Assessment of PM2.5 concentrations and exposure throughout China using ground observations

Time series analysis of PM2.5 pollution risk based on the supply and demand of PM2.5 removal service: a case study of the urban areas of Beijing

Demonstrating the temporal changes in PM2.5 pollution risk in regions facing serious PM2.5 pollution problems can provide scientific evidence for the air pollution control of the region. However, research on the variation of PM2.5 pollution risk on a fine temporal scale is very limited. Therefore, we developed a method for quantitative characterizing PM2.5 pollution risk based on the supply and demand of PM2.5 removal services, analyzed the time series characteristics of PM2.5 pollution risk, and explored the reasons for the temporal changes using the urban areas of Beijing as the case study area. The results show that the PM2.5 pollution risk in the urban areas of Beijing was close between 2008 and 2012, decreased by approximately 16.3% in 2016 compared to 2012, and further decreased by approximately 13.2% in 2021 compared to 2016. The temporal variation pattern of the PM2.5 pollution risk in 2016 and 2021 showed significant differences, including an increase in the number of risk-free days, a decrease in the number of heavily polluted days, and an increase in the stability of the risk day sequence. The significant reduction in risk level was mainly attributed to Beijing's air pollution control measures, supplemented by the impact of COVID-19 control measures in 2021. The results of PM2.5 pollution risk decomposition indicate that compared to the previous 2 years, the stability and predictability of the risk variation in 2016 increased, but the overall characteristics of high risk from November to February and low risk from April to September did not change. The high risk from November to February was mainly due to the demand for coal heating during this period, a decrease in PM2.5 removal service supply caused by plant leaf fall, and the common occurrence of temperature inversions in winter, which hinders the diffusion of air pollutants. This study provides a method for the analysis of PM2.5 pollution risk on fine temporal scales and may provide a reference for the PM2.5 pollution control in the urban areas of Beijing.

Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning

Global fine particulate matter (PM2.5) assessment is impeded by a paucity of monitors. We improve estimation of the global distribution of PM2.5 concentrations by developing, optimizing, and applying a convolutional neural network with information from satellite-, simulation-, and monitor-based sources to predict the local bias in monthly geophysical a priori PM2.5 concentrations over 1998-2019. We develop a loss function that incorporates geophysical a priori estimates and apply it in model training to address the unrealistic results produced by mean-square-error loss functions in regions with few monitors. We introduce novel spatial cross-validation for air quality to examine the importance of considering spatial properties. We address the sharp decline in deep learning model performance in regions distant from monitors by incorporating the geophysical a priori PM2.5. The resultant monthly PM2.5 estimates are highly consistent with spatial cross-validation PM2.5 concentrations from monitors globally and regionally. We withheld 10% to 99% of monitors for testing to evaluate the sensitivity and robustness of model performance to the density of ground-based monitors. The model incorporating the geophysical a priori PM2.5 concentrations remains highly consistent with observations globally even under extreme conditions (e.g., 1% for training, R2 = 0.73), while the model without exhibits weaker performance (1% for training, R2 = 0.51).

Spatiotemporal patterns of surface ozone exposure inequality in China

Rising surface ozone (O3) levels in China are increasingly emphasizing the potential threats to public health, ecological balance, and economic sustainability. Using a 1 km × 1 km dataset of O3 concentrations, this research employs subpopulation demographic data combined with a population-weighted quality model. Its aim is to evaluate quantitatively the differences in O3 exposure among various subpopulations within China, both at a provincial and urban cluster level. Additionally, an exposure disparity indicator was devised to establish unambiguous exposure risks among significant urban agglomerations at varying O3 concentration levels. The findings reveal that as of 2018, the population-weighted average concentration of O3 for all subgroups has experienced a significant uptick, surpassing the average O3 concentration (118 μg/m3). Notably, the middle-aged demographic exhibited the highest O3 exposure level at 135.7 μg/m3, which is significantly elevated compared to other age brackets. Concurrently, there exists a prominent positive correlation between educational attainment and O3 exposure levels, with the medium-income bracket showing the greatest susceptibility to O3 exposure risks. From an industrial vantage point, the secondary sector demographic is the most adversely impacted by O3 exposure. In terms of urban-rural structure, urban groups in all regions had higher levels of exposure to O3 than rural areas, with North and East China having the most significant levels of exposure. These findings not only emphasize the intricate interplay between public health and environmental justice but further highlight the indispensability of segmented subgroup strategies in environmental health risk assessment. Moreover, this research furnishes invaluable scientific groundwork for crafting targeted public health interventions and sustainable air quality management policies.

Emission inventory and air quality impact of non-road construction equipment in different emission stages

Non-road construction equipment (NRCE) is an important source of air pollution, and it is crucial to fully understand the impact of NRCE on atmospheric PM2.5 and O3 pollution. However, systematic assessment of the impact of NRCE emissions on the atmosphere is lacking, especially with the latest implementation of the Stage IV Standard, and current research progress is insufficient for the development of effective control measures. This study estimated NRCE emission inventories at different emission standard stages and their impact on the atmosphere, using the "2 + 26" cities as the case study area. The results showed that the total NRCE emissions of CO, NOx, VOC, and PM2.5 were 387, 418, 82, and 24 kt in 2015 and 319, 262, 62, and 15 kt in 2020 and are predicted to be 270, 226, 48, and 10 kt in 2025, respectively. Simulation results showed that the contributions of NRCE to NO3-, NO2, PM2.5, and O3 were 16.7 %, 18.9 %, 7.7 %, and 8.2 % in 2015 to 13.6 %, 18.4 %, 6.5 %, and 6.7 % in 2020, respectively. In both 2015 and 2020, NRCE emissions in southern cities showed greater impacts on the average concentrations in the "2 + 26" cities than those in northern cities. The contributions of local NRCE emissions to local PM2.5 and O3 concentrations in the 28 cities ranged from 30 %-59 % and 13 %-39 %, respectively. The O3 sensitivity estimated by the HDDM illustrated that nonlinear characteristics highlighted the importance of coordinated control of NOx and VOC and can inspire development of post-processing technology and electricity substitution. The belt-like area connecting Zhengzhou to Beijing showed higher exposure concentrations of PM2.5 and O3, and the concentration exposure in urban areas was much higher than that in the rural and other areas. The environmental impact assessment of NRCE emissions can provide guidance for its management and development.

Influencing factors of PM2.5 concentration in the typical urban agglomerations in China based on wavelet perspective

PM2.5 is one of the most harmful air pollutants affecting sustainable economic and social development in China. The analysis of influencing factors affecting PM2.5 concentration is significant for the improvement of air quality. In this study, three typical urban agglomerations in China (Beijing‒Tianjin‒Hebei [BTH], the Yangtze River Delta [YRD], and the Pearl River Delta [PRD]) were studied using innovative trend analysis, a Bayesian statistical model, and partial wavelet and multiwavelet coherence to analyze PM2.5 concentration variations and multi-scale coupled oscillations between PM2.5 concentration and air pollutants/meteorological factors. The results showed that: (1) PM2.5 concentration time-series showed significant downward trends, which decreased as follows: BTH > YRD > PRD. The higher the pollution level, the greater the change trend. In BTH and the PRD, PM2.5 had obvious trends and seasonal change points; whereas, the PM2.5 time-series change point in the YRD was not obvious. (2) PM2.5 had significant intermittent resonance cycles with air pollutants and meteorological factors in different time domains. There were differences in the main controlling factors affecting PM2.5 among the three urban agglomerations. (3) The explanatory ability of air pollutant combinations for variations in PM2.5 was higher than that of meteorological factor combinations. However, the synergistic effect of air pollutants/meteorological factors could better explain the PM2.5 concentration variations on all time-frequency scales. The results of this study provide a reference for ecological improvement as well as collaborative governance of air pollution.

