Trends of PM2.5 concentrations in China: A long term approach

Contamination degree and health implications of indoor air pollution: Operating field measurements in market environments

This study investigated the contamination levels, ecological and human health risks, and potential sources of eleven potentially toxic metals (PETs), particulate matter 2.5 (PM2.5), and carbon dioxide (CO2) collected from urban-rural-periphery markets exposed to various anthropogenic activities, accentuated by the ever-increasing stress of anthropogenic activities. Contamination aspects, associated ecological risks, and hazards to human health will be discussed herein, besides determining and presenting possible sources of PM2.5 and PETs: Cr, Cu, Co Cd, Ni, Pb, Zn, Mg, Al, Mn, and Fe. The study describes the settled dust particles from various indoor dust-collecting environments of a few supermarket chains in Kastamonu Province, Türkiye. The indoor and outdoor average levels (I/O), ratio of CO2 and PM2.5 concentrations was between 1.05-1.28 and 1.23-1.70 across these markets. The overall concentrations of PETs (mg kg-1) indoors were observed in the following descending order: Fe (6492.73) > Al (2290.80) > Mg (719.86) > Zn (150.20) > Mn (162.13) > Ni (38.73) > Cr (18.06) > Pb (28.33) > Cu (13.67) > Co (7.87) > Cd (1.69). The I/O PM2.5, CO2, and PETs concentration ratios generally exhibited a multi-distribution, with peaks between 4:00-7:00 p.m., likely associated with customer density. The mean levels of children's exposure to dust particles from urban markets occurred principally through ingestion (7.53E+02), followed by dermal contact (1.10E-03) and inhalation (7.95E-06). The findings of this pioneering study offer crucial data to inform future monitoring and policy for protecting coastal ecosystems and public health.

Insights into the seasonal characteristics of single particle aerosols in Chengdu based on SPAMS

To investigate the seasonal characteristics in air pollution in Chengdu, a single particle aerosol mass spectrometry was used to continuously observe atmospheric fine particulate matter during one-month periods in summer and winter, respectively. The results showed that, apart from O3, the concentrations of other pollutants (CO, NO2, SO2, PM2.5 and PM10) were significantly higher in winter than in summer. All single particle aerosols were divided into seven categories: biomass burning (BB), coal combustion (CC), Dust, vehicle emission (VE), K mixed with nitrate (K-NO3), K mixed with sulfate and nitrate (K-SN), and K mixed with sulfate (K-SO4) particles. The highest contributions in both seasons were VE particles (24%). The higher contributions of K-SO4 (16%) and K-NO3 (10%) particles occurred in summer and winter, respectively, as a result of their different formation mechanisms. S-containing (K-SO4 and K-SN), VE, and BB particles caused the evolution of pollution in both seasons, and they can be considered as targets for future pollution reduction. The mixing of primary sources particles (VE, Dust, CC, and BB) with secondary components was stronger in winter than in summer. In summer, as pollution worsens, the mixing of primary sources particles with 62 [NO3]- weakened, but the mixing with 97 [HSO4]- increased. However, in winter, the mixing state of particles did not exhibit an obvious evolution rules. The potential source areas in summer were mainly distributed in the southern region of Sichuan, while in winter, besides the southern region, the contribution of the western region cannot be ignored.

Multi-organ toxicity caused by PM2.5 in mice with cardiovascular diseases: The role of PAHs played from the most polluted episodes in China

PAHs pollutants, as the key toxic components in PM2.5, have been proved to be closely related to the morbidity and mortality of people with cardiovascular diseases, however, their effects on organs and tissues other than cardiovascular/lung systems have not been deeply discussed. Here we collected PM2.5 samples from 2017 to 2020 in Xi'an, the city with one of the highest PM2.5 level in China, investigated the effects of PM2.5-bound PAHs on lung, spleen, liver and kidney by using the ApoE-/- mice model with high-fat diet. Firstly, six key toxic components in PAHs were screened to determine their relative importance in pollutants. The results showed that PAHs had the most significant toxicity in lung, followed by liver, kidney and spleen. In addition, PAHs activated systemic inflammation by enhancing the production of IL-6, particularly through strong protein interactions, mainly via van der Waals forces. This process exacerbated cardiovascular damage and led to elevated levels of pro-inflammatory cytokines circulating in the bloodstream, thereby increasing multi-organ toxicity. The results of this study deepened the understanding of comprehensive impacts of PAHs on cardiovascular patients, and suggest more strict emission source-control strategies on PAHs prevention especially for the susceptible population with cardiovascular diseases.

Microscopic Characterization of Individual Aerosol Particles in a Typical Industrial City and Its Surrounding Rural Areas in China

Transmission electron microscopy was used to analyze individual aerosol particles collected in Lanzhou (urban site) and its surrounding areas (rural site) in early 2023. The results revealed that from the pre-Spring Festival period to the Spring Festival period, the main pollutants at the urban site decreased significantly, while the PM2.5 and SO2 concentrations increased at the rural site. During the entire sampling period, the main particles at the urban site were organic matter (OM), secondary inorganic aerosols (SIA), and OM-SIA particles, while those at the rural site were OM, SIA, and soot particles. The degree of external mixing of single particles in both sites increased from the pre-Spring Festival period to the Spring Festival period. The proportion of the OM particles increased by 11% at the urban site, and the proportion of SIA particles increased by 24% at the rural site. During the Spring Festival, the aging of the soot particles was enhanced at the urban site and weakened at the rural site. At the urban site, the SIA particle size was more strongly correlated with the thickness of the OM coating during the pre-Spring Festival period, while the correlation was stronger at the rural site during the Spring Festival.

Toward Better and Healthier Air Quality: Global PM2.5 and O3 Pollution Status and Risk Assessment Based on the New WHO Air Quality Guidelines for 2021

To reduce the high burden of disease caused by air pollution, the World Health Organization (WHO) released new Air Quality Guidelines (AQG) on September 22, 2021. In this study, the daily fine particulate matter (PM2.5) and surface ozone (O3) data of 618 cities around the world is collected from 2019 to 2022. Based on the new AQG, the number of attainment days for daily average concentrations of PM2.5 (≤ 15 µg m-3) and O3 (≤ 100 µg m-3) is approximately 10% and 90%, respectively. China and India exhibit a decreasing trend in the number of highly polluted days (> 75 µg m-3) for PM. Every year over 68% and 27% of cities in the world are exposed to harmful PM2.5 (> 35 µg m-3) and O3 (> 100 µg m-3) pollution, respectively. Combined with the United Nations Sustainable Development Goals (SDGs), it is found that more than 35% of the world's cities face PM2.5-O3 compound pollution. Furthermore, the exposure risks in these cities (China, India, etc.) are mainly categorized as "High Risk", "Risk", and "Stabilization". In contrast, economically developed cities are mainly categorized as "High Safety", "Safety", and "Deep Stabilization." These findings indicate that global implementation of the WHO's new AQG will minimize the inequitable exposure risk from air pollution.

Energy and air? The impact of energy efficiency improvement on air quality in China

The global economic growth is hindered by resources shortage, energy demand, air pollution and climate. Energy efficiency can reduce some pollutants while potentially increase others. This study refers to sulfur dioxide (SO2), nitrogen oxides (NOx), and dust and smoke (DS) as primary pollutants to distinguish it from secondary ones. The influence of energy efficiency, socioeconomic, and natural climatic factors on air quality is analyzed under the theory of STIRPAT. It is highly coupled between energy efficiency and the spatial distribution of air quality. Increased energy efficiency can improve air quality by reducing SO2 and NOx, but the impact on DS is insignificant. Air pollutants decrease by about 0.531% for every 1% increase in temperature and 0.105% for every 1% increase in precipitation. Consumption will reduce air pollution, and there is an inverted U-shaped relationship between population density, economic scale, urbanization, technology innovation, and air pollution. It is worth mentioning that this work adds temperature and precipitation to the STIRPAT as natural climatic factors, analyzing the impact of energy efficiency on air pollution under the two-factor restrictions of socioeconomic and natural climatic factors. Finally, management suggestions are made to improve air quality.

Assessing the effects of short-term traffic restriction policies on traffic-related air pollutants

The implementation of short-term traffic restriction policies (TRPs) during major events positively influences the traffic emission reduction. However, few studies explore the impact of diesel vehicle emissions on air quality during short-term TRP. In particular, the intertwined influences of short-term TRP and Spring Festival remains unclear. Based on Beijing 2022 Olympic Games, this study analyzed the spatiotemporal changes in urban air quality and diesel vehicle emission during short-term TRP. The results showed that the TRPs and Spring Festival contributed equally to the improvement of air quality and reduction of diesel vehicle emissions. The "interruption-recovery" pattern of short-term TRPs is characterized by a longer duration and a slower decline/recovery rate. Additionally, the individual contribution rate of diesel vehicle emissions to urban air pollutants was 15-20 % higher than that of meteorological factors during short-term TRPs.

Responses of sulfate and nitrate to anthropogenic emission changes in eastern China - in perspective of long-term variations

We investigate responses of sulfate (SO₄^2-) and nitrate (NO₃^-) to anthropogenic emission changes in 2006-2017 by fixing meteorology at the 2009 level using nested 3D chemical transport model GEOS-Chem. We find that sulfate concentration decreases following SO₂ emissions, but with a relatively smaller reduction rate (by 16 % in North China Plain (NCP) and 28 % in Yangtze River Delta (YRD)) due to larger sulfur oxidation ratio (SOR) at lower SO₂ level. SOR follows a power law with SO₂ emissions in general except in winter in NCP, when and where both SO₂ emission reduction and atmospheric oxidation capacity are critical to the inter-annual variations of SOR. Nitrate concentration ([pNO₃^-]) decreases along with NOx emission reduction in summer, but increases slightly in winter in 2011-2017. Equilibrium with gas phase HNO₃, NO₃^- in particle phase (pNO₃^-) is determined by total HNO₃ (TN = [pNO₃^-] + [gHNO₃]) oxidized from NO₂ and gas-particle partitioning (ε(NO₃^-) = [pNO₃^-]/TN). TN is decreasing faster in summer (~33 %) than in winter (~25 %) in 2011-2017. In contrast, ε(NO₃^-) changes marginally in summer (within 5 %) but increases by 36 % in NCP and by 51 % in YRD in winter in 2006-2017. The increasing of ε(NO₃^-) in winter is attributed to the strong reduction of [pSO₄^2-], which increases the relative abundance of NH₃ and thus favors partitioning of NO₃^- to the particle phase. The effect of increasing ε(NO₃^-) overcomes that of decreasing TN in winter. We suggest reduce SO₂ emissions to further reduce [pSO₄^2-] in eastern China. In addition, we recommend reduce NOx emissions in summer, and reduce atmospheric oxidation capacity and relative abundance of NH₃ in winter to reduce [pNO₃^-].

Concentration Characteristics and Correlations with Other Pollutants of Atmospheric Particulate Matter as Affected by Relevant Policies

With the increase in global environmental pollution, it is important to understand the concentration characteristics and correlations with other pollutants of atmospheric particulate matter as affected by relevant policies. The data presented in this paper were obtained at monitoring stations in Xi'an, China, in the years from 2016 to 2020, and the spatial distribution characteristics of the mass and quantity concentrations of particulate matter in the atmosphere, as well as its correlation with other pollutants, were analyzed in depth. The results showed that the annual average concentrations of PM10 and PM2.5 decreased year by year from 2016 to 2020. The annual concentrations of PM2.5 decreased by 20.3 μg/m3, and the annual concentrations of PM10 decreased by 47.3 μg/m3. The days with concentrations of PM10 exceeding the standards decreased by 82 days, with a decrease of 66.7%. The days with concentrations of PM2.5 exceeding the standards decreased by 40 days, with a decrease of 35.4%. The concentration values of PM10 and PM2.5 were roughly consistent with the monthly and daily trends. The change in monthly concentrations was U-shaped, and the change in daily concentrations showed a double-peak behavior. The highest concentrations of particulate matter appeared at about 8:00~9:00 am and 11:00 pm, and they were greatly affected by human activity. The proportion of particles of 0~1.0 μm decreased by 1.94%, and the proportion of particles of 0~2.5 μm decreased by 2.00% from 2016 to 2020. A multivariate linear regression model to calculate the concentrations of the pollutants was established. This study provides a reference for the comprehensive analysis and control of air pollutants in Xi'an and even worldwide.

Acute effects of black carbon on mortality in nine megacities of China, 2008-2016: a time-stratified case-crossover study

Black carbon (BC) may have more adverse effects on human health than other constituents of PM_2.5. The daily mean concentrations of BC in China are much higher than those in developed countries and are estimated to account for more than a quarter of global anthropogenic BC emissions. However, reports on the health effects of BC in China have been limited. Thus, a time-stratified case-crossover study was conducted to evaluate the impacts of BC on daily mortality risk in nine Chinese megacities from 2008-2016. Our results show that for all-cause mortality, when compared to the interquartile range (IQR) of BC concentration increased, odds ratios (ORs) were in the range of 1.01-1.06 (95% CIs: 0.99-1.10). For cardiovascular mortality, ORs were in the range of 1.02-1.07 (95% CIs: 1.003-1.12), and for respiratory mortality, ORs were in the range of 1.01-1.15 (95% CIs: 1.00-1.18). The effects of BC in the nine cities were robust after adjusting for PM_2.5, or even became more prominent. Furthermore, BC had stronger effects in spring and winter in northern cities, whereas in mid-latitude cities, BC had stronger effects in the warm seasons. In southern cities, BC had stronger effects in the cool and dry seasons. Our findings support an association between residential exposure to BC and mortality and thus provide further evidence that BC negatively impacts human health and is helpful for decision-making.

Statistical Inference of Dynamic Conditional Generalized Pareto Distribution with Weather and Air Quality Factors

Air pollution is a major global problem, closely related to economic and social development and ecological environment construction. Air pollution data for most regions of China have a close correlation with time and seasons and are affected by multidimensional factors such as meteorology and air quality. In contrast with classical peaks-over-threshold modeling approaches, we use a deep learning technique and three new dynamic conditional generalized Pareto distribution (DCP) models with weather and air quality factors for fitting the time-dependence of the air pollutant concentration and make statistical inferences about their application in air quality analysis. Specifically, in the proposed three DCP models, a dynamic autoregressive exponential function mechanism is applied for the time-varying scale parameter and tail index of the conditional generalized Pareto distribution, and a sufficiently high threshold is chosen using two threshold selection procedures. The probabilistic properties of the DCP model and the statistical properties of the maximum likelihood estimation (MLE) are investigated, simulating and showing the stability and sensitivity of the MLE estimations. The three proposed models are applied to fit the PM2.5 time series in Beijing from 2015 to 2021. Real data are used to illustrate the advantages of the DCP, especially compared to the estimation volatility of GARCH and AIC or BIC criteria. The DCP model involving both the mixed weather and air quality factors performs better than the other two models with weather factors or air quality factors alone. Finally, a prediction model based on long short-term memory (LSTM) is used to predict PM2.5 concentration, achieving ideal results.

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.

A Study on the Long-Term Variations in Mass Extinction Efficiency Using Visibility Data in South Korea

Fine particulate matter (PM) release is regulated by environmental policies in most countries. This study investigated long–term trends in the mass extinction efficiency (Qe) of aerosols in Northeast Asia. For this purpose, the Qe was calculated using visibility, PM2.5 recorded between 2015 and 2020, and PM10 recorded between 2001 and 2020 at eight Korean sites. The Qe of PM10 (Qe,10) showed an increasing trend with 0.06~0.22 (m2/g)/yr in seven cities except for Jeju. The Qe of PM2.5 (Qe,2.5) also showed an increasing trend with 0.28–2.47 (m2/g)/yr in all cities. In this study, PM10 and PM2.5, were divided into low, moderate, and high concentrations, and the Qe value change by year was examined. Qe,10 showed a tendency to decrease at low concentrations (19–21 μg/m3). However, at moderate (69–71 μg/m3) and high concentrations (139–141 μg/m3), Qe,10 increased in most regions. Qe,2.5 showed an increasing trend at low concentration (9–11 μg/m3), moderate concentration (29–31 μg/m3), and high concentration (69–71 μg/m3), except for Suwon and Pohang, where data were insufficient for analysis. Both Qe,10 and Qe,2.5 showed an increasing trend. The increase in Qe indicated that the visibility-impairing effect of PM can increase even if the same concentration of PM is present. The visibility-impairing effects of PM vary based on the composition, size and other characteristics of the particles in the atmosphere at a given point in time and not simply the quantity of particles. This means that reducing the quantity of particles does not reliably produce a proportionate improvement in visibility. Air quality policies must take the variable nature of PM particles and their effect on visibility into account so that more consistent improvements in air quality can be achieved.

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.

Optical properties of mountain primary and secondary brown carbon aerosols in summertime

Brown carbon (BrC) can affect atmospheric radiation due to its strong absorption ability from the near ultraviolet to the visible range, thereby influencing global climate. However, given the complexity of BrC's chemical composition, its optical properties are still poorly understood, especially in mountainous areas. In this study, the black carbon (BC) tracer method is used to explore the light-absorbing properties of primary and secondary BrC at Mount Hua, China during the 2018 summer period. The primary BrC absorption contributes to 10-15% of the total BrC absorption at a wavelength of 370 nm. From the positive matrix factorization analysis, traffic emissions are found to be a major source of primary BrC absorption (44%), followed by industry and biomass-burning emissions (29%). The secondary BrC accounts for 87% of the total BrC absorption at a wavelength of 370 nm, indicating that BrC is dominated by secondary formation. The observation of a higher secondary BrC absorption diurnal pattern at Mount Hua can be affected by secondary BrC in the residual layer after sunrise and the formation of light-absorbing chromophores by photochemical oxidation in the afternoon. The estimated average mass absorption efficiencies of primary and secondary BrC (MAE_{_pri} and MAE_{_sec}, respectively) are 0.4 m²/g and 2.1 m²/g at wavelengths of 370 nm, respectively, indicating a stronger light-absorbing ability for secondary BrC than for primary BrC. There is no significant difference in MAE_{_pri} within a daily variation, but the daytime MAE_{_sec} value is higher than that during the night. Our study shows that secondary BrC is important to light absorption in mountainous areas.

Separate and combined effects of 3D building features and urban green space on land surface temperature

Deduction of urban green space (UGS) and the multidimensional growth of building have exacerbated the urban heat island (UHI). Yet thorough investigations into how 3D building features and UGS combinedly influence urban land surface temperature (LST) are limited, especially at the road-based blocks scale. Therefore, the study uses the boosted regression tree (BRT) model to explore the relative contribution and marginal effects of the influential factors on LST, and quantify the warming/cooling effects of buildings and UGS. Results show that, (1) building coverage ratio (BCR) is the most influential factor among seven building metrics with a relative contribution of 44.6%. Besides, high-rise buildings tend to alleviate LST while low- and mid-rise buildings heat the surroundings. (2) Green coverage ratio (GCR), edge density (ED), and patch density (PD) are the most influential factors among six UGS metrics, with the relative contribution of 21.0%, 20.9%, and 20.4%, respectively. (3) Comprehensively considering 13 metrics, we find that the dominant influential factor is BCR with a relative contribution of 28.3%, while the regulation amplitudes to LST of aggregation index (AI) and GCR dramatically reduced. These findings indicate that the cooling effect of UGS will be obscured when the buildings coverage is large. Hence, only relying on UGS to alleviate the heat island effect seems inadequate, the keys are the reasonable planning and optimization of 3D built environment.

Analysis of PM2.5, PM10, and Total Suspended Particle Exposure in the Tema Metropolitan Area of Ghana

Dust levels around the Tema industrial area of the Greater Accra Region have seen no reduction in recent years. Even though at some periods in time a natural drop in dust pollution levels is assured, the overall variation characteristics of the concentration of PM2.5, PM10, and Total Suspended Particles (TSP) have not been studied in recent years. This paper examines the levels of dust pollution across four (4) locations within the Tema metropolitan area with a specific interest in selecting locations and periods (weeks) significantly affected by dust pollution within the study area. Data collection was done over a nine-month period using the Casella 712 Microdust Pro Kit equipment. Measurements were done day and night at sampling points about 100 m apart in a given location. Monitoring was conducted once a week during the day and at night with a sampling period of 24 h per location, for thirty-six weeks. The generalized linear models were explored in selecting locations and weeks significantly affected by dust pollution. The study results showed no significant difference between pollution levels across the four selected locations. Eight, eleven, and five weeks out of the 36 weeks recorded significantly high concentrations of PM2.5, PM10, and TSP respectively. In addition, two out of the selected four areas (the oil jetty area and the VALCO hospital area) were found to have significantly high concentrations of dust pollution. The study recommends that an urgent air quality control policy intervention be put in place to control the highly alarming levels of dust pollution concentrations to guarantee and protect human health within the study area and beyond.

Measuring and Quantifying Impacts of Environmental Parameters on Airborne Particulate Matter in Under-Viaducts Spaces in Wuhan, China

Particulate pollution caused by urban traffic emissions has become a significant public hazard. Many urban roads of under-viaduct spaces (UVSs) have become concentrated areas of particulate pollution. This study aims to explore the effects of landscape parameters on particulate matter in UVSs in Wuhan, China. We selected 14 types of UVS sections and nine potential environmental parameters to monitor four types of particulate matter (PM_1.0, PM_2.5, PM₁₀, and TSP). Finally, linear regression analysis was employed to quantify the relative contributions of environmental parameters to the reduction in the concentration of the four types of particulate matter in the summer and winter. The results showed that particulate matter concentrations exhibit spatial and seasonal differences in UVSs. A single landscape parameter was correlated with particulate matter concentration, while compound environmental parameters had significant effects on the particulate matter concentration in UVSs. Meteorological factors and greening structures had a dominant impact on the particulate matter concentrations in summer and winter, respectively. Therefore, adjusting and optimizing the environmental parameters could reduce particulate pollution in UVSs and could have practical significance for the planning and design of UVSs.

Air pollution could drive global dissemination of antibiotic resistance genes

Antibiotic-resistant pathogens pose a significant threat to human health. Several dispersal mechanisms have been described, but transport of both microbes and antibiotic resistance genes (ARGs) via atmospheric particles has received little attention as a pathway for global dissemination. These atmospheric particles can return to the Earth's surface via rain or snowfall, and thus promote long-distance spread of ARGs. However, the diversity and abundance of ARGs in fresh snow has not been studied and their potential correlation with particulate air pollution is not well explored. Here, we characterized ARGs in 44 samples of fresh snow from major cities in China, three in North America, and one in Europe, spanning a gradient from pristine to heavily anthropogenically influenced ecosystems. High-throughput qPCR analysis of ARGs and mobile genetic elements (MGEs) provided strong indications that dissemination of ARGs in fresh snow could be exacerbated by air pollution, severely increasing the health risks of both air pollution and ARGs. We showed that snowfall did effectively spread ARGs from point sources over the Earth surface. Together our findings urge for better pollution control to reduce the risk of global dissemination of antibiotic resistance genes.

Exceedances and trends of particulate matter (PM2.5) in five Indian megacities

Fine particulate matter (PM_2.5) is the leading environmental risk factor that requires regular monitoring and analysis for effective air quality management. This work presents the variability, trend, and exceedance analysis of PM_2.5 measured at US Embassy and Consulate in five Indian megacities (Chennai, Kolkata, Hyderabad, Mumbai, and New Delhi) for six years (2014-2019). Among all cities, Delhi is found to be the most polluted city followed by Kolkata, Mumbai, Hyderabad, and Chennai. The trend analysis for six years for five megacities suggests a statistically significant decreasing trend ranging from 1.5 to 4.19 μg/m³ (2%-8%) per year. Distinct diurnal, seasonal, and monthly variations are observed in the five cities due to the different site locations and local meteorology. All cities show the highest and lowest concentrations in the winter and monsoon months respectively except for Chennai which observed the lowest levels in April. All the cities consistently show morning peaks (~08: 00-10:00 h) and the lowest level in late afternoon hours (~15:00-16:00 h). We found that the PM_2.5 levels in the cities exceed WHO standards and Indian NAAQS for 50% and 33% of days in a year except for Chennai. Delhi is found to have more than 200 days of exceedances in a year and experiences an average 15 number of episodes per year when the level exceeds the Indian NAAQS. The trends in the exceedance with a varying threshold (20-380 μg/m³) suggest that not only is the annual mean PM_2.5 decreasing in Delhi but also the number of exceedances is decreasing. This decrease can be attributed to the recent policies and regulations implemented in Delhi and other cities for the abatement of air pollution. However, stricter compliance of the National Clean Air Program (NCAP) policies can further accelerate the reduction of the pollution levels.

The Impact of the Control Measures during the COVID-19 Outbreak on Air Pollution in China

The outbreak of the COVID-19 virus in Wuhan, China, in January 2020 just before the Spring Festival and subsequent country-wide measures to contain the virus, effectively resulted in the lock-down of the country. Most industries and businesses were closed, traffic was largely reduced, and people were restrained to their homes. This resulted in the reduction of emissions of trace gases and aerosols, the concentrations of which were strongly reduced in many cities around the country. Satellite imagery from the TROPOspheric Monitoring Instrument (TROPOMI) showed an enormous reduction of tropospheric NO2 concentrations, but aerosol optical depth (AOD), as a measure of the amount of aerosols, was less affected, likely due to the different formation mechanisms and the influence of meteorological factors. In this study, satellite data and ground-based observations were used together to estimate the separate effects of the Spring Festival and the COVID-19 containment measures on atmospheric composition in the winter of 2020. To achieve this, data were analyzed for a period from 30 days before to 60 days after the Spring Festivals in 2017–2020. This extended period of time, including similar periods in previous years, were selected to account for both the decreasing concentrations in response to air pollution control measures, and meteorological effects on concentrations of trace gases and aerosols. Satellite data from TROPOMI provided the spatial distributions over mainland China of the tropospheric vertical column density (VCD) of NO2, and VCD of SO2 and CO. The MODerate resolution Imaging Spectroradiometer (MODIS) provided the aerosol optical depth (AOD). The comparison of the satellite data for different periods showed a large reduction of, e.g., NO2 tropospheric VCDs due to the Spring Festival of up to 80% in some regions, and an additional reduction due to the COVID-19 containment measures of up to 70% in highly populated areas with intensive anthropogenic activities. In other areas, both effects are very small. Ground-based in situ observations from 26 provincial capitals provided concentrations of NO2, SO2, CO, O3, PM2.5, and PM10. The analysis of these data was focused on the situation in Wuhan, based on daily averaged concentrations. The NO2 concentrations started to decrease a few days before the Spring Festival and increased after about two weeks, except in 2020 when they continued to be low. SO2 concentrations behaved in a similar way, whereas CO, PM2.5, and PM10 also decreased during the Spring Festival but did not trace NO2 concentrations as SO2 did. As could be expected from atmospheric chemistry considerations, O3 concentrations increased. The analysis of the effects of the Spring Festival and the COVID-19 containment measures was complicated due to meteorological influences. Uncertainties contributing to the estimates of the different effects on the trace gas concentrations are discussed. The situation in Wuhan is compared with that in 26 provincial capitals based on 30-day averages for four years, showing different effects across China.

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.

Yearly changes of the sulfate-nitrate-ammonium aerosols and the relationship with their precursors from 1999 to 2016 in Beijing

The change in particulate matter (PM)_2.5 composition in relation to precursors over recent decades has not been elucidated clearly in Beijing. Using ground-based measurements from the literature, this study investigated the yearly time series of PM_2.5 and its chemical composition over Beijing from 1999 to 2016 to identify the driving forces underlying these changes. The PM_2.5 concentration declined slightly, due to the organic carbon, elemental carbon, and dust rather than to either sulfate-nitrate-ammonium (SNA) aerosols. Before 2013, the trend of SNA aerosols was opposite to that of PM_2.5; however, subsequently, SNA aerosols have represented the major contribution to the reduction of PM_2.5, coinciding with a large decline of regional precursor gases. The yearly time series of SNA aerosols can be explained better by regional precursor gases than by local ones. Generally, precursor gases emissions over the region of North China Plain can be controlled if Beijing's air quality is to be improved.

Pareto law-based regional inequality analysis of PM2.5 air pollution and economic development in China

Regional inequality has caused large social and economic problems in China. Numerous researchers have sought to understand the status of economic inequality in the past decades. However, studies are lacking on other aspects of regional inequality, particularly when multiple facets must be considered. In this study, we have innovatively proposed a Pareto law-based method that can help assess multiple dimensions of regional inequality simultaneously. With this approach, we can rank multiple aspects of inequality and provide robust, reasonable goals for different groups of administrative districts. The proposed approach was successfully implemented by using Chinese data for 2015 and 2016, a period during which China was experiencing both severe PM_2.5 pollution and economic regional inequality. The results indicate that (1) Shanghai and Shenzhen represent the optimal condition of economic development; (2) different from the spatial distribution of economic inequality alone, inequality was higher in central China for both economic development and PM_2.5 air quality; (3) in the context of severe economic inequality in China, the tradeoff between economic development and air quality will result in a relatively equitable condition. In addition, the proposed method is open-ended and can be extended to incorporate more aspects of regional inequality. This approach appears to possess substantial potential for integration into decision-making regarding regional inequality.

The relationships between surface-column aerosol concentrations and meteorological factors observed at major cities in the Yangtze River Delta, China

Monitoring of particulate matter (PM) is important in air quality, public health, and epidemiological studies and in decision-making for policy implementation. In the present study, the temporal variability of surface-measured PM concentrations ([PM]) and their relationship with meteorological variables and aerosol optical depth (AOD), with the aid from source apportionment studies, are investigated at four urban cities in the Chinese Yangtze River Delta (YRD) region during January 2014 to December 2017. The annual mean concentrations of [PM_2.5] ([PM₁₀]) observed at Shanghai (SH), Nanjing (NJ), Hangzhou (HZ), and Hefei (HF) were 46.98 ± 12.21, 54.84 ± 46.14, 52.82 ± 16.98, and 64.03 ± 20.57 μg m^-3 (68.07 ± 14.33, 96.48 ± 26.86, 83.08 ± 22.38, and 97.61 ± 20.19 μg m^-3), respectively. However, the [PM] exceeded the Chinese National Air Quality Standards of GB3095-2012, being higher (lower) during winter (summer). The [PM] was found higher in the morning (08:00-10:00 LT) and evening (18:00-20:00 LT) and lower in early morning (04:00 LT) and afternoon (14:00 LT) attributed to the dynamics of boundary layer height and varied emission sources. With an annual mean of 0.6-0.7, the PM ratio (PMr = PM_2.5/PM₁₀) was observed to have a single peak distribution in all seasons indicating the dominance of fine particles (PM_2.5). Further, the [PM₁₀] and [PM_2.5] were highly correlated (r ≥ 0.90) in all cities, with slope > 0.70 representing the abundance of fine particles, except for NJ (< 0.70). A low correlation (< 0.5) was noticed between [PM₁₀] and AOD₅₅₀ suggesting that the aerosol particles had a large influence on AOD, contributing less to PM₁₀. Finally, the concentration bivariate probability function (CBPF) and trajectory statistical models like potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) suggested that local and regional sources contributed a lot for the high [PM_2.5] observed at the four cities in the YRD, China.

Temporal and Spatial Features of the Correlation between PM2.5 and O3 Concentrations in China

In recent years, particulate matter of 2.5 µm or less (PM2.5) pollution in China has decreased but, at the same time, ozone (O3) pollution has become increasingly serious. Due to the different research areas and research periods, the existing analyses of the correlation between PM2.5 and O3 have reached different conclusions. In order to clarify the relationship between PM2.5 and O3, this study selected mainland China as the research area, based on the PM2.5 and O3 concentration data of 1458 air quality monitoring stations, and analyzed the correlation between PM2.5 and O3 for different time scales and geographic divisions. Moreover, by combining the characteristics of the pollutants, topography, and climatic features of the study area, we attempted to discuss the causes of the spatial and temporal differences of R-PO (the correlation between PM2.5 and O3). The study found that: (1) R-PO tends to show a positive correlation in summer and a negative correlation in winter, (2) the correlation coefficient of PM2.5 and O3 is lower in the morning and higher in the afternoon, and (3) R-PO also shows significant spatial differences, including north-south differences and coastland-inland differences.

Seasonal characteristics and health risks of PM2.5-bound organic pollutants in industrial and urban areas of a China megacity

Organic pollutants are important harmful components in atmospheric fine particulate matters (PM_2.5), health risks of which varied with temporal and spatial distributions. To clarify the characteristics of atmospheric organic pollution, the concentrations, sources, and human health risks of typical organic compositions in PM_2.5 samples from both industrial and urban areas of Nanjing in eastern China were investigated monthly for a year. Results showed that, the concentrations of PM_2.5-bound polycyclic aromatic hydrocarbons (PAHs) and n-alkanes were higher in winter and spring than those in summer and autumn. The organic pollution was slightly higher in industrial than urban area, though the PAHs in autumn and the n-alkanes in warm season (summer and autumn) were higher in urban area. With regards to the pollutant sources, the atmospheric PAHs were dominated by motor vehicle exhaust in the urban area, and combined with coal combustion emission in the industrial area. Airborne n-alkanes were mainly from biological source accompanied by fossil fuel combustion in industrial area. The PM_2.5-bound PAHs indicated higher risks to adults in industrial area than in urban area with the seasonal patterns: winter > spring > autumn > summer. More attention should be paid to the health risks of exposure to organic pollutants accumulated in PM_2.5 during cold season. Controlling vehicle emissions might be the key measure for alleviating atmospheric PAHs and n-alkanes pollution in megacities, while coal purification can be an effective control method in industrial areas.

Effects of air pollution control measures on air quality improvement in Guangzhou, China

The ambient air quality of Guangzhou in 2016 has significantly improved since Guangzhou and its surrounding cities implemented a series of air pollution control measures from 2014 to 2016. This study not only estimated the effects of meteorology and emission control measures on air quality improvement in Guangzhou but also assessed the contributions of emissions reduction from various sources through the combination of observation data and simulation results from Weather Research and Forecasting - Community Multiscale Air Quality (WRF-CMAQ) modeling system. Results showed that the favorable meteorological conditions in 2016 alleviated the air pollution. Compared to change in meteorology, implementing emission control measures in Guangzhou and surrounding cities was more beneficial for air quality improvement, and it could reduce the concentrations of SO₂, NO₂, PM_2.5, PM₁₀, and O₃ by 9.7 μg m^-3 (48.4%), 9.2 μg m^-3 (17.7%), 7.7 μg m^-3 (14.6%), 9.7 μg m^-3 (13.4%), and 12.0 μg m^-3 (7.7%), respectively. Furthermore, emission control measures that implemented in Guangzhou contributed most to the concentration reduction of SO₂, NO₂, PM_2.5, and PM₁₀ (46.0% for SO₂, 15.2% for NO₂, 9.4% for PM_2.5, and 9.1% for PM₁₀), and it increased O₃ concentration by 2.4%. With respect to the individual contributions of source emissions reduction, power sector emissions reduction showed the greatest contribution in reducing the concentrations of SO₂, NO₂, PM_2.5, and PM₁₀ due to the implementation of Ultra-Clean control technology. As for O₃ mitigation, VOCs product-related source emissions reduction was most effective, and followed by transportation source emissions reduction, while the reductions of power sector, industrial boiler, and industrial process source might not be as effective. Our findings provide scientific advice for the Guangzhou government to formulate air pollution prevention and control policies in the future.

Estimating Secondary Organic Aerosol Production from Toluene Photochemistry in a Megacity of China

The production of secondary organic aerosols (SOA) from toluene photochemistry in Shanghai, a megacity of China, was estimated by two approaches, the parametrization method and the tracer-based method. The temporal profiles of toluene, together with other fifty-six volatile organic compounds (VOCs), were characterized. Combing with the vapor wall loss corrected SOA yields derived from chamber experiments, the estimated toluene SOA by the parametrization method as embodied in the two-product model contributes up to ∼40% of the total SOA budget during summertime. 2,3-Dihydroxy-4-oxopentanoic acid (DHOPA), a unique product from the OH-initiated oxidation of toluene in the presence of elevated NO_x, was used as a tracer to back calculate the toluene SOA concentrations. By taking account for the effect of gas-particle partitioning processes on the fraction of DHOPA in the particle phase, the estimated toluene SOA concentrations agree within ∼33% with the estimates by the parametrization method. The agreement between these two independent approaches highlight the need to update current model frameworks with recent laboratory advances for a more accurate representation of SOA formation in regions with substantial anthropogenic emissions.

Acute effects of fine particulate matter (PM2.5) on hospital admissions for cardiovascular disease in Beijing, China: a time-series study

BACKGROUND

Air pollution and cardiovascular disease are increasing problems in China. However, the short-term association between fine particulate matter (PM_2.5) and cardiovascular disease (CVD) is not well documented. The purpose of this study is to estimate the short-term effects of PM_2.5 on CVD admissions in Beijing, China.

METHODS

In total, 460,938 electronic hospitalization summary reports for CVD between 2013 and 2017 were obtained. A generalized additive model using a quasi-Poisson distribution was used to investigate the association between exposure to PM_2.5 and hospitalizations for total and cause-specific CVD, including coronary heart disease (CHD), atrial fibrillation (AF), and heart failure (HF) after controlling for the season, the day of the week, public holidays, and weather conditions. A stratified analysis was also conducted for age (18-64 and ≥ 65 years), sex and season.

RESULTS

For every 10 μg/m³ increase in the PM_2.5 concentration from the previous day to the current (lag 0-1) there was a significant increase in total CVD admissions (0.30, 95% CI: 0.20, 0.39%), with a strong association for older adults (aged ≥65 years), CHD (0.34, 95% CI: 0.22 to 0.45%) and AF (0.29, 95% CI, 0.03 to 0.55%). However, the observed increased risk was not statistically significant for HF hospitalizations. The associations in the single-pollutant models were robust to the inclusion of other pollutants in a two-pollutant model. No differences were found after stratification by sex and season.

CONCLUSIONS

Exposure to PM_2.5 increased the risk of hospitalizations from CVD, especially for CHD, and appeared to have more influence in the elderly. Precautions and protective measures and efforts to reduce exposure to PM_2.5 should be strengthened, especially for the elderly.

Exposures to Air Pollution and Noise from Multi-Modal Commuting in a Chinese City

Background: Modern urban travel includes mixtures of transit options, which potentially impact individual pollution exposures and health. This study aims to investigate variations in traffic-related air pollution and noise levels experienced in traffic in Chengdu, China. Methods: Real-time PM_2.5, black carbon (BC), and noise levels were measured for four transportation modes (car, bus, subway, and shared bike) on scripted routes in three types of neighborhoods (urban core, developing neighborhood, and suburb). Each mode of transportation in each neighborhood was sampled five times in summer and winter, respectively. After quality control, mixed effect models were built for the three pollutants separately. Results: Air pollutants had much higher concentrations in winter. Urban Core had the highest PM_2.5 and BC concentrations across seasons compared to the other neighborhoods. The mixed effect model indicated that car commutes were associated with lower PM_2.5 (−34.4 μg/m³; 95% CI: −47.5, −21.3), BC (−2016.4 ng/m³; 95% CI: −3383.8, −648.6), and noise (−9.3 dBA; 95% CI: −10.5, −8.0) levels compared with other modes; subway commutes had lower PM_2.5 (−11.9 μg/m³; 95% CI: 47.5, −21.3), but higher BC (2349.6 ng/m³; 95% CI: 978.1, 3722.1) and noise (3.0 dBA; 95% CI: 1.7, 4.3) levels than the other three modes of transportation. Conclusion: Personal exposure to air pollution and noise vary by season, neighborhood, and transportation modes. Exposure models accounting for environmental, meteorological, and behavioral factors, and duration of mixed mode commuting may be useful for health studies of urban traffic microenvironments.

Increased secondary aerosol contribution and possible processing on polluted winter days in China

China experiences severe particulate pollution, especially in winter, and determining the characteristics of particulate matter (PM) during pollution events is imperative for understanding the sources and causes of the pollution. However, inconsistencies have been found in the aerosol composition, sources and secondary processing among reported studies. Modern meta-analysis was used to probe the PM chemical characteristics and processing in winter at four representative regions of China, and the first finding was that secondary aerosol formation was the major effect factor for PM pollution. The secondary inorganic species behaved differently in the four regions: sulfate, nitrate, and ammonium increased in the Beijing-Tianjin-Hebei (BTH) and Guanzhong (GZ) areas, but only nitrate increased in the Pearl River Delta (PRD) and Yangtze River Delta (YRD) regions. The increased production of secondary organic aerosol (SOA) was probably caused by aqueous-phase processing in the GZ and BTH regions and by photochemical reactions in the PRD. Finally, we suggest future AMS/ACSM observations should focus on the aerosol characteristics in rural areas in winter in China.

Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data

Atmospheric fine particulate matters (PM_2.5) have raised global concerns because of their markedly adverse effects on public health and environmental quality. In parallel with technological variations and social changes in the evolving industrialization pathways in China, there is an acute need to evaluate the long-term spatiotemporal trend of PM_2.5 concentrations across China after years of elevation. Toward this end, an integrated high-resolution satellite-derived (1998-2016) and ground-measured (2015-2017) PM_2.5 data base was applied. Satellite-derived annual mean PM_2.5 grids were firstly validated via comparison with collocated surface in situ PM_2.5 measurements and were then used for trend analyses. The estimated linear trends from gridded PM_2.5 data indicated that PM_2.5 concentrations in China increased mainly before 2008 and have decreased since then, with prominent decreases observed primarily in south China. To corroborate the satellite-based PM_2.5 trend estimations, surface in situ PM_2.5 measurements from the period from 2015 to 2017 were applied to further evaluate the decreasing rate after 2014, at which time the Chinese "Air Pollution Prevention and Control Action Plan" was enforced. The results revealed that the national mean PM_2.5 concentrations decreased by about 6.5 μg/m³ from 2015 to 2017, with prominent decreases (by a rate of 5-10 μg/m³ per year) observed primarily associated with large PM_2.5 concentrations in Central China, North China, Northeast China, and East China during the period from October to December. Our systematic trend assessment provides a deepened understanding of PM_2.5 variations across China in the past few years in association with the newly promoted action plan and offers a brief guideline for relevant policy making in the future.

Review of total suspended particles (TSP) and PM2.5 concentration variations in Asia during the years of 1998-2015

In Asian countries such as China, Malaysia, Pakistan, India, Taiwan, Korea, Japan and Hong Kong, ambient air total suspended particulates and PM_2.5 concentration data were collected and discussed during the years of 1998-2015 in this study. The aim of the present study was to (1) investigate and collect ambient air total suspended particulates (TSP) and PM_2.5 concentrations for Asian countries during the past two decades. (2) Discuss, analyze and compare those particulates (TSP and PM_2.5) annual concentration distribution trends among those Asian countries during the past two decades. (3) Test the mean concentration differences in TSP and PM_2.5 among the Asian countries during the past decades. The results indicated that the mean TSP concentration order was shown as China > Malaysia > Pakistan > India > Taiwan > Korea > Japan. In addition, the mean PM_2.5 concentration order was shown as Vietnam > India > China > Hong Kong > Mongolia > Korea > Taiwan > Japan and the average percentages of PM_2.5 concentrations for Taiwan, China, Japan, Korea, Hong Kong, Mongolia and Other (India and Vietnam) were 8, 21, 6, 8, 14, 13 and 30%, respectively, during the past two decades. Moreover, t test results revealed that there were significant mean TSP and PM_2.5 concentration differences for either China or India to any of the countries such as Taiwan, Korea and Japan in Asia during the past two decades for this study. Noteworthy, China and India are both occupied more than 60% of the TSP and PM_2.5 particulates concentrations out of all the Asia countries. As for Taiwan, the average PM_2.5 concentration displayed increasing trend in the years of 1998-1999. However, it showed decreasing trend in the years of 2000-2010. As for Korea, the average PM_2.5 concentrations showed decreasing trend during the years of 2001-2013. Finally, the average PM_2.5 concentrations for Mongolia displayed increasing trend in the years of 2004-2013.

PM2.5, Population Exposure and Economic Effects in Urban Agglomerations of China Using Ground-Based Monitoring Data

This paper adopts the PM_2.5 concentration data obtained from 1497 station-based monitoring sites, population and gross domestic product (GDP) census data, revealing population exposure and economic effects of PM_2.5 in four typical urban agglomerations of China, i.e., Beijing-Tianjin-Hebei (BTH), the Yangtze River delta (YRD), the Pearl River delta (PRD), and Chengdu-Chongqing (CC). The Cokriging interpolation method was used to estimate the PM_2.5 concentration from station-level to grid-level. Next, an evaluation was conducted mainly at the grid-level with a cell size of 1 × 1 km, assisted by the urban agglomeration scale. Criteria including the population-weighted mean, the cumulative percent distribution and the correlation coefficient were applied in our evaluation. The results showed that the spatial pattern of population exposure in BTH was consistent with that of PM_2.5 concentration, as well as changes in elevation. The topography was also an important factor in the accumulation of PM_2.5 in CC. Moreover, the most polluted urban agglomeration based on the population-weighted mean was BTH, while the least was PRD. In terms of the cumulative percent distribution, only 0.51% of the population who lived in the four urban agglomerations, and 2.33% of the GDP that was produced in the four urban agglomerations, were associated with an annual PM_2.5 concentration smaller than the Chinese National Ambient Air Quality Standard of 35 µg/m³. This indicates that the majority of people live in the high air polluted areas, and economic development contributes to air pollution. Our results are supported by the high correlation between population exposure and the corresponding GDP in each urban agglomeration.