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Docked vessels in ports can be one of the dirtiest emitters in terms of local air pollutants. This paper evaluates whether the regulation of Emission Control Areas in China reduces air pollutants emissions in port cities. The transboundary spillovers of air pollution, induced by meteorological conditions, results in air pollution concentrations at specific locations encompassing both locally generated pollutants and those originating from elsewhere. Therefore, it is challenging to discern the emission reduction effects of environmental regulatory policies through air pollutant monitoring concentrations. This paper develops an approach to isolate transboundary spillovers induced by the wind, estimate locally produced pollution, and utilize the concentrations of locally produced pollution to identify the treatment effects of the Chinese Emission Control Areas policy. The results find that the policy has significantly reduced the locally produced sulfur dioxide concentrations by 3.41% in port cities. However, the omission of transboundary spillovers could yield contrasting results, suggesting that ignoring the pollution transport could lead to erroneous conclusions. The research findings of this study hold significant policy implications, highlighting the importance of accounting for transboundary transport of air pollution in evaluating the local government's environmental efforts and implementing supra-city environmental policies to prevent air pollution. • We investigate the efficacy of marine fuel restrictions policy. • We develop a new approach to account for the transboundary air pollution spillovers. • The policy significantly reduced local sulfur dioxide concentrations in ports. • Ignoring pollutant spillovers may bias the estimate of environmental policy effects. [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]

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]

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]

The increase in air pollution has posed numerous new challenges for human society, making the exploration of an effective method for predicting air pollutant concentrations highly significant. The current research faces several primary challenges: the neglect of non-Euclidean characteristics of site distribution on data and the strong spatiotemporal dependencies in the dispersion process of pollutants. To address these issues, this paper constructs a spatiotemporal hybrid prediction model – the MGAtt-LSTM method – for predicting PM 2.5 concentrations, which employs the dynamic multi-graph attention module (MGAtt) to tackle spatial dependencies and Long Short-Term Memory networks (LSTM) to address temporal dependencies. Additionally, extensive experiments are conducted by using historical air pollutant monitoring data and meteorological data from the Beijing-Tianjin-Hebei region. The results demonstrate that the proposed MGAtt-LSTM model achieved superior performance in concentration prediction compared to existing benchmark models. • The neural network consists of multi-graph attention network and LSTM network. • Air pollution data in North China and meteorological data are used to forecast. • The uneven distribution of pollutant sites is considered to predict PM 2.5 concentrations precisely. • The use of multi-graph attention networks addresses the issue of traditional GCN methods relying on fixed graph structures. [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]

• 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]

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]

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]

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]

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]

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]

Abstract: The unprecedented resources and energy needed to support the high growth of urbanization with the emerging issues of environmental degradation and GHG emissions is increasingly dramatic in China. A series of national and local policies have been implemented for achieving the co-benefits of reducing emissions of greenhouse gas (GHG) and air pollution for China's sustainable development. In this paper, the achievement of climate change mitigation and air pollution reduction in different sectors through implementing policies is reviewed. This paper reports on the types of policy measures that have been introduced in two cases (i.e. Tiexi District of Shenyang and Baoshan District of Shanghai) to affect air quality and energy efficiency improvements, which are then collectively examined in terms of their impacts on GHG and air pollutant emissions. Recommendations are made for achieving co-benefits effectively through the integrated approach by comprehensively concentrating on the short and long-term environmental protection and energy conservation at local and national levels based on the analysis made in the paper. The limited coordination and lack of capacity in different government bodies may be the main barriers to the implementation of a co-benefits approach. Enhancing the cooperation and capacity building could overcome these obstacles. [Copyright &y& Elsevier]

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]

Regional specification of PM 2.5 pollution characteristics is crucial for pollution control and policymaking. Spatiotemporal variations of six criteria air pollutants and influencing factors in China were studied using hourly concentrations of PM 2.5 , PM 10 , SO 2 , NO 2 , CO, and O 3 from 2015 to 2016. China was categorized into eight regions: north-east, northern coastland, eastern coastland, southern coastland, Yellow River middle reaches, Yangtze River middle reaches, south-west, and north-west. The 29 exemplary cities in China were also researched. It was found that the PM 2.5 concentration in the northern coastland (Beijing–Tianjin–Hebei–Shandong) was the highest (72.28 μg.m−3) among the eight regions, particularly in the city of Baoding, Hebei, which had an annual average PM 2.5 concentration of 98.53 μg.m−3. Average PM 2.5 concentrations in 2015 and 2016 of China were 50.16 μg.m−3 and 46.61 μg.m−3, respectively. Compared with 2015, the PM 2.5 concentration decreased by 8.41% in 2016, the decline of PM 2.5 in summer was the largest, followed by autumn, spring and winter. The average mean PM 2.5 concentrations of the 29 exemplary cities in 2015 and 2016 were 54.66 μg.m−3 and 48.37 μg.m−3, respectively, exceeding the limit for grade 2 of the national standards (35 μg.m−3). National air pollution distribution has exploded geographically with influence of regional economic factors. Gaseous pollutant as well as geographical and socio-economic conditions influenced PM 2.5 emissions. Effects of these factors on PM 2.5 emissions varied across regions and decreased continuously from the northern region to the south-west and eastern coastland regions. This paper clearly identifies the regional characteristics and distribution of PM 2.5 , focusing on the effects of gaseous pollutant, geography and socio-economic development. Secondary transformation and vehicle exhaust across regions should be further studied. Image 1 • The results show that average PM 2.5 concentrations in 2015 and 2016 were 50.16 and 46.61 μg/m3, respectively. • Compared to 2015, the concentration decreased by 8.41% in 2016. • Considering regional differences, the northern coastland, Yellow River middle reaches and Yangtze River middle reaches had higher PM 2.5 concentrations relative to other areas. • Gaseous pollutant, socio-economic and geographic influence PM 2.5 emissions. • It was found that the influence factors on PM 2.5 emissions vary by region and decrease continuously from the central to western and eastern regions. • Results reveal heavy air pollution at lower elevations and in basins. • Secondary transformation and vehicle exhaust in different regions need further study. [ABSTRACT FROM AUTHOR]