Spatial-temporal evolution patterns and drivers of PM2.5 chemical fraction concentrations in China over the past 20 years

The quantitative assessment of the spatial and temporal variability and drivers of fine particulate matter (PM2.5) fraction concentrations are important for pollution control and public health preservation in China. In this study, we investigated the spatial temporal variation of PM2.5 chemical component based on the PM2.5 chemical component datasets from 2000 to 2019 and revealed the driving forces of the differences in the spatial distribution using geodetector model (GD), multi-scale geographically weighted regression model (MGWR), and a two-step clustering approach. The results show that: the PM2.5 chemical fraction concentrations show a trend of first increasing (2000-2007) and then decreasing (2007-2019). From 2000 to 2019, the change rates of PM2.5, organic matter (OM), black carbon (BC), sulfates (SO2- 4), ammonium (NH+ 4), and nitrates (NO- 3) were -0.59, -0.23, -0.07, -0.15, -0.02, and 0.04μg/m3/yr in the entirety of China. The secondary aerosol (i.e., SO2- 4, NO- 3, and NH+ 4; SNA) had the highest fraction in PM2.5 concentrations (55.6-68.1% in different provinces), followed by OM and BC. Spatially, North, Central, and East China are the regions with the highest PM2.5 chemical component concentrations in China; meanwhile, they are also the regions with the most significant decrease in PM2.5 chemical fraction concentrations. The GD and MGWR model shows that among all variables, the number of enterprises, disposable income, private car ownership, and the share of secondary industry non-linearly enhance the differences in the spatial distribution of PM2.5 component concentrations. Electricity consumption has the strongest influence on NH+ 4 emissions in Northwest China and BC and OM emissions in Northeast China.

Air pollution and associated health impact and economic loss embodied in inter-provincial electricity transfer in China

As the largest producer and consumer of coal in the world, China heavily relies on coal resources for thermal power generation. Owing to the unbalanced distribution of energy resources, electricity transfer among regions in China plays a key role in promoting economic growth and ensuring energy safety. However, little is known about air pollution and the related health impacts resulting from electricity transfer. This study assessed PM_2.5 pollution and related health and economic losses attributable to the inter-provincial electricity transfer in mainland China in 2016. The results show that a large amount of virtual air pollutant emissions were transferred from energy-abundant northern, western and central China to well-developed and populated eastern coastal regions. Correspondingly, the inter-provincial electricity transfer dramatically reduced the atmospheric levels of PM_2.5 and related health and economic losses in eastern and southern China, while increasing those in northern, western and central China. The health benefits attributable to inter-provincial electricity transfer were mainly found in Guangdong, Liaoning, Jiangsu and Shandong, whereas the extra health loss is concentrated in Hebei, Shanxi, Inner Mongolia, and Heilongjiang. Overall, the inter-provincial electricity transfer led to an extra increase of 3600 (95 % CI: 3200-4100) PM_2.5-related deaths and 345 (95 % CI: 294-389) million USD of economic loss in China in 2016. The results could assist air pollution mitigation strategies for the thermal power sector in China by strengthening the cooperation between suppliers and consumers of electricity.

The interactive effects of extreme temperatures and PM2.5 pollution on mortalities in Jiangsu Province, China

Exposure to extreme temperatures or fine particles is associated with adverse health outcomes but their interactive effects remain unclear. We aimed to explore the interactions of extreme temperatures and PM2.5 pollution on mortalities. Based on the daily mortality data collected during 2015-2019 in Jiangsu Province, China, we conducted generalized linear models with distributed lag non-linear model to estimate the regional-level effects of cold/hot extremes and PM2.5 pollution. The relative excess risk due to interaction (RERI) was evaluated to represent the interaction. The relative risks (RRs) and cumulative relative risks (CRRs) of total and cause-specific mortalities associated with hot extremes were significantly stronger (p < 0.05) than those related to cold extremes across Jiangsu. We identified significantly higher interactions between hot extremes and PM2.5 pollution, with the RERI range of 0.00-1.15. The interactions peaked on ischaemic heart disease (RERI = 1.13 [95%CI: 0.85, 1.41]) in middle Jiangsu. For respiratory mortality, RERIs were higher in females and the less educated. The interaction pattern remained consistent when defining the extremes/pollution with different thresholds. This study provides a comprehensive picture of the interactions between extreme temperatures and PM2.5 pollution on total and cause-specific mortalities. The projected interactions call for public health actions to face the twin challenges, especially the co-appearance of hot extremes and PM pollution.

Perchlorate in fine particulate matter in Shenzhen, China, and implications for human inhalation exposure

The wide application of perchlorate in military and aerospace industries raises potential exposure risks for humans. Previous studies have mainly focused on perchlorate in drinking water, foodstuffs and dust, while its exposure in fine particulate matter (PM_2.5) has received less attention. Thus, we investigated its concentrations and temporal variability in PM_2.5 from October 2020 to September 2021 in Shenzhen, southern China. We also assessed the native population's intake and uptake of perchlorate in PM_2.5 via inhalation. Measured PM_2.5 concentrations in samples from Shenzhen ranged from 2.0 to 91.9 μg m^-3. According to air quality guidelines proposed by the World Health Organization, 12.7% of all the samples exceeded interim target 1 (> 35 μg m^-3), and only 37.3% met interim target 3 (< 15 μg m^-3). Logistic regression analysis showed that perchlorate concentrations positively correlated with the PM_2.5 concentrations and negatively correlated with precipitation. The median estimated daily intake (EDI) was highest for infants (0.029 ng kg^-1 day^-1), and both EDIs and estimated daily uptakes (EDUs) gradually decreased with age. All the EDIs and EDUs were below the reference dose provided by the US National Academy of Sciences (NAS), indicating that exposure to perchlorate in PM_2.5 posed negligible health risks for Shenzhen residents. However, the exposure of infants and specific groups who tend to be more highly exposed than average still warrants attention.

A new approach of air pollution regionalization based on geographically weighted variations for multi-pollutants in China

Air pollution regionalization is a key and necessary action to identify pollution regions for implementing control measures. Here we present a new approach called Geographically Weighted Rotation Empirical Orthogonal Function (GWREOF) for air pollution regionalization in China. Compared with previous methods, such as EOF, REOF, and K-mean, GWREOF better accounts for the variability of air pollution conditions driven by emission patterns and meteorology with centralized spatial locations. We apply GWREOF to multiple air pollutants (such as PM_2.5, O₃, and other monitored air pollutants) and air quality metrics using their measured spatial and temporal variations in 337 Chinese cities over 2015-2020. We find that the regionalization results for different air pollutants are highly similar, primarily determined by topography and meteorological conditions in China. Therefore, we propose an integrated regionalization result, which identifies 18 air pollution control regions in China and can be applied to multiple pollutants and different years. We further analyze PM_2.5, O₃, and O_X (O₃ + NO₂) pollution levels and their correlations in these regions. PM_2.5 and O₃ correlations are generally strongly positive in southern China while negative in northern China. However, PM_2.5 and O_X correlations are broadly positive in China, reflecting the crucial role of atmospheric oxidizing capacity. Regional-specific and coordinated control measures are in need as China's air pollution strategy transits from PM_2.5-focused to PM_2.5-O₃ synergic control.

