Spatial and temporal distribution, chemical characteristics, and sources of ambient particulate matter in the Beijing-Tianjin-Hebei region

Regulation-driven changes in PM2.5 sources in China from 2013 to 2019, a critical review and trend analysis

Identifying changes in source-specific fine particles (PM2.5) over time is essential for evaluating the effectiveness of regulatory measures and informing future policy decisions. After the extreme haze events in China during 2013-14, more comprehensive and stringent policies were implemented to combat PM2.5 pollution. To determine the effectiveness of these policies, it is necessary to assess the changes in the specific source types to which the regulations pertain. Multiple studies have been conducted over the past decade to apportion PM2.5. The purpose of this study was to explore the available literature and conduct a critical review of the reliable results. In total, 5008 articles were screened, but only 48 studies were included for further analysis given our inclusion criteria including covering a monitoring period of ≥1 year and having enough speciation data to provide mass closure. Using these studies, we analyzed temporal and spatial trends across China from 2013 to 2019. We observed the overall decrease in the concentration contributions from all main source categories. The reductions from industry, coal and heavy oil combustion, and the related secondary sulfate were more notable, especially from 2013 to 2016-17. The contributions from biomass burning initially decreased but then increased slightly after 2016 in some locations despite new constraints on agricultural and household burning practices. Although the contributions from vehicle emissions and related secondary nitrate decreased, they gradually became the primary contributors to PM2.5 by ∼2017. Despite the substantial improvements achieved by the air pollution regulation implementations, further improvements in air quality will require additional aggressive actions, especially those targeting vehicular emissions. Ultimately, source apportionment studies based on extended duration, fixed-site sampling are recommended to provide a more thorough understanding of the sources impacting areas and transformations in PM2.5 sources prompted by regulatory actions.

Exploring efficient strategies for air quality improvement in China based on its regional characteristics and interannual evolution of PM2.5 pollution

Fine particulate matter (PM2.5) harms human health and hinders normal human life. Considering the serious complexity and obvious regional characteristics of PM2.5 pollution, it is urgent to fill in the comprehensive overview of regional characteristics and interannual evolution of PM2.5. This review studied the PM2.5 pollution in six typical areas between 2014 and 2022 based on the data published by the Chinese government and nearly 120 relevant literature. We analyzed and compared the characteristics of interannual and quarterly changes of PM2.5 concentration. The Beijing-Tianjin-Hebei region (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) made remarkable progress in improving PM2.5 pollution, while Fenwei Plain (FWP), Sichuan Basin (SCB) and Northeast Plain (NEP) were slightly inferior mainly due to the relatively lower level of economic development. It was found that the annual average PM2.5 concentration change versus year curves in the three areas with better pollution control conditions can be merged into a smooth curve. Importantly, this can be fitted for the accurate evaluation of each area and provide reliable prediction of its future evolution. In addition, we analyzed the factors affecting the PM2.5 in each area and summarize the causes of air pollution in China. They included primary emission, secondary generation, regional transmission, as well as unfavorable air dispersion conditions. We also suggested that the PM2.5 pollution control should target specific industries and periods, and further research need to be carried out on the process of secondary production. The results provided useful assistance such as effect prediction and strategy guidance for PM2.5 pollution control in Chinese backward areas.

Insights into PM2.5 pollution of four small and medium-sized cities in Chinese representative regions: Chemical compositions, sources and health risks

Fine particles (PM2.5) pollution is still a severe issue in some cities in China, where the chemical characteristics of PM2.5 remain unclear due to limited studies there. Herein, we focused on PM2.5 pollution in small and medium-sized cities in key urban agglomerations and conducted a comprehensive study on the PM2.5 chemical characteristics, sources, and health risks. In the autumn and winter of 2019-2020, PM2.5 samples were collected simultaneously in four small and medium-sized cities in four key regions: Dingzhou (Beijing-Tianjin-Hebei region), Weinan (Fenwei Plain region), Fukang (Northern Slope of the Tianshan Mountain region), and Bozhou (Yangtze River Delta region). The results showed that secondary inorganic ions (43.1 %-67.0 %) and organic matter (OM, 8.6 %-36.4 %) were the main components of PM2.5 in all the cities. Specifically, Fukang with the most severe PM2.5 pollution had the highest proportion of SO42- (31.2 %), while the dominant components in other cities were NO3- and OM. The Multilinear Engine 2 (ME2) analysis identified five sources of PM2.5 in these cities. Coal combustion contributed most to PM2.5 in Fukang, but secondary sources in other cities. Combined with chemical characteristics and ME2 analysis, it was preliminarily determined that the primary emission of coal combustion had an important contribution to high SO42- in Fukang. Potential source contribution function (PSCF) analysis results showed that regional transport played an important role in PM2.5 in Dingzhou, Weinan and Bozhou, while PM2.5 in Fukang was mainly affected by short-range transport from surrounding areas. Finally, the health risk assessment indicated Mn was the dominant contributor to the total non-carcinogenic risks and Cr had higher carcinogenic risks in all cities. The findings provide a scientific basis for formulating more effective abatement strategies for PM2.5 pollution.

