of land misallocation and urban sprawl on air pollution. We first set up the utility maximization problem of each economic entity in the process of land misallocation in the mathematical model, and form a general equilibrium theory model to analyze the relationship between land misallocation, urban sprawl and air pollution, and then put forward the research hypotheses. We further use the big data of land market transactions of China Land Market Network and LandScan global population data to build the land misallocation index and urban sprawl index respectively, and use an extended mediating effect model to empirically test the hypotheses. The results suggest that the effect of land misallocation on air pollution is significantly positive, and the impact of land misallocation on urban sprawl largely depends on the return rate of land relative to labor (RRIRL). When the RRIRL is relatively low, the impact is weak, or even has a negative effect to some extent. At this time, low transportation costs are conducive to reducing air pollution. On the contrary, when the RRIRL is relatively high, land misallocation has a significant role in promoting urban sprawl. At this time, high traffic costs are conducive to reducing air pollution. In addition, we also find that there is an inverted U-shaped relationship between urban sprawl and air pollution. When the urban sprawl level is low, its impact on air pollution increases with the increase of its level, but once it exceeds a specific node, the impact will decrease. Based on the characteristics of the relationship among land misallocation, urban sprawl and air pollution under different RRIRL, we summarize four environmentally friendly urban development paths. We bring the land bid rent theory and environmental pollution research into a same theoretical model framework, which provides a modeling idea and research perspective with a micro-foundation for subsequent research on land misallocation. We integrate the research conclusions of existing literature and unify them under the research frameworks concerning different RRIRL in the empirical results, which greatly improving their applicability. The four urban development paths summarized in this paper will also provide inspiration for future researchers and policy makers. • We posit that urban sprawl is an important intermediate mechanism by which land misallocation affects air pollution. • We build a general equilibrium framework. • Land misallocation leads to an increase in air pollutant concentrations. • Land misallocation has a nonlinear impact on air pollution by restraining urban sprawl. [ABSTRACT FROM AUTHOR]

Industrial agglomeration is a major source of regional economic development and the main pattern enterprises employ after having developed to a certain stage. Industrial agglomeration also affects the emissions of air pollutants in production. Based on provincial panel data for China from 2006 to 2019, this paper introduces the full generalized least squares (FGLS) panel econometrics model. By considering spatial correlation, the potential endogenous problem has been controlled using the instrumental variable and the effects of the co-agglomeration of manufacturing and producer services on three major air pollutants, i.e., SO2, PM2.5, and NOx, have been empirically estimated. The empirical results show that: (1) The agglomeration of manufacturing increases the emission of PM2.5 in the air, while the agglomeration of producer services and the co-agglomeration of manufacturing and producer services reduce it. Moran correlation index test showed that SO2 and NOx had no significant spatial correlation. (2) The agglomeration of manufacturing, the agglomeration of producer services, and co-agglomeration exert the most significant effects on PM2.5 in the air in central and western China. This is probably because of the availability of basic natural resources in these areas. (3) The energy consumption structure mediates the effect of the agglomeration of manufacturing on PM2.5, and human capital mediates the effect of the agglomeration of producer services on PM2.5 emissions. Based on the results, policy suggestions to improve the atmospheric environment during the process of industrial agglomeration are proposed. [ABSTRACT FROM AUTHOR]

In this paper, complex network theory has been applied to reveal the transport patterns and cooperative regions of fine particulate matter (PM 2.5) over China from 2015 to 2019. The results show that the degrees, weighted degrees, and edge lengths of PM 2.5 cities follow power law distributions. We find that the cities in the Beijing–Tianjin–Hebei–Henan–Shandong (BTHHS) region have a strong ability to export PM 2.5 pollution to other cities. By analyzing the transport routes, we show that a mass of links extends southward from the BTHHS to the Yangtze River Delta (YRD) regions with 1 or 2 d time lags. Hence, we conclude that earlier emission reduction in the BTHHS region and early warning measures in the YRD region will provide better air pollution mitigation in both regions. Moreover, significant links are concentrated in wintertime, suggesting the impact of the winter monsoon. In addition, all cities have been divided into nine clusters according to their spatial correlations. We suggest that the cities in the same clusters should be regarded as a whole to control the level of air pollution. This approach is able to characterize the transport and cluster for other air pollutants, such as ozone and NOx. [ABSTRACT FROM AUTHOR]

The development of industry has brought about the pollution of the atmospheric environment. Pollution is harmful to people's health. Realizing the real-time monitoring of atmospheric environmental quality parameters can improve the above-mentioned effects. China's existing environmental monitoring systems focus on the accuracy of the system hardware itself for assessment, lack of data analysis and forecasting and early warning, and cannot provide managers and ordinary people with decision-making and activity guidance. This paper develops an IPV6-based high-spatial–temporal precision air pollutant monitoring and early warning platform. The feasibility of the system is verified through networking tests, operation tests, and early warning tests. Through actual data analysis and comparison, it is concluded that the monitoring system has field feasibility, and the atmospheric environment monitoring for the target observation area has achieved the desired observation function. This system integrates GIS technology and B/S architecture to analyze changes in the regional environment to provide support for regional environmental air quality management. The forecast and early warning module constructed by combining the weight method of the influence of different input factors on the environmental quality index and minute-level observations can provide technical support for the government to improve the level of supervision. [ABSTRACT FROM AUTHOR]

Weak institutions hinder the improvement of air quality in developing countries. This paper focuses on whether public environmental participation can correct the adverse effects of government behavior on air quality in weak institutional settings using the spatial econometric model based on China's panel data during 2003-2017. The results show that local government intervention is not conducive to environmental improvement. This adverse impact has spatial spillover effects due to competition among local governments for promotion. The public cannot rely on their own strength to form constraints on local government behavior. However, with the central government's help, public environmental participation can effectively restrain the adverse effects of improper intervention by local governments on air quality. After considering the heterogeneity of air pollutants, with the central government's assistance, public environmental participation only has a statistically significant improvement effect on sulfur dioxide. We believe that the characteristics of pollutants and local governments' strategic response to the public are the main reasons for this result. The findings indicate that local governments are mainly accountable to the central government under the performance-based appointment system. The impact of public environment participation is highly dependent on the central government. [ABSTRACT FROM AUTHOR]