Spatiotemporal characteristics and socioeconomic factors of PM2.5 heterogeneity in mainland China during the COVID-19 epidemic

Spatiotemporal variation of PM_2.5 in 2018 and 2020 were compared to analyze the impacts of COVID-19, the spatial heterogeneity of PM_2.5, and meteorological and socioeconomic impacts of PM_2.5 concentrations heterogeneity in China in 2020 were investigated. The results showed that the annual average PM_2.5 concentration in 2020 was 32.73 μg/m³ existing a U-shaped variation pattern, which has decreased by 6.38 μg/m³ compared to 2018. A consistent temporal pattern was found in 2018 and 2020 with significant high values in winter and low in summer. PM_2.5 declined dramatically in eastern and central China, where are densely populated and economically developed areas during the COVID-19 epidemic compared with previous years, indicating that the significantly decline of social activities had an important effect on the reduction of PM_2.5 concentrations. The lowest PM_2.5 was found in August because that precipitation had a certain dilution effect on pollutants. January was the most polluted due to centralized coal burning for heating in North China. Overall, the PM_2.5 concentrations in China were spatially agglomerated. The highly polluted contiguous zones were mainly located in northwest China and the central plains city group, while the coastal area and Inner Mongolia were areas with good air quality. Negative correlations were found between natural factors (temperature, precipitation, wind speed and relative humidity) and PM_2.5 concentrations, with precipitation has the greatest impact on PM_2.5, which are beneficial for reducing PM_2.5 concentrations. Among the socio-economic factors, proportion of the secondary industry, number of taxis, per capita GDP, population, and industrial nitrogen oxide emissions have positive correlation effects on PM_2.5, while the overall social electricity consumption, industrial sulfur dioxide emissions, green coverage in built-up areas, and total gas and liquefied gas supply have negative correlation effects on the PM_2.5.

Ozone exposure and health risks of different age structures in major urban agglomerations in People's Republic of China from 2013 to 2018

High concentration of surface ozone (O₃) will cause health risks to people. In order to analyze the spatiotemporal characteristics of O₃ and assess O₃ exposure and health risks for different age groups in China, we applied multiple methods including standard deviation ellipse, spatial autocorrelation, and exposure-response functions. Results show that O₃ concentrations increased in 64.5% of areas in China from 2013 to 2018. The central plain urban agglomeration (CPU), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) witnessed the greatest incremental rates of O₃ by 16.7%, 14.3%, and 13.1%. Spatially, the trend of O₃ shows a significant positive autocorrelation, and high trend values primarily in central and east China. The proportion of the total population exposed to high O₃ (above 160 μg/m³) increased annually. Compared to 2013, the proportion of the young, adult, and old populations exposed to high O₃ increased to different extents in 2018 by 26.8%, 29.6%, and 27.2%, respectively. The extent of population exposure risk areas in China expanded in size, particularly in north and east China. The total premature respiratory mortalities attributable to long-term O₃ exposure in six urban agglomerations were about 177,000 in 2018 which has increased by 16.4% compared to that in 2013. Among different age groups, old people are more vulnerable to O₃ pollution, so we need to strengthen their relevant health protection of them.

Fuzzy-Based Ecological Vulnerability Assessment Driven by Human Impacts in China

Human activities have a significant impact on global ecosystems. Assessing and quantifying ecological vulnerability is a fundamental challenge in the study of the ecosystem’s capacity to respond to anthropogenic disturbances. However, little research has been conducted on EVA’s existing fuzzy uncertainties. In this paper, an ecological vulnerability assessment (EVA) framework that integrated the Exposure-Sensitivity-Adaptive Capacity (ESC) framework, fuzzy method, and multiple-criteria decision analysis (MCDA), and took into account human impacts, was developed to address the uncertainties in the assessment process. For the first time, we conducted a provincial-scale case study in China to illustrate our proposed methodology. Our findings imply that China’s ecological vulnerability is spatially heterogeneous due to regional differences in exposure, sensitivity, and adaptive capacity indices. The results of our ecological vulnerability assessment and cause analysis can provide guidance for further decision-making and facilitate the protection of ecological quality over the medium to long term. The developed EVA framework can also be duplicated at multiple spatial and temporal dimensions utilizing context-specific datasets to assist environmental managers in making informed decisions.

Spatio-Temporal Characteristics of Air Quality Index (AQI) over Northwest China

In recent years, air pollution has become a serious threat, causing adverse health effects and millions of premature deaths in China. This study examines the spatial-temporal characteristics of ambient air quality in five provinces (Shaanxi (SN), Xinjiang (XJ), Gansu (GS), Ningxia (NX), and Qinghai (QH)) of northwest China (NWC) from January 2015 to December 2018. For this purpose, surface-level aerosol pollutants, including particulate matter (PMx, x = 2.5 and 10) and gaseous pollutants (sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3)) were obtained from China National Environmental Monitoring Center (CNEMC). The results showed that fine particulate matter (PM2.5), coarse particulate matter (PM10), SO2, NO2, and CO decreased by 28.2%, 32.7%, 41.9%, 6.2%, and 27.3%, respectively, while O3 increased by 3.96% in NWC during 2018 as compared with 2015. The particulate matter (PM2.5 and PM10) levels exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II standards as well as the WHO recommended Air Quality Guidelines, while SO2 and NO2 complied with the CAAQS Grade II standards in NWC. In addition, the average air quality index (AQI), calculated from ground-based data, improved by 21.3%, the proportion of air quality Class I (0–50) improved by 114.1%, and the number of pollution days decreased by 61.8% in NWC. All the pollutants’ (except ozone) AQI and PM2.5/PM10 ratios showed the highest pollution levels in winter and lowest in summer. AQI was strongly positively correlated with PM2.5, PM10, SO2, NO2, and CO, while negatively correlated with O3. PM10 was the primary pollutant, followed by O3, PM2.5, NO2, CO, and SO2, with different spatial and temporal variations. The proportion of days with PM2.5, PM10, SO2, and CO as the primary pollutants decreased but increased for NO2 and O3. This study provides useful information and a valuable reference for future research on air quality in northwest China.

Exposure Risk of Global Surface O3 During the Boreal Spring Season

Surface ozone (O₃) is an oxidizing gaseous pollutant; long-term exposure to high O₃ concentrations adversely affects human health. Based on daily surface O₃ concentration data, the spatiotemporal characteristics of O₃ concentration, exposure risks, and driving meteorological factors in 347 cities and 10 major countries (China, Japan, India, South Korea, the United States, Poland, Spain, Germany, France, and the United Kingdom) worldwide were analyzed using the MAKESENS model, Moran' I analysis, and Generalized additive model (GAM). The results indicated that: in the boreal spring season from 2015 to 2020, the global O₃ concentration exhibited an increasing trend at a rate of 0.6 μg/m³/year because of the volatile organic compounds (VOCs) and NOx changes caused by human activities. Due to the lockdown policies after the outbreak of COVID-19, the average O₃ concentration worldwide showed an inverted U-shaped growth during the study period, increasing from 21.9 μg/m³ in 2015 to 27.3 μg/m³ in 2019, and finally decreasing to 25.9 μg/m³ in 2020. According to exposure analytical methods, approximately 6.32% of the population (31.73 million people) in the major countries analyzed reside in rapidly increasing O₃ concentrations. 6.53% of the population (32.75 million people) in the major countries were exposed to a low O₃ concentration growth environment. Thus, the continuous increase of O₃ concentration worldwide is an important factor leading to increasing threats to human health. Further we found that mean wind speed, maximum temperature, and relative humidity are the main factors that determine the change of O₃ concentration. Our research results are of great significance to the continued implementation of strict air quality policies and prevention of population hazards. However, due to data limitations, this research can only provide general trends in O₃ and human health, and more detailed research will be carried out in the follow-up.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12403-022-00463-7.

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.

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

Based on multiple linear regression (MLR) models, we estimated the PM2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM2.5 estimation. We also found that the quality of estimated PM2.5 shows some seasonal variation; the estimated PM2.5 values consistently have the highest correlation with in situ PM2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM2.5 estimation, our analyses of the difference in estimated PM2.5 reveal the importance of utilizing the proper AOD for the best quality of PM2.5 estimation.