The role of large-scale atmospheric circulations on long-term variations of PM10 concentrations over Turkey

PM10 is widely identified as an important atmospheric pollutant posing a serious threat to human health and environment as well as it influences the climate system. To unearth the mechanism involved in its sources and circulation behavior in environment, this study focuses on the role of large-scale atmospheric circulation on the long-term variability of PM10 over Turkey by applying rotated empirical orthogonal functions (REOF) analysis. As a result of the implementation of REOF to the daily PM10 data for 80 air quality stations throughout the period 2010-2020, first REOF mode (REOF1 44.9% in winter, 43.2% in spring, 39.5% in summer and 31.6% in fall) for all the four seasons indicated the role of local emission sources on the variations of PM10, which show high PM10 values in different geographical regions. The results of the second mode (REOF2, 17.9% in winter, 14.0% in spring, 14.0% in summer and 16.3% in fall) indicate the role of large-scale atmospheric circulations on the values of PM10. From the REOF2 analysis and extracted synoptic composite maps, the strength of southerly winds and the presence of southwesterly winds at low levels are very important in transporting of dust pollutants from the Arabian Peninsula and Northern Africa, respectively, to the eastern (EAR) and southeastern (SEAR) regions of Turkey during winter. In spring, sand particles in the interior terrestrial part of the country are carried to the northern regions by the effect of large-scale southerly winds, which cause above-normal PM10 concentrations in the Black Sea region of Turkey. In summer, dust particles together with warm dry air intrusion to the eastern region of Turkey by strong easterly winds are sourced by Caspian Sea and result in high PM10 values. Our findings emphasize that the long-term variations in air quality over Turkey are affected secondary by the variations in the large-scale atmospheric circulations with primary contributions from the changes in local emission sources.

Variation of PM2.5 and PM10 in emissions and chemical compositions in different seasons from a manure-belt laying hen house

Particulate matter (PM) emissions from animal houses and the corresponding hazard have raised increasing attention during recent years. In this study, a large-scale manure-belt laying hen house located in Beijing, China was selected as the experimental site for the study of the emission rates (ER) and chemical compositions of PM2.5 and PM10 in 3 seasons, namely, summer, autumn, and winter, to investigate their possible influences on ambient air quality and human health. The results showed that the mean ER from the hen house in summer, autumn, and winter were 9.0 ± 1.7, 2.4 ± 0.7, and 1.9 ± 0.7 mg hen-1 d-1 for PM2.5 (P < 0.05), and 30.7 ± 1.1, 12.8 ± 1.5, and 10.9 ± 0.9 mg hen-1 d-1 for PM10 (P < 0.05), respectively. Moreover, large amounts of secondary inorganic aerosols (SIA) were observed inside the house in summer, accounting for 11.4 and 9.6% of indoor PM2.5 and PM10 mass, respectively, compared with the value of <1.4% in autumn and winter. Among the 31 detected elements in indoor PM, arsenic concentration exceeded the threshold set in legislation. Zn had a notably high concentration of 3,403 to 4,432 ng m-3 in indoor PM10, which was 28 to 71 times higher than that in ambient PM10. The findings suggest that the poultry-raising house emit PM2.5 and PM10 containing SIA and toxic heavy-metal elements such as As and Zn to the ambient with much more emissions in summer than in autumn and winter. Considering the increasing development of poultry-raising farming in China, the potential hazard derived from the exhaust of PM2.5 and PM10 should be focused on, especially during summer.

Estimating the geographical patterns and health risks associated with PM2.5-bound heavy metals to guide PM2.5 control targets in China based on machine-learning algorithms

PM2.5 is the main component of haze, and PM2.5-bound heavy metals (PBHMs) can induce various toxic effects via inhalation. However, comprehensive macroanalyses on large scales are still lacking. In this study, we compiled a substantial dataset consisting of the concentrations of eight PBHMs, including As, Cd, Cr, Cu, Mn, Ni, Pb and Zn, across different cities in China. To improve prediction accuracy, we enhanced the traditional land-use regression (LUR) model by incorporating emission source-related variables and employing the best-fitted machine-learning algorithm, which was applied to predict PBHM concentrations, analyze geographical patterns and assess the health risks associated with metals under different PM2.5 control targets. Our model exhibited excellent performance in predicting the concentrations of PBHMs, with predicted values closely matching measured values. Noncarcinogenic risks exist in 99.4% of the estimated regions, and the carcinogenic risks in all studied regions of the country are within an acceptable range (1 × 10-5-1 × 10-6). In densely populated areas such as Henan, Shandong, and Sichuan, it is imperative to control the concentration of PBHMs to reduce the number of patients with cancer. Controlling PM2.5 effectively decreases both carcinogenic and noncarcinogenic health risks associated with PBHMs, but still exceed acceptable risk level, suggesting that other important emission sources should be given attention.

Multistation collaborative prediction of air pollutants based on the CNN-BiLSTM model

The development of industry has led to serious air pollution problems. It is very important to establish high-precision and high-performance air quality prediction models and take corresponding control measures. In this paper, based on 4 years of air quality and meteorological data from Tianjin, China, the relationships between various meteorological factors and air pollutant concentrations are analyzed. A hybrid deep learning model consisting of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) is proposed to predict pollutant concentrations. In addition, a Bayesian optimization algorithm is applied to obtain the optimal combination of hyperparameters for the proposed deep learning model, which enhances the generalization ability of the model. Furthermore, based on air quality data from multiple stations in the region, a multistation collaborative prediction method is designed, and the concept of a strongly correlated station (SCS) is defined. The predictive model is modified using the idea of SCS and is used to predict the pollutant concentration in Tianjin. The coefficient of determination R2 of PM2.5, PM10, SO2, NO2, CO, and O3 are 0.89, 0.84, 0.69, 0.83, 0.92, and 0.84, respectively. The results show that our model is capable of dealing with air pollutant prediction with satisfactory accuracy.

Lung cancer risk and exposure to air pollution: a multicenter North China case-control study involving 14604 subjects

BACKGROUND

For North Chinese lung cancer patients, there is limited study on the distribution of air pollution and smoking related features based on analyses of large-scale, high-quality population datasets. The aim of the study was to fully analyze risk factors for 14604 Subjects.

