Spatiotemporal trends in PM2.5 levels from 2013 to 2017 and regional demarcations for joint prevention and control of atmospheric pollution in China

Emission control status and future perspectives of diesel trucks in China

Chinese diesel trucks are the main contributors to NOx and particulate matter (PM) vehicle emissions. An increase in diesel trucks could aggravate air pollution and damage human health. The Chinese government has recently implemented a series of emission control technologies and measures for air quality improvement. This paper summarizes recent control technologies and measures for diesel truck emissions in China and introduces the comprehensive application of control technologies and measures in Beijing-Tianjin-Hebei and surrounding regions. Remote online monitoring technology has been adopted according to the China VI standard for heavy-duty diesel trucks, and control measures such as transportation structure adjustment and heavy pollution enterprise classification control continue to support the battle action plan for pollution control. Perspectives and suggestions are provided for promoting pollution control and supervision of diesel truck emissions: adhere to the concept of overall management and control, vigorously promote the application of systematic and technological means in emission monitoring, continuously facilitate cargo transportation structure adjustment and promote new energy freight vehicles. This paper aims to accelerate the implementation of control technologies and measures throughout China. China is endeavouring to control diesel truck exhaust pollution. China is willing to cooperate with the world to protect the global ecological environment.

Advancing nuanced pollution control: Local improvements and spatial spillovers of policies on key enterprises

This paper examines the impact of air pollution control policies targeting key polluting enterprises, highlighting a strategic shift towards precision pollution control that concentrates on high-emission, high-risk businesses. The paper explores the efficacy of these policies and their potential spatial spillover effects, utilizing panel data from 259 Chinese cities from 2013 to 2021. Employing the difference-in-differences (DID) model and spatial Durbin model, the study analyzes both the direct local effects and the broader spatial consequences of these regulatory measures on air quality. The findings indicate a significant reduction in air pollutant concentrations in urban areas, attributing this improvement to factors such as industrial restructuring, increased investment in science and technology, and economic growth. Spatial econometric analysis further reveals a substantial positive correlation in air quality among Chinese cities. However, estimates of the spillover effect indicate that while such policies successfully reduce pollution locally, they could unintentionally degrade air quality in adjacent areas. The study highlights the need for nuanced policy strategies to mitigate unintended spatial spillovers and enhance overall effectiveness. It recommends tailored policies that integrate environmental and socioeconomic objectives, national and regional coordination for consistent enforcement, technology-driven compliance strategies, and incentives for sustainable enterprise practices.

The impacts of regional transport on anthropogenic source contributions of PM2.5 in a basin city, China

PM2.5 pollution events are often happened in urban agglomeration locates in mountain-basin regions due to the complex terra and intensive emissions. Source apportionment is essential for identifying the pollution sources and important for developing local mitigation strategies, however, it is influenced by regional transport. To understand how the regional transport influences the atmospheric environment of a basin, we connected the PM2.5 source contributions estimated by observation-based receptor source apportionment and the regional contributions estimated by a tagging technology in the comprehensive air quality model with extensions (CAMx) via an artificial neural network (ANNs). The result shows that the PM2.5 in Xi'an was from biomass burning, coal combustion, traffic related emissions, mineral dust, industrial emissions, secondary nitrate and sulfate. 48.8 % of the PM2.5 in study period was from Xi'an, then followed by the outside area of Guanzhong basin (28.2 %), Xianyang (14.6 %) and Weinan (5.8 %). Baoji and Tongchuan contributed trivial amount. The sensitivity analysis showed that the transported PM2.5 would lead to divergent results of source contributions at Xi'an. The transported PM2.5 from the outside has great a potential to alter the source contributions implying a large uncertainty of the source apportionment introduced when long-range transported pollutants arrived. It suggests that a full comprehension on the impacts of regional transport can lower the uncertainty of the local PM2.5 source apportionment and reginal collaborative actions can be of great use for pollution mitigation.

Revealing the impact of China's clean air policies on synergetic control of CO2 and air pollutant emissions: Evidence from Chinese cities

China is presently confronted with the intricate challenge of simultaneously mitigating air pollution and decelerating the pace of climate change. An integrated perspective to investigate the synergetic control of CO2 and air pollutant emissions is in an urgent need. Using data for 284 Chinese cities from 2009 to 2017, we introduced an indicator called coupling and coordination degree of CO2 and air pollutant emissions control (CCD) and found an upward and spatial agglomeration trend of CCD distribution during the research period. Then, this study posed a specific focus on the impact of China's Air Pollution Prevention and Control Action Plan (APPCAP). The DID model revealed that implementation of the APPCAP resulted in a 4.0% increase in CCD for cities with special emission limits, attributed to industrial structural adjustments and the promotion of technology innovation. Furthermore, we also identified positive spillover effects of the APPCAP on neighboring control group cities situated within 350 km of the treatment group cities, providing an explanation for the spatial agglomeration trend observed in CCD distribution. These findings hold significant implications for the synergetic control in China and underscored the potential benefits of industrial structural adjustments and technology innovation in mitigating environmental pollution.

Spatiotemporal dynamics, traceability analysis, and exposure risks of antibiotic resistance genes in PM2.5 in Handan, China

Fine particulate matter (PM2.5) seriously affects environmental air quality and human health, and antibiotic resistance genes (ARGs) in PM2.5 posed a great challenge to clinical medicine. The year of 2013-2017 was an important 5-year period for the implementation of Air Pollution Prevention and Control Action Plan (APPCAP) in China. Here, we took Handan, a PM2.5 polluted city in northern China, as the research object and analyzed ARGs in PM2.5 in winter (January) from 2013 to 2017. The results showed that the abundance of ARGs was the highest in 2013 (3.7 × 10-2 copies/16S rRNA), and ARGs were positively correlated with air quality index (AQI) (r = 0.328, P < 0.05) and PM2.5 concentration (r = 0.377, P = 0.020 < 0.05) in the 5-year period. The ARGs carried by PM2.5 in four functional regions of sewage treatment plant, steel works, university, and park showed that sul1 and qepA had higher abundance in each functional region, and the total ARG abundance in sewage treatment plant (1.3 × 10-1 copies/16S rRNA) was the highest, while lowest in park (2.0 × 10-3 copies/16S rRNA). Potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) model were used to trace the pollutants at the sampling points, which indicated that the surrounding cities contributed more than quarter to the sampling points. Therefore, regional transportation reduces the spatial distribution difference of ARGs in PM2.5. The exposure dose of ARGs in different functional regions illustrated that the total inhaled dose of ARGs in sewage treatment plant (1.7 × 105 copies/d) was the highest, while lowest in park (3.2 × 104 copies/d). This study is of great significance for assessing the distribution and sources of ARGs under the clean air initiative in China.

Long-term spatiotemporal evolution and coordinated control of air pollutants in a typical mega-mountain city of Cheng-Yu region under the "dual carbon" goal

Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for megacities to formulate relevant air pollution prevention and control measures and achieve carbon neutrality goals. Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain-city in China, environmental problems are complex and sensitive. This research aims to investigate the exceeding standard levels and spatio-temporal evolution of criteria pollutants between 2014 and 2020. The results indicated that PM10, PM2.5, CO and SO2 were decreased significantly by 45.91%, 52.86%, 38.89% and 66.67%, respectively. Conversely, the concentration of pollutant O3 present a fluctuating growth and found a "seesaw" phenomenon between it and PM. Furthermore, PM and O3 are highest in winter and summer, respectively. SO2, NO2, CO, and PM showed a "U-shaped", and O3 showed an inverted "U-shaped" seasonal variation. PM and O3 concentrations are still far behind the WHO, 2021AQGs standards. Significant spatial heterogeneity was observed in air pollution distribution. These results are of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, and formulate a regional carbon peaking roadmap under climate coordination. Besides, it can provide an important platform for exploring air pollution in typical terrain around the world and provide references for related epidemiological research.Implications: Chongqing is one of the dual-core key megacities in Cheng-Yu region and as a typical mountain city, environmental problems are complex and sensitive. Under the background of the "14th Five-Year Plan", the construction of the "Cheng-Yu Dual-City Economic Circle" and the "Dual-Carbon" goal, this article comprehensively discussed the annual and seasonal excess levels and spatiotemporal evolution of pollutants under the multiple policy and the newest international standards (WHO,2021AQG) backgrounds from 2014 to 2020 in Chongqing. Furthermore, suggestions and measures related to the collaborative management of pollutants were discussed. Finally, limitations and recommendations were also put forward.Clarifying the spatiotemporal distribution and impact mechanism of pollution is the prerequisite for cities to formulate relevant air pollution control measures and achieve carbon neutrality goals. This study is of great significance for Chongqing to achieve "double control and double reduction" of PM2.5 and O3 pollution, study and formulate a regional carbon peaking roadmap under climate coordination and an action plan for sustained improvement of air quality.In addition, this research can advanced our understanding of air pollution in complex terrain. Furthermore, it also promote the construction of the China national strategic Cheng-Yu economic circle and build a beautiful west. Moreover, it provides scientific insights for local policymakers to guide smart urban planning, industrial layout, energy structure, and transportation planning to improve air quality throughout the Cheng-Yu region. Finally, this is also conducive to future scientific research in other regions of China, and even megacities with complex terrain in the world.