The implementation of pollutant emission control has made initial achievements in the plant power, iron, and steel industries in China. To further improve air quality, it is of great significance to carry out research on zero-impact emissions of the petrochemical industry. Based on the existing concept and practice of zero emissions, this study proposes the concept of zero-impact emissions, taking emission concentration as the constraint. A typical petrochemical enterprise (namely Enterprise A) in Shanghai Chemical Industry Park as the research object, and used the CALPUFF model to simulate the target pollutant emissions (i.e. sulfur dioxide (SO2), nitrogen oxide (NOx), particulate matter (PM), and volatile organic compounds (VOCs)). The current emission standard, spatial distributions, and emission heights of chimneys in Enterprise A was considered as the baseline emission scenario and taking the zero-impact emission as a target for simulation. The results show that the current emission standards of NOx and VOCs (benzene) exceeded the zero-impact emission limits, and needed to be reduced by 22% and 87.5%, respectively. Moreover, the areas that exceeded the zero-impact concentration limits were located northwest of the chimneys and Hangzhou Bay. In terms of seasonal effects, the wind conditions in spring were more adverse for the enterprise to achieve zero-impact emissions. Based on the simulation, the zero-impact emission limits of SO2, NOx, PM, and VOCs (benzene) for Enterprise A were 50 mg/m³, 78 mg/m³, 10 mg/m³, and 0.32 mg/m³, respectively.Implications: Through case study, this paper solves the environmental management issue which is of universal significance for chemical industry park. The concept of zero-impact emissions and the determination method of zero-impact concentration limits proposed in this study could be used as references for related research on cutting emissions. Although the conclusion of this study about the emission limits is not suitable for other enterprises to apply directly, the calculation method of zero-impact emission limit can be used by other enterprises. Furthermore, the zero-impact emission limits on park scale can be determined after comprehensive evaluation based on the calculation results of multiple enterprises. [ABSTRACT FROM AUTHOR]

With the rapid development of China's industrialization, the extensive development of industrial models has led to increasing environmental pressure. In this paper, we use China as an example, combined with the Tapio elastic analysis method (Tapio model), modified gravity model, social network analysis (SNA), quadratic assignment procedure (QAP) regression and logit model, to explore a network effect on the decoupling of industrial waste gas emissions and industrial added value (industrial GDP). We use the special network structure of industrial waste gas emissions and industrial GDP to capture the network effect. The results show the following: (1) For industrial sulfur dioxide (SO 2), nitrogen oxide (NO x) and soot (dust) (SD), most provinces in China show obvious strong decoupling, and provinces with faster economic growth, such as the eastern coastal areas, have a larger proportion of strong decoupling than provinces with poor economic development. (2) According to the network analysis of the industrial GDP, SO 2 , NO x and SD, economically developed provinces such as Shanghai, Jiangsu, Zhejiang, Beijing and Guangdong are generally located at the core of the network and have a strong spatial spillover effect on other provinces; (3) The QAP regression results show that the spillover effects of industrial SO 2 , NO x and SD are largely influenced by industry structural similarities, geographic proximity, and the development of economy. (4) The logit regression results show that the decoupling of industrial waste gas emissions will be greatly affected by the spatial network structure of industrial waste gas emissions and industrial GDP. The provinces at the core of the network are more likely to undergo strong decoupling. Finally, based on the research results, this paper provides some recommendations. • A network effect on decoupling of industrial emissions and industrial added value. • The modified gravity model is used to build the network. • Provinces with faster economic development are more prone to pollution decoupling. • Provinces at the core of the network are more prone to pollution decoupling. [ABSTRACT FROM AUTHOR]

Benzene has been measured in indoor environments for many decades and has been identified to cause variety of health effects. Children spend most of their time indoors such as daycare centre, preschool and school, they are more likely to be exposed to indoor air pollutants. This paper was aimed to review the exposure to benzene among children within indoor environments from worldwide studies from 2003 to 2018. Based on 24 papers evaluated, 54% were conducted in primary schools. The highest concentration of benzene was found in preschools in China at 148.0 µg/m3. The benzene levels were found higher in indoors than outdoors for most of the studies. Active sampling techniques were used in 42% of studies that enable the determination of acute health effects on children during short-period of exposure time. Differences in sampling techniques and durations make it hard to compare the outcomes of the studies with health-effect guidelines. This review indicated a diversity of sampling approaches and techniques, pointing to the importance of establishment of standard method for collecting and reporting data. [ABSTRACT FROM AUTHOR]

Air pollution has been an environmental problem exerting serious impact on human health. An accurate prediction of air pollutant concentrations in space and time is essential to the mitigation and minimization of exposure to air pollution, particularly in cities. To take advantage of data collected at monitoring stations and the effect of secondary information such as meteorological factors, this paper proposes a flexible hierarchical Bayesian model (HBM) which can predict air pollutant concentrations in space and time. Within the framework, spatial temporal Kriging (STK) is employed for interpolation and the obtained results are used as priors. Because of the undesirable performance of the STK, the likelihood in the form of a nonlinear regression (NLR) with skewed normal residuals is employed to take into account the effect of meteorological factors to derive the posterior distribution of air pollutant concentrations. Due to high dimensionality and complexity, the formulation of the posterior distribution is non-analytic. We thus need to draw samples from the estimated parameters of the posterior distribution with Markov chain Monte Carlo (MCMC) method, and approximate the population characteristics with the sample characteristics. We evaluate the HBM with the concentrations of air pollutants and the meteorological variables from Northern China. For all pollutants, in the cross-validation (CV) experiments, the HBM reduces the root mean square error (RMSE) of NLR and STK by at least 0.08 and 0.4, and R 2 by at least 0.02 and 0.4. The empirical results show that the proposed HBM generally outperforms the NLR and STK in terms of efficiency, accuracy and robustness. The proposed framework is general enough for the analysis of spatial-temporal data of all kinds. [ABSTRACT FROM AUTHOR]

Information on the vertical distribution of air pollutants is essential for understanding their spatiotemporal evolution underlying urban atmospheric environment. This paper presents the SO 2 profiles based on ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements from March 2018 to February 2019 in Hefei, East China. SO 2 decrease rapidly with increasing heights in the warm season, while lifted layers were observed in the cold season, indicating accumulation or long-range transport of SO 2 in different seasons might occur at different heights. The diurnal variations of SO 2 were roughly consistent for all four seasons, exhibiting the minimum at noon and higher values in the morning and late afternoon. Lifted layers of SO 2 were observed in the morning for fall and winter, implying the accumulation or transport of SO 2 in the morning mainly occurred at the top of the boundary layer. The bivariate polar plots showed that weighted SO 2 concentrations in the lower altitude were weakly dependent on wind, but in the middle and upper altitudes, higher weighted SO 2 concentrations were observed under conditions of middle-high wind speed. Concentration weighted trajectory (CWT) analysis suggested that potential sources of SO 2 in spring and summer were local and transported mainly occurred in the lower altitude from southern and eastern areas; while in fall and winter, SO 2 concentrations were deeply affected by long-range transport from northwestern and northern polluted regions in the middle and upper altitudes. Our findings provide new insight into the impacts of regional transport at different heights in the boundary layer on SO 2 pollution. [Display omitted] [ABSTRACT FROM AUTHOR]