• We focus on air pollution and health issues in China. • The data come from China Health and Retirement Longitudinal Study. • We consider the effects of pollutants (i.e. SO 2 , NO x and PM 2.5) at different pollution levels and identify the severity of pollution across Chinese provinces. • We find Household income growth can significantly reduce health risks of gaseous air pollution, but the health benefits from income vary among different regions. China's remarkable economic growth during the past four decades has resulted in a number of environmental problems. However, very few studies have addressed the interactive impact of both income and air pollution on public health. Thus, an innovative contribution of this paper is to test whether household income growth (HIG) can mitigate the health risks resulting from air pollution in China. More specifically, using pooled data from the China Health and Retirement Longitudinal Study and official statistical data, we try to estimate the interactive impact of HIG and air pollution-related health risks by employing an ordered logistic regression model, and compare these effects across different levels of pollution and education attainment. Our results indicate that HIG can significantly reduce the health risks of air pollution, especially those health risks caused by gaseous pollutants (SO 2 and NO x). However, HIG fails to offset the adverse health effect induced by particulate matter (PM). Further, heterogeneity analysis shows that the health benefits from incomes vary greatly among different regions. HIG can mitigate the health risks of NO x to people in heavily polluted regions. In terms of education level, HIG can significantly raise the self-rated health level for groups with different educational backgrounds, but HIG of the highly educated group can significantly offset the health losses due to air pollution. [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]

Particulate matter (PM) is one of the major air pollutants in China. Traffic-related microenvironments are the typical scenarios exposed to high PM concentrations. This paper investigates the personal exposure to PM during commuting in Nanjing in four transportation modes, i.e. subway, bicycle, bus and walking. The measurements were conducted in a heavy-traffic street during rush hours in summer and winter. The result reveals significant PM concentration differences between various commuting modes. Passengers in subway cabin are exposed to lowest PM 1 (38.3 μg/m3) and PM 2.5 (54.4 μg/m3) concentrations, while passengers in subway station are exposed to highest PM 2.5 (90.5 μg/m3). Pedestrians are exposed to highest PM 1 (59.5 μg/m3). Both outdoor and indoor-generated particles contribute a lot to the particles in subway station and 63.4% of the PM 2.5 generated in subway station are between 1 μm and 2.5 μm in size. Most particles in subway cabin are from subway station and most particles in bus cabin come from the outdoor air, while indoor sources contribute little. Spatial particle concentration variations were observed in subway station. The particle concentration at a deeper level is usually higher than the concentration at a shallower level. Substantial particles within 1–2.5 μm in size were observed at the platform and the portion within 1–2.5 μm decreased at shallower levels. The PM inhalation during subway trip is lowest while the inhalations during walking and cycling are more than 5 times higher. During a short distance subway trip, the exposure in subway station contributes most of the total particle inhalation during the entire subway trip. • Most PM in subway cabin is from station while in-cabin source contributes little. • Cyclists and pedestrians inhale 5 times more PM than subway commuters. • PM levels show linear decreasing from lower to upper floor in subway station. [ABSTRACT FROM AUTHOR]

Previous studies have suggested a change of birth weight linked with elevated ambient air pollutant concentrations during the pregnancy. However, investigations of the influence of higher pollutant levels on birth weight change are limited. The goal of this study is to evaluate whether the air pollution of Ningbo is associated with birth weight, and which trimester could be a window period for maternal exposure to air pollution. A total of 170,008 live births were selected in the Ningbo city of Zhejiang, China, from 2015 to 2017. We estimated the association between the decreased birth weight and the increased air pollutant concentrations in the three trimesters and full gestation. The effects of interaction among pollutants were identified using a co-pollutant adjustment model. An interquartile range increases in PM 2.5 (10.55 μg/m3), SO 2 (4.6 μg/m3), CO (125.59 μg/m3), and O 3 (14.54 μg/m3) concentrations during the entire gestation were associated with 3.65 g (95% confidence interval: −6.02 g, −1.29 g), 5.02 g (−6.89 g, −3.14 g), 2.64 g (−4.65 g, −0.63 g) and 2.9 g (−4.8 g, 1 g) decreases, respectively, in birth weight. With each interquartile range increment in NO 2 concentration was associated with an 8.05 g (6.24 g, 9.85 g) increase in birth weight. In the first trimester, only the PM 2.5 exposure seemed to be associated with the greatest decline in birth weight. After adjustment for co-pollutant, both PM 2.5 and SO 2 were still associated with birth weight, except for CO for O 3 adjustment, O 3 for SO 2 adjustment, and O 3 for NO 2 adjustment. Maternal exposure to air pollution may be associated with a decrease of birth weight, but the contribution of various pollutants is necessary to verify by future research. Image 1 • The association between air pollution of Ningbo, China, and birth weight is evaluated using a co-pollutant adjustment model. • A large number of subjects (170008 births) are included in this study. • Air pollutants including PM 2.5 , SO 2 , CO, and O 3 are analysed in this paper. Main Findings : During the entire gestation, an increase of PM 2.5 , SO 2 , CO, and O 3 is inversely associated with decrease in birth weight, and the level of NO 2 is positively linked with birth weight. [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]