Global, continental, and national variation in PM2.5, O3, and NO2 concentrations during the early 2020 COVID-19 lockdown

Lockdowns implemented in response to COVID-19 have caused an unprecedented reduction in global economic and transport activity. In this study, variation in the concentration of health-threatening air pollutants (PM_2.5, NO₂, and O₃) pre- and post-lockdown was investigated at global, continental, and national scales. We analyzed ground-based data from >10,000 monitoring stations in 380 cities across the globe. Global-scale results during lockdown (March to May 2020) showed that concentrations of PM_2.5 and NO₂ decreased by 16.1% and 45.8%, respectively, compared to the baseline period (2015-2019). However, O₃ concentration increased by 5.4%. At the continental scale, concentrations of PM_2.5 and NO₂ substantially dropped in 2020 across all continents during lockdown compared to the baseline, with a maximum reduction of 20.4% for PM_2.5 in East Asia and 42.5% for NO₂ in Europe. The maximum reduction in O₃ was observed in North America (7.8%), followed by Asia (0.7%), while small increases were found in other continents. At the national scale, PM_2.5 and NO₂ concentrations decreased significantly during lockdown, but O₃ concentration showed varying patterns among countries. We found maximum reductions of 50.8% for PM_2.5 in India and 103.5% for NO₂ in Spain. The maximum reduction in O₃ (22.5%) was found in India. Improvements in air quality were temporary as pollution levels increased in cities since lockdowns were lifted. We posit that these unprecedented changes in air pollutants were mainly attributable to reductions in traffic and industrial activities. Column reductions could also be explained by meteorological variability and a decline in emissions caused by environmental policy regulations. Our results have implications for the continued implementation of strict air quality policies and emission control strategies to improve environmental and human health.

Study on the characteristics of black carbon during atmospheric pollution conditions in Beijing

Black carbon (BC), not only has a negative impact on human health, but also contributes to visibility degradation and the attenuation of solar radiation due to light absorption. In this paper, we investigated the variations of BC concentration, BC optical characteristics and its effects on the physical and optical properties of atmospheric aerosols based on AERONET data during atmospheric pollution conditions in Beijing from 2012 to 2017. The results indicated that the average annual ground-level BC concentration and BC/PM_2.5 were 8.9 μg m^-3 and 6.7%, respectively, from 2012 to 2017 during atmospheric pollution conditions in Beijing. The annual mean ground-level BC concentration showed weak variation, but the monthly variation was pronounced during atmospheric pollution conditions. Moreover, the BC column concentration had a higher correlation with absorptive aerosol optical thickness (AAOT) at 870 nm (R² = 0.93) than 440 nm (R² = 0.73). The difference in AAOT between 440 nm and 870 nm was more significant under high BC column concentration. The seasonal variation of the BC column concentration that contributed to the AAOT at 870 nm displayed a consistent monthly average variation tendency. The BC column concentrations were divided into three segments of low, moderate, and high according to the results of the approximately normal distribution of the BC column concentration. Compared with high BC concentration, the single scattering albedo (SSA) and asymmetry parameter were enhanced by 0.05 and 0.04 in low BC concentrations, respectively. On the contrary, the fine mode fraction (FMF) was dropped by 12.5% in low BC concentrations. A higher BC concentration contributed to the enhancement in the AAOT and the extinction ratio of the fine mode aerosol. Meanwhile, the atmospheric particles' forward scattering ability was also attenuated under a high BC concentration.

The spatiotemporal characteristics of the air pollutants in China from 2015 to 2019

China’s rapid industrialization and urbanization have led to poor air quality, and air pollution has caused great concern among the Chinese public. Most analyses of air pollution trends in China are based on model simulations or satellite data. Studies using field observation data and focusing on the latest data from environmental monitoring stations covering the whole country to assess the latest trends of different pollutants in different regions are relatively rare. The State Council of China promulgated the toughest-ever Air Pollution Prevention and Control Action Plan (Action Plan) in 2013. This led to a major improvement in air quality. We use the hourly Air Quality Index (AQI) and mass concentrations of PM_2.5, PM₁₀, CO, NO₂, O₃, and SO₂ in 362 cities from 2015 to 2019, obtained from the Ministry of Ecology and Environment, to study their temporal and spatial changes and assess the effectiveness of the policy on the atmospheric environment since its promulgation and implementation. We found that the national and regional air quality in China continues to improve, with PM_2.5, PM₁₀, AQI, CO, and SO₂ exhibiting negative trends. However, O₃ and NO₂ pollution is an urgent problem that needs to be solved and the current control strategy for PM_2.5 will only partially reduce the PM_2.5 pollution in the western region. Although the implementation of "Action Plan" measures has effectively improved air quality, China’s air pollution is still serious and far from the WHO standard. Implementing measures for continuous and effective emissions control is still a top priority.

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.

Efforts in reducing air pollution exposure risk in China: State versus individuals

China has made great efforts towards air pollutant concentration control during the past five years, which has led to positive outcomes. However, air pollutant concentration focused efforts were considered separately from human exposure risk. And this might result in a misunderstanding that reducing exposure risk can only rely on the national level measures of air pollutant control. This study integrates the first Chinese survey of human activity patterns and the spatially continuous high-resolution PM_2.5 concentration maps to reveal the spatial and temporal variations of China's air pollution exposure risk from 2013 to 2017. More importantly, the effects on risk reduction from multi-scale and multi-object perspectives (reductions of ambient PM_2.5 concentrations by national or provincial measures and changes of individual behavior patterns by personal efforts) are deeply investigated. Results show that the reductions of PM_2.5 concentration and associated reductions of exposure risk from 2013 to 2017 were 40% and 35.7%, respectively. They also showed that both the reduction of PM_2.5 concentrations and change of personal behavior patterns were effective for risk reduction when China's total PM_2.5 exposure risk was higher than 1.58. However, only individual behavior changes contributed to risk reduction for scenarios with state-level risk value below 1.58. For regional strategies, threshold values for PM_2.5 exposure risk control differentiating national measures or personal efforts were spatially and temporally dependent. The role of personal behavior changes on PM_2.5 exposure risk reduction was growing in these five years with concentration rapidly decreasing regions. The findings suggest that people-centered air pollution exposure risk prevention not only depends on government management for air pollution control, but also on individual changes of activity patterns. Efforts from the state and individuals are both essential for reducing air pollution exposure risk in China, especially growing individual efforts are needed in regions with the decreasing air pollutant concentrations in the coming future. Moreover, this study mainly discussed the PM_2.5 exposure risk from the macroscopic perspective, the research at the microcosmic perspective is also needed in the further study.

The haze extreme co-movements in Beijing-Tianjin-Hebei region and its extreme dependence pattern recognitions

Extreme haze was often observed at many locations in Beijing-Tianjin-Hebei region within several hours when they occurred, which is referred to as extreme co-movements and extreme dependence in statistics. This article applies tail quotient correlation coefficient to explore the temporal and spatial extreme dependence patterns of haze in this region. Hourly PM2.5 station-level data during 2014-2018 are used, and the results show that the tail quotient correlation coefficient between stations increases with month. Specifically, the simultaneous extreme dependence was strong in the fourth season, while the haze was severe. In the first season, while the haze was also severe, the extreme hazes only show strong co-movements with a time difference. These observations lead to the study of two special scenarios, that is, the concurrence/extreme dependence of the worst extreme haze and its lag effects. City clusters suffering simultaneous extreme haze or with certain time difference as well as the most frequently co-movement cities are identified. The extreme co-movements of these cities and the reasons for their occurrences have strong implications for improving the PM2.5 joint prevention and control in the Beijing-Tianjin-Hebei region. The importance of lag effects is also reflected in the precedence order of the extreme haze's appearance. It is especially useful when setting the mechanism of the early warning system which can be triggered by the first appearance of extreme haze. The precedence orders also avail in investigating the transmission path of the haze, based on which more precise meteorological models can be made to benefit the haze forecasting of the region.