METHODS

Participants and controls were recruited in 11 cities of North China. Participants' basic information (sex, age, marital status, occupation, height, and weight), blood type, smoking history, alcohol consumption, history of lung-related diseases and family history of cancer were collected. PM2.5 concentration data for each year in each city of the study area from 2005 to 2018 were extracted based on geocoding of each person's residential address at the time of diagnosis. Demographic variables and risk factors were compared between cases and matched controls using a univariate conditional logistic regression model. Multivariate conditional logistic regression models were applied to estimate the odds ratio (OR) and 95% confidence interval (CI) for risk factors in univariate analysis. The nomogram model and the calibration curve were developed to predict lung cancer probability for the probability of lung cancer.

RESULTS

There was a total of 14604 subjects, comprising 7124 lung cancer cases and 7480 healthy controls included in the study. Marital status of unmarried persons, people with a history of lung-related disease, corporate personnel and production /service personnel were protective factors for lung cancer. People younger than 50 years old, people who were smoking and quit smoking, people who had been drinking consistently, people with family history of cancer and PM2.5 exposure were proven to be a risk factor for lung cancer. The risk of lung cancer varied with sex, smoking status and air pollution. Consistent alcohol consumption, persistent smoking and smoking quit were risk factors for lung cancer in men. By smoking status, male was risk factor for lung cancer in never smokers. Consistent alcohol consumption added risk for lung cancer in never smokers. The combined effects of PM2.5 pollution exposure and ever smoking aggravated the incidence of lung cancer. According to air pollution, lung cancer risk factors are completely different in lightly and heavily polluted areas. In lightly polluted areas, a history of lung-related disease was a risk factor for lung cancer. In heavily polluted areas, male, consistent alcohol consumption, a family history of cancer, ever smokers and smoking quit were all risk factors for lung cancer. A nomogram was plotted and the results showed that PM2.5 was the main factor affecting the occurrence of lung cancer.

CONCLUSIONS

The large-scale accurate analysis of multiple risk factors in different air quality environments and various populations, provide clear directions and guidance for lung cancer prevention and precise treatment.

Variations and drivers of aerosol vertical characterization after clean air policy in China based on 7-years consecutive observations

Understanding the aerosol vertical characterization is of great importance to both climate and atmospheric environment. This study investigated the variations of aerosol profiles over eight regions of interest in China after clean air policy (2013-2019) and discussed the drivers of the vertical aerosol structure, using observations from active satellite measurements (CALIPSO). From the annual variation, the amplitude of extinction coefficient profiles showed a decreasing trend with fluctuations, and the maximum was 0.21 km^-1 in Beijing-Tianjin-Hebei (JJJ). For regions suffered from air pollution, the variation was greatest below 0.45 km, while it was between 1-1.5 km for Sichuan Basin. The correlation coefficient between the relative humidity (RH) and the extinction coefficient indicated that the increase of RH inhibited the decrease of the extinction coefficient in the Yangtze River Delta. In most regions, the main aerosol subtypes were polluted dust and polluted continental, but they were coarser in JJJ and North West. The frequency of concurrency of dust and polluted dust aerosols decreased in JJJ, but polluted continental aerosols occurred more frequently. Further, the aerosol extinction coefficient profiles under different pollution conditions showed that it changed most during heavy pollution periods in JJJ, especially in 2017, with a significant aerosol loading between ∼700 and 1200 m. The atmospheric reanalysis data revealed that the weak convergence at low level and the divergence at high level supported the upward transport of aerosols in 2017. Overall, the differences in divergence allocation, RH, and wind filed were the main meteorological drivers.

Speciation and source changes of atmospheric arsenic in Qingdao from 2016 to 2020 - Response to control policies in China

Arsenic (As) is a toxic pollutant in the atmosphere. The atmospheric As concentration is high over the East Asian continent. At present, there is less research on the long-term trend of atmospheric arsenic pollution, which is not conducive to understanding its behavior. Total suspended particulate matter (TSP) samples were collected in Qingdao in autumn and winter from 2016 to 2020 to analyze total arsenic (TAs), As(V) and As(III). The interannual variation patterns, influencing factors and health risks of arsenic concentrations in aerosols were discussed. The results showed that As(V) is the dominant species of arsenic in aerosols. The average concentration of TAs gradually decreased and the proportion of As(III) increased during autumn and winter from 2016 to 2020. The levels of TAs, As(V) and As(III) in aerosols increased during the heating period and on polluted days. Negative correlation between TAs/TSP and TSP indicated that higher concentrations of TSP in the atmosphere would reduce the content of TAs in particulate matter. The increase of secondary aerosol particles played a dilution effect. Mobile source emissions, biomass and coal combustion were main sources of atmospheric arsenic. The distribution range of large potential sources of atmospheric arsenic decreased from 2016 to 2020, and concentrated, mainly in parts of Shandong province and its offshore areas. Local sources contributed the most to atmospheric arsenic pollution in Qingdao in autumn and winter. TAs, As(V) and As(III) posed a low non-carcinogenic risk and a negligible carcinogenic risk to adults and children. This study reveals the influence of strict air pollution control policies on the speciation and source of arsenic in aerosols.