A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3) for the period 2013–2018 at high spatial (15km×15km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R2=0.52 –0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4 –39.3 µg/m3 for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM 2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R2=0.74 –0.86 and RMSE =16.8 –33.6 µg/m3 in different cities). Furthermore, through the comparison to satellite-estimated PM 2.5 concentrations, we show that the accuracy of the PM 2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at 10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at 10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users. [ABSTRACT FROM AUTHOR]

Low-level temperature inversions play crucial roles in inhibiting vertical exchange of energy and mass, and may lead to air pollutants accumulation. The climatological study of them provides a fundamental overview of the static stability of atmosphere and is indispensable for air pollution controls. Based on high-vertical-resolution radiosondes at 00 and 12 UTC during 2011–2018, this work comprehensively examined the spatial and temporal variability of three characteristics (occurrences, strengths, and depths) of low-level inversions in China. Results revealed that inversions are prevalent (more than 80% of the observation records) over Eastern China. The northern part of Eastern China is dominated by the thick strong surface-based inversions with median thickness and strength of 200 m and 3 K, while the southern part by thin weak elevated ones of 80 m and 0.8 K. Tibetan Plateau experiences rare inversions (less than 50% of the time) which tend to be strong thick surface-based inversions with median depth and intensity of 170 m and 4 K. Inversion depths and strengths are positively correlated. The three properties of inversions present remarkable seasonal variations. Generally, inversions are strongest, thickest, and most frequent in winter and least so in summer, with median intensity and depth ranging from 0.3 K and 80 m to 5 K and 220 m. This paper also analyzed the monthly variations of inversions of four representative stations (Harbin, Urumqi, Beijing, and Chongqing) in details. These four cities are known for frequent severe air pollution events. The quantitative investigations of inversions may play a significant role in atmospheric environmental management. [ABSTRACT FROM AUTHOR]

In this paper, we analyzed the spatial and temporal causality and graph-based centrality relationship between air pollutants and PM2.5 concentrations in China from 2013 to 2017. NO2, SO2, CO and O3 were considered the main components of pollution that affected the health of people; thus, various joint regression models were built to reveal the causal direction from these individual pollutants to PM2.5 concentrations. In this causal centrality analysis, Beijing was the most important area in the Jing-Jin-Ji region because of its developed economy and large population. Pollutants in Beijing and peripheral cities were studied. The results showed that NO2 pollutants play a vital role in the PM2.5 concentrations in Beijing and its surrounding areas. An obvious causality direction and betweenness centrality were observed in the northern cities compared with others, demonstrating the fact that the more developed cities were most seriously polluted. Superior performance with causal centrality characteristics in the recognition of PM2.5 concentrations has been achieved. [ABSTRACT FROM AUTHOR]

Due to the acceleration of urbanization and industrialization, air pollutants has been increasing, posing a major threat to human health and the ecological environment. During the past period of rapid growth, with the booming development of real estate, the air pollutants brought about by the construction of housing buildings have become more and more serious, especially sulfur dioxide, nitrogen dioxide, and dust, which cast a great threat to human life and seriously jeopardize human health. Compared with the traditional construction of houses, prefabricated buildings construction procedures are reduced, to some extent, can reduce air pollutants. Therefore, this paper takes an empirical perspective to include prefabricated buildings and air pollutants into the same research framework and analyze the inherent logical relationship. We found that prefabricated buildings have a substantial role in mitigating urban air pollution. This conclusion remained solid after various robustness tests including constructing instrumental variables. The effect is observed to be greater in cities above the non-sub-provincial level and those positioned within central regions. The conclusions explore new areas for the green and sustainable development of the construction industry, which in the future will have to fill the requirements of green development in order to survive. Else, perspectives from the construction industry, it provides a realistic solution to economic development and ecological protection, helping to alleviate or resolve the conflict between development and survival. • The air pollution abatement effect of prefabricated buildings is investigated. • The results are robust after controlling the endogenous issue. • The heterogeneity is supported for different samples and air pollutants. [ABSTRACT FROM AUTHOR]

Coal burning is considered to be the main anthropogenic source of air pollutants, which can cause serious environmental pollution. As one of the important coal burning sources, coal-fired boilers have become the important challenges of the policy of "carbon peaking" and "carbon neutrality" of Chinese government. In this paper, the emission inventory of air pollutants (TSP, SO 2 , NO X , VOCs), and greenhouse gases (GHG, including CO 2 , CH 4 , N 2 O) from coal-fired industrial boilers (CFIB) from 2006 to 2020 are established by using the "bottom-up" emission factor method. Besides, Kaya identical equation and Logarithmic mean Divisia index (LMDI) model are used to quantify the driving forces of the changing tendency in pollutants emission based on five driving factors, which include pollution production coefficient (f), control technology level (β), economy (G OV), industrial energy intensity (e) and coal consumption structure of industrial boilers (s). Combined with existing relevant policies, the future emission tendency of air pollutants and GHG in 2025 and 2030 is projected. The result shows that policy adjustment has a significant impact on pollutant emission trends. In 2020, emissions of TSP, SO 2 , NO X , VOCs and GHG from CFIB in China are 679.5 kt, 644.7 kt, 603.9 kt, 40.7 kt and 4.4 × 105 kt CO 2 e, respectively. Control technology level is the main factor to the emission reduction of SO 2 and TSP, which has caused a decrease of 4278.6 kt and 3337.3 kt in emissions of SO 2 and TSP compared to those of 2006 respectively, while the influencing effect on NO X , GHG and VOCs was relatively weak. In addition, in the scenario of improved control technology, pollutant emissions could be reduced by 62.6%–94.9% until 2030. • Policy adjustment can significantly impact the emissions of air pollutants of CFIB. • Control technology level is the main factor of the emission reduction. • The emission reduction rate of energy intensity was lower than coal consumption. [ABSTRACT FROM AUTHOR]