This paper analyzes how the spatial spillover effects of air pollution on city-level average housing prices vary with geographical, atmospheric and institutional factors in China. By using the panel data of Chinese major cities over the period 2005–2015, our empirical estimations show that: first, the negative spatial externality of transboundary neighboring air pollution on housing prices is significant only when the distance between cities is less than 200 km, and the impact magnitude significantly declines with distance; second, the spillover effect of air pollution is sensitive to wind direction; third, the negative externality of air pollution on housing prices is significantly aggravated where there is higher ventilation intensity between the cities and lower effectiveness levels relating to local environmental governance. These findings are robust to differing model settings and differing indicators for key variables. Our estimate suggests that, on average, a doubling of the total imported air pollutants from neighboring cities less than 100 km distant will be likely to reduce local housing prices by 6%. The paper concludes with policy suggestions on how to mitigate the negative externality of transboundary air pollution through collaborative cross-boundary urban governance. • The spatial spillover effects of air pollution on housing prices vary with geographic, atmospheric and institutional factors. • The negative externality of transboundary air pollution on housing prices is significant only when the distance is less than 200 km. • The estimate of spillover effect of air pollution is sensitive to whether the study has proper control of wind direction. • Higher levels of ventilation intensity and emission rate of industry pollutant aggravates the spillover effect of air pollution. • A doubling of total imported air pollutants from cities within the radius of 100 km will depress local housing prices by 6%. [ABSTRACT FROM AUTHOR]

• Compared with NO 2 and O 3 exposure, PM 2.5 exposure is the greatest threat to cognitive function. • Cognitive function is spatially correlated. • Both short- and long-term air pollution exposure can affect cognitive function, but long-term damage was more serious. • Air pollution have different effects on the cognitive function of people with different characteristics. • People with low cognitive function are more affected by air pollution exposure. In the field of environmental health, the impact of air pollution on people's cognitive function is receiving increasing attention. Various air pollution exposures and different exposure periods result in different degrees of damage to cognition. This paper first used CFPS cognitive tests to evaluate the cognitive function of 15,163 adults in 25 provinces of China. Next, based on the geographical location of the population, the kriging interpolation method was applied to evaluate the different exposure periods for various air pollutants (PM 2.5 , NO 2 and O 3). Air pollution exposures lasting 3 years and more were referred to in this paper as long-term exposures, while those lasting<3 years were short-term exposures. This paper used an ordinary least squares (OLS) regression model to explore the differential effects of various air pollutant exposures and discussed the impact of long- and short-term exposure to pollutants. Subsequently, Moran's index was used to test the spatial connection for cognitive function, and the spatial error model was used for analysis in the spatial autoregressive model. This research also conducted a heterogeneity study on the justice of air pollutant exposure among people with different characteristics. The population was classified according to cognitive function and geographic location using OLS regression and quantile regression, and a propensity score matching (PSM) model was used for cross-validation to explore whether people with different characteristics and attributes were differentially exposed to air pollution. We found that there were significant negative relationships between air pollutant exposure and cognitive function, especially PM 2.5 exposure and long-term exposure. In addition, air pollution had significantly different impacts on cognition based on the different characteristics and attributes of the person exposed. This study helps by analyzing the socioeconomic factors that affect the level of exposure and suggests that groups who are vulnerable to environmental pollution should be protected and the occurrence of injustice reduced. The study also provides a reference for the distribution of pollution sources and the allocation of health resources, which can be useful for population distribution planning. [ABSTRACT FROM AUTHOR]

Based on household-level survey data collected from Qinghai Province of China, we estimated the greenhouse gas (GHG) and air pollutant emissions from spatial perspectives, including household energy consumption in pastoral, agropastoral, and agricultural zones. The findings suggest that the total annual GHG and pollutant emissions per capita in the area was 2296.32 kg per year. The highest amount of pollutants was emitted from the pastoral zones, followed by the agropastoral and agricultural zones. CO 2 is the primary GHG emitted by household energy consumption. Dung burning was the cause of the high PM 2.5 emissions in the pastoral areas, while the use of coal was the primary cause of GHG and pollutant emissions in the agropastoral and agricultural zones. • We used empirical data from rural households in Qinghai, China. • This paper indicates the reliance of local energy consumption on non-commercial energy. • We estimate greenhouse gas and pollutant emissions resulting from solid-fuel combustion. • We compare indoor pollutant emissions from pastoral, agropastoral, and agricultural zones spatially. • This paper provides policy recommendations for energy transition towards clean energy. Energy consumption in the household sector has become an important issue in China's energy consumption and an important unit of China's clean energy transformation. Currently, the potential air pollution, carbon emissions and health risks caused by energy consumption in many areas cannot be ignored, and refined and regionalized index-based research data necessary to support decision making are lacking. Based on household-level survey data collected from Qinghai Province, China, we estimated greenhouse gas (GHG) and air pollutant emissions from spatial perspectives, including household energy consumption in pastoral, agropastoral, and agricultural zones. The findings suggest that the total annual GHG and pollutant emissions per capita in the area was 2296.32 kg per year. The highest amount of pollutants was emitted from the pastoral zones, followed by the agropastoral and agricultural zones. CO 2 is the primary GHG emitted by household energy consumption. Dung burning was the cause of the high PM 2.5 emissions in the pastoral areas, while the use of coal was the primary cause of GHG and pollutant emissions in the agropastoral and agricultural zones. These findings highlight the need to integrate household energy policies with rural development to enable a complete transition towards cleaner fuels. [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]