Heterogeneous changes of chemical compositions, sources and health risks of PM2.5 with the "Clean Heating" policy at urban/suburban/industrial sites

China has enacted the "Clean Heating" (CH) policy in north China. The domain-specific impacts on PM2.5 constituents and sources in small cities are still lacking, which obstruct the further policy optimization. Here, we performed an intensive observation covering the heating period (HP) and pre-heating period (PHP) in winter of 2017 at urban (UR), industrial (IS), and suburban (SUR) sites in one of the "2 + 26" cities. The mean PM2.5 concentrations at UR and IS decreased by 15.2 % and 4.6 %, while increased by 9.8 % at SUR in the HP compared with the PHP, indicating the heterogeneous responses. The lowest contribution percentages of coal combustion (14.6 %) and industrial emissions (17.1 %) to PM2.5 at UR in the HP implied the CH policy played more effective role. The most increase in NO3-/SO42- ratio by 26.8 % and the highest NO3- concentration at UR in the HP were linked mainly with the thermal-NOx emitted from natural gas (NG) burning in view of NOx emission reductions from other sources. The highest concentrations of OC, SO42-, K+, and Cl-, and contribution percentages of biomass burning (20.0 %) and coal combustion (24.8 %) to PM2.5 at SUR in the HP evidenced the enhanced usage of biomass/coal. Coal banning in the HP at IS and UR led to the obvious decreases in OC, SO42-, As, and Sb. Secondary nitrate became the largest PM2.5 source at IS and UR in the HP. Coal banning, emission control on large-size enterprises and ignored control on small-size enterprises efficiently modified the concentrations and health risks of heavy metals. The lowest carcinogenic risks moved from SUR in the PHP to UR in the HP. The policies on de-NOx of NG-burning related enterprises, reduction of biomass/coal usage in suburban area, and strict regulation of small-size enterprises were urgently need to further improve the air quality.

Spatial and temporal variations of PM2.5 concentrations in Chinese cities during 2015-2019

The study analyzed the current status and changing trends of PM_2.5 pollution, and used Kriging spatial interpolation, spatial autocorrelation analysis, and scan statistics to explore the spatiotemporal characteristics and identify hotspots. The results showed that PM_2.5 pollution during 2015-2019 displayed a downward trend year by year, with a pronounced seasonal difference of higher concentrations in winter and lower concentrations in summer. By 2019, there were still 110 cities (n = 194) failed to meet China's annual grade II air quality standard (35 μg/m³). The spatial distribution of PM_2.5 was characterized by marked heterogeneity, with a significant spatial dependence and clustering characteristics. The pollution hotspots of PM_2.5 were mainly concentrated in eastern and central China, especially in the Beijing-Tianjin-Hebei region and its surrounding area. The results of this study will assist Chinese authorities in developing strategies for preventing and controlling air pollution, especially in hotspot regions and during peak periods.

Exposure to fine particulate matter constituents and cognitive function performance, potential mediation by sleep quality: A multicenter study among Chinese adults aged 40-89 years

Although exposure to fine particulate matter (PM_2.5) has been associated with cognitive decline, little is known about which PM_2.5 constituents are more harmful. Recent study on the association between PM_2.5 and sleep quality prompted us to propose that sleep quality may mediate the adverse effects of PM_2.5 components on cognitive decline. Understanding the association between PM_2.5 constituents and cognitive function, as well as the mediating role of sleep quality provides a future intervention target for improving cognitive function. Using data involving 1834 participants from a multicenter cross-sectional study in nine cities of the Beijing-Tianjin-Hebei (BTH) region in China, we undertook multivariable linear regression analyses to quantify the association of annual moving-average PM_2.5 and its chemical constituents with cognitive function and to assess the modifying role of exposure characteristic in this association. Besides, we examined the extent to which this association of PM_2.5 constituents with cognitive function was mediated via sleep quality by a mediation analysis. We observed significantly negative associations between an increase of one interquartile range increase in PM_2.5 [-0.876 (95 % CI: -1.205, -0.548)], organic carbon [-0.481 (95 % CI: -0.744, -0.219)], potassium [-0.344 (95 % CI: -0.530, -0.157)], iron [-0.468 (95 % CI: -0.646, -0.291)], and ammonium ion [-0.125 (95 % CI: -0.197, -0.052)] and cognitive decline. However, we didn't find any individual components more harmful than PM_2.5. Poor sleep quality partially mediated the estimated associations, which were explained ranging from 2.28 % to 11.99 %. Stratification analyses showed that people living in areas with lower greenspace were more susceptible to specific PM_2.5 components. Our study suggests that the adverse effect of suffering from PM_2.5 components is more pronounced among individuals with poor sleep quality, amplifying environmental inequalities in health. Besides reducing environmental pollution, improving sleep quality may be another measure worth considering to improve cognition if our research is confirmed in the future.

Spatiotemporal variations and sources of PM2.5 in the Central Plains Urban Agglomeration, China

The Central Plains Urban Agglomeration (CPUA) is the largest region in central China and suffers from serious air pollution. To reveal the spatiotemporal variations and the sources of fine particulate matter (PM_2.5, with an aerodynamic diameter of smaller than 2.5 μm) concentrations of CPUA, multiple and transdisciplinary methods were used to analyse the collected millions of PM_2.5 concentration data. The results showed that during 2017 ~ 2020, the yearly mean concentrations of PM_2.5 for CPUA were 68.3, 61.5, 58.7, and 51.5 μg/m³, respectively. The empirical orthogonal function (EOF) analysis suggested that high PM_2.5 pollution mainly occurred in winter (100.8 μg/m³, 4-year average). The diurnal change in PM_2.5 concentrations varied slightly over the season. The centroid of the PM_2.5 concentration moved towards the west over time. The spatial autocorrelation analysis indicated that PM_2.5 concentrations exhibited a positive spatial autocorrelation in CPUA. The most polluted cities distributed in the northern CPUA (Handan was the centre) formed a high-high agglomeration, and the cities located in the southern CPUA (Xinyang was the centre) formed a low-low agglomeration. The backward trajectory model and potential source contribution function were employed to discuss the regional transportation of PM_2.5. The results demonstrated that internal-region and cross-regional transport of anthropogenic emissions were all important to PM_2.5 pollution of CPUA. Our study suggests that joint efforts across cities and regions are necessary.