Assessment of Regional Differences in the Implementation of the Air Pollution Prevention and Control Action Plan in China

Air pollution is still an important risk factor that endangers the health of Chinese people, leading the government to implement a series of policies to address air pollution. This study takes the Air Pollution Prevention and Control Action Plan (APPCAP) proposed in 2013 as the object and uses the combined data set of China's 2000-2019 economic panel data and PM_2.5 remote sensing data to analyse the implementation effect of the policy by the multiperiod difference-in-differences method, considering regional heterogeneity. The results show that the implementation of the APPCAP significantly reduced the PM_2.5 concentration in China, and the effect was stronger in the Yangtze River Delta region. Future governance policies should further consider local characteristics and determine pollution control goals and measures according to local conditions.

The contribution of industrial emissions to ozone pollution: identified using ozone formation path tracing approach

Wintertime meteorological conditions are usually unfavorable for ozone (O₃) formation due to weak solar irradiation and low temperature. Here, we observed a prominent wintertime O₃ pollution event in Shijiazhuang (SJZ) during the Chinese New Year (CNY) in 2021. Meteorological results found that the sudden change in the air pressure field, leading to the wind changing from northwest before CNY to southwest during CNY, promotes the accumulation of air pollutants from southwest neighbor areas of SJZ and greatly inhibits the diffusion and dilution of local pollutants. The photochemical regime of O₃ formation is limited by volatile organic compounds (VOCs), suggesting that VOCs play an important role in O₃ formation. With the developed O₃ formation path tracing (OFPT) approach for O₃ source apportionment, it has been found that highly reactive species, such as ethene, propene, toluene, and xylene, are key contributors to O₃ production, resulting in the mean O₃ production rate (P_O3) during CNY being 3.7 times higher than that before and after CNY. Industrial combustion has been identified as the largest source of the P_O3 (2.6 ± 2.2 ppbv h^-1), with the biggest increment (4.8 times) during CNY compared to the periods before and after CNY. Strict control measures in the industry should be implemented for O₃ pollution control in SJZ. Our results also demonstrate that the OFPT approach, which accounts for the dynamic variations of atmospheric composition and meteorological conditions, is effective for O₃ source apportionment and can also well capture the O₃ production capacity of different sources compared with the maximum incremental reactivity (MIR) method.

Long-Term Air Pollution Characteristics and Multi-scale Meteorological Factor Variability Analysis of Mega-mountain Cities in the Chengdu-Chongqing Economic Circle

Currently, air quality has become central to global environmental policymaking. As a typical mountain megacity in the Cheng-Yu region, the air pollution in Chongqing is unique and sensitive. This study aims to comprehensively investigate the long-term annual, seasonal, and monthly variation characteristics of six major pollutants and seven meteorological parameters. The emission distribution of major pollutants is also discussed. The relationship between pollutants and the multi-scale meteorological conditions was explored. The results indicate that particulate matter (PM), SO2 and NO2 showed a "U-shaped" variation, while O3 showed an "inverted U-shaped" seasonal variation. Industrial emissions accounted for 81.84%, 58% and 80.10% of the total SO2, NOx and dust pollution emissions, respectively. The correlation between PM2.5 and PM10 was strong (R = 0.98). In addition, PM only showed a significant negative correlation with O3. On the contrary, PM showed a significant positive correlation with other gaseous pollutants (SO2, NO2, CO). O3 is only negatively correlated with relative humidity and atmospheric pressure. These findings provide an accurate and effective countermeasure for the coordinated management of air pollution in Cheng-Yu region and the formulation of the regional carbon peaking roadmap. Furthermore, it can improve the prediction accuracy of air pollution under multi-scale meteorological factors, promote effective emission reduction paths and policies in the region, and provide references for related epidemiological research.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s11270-023-06279-8.

Quantifying the Spatiotemporal Heterogeneity of PM2.5 Pollution and Its Determinants in 273 Cities in China

Fine particulate matter (PM_2.5) pollution brings great negative impacts to human health and social development. From the perspective of heterogeneity and the combination of national and urban analysis, this study aims to investigate the variation patterns of PM_2.5 pollution and its determinants, using geographically and temporally weighted regression (GTWR) in 273 Chinese cities from 2015 to 2019. A comprehensive analytical framework was established, composed of 14 determinants from multi-dimensions, including population, economic development, technology, and natural conditions. The results indicated that: (1) PM_2.5 pollution was most severe in winter and the least severe in summer, while the monthly, daily, and hourly variations showed "U"-shaped, pulse-shaped and "W"-shaped patterns; (2) Coastal cities in southeast China have better air quality than other cities, and the interaction between determinants enhanced the spatial disequilibrium of PM_2.5 pollution; (3) The determinants showed significant heterogeneity on PM_2.5 pollution-specifically, population density, trade openness, the secondary industry, and invention patents exhibited the strongest positive impacts on PM_2.5 pollution in the North China Plain. Relative humidity, precipitation and per capita GDP were more effective in improving atmospheric quality in cities with serious PM_2.5 pollution. Altitude and the proportion of built-up areas showed strong effects in western China. These findings will be conductive to formulating targeted and differentiated prevention strategies for regional air pollution control.

Characteristics of wintertime carbonaceous aerosols in two typical cities in Beijing-Tianjin-Hebei region, China: Insights from multiyear measurements

In order to investigate the impact of "Blue Sky War" implemented during 2018-2020 on carbonaceous aerosols in Beijing-Tianjin-Hebei (BTH) region, China, fine particulate matter (PM_2.5) samples were collected simultaneously in Tianjin and Handan in three consecutive winters from 2018 to 2020. Organic carbon (OC) and elemental carbon (EC) in PM_2.5 were measured with the same thermal-optical methods and analysis protocols. Significant reductions in primary organic carbon (POC) and EC concentrations were observed both in Tianjin and Handan, with decreasing rates of 0.65 and 2.95 μg m^-3 yr^-1 for POC and 0.13 and 0.64 μg m^-3 yr^-1 for EC, respectively. The measured absorption coefficients of EC (b_{abs, EC}) also decreased year by year, with a decreasing rate of 1.82 and 6.16 Mm^-1 yr^-1 in Tianjin and Handan, respectively. The estimated secondary organic carbon (SOC) concentrations decreased first and then increased in both Tianjin and Handan, accounting for more than half of the total OC in winter of 2020-2021 and with increasing contributions especially in highly polluted days. SOC was recognized as one of key factors influencing EC light absorption. EC in the two cities was relatively more related to coal combustion and industrial sources. The reductions of primary carbonaceous components may be attributed to the air quality regulations targeting coal combustion and industrial sources emissions in BTH area. Potential source contribution function (PSCF) analysis results indicated that the major source areas of OC and EC in Tianjin were the southwest region of the sampling site, while the southeast areas for Handan. These findings demonstrated the effectiveness of air quality regulation in primary emissions in typical polluted cities in BTH region and highlighted the needs for further control and in-depth investigation of SOC formation along with implementation of air pollution control act in the future.

Synergetic PM2.5 and O3 control strategy for the Yangtze River Delta, China

PM_2.5 concentrations have dramatically reduced in key regions of China during the period 2013-2017, while O₃ has increased. Hence there is an urgent demand to develop a synergetic regional PM_2.5 and O₃ control strategy. This study develops an emission-to-concentration response surface model and proposes a synergetic pathway for PM_2.5 and O₃ control in the Yangtze River Delta (YRD) based on the framework of the Air Benefit and Cost and Attainment Assessment System (ABaCAS). Results suggest that the regional emissions of NOx, SO₂, NH₃, VOCs (volatile organic compounds) and primary PM_2.5 should be reduced by 18%, 23%, 14%, 17% and 33% compared with 2017 to achieve 25% and 5% decreases of PM_2.5 and O₃ in 2025, and that the emission reduction ratios will need to be 50%, 26%, 28%, 28% and 55% to attain the National Ambient Air Quality Standard. To effectively reduce the O₃ pollution in the central and eastern YRD, VOCs controls need to be strengthened to reduce O₃ by 5%, and then NOx reduction should be accelerated for air quality attainment. Meanwhile, control of primary PM_2.5 emissions shall be prioritized to address the severe PM_2.5 pollution in the northern YRD. For most cities in the YRD, the VOCs emission reduction ratio should be higher than that for NOx in Spring and Autumn. NOx control should be increased in summer rather than winter when a strong VOC-limited regime occurs. Besides, regarding the emission control of industrial processes, on-road vehicle and residential sources shall be prioritized and the joint control area should be enlarged to include Shandong, Jiangxi and Hubei Province for effective O₃ control.

The effect of joint prevention and control plan on atmospheric pollution governance and residents' willingness to pay

This study investigates the governance effect of China's joint prevention and control of atmospheric pollution (JPCAP) plan and residents' willingness to pay for clean air. First, this study delves into the JPCAP plan's atmospheric pollution governance effect using the difference-in-difference and spatial difference-in-difference models. The results showed that the atmospheric pollution in Beijing-Tianjin-Hebei (BTH) and surrounding cities have significant spatial autocorrelation characteristics. From the autumn and winter of 2017 to 2019, the JPCAP plan implemented by BTH atmospheric pollution transmission channel cities significantly reduced atmospheric pollution. However, the atmospheric pollution governance effect of the JPCAP plan is weaker in 2018-2019 than in 2017-2018. Second, this study introduced the air quality index and three atmospheric pollutants-PM2.5, NO2, and SO2-into the hedonic price model and investigated the residents' willingness to pay by employing the spatial error model and spatial lag model. Finally, subsample and quantile regression were used to discuss the heterogeneity of residents' willingness to pay. The results show that the reduction in atmospheric pollution increases residents' willingness to pay for clean air. Residents have different willingness to pay for reducing different atmospheric pollutants, and there is heterogeneity in willingness to pay across regions and consumption levels. Residents in areas with the JPCAP plan have a higher willingness to pay than those without the JPCAP plan, and there is no spatial autocorrelation characteristic of the willingness to pay of residents in BTH and surrounding cities.