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.

Spatial and temporal variations of air quality and six air pollutants in China during 2015-2017

Air pollution has aroused significant public concern in China, therefore, long-term air-quality data with high temporal and spatial resolution are needed to understand the variations of air pollution in China. However, the yearly variations with high spatial resolution of air quality and six air pollutants are still unknown for China until now. Therefore, in this paper, we analyze the spatial and temporal variations of air quality and six air pollutants in 366 cities across mainland China during 2015–2017 for the first time to the best of our knowledge. The results indicate that the annual mean mass concentrations of PM2.5, PM10, SO₂, and CO all decreased year by year during 2015–2017. However, the annual mean NO₂ concentrations were almost unchanged, while the annual mean O₃ concentrations increased year by year. Anthropogenic factors were mainly responsible for the variations of air quality. Further analysis suggested that PM2.5 and PM10 were the main factors influencing air quality, while NO₂ played an important role in the formation of PM2.5 and O₃. These findings can provide a theoretical basis for the formulation of future air-pollution control policy in China.

Exploring the Spatial Variation Characteristics and Influencing Factors of PM2.5 Pollution in China: Evidence from 289 Chinese Cities

Haze pollution has become an urgent environmental problem due to its impact on the environment as well as human health. PM2.5 is one of the core pollutants which cause haze pollution in China. Existing studies have rarely taken a comprehensive view of natural environmental conditions and socio-economic factors to figure out the cause and diffusion mechanism of PM2.5 pollution. This paper selected both natural environmental conditions (precipitation (PRE), wind speed (WIN), and terrain relief (TR)) and socio-economic factors (human activity intensity of land surface (HAILS), the secondary industry's proportion (SEC), and the total particulate matter emissions of motor vehicles (VE)) to analyze the effects on the spatial variation of PM2.5 concentrations. Based on the spatial panel data of 289 cities in China in 2015, we used spatial statistical methods to visually describe the spatial distribution characteristics of PM2.5 pollution; secondly, the spatial agglomeration state of PM2.5 pollution was characterized by Moran’s I; finally, several regression models were used to quantitatively analyze the correlation between PM2.5 pollution and the selected explanatory variables. Results from this paper confirm that in 2015, most cities in China suffered from severe PM2.5 pollution, and only 17.6% of the sample cities were up to standard. The spatial agglomeration characteristics of PM2.5 pollution in China were particularly significant in the Beijing–Tianjin–Hebei region. Results from the global regression models suggest that WIN exerts the most significant effects on decreasing PM2.5 concentration (p < 0.01), while VE is the most critical driver of increasing PM2.5 concentration (p < 0.01). Results from the local regression model show reliable evidence that the relation between PM2.5 concentrations and the explanatory variables varied differently over space. VE is the most critical factor that influences PM2.5 concentrations, which means controlling motor vehicle pollutant emissions is an effective measure to reduce PM2.5 pollution in Chinese cities.

Assessment of volatile methylsiloxanes in environmental matrices and human plasma

Volatile methylsiloxanes (VMSs) are widely used in various personal-care products and industrial additives and products. This study focused on VMSs exposure in the general population, workers, and the families of workers living in residential and industrial areas of southwestern China. VMSs concentrations in indoor environmental matrices from six industrial facilities were 3.4 × 10² to 9.0 × 10² μg m^-3 in gas-phase samples, 4.7 × 10² to 1.5 × 10⁴ μg g^-1 in PM_2.5 samples, and 2.3 × 10² to 7.2 × 10³ μg g^-1 in dust samples, which were two to four orders of magnitude higher than the concentrations measured in residential areas. Exposure to VMSs was investigated by analysis of plasma samples from workers in residential and industrial areas for the presence of cyclic (D4-D6) and linear (L3-L16) VMSs. VMSs concentrations in plasma samples ranged from 84 to 2.3 × 10² ng ml^-1 in workers, one to two orders of magnitude higher than those in the general population (2.2 ng ml^-1). Daily VMSs indoor exposure via inhalation and ingestion in individuals from residential and industrial areas were estimated and assessed under working-time and leisure-time conditions. This study showed that exposure to VMSs in industrial areas is approximately two to four or one to two orders of magnitude higher than that in residential areas during the working- or leisure-time scenario, respectively. Furthermore, the families of workers (the non-occupational group) experienced higher levels of exposure to VMSs in their homes compared with the general population. The ratios of exposure to linear VMSs via PM_2.5 inhalation to that via the gas phase ranged from 7.8% to 43.1% in industrial areas. This study suggests that intake of linear VMSs via PM_2.5 inhalation should be considered when estimating human exposure to VMSs in areas with high levels of PM_2.5 air pollution.

Spatial and Temporal Variabilities of PM2.5 Concentrations in China Using Functional Data Analysis

As air pollution characterized by fine particulate matter has become one of the most serious environmental issues in China, a critical understanding of the behavior of major pollutant is increasingly becoming very important for air pollution prevention and control. The main concern of this study is, within the framework of functional data analysis, to compare the fluctuation patterns of PM2.5 concentration between provinces from 1998 to 2016 in China, both spatially and temporally. By converting these discrete PM2.5 concentration values into a smoothing curve with a roughness penalty, the continuous process of PM2.5 concentration for each province was presented. The variance decomposition via functional principal component analysis indicates that the highest mean and largest variability of PM2.5 concentration occurred during the period from 2003 to 2012, during which national environmental protection policies were intensively issued. However, the beginning and end stages indicate equal variability, which was far less than that of the middle stage. Since the PM2.5 concentration curves showed different fluctuation patterns in each province, the adaptive clustering analysis combined with functional analysis of variance were adopted to explore the categories of PM2.5 concentration curves. The classification result shows that: (1) there existed eight patterns of PM2.5 concentration among 34 provinces, and the difference among different patterns was significant whether from a static perspective or multiple dynamic perspectives; (2) air pollution in China presents a characteristic of high-emission “club” agglomeration. Comparative analysis of PM2.5 profiles showed that the heavy pollution areas could rapidly adjust their emission levels according to the environmental protection policies, whereas low pollution areas characterized by the tourism industry would rationally support the opportunity of developing the economy at the expense of environment and resources. This study not only introduces an advanced technique to extract additional information implied in the functions of PM2.5 concentration, but also provides empirical suggestions for government policies directed to reduce or eliminate the haze pollution fundamentally.

Spatiotemporal patterns of recent PM2.5 concentrations over typical urban agglomerations in China

China experiences severe particulate matter pollution associated with rapid economic growth and accelerated urbanization. In this study, concentrations of PM_2.5 (fine particulate matter with an aerodynamic diameter ≤ 2.5 μm) throughout China, and specifically in nine typical urban agglomerations and one economic region, were statistically analyzed using high-resolution ground-based PM_2.5 observations from June 2014 to May 2018. The spatial variation of PM_2.5 was also explored via spatial autocorrelation analysis. High annual mean PM_2.5 concentrations were predominantly concentrated in the Beijing-Tianjin-Hebei, Central Plain, Northern Slope of Tianshan Mountain, and Cheng-Yu urban agglomerations, as well as the Huaihai Economic Region. The proportion of air quality nationwide monitoring sites where annual average PM_2.5 concentrations exceeded the Chinese Ambient Air Quality Standards (CAAQS) Grade II annual standard were 82.8%, 77.1%, and 70.8% in 2015, 2016, and 2017, respectively. Moreover, the frequency of PM_2.5 concentrations meeting the CAAQS Grade I 24-h standard increased in five national-level urban agglomerations, and the average annual PM_2.5 decreased from 2015 to 2017 with a reduction rate of over 20%. The southern Beijing-Tianjin-Hebei agglomeration and surrounding areas revealed the highest PM_2.5 pollution in four seasons. Monthly mean PM_2.5 typically exhibited a characteristic "U" shape. Diurnal mean PM_2.5 concentrations were generally consistent with typical urban agglomerations, with maximum and minimum PM_2.5 values occurring at approximately 08:00-12:00 and 15:00-17:00, respectively, except for the Northern Slope of Tianshan Mountain urban agglomeration (NSTM-UA) (14:00 and 08:00, respectively). A positive spatial autocorrelation of PM_2.5 concentrations was observed in all urban agglomerations (except NSTM-UA); high-high agglomeration centers of PM_2.5 pollution were located far inland with a circular distribution, and low-low agglomeration centers formed at the periphery of the high-high agglomeration region. This study is key for understanding the difference in PM_2.5 concentrations among urban agglomerations and region-oriented air pollution control strategies are highly suggested.

Estimating Ground-Level Concentrations of Multiple Air Pollutants and Their Health Impacts in the Huaihe River Basin in China

Air pollutants existing in the environment may have negative impacts on human health depending on their toxicity and concentrations. Remote sensing data enable researchers to map concentrations of various air pollutants over vast areas. By combining ground-level concentrations with population data, the spatial distribution of health impacts attributed to air pollutants can be acquired. This study took five highly populated and severely polluted provinces along the Huaihe River, China, as the research area. The ground-level concentrations of four major air pollutants including nitrogen dioxide (NO₂), sulfate dioxide (SO₂), particulate matters with diameter equal or less than 10 (PM₁₀) or 2.5 micron (PM_2.5) were estimated based on relevant remote sensing data using the geographically weighted regression (GWR) model. The health impacts of these pollutants were then assessed with the aid of co-located gridded population data. The results show that the annual average concentrations of ground-level NO₂, SO₂, PM₁₀, and PM_2.5 in 2016 were 31 µg/m³, 26 µg/m³, 100 µg/m³, and 59 µg/m³, respectively. In terms of the health impacts attributable to NO₂, SO₂, PM₁₀, and PM_2.5, there were 546, 1788, 10,595, and 8364 respiratory deaths, and 1221, 9666, 46,954, and 39,524 cardiovascular deaths, respectively. Northern Henan, west-central Shandong, southern Jiangsu, and Wuhan City in Hubei are prone to large health risks. Meanwhile, air pollutants have an overall greater impact on cardiovascular disease than respiratory disease, which is primarily attributable to the inhalable particle matters. Our findings provide a good reference to local decision makers for the implementation of further emission control strategies and possible health impacts assessment.

Exposure to Concentrated Ambient PM2.5 Compromises Spermatogenesis in a Mouse Model: Role of Suppression of Hypothalamus-Pituitary-Gonads Axis

Epidemiological studies link ambient fine particulate matter (PM2.5) pollution to abnormalities in the male reproductive system. However, few toxicological studies have investigated this potentially important adverse effect of PM2.5 pollution. Therefore, in the present study, we analyzed the effects of PM2.5 exposure on spermatogenesis and hypothalamic-pituitary-gonadal (HPG) axis in a murine model. Fourteen male C57BL/6J mice were subjected to a 4-month exposure to filtered air or concentrated ambient PM2.5 (CAP). Their sperm count, testicular histology, spermatogenic parameters, and the major components of HPG axis were assessed. Exposure to CAP significantly reduced sperm count in the epididymis. This was accompanied by Sertoli cell vacuolization, immature germ cell dislocation, and decreases in pachytene spermatocytes and round spermatids of stage VII seminiferous tubules, suggesting a marked impairment of spermatogenesis in these mice. This impairment of spermatogenesis appeared to be attributable to a suppression of HPG axis subsequent to CAP exposure-induced hypothalamic inflammation, as exposure to CAP significantly increased TNFα and IL1b mRNA levels and meanwhile decreased gonadotropin-releasing hormone mRNA expression in the hypothalamus. Moreover, CAP exposure significantly reduced circulating testosterone and follicle-stimulating hormone, testicular testosterone and mRNA expression of follicle-stimulating hormone target gene SHBG and luteinizing hormone target genes P450scc, 17βHSD, and StAR. The present data demonstrate that exposure to ambient PM2.5 impairs spermatogenesis in murine model, raising the concern over effects of ambient PM2.5 pollution on the male reproductive function.

Estimation of PM2·5-associated disease burden in China in 2020 and 2030 using population and air quality scenarios: a modelling study

BACKGROUND

Air pollution and its adverse effects on public health remain a considerable problem in China, where policies have been implemented to improve the situation. We aimed to estimate the disease burden associated with particulate matter (PM)_2·5 across China for 2020 and 2030 to identify the populations and regions most at risk, quantify the health benefits of air quality improvement targets, and determine the effect of population growth and ageing on this disease burden.

METHODS

In this modelling study, we investigated premature deaths associated with PM_2·5 across China on the basis of air quality scenarios proposed by the expert group involved in the formulation of the 13th Five-Year Plan for Eco-Environmental Protection and population scenarios based on the Shared Socioeconomic Pathways of the Intergovernmental Panel on Climate Change. We used the integrated exposure-response model used for the Global Burden of Disease Study to estimate the number of PM_2·5-related premature deaths under each scenario.

FINDINGS

The projected health benefits of the air-quality-improving targets are substantial, and could reduce the number of PM_2.5-related premature deaths in China by approximately 129 278 by 2020 and 217 988 by 2030, compared with 2010. However, since China's population is increasing and ageing, the number of PM_2.5-related premature deaths was estimated to increase by 84 102 by 2020 and by 244 191 by 2030, indicating that the health benefits induced by air quality improvements could be offset by the effect of the population increasing in size and ageing.

INTERPRETATION

To reduce the future disease burden in China, targets that are stricter than the interim target and stringent policies to improve air quality and protect public health are needed, especially for at-risk population groups, such as older individuals (aged >55 years) and patients with cardiovascular diseases, particularly in regions with a high disease burden.

FUNDING

National Key Research and Development Program of China, National Natural Science Foundation of China, Beijing Natural Science Foundation, National High-level Talents Special Support Plan of China for Young Talents, and Special Foundation of Basic Science and Technology Resources Survey of Ministry of Science and Technology of China.

PM2.5-Bound Toxic Elements in an Urban City in East China: Concentrations, Sources, and Health Risks

Concentrations of PM_2.5-bound trace elements have increased in China, with increasing anthropogenic emissions. In this study, long-term measurements of PM_2.5-bound trace elements were conducted from January 2014 to January 2015 in the urban city of Jinan, east China. A positive matrix factorization model (PMF) and health risk assessment were used to evaluate the sources and health risks of these elements, respectively. Compared with most Chinese megacities, there were higher levels of arsenic, manganese, lead, chromium, and zinc in this city. Coal combustion, the smelting industry, vehicle emission, and soil dust were identified as the primary sources of all the measured elements. Heating activities during the heating period led to a factor of 1.3–2.8 higher concentrations for PM_2.5 and all measured elements than those during the non-heating period. Cumulative non-carcinogenic and carcinogenic risks of the toxic elements exceeded the safety levels by 8–15 and 10–18 times, respectively. Arsenic was the critical element having the greatest health risk. Coal combustion caused the highest risk among the four sources. This work provides scientific data for making targeted policies to control air pollutants and protect human health.