The air pollutant diffusion trend prediction plays an important role in the environment protection. The approaches in the existing studies rarely consider spatial and temporal information sequentially or cannot use spatiotemporal structure information effectively. In this paper, we propose a new deep learning-based air pollutant diffusion trend prediction model, called GATBL-Learning. It is an end-to-end neural network model consisting of Graph Attention Network (GAT) and Encoder-Decoder model. Since each city in the region will set as a node, GAT can extract spatial information from them by aggregation operation. Then Encoder-Decoder model, consisting of Bi-LSTM as Encoder and LSTM as Decoder, mines time series features and outputs the data containing spatiotemporal features. In addition, we also observe that the changes of air pollutants show seasonality discipline and training the seasonal model can improve the model adaptability. The results demonstrate the proposed prediction model is effective and superior. [ABSTRACT FROM AUTHOR]

Exposure to air pollutants has become a major global health threat and is a dominant contributor to increasing respiratory mortality. Accurate projections of respiratory mortality are crucial to mitigating the adverse influences of air pollution on public health and guiding long-term policy implementation. However, the forecasting models' accuracy is difficult to guarantee because seasonal factors perturb the raw series of air pollutants and respiratory mortality. This paper proposes an enhanced seasonal and self-adaptive multivariate grey convolution model to solve this dilemma. The innovations of the proposed model reside in the following areas: firstly, a seasonal operator vector of multidimensional data is initially built to digitize the seasonality of air pollutants and related mortality. Secondly, a multivariate grey convolution model with an adaptive time-lag cumulative optimization item is put forward. The weight coefficients of each driving variable at different periods are intended to adapt dynamically. Thirdly, the self-adaptive time-delay coefficient is determined by the Particle Swarm Algorithm, enhancing the accuracy of predictions. Theoretically, the generalizability and adaptability of the newly presented model are well-validated. Moreover, its practical applicability and higher forecasting performance are further verified in estimating the respiratory mortality attributable to the comprehensive effects of air pollutants in China. [ABSTRACT FROM AUTHOR]

With the gradual improvement of PM2.5 pollution in China, the problem of ozone pollution is becoming more and more serious. In recent years, ozone has become the only one of the six major air pollutants increasing year on year in China, and the primary pollutant of air pollution in many cities of China in summer. In this paper, pollution characteristics of ozone and its relationship with meteorological conditions in China's provincial capitals and some economically developed cities (27 cities in total), and temporal-spatial distribution rules of ozone in China were analyzed, and influence and main source of ozone in China were summarized. The results were as below: daily change of ozone concentration showed as single-peak type, and the maximum generally appeared at 15 :00 - 16 :00, while the minimum generally occurred at 07 :00 - 08 :00. Seasonal change of ozone concentration mainly showed as single-peak type. In summer, ozone concentration was the highest, followed by spring and autumn, and its concentration in winter was the lowest. The influence of meteorological factors on ozone showed as that there was significantly positive correlation between ozone and temperature, radiation quantity, and significantly negative correlation between ozone and humidity, NOx. Ozone near surface will have adverse effects on biosphere, climate and ecological environment. The source of ozone can be divided into two kinds : man-made and natural, and the most important source of near-surface ozone is man-made. [ABSTRACT FROM AUTHOR]

As a natural foundation and fill material for construction in loess areas, there exists a significant impact on the stability of the engineering construction due to the permeation characteristics of the eolic sediment Malan loess. In this study, a series of air permeability tests were conducted for intact loess and remolded loess by using an improved air permeameter. We determined the influence of the water content, wetting and drying, dry density on the air permeability ka, and the use of microstructure models, scanning electron microscope (SEM) micrographs, and 3D super depth of field microscope micrographs to elucidate the microscopic mechanism of the air permeation. The results show that the air permeability ka of the remolded loess increased with the initial water content of remolding loess at dry density of ρd ≦ 1.6 g/cm3 increase, which is because of the formation of aggregates, and menisci water effect in loess; however, at dry density of ρd > 1.6 g/cm3, the air permeability started to decrease. The ka reduced remarkably as the water content of the intact and remolded loess increased during wetting process, and the air permeability during the drying process was always higher than the air permeability in the wetting process at the same water content. With the increase in the dry density, the air permeability of the remolded loess decreased; from a microstructural perspective, large and round pores tend to be small and elongated and irregular pores at high dry density. This paper will contribute to elucidating air permeability characteristics of loess and reducing the permeation risk in stability of the engineering construction in the Loess Plateau. [ABSTRACT FROM AUTHOR]

• The causal impact of green space on air pollution remains understudied. • Here, the Normalized Difference Vegetation Index (NDVI) was used to assess green space level in each county of China. • Two-stage least squares estimation (2SLS) and multiplex IV was employed to verify the robustness. • The increased greenspace significantly decreases the PM 2.5 concentration and other air pollutants. • Green spaces show a more pronounced air pollution reduction effect in southern or higher administrative-level cities. Can green vegetation absorb air pollutants and control regional air pollution? The existing research has not reached a consistent conclusion on this issue. Using the multi-level data in China, this paper provides empirical evidence of the causal impact of greenspace on PM 2.5 through the abundant fixed effect and controls. Besides, this paper applied the soil humidity, Normalized Difference Vegetation Index (NDVI) last month, NDVI in the same month last year, and NDVI prefecture-level city average as the instrumental variables, respectively, to test the robustness of the estimation. The results show that increased greenspace significantly decreases the PM 2.5 concentration and other air pollutants. In further analysis, we found that the decreasing effect turns significant when NDVI exceeds 0.3. In addition, there are regional heterogeneities in the impact of greenspace on air pollution. Greenspaces show a more pronounced air pollution reduction effect in southern or higher administrative-level cities. This paper suggests that increasing greenspace is an economical and effective way to achieve co-management of multiple air pollutants. [ABSTRACT FROM AUTHOR]