Epidemiological characteristics and risk factors of lung adenocarcinoma: A retrospective observational study from North China

Background

The main aim of the study was to determine the risk factors of lung adenocarcinoma and to analyze the variations in the incidence of lung adenocarcinoma according to time, sex, and smoking status in North China.

Methods

Patients with lung cancer in local household registries diagnosed and treated for the first time in the investigating hospital were enrolled from 11 cities in North China between 2010 and 2017. Baseline characteristics and tumor-related information were extracted from the patients’ hospital medical record, clinical course records, and clinical examination. Some of the variables, such as smoking, alcohol consumption, medical history, and family history of cancer, were obtained from interviews with the enrolled patients. The statistical method used were the chi-square test and multi-factor logistic regression analysis. The time trend was statistically analyzed using Joinpoint regression models, and p values were calculated.

Results

A total of 23,674 lung cancer cases were enrolled. People in severely polluted cities were at higher risk for lung adenocarcinoma (p &lt; 0.001). Most patients with lung adenocarcinoma had no history of lung-related diseases (p = 0.001). Anatomically, lung adenocarcinoma was more likely to occur in the right lung (p &lt; 0.001). Non-manual labor workers were more likely to develop from lung adenocarcinoma than manual workers (p = 0.015). Notably, non-smokers were more likely to develop lung adenocarcinoma than smokers (p &lt; 0.001). The proportion of lung adenocarcinoma increased significantly in Hebei Province (p &lt; 0.001). Among non-smokers, the proportion of lung adenocarcinoma showed a higher rise than in smokers (p &lt; 0.001).

Conclusions

Lung adenocarcinoma is the most common histological type of lung cancer in North China (Hebei Province), and the proportion of lung adenocarcinoma is increasing, especially among non-smokers. Lung adenocarcinoma is more common in women, severely polluted cities, individuals with no history of lung-related diseases, in the right lung, and in non-smokers. These can serve as a great guide in determining the accuracy of lung adenocarcinoma high-risk groups and lung cancer risk assessment models.

Migration and diffusion characteristics of air pollutants and meteorological influences in Northwest China: a case study of four mining areas

In the process of exploiting mineral resources, dust enters the environment through air suspended particles and surface runoff, which has a serious impact on the atmospheric environment and human health. From all-year and seasonal scenarios, the migration trajectories and cumulative concentration based on the secondary development of Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) in four mining areas (SF, BC, SJZ, and MJT) in Northwest China are studied. The convergent cross mapping (CCM) method is used to study the causal relationship between concentration and meteorological factors. In this process, the problem of missing non-station meteorological data is solved with the help of the inverse distance weighted interpolation method, and the problem in which the convergence requirements of the CCM algorithm cannot meet the requirements is solved with the bootstrap method. The results indicated that the short path has the characteristics of slow movement, short migration path, low altitude(< 1 km), and high contribution rate, while the long path has the opposite characteristics. Furthermore, the results demonstrated that the concentration is centered on the pollution source and diffuses around, with a diffusion radius of 220-270 km, showing a serious pollution center and slight gradient settlement on the edge, but the overall distribution of accumulated concentration is uneven. The results also show that temperature (TEMP and S_TEMP), evaporation, and air pressure are the main meteorological factors affecting the all-year concentration. The concentration and meteorological factors in the four mining areas also show significant seasonal characteristics, and the correlation in spring, summer, and autumn is stronger than that in winter. This study not only provides a reference for the green and sustainable exploitation of mineral resources but also provides theoretical support for the joint prevention and control of transboundary pollution.

Associations of Fine Particulate Matter Constituents with Metabolic Syndrome and the Mediating Role of Apolipoprotein B: A Multicenter Study in Middle-Aged and Elderly Chinese Adults

Fine particulate matter (PM_2.5) was reported to be associated with metabolic syndrome (MetS), but how PM_2.5 constituents affect MetS and the underlying mediators remains unclear. We aimed to investigate the associations of long-term exposure to 24 kinds of PM_2.5 constituents with MetS (defined by five indicators) in middle-aged and elderly adults and to further explore the potential mediating role of apolipoprotein B (ApoB). A multicenter study was conducted by recruiting subjects (n = 2045) in the Beijing-Tianjin-Hebei region from the cohort of Sub-Clinical Outcomes of Polluted Air in China (SCOPA-China Cohort). Relationships among PM_2.5 constituents, serum ApoB levels, and MetS were estimated by multiple logistic/linear regression models. Mediation analysis quantified the role of ApoB in "PM_2.5 constituents-MetS" associations. Results indicated PM_2.5 was significantly related to elevated MetS prevalence. The MetS odds increased after exposure to sulfate (SO₄^2-), calcium ion (Ca²⁺), magnesium ion (Mg²⁺), Si, Zn, Ca, Mn, Ba, Cu, As, Cr, Ni, or Se (odds ratios ranged from 1.103 to 3.025 per interquartile range increase in each constituent). PM_2.5 and some constituents (SO₄^2-, Ca²⁺, Mg²⁺, Ca, and As) were positively related to serum ApoB levels. ApoB mediated 22.10% of the association between PM_2.5 and MetS. Besides, ApoB mediated 24.59%, 50.17%, 12.70%, and 9.63% of the associations of SO₄^2-, Ca²⁺, Ca, and As with MetS, respectively. Our findings suggest that ApoB partially mediates relationships between PM_2.5 constituents and MetS risk in China.