Population-weighted exposure to PM2.5 pollution in China: An integrated approach

Fine particulate matter air pollution (PM_2.5) is a major risk factor for premature death globally. Studies of the PM_2.5 health burden usually treat exposure to ambient air pollution (AAP) and household air pollution from solid fuels (HAP) as separate risk factors. AAP and HAP can, however, be closely interrelated. Taking as the starting point that the total exposure to PM_2.5 is what matters for health, and recognizing the curvilinear form of exposure-response functions for important health effects, we develop a method for estimating the total annual mean population-weighted personal exposure, denoted integrated population-weighted exposure (IPWE). To establish the IPWE in China, we used recent emission inventories, Chemical Transport Models, China Census data on population and residential fuel use, and estimates of the PM_2.5 exposure among solid fuel users. We found an IPWE of 151 [123-179] μg/m³, of which 62-74% was attributable to residential solid fuels through HAP exposure and the residential sector emissions' contribution to AAP. We found large disparities in the PM_2.5 exposure burden, with an estimated IPWE in rural populations nearly twice the level in urban populations. Using the IPWE metric, we estimated that 1.15 [1.09-1.19] million premature deaths were attributable to PM_2.5 exposure annually in the period 2010-2013. Using the same data set, but calculating premature deaths from AAP and HAP in isolation, the estimated number was nearly 50% higher. The IPWE metric enables integration across AAP and HAP in policy analyses and could mitigate the concern of a potential double counting of the health burden that may arise from treating AAP and HAP as separate health risk factors.

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

The promulgation and implementation of the Air Pollution Prevention and Control Action Plan (APPCAP) have greatly accelerated air quality improvements in China. In this study, these improvements were assessed and analyzed using arithmetic mean and percentile methods. Air quality status and trends were illustrated meticulously. Air pollution risks remaining since the implementation of the APPCAP were also identified. In addition, a complex network correlation model was created and used to demarcate highly inter-correlated regions within China, which were identified using long-term PM_2.5 concentration data. The results indicate that the annual mean PM_2.5 concentration decreased by more than 30% throughout the country since the implementation of the APPCAP. However, more than 1 billion people were still exposed to polluted air containing PM_2.5 concentrations exceeding the WHO Interim Target-1 (WHO IT-1). Cities with populations of more than 10 million were generally among the most polluted regions in China, while PM_2.5 concentrations in locations with populations of less than 1 million met WHO IT-1 standards. Moreover, PM_2.5 network correlation analysis defined 7 key Joint Prevention and Control of Atmospheric Pollution (JPCAP) regions with strong synchronicity in PM_2.5 mass concentrations; these results suggest that JPCAP could be implemented separately with in each of these demarcated regions. The atmospheric pollution control concepts and methods proposed herein are also broadly applicable for the implementation of JPCAP policies in other regions worldwide.

Genetic variants in nuclear DNA along with environmental factors modify mitochondrial DNA copy number: a population-based exome-wide association study

Background

Mitochondrial DNA (mtDNA) copy number has been found associated with multiple diseases, including cancers, diabetes and so on. Both environmental and genetic factors could affect the copy number of mtDNA. However, limited study was available about the relationship between genetic variants and mtDNA copy number. What’s more, most of previous studies considered only environmental or genetic factors. Therefore, it’s necessary to explore the genetic effects on mtDNA copy number with the consideration of PM_2.5 exposure and smoking.

Results

A multi-center population-based study was performed with 301 subjects from Zhuhai, Wuhan and Tianjin. Personal 24-h PM_2.5 exposure levels, smoking and mtDNA copy number were evaluated. The Illumina Human Exome BeadChip, which contained 241,305 single nucleotide variants, was used for genotyping. The association analysis was conducted in each city and meta-analysis was adopted to combine the overall effect among three cities. Seven SNPs showed significant association with mtDNA copy number with P value less than 1.00E-04 after meta-analysis. The following joint analysis of our identified SNPs showed a significant allele-dosage association between the number of variants and mtDNA copy number (P = 5.02 × 10^− 17). Further, 11 genes were identified associated with mtDNA copy number using gene-based analysis with a P value less than 0.01.

Conclusion

This study was the first attempt to evaluate the genetic effects on mtDNA copy number with the consideration of personal PM_2.5 exposure level. Our findings could provide more evidences that genetic variants played important roles in modulating the copy number of mtDNA.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5142-7) contains supplementary material, which is available to authorized users.

Relevance Analysis on the Variety Characteristics of PM2.5 Concentrations in Beijing, China

Air pollution has become one of the most serious environmental problems in the world. Considering Beijing and six surrounding cities as main research areas, this study takes the daily average pollutant concentrations and meteorological factors from 2 December 2013 to 13 October 2017 into account and studies the spatial and temporal distribution characteristics and the relevant relationship of particulate matter smaller than 2.5 μm (PM2.5) concentrations in Beijing. Based on correlation analysis and geo-statistics techniques, the inter-annual, seasonal, and diurnal variation trends and temporal spatial distribution characteristics of PM2.5 concentration in Beijing are studied. The study results demonstrate that the pollutant concentrations in Beijing exhibit obvious seasonal and cyclical fluctuation patterns. Air pollution is more serious in winter and spring and slightly better in summer and autumn, with the spatial distribution of pollutants fluctuating dramatically in different seasons. The pollution in southern Beijing areas is more serious and the air quality in northern areas is better in general. The diurnal variation of air quality shows a typical seasonal difference and the daily variation of PM2.5 concentrations present a “W” type of mode with twin peaks. Besides emission and accumulation of local pollutants, air quality is easily affected by the transport effect from the southwest. The PM2.5 and PM10 concentrations measured from the city of Langfang are taken as the most important factors of surrounding pollution factors to PM2.5 in Beijing. The concentrations of PM10 and carbon monoxide (CO) concentrations in Beijing are the most significant local influencing factors to PM2.5 in Beijing. Extreme wind speeds and maximal wind speeds are considered to be the most significant meteorological factors affecting the transport of pollutants across the region. When the wind direction is weak southwest wind, the probability of air pollution is greater and when the wind direction is north, the air quality is generally better.

Analysis of Spatial-Temporal Characteristics of the PM2.5 Concentrations in Weifang City, China

Air pollution, which accompanies industrial progression and urbanization, has become an urgent issue to address in contemporary society. As a result, our understanding and continued study of the spatial-temporal characteristics of a major pollutant, defined as 2.5-micron or less particulate matter (PM2.5), as well as the development of related approaches to improve the environment, has become vital. This paper studies the characteristics of yearly, quarterly, monthly, daily, and hourly PM2.5 concentrations, and discusses the influencing factors based on the hourly data of nationally controlled and provincially controlled monitoring stations, from 2012 to 2016, in Weifang City. The main conclusion of this study is that the annual PM2.5 concentrations reached a peak in 2013. With efficient aid from the government, this value has decreased annually and has high spatial characteristics in the northwest and low spatial characteristics in the southeast. Second, the seasonal and monthly PM2.5 concentrations form a U-shaped trend, meaning that the concentration is high in the summer and low in the winter. These trends are highly relevant to the factors of plantation, humidity, temperature, and precipitation. Third, within a week, higher PM2.5 concentrations appear on Mondays and Saturdays, whereas the lowest concentration occurs on Wednesdays. It can be inferred that PM2.5 concentrations tend to be highly dependent on human activities and living habits. Lastly, there are hourly discrepancies within the peaks and troughs depending on the month, and the overall daytime PM2.5 concentrations and reductive rates are higher in the daytime than in the nighttime.

Comparison of Ground-Based PM2.5 and PM10 Concentrations in China, India, and the U.S

Urbanization and industrialization have spurred air pollution, making it a global problem. An understanding of the spatiotemporal characteristics of PM_2.5 and PM₁₀ concentrations (particulate matter with an aerodynamic diameter of less than 2.5 μm and 10 μm, respectively) is necessary to mitigate air pollution. We compared the characteristics of PM_2.5 and PM₁₀ concentrations and their trends of China, India, and the U.S. from 2014 to 2017. Particulate matter levels were lowest in the U.S., while China showed higher concentrations, and India showed the highest. Interestingly, significant declines in PM_2.5 and PM₁₀ concentrations were found in some of the most polluted regions in China as well as the U.S. No comparable decline was observed in India. A strong seasonal trend was observed in China and India, with the highest values occurring in winter and the lowest in summer. The opposite trend was noted for the U.S. PM_2.5 was highly correlated with PM₁₀ for both China and India, but the correlation was poor for the U.S. With regard to reducing particulate matter pollutant concentrations, developing countries can learn from the experiences of developed nations and benefit by establishing and implementing joint regional air pollution control programs.