Air pollution in China has changed substantially since 2013, and the effects such changes bring to the human health and environment has been an increasingly hot topic in many scientific fields. Such studies, however, are often hindered by a lack of long-term air quality dataset in China of high accuracy and spatiotemporal resolutions. In this study, a six-year long high-resolution Chinese air quality reanalysis datasets (CAQRA) has been developed by assimilating over 1000 surface air quality monitoring sites from China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). Surface fields of six conventional air pollutants in China, namely PM2.5, PM10, SO2, NO2, CO and O3 for period 2013-2018, are provided at high spatial (15 km ×15 km) and temporal (1 hour) resolutions. This paper aims to document this dataset by providing the detailed descriptions of the assimilation system and presenting the first validation results for the reanalysis dataset. A five-fold cross validation (CV) method was used to assess the quality of CAQRA. The CV results show that the CAQRA has excellent performances in reproducing the magnitude and variability of the surface air pollutants in China (CV R2 = 0.52-0.81, CV RMSE = 0.54 mg/m3 for CO and 16.4-39.3 μg/m3 for other pollutants at the hourly scale). The interannual changes of the air quality in China were also well represented by CAQRA. Through the comparisons with the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) produced by the European Centre for Medium-Range Weather Forecasts (ECWMF) based on assimilating satellite products, we show that the CAQRA has higher accuracy in representing the surface gaseous air pollutants in China due to the assimilation of surface observations. The finer horizontal resolution of CAQRA also makes it more suitable for the air quality studies in the regional scale. We further validate the PM2.5 reanalysis dataset against the independent datasets from the U.S. Department of State Air Quality Monitoring Program over China, and the accuracy of PM2.5 reanalysis was also compared to that of the satellite estimated PM2.5 concentrations. The results indicate that the PM2.5 reanalysis shows good agreement with the independent observations (R2 = 0.74-0.86, RMSE = 16.8-33.6 μg/m3 in different cities) and its accuracy is higher than most satellite estimates. This dataset would be the first high-resolution air quality reanalysis dataset in China that can simultaneously provide the surface concentrations of six conventional air pollutants in China, which should be of great value for many studies, such as the assessment of health impacts of air pollution, investigation of the changes of air quality in China and providing training data for the statistical or AI (Artificial Intelligence) based forecast. The whole datasets are freely available at: https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a teaser product which contains the monthly and annual mean of the CAQRA has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the potential users to download and to evaluate the improvement of CAQRA. [ABSTRACT FROM AUTHOR]

Volatile organic compounds (VOCs) are important precursors of ozone and PM2.5. At present, VOCs have gradually become the focus of air pollution control after fine particles in China, and a series of documents, standards and planning on VOCs were issued from the state to each area. The analysis on source apportionment and control of VOCs have also become a hot spot in atmospheric environmental protection research of China at present stage. The research work on VOCs in ambient air is the basis and premise of scientific and effective prevention and control of VOCs pollution. In this paper, research progress on the monitoring of VOCs is introduced systemically, and main monitoring and evaluation methods of atmospheric VOCs are explored from monitoring analysis method, chemical reaction activity and health risk assessment. Moreover, concentration characteristics and source apportionment of VOCs in the regions at home and abroad that have caaried out VOCs research work are compared and summarized. The research could provide reference for source apportionment of VOCs in other cities of China. [ABSTRACT FROM AUTHOR]

When performing spatial regression analysis in environmental data applications, spatial heterogeneity in the regression coefficients is often observed. Spatially varying coefficient models, including geographically weighted regression and spline models, are standard tools for quantifying such heterogeneity. In this paper, we propose a spatially varying coefficient model that represents the spatially varying parameters as a mixture of local polynomials at selected locations. The local polynomial parameters have attractive interpretations, indicating various types of spatial heterogeneity. Instead of estimating the spatially varying regression coefficients directly, we develop a penalized least squares regression procedure for the local polynomial parameter estimation, which both shrinks the parameter estimation and penalizes the differences among parameters that are associated with neighboring locations. We develop confidence intervals for the varying regression coefficients and prediction intervals for the response. We apply the proposed method to characterize the spatially varying association between particulate matter concentrations (PM2.5) and pollutant gases related to the secondary aerosol formulation in China. The identified regression coefficients show distinct spatial patterns for nitrogen dioxide, sulfur dioxide, and carbon monoxide during different seasons. [ABSTRACT FROM AUTHOR]

Nowadays, the gaseous pollutant emissions, including particulate matter (PM), sulfur dioxide (SO2), and nitrogen oxide (NOx) from household coal combustion, cause great threat to environment and public health by contributing to severe haze in China. Particularly, a clean coke free of the major pollutants precursors (sulfur and nitrogen compounds) by sulfur fixation and denitrification has been deemed as an effective strategy to reducing pollutants. In this paper, a preprocessed coke was prepared by co-pyrolysis of high-sulfur coal with the assistance of calcium-based and iron-based complexes at high temperature. The results show that high-temperature co-pyrolysis could remove the volatile compounds that are major precursors for the formation of gaseous pollutants from the raw coal. During the coking process, the sulfur can be removed by being fixed in the form of CaS in presence of a Ca-based complex, which could be beneficial for the CaSO4 during the coke combustion. The volatile nitrogen is transferred to the gas phase with the addition of Fe-based complexes, which effectively reduce the residual nitrogen in coke. As a result, Ca-based additives captured the released SO2 and formed CaSO4 during the combustion process. In addition, in the presence of Fe-based complexes, both char and CO react with NOx to form N2, which leads to a reduction in NOx emissions during combustion. Additionally, the replacement of current residential coal with a new type of clean coke is a facile method for reducing gaseous pollutant emissions from household activities to protect the atmospheric environment. The average emission factors (EFs) of PM, SO2, and NOx for the prepared clean coke were small during combustion and were much lower than the EFs of the tested raw coal, semicoke, and briquettes. [ABSTRACT FROM AUTHOR]

Knowledge of vertical distribution of air pollutants is important for understanding the mechanisms underlying haze pollution. In this paper, we characterize the vertical structure of air pollutants based on ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements in the wintertime in Hefei, East China. Both NO 2 and aerosol show exponentially decreasing profile with high concentrations concentrated close to the surface, suggesting the major emissions are at ground level. SO 2 profiles extend to slightly higher altitudes than NO 2 , which might be due to the SO 2 emissions from elevated point sources (e.g., power plants). Elevated HCHO was observed in the middle layer during noon time, probably indicating that the larger effect of the photochemical formation of atmospheric HCHO rather than direct HCHO emissions. Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) models were used to evaluate the potential sources of Hefei's air pollution in the lower, middle and upper boundary layer. The results revealed that the major regional source of NO 2 and aerosol were located in the northern and eastern part of Anhui and the transport mainly occured in the lower layer. Transported SO 2 from the northwestern regions in the middle layer made great contribution to SO 2 in Hefei. HCHO was mostly produced locally while the transport of HCHO from potential source areas was negligible. To further explore the potential source contributions affecting haze pollution, PSCF and CWT analysis for heavy haze episodes suggested that the potential source areas for aerosol were almost located in the northern region, the northwestern region, and the YRD region. Our findings provide a more comprehensive understanding for regional transport through the study on potential sources at different altitudes, which are useful for designing collaborative air pollution control strategies on a regional scale. • Vertical distribution of air pollutants is investigated to identify potential sources at different altitudes during winter. • Both NO 2 and aerosol show exponentially decreasing profile with high concentrations concentrated close to the surface. • SO 2 and HCHO profile typically extended to higher altitudes than NO 2 and aerosol did during winter. • The main potential sources for aerosol were located in the northern region, the northwestern region, and the YRD region. • This study is useful for designing collaborative air pollution control strategies on a regional scale. [ABSTRACT FROM AUTHOR]