This paper introduces a methodology to examine the effect of technological learning on industrial air pollutants intensity in China. It is based upon the theory of learning curves and environmental learning curves. In this way it is possible to estimate the effect of technological learning on industrial air pollutants intensity in two steps. First, we build a multi-factor environmental learning curve model to investigate the relationship between technological learning and industrial air pollutants intensity. At the second stage of the analysis, we assess whether and how different types of technological learning can reduce industrial air pollutants intensity based on a mediating effect model. The results show that there is a causal relationship between technological learning and industrial air pollutants intensity. Moreover, different types of technological learning have the same influence way on industrial air pollutants intensity: learning-by-doing can significantly reduce industrial air pollutants intensity through energy efficiency; and the same is true with learning-by-researching and learning-by-importing. In addition, different types of technological learning have different influence degree on industrial air pollutants intensity: learning-by-doing is the leading contributor to the reduction in industrial air pollutants intensity, while the contribution of learning-by-researching is the smallest. [ABSTRACT FROM AUTHOR]

Severe outdoor haze pollution has brought about associated public health impacts for China in recent years. Haze pollution therefore has become one of the most serious environmental problems in China. To alleviate this, the government issued its National Air Pollution Control NACAP (NAPCAP) in 2013 containing pollution reduction targets for 2017 and beyond. However, there are doubts whether these targets can be achieved. This paper examines this issue by modeling the statistical relationship between PM 2.5 concentration and air pollutant emissions (SO 2 , NO x , etc.), together with wind and neighboring transfer impacts at the city level, to identify ways of calculating intended maximum emission levels in the Beijing-Tianjin-Hebei area. The results indicate that local air pollutant emissions, lagged PM 2.5 concentrations, wind speed, and PM 2.5 concentrations in adjacent areas have a significant impact on PM 2.5 concentrations in the Beijing-Tianjin-Hebei area. The 2017 haze concentration control targets could not be achieved through the current NAPCAP plan, with PM 2.5 concentration levels in many locations being predicted as between two and three times those targeted. However, the 2017 concentration target can be fulfilled given the pollutant reduction rates of NAPCAP plus Enhanced Measures of Air Pollution Control for Beijing-Tianjin-Hebei region (EMAPC) under the same wind conditions as 2016. There are large deviations between the simulation and monitor values of PM 2.5 concentration when it is less windy, and the simulation value of most cities are generally lower than the monitor values. The unsuccessful implementation of pollutant reduction measures and inaccuracy of the monitor data are two possible reasons to explain the relatively large deviations involved. Finally, some preliminary institutional causes of haze pollution and solutions for its control in the Beijing-Tianjin-Hebei area are discussed. [ABSTRACT FROM AUTHOR]

The increasingly high frequency of heavy air pollution in most regions of China signals the urgent need for the transition to an environmentally friendly production performance by socioeconomic sectors for the sake of people's health and sustainable development. Focusing on CO 2 and major air pollutants, this paper presents a comprehensive environmental efficiency index based on evaluating the environmental efficiency of major socioeconomic sectors, including agriculture, power, industry, residential and transportation, at the province level in China in 2010 based on a slack-based measure DEA model with non-separable bad output and weights determined by the coefficient of variation method. In terms of the environment, 5, 16, 6, 7 and 4 provinces operated along the production frontier for the agricultural, power, industrial, residential and transportation sectors, respectively, in China in 2010, whereas Shanxi, Heilongjiang, Ningxia, Hubei and Yunnan showed lowest efficiency correspondingly. The comprehensive environmental efficiency index varied from 0.3863 to 0.9261 for 30 provinces in China, with a nationwide average of 0.6383 in 2010; Shanghai ranked at the top, and Shanxi was last. Regional disparities in environmental efficiency were identified. A more detailed inefficiency decomposition and benchmarking analysis provided insight for understanding the source of comprehensive environmental inefficiency and, more specifically, the reduction potential for CO 2 and air pollutants. Some specific research and policy implications were uncovered from this work. [ABSTRACT FROM AUTHOR]

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 μm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13–24 h prediction tasks (MAPE = 31.47%). [ABSTRACT FROM AUTHOR]

The frequent occurrence of haze, does not only pollute the indoor air quality seriously, but also endanger human health. University library is one of the most high frequency and close contact college environment for students, so it should keep good indoor air quality. But with the arrival of the northern heating period, outdoor airborne particulate matter concentration would increase and permeate the room through the windows and doors gap. With the frequently movement of people, it suspends in the air for a long time, seriously affects environmental quality of the reading room. In this paper, the diffusion mechanism of outdoor PM2.5 into the indoor is studied under different levels of haze pollution of a university library in north. Through the use of aerosol detector and other related equipment on the indoor and outdoor fine particulate PM2.5 pollutant concentration measured, analysis indoor and outdoor fine particulate matter pollution correlation, and determine the relevant factors. [ABSTRACT FROM AUTHOR]

Since the Guideline of Strengthen Preservation and Development of Traditional Village enacted, the traditional villages gradually gain academic concern and many achievements are acquired. But the study about outdoor wind environment is still insufficient. In this paper, the outdoor wind environment of Liyuanba Village in Bazhong City of Sichuan Province – a typical traditional village in the northeastern Sichuan Basin, is simulated by CFD methods. According to the simulation result, the outdoor wind environment in winter is comfortable for people in general, but there are still eddies and corner flows in some areas. To improve the outdoor wind environment, the suggestions such as changing the direction of courtyard and building are put forward. Furthermore, the optimization is also simulated and these two simulation results are compared in the end. [ABSTRACT FROM AUTHOR]

Previous studies analysing the effects of air pollution on work efficiency did not pay much attention to indoor impacts. This paper attempts to fill this gap by using technical data from Chinese Basketball Association (CBA) players. For the first time, we present personal work efficiency using a comprehensive evaluation index, the player efficiency rating (PER). The information is then combined with daily air quality data. We find that air pollution reduces work productivity: for every 10% increase in the air quality index (AQI), the average PER decreases by 0.4221, and the effects vary by air pollutant. Heterogeneity analysis shows that at the level of competition venues, the negative impact of air pollution on work efficiency can be weakened at home, while the rising temperature in the competition city can amplify the negative impact of air pollution on work efficiency. At the level of individual players, older players and foreign players are less affected by air pollution. Moreover, we find that air pollution affects work efficiency by reducing total work output and work accuracy. Our research constructs a comprehensive evaluation index for work efficiency, enriches research on the influence of air pollution on work efficiency, and provides a policy basis for identifying groups that are sensitive to air pollution, which might help with air pollution control policy making. [ABSTRACT FROM AUTHOR]

The change of land-use from natural to artificial surface induced by urban expansion can deeply impact the city environment. In this paper, the model WRF/Chem is applied to explore the effect of this change on regional meteorology and air quality over South China, where people have witnessed a rapid rate of urbanization. Two sets of urban maps are adopted to stand for the pre-urbanization and the present urban land-use distributions. Month-long simulations are conducted for January and July, 2014. The results show that urban expansion can obviously change the weather conditions around the big cities of South China. Especially in the Pearl River Delta region (PRD), the urban land-use change can increase the sensible heat flux by 40 W/m 2 in January and 80 W/m 2 in July, while decrease the latent heat flux about −50 W/m 2 in January and −120 W/m 2 in July. In the consequent, 2-m air temperature (T 2 ) increases as much as 1 °C and 2 °C (respective to January and July), planetary boundary layer height (PBLH) rises up by 100–150 m and 300 m, 10-m wind speed (WS 10 ) decreases by −1.2 m/s and −0.3 m/s, and 2-m specific humidity is reduced by −0.8 g/kg and −1.5 g/kg. Also, the precipitation in July can be increased as much as 120 mm, with more heavy rains and rainstorms. These variations of meteorological factors can significantly impact the spatial and vertical distribution of air pollutants as well. In PRD, the enhanced updraft can reduce the surface concentrations of PM 10 by −40 μg/m 3 (30%) in January and −80 μg/m 3 (50%) in July, but produce a correlating increase in the concentrations at higher atmospheric layers. However, according to the increase in T 2 and the decrease in surface NO, the surface concentrations of O 3 in PRD can increase by 2–6 ppb in January and 8–12 ppb in July. Meanwhile, there is a significant increase in the O 3 concentrations at upper layers above PRD, which should be attributed to the increase in air temperature and the enhanced upward transport of O 3 and its precursors. As for some relative small cities, such as Haikou, there is very little variation in surface PM 10 and O 3 in both months, implying less urbanization in these areas. Moreover, the depletion of O 3 by NO may be the main cause of the reduction of O 3 at upper layers in these small cities. [ABSTRACT FROM AUTHOR]

Vehicle emissions have become a major source of air pollution in Shandong province, which has experienced a sharp growth of vehicle numbers in recent years and now has the largest vehicle population in China. This paper combines the COPERT IV model with the vehicle age distribution to estimate the temporal trends and map the spatial distributions of vehicle emissions in Shandong province during the period ranging from 2000 to 2014. Both conventional air pollutants and greenhouse gases are included. In addition, a high-resolution vehicle emission inventory at the prefecture level is developed and mapped on a 0.05° × 0.05° grid based on road information. Our results show that the emissions of all of the conventional air pollutants have decreased to various extents over the recent past, but greenhouse gas emissions have continued to increase due to the lack of effective control strategies. The total emissions of CO, NMVOC, NO X , PM 10 , CO 2 , CH 4 and N 2 O from the Shandong vehicle fleet changed from 1734.5 Gg, 277.9 Gg, 177.0 Gg, 12.4 Gg, 19239.7 Gg, 11.3 Gg and 0.6 Gg, respectively, in 2000 to 1723.3 Gg, 234.2 Gg, 513.8 Gg, 29.5 Gg, 138,419.5 Gg, 15.3 Gg and 3.9 Gg, respectively, in 2014. Vehicle emissions were mainly concentrated in cities and became more dispersed in Shandong province between 2000 and 2014. [ABSTRACT FROM AUTHOR]

Recently, comprehensive attention has been attracted to global warming. Meanwhile, China is a country with huge energy consumption and greenhouse gas emission. Therefore, it is urgent for China to reduce the air pollutants embodied in each unit of a good for a less greenhouse gas earth. In this paper, air pollutants embodied in goods consumption as well as import-export trade in China was accounted in 1995–2009. Besides, we checked the structural decomposition analysis of emissions' variation. The results showed: (1) in 1995–2009, air pollutants embodied in goods for consumption and import-export trade in China displayed a rising trend. Emission of carbon dioxide was the largest and methane, carbon monoxide, sulphur oxides, non-methane volatile organic compounds, nitrogen oxides, ammonia, nitrous oxide were in proper sequence based at the average level. (2) Air pollutants embodied in goods exported were larger than those in imported and difference among nations existed. (3) The percentages of carbon dioxide, methane, nitrogen oxides, sulphur oxides, carbon monoxide, non-methane volatile organic compounds embodied in Construction were larger than in other departments on the consumption side, while percentages of carbon dioxide, nitrogen oxides, sulphur oxides, carbon monoxide, non-methane volatile organic compounds embodied in Electrical and Optical Equipment was bigger on the trade side. (4) Emission reduction techniques reduced air pollutants embodied in goods consumption and import and export trade in China. Consequently, China ought to pay attention to emission reduction of carbon dioxide and raise awareness of air pollutants controlling, especially in Construction, Electrical and Optical Equipment and other departments. More importantly, China ought to strengthen cooperation with other nations in emission reduction and developing technologies so as to cut down air pollutants, not only for China but also for the earth. [ABSTRACT FROM AUTHOR]

Assessment of human health impact caused by air pollution is crucial for evaluating environmental hazards. In this paper, concentrations of six air pollutants (PM 10 , PM 2.5 , NO 2 , SO 2 , O 3 , and CO) were first derived from satellite observations, and then the overall human health risks in China caused by multiple air pollutants were assessed using an aggregated health risks index. Unlike traditional approach for human health risks assessment, which relied on the in-situ air pollution measurements, the spatial distribution of aggregated human health risks in China were obtained using satellite observations in this research. It was indicated that the remote sensing data have advantages over in-situ data in accessing human health impact caused by air pollution. [ABSTRACT FROM AUTHOR]

Emissions of the fine particulate matters (diameter of 2.5 μm or less) caused by both the primary particle emissions and the precursor emission sources such as sulfur dioxide and nitrogen oxides, have contributed significantly to poor urban air quality in China, and have attracted tremendous public attention over the past few years. This study provides an interdisciplinary study to investigate the key contributors driving air pollution emissions changes in China from 1997 to 2012, by applying the Logarithmic Mean Divisia Index method. The decomposition results are presented in both multiplicative and additive approaches to show the relative and absolute contribution of each factor in affecting emission changes. Changes in total particulate matter emissions are attributed to variations in primary particle, sulfur dioxide and nitrogen oxides emissions. It is manifested that the economic growth effect and energy intensity effect have always been the two key drivers in affecting the changes in air pollutant emissions over the period. The effects of emission efficiency, production structure and population growth contribute less significantly to overall emission changes, and the impacts of different factors vary among different pollutants. Since current strategies and policies in combatting particulate matter emissions are inefficient, this paper provides a guideline for the Chinese Government to deal with the air pollution problem for sustainable development in China. [ABSTRACT FROM AUTHOR]

To address ecological and environmental issues, central environmental inspection (CEI) coordinated by the Chinese Ministry of Ecology and Environment has been implemented since 2016. This paper aims to comprehensively evaluate how and how much CEI affects air quality. The results of the difference-in-differences models show that CEI improved the air quality and reduced the concentrations of PM 2.5 , PM 10 , NO 2 , and SO 2 by 8.8%, 8.1%, 7.9%, and 2.4%, respectively. Moreover, environmental effectiveness was strengthened over the course of four rounds of inspection. The mediating model results indicate that effectiveness was achieved through active public participation, administrative punishments from the central inspectors, and positive rectification actions from the local governments. The greatest improvement in air quality occurred during the on-site inspection period, after which the effects gradually weakened. A review inspection was carried out to supervise the rectification tasks. The adoption of review inspection made the effects on air quality improvement reappear, which verifies that CEI in China is not just a temporary campaign-style enforcement but a normalized and effective governance of air pollution. • Central environmental inspection dropped air pollutant concentrations significantly. • Environmental effectiveness was strengthened over the course of four rounds of inspection. • The largest improvement in air quality occurred during the on-site inspection period. • The effectiveness was achieved through mediating effects of public participation and negative political incentives. [ABSTRACT FROM AUTHOR]

Self-reported life satisfaction of China's population has not improved as much as expected during the economic boom, which was accompanied by a significant decline in environmental performance. Is environmental pollution the culprit for the lagging subjective well-being? To explore this issue, this paper adopts sentiment analysis to construct a real-time daily subjective well-being metric at the city level based on the big data of online search traces. Using daily data from 13 Chinese cities centred on Beijing between August 2014 and December 2019, we look at the corelation between subjective well-being and air pollution and the heterogeneity in this relationship based on two separate identification strategies. We find that air pollutants are negatively correlated with subjective well-being, and well-being tends to decline more from pollution during hot seasons. In addition, residents in wealthier regions tend to be more sensitive to air pollution. This result may be explained by the differences in the subjective perception of air pollution and personal preferences at different levels of income. These findings provide information about concerns of the public, thereby helping the government to take appropriate actions to respond to the dynamics of subjective well-being. • Sentiment analysis and big data are adopted to construct a daily city-level subjective well-being metric. • Higher levels of airborne particulates lead to a decline in subjective well-being. • Peoples' well-being tends to decline more from pollution during hot seasons. • Residents in wealthier regions tend to be more sensitive to air pollution. [ABSTRACT FROM AUTHOR]

The values of OM emission factors of CO 2 , co-abatement rates per unit CO 2 emission reduction, and the health co-benefits per unit of CO 2 emission reduction in each grid in China. The depth of color indicates the relative size of the health co-benefits. Deeper colors represent higher CO 2 OM emission factors (t/MWh) in (a), higher co-abatement rates of each kiloton of CO 2 emission reduction (tSO 2 /kt CO 2 , tNOx/kt CO 2 , tPM 2.5 /kt CO 2) in (b), (c), and (d), and larger health co-benefits of each kiloton of CO 2 reduction (¥/kt CO 2) in (e). The values in (e) are mean values. The circular diagram in each grid shows the share of trans-grid health co-benefits (%) if carbon emissions are reduced in the respective grid. Tibet is blank due to lack of data. This graphical abstract implies that, if policy makers want to increase synergies between carbon and conventional air pollutants (CAP) reductions, they should allocate more carbon emission reduction targets to those regions with higher CAP co-abatement per unit of CO 2 emission reduction. Therefore, the grids that should take more responsibility to reduce emissions are as follows: the south, northwest, and east grid for SO 2 co-reduction; the northeast, north, and northwest grid for NOx co-reduction; and the northeast, south, and east grid for PM 2.5 co-reduction. However, if policy makers want to increase synergies between carbon emission reduction and health co-benefits, they should allocate more carbon emission reduction targets to those regions with larger health co-benefits per unit of CO 2 emission reduction. Therefore, the grids that should take more responsibility to reduce emissions are as follows: the south, central, east, north, northwest, and northeast grid. Besides, the graphical abstract also implies that, the share of trans-grid health co-benefits due to the same amount CO 2 emission reductions can be as high as 14.1% if CO 2 reduction takes place in the east grid. Therefore, in this study, we advocate that health co-benefits and the trans-regional distribution of health co-benefits should be incorporated into regional carbon emission reduction policy making in order to increase mitigation efficiency. • The health co-benefits per unit CO 2 reduced in China's power grids were quantified. • The difference in health co-benefits per unit CO 2 reduced could reach fivefold. • The share of health co-benefits transfer effect could be as high as 14.1%. • Health co-benefits and their trans-regional distribution should be considered. How to maximize the synergies between CO 2 and conventional air pollutants (CAP) emission reductions have been extensively discussed in previous studies. However, few studies have explored the essential policy concern, which is, how to maximize the social benefits including both carbon mitigation and human health co-benefits from the co-abated conventional air pollutants. Besides, for some sectors that have extensive inter-regional trade, using more low-carbon technologies to produce goods (and thus reducing its own CO 2 emissions) in one region may influence the need of goods imported from another region and consequently the associated conventional air pollutants emissions. Therefore, the health co-benefits may also be transferred to the other regions. This transfer effect has seldom been quantified before. To bridge these research gaps, this paper took China's power sector as an example and quantified the operating margin (OM) emission factors of SO 2 , NO X and PM 2.5 in China's six power grids. Then the co-abatement rates of these three pollutants when reducing each kiloton of CO 2 in different grids were calculated. Finally, considering different grids' meteorological conditions, population densities, baseline incidences, baseline death rates, and the inter-grid electricity trade, this paper computed and compared the values and regional distribution of health co-benefits per kiloton of CO 2 reduced. It is found that grids with the highest co-abatement rates in conventional air pollutants do not necessarily bring maximum health co-benefits. Although the northeast grid could achieve the largest co-reduction in NO X and PM 2.5 , it is the south grid that has the largest health co-benefits. The differences in health co-benefits of the same amount of CO 2 reduced among grids vary as much as fivefold. Besides, the share of trans-grid health co-benefits can be as high as 14.1% if CO 2 reduction takes place in the east grid. Therefore, it is suggested that the health co-benefits of CO 2 reduction and their trans-regional distribution should be taken seriously when making regional carbon emission reduction plans, in order to maximize the social benefits of CO 2 reduction. [ABSTRACT FROM AUTHOR]

Quantifying environmental load displacement from developed countries (the North) to developing countries (the South) is of particular importance for understanding the environmental implications of consumption in the North and global sustainability. Based on a global input–output model, this paper estimates emissions transfers between the North and the South for eight types of air pollutants from a consumption perspective, with a focus on emissions transfers between the North and China. The results show that 14%–30% of air pollutant emissions in the South were caused by consumption in the North in 2007. There is a large ‘pollution deficit’ between the North and South, which significantly increased during the period 1995–2007, that favors the theory of ecologically unequal exchange. Although the emissions per capita of the North from production for most air pollutants decreased over this period, the emissions per capita from consumption increased or decreased more mildly. The decomposition of emissions transfers further shows that South–South trade in intermediates has played an increasingly important role in environmental load displacement from the North to the South. It is important to assess the sustainability of so-called ‘weightless economies’ in the North from a global perspective that takes environmental load displacement into account. [ABSTRACT FROM AUTHOR]

The rapid growth of energy consumption in China has led to increased emissions of air pollutants. As a response, in its 12th Five Year Plan the Chinese government proposed mitigation targets for SO 2 and NO x emissions. In this paper, we have investigated mitigation measures taken in different sectors and their corresponding impacts on the energy system. Additionally, as non-fossil energy development has gained attention in addressing energy and environmental challenges in China, we investigated the impact of non-fossil energy development on air pollutant emissions, and explored interactions and co-benefits between these two types of policies. An extended Global Change Assessment Model (GCAM) was used in this study. The extended version of GCAM includes an additional air pollutant emissions control module coupling multiple end-of-pipe (EOP) control technologies with energy technologies, as well as more detailed end-use sectors in China. We find that implementing EOP control technologies would reduce air pollution in the near future, but with little room left to implement these EOP technologies, other cleaner and more efficient technologies are also effective. These technologies would reduce final energy consumption, increase electricity’s share in final energy, and increase the share of non-fossil fuels in primary energy and electricity consumption. Increasing non-fossil energy usage at China’s proposed adoption rate would in turn also reduce SO 2 and NO x emissions, however, the reductions from this policy alone still lag behind the targets for air pollutant reduction. A combination of air pollutant controls and non-fossil energy development could synergistically help realize the respective individual targets, and would result in lower costs than would addressing these issues separately. [ABSTRACT FROM AUTHOR]

In China, industrial energy use accounts for two thirds of total energy consumption, and this is expected to remain the same in the medium and long-term. China has embarked on a path towards more sustainable energy use to meet domestic (e.g. air quality) and global needs (e.g. climate change), and to sustain its economic welfare. However, most energy-economy models for China have shown limitations to evaluate policy instruments and technology diffusion in industries, in relation to the multiple policy goals. In this paper, the advantages and weaknesses of 19 current energy models for China are evaluated, including important co-benefits as reduced air pollutant emissions. Results show that the co-benefits of energy use and emission policies are rarely modeled on industrial level. Based on the critical assessment of the state-of-the-art energy models, we develop recommendations for modeling industrial energy use, with an emphasis on improved incorporation of (economic, environmental and energy) policy effects, technology representation, co-benefit modeling, and uncertainty analysis. [ABSTRACT FROM AUTHOR]

Policies have been the main motivator to stimulate the diffusion of new energy vehicles, which are vital to greenhouse gas control and air pollutant governance. However, the policy mechanisms are complicated and sometimes fall into inefficiency. Thus, a two-stage Stackelberg game model is developed in this study to investigate the impacts of four main policies, namely, subsidy, dual credit policy, restrictions on fuel vehicles, and charging pile construction, on new energy vehicle diffusion. Furthermore, the moderating role of market types is incorporated in analyzing policy effectiveness. Thus, policy performances can be evaluated under different markets. Results show that subsidy, dual credit policy, and charging pile construction have a positive effect on new energy vehicle diffusion. And controlling the trading price of new energy credit within a certain range proposed in this study is vital in maximizing policy effectiveness. However, the effect of restrictions on fuel vehicles shows an inverted U-shaped relationship, indicating that imposing excessive restrictions on fuel vehicles is unwise. Moreover, simultaneously implementing the four incentive policies is unnecessary. Replacing subsidy with dual credit policy is advisable, and the government should devote more resources to construct charging piles instead of implementing restrictions on fuel vehicles. Furthermore, the average price sensitivity of the consumption market has a negative moderating effect on policy effectiveness. The government must strengthen the policies under markets with high average price sensitivity. By applying Stackelberg game and computation experiment, this paper theoretically contributes to understanding the impact of multiple policies on the diffusion of new energy vehicles, and gaining insight into the performance of various policies under different consumption markets. [Display omitted] • Policies' effects get maximum when NEV-credit's price is within a certain range. • Restriction on FV presents inverted U-shaped relation with NEV diffusion. • Dual credit policy and subsidy are replaceable in high intensity. • It's better to make low-intensity FV restrictions when constructing charging plies. • Market's average price-sensitivity has negative moderating effect on NEV diffusion. [ABSTRACT FROM AUTHOR]

Air pollution is not only an environmental problem, but also an economic problem. Faced with the deteriorating air pollution, the role of financial institutions is increasingly valued. Because different air pollutants come from diverse sources, the same economic means have different impacts. To investigate this question, this paper adopts difference-in-differences to test China's green finance policy issued in 2017. The results indicate that green finance policy is capable of controlling overall situation of air pollution. As for the specific six pollutants, it shows superiority in reducing SO 2 , NO 2 and PM 2.5 , but inferiority in reducing CO, O 3 and PM 10. Besides, the control effects of green finance policy are through local companies' economic behaviors. Compared with the economically developed regions, the emission reduction effects of green finance policy in the economically underdeveloped regions are better. These findings offer references for how to control air pollution with economic means from the perspective of micro practice. • Pilot provinces have taken the lead in exploring green finance. • Air pollution is not only an environmental problem, but also an economic problem. • The green finance policy can reduce the air pollution. • The effect of green finance is better in the underdeveloped regions. [ABSTRACT FROM AUTHOR]

With fast economic development, industrialization and urbanization, China faces increasing pressures on carbon emission reduction, and especially on air pollutants (SO 2 , NOx, PM) reduction, particularly the notorious haze issue caused by air pollution in recent years. Pursuing co-benefits is an effective approach to simultaneously respond to both carbon and air pollutant problems. In this paper, the AIM/CGE (Asia–Pacific Integrated Assessment Model/Computational General Equilibrium) model and GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies)-China model are combined together to project future CO 2 and air pollutants emissions in China, as well as reduction costs and co-benefit effects. Considering implementation of carbon mitigation policy and air pollutant mitigation technologies, four scenarios (S1, S2, S3 and S4) are analyzed. Results indicate that by implementing both carbon and air pollutant mitigation (S4), CO 2 emission per GDP can be reduced by 41% by 2020, compared with the 2005 level, and SO 2 , NOx and PM2.5 emissions would change by a factor 0.8, 1.26 and 1.0 of the 2005 level, respectively in 2030. The real co-benefits of emission reductions (S2 minus S4) for SO 2 , NOx and PM2.5 are 2.4 Mt, 2.1 Mt and 0.3 Mt in 2020, and the corresponding cost reduction co-benefits are 4, 0.11, and 0.8 billion €, respectively. Provincial disparity analysis reveals that regions with higher co-benefits are those with higher GDP such as Guangdong, Shandong and Jiangsu, energy production bases such as Inner Mongolia and Shanxi, low coal quality provinces such as Sichuan (for SO 2 ), and industrial base provinces such as Liaoning. Cost-effectiveness is finally discussed for policy implications, which suggests that investment in less developed western regions is more cost-effective and easier in reducing CO 2 or air pollutant emissions than in developed eastern regions. [ABSTRACT FROM AUTHOR]