Phthalates in PM2.5 from Shenzhen, China and human exposure assessment factored their bioaccessibility in lung

Temporal variability of phthalates (PAEs) in PM_2.5 from Shenzhen during 2015-2016 was measured and the associated human exposure via inhalation was assessed. The PM_2.5 concentrations ranged from 30.7 to 115 μg m^-3, greater than the air quality guidelines of interim target-3 (10-15 μg m^-3) and interim target-2 (15-25 μg m^-3) set by World Health Organization. PAEs were detected in 94.7% samples and the 95th percentile concentrations of total PAEs (∑₆PAEs) in Longgang and Nanshan districts were 324 and 44.7 ng m^-3, respectively. Di-2-ethylhexyl phthalate was the dominant species, accounting for an average of 81.9% of ∑₆PAEs. The mean and 95th percentile concentrations of ∑₆PAEs in PM_2.5 were used to calculate a "typical" and "high" total daily intake and uptake, respectively. The estimated total daily intakes of PAEs varied and depended on body weight in each age group. Infants had the highest "typical" and "high" daily intake of 43.4 and 179 ng kg-body weight (bw) ^-1 day^-1 for boys, and 42.0 and 173 ng kg-bw^-1 day^-1 for girls, respectively. However, after taking the bioaccessibility of PAEs in PM_2.5 into account, the total daily "typical" and "high" uptakes dropped to 27.3 and 113 ng kg-bw^-1 day^-1 for male infants, and 29.0 and 120 ng kg-bw^-1 day^-1 for female infants, respectively. Both of the data on the daily "high" intake and uptake were much lower than the tolerable daily intake set by the European Food Safety Agency. It merits attention that infants were subject to greater PAE exposure than adults.

Spatiotemporal Assessment of PM2.5-Related Economic Losses from Health Impacts during 2014⁻2016 in China

Background: Particulate air pollution, especially PM_2.5, is highly correlated with various adverse health impacts and, ultimately, economic losses for society, however, few studies have undertaken a spatiotemporal assessment of PM_2.5-related economic losses from health impacts covering all of the main cities in China. Methods: PM_2.5 concentration data were retrieved for 190 Chinese cities for the period 2014–2016. We used a log-linear exposure–response model and monetary valuation methods, such as value of a statistical life (VSL), amended human capital (AHC), and cost of illness to evaluate PM_2.5-related economic losses from health impacts at the city level. In addition, Monte Carlo simulation was used to analyze uncertainty. Results: The average economic loss was 0.3% (AHC) to 1% (VSL) of the total gross domestic product (GDP) of 190 Chinese cities from 2014 to 2016. Overall, China experienced a downward trend in total economic losses over the three-year period, but the Beijing–Tianjin–Hebei, Shandong Peninsula, Yangtze River Delta, and Chengdu-Chongqing regions experienced greater annual economic losses. Conclusions: Exploration of spatiotemporal variations in PM_2.5-related economic losses from long-term health impacts could provide new information for policymakers regarding priority areas for PM_2.5 pollution prevention and control in China.

Transfers of embodied PM2.5 emissions from and to the North China region based on a multiregional input-output model

Atmospheric PM_2.5 pollution has become a global issue, and is increasingly being associated with social unrest. As a resource reliant local economy and heavy industry cluster, the North China region has become China's greatest emitter, and the source of much pollution spillover to outside regions. To address this issue, the current study investigates the transfers of embodied PM_2.5 emissions to and from the North China region (which is taken to include Hebei, Henan, Shandong, and Shanxi, and is referred to here as HHSS). The study uses a top-down pollutant emission inventory and environmentally extended multi-regional input-output (EE-MRIO) model. The results indicate that the HHSS area exported a total of 660 Gg of embodied PM_2.5 to other domestic provinces, mainly producing outflows to China's central coastal area (Jiangsu, Zhejiang, and Shanghai) and the Beijing-Tianjin region. HHSS also imported 224 Gg of embodied PM_2.5 from other domestic regions, primarily from Inner Mongolia and the northeast. Furthermore, the transfer of embodied emissions often occurred between geographically adjacent areas to save costs; Beijing and Tianjin mainly transferred embodied pollution to Hebei and Shanxi, whilst Jiangsu, Shanghai, and Zhejiang tended to import embodied air pollutants from Shandong and Henan. At the sectoral level, the melting and pressing of metals, the production of non-metallic products, and electric and heat power production were the three dominant economic sectors for PM_2.5 emissions, together accounting for 81% of total discharges. Capital formation played a key role in outflows (75%) in all sectors. Moreover, the virtual pollutant emissions exported to foreign countries also significantly affected HHSS' discharges significantly, making up 340 Gg. Allocating responsibility for some proportion of HHSS' emissions to the Beijing-Tianjin area and the central coastal provinces may be an effective approach for mitigating releases in HHSS.

Atmospheric Environment Vulnerability Cause Analysis for the Beijing-Tianjin-Hebei Metropolitan Region

Assessing and quantifying atmospheric vulnerability is a key issue in urban environmental protection and management. This paper integrated the Analytical hierarchy process (AHP), fuzzy synthesis evaluation and Geographic Information System (GIS) spatial analysis into an Exposure-Sensitivity-Adaptive capacity (ESA) framework to quantitatively assess atmospheric environment vulnerability in the Beijing-Tianjin-Hebei (BTH) region with spatial and temporal comparisons. The elaboration of the relationships between atmospheric environment vulnerability and indices of exposure, sensitivity, and adaptive capacity supports enable analysis of the atmospheric environment vulnerability. Our findings indicate that the atmospheric environment vulnerability of 13 cities in the BTH region exhibits obvious spatial heterogeneity, which is caused by regional diversity in exposure, sensitivity, and adaptive capacity indices. The results of atmospheric environment vulnerability assessment and the cause analysis can provide guidance to pick out key control regions and recognize vulnerable indicators for study sites. The framework developed in this paper can also be replicated at different spatial and temporal scales using context-specific datasets to support environmental management.

Spatiotemporal Changes in Fine Particulate Matter Pollution and the Associated Mortality Burden in China between 2015 and 2016

In recent years, research on the spatiotemporal distribution and health effects of fine particulate matter (PM2.5) has been conducted in China. However, the limitations of different research scopes and methods have led to low comparability between regions regarding the mortality burden of PM2.5. A kriging model was used to simulate the distribution of PM2.5 in 2015 and 2016. Relative risk (RR) at a specified PM2.5 exposure concentration was estimated with an integrated exposure-response (IER) model for different causes of mortality: lung cancer (LC), ischaemic heart disease (IHD), cerebrovascular disease (stroke) and chronic obstructive pulmonary disease (COPD). The population attributable fraction (PAF) was adopted to estimate deaths attributed to PM2.5. 72.02% of cities experienced decreases in PM2.5 from 2015 to 2016. Due to the overall decrease in the PM2.5 concentration, the total number of deaths decreased by approximately 10,658 per million in 336 cities, including a decrease of 1400, 1836, 6312 and 1110 caused by LC, IHD, stroke and COPD, respectively. Our results suggest that the overall PM2.5 concentration and PM2.5-related deaths exhibited decreasing trends in China, although air quality in local areas has deteriorated. To improve air pollution control strategies, regional PM2.5 concentrations and trends should be fully considered.