Crop residue burning is an important source of air pollutants and strongly affects the regional air quality and global climate change. This study presents a detailed emission inventory of major air pollutants from crop residue burning for the year of 2014 in China by the bottom-up method. Activity data were investigated for 296 prefecture-level cities. Emission factors were determined for indoor and in-field burning separately. Regional differences were considered for the proportion of residue burned (PCRB), the ratio between indoor and in-field burning, and the ratio of straw to grain production. The emissions were estimated at prefecture-city level as the first step; then they were redistributed within a city based on 1-km resolution land-use, MODIS fire counts, and rural population. Temporal variation was determined according to farming practices in different regions and MODIS fire counts. Uncertainties were estimated using the Monte Carlo method. The total emissions from crop residue burning in China were estimated to be 0.13 (−47–92%) for BC, 0.71 (−48–92%) for OC, 1.77 (−48–91%) for PM 2.5 , 2.04 (−50–100%) for PM 10 , 0.16 (−59–133%) for SO 2 , 0.53 (−55–105%) for NO X , 0.12 (−47–93%) for NH 3 , 1.07 (−55–102%) for CH 4 , 1.85 (−43–74%) for NMVOC, 18.33 (−46–85%) for CO and 305.20 (−45–80%) for CO 2 , in unit of Tg yr−1. Our results are remarkably lower than those reported in previous studies, mainly because of the PCRB has decreased significantly in recent years. For most of the pollutants, indoor burning accounted for about 50–70% of the emissions. Rice, wheat and corn contributed more than 85% of the emissions, but their relative contributions varied a lot with region and season. High emissions were mostly located in the eastern China, central China and northeastern China, and temporally peaked in April, June and October, with different intensities in the north and the south. This study provides a useful basis for air quality modeling and the policy making of pollution control strategies. • This paper presents an up-to-date and high-resolution inventory of air pollutant emissions from crop residue burning in China. • City-level activity data and high-resolution land use, fire counts and population were combined for spatial distribution. • Farming practices in different regions and statistics of fire counts were used to determine temporal distribution. • High emissions were generally located in Eastern, Central and Northeastern China, and temporally peaking in April, June and October. [ABSTRACT FROM AUTHOR]

Rapid industrialization and urbanization have presented a dilemma to traditional environmental policies, and severe haze pollution has attracted particular attention in China. This paper employed the exploratory spatial data analysis technique to analyze global and local spatial auto-correlation of environmental regulations (ERS) and haze pollution. Static and dynamic spatial panel data models were further established to investigate the influence of ERS and its spatial spillover effect on haze pollution across 31 provinces of China in 2005-2015. Results showed that significant spatial autocorrelation existed for both ERS and haze pollution in China, forming different aggregation regions in terms of geographical distribution. There was strong evidence for the "inverted-U"-shaped EKC relationship between ERS and haze pollution regardless of whether it was the static or dynamic spatial econometric model. In addition, haze pollution was not only affected by economic growth, population size, and urbanization, but also by the haze pollution in the previous period. Conclusions and control strategies that would be of great significance for implementing effective management measures were synoptically discussed. [ABSTRACT FROM AUTHOR]

Air pollution in the form of particulate matter (PM) is becoming one of the greatest current threats to human health on a global scale. This paper firstly presents a Bayesian space–time hierarch piecewise regression model (BSTHPRM) which can self-adaptively detect the transitions of local trends, accounting for spatial correlations. The spatiotemporal trends of the approximately anthropogenic PM 2.5 removed natural dust (PM 2.5_No Dust) concentrations and the corresponding population's PM 2.5_No Dust exposure (PPM 2.5 E) in the global continent from 1998 to 2016 were investigated by the presented BSTHPRM. The total areas of the high and higher PM 2.5_No Dust -polluted regions, whose spatial relative magnitude of PM 2.5_NoDust pollution to the global continental overall level was between 1.89 and 14.68, accounted for about 13.4% of the global land area, and the corresponding exposed populations accounted for 56.0% of the global total population. The spatial heterogeneity of the global PM 2.5_NoDust pollution increased generally from 1998 to 2016. The areas of hot, warm, and cold spots with increasing trends of PM 2.5_NoDust concentration initially contracted and then later expanded. The local trends of the global continental PM 2.5_NoDust concentrations and PPM 2.5 E can be parted into three changing stages, early, medium, and later stages, using the BSTHPRM. The area proportions of the regions experiencing a decreasing trend of PM 2.5_NoDust concentrations and PPM 2.5 E were greater in the medium stage than in the early and later stages. The local trends of PM 2.5_NoDust concentration and PPM 2.5 E in the two higher PM 2.5_NoDust polluted areas, northern India and eastern and southern China, increased in the early stage and then decreased in the medium stage. In the later stage (recent years), northern India displayed a stronger increasing trend; nevertheless, the follow-up decreasing trend still occurred in eastern and southern China. In the first two stages, more than half of the areas in Europe experienced a decreasing trend of PM 2.5_NoDust concentration and PPM 2.5 E; later, more than half of areas in Europe exhibited increasing trends in the later stage. North America and South America experienced a similar local trend of PPM 2.5 E to Europe. The PPM 2.5 E trend in Africa generally increased during the study period. • First presentation of a Bayesian space-time hierarchy piecewise regression model detecting self-adaptively transitions. • Firstly applies a developed Bayesian space-time model in investigating the global population's exposure to PM 2.5. • Deeply analyses the spatiotemporal trends of the global continental PM 2.5 pollution removed natural dust. • Details the spatiotemporal trends of the global population's exposure to anthropogenic PM 2.5. [ABSTRACT FROM AUTHOR]

China's economic growth is accompanied by air pollution, which has harmed public health. Former scholars have explored the influencing factors of public health from different perspectives with different methods, among which family income and environmental pollution are two important factors. The paper explored the combined effects of economic level and air pollution on public health at the micro and macro level using the multivariate ordered Logit model and panel data regression model. A comparative analysis was also explored between China's eastern and western regions. The results demonstrated that: (1) Both at the micro and macro level, economic level was positively correlated with public health, while air pollution was negatively correlated with it. (2) The improvement of the economic level made public health more susceptible to air pollution at the micro level and at the macro level. (3) The higher the economic level, the lower the effect of economic growth in reducing the adverse effects of air pollution on public health. This paper proposed that sustainable economic growth should be pursued, the emissions of air pollutants should be reduced, regional development should be balanced and the development gap between China's eastern and western regions should be narrowed. [ABSTRACT FROM AUTHOR]

Abstract Cement production needs effective ways to promote the sustainability level due to its huge energy consumption and intensive environmental emissions. In this paper, we conduct an emergy accounting to assess the performance of the resource utilization and environmental emissions of a New Suspension Preheaters (NSP) cement plant in China. The sustainability level associated with utilization efficiency and environmental load of the cement production process are quantified based on the emergy-based indicators. It can be concluded that in the total emergy input, nonrenewable resource input and economic investment contribute most because the raw materials of lime and sandstone are from surrounding environment, and the dependence on energy and materials purchased from the economy. Particularly, emergy-based environmental emission indices for CO 2 , SO 2 , NO x , PM 10 and waste water are calculated to reveal the life-cycle cost of cement production and the loses of both natural capital and human health. Regarding the emission intensity of total emergy input, CO 2 emission intensity is calculated to be the highest, followed by wastewater, PM 10 and SO 2 , while NO x is the lowest. [ABSTRACT FROM AUTHOR]

Abstract: China is currently facing the threat of serious air pollution and has adopted a series of governance policies. However, the fact that PM 2.5 indicator in some regions often reached far higher than the official threshold since 2015 has reflected that China's current Air Quality Index system for measuring and evaluating air quality is still not perfect. Therefore, this paper extended the current air quality indicator system officially used by China. Using improved Entropy-weighted Factor Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Method, we calculated the air quality scores of 26 cities in China's most economically developed region - the Yangtze River Delta region based on their daily average concentration numbers of main air pollutants from February 2015 to January 2018. Based on the air quality calculation result, we further studied the effectiveness of air pollution control policies of this region by using the Box-Jenkins Model with fuzzy strategy parameter adjustment. Our findings are: the pollution control policy is more effective to Shanghai, Jiangsu and Zhejiang. The air quality score of Shanghai has improved by 51.07% compared with its level before the policy. Meanwhile, the air quality improvement in Zhejiang province has shown the characteristics of "Campaign-style Governance" right before the G20 Summit, especially that in Hangzhou, the host city of the Summit. On the other hand, maybe due to late start of air pollution control programs, the cities of Anhui province haven't shown obvious improvements in air quality, and some cities have even seen deterioration in air quality scores during our study period. Graphical abstract Image 1 Highlights • We extended the current air quality indicator system officially used by China. • We used the Entropy-weighted Factor Method and TOPSIS Method for assessing. • We studied the control policies by Box-Jenkins Model with fuzzy strategy parameter. • Results and corresponding reasons were analyzed according to provinces and cities. • Policy recommendations for air pollution control in YRD were proposed. [ABSTRACT FROM AUTHOR]

Highlights • Ghosh input-output model. • Structural decomposition analysis of air pollutant emissions. • Covering China, India, USA, and Japan. • Key role of adjusting allocation structures. Abstract China’s air quality has become a major issue affecting people’s livelihood and continues to deteriorate in recent times. It is an important issue of common concern for economists and policymakers to explore the drivers of the growth of air pollution emissions and the deteriorating environmental quality in China. From the perspective of supply-side structures, this paper adopts Ghosh input-output model to decompose the factors affecting the changes of air pollutant emissions into economic activities, economic structures, allocation structures and emission intensity. Using this model, we conduct a structural decomposition analysis of air pollutant emissions in China, India, USA, and Japan for 1995–2009. The results reveal that China’s economic structure initially promoted air pollutant emissions, but later played a role in reducing them. Further, whereas in Japan and particularly, China, allocation structures were found to be a key factor in increasing air pollutant emissions, in America and India, it played a critical role in reducing emissions. Our findings suggest that adjusting the distribution structure of intermediate products is crucial to reduce air pollution. [ABSTRACT FROM AUTHOR]

In order to investigate the relationship between outdoor air pollution and sick building syndrome (SBS) symptoms, the CCHH (China, Children, Home, Health) group in Shanghai conducted a cross-sectional survey on the current incidence of SBS symptoms among residents in five districts of Shanghai during April 2011–April 2012. It also collected the related outdoor air pollutants data from Shanghai environmental monitoring center (SEMC), including nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ) and particles with aerodynamic diameter less than 10 µm (PM 10 ). The logistic regression models were used in this paper to study the associations between air pollutants and SBS symptoms. Results were found that the daily mean concentrations of NO 2 and SO 2 met standard requirement, but that of PM 10 exceeded the limit values of the standard in China during this survey. The significant differences were found between the pollutants concentration in urban and suburban area. In addition, the incidences of three categories of SBS symptoms among residents were 79.1% for general symptoms (GS), 65.2% for mucous membrane symptoms (MS) and 35.7% for skin symptoms (SS), respectively. Multiple logistic regression analysis by two case studies illustrated that three outdoor air pollutants, both their concentration quartiles and equal increment, were all associated to and taken as the risk factors for SBS symptoms, for GS OR NO2  = 1.62 (1.18–2.22), MS OR NO2  = 1.58 (1.21–2.06) and SS OR PM10  = 1.21 (1.09–1.35). Furthermore, the new pollutants expressed as the mixtures of their combination were also significantly associated with part of the SBS symptoms. The synthetic air quality indexes of all surveyed residents were calculated as III-level, which represented mild pollution. Therefore, it would be an effective way to decrease the incidence of SBS symptoms in residence to reduce the outdoor air pollution and control the penetration from outdoor to indoor environment. [ABSTRACT FROM AUTHOR]