Variations and Sources of Organic Aerosol in Winter Beijing under Markedly Reduced Anthropogenic Activities During COVID-2019

The COVID-19 outbreak provides a "controlled experiment" to investigate the response of aerosol pollution to the reduction of anthropogenic activities. Here we explore the chemical characteristics, variations, and emission sources of organic aerosol (OA) based on the observation of air pollutants and combination of aerosol mass spectrometer (AMS) and positive matrix factorization (PMF) analysis in Beijing in early 2020. By eliminating the impacts of atmospheric boundary layer and the Spring Festival, we found that the lockdown effectively reduced cooking-related OA (COA) but influenced fossil fuel combustion OA (FFOA) very little. In contrast, both secondary OA (SOA) and O3 formation was enhanced significantly after lockdown: less-oxidized oxygenated OA (LO-OOA, 37% in OA) was probably an aged product from fossil fuel and biomass burning emission with aqueous chemistry being an important formation pathway, while more-oxidized oxygenated OA (MO-OOA, 41% in OA) was affected by regional transport of air pollutants and related with both aqueous and photochemical processes. Combining FFOA and LO-OOA, more than 50% of OA pollution was attributed to combustion activities during the whole observation period. Our findings highlight that fossil fuel/biomass combustion are still the largest sources of OA pollution, and only controlling traffic and cooking emissions cannot efficiently eliminate the heavy air pollution in winter Beijing.

Heavy haze pollution during the COVID-19 lockdown in the Beijing-Tianjin-Hebei region, China

To investigate the characteristics of particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) and its chemical compositions in the Beijing-Tianjin-Hebei (BTH) region of China during the novel coronavirus disease (COVID-19) lockdown, the ground-based data of PM_2.5, trace gases, water-soluble inorganic ions, and organic and elemental carbon were analyzed in three typical cities (Beijing, Tianjin, and Baoding) in the BTH region of China from 5-15 February 2020. The PM_2.5 source apportionment was established by combining the weather research and forecasting model and comprehensive air quality model with extensions (WRF-CAMx). The results showed that the maximum daily PM_2.5 concentration reached the heavy pollution level (>150 μg/m³) in the above three cities. The sum concentration of SO₄^2-, NO₃^- and NH₄⁺ played a dominant position in PM_2.5 chemical compositions of Beijing, Tianjin, and Baoding; secondary transformation of gaseous pollutants contributed significantly to PM_2.5 generation, and the secondary transformation was enhanced as the increased PM_2.5 concentrations. The results of WRF-CAMx showed obviously inter-transport of PM_2.5 in the BTH region; the contribution of transportation source decreased significantly than previous reports in Beijing, Tianjin, and Baoding during the COVID-19 lockdown; but the contribution of industrial and residential emission sources increased significantly with the increase of PM_2.5 concentration, and industry emission sources contributed the most to PM_2.5 concentrations. Therefore, control policies should be devoted to reducing industrial emissions and regional joint control strategies to mitigate haze pollution.

Composition and sources of particulate matter in the Beijing-Tianjin-Hebei region and its surrounding areas during the heating season

This paper aimed to analyze the composition and pollution sources of particulate matter (PM) in the Beijing-Tianjin-Hebei region and its surrounding areas (henceforth the BTH region) during the heating season to support the mitigation and control of regional air pollution. Manual monitoring data from the China National Environmental Monitoring Network for Atmospheric PM in the BTH region were collected and analyzed during the 2016 and 2018 heating seasons. The positive definite matrix factor analysis (PMF) model was used to analyze the PM sources in BTH cities during the heating season. The main PM components were organic matter (OM), nitrate (NO₃^-), sulfate (SO₄^2-) and ammonium salt (NH₄⁺). Direct emission sources have decreased since 2016, indicating the effectiveness of governmental controls on these sources; however, secondary pollution showed an increasing trend, suggesting control measures should be strengthened. Daily regional average concentrations of OM, SO₄^2-, NH₄⁺, elemental carbon (EC), chloride (Cl^-) and trace elements all showed similar trends. When air quality worsened, the concentrations of the main PM components increased, but trends of change varied among components. In 2018, concentrations of OM and chloride were highest in the Taihang Mountains, and NO₃ concentrations were highest in Anyang, Hebi, Jiaozuo and Xinxiang. The SO₄^2- concentration was highest in the southern section of the Taihang Mountains. The NH₄⁺ and EC concentrations were generally highest in the central and southern regions. The concentration of crustal substances was highest in some cities in the north and central parts of the BTH region. In the 2018 heating season, the pollution level of five transmission channels showed an increasing trend in the Northwest, Southeast, Yanshan, South and Taihang Mountain channels. These findings provide a scientific basis for the continued management of atmospheric PM pollution.

PM2.5 induces the distant metastasis of lung adenocarcinoma via promoting the stem cell properties of cancer cells

Lung cancer is the most common cancer in China and second worldwide, of which the incidence of lung adenocarcinoma is rising. As an independent factor, air pollution has drawn the attention of the public. An increasing body of studies has focused on the effect of PM_2.5 on lung adenocarcinoma; however, the mechanism remains unclear. We collected the PM_2.5 in two megacities, Beijing (BPM) and Shijiazhuang (SPM), located in the capital of China, and compared the different components and sources of PM_2.5 in the two cities. Vehicle emissions are the primary sources of BPM, whereas SPM is industrial emissions. We found that chronic exposure to PM_2.5 promotes the tumorigenesis and metastasis of lung adenocarcinoma in patient-derived xenograft (PDX) models, as well as the migration and invasion of lung adenocarcinoma cell lines. SPM has more severe effects in vivo and in vitro. The underlying mechanisms are related to the stem cell properties of cancer cells, the epithelial-mesenchymal transition (EMT) process, and the corresponding miRNAs. It is hopeful to provide a theoretical basis for improving air pollution in China, especially in the capital area, and is of the significance of long-term survival of lung cancer patients.