Regional demarcation of synergistic control for PM2.5 and ozone pollution in China based on long-term and massive data mining

Implementing an inter-regional synergistic control policy for fine particulate matter (PM_2.5) and ground-level ozone (O₃) could improve regional air quality. However, little is known about the effectiveness and accuracy of synergistic control region delineation. This study aimed to construct a network model and apply it to a case study of regional delineation in China at different scales to quantify the interactions between regions. Firstly, the Cumulative Risk Index (CRI) was proposed and quantified from a health risk perspective based on the daily mean PM_2.5 and daily maximum 8-h average O₃ concentrations from 2015 to 2020 in China. Then, the complex network topology parameters were introduced to determine the optimal threshold for different network constructions, and the Girvan-Newman (GN) algorithm was used to divide the network into independent regions. Results showed that the correlation between cities is more robust than that between provinces. There are four-seven major provincial-scale regions with strong synchronicity in CRI, suggesting that PM_2.5 and O₃ synergistic control policies shall be implemented jointly within these demarcated regions. Moreover, urban-scale CRI network analysis indicated that the existing key control areas (2 + 26 cities) need to be expanded to 40-50 cities and refined into seven independent urban regions. Meanwhile, the Fen-Wei Plain can be focused on six cities: Xi'an, Baoji, Xianyang, Weinan, Yuncheng, and Tongchuan. This study could improve our understanding of the synergistic control regions for PM_2.5 and O₃ pollution, and the results could be used to develop joint control policies for both pollutants.

Spatiotemporal characterization of aerosols and trace gases over the Yangtze River Delta region, China: impact of trans-boundary pollution and meteorology

BACKGROUND

The spatiotemporal variation of observed trace gases (NO₂, SO₂, O₃) and particulate matter (PM_2.5, PM₁₀) were investigated over cities of Yangtze River Delta (YRD) region including Nanjing, Hefei, Shanghai and Hangzhou. Furthermore, the characteristics of different pollution episodes, i.e., haze events (visibility < 7 km, relative humidity < 80%, and PM_2.5 > 40 µg/m³) and complex pollution episodes (PM_2.5 > 35 µg/m³ and O₃ > 160 µg/m³) were studied over the cities of the YRD region. The impact of China clean air action plan on concentration of aerosols and trace gases is examined. The impacts of trans-boundary pollution and different meteorological conditions were also examined.

RESULTS

The highest annual mean concentrations of PM_2.5, PM₁₀, NO₂ and O₃ were found for 2019 over all the cities. The annual mean concentrations of PM_2.5, PM₁₀, and NO₂ showed continuous declines from 2019 to 2021 due to emission control measures and implementation of the Clean Air Action plan over all the cities of the YRD region. The annual mean O₃ levels showed a decline in 2020 over all the cities of YRD region, which is unprecedented since the beginning of the China's National environmental monitoring program since 2013. However, a slight increase in annual O₃ was observed in 2021. The highest overall means of PM_2.5, PM₁₀, SO₂, and NO₂ were observed over Hefei, whereas the highest O₃ levels were found in Nanjing. Despite the strict control measures, PM_2.5 and PM₁₀ concentrations exceeded the Grade-1 National Ambient Air Quality Standards (NAAQS) and WHO (World Health Organization) guidelines over all the cities of the YRD region. The number of haze days was higher in Hefei and Nanjing, whereas the complex pollution episodes or concurrent occurrence of O₃ and PM_2.5 pollution days were higher in Hangzhou and Shanghai.The in situ data for SO₂ and NO₂ showed strong correlation with Tropospheric Monitoring Instrument (TROPOMI) satellite data.

CONCLUSIONS

Despite the observed reductions in primary pollutants concentrations, the secondary pollutants formation is still a concern for major metropolises. The increase in temperature and lower relative humidity favors the accumulation of O₃, while low temperature, low wind speeds and lower relative humidity favor the accumulation of primary pollutants. This study depicts different air pollution problems for different cities inside a region. Therefore, there is a dire need to continuous monitoring and analysis of air quality parameters and design city-specific policies and action plans to effectively deal with the metropolitan pollution.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12302-022-00668-2.

Analysis of the air pollution reduction and climate change mitigation effects of the Three-Year Action Plan for Blue Skies on the "2+26" Cities in China

City clusters play an important role in air pollutant and greenhouse gas (GHG) emissions reduction in China, primarily due to their high fossil energy consumption levels. The "2 + 26" Cities, i.e., Beijing, Tianjin and 26 other perfectures in northern China, has experienced serious air pollution in recent years. We employ the Greenhouse Gas and Air Pollution Interactions and Synergies model adapted to the "2 + 26" Cities (GAINS-JJJ) to evaluate the impacts of structural adjustments in four major sectors, industry, energy, transport and land use, under the Three-Year Action Plan for Blue Skies (Three-Year Action Plan) on the emissions of both the major air pollutants and CO₂ in the "2 + 26" Cities. The results indicate that the Three-Year Action Plan applied in the "2 + 26" Cities reduces the total emissions of primary fine particulate matter with an aerodynamic diameter of ≤ 2.5 μm (PM_2.5), SO₂, NO_x, NH₃ and CO₂ by 17%, 25%, 21%, 3% and 1%, respectively, from 2017 to 2020. The emission reduction potentials vary widely across the 28 prefectures, which may be attributed to the differences in energy structure, industrial composition, and policy enforcement rate. Among the four sectors, adjustment of industrial structure attains the highest co-benefits of CO₂ reduction and air pollution control due to its high CO₂ reduction potential, while structural adjustments in energy and transport attain much lower co-benefits, despite their relatively high air pollutant emissions reductions, primarily resulting from an increase in the coal-electric load and associated carbon emissions caused by electric reform policies..

Co-exposure to multiple air pollutants and sleep disordered breathing in patients with or without obstructive sleep apnea: A cross-sectional study

BACKGROUND

Air pollution may be a contributing risk factor for obstructive sleep apnea (OSA). However, the health effects of co-exposure to multiple air pollutants on OSA patients remain unclear.

OBJECTIVES

To assess the joint effect of multi-pollutant on sleep disordered breathing (SDB) parameters in patients with or without OSA and identify the dominant pollutants.

METHODS

A total of 2524 outpatients from April 2020 to May 2021 were recruited in this cross-sectional study. Ambient air pollutant data were obtained from the nearest central monitoring stations to participants' residential address. SDB parameters were measured by the ApneaLink devices, including apnea-hypopnea index (AHI), hypopnea index (HI), oxygen desaturation index (ODI), average oxygen saturation (SpO₂), percentage sleep time with <90% saturation (T90), and desaturation. Bayesian kernel machine regression (BKMR) was applied to evaluate the effects of multiple pollutants.

RESULTS

Significant associations were observed between air pollutants and SDB parameters (including increases in AHI, HI, ODI, and desaturation) among patients with OSA. Co-exposure to air pollutants was positively correlated with AHI, HI, and ODI. PM₁₀ and O₃ dominated the effects of pollutant mixtures on OSA, with the highest posterior inclusion probability (PIP) values of 0.592 and 0.640, respectively. Stratified analysis showed that, compared to male patients with OSA, stronger effects on the SDB parameters were observed in female patients. Stronger associations were also found in the warm season than those in the cold season.

CONCLUSION

Co-exposure to air pollutants was associated with SDB parameters among patients with OSA, PM₁₀ and O₃ might play the dominant roles.

Evaluating the real changes of air quality due to clean air actions using a machine learning technique: Results from 12 Chinese mega-cities during 2013-2020

China has implemented two national clean air actions in 2013-2017 and 2018-2020, respectively, with the aim of reducing primary emissions and hence improving air quality at a national level. It is important to examine the effectiveness of such emission reductions and assess the resulting changes in air quality. However, such evaluation is difficult as meteorological factors can amplify, or obscure the changes of air pollutants, in addition to the emission reduction. In this study, we applied the random forest machine learning technique to decouple meteorological influences from emissions changes, and examined the deweathered trends of air pollutants in 12 Chinese mega-cities during 2013-2020. The observed concentrations of all criteria pollutants except O₃ showed significant declines from 2013 to 2020, with PM_2.5 annual decline rates of 6-9% in most cities. In contrast, O₃ concentrations increased with annual growth rates of 1-9%. Compared with the observed results, all the pollutants showed smoothed but similar variation in trend and annual rate-of-change after weather normalization. The response of O₃ to NO₂ concentrations indicated significant regional differences in photochemical regimes, and the differences between observed and deweathered results provided implications for volatile organic compound emission reductions in O₃ pollution mitigation. We further evaluated the effectiveness of first and second clean air actions by removing the meteorological influence. We found that the meteorology can make negative or positive contribution in reducing pollutant concentrations from emission reduction, depending on type of pollutants, locations, and time period. Among the 12 mega-cities, only Beijing showed a positive meteorological contribution in amplifying reductions in main pollutants except O₃ during both clean air action periods. Considering the large and variable impact of meteorological effects in changing air quality, we suggest that similar deweathered analysis is needed as a routine policy evaluation tool on a regional basis.

Optimal Planning of Air Quality-Monitoring Sites for Better Depiction of PM2.5 Pollution across China

A myriad of studies have attempted to use ground-level observations to obtain gap-free spatiotemporal variations of PM_2.5, in support of air quality management and impact studies. Statistical methods (machine learning, etc.) or numerical methods by combining chemical transport modeling and observations with data assimilation techniques have been typically applied, yet the significance of site placement has not been well recognized. In this study, we apply five proper orthogonal decomposition (POD)-based sensor placement algorithms to identify optimal site locations and systematically evaluate their reconstruction ability. We demonstrate that the QR pivot is relatively more reliable in deciding optimal monitoring site locations. When the number of planned sites (sensors) is limited, using a lower number of modes would yield lower estimation errors. However, the dimension of POD modes has little impact on reconstruction quality when sufficient sensors are available. The locations of sites guided by the QR pivot algorithm are mainly located in regions where PM_2.5 pollution is severe. We compare reconstructed PM_2.5 pollution based on QR pivot-guided sites and existing China National Environmental Monitoring Center (CNEMC) sites and find that the QR pivot-guided sites are superior to existing sites with respect to reconstruction accuracy. The current planning of monitoring stations is likely to miss sources of pollution in less-populated regions, while our QR pivot-guided sites are planned based on the severity of PM_2.5 pollution. This planning methodology has additional potentials in chemical data assimilation studies as duplicate information from current CNEMC-concentrated stations is not likely to boost performance.

Characterization of Atmospheric Fine Particles and Secondary Aerosol Estimated under the Different Photochemical Activities in Summertime Tianjin, China

In order to evaluate the pollution characterization of PM_2.5 (particles with aerodynamic diameters less than or equal to 2.5 μm) and secondary aerosol formation under the different photochemical activity levels, CO was used as a tracer for primary aerosol, and hourly maximum of O₃ (O_3,max) was used as an index for photochemical activity. Results showed that under the different photochemical activity levels of L, M, LH and H, the mass concentration of PM_2.5 were 29.8 ± 17.4, 32.9 ± 20.4, 39.4 ± 19.1 and 42.2 ± 18.9 μg/m³, respectively. The diurnal patterns of PM_2.5 were similar under the photochemical activity and they increased along with the strengthening of photochemical activity. Especially, the ratios of estimated secondary aerosol to the observed PM_2.5 were more than 58.6% at any hour under the photochemical activity levels of LH and H. The measured chemical composition included water soluble inorganic ions, organic carbon (OC), and element carbon (EC), which accounted for 73.5 ± 14.9%, 70.3 ± 24.9%, 72.0 ± 21.9%, and 65.8 ± 21.2% in PM_2.5 under the photochemical activities of L, M, LH, and H, respectively. Furthermore, the sulfate (SO₄²⁻) and nitrate (NO₃⁻) were nearly neutralized by ammonium (NH₄⁺) with the regression slope of 0.71, 0.77, 0.77, and 0.75 between [NH₄⁺] and 2[SO₄²⁻] + [NO₃⁻]. The chemical composition of PM_2.5 was mainly composed of SO₄²⁻, NO₃⁻, NH₄⁺ and secondary organic carbon (SOC), indicating that the formation of secondary aerosols significantly contributed to the increase in PM_2.5. The formation mechanism of sulfate in PM_2.5 was the gas-phase oxidation of SO₂ to H₂SO₄. Photochemical production of nitric acid was intense during daytime, but particulate nitrate concentration was low in the afternoon due to high temperature.

Exploring the Spatiotemporal Evolution and Socioeconomic Determinants of PM2.5 Distribution and Its Hierarchical Management Policies in 366 Chinese Cities

From 2013 to 2017, progress has been made by implementing the Air Pollution Prevention and Control Action Plan. Under the background of the 3 Year Action Plan to Fight Air Pollution (2018–2020), the pollution status of PM2.5, a typical air pollutant, has been the focus of continuous attention. The spatiotemporal specificity of PM2.5 pollution in the Chinese urban atmospheric environment from 2018 to 2020 can be summarized to help conclude and evaluate the phased results of the battle against air pollution, and further, contemplate the governance measures during the period of the 14th Five-Year Plan (2021–2025). Based on PM2.5 data from 2018 to 2020 and taking 366 cities across China as research objects, this study found that PM2.5 pollution has improved year by year from 2018 to 2020, and that the heavily polluted areas were southwest Xinjiang and North China. The number of cities with a PM2.5 concentration in the range of 25–35 μg/m³ increased from 34 in 2018 to 86 in 2019 and 99 in 2020. Moreover, the spatial variation of the PM2.5 gravity center was not significant. Concretely, PM2.5 pollution in 2018 was more serious in the first and fourth quarters, and the shift of the pollution's gravity center from the first quarter to the fourth quarter was small. Global autocorrelation indicated that the space was positively correlated and had strong spatial aggregation. Local Moran's I and Local Geti's G were applied to identify hotspots with a high degree of aggregation. Integrating national population density, hotspots were classified into four areas: the Beijing–Tianjin–Hebei region, the Fenwei Plain, the Yangtze River Delta, and the surrounding areas were selected as the key hotspots for further geographic weighted regression analysis in 2018. The influence degree of each factor on the average annual PM2.5 concentration declined in the following order: (1) the proportion of secondary industry in the GDP, (2) the ownership of civilian vehicles, (3) the annual grain planting area, (4) the annual average population, (5) the urban construction land area, (6) the green space area, and (7) the per capita GDP. Finally, combined with the spatiotemporal distribution of PM2.5, specific suggestions were provided for the classified key hotspots (Areas A, B, and C), to provide preliminary ideas and countermeasures for PM2.5 control in deep-water areas in the 14th Five-Year Plan.

Risk Assessment and Prediction of Air Pollution Disasters in Four Chinese Regions

Evaluating the regional trends of air pollution disaster risk in areas of heavy industry and economically developed cities is vital for regional sustainable development. Until now, previous studies have mainly adopted a traditional weighted comprehensive evaluation method to analyze the air pollution disaster risk. This research has integrated principal component analysis (PCA), a genetic algorithm (GA) and a backpropagation (BP) neural network to evaluate the regional disaster risk. Hazard risk, hazard-laden environment sensitivity, hazard-bearing body vulnerability and disaster resilience were used to measure the degree of disaster risk. The main findings were: (1) the air pollution disaster risk index of Liaoning Province, Beijing, Shanghai and Guangdong Province increased year by year from 2010 to 2019; (2) the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of each regional air pollution disaster risk index in 2019, as predicted by the PCA-GA-BP neural network, were 0.607, 0.317 and 20.3%, respectively; (3) the predicted results were more accurate than those using a PCA-BP neural network, GA-BP neural network, traditional BP neural network, support vector regression (SVR) or extreme gradient boosting (XGBoost), which verified that machine learning could be used as a method of air pollution disaster risk assessment to a considerable extent.

Cluster analysis of PM2.5 pollution in China using the frequent itemset clustering approach

In recent years, severe air pollution has frequently occurred in China at the regional scale. The clustering method to define joint control regions is an effective approach to address severe regional air pollution. However, current cluster analysis research on the determination of joint control areas relies on the Pearson correlation coefficient as a similarity measure. Due to nonlinearity and outliers in air pollution data, the correlation coefficient cannot accurately reveal the similarity in air quality between different cities. To bridge this gap, we proposed a method to delineate spatial patterns of PM_2.5 pollution and regional boundaries of polluted areas using the frequent itemset clustering approach. The frequent itemsets between cities were first mined, and the support values were employed as interestingness metrics to describe the significance of similar variation patterns between cities. Then, the hierarchical clustering method was applied to identify appropriate areas for joint pollution control. The proposed clustering algorithm exhibits the advantages of not requiring model assumptions and a robustness to the outliers, which is a cost-effective approach to define joint control regions. By analysing urban PM_2.5 pollution in China from 2015 to 2018, we obtained results demonstrating that the frequent itemset clustering approach can efficiently determine pollution patterns and can effectively identify regional divisions. The clustering approach could facilitate a greater understanding of PM_2.5 spatiotemporal aggregation to design joint control measures among areas. The findings and methodology of this research have important implications for the formulation of clean air policies in China.

Association between air pollution and emergency room visits for eye diseases and effect modification by temperature in Beijing, China

The growing burden of eye disease worldwide has aroused increasing concern upon its environmental etiology. This study aims to evaluate the associations of air pollutants with emergency room visits for eye diseases and the effect modification by temperature. Based on 24,389 cases from a general hospital during 2014-2019 in Beijing, China, this study used generalized additive models to examine the associations of air pollutants and emergency room visits for total eye diseases (ICD10: H00-H59) and conjunctivitis (ICD10: H10). Short-term exposures to PM2.5, PM10, CO, and NO2 were associated with increased visits for total eye diseases and conjunctivitis, and stronger effect estimates were observed in high (>75th) temperature group for PM2.5, PM10, CO, and NO2 and low (<75th) temperature group for CO and NO2. For instance, a 10 μg/m3 increase in PM2.5 at lag0-1 were associated with a 0.73% (95% CI: 0.23%, 1.24%) increase in total eye disease visits and a 1.34% (95% CI: 0.55%, 2.13%) increase in conjunctivitis visits, respectively. Meanwhile, a 10 μg/m3 increase in PM2.5 was associated with a 1.57% (95% CI: 0.49%, 2.64%) change in high temperature group and a 0.48% (95% CI: -0.24%, 1.19%) change in medium temperature group (P for interaction = 0.04) in total eye disease visits. Our study emphasizes the importance of controlling the potential hazards of air pollutants on eyes, especially on days with relatively higher or colder temperature.

Temporal and Spatial Analysis of PM2.5 and O3 Pollution Characteristics and Transmission in Central Liaoning Urban Agglomeration from 2015 to 2020

The central Liaoning urban agglomeration is an important heavy industry development base in China, and also an important part of the economy in northeast China. The atmospheric environmental problems caused by the development of heavy industry are particularly prominent. Trajectory clustering, potential source contribution (PSCF), and concentration weighted trajectory (CWT) analysis are used to discuss the temporal and spatial pollution characteristics of PM2.5 and ozone concentrations and reveal the regional atmospheric transmission pattern in central Liaoning urban agglomeration from 2015 to 2020. The results show that: (1) PM2.5 in the central Liaoning urban agglomeration showed a decreasing trend from 2015 to 2020. The concentration of PM2.5 is the lowest in 2018. Except for Benxi (34.7 µg/m3), the concentrations of PM2.5 in other cities do not meet the standard in 2020. The ozone concentration in Anshan, Liaoyang, and Shenyang reached the peaks in 2017, which are 68.76 µg/m3, 66.27 µg/m3, and 63.46 µg/m3 respectively. PM2.5 pollution is the highest in winter and the lowest in summer. The daily variation distribution of PM2.5 concentration showed a bimodal pattern. Ozone pollution is the most serious in summer, with the concentration of ozone reaching 131.14 µg/m3 in Shenyang. Fushun is affected by Shenyang intercity pollution, and the ozone concentration is high. (2) In terms of spatial distribution, the high values of PM2.5 are concentrated in monitoring stations in urban areas. On the contrary, the concentration of ozone in suburban stations is higher. The high concentration of ozone in the northeast of Anshan, Liaoyang, Shenyang to Tieling, and Fushun extended in a band distribution. (3) Through cluster analysis, it is found that PM2.5 and ozone in Shenyang are mainly affected by short-distance transport airflow. In winter, the weighted PSCF high-value area of PM2.5 presents as a potential contribution source zone of the northeast trend with wide coverage, in which the contribution value of the weighted CWT in the middle of Heilongjiang is the highest. The main potential source areas of ozone mass concentration in spring and summer are coastal cities and the Bohai Sea and the Yellow Sea. We conclude that the regional transmission of pollutants is an important factor of pollution, so we should pay attention to the supply of industrial sources and marine sources of marine pollution in the surrounding areas of cities, and strengthen the joint prevention and control of air pollution among regions. The research results of this article provide a useful reference for the central Liaoning urban agglomeration to improve air quality.

Effects of the joint prevention and control of atmospheric pollution policy on air pollutants-A quantitative analysis of Chinese policy texts

Joint prevention and control of atmospheric pollution (JPCAP) policies play a vital role in alleviating regional pollution. Based on Latent Dirichlet Allocation (LDA) model, we construct two policy strength measures of effectiveness and number, and investigate the effects of policy strength on air pollutant emissions for four types of JPCAP policies. The results show that the effects of economic incentive policy tools and supporting policy tools on emission reduction deviate significantly from policy preferences. Economic incentive policy tools are the most effective in promoting emission reductions in SO₂, NOx and dust, but their effectiveness are the lowest in reality. Supporting policy tools, with the highest strength, have little effect on emission reduction. Command-control policies and persuasion policies are both relatively high in quantity and effectiveness. In addition, policy strength plays a more important role in reducing air pollutants in key regions than in non-key regions. JPCAP policies have gradually changed from a single policy tool to multiple policy tools, and the government shifted its attention to improving the legal effectiveness of policies after 2015. Finally, we propose some policy implications to optimize JPCAP policies and address regional air pollution problem.

Strategies towards PM2.5 attainment for non-compliant cities in China: A case study

The northern part of the Yangtze River Delta (YRD) region in China suffers from high concentrations of fine particular matter (PM_2.5) during the past years yet received much less attention compared to the other parts of the YRD region. In this study, we integrated observational data, control policies and strategies, and air quality simulations to develop PM_2.5 attainment demonstration by year 2030 for the city of Bengbu, which represents a typical non-compliant city in the northern YRD region. In 2018, the annual average PM_2.5 concentration in Bengbu was 51.8 μg/m³, which was 48 % higher than the standard of 35 μg/m³ set by the National Ambient Air Quality Standards (NAAQS). Different future emission scenarios were developed for year 2025 as mid-term and year 2030 as long-term. Integrated meteorology and air quality modeling system together with monitoring data was applied to predict the air quality under the future emission scenarios. Results show that when a conservative emission reduction ratio of 40 % was assumed for surrounding regions, the annual average PM_2.5 concentration in Bengbu could meet the target value by 2030, in which case emissions of SO₂, NOx, PM_2.5, VOCs, and NH₃ need to be reduced by 70.6 %, 43.5 %, 47.2 %, 33.4 %, and 47.5 %, respectively. PM_2.5 concentration in Bengbu is not only controlled by local emission reductions but also affected by emission reductions of surrounding regions as well as contribution from long-range transport. More attentions need to be paid to the control of VOCs emissions in the near future to avoid increase of ozone concentrations while reducing PM_2.5. Our results provide scientific support for the local government to formulate future air pollution prevention and control strategies, sub-regional joint-control among surrounding cities, as well as trans-regional joint-control between the north China and the YRD region.

Health and economic benefits of clean air policies in China: A case study for Beijing-Tianjin-Hebei region

Exposure to PM_2.5 is associated with many adverse health effects, leading to additional social costs. The Blue Sky Protection Campaign (BSPC) has been implemented in 2018 in the Beijing-Tianjin-Hebei (BTH) area to control air pollution. This study assesses PM_2.5-related health and economic benefits of the BSPC in the BTH region. Results show that by 2020, PM_2.5 reduction can avoid 3561 thousand morbidity cases (equivalent to a 24% reduction in the 2020 baseline scenario) and 24 thousand premature deaths (12%) in the BTH region, with the majority benefit in Hebei. By 2030, the avoided morbidity and mortality cases will be 2943 (18%) thousand and 20 (9%) thousand, respectively. PM_2.5 reductions are highly effective in reducing work time loss, which will decrease the total annual work time by 1.7 × 10⁸ h (24%) in the BTH region by 2020. From the economic aspect, the reduced PM_2.5 concentration will save 30 million USD (25%) health expenditures and avoid 60 billion USD (13%) economic loss by using the value of statistical life (VSL) by 2020. In 2030, the health expenditures and economic loss will also decrease significantly, with 17 million USD (18%) and 63 billion USD (10%), respectively, in the BTH region. Besides, the economic benefits far exceed the policy costs of the BSPC, and the Δ benefit/Δ cost ratios of Beijing are significantly higher than those of Hebei. The BSPC in BTH has significant positive health and economic impacts. This study can provide a basis for future PM_2.5-related health risk studies at an urban level in China.

Non-optimum temperature-related mortality burden in China: Addressing the dual influences of climate change and urban heat islands

Under the dual effects of climate change and urban heat islands (UHI), non-optimum temperature-related mortality burdens are complex and uncertain, and are rarely discussed in China. In this study, by applying city-specific exposure-response functions to multiple temperature and population projections under different climate and urbanization scenarios, we comprehensively assessed the non-optimum temperature-related mortality burdens in China from 2000 to 2050. Our results showed that temperature-related deaths will decrease from 1.19 million in 2010 to 1.08-1.17 million in 2050, with the exception of the most populous scenario. Excess deaths attributable to non-optimal temperatures under representative concentration pathway 8.5 (RCP8.5) were 2.35% greater than those under RCP4.5. This indicates that the surge in heat-related deaths caused by climate change will be offset by the reduction in cold-related deaths. As the climate changes, high-risk areas will be confronted with more severe health challenges, which requires health protection resource relocation strategies. Simultaneously, the net effects of UHIs are beneficial in the historical periods, preventing 3493 (95% CI: 22-6964) deaths in 2000. But UHIs will cause an additional 6951 (95% CI: -17,637-31,539, SSP4-RCP4.5) to 17,041 (95% CI: -10,516-44,598, SSP5-RCP8.5) deaths in 2050. The heavier health burden in RCP8.5 than RCP4.5 indicates that a warmer climate aggravates the negative effects of UHIs. Considering the synergistic behavior of climate change and UHIs, UHI mitigation strategies should not be developed without considering climate change. Moreover, the mortality burden exhibited strong spatial variations, with heavy burdens concentrated in the hotspots including Beijing-Tianjin Metropolitan Region, Yangtze River Delta, Chengdu-Chongqing City Group, Guangzhou, Wuhan, Xi'an, Shandong, and Henan. These hotspots should be priority areas for the allocation of the national medical resources to provide effective public health interventions.

Effects of air pollution control devices on volatile organic compounds reduction in coal-fired power plants

Air pollution control devices (APCDs) have been fitted to many coal-fired power plants to decrease the impacts of pollutants generated during coal combustion. APCDs remove conventional pollutants but also decrease volatile organic compound (VOC) emissions. In this study, flue gas samples were collected from different points in seven typical coal-fired power and two industrial boilers, and the VOC concentrations in the flue gas samples were determined by gas chromatography-mass spectrometry (GC-MS). Selective catalytic reduction (SCR) systems and electrostatic precipitators (ESP) can synergistically remove VOCs, the mean removal rate of VOCs by ESP was 42% ± 9%. This was caused by the catalyst in SCR systems and the condensation process in the ESP. Wet flue gas desulfurization (WFGD) affected different VOCs in different ways, increasing the halogenated hydrocarbons and aromatic hydrocarbons concentrations but decreasing the oxygenated VOCs concentrations by 12%. Wet electrostatic precipitators (WESP) increased VOC emissions. By calculating Ozone formation potential (OFP), aromatic hydrocarbons are important contributors to ozone production. The emission factor of the power plant was 0.69 g/GJ, and the Chinese annual emission was about 1.2 × 10⁴ t. VOCs emissions in different regions were affected by factors such as the economy and population. VOC emissions can be decreased by using the most appropriate unit load and improving the VOC removal efficiencies of the APCDs.

Control Models and Spatiotemporal Characteristics of Air Pollution in the Rapidly Developing Urban Agglomerations

This paper systematically summarizes the hierarchical cross-regional multi-directional linkage in terms of air pollution control models implemented in the Beijing-Tianjin-Hebei urban agglomeration, including the hierarchical linkage structure of national-urban agglomeration-city, the cross-regional linkage governance of multiple provinces and municipalities, the multi-directional linkage mechanism mainly involving industry access, energy structure, green transportation, cross-regional assistance, monitoring and warning, consultation, and accountability. The concentration data of six air pollutants were used to analyze spatiotemporal characteristics. The concentrations of SO₂, NO₂, PM₁₀, PM_2.5, CO decreased, and the concentration of O₃ increased from 2014 to 2017; the air pollution control has achieved good effect. The concentration of O₃ was the highest in summer and lowest in winter, while those of other pollutants were the highest in winter and lowest in summer. The high pollution ranges of O₃ diffused from south to north, and those of other pollutants decreased significantly from north to south. Finally, we suggest strengthening the traceability and process research of heavy pollution, increasing the traceability and process research of O₃ pollution, promoting the joint legislation of different regions in urban agglomeration, create innovative pollution discharge supervision mechanisms, in order to provide significant reference for the joint prevention and control of air pollution in urban agglomerations.

Ridership exceedance exposure risk: Novel indicators to assess PM2.5 health exposure of bike sharing riders

Identifying the fine particulate matter (PM_2.5) exposure risk for bicycle riders is crucial for promoting the development of theory and technology in transportation-related air pollution assessment as well as urban health planning. Previous studies have employed daily mean PM_2.5 concentrations and designed routes to evaluate air pollution exposure risk. However, because the daily mean PM_2.5 concentrations cannot fully illustrate the intra-day variations in PM_2.5, which are typically higher than daily mean values, the adverse effects of PM_2.5 concentrations remain underestimated. Moreover, the quantity and representativeness of monitoring samples make large spatial-scale and multi-temporal-scale analysis challenging. By defining hourly exceedance PM_2.5 concentration and sharing bicycle rider data, two novel indicators were proposed in our study: exceedance exposure risk of PM_2.5 for sharing bicycle riders (EPSR) and accumulative exceedance exposure risk of PM_2.5 for sharing bicycle riders (AEPSR). Standard deviation ellipse analysis was conducted to investigate the multi-temporal variation of ESPR and AEPSR. A geographically weighted regression model was applied to quantify the relationship between city function zones and exceedance PM_2.5 exposure risk for sharing bicycle riders. Results revealed that the mean values of EPSR and AEPSR during morning peak periods ranged between 0.109 min μg/m³ and 1.27 min μg/m³ and 6.83 min μg/m³ and 43.41 min μg/m³, respectively, whereas the mean values of EPSR and AEPSR during evening peak periods ranged between 0.19 min μg/m³ and 4.28 min μg/m³ and 14.67 min μg/m³ and 357.66 min μg/m³, respectively. This implied that sharing bicycle riders were exposed to higher PM_2.5-related risks during the evening than in the morning. When considering the accumulative effects, the average centers of the AEPSR moved to the north side as compared to the average centers of the EPSR. Expanding areas of EPSR shrunk by 20.25 km². This indicated that accumulative effects aggregated spatial clusters of exceedance PM_2.5 exposure risk for sharing bicycle riders more tightly to the north of the study areas. Spatiotemporal variation of EPSR and AEPSR led us to investigate the mechanism behind this phenomenon. Spatial associations between city function zones and EPSR and AEPSR showed that sharing bicycle riders experienced more severe exceedance PM_2.5 exposure risk around financial/corporations and leisure service areas, with R² values of 0.33 and 0.35, respectively. This spatial association tended to be more significant during the evening peak periods. By developing two novel indicators, the increasing health threats for bicycle riders caused by exceedance PM_2.5 were investigated in this study. The mechanism results should be included for developing mitigation strategies to alleviate the adverse effects of air pollution for public rider participators and achieving the goal of eco-health cities.

Short-Term Exposure to Ambient Air Pollution and Increased Emergency Room Visits for Skin Diseases in Beijing, China

Skin diseases have become a global concern. This study aims to evaluate the associations between ambient air pollution and emergency room visits for skin diseases under the background of improving air quality in China. Based on 45,094 cases from a general hospital and fixed-site monitoring environmental data from 2014–2019 in Beijing, China, this study used generalized additive models with quasi-Poisson regression to estimate the exposure–health associations at lag 0–1 to lag 0–7. PM_2.5 and NO₂ exposure were associated with increased emergency room visits for total skin diseases (ICD10: L00-L99). Positive associations of PM_2.5, PM₁₀, O₃ and NO₂ with dermatitis/eczema (ICD-10: L20–30), as well as SO₂ and NO₂ with urticaria (ICD-10: L50) visits were also found. For instance, a 10 μg/m³ increase in PM_2.5 was associated with increases of 0.7% (95%CI: 0.2%, 1.2%) in total skin diseases visits at lag 0–5 and 1.1% (95%CI: 0.6%, 1.7%) in dermatitis/eczema visits at lag 0–1, respectively. For PM_2.5, PM₁₀ and CO, stronger annual associations were typically observed in the high-pollution (2014) and low-pollution (2018/2019) years. For instance, a 10 μg/m³ increase in PM_2.5 at lag 0–5 was associated with increases of 1.8% (95%CI: 1.0%, 2.6%) and 2.3% (95%CI: 0.4%, 4.3%) in total skin disease visits in 2014 and 2018, respectively. Our study emphasizes the necessity of controlling the potential health hazard of air pollutants on skin, although significant achievements in air quality control have been made in China.

Enhanced atmospheric oxidation capacity and associated ozone increases during COVID-19 lockdown in the Yangtze River Delta

Aggressive air pollution control in China since 2013 has achieved sharp decreases in fine particulate matter (PM_2.5), along with increased ozone (O₃) concentrations. Due to the pandemic of coronavirus disease 2019 (COVID-19), China imposed nationwide restriction, leading to large reductions in economic activities and associated emissions. In particular, large decreases were found in nitrogen oxides (NO_x) emissions (>50%) from transportation. However, O₃ increased in the Yangtze River Delta (YRD), which cannot be fully explained by changes in NO_x and volatile organic compound (VOCs) emissions. In this study, the Community Multi-scale Air Quality model was used to investigate O₃ increase in the YRD. Our results show a significant increase of atmospheric oxidation capacity (AOC) indicated by enhanced oxidants levels (up to +25%) especially in southern Jiangsu, Shanghai and northern Zhejiang, inducing the elevated O₃ during lockdown. Moreover, net P(HO_x) of 0.4 to 1.6 ppb h^-1 during lockdown (Case 2) was larger than the case without lockdown (Case 1), mainly resulting in the enhanced AOC and higher O₃ production rate (+12%). This comprehensive analysis improves our understanding on AOC and associated O₃ formation, which helps to design effective strategies to control O₃.

The objects, agents, and tools of Chinese co-governance on air pollution: a review

The social and economic development in China has not only made a series of great achievements but also suffered from increasingly serious air pollution. It is of great significance to explore the co-governance mechanism of air pollution in order to promote high-quality development and the construction of "beautiful China." Based on an analysis using the concept of co-governance, this paper reviews the research from four aspects: the multi-object relationships, multi-agent framework, and the co-governance technical tools and policy tools. The results show that the current research has many deficiencies: a lack of research on the size, direction, and driving factors of the correlation of objects; the construction of the multi-agent framework focused only on concepts and lacking the design of core mechanisms; evaluating only the effect of tools but ignoring the optimal combination of governance tools, and paying attention only to the traditional pollutants and disregarding the latest air pollution. Accordingly, this paper finds that the research should be expanded from four aspects, which include taking into account the co-governance of new air pollution, clarifying the relationship between the various types of air pollutants and the driving factors, building a multi-disciplinary research framework for co-governance, and optimizing the combination of governance policies and technical tools in order to realize high-quality development of China.

Policy-driven changes in the health risk of PM2.5 and O3 exposure in China during 2013-2018

China issued a series of control measures to mitigate PM_2.5 pollution, including long-term (i.e., Air Pollution Prevention and Control Action Plan, APPCAP) and short-term (emergency measures in autumn and winter) acts. However, the O₃ concentration increased significantly as PM_2.5 levels sharply decreased when these measures were implemented. Therefore, the policy-driven positive/negative health effects of PM_2.5/O₃ need to be comprehensively estimated. The health impact function (HIF) is applied to evaluate the health burden attributable to long- and short-term PM_2.5 and O₃ exposure. The results show that the PM_2.5 concentration decreased by 42.95% in 74 cities, whereas O₃ pollution is increased by 17.56% from 2013 to 2018. Compared with 2013, the number of premature deaths attributable to long- and short-term PM_2.5 exposure decreased by almost 5.31 × 10⁴ (95% confidence interval [CI]: 2.87 × 10⁴-4.71 × 10⁴) (10.13%) and 3.00 × 10⁴ (95% CI: 1.66 × 10⁴-4.39 × 10⁴) (72.49%), respectively, in 2018. In contrast, O₃-attributable deaths, increased by 1.98 × 10⁴ (95% CI: 0.31 × 10⁴-3.59 × 10⁴) (130.57%) and 0.91 × 10⁴ (95% CI: 0.50 × 10⁴-1.33 × 10⁴) (76.16%) for long- and short-term exposure, respectively. The number of avoidable deaths attributed to PM_2.5 reduction is larger than the level of premature deaths related to increasing O₃. Although annual mean PM_2.5 concentrations have fallen rapidly, the benefits of reducing long-term exposure are limited, whereas the deaths associated with acute exposure decrease more significantly due to the reduction of heavy-pollution days by implementing emergency measures. The results show appreciable effectiveness in protecting human health and illustrate that synchronous control of PM_2.5 and O₃ pollution should be emphasized.

Inferring Near-Surface PM2.5 Concentrations from the VIIRS Deep Blue Aerosol Product in China: A Spatiotemporally Weighted Random Forest Model

Much of the population is exposed to PM2.5 (particulate matter) pollution in China, and establishing a high-precision PM2.5 grid dataset will be very valuable for air pollution and related studies. However, limited by the traditional models themselves and input data sources, PM2.5 estimations are of low accuracy with narrow spatial coverage. Therefore, we develop a new spatiotemporally weighted random forest (SWRF) model to improve the estimation accuracy and expand the spatial coverage of PM2.5 concentrations using the latest release of the Visible infrared Imaging Radiometer (VIIRS) Deep Blue (DB) aerosol product, along with meteorological variables, and socioeconomic data. Compared with traditional methods and the results of previous similar studies, our satellite-derived PM2.5 distribution shows better consistency with surface-measured records, having a high out-of-sample (out-of-station) cross-validation (CV) coefficient of determination (CV-R2), root mean squared error (RMSE), and mean absolute error (MAE) of 0.87 (0.85), 11.23 (11.53) μg m−3 and 8.25 (8.78) μg m−3, respectively. The monthly, seasonal, and annual mean PM2.5 were also successfully captured (CV-R2 = 0.91–0.92, RMSE = 4.35–6.72 μg m−3). Then, the spatial characteristics of PM2.5 pollution in 2018 were investigated, showing that although air pollution has diminished in recent years, China still faces a high PM2.5 pollution risk overall, especially in winter (average = 50.43 + 16.81 μg m−3). In addition, 19 provinces or administrative regions have annual PM2.5 concentrations >35 μg m−3, particularly the Xinjiang Uygur Autonomous Region (~55.25 μg m−3), Tianjin (~49.65 μg m−3), and Henan Province (~48.60 μg m−3). Our estimated surface PM2.5 concentrations are accurate, which could benefit further research on air pollution in China.

Spatial Characteristics of PM2.5 Pollution among Cities and Policy Implication in the Northern Part of the North China Plain

In the recent decade, the North China Plain (NCP) has been among the region’s most heavily polluted by PM2.5 in China. For the nonattainment cities in the NCP, joint pollution control with related cities is highly needed in addition to the emission controls in their own cities. However, as the basis of decision-making, the spatial characteristics of PM2.5 among these cities are still insufficiently revealed. In this work, the spatial characteristics among all nonattainment cities in the northern part of the North China Plain (NNCP) region were revealed based on data mining technologies including clustering, coefficient of divergence (COD), network correlation model, and terrain and meteorology analysis. The results indicate that PM2.5 pollution of cities with a distance of less than 180 km exhibits homogeneity in the NCP region. Especially, the sub-region, composed of Xinxiang, Hebi, Kaifeng, Zhengzhou, and Jiaozuo, was strongly homogeneous and a strong correlation exists among them. Compared with spring and summer, much stronger correlations of PM2.5 between cities were found in autumn and winter, indicating a strong need for joint prevention and control during these periods. All nonattainment cities in this region were divided into city-clusters, depending on the seasons and pollution levels to further helping to reduce their PM2.5 concentrations effectively. Air stagnation index (ASI) analysis indicates that the strong correlations between cities in autumn were more attributed to the transport impacts than those in winter, even though there were higher PM2.5 concentrations in winter. These results provided an insight into joint prevention and control of pollution in the NCP region.

Source specific PM2.5 associated with heart rate variability in the elderly with coronary heart disease: A community-based panel study

There is increasingly concern that PM_2.5 constituents play a significant role in PM_2.5-related cardiovascular outcomes. However, little is known about the associations between specific constituents of PM_2.5 and risk for cardiovascular health. To evaluate the exposure to specific chemicals of PM_2.5 from various sources and their cardiac effects, a longitudinal investigation was conducted with four repeated measurements of elderly participants' HRV and PM_2.5 species in urban Beijing. Multiple chemicals in PM_2.5 (metals, ions and PAHs) were characterized for PM_2.5 source apportionment and personalized exposure assessment. Five sources were finally identified with specific chemicals as the indicators: oil combustion (1.1%, V & PAHs), secondary particle (11.3%, SO₄^2- & NO₃^-), vehicle emission (1.2%, Pd), construction dust (28.7%, Mg & Ca), and coal combustion (57.7%, Se & As). As observed, each IQR increase in exposure to oil combustion (V), vehicle emission (Pd), and coal combustion (Se) significantly decreased rMSSD by 13.1% (95% CI: -25.3%, -1.0%), 27.4% (95% CI: -42.9%, -7.6%) and 24.7% (95% CI: -39.2%, -6.9%), respectively, while those of PM_2.5 mass with decreases of rMSSD by 11.1% (95% CI: -19.6%, -1.9%) at lag 0. Elevated exposures to specific sources/constituents of PM_2.5 disrupt cardiac autonomic function in elderly and have more adverse effects than PM_2.5 mass. In the stratified analysis, medication and gender modify the associations of specific chemicals from variable sources with HRV. The findings of this study provide evidence on the roles of influential constituents of ambient air PM_2.5 and their sources in terms of their adverse cardiovascular health effects.

Administrative Mechanism of Joint Participation and Cooperation in the Early Stages of the COVID-19 Outbreak in Wuhan

Introduction

From December 2019 to January 2020, a novel coronavirus disease (officially COVID-19) was reported in Wuhan and continued to spread all China. This study describes the administrative mechanism of joint participation and cooperation during the early stages of the COVID-19 outbreak in Wuhan and the rest of the country by health practitioners and administrative authorities.

Methods

This study adopted a qualitative design. An analytical framework based on the theory of policy participation that included stimulus, setting, and position of policy participation was constructed. Qualitative data of policy participation by health practitioners and administrative authorities consisted of publicly available data.

Results

Early during the outbreak, from December 2019 to January 2020, three main stages occurred according to the containment situation. The first stage was characterized by limited knowledge of the transmission dynamics of the virus and a consequently weak response. In the second stage, the disease spreads rapidly because of travel during a national festival. In the third stage, particularly when top Chinese leaders delivered instructions to intensify containment efforts, diverse departments initiated joint prevention and control measures to combat COVID-19.

Conclusion

The administrative mechanism of joint participation and cooperation was instrumental in avoiding a substantial increase in both cases and fatalities in the initial stage of the outbreak. This joint participation provides valuable experience and initiatives for major public health emergency preparedness, and the new empirical evidence further highlights the importance of policy participation theory in epidemic prevention in other countries.

Spatiotemporal characteristics of PM2.5 concentration in the Yangtze River Delta urban agglomeration, China on the application of big data and wavelet analysis

PM_2.5 pollution has been one of the main environmental issues of concern for the Yangtze River Delta Urban Agglomeration (YRDUA) during the recent decade. In this paper, allied with big data and wavelet analysis, spatiotemporal variations of PM_2.5 and its influencing factors (air pollutants and meteorological factors) are studied based on hourly concentrations of PM_2.5 from 2015 to 2018 in the YRDUA. Results showed that PM_2.5 presented a step-shaped decline from northwest to southeast in space and significant multi-scale temporal variations in time. On the macroscopic level, PM_2.5 concentrations decreased from 2015 to 2018, showing a U-shaped pattern within a year. On the microscopic level, it had a four-stage annual variation (January to March, April to June, July to September, October to December) and the mutation events mainly occurred in winter. There were two dominant periods of PM_2.5, an annual cycle on the time scale of 250-480 d and a semi-annual cycle on the time scale of 130-220 d. In addition, PM_2.5 showed time scale-dependent correlations with air pollutants and meteorological factors. Among air pollutants, the correlation between PM_2.5 and CO was the most consistent, and the correlation between PM_2.5 and SO₂/NO₂ improved with the increase of time scale, while the correlation between PM_2.5 and O₃ was positive at shorter time scales but negative at broader time scales. Among meteorological factors, the correlations between PM_2.5 and wind speed, precipitation, temperature, air pressure and relative humidity were mainly reflected at broader time scales. These findings would be helpful to improve the accuracy of prediction model and provide references for the ongoing joint prevention and control.

Mining sequential patterns of PM2.5 pollution between 338 cities in China

Serious PM2.5 air pollution has persistently plagued and endangered most urban areas in China in recent years, and targeted policies are necessary to improve urban air quality ranging from macro policy (national level) to medium policy (city level) to micro policy (site specific). However, the macro-pattern study of air pollution between Chinese cities is inadequate, and not conducive to the formulation of macro-policy. To bridge this gap, we applied a sequential pattern mining algorithm to analyze the spatial-temporal patterns of PM2.5 pollution across Chinese cities during the period 2015 to 2018. The sequential patterns were collected from three levels of granularity on geographic areas and ten temporal scenarios covering time intervals from 10 to 100 h. Many underlying associative relationships were revealed between different cities by the mined patterns. The patterns were heterogeneous and presented five characteristics (i.e., clustering, symmetry, imbalance, decay, and stability). Each of the urban areas under investigation at different granularities was analyzed to identify the occurrence of associative relationships between it and other urban areas; moreover, we determined the degree of severity of such relationships. Our research results provide solid data that can be used as a reference by the various levels of Chinese governments for decision-making; overall, they can be used to improve the design of joint policies to prevent and control PM2.5 pollution in Chinese urban areas.

Joint Governance Regions and Major Prevention Periods of PM2.5 Pollution in China Based on Wavelet Analysis and Concentration-Weighted Trajectory

China has made some progress in controlling PM2.5 (particulate matter with an aerodynamic diameter of ≤2.5 μm) pollution, but there are still some key areas that need further strengthening. Considering that excessive prevention and control efforts affect economic development, this paper combined an empirical orthogonal function, a continuous wavelet transform, and a concentration-weighted trajectory method to study joint regional governance during key pollution periods to provide suggestions for the efficient control of PM2.5. The results from our panel of data of PM2.5 in China from 2016 to 2018 could be decomposed into two modes. In the first mode, the pollution center was in central Shaanxi Province, and the main eruption period was from November to January of the following year. As the center of this region, Xi’an should cooperate with the four cities in eastern Sichuan (Nanchong, Guangan, Bazhong, and Dazhou) to control PM2.5, since the eruption occurred in this area. Moreover, governance should last for at least two cycles, where one cycle is at least 23 days. The pollution center of the second mode was in the western part of Xinjiang. Therefore, after the prevention and control efforts during the first mode are completed, the regional city of Kashgar should continue to build a joint governance zone for PM2.5 along the Tianshan mountains in the east, focusing on prevention and control over two cycles (where one cycle is 28 days).

Spatial homogeneity and heterogeneity of ambient air pollutants in Tehran

To investigate spatial inequality of ambient air pollutants and comparison of their heterogeneity and homogeneity across Tehran, the following quantitative indicators were utilized: coefficient of divergence (COD), the 90^th percentile of the absolute differences between ambient air pollutant concentrations and coefficient of variation (CV). Real-time hourly concentrations of particulate matter (PM) and gaseous air pollutants (GAPs) of twenty-two air quality monitoring stations (AQMSs) were obtained from Tehran Air Quality Control Company (TAQCC) in 2017. Annual mean concentrations of PM_2.5, PM_10-2.5, and PM₁₀ (PM_X) ranged from 21.7 to 40.5, 37.3 to 75.0 and 58.0 to 110.4 μg m^-3, respectively. Annual mean PM_2.5 and PM₁₀ concentrations were higher than the World Health Organization air quality guideline (WHO AQG) and national standard levels. NO₂, O₃, SO₂ and CO annual mean concentrations ranged from 27.0 to 76.8, 15.5 to 25.1, 4.6 to 12.2 ppb, and 1.9 to 3.8 ppm over AQMSs, respectively. Our generated spatial maps exhibited that ambient PM_X concentrations increased from the north into south and south-western areas as the hotspots of ambient PM_X in Tehran. O₃ hotspots were observed in the north and south-west, while NO₂ hotspots were in the west and south. COD values of PM_X demonstrated more results lower than the 0.2 cut off compared to GAPs; indicating high to moderate spatial homogeneity for PM_X and moderate to high spatial heterogeneity for GAPs. Regarding CV approach, the spatial variabilities of air pollutants followed in the order of O₃ (87.3%) > SO₂ (65.2%) > CO (61.8%) > PM_10-2.5 (52.5%) > PM_2.5 (48.9%) > NO₂ (48.1%) > PM₁₀ (42.9%), which were mainly in agreement with COD results, except for NO₂. COD values observed a statistically (P < 0.05) positive correlation with the values of the 90^th percentile across AQMSs. Our study, for the first time, highlights spatial inequality of ambient PM_X and GAPs in Tehran in detail to better facilitate establishing new intra-urban control policies.

Source and exposure apportionments of ambient PM2.5 under different synoptic patterns in the Pearl River Delta region

PM_2.5 is one of the most notorious ambient pollutants in the Pearl River Delta (PRD) region during episodic conditions. In this work, the Comprehensive Air Quality Model with extension (CAMx) was used together with the Particulate Source Apportionment Technology (PSAT) module to analyze the influences of different sources on PM_2.5 concentration in the PRD region under different synoptic patterns (sea high pressure, sub-tropical high pressure and equalizing pressure field). The result shows that the PM_2.5 concentration increases to different degrees under the three synoptic patterns. The emissions outside the PRD region contribute more than 54% under episodic conditions. The source category contribution varies little under different synoptic patterns. Area (46%), mobile (21%) and industry point source (16%) are the major contributors over the three episodic cases. The regional source contributions (from other cities within the PRD) to Foshan, Zhongshan and Zhaoqing are larger and can reach up to 33%. People living in the PRD region are more exposed to pollutants produced from the area and mobile sources. About 80% of the population is exposed to PM_2.5 levels exceeding the IT-3 standard during the pollution episodes.

Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China

Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enough to decode it for better regulation. Therefore, we use the traditional atmospheric model, Weather Research and Forecasting coupled with Community Multi-scale Air Quality (WRF-CMAQ), and machine learning models, Extreme Learning Machine (ELM), Multi-layer Perceptron (MLP), Random Forest (RF) and Recurrent Neural Network (RNN) to analyze and predict the ozone in the surface air in Hangzhou, China, using meteorology and air pollutants as input. We firstly quantitatively demonstrate that the dew-point deficit, instead of temperature and relative humidity, is the predominant meteorological factor in shaping tropospheric ozone. Urban heat island, daily direct solar radiation time, wind speed and wind direction play trivial role in impacting tropospheric ozone. NO₂ is the primary influential factors both for hourly ozone and daily O₃-8 h due to the titration effect. The most environmental-friendly way to mitigate the ozone pollution is to lower the volatile organic compounds (VOCs) with the highest ozone formation potentials. We deduce that the tropospheric ozone formation process tends to be not only non-linear but also non-smooth. Compared with the traditional atmospheric models, machine learning, whose characteristics are rapid convergence, short calculating time, adaptation of forecasting episodes, small program memory, higher accuracy and less cost, is able to predict tropospheric ozone more accurately.

Spatial assessment of air resources in China from 2013 to 2017

The concept of air resource management originated in the United States and subsequently became the foundation of most air quality control programs in developed countries. However, little is known about its validity and quantitative assessment methods in regional atmospheric environment management. The aim of this study was to construct an air resource endowment (ARE) index and apply it in a case study for assessing the distribution of ARE across mainland China from 2013 to 2017. The quantification of the ARE index includes two terms: the atmospheric diffusion coefficient (A value) and self-purification ability (B value), which can be calculated via Weather Research and Forecasting modeling (WRF-CALMET). The results indicated that about 15% of China's land area enjoys high ARE, around 20-25% of China's land area was considered to have relatively high or relatively low ARE indices, and ARE in the rest of China's land area (40%) was considered to be low. Further, a complex network correlation model was created and used to demarcate highly inter-correlated regions based on the data mining of the ARE index. Six key Joint Prevention and Control of Atmospheric Pollution (JPCAP) regions with strong synchronicity in the ARE index were identified, which suggests that JPCAP could be implemented separately within each of these demarcated regions. The concepts and analysis methods proposed in this study for determining ARE and regional divisions can have broad significance for JPCAP implementation in China.

Spatial-Temporal Evolution of PM2.5 Concentration and its Socioeconomic Influence Factors in Chinese Cities in 2014⁻2017

PM_2.5 is a main source of China’s frequent air pollution. Using real-time monitoring of PM_2.5 data in 338 Chinese cities during 2014–2017, this study employed multi-temporal and multi-spatial scale statistical analysis to reveal the temporal and spatial characteristics of PM_2.5 patterns and a spatial econometric model to quantify the socio-economic driving factors of PM_2.5 concentration changes. The results are as follows: (1) The annual average value of PM_2.5 concentration decreased year by year and the monthly average showed a U-shaped curve from January to December. The daily mean value of PM_2.5 concentration had the characteristics of pulse-type fluctuation and the hourly variation presented a bimodal curve. (2) During 2014–2017, the overall PM_2.5 pollution reduced significantly, but that of more than two-thirds of cities still exceeded the standard value (35 μg/m³) regulated by Chinese government. PM_2.5 pollution patterns showed high values in central and eastern Chinese cities and low values in peripheral areas, with the distinction evident along the same line that delineates China’s uneven population distribution. (3) Population agglomeration, industrial development, foreign investment, transportation, and pollution emissions contributed to the increase of PM_2.5 concentration. Urban population density contributed most significantly while economic development and technological progress reduced PM_2.5 concentration. The results also suggest that China in general remains a “pollution shelter” for foreign-funded enterprises.