Industrial structures in China's resource-based cities are unlikely to materially change in the short term, and the large quantities of air pollutants emitted by residents' energy consumption cause serious air pollution. It is therefore urgently required to frame policies that can be adopted to realize the sustainable development of a social economy under the constraint of atmospheric environment in China's resource-based cities. In this paper, we develop a dynamic optimization model by using input–output analysis with gross regional product (GRP) maximization as the objective function. This model contains three submodels: the socioeconomic submodel, atmo-environmental control submodel, and energy submodel. The model is applied to Tangshan City, a typical resource-based industrial city, to conduct a dynamic simulation with 13 terms from 2013 to 2025 and based on the data in 2012. The goal for the model operation is to realize the optimal development of a social economy under the restrain of certain emission levels of air pollutants. This goal is achieved by introducing a comprehensive policy that includes a residential clean energy promotion policy and an industry emission reduction policy, and industrial restructuring also plays a role by industrial subsidies. The simulation results demonstrate that sulfur dioxide ( S O 2 ) and nitrogen oxides ( N O x ) can be reduced by 53% and 45%, respectively, in Tangshan City in 2025 compared with their values in 2012 with an annual GRP growth rate of 6.2%. The total output of the clean energy in the simulation period is 41.4 million tons of standard coal. The goal of improving the atmospheric environment and sustainable development of social economies in resource-based cities can be achieved by coordinating the relationship among economic development, atmospheric environmental protection, and energy consumption through a dynamic optimization model with a comprehensive policy and industrial restructuring. [ABSTRACT FROM AUTHOR]

Sulfur dioxide (SO2) in the planetary boundary layer (PBL) as a kind of gaseous pollutant has a strong effect regarding atmospheric environment, air quality, and climate change. As one of the most polluted regions in China, air quality in Beijing-Tianjin-Hebei (BTH) region has attracted more attention. This paper aims to study the characteristics of SO2 distribution and variation over BTH. Spatial and temporal variations for a long term (2006–2017) over BTH derived from OMI PBL SO2 products were discussed. The temporal trends confirm that the SO2 loading falls from average 0.88 DU to 0.16 DU in the past 12 years. Two ascending fluctuations in 2007 and 2011 appeared to be closely related to the economic stimulus of each five-year plan (FYP). The spatial analysis indicates an imbalanced spatial distribution pattern, with higher SO2 level in the southern BTH and lower in the northern. This is a result of both natural and human factors. Meanwhile, the SO2 concentration demonstrates a decreasing trend with 14.92%, 28.57%, and 27.43% compared with 2006, during the events of 2008 Olympic Games, 2014 Asia-Pacific Economic Cooperation (APEC) summit, and 2015 Military Parade, respectively. The improvement indicates that the direct effect is attributed to a series of long-term and short-term control measures, which have been implemented by the government. The findings of this study are desirable to assist local policy makers in the BTH for drawing up control strategies regarding the mitigation of environmental pollution in the future. [ABSTRACT FROM AUTHOR]

The provinces of China have suffered from severe PM2.5 pollution in recent years, presenting a significant threat to human health. Identifying associations between mortality rate and PM2.5 level is extremely useful for a range of purposes, such as the development of preventive measures, increasing health awareness, and establishing disaster warning systems. Based on remote sensing data, station monitoring data, and statistical data, this paper uses the exposure response function, regression analysis, and kriging to evaluate the number of deaths in China’s 31 provinces caused by PM2.5 pollution in 2015. Variations in the number of deaths and mortality rates in China under different PM2.5 concentration control standards have been simulated by a range of countries and organizations helping to develop optimal control standards for each province individually according to actual PM2.5 concentration. These results show that: 1) PM2.5 pollution has an important effect on the mortality rate in China. The rate caused by PM2.5 pollution in 2015 accounted for 1.75‰, or approximately 2.62 million people and 31.14% of all deaths in China. 2) Strict control standards for PM2.5 concentration can bring significant health benefits, with projections that if PM2.5 concentration in China's provinces were controlled to the level set by China, the EU, Japan, USA, and Australia, the number of deaths caused by PM2.5 pollution would be reduced by approximately 0.95, 1.52, 2.02, 2.26, and 2.49 million people, respectively, or 36.24%, 58.08%, 79.91%, 86.47%, and 95.20% compared with baseline year data. 3) Choosing appropriate control targets for limiting PM2.5 concentrations in different provinces in China is an effective way to obtain optimal health benefits. Beijing, Tianjin, Hebei, Shandong, and Henan should adopt a 35 μg/m3 standard with a 25 μg/m3 standard appropriate for Shanxi, Liaoning, Jilin, Shanghai, Jiangsu, Zhejiang, Anhui, Hubei, Hunan, Chongqing, Shanxi, and Xinjiang; 13 provinces, including Inner Mongolia, Heilongjiang, Fujian, Jiangxi, Guangdong, Guangxi, Sichuan, Guizhou, Yunnan, Tibet, Gansu, Qinghai, and Ningxia, should adopt the 15 μg/m3 standard; and Hainan should consider choosing a 12 μg/m3 standard. [ABSTRACT FROM AUTHOR]

Ambient air pollution is a major public health concern impacting all aspects of human health. There is a lack of studies on the impact of ambient air pollution on health-related quality of life (HRQoL) of older Chinese adults. Our study answers two questions: How concentrations of ambient air pollutants are associated with HRQoL among older adults in China and, second, what are the possible mechanisms through which ambient air pollution affects HRQoL. From the 2018 National Health Service Survey, we sampled 5717 aged 65 years or older residents for the eastern province of Shandong, China. Data on individual exposures to PM 2.5 and PM 10 (particulate matter with diameter less than or equal to 2.5 μm and 10 μm) and sulfur dioxide (SO 2) were collected from the ChinaHighAirPollutants (CHAP) datasets. Mixed-effects Tobit regression models and mixed-effects ordered Probit regression models were employed to examine the associations of long-term exposure to ambient air pollution with the European Quality of Life 5 Dimensions 3 Level Version (EQ-5D-3L) scale comprising mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Socioeconomic, demographic and behavioral factors relating to HRQoL were also examined. The results show that for each 1 μg/m3 increase, EQ-5D-3L scores fell 0.002 for PM 2.5 ; 0.001 for PM 10 and 0.002 for SO 2. Long term exposure to PM 2.5 , PM 10 and SO 2 were also associated with increased prevalence of pain/discomfort and anxiety/depression. The reduced HRQoL effects of ambient air pollution were exacerbated by higher socioeconomic status (affluent, urban and higher level of education). Our findings suggested that HRQoL of older Chinese adults was not only associated with demographic, socioeconomic, and health-related factors, but also negatively correlated with air pollution, especially through increased pain/discomfort and anxiety/depression. The paper proposes policy recommendations. [Display omitted] • Ambient air pollution was associated with lower HRQoL among older adults in China. • Long term air pollution was associated with increased prevalence of pain and anxiety. • The reduced HRQoL effects was exacerbated by higher socioeconomic status. [ABSTRACT FROM AUTHOR]