Spatiotemporal Patterns and Regional Transport of Ground-Level Ozone in Major Urban Agglomerations in China

Ground-level ozone (O3) pollution has become a serious environmental issue in major urban agglomerations in China. To investigate the spatiotemporal patterns and regional transports of O3 in Beijing–Tianjin–Hebei (BTH-UA), the Yangtze River Delta (YRD-UA), the Triangle of Central China (TC-UA), Chengdu–Chongqing (CY-UA), and the Pearl River Delta urban agglomeration (PRD-UA), multiple transdisciplinary methods were employed to analyze the O3-concentration data that were collected from national air quality monitoring networks operated by the China National Environmental Monitoring Center (CNEMC). It was found that although ozone concentrations have decreased in recent years, ozone pollution is still a serious issue in China. O3 exhibited different spatiotemporal patterns in the five urban agglomerations. In terms of monthly variations, O3 had a unimodal structure in BTH-UA but a bimodal structure in the other urban agglomerations. The maximum O3 concentration was in autumn in PRD-UA, but in summer in the other urban agglomerations. In spatial distribution, the main distribution of O3 concentration was aligned in northeast–southwest direction for BTH-UA and CY-UA, but in northwest–southeast direction for YRD-UA, TC-UA, and PRD-UA. O3 concentrations exhibited positive spatial autocorrelations in BTH-UA, YRD-UA, and TC-UA, but negative spatial autocorrelations in CY-UA and PRD-UA. Variations in O3 concentration were more affected by weather fluctuations in coastal cities while the variations were more affected by seasonal changes in inland cities. O3 transport in the center cities of the five urban agglomerations was examined by backward trajectory and potential source analyses. Local areas mainly contributed to the O3 concentrations in the five cities, but regional transport also played a significant role. Our findings suggest joint efforts across cities and regions will be necessary to reduce O3 pollution in major urban agglomerations in China.

Characteristics of airborne particles retained on conifer needles across China in winter and preliminary evaluation of the capacity of trees in haze mitigation

To fully understand the characteristics of particulate matter (PM) retained on plant leaves (PMR) and the effect of vegetation on haze on a large spatial scale, we investigated needle samples collected from 78 parks and campuses in 31 cities (30 provincial cities) of China and developed a comprehensive method to characterise PMR. Both the PMR load (including water-insoluble particulate matter (WIPM), water-soluble inorganic ions (WSIS) and water-soluble organic matter (WSOM)), with a mean value of 554 ± 345 mg m^-2 leaf area, and component profiles of PMR showed obvious spatial variation across the cities. Though haze pollution levels vary greatly among the 31 cities, the PM retention capacity of needles does not depend on haze level because PMR generally reaches saturation before precipitation in winter. The water-soluble component (WSC, the sum of WSIS and WSOM) accounted for 52.3% of PMR on average, among which WSIS and WSOM contributed 21.4% and 30.9% to PMR, respectively. The dominant ions of WSIS in PMR in the cities were Ca²⁺, K⁺ and NO₃^-, indicating that raised dust, biomass combustion and traffic exhaust are significant sources of PM in China. Compared with previous reports, the particle size distributions of PMR and PM across China were consistent, with fine PM (PM_2.5) constituting a substantial proportion (43.8 ± 17.0%) of PMR. These results prove that trees can effectively remove fine particles from the air, thereby reducing human exposure to inhalable PM. We proposed a method to estimate the annual amount of PMR on Cedrus deodara, with an average value of 11.9 ± 9.6 t km^-2 canopy yr^-1 in China. Compared with the load of dust fall (atmospheric particles naturally falling on the ground, average of 138 ± 164 t km^-2 land area yr^-1 in China), we conclude that trees play a significant role in mitigating haze pollution.

High contribution of vehicle emissions to fine particulate pollutions in Lanzhou, Northwest China based on high-resolution online data source appointment

The quantitative estimation of urban particulate matter (PM) sources is essential but limited because of various reasons. The hourly online data of PM_2.5, organic carbon (OC), elemental carbon (EC), water-soluble ions, and elements from December 2019 to November 2020 was used to conduct PM source appointment, with an emphasis on the contribution of vehicle emissions to fine PM pollution in downtown Lanzhou, Northwest China. Vehicle emissions, secondary formation, mineral dust, and coal combustion were found to be the major PM sources using the positive matrix factorization model. The seasonal mean PM_2.5 were estimated to be 72.8, 39.2, 24.3, and 43.6 μg·m^-3 and vehicle emissions accounted for 35.7%, 25.8%, 30.0%, and 56.6% in winter, spring, summer, and autumn, respectively. Vehicle emissions were the largest source of PM considering the high PM pollution in winter and its significantly large contribution in autumn. Furthermore, the contribution of vehicle emissions increased with increasing PM in winter and autumn. Vehicle emissions were also the most important source of EC, accounting for 70.3%, 91.0%, 83.5%, and 93.7% of the total EC in winter, spring, summer, and autumn, respectively. With the reduction in industrial emissions and increase in vehicle numbers in China in recent years, vehicle emissions are going to be the largest source of urban PM pollution. To sustainably improve air quality in Lanzhou and other Chinese cities, efforts should be made to control vehicle emissions such as promoting clean-energy vehicles and encouraging public transportation.

Application and validation of the fugitive dust source emission inventory compilation method in Xiong'an New Area, China

The development of a refined fugitive dust emission inventory is vital for prevention and control of air pollution. In this study, a fugitive dust emission inventory of soil dust (SD), road dust (RD), and construction dust (CD) in Xiong'an New Area (XANA) for 2020 was developed by collecting activity data and combining remote sensing and field investigation data based on a popular compilation technology in China. The CALPUFF model was used to elucidate the contribution characteristics of dust sources to ambient particulate matter (PM), and the accuracy of the dust emission inventory compilation method was verified. The results show that the total emissions of PM₁₀ and PM_2.5 were 43,081.14 tons and 9701.69 tons, respectively. Meanwhile, RD and CD were the main emission sources, accounting for over 98.49% of the total emissions. The total contribution from the different types of dust sources to the ambient PM₁₀ was 42.59 μg/m³ (29.38%), with the contribution of RD (32.63 μg/m³, 22.51%) being approximately three times that of CD (9.78 μg/m³, 6.74%). Roads were the main source of fugitive dust, but large-scale infrastructure construction was the main cause of the high emission and high contribution of RD. The results show that the emission inventory compilation method can be used to estimate the emissions of dust sources, while the method used to calculate the emission of SD may be more suitable for dry and semi-dry areas with less rainfall. It was also found that when the dust emissions stay stable, the contribution of dust sources to the ambient PM₁₀ in different seasons can vary by 3-4 times. Therefore, under adverse meteorological conditions, it is necessary to strengthen the control of various dust sources and reduce the influence of human factors on them.

Size-resolved source apportionment of particulate matter from a megacity in northern China based on one-year measurement of inorganic and organic components

This research apportioned size-resolved particulate matter (PM) contributions in a megacity in northern China based on a full year of measurements of both inorganic and organic markers. Ions, elements, carbon fractions, n-alkanes, polycyclic aromatic hydrocarbons (PAHs), hopanes and steranes in 9 p.m. size fractions were analyzed. High molecular weight PAHs concentrated in fine PM, while most other organic compounds showed two peaks. Both two-way and three-way receptor models were used for source apportionment of PM in different size ranges. The three-way receptor model gave a clearer separation of factors than the two-way model, because it uses a combination of chemical composition and size distributions, so that factors with similar composition but distinct size distributions (like more mature and less mature coal combustion) can be resolved. The three-way model resolved six primary and three secondary factors. Gasoline vehicles and coal and biomass combustion, nitrate and high relative humidity related secondary aerosol, and resuspended dust and diesel vehicles (exhaust and non-exhaust) are the top two contributors to pseudo-ultrafine (<0.43 μm), fine (0.43-2.1 μm) and coarse mode (>2.1 μm) PM, respectively. Mass concentration of PM from coal and biomass combustion, industrial emissions, and diesel vehicle sources showed a bimodal size distribution, but gasoline vehicles and resuspended dust exhibited a peak in the fine and coarse mode, separately. Mass concentration of sulphate, nitrate and secondary organic aerosol exhibited a bimodal distribution and were correlated with temperature, indicating strong photochemical processing and repartitioning. High relative humidity related secondary aerosol was strongly associated with size shifts of PM, NO₃^- and SO₄^2- from the usual 0.43-0.65 μm to 1.1-2.1 μm. Our results demonstrated the dominance of primary combustion sources in the <0.43 μm particle mass, in contrast to that of secondary aerosol in fine particle mass, and dust in coarse particle mass in the Northern China megacity.

Polyethylene mesh knitted fabrics mulching the soil to mitigate China's haze: A potential source of PBDEs

The fate of polybrominated diphenyl ethers (PBDEs) from polyethylene mesh knitted fabrics (PMKFs) to mulched soil and nearby plants was studied. PBDEs in the soil sample collected from Tianjin University of Commerce in April 2019 increased significantly after 6 months of PMKF mulching owing to PMKFs as the main input source. The compositional profiles/congener patterns of the PBDEs in the soil and PMKFs became similar after 6 months. High correlations were found between ΣPBDEs in the soil and PMKFs in October 2019, with no significant correlation in April. Plants could take up, accumulate and biotransform PBDEs in contaminated soil. The uptake of BDE-209 by plants was the highest compared with other lesser brominated PBDE congeners, due to its higher log Kow value and molecular weight or size. BDE-47 taken up in the plant was biotransformed via hydroxylation. These results prove that the government's PMKF solution to haze is causing environmental problems in bare soil, i.e., PBDE pollution in both soil and nearby plants. The present study provides important pieces of evidence for government and policymakers, and it is recommended that one environmental problem is not solved by creating another.

Aircraft-based observation of gaseous pollutants in the lower troposphere over the Beijing-Tianjin-Hebei region

To investigate the spatial and vertical distribution of atmospheric pollutants (SO₂, NO_x, CO and O₃), aircraft-based measurements (model: Yun-12, 12 flights, 27 h total flight time) were conducted from near the surface up to 2400 m over the Beijing-Tianjin-Hebei (BTH) region between June 17th and July 22nd 2016. The results showed that high concentrations of primary gaseous pollutants (SO₂, NO_x, CO) were generally present in Beijing, Tianjin, Langfang and Tangshan areas, while high values of O₃ frequently appeared in areas far from the city. The flights at noon and dusk measured higher O₃ concentrations at 600 m and lower O₃ concentrations at higher altitudes, implying a strong influence by photochemical production. Back trajectory analysis suggested that the high levels of gaseous pollutants, especially at 600 m, were associated with pollution sources transported from the southerly direction during the observation period. The first simultaneous vertical distribution measurements using aircraft and tethered balloon were conducted in Gaocun (a rural site between Beijing and Tianjin) on June 17th. The results indicated that an inversion layer at the top of the planetary boundary layer (PBL) significantly suppressed vertical exchange through the PBL and resulted in a "two-layer" vertical distribution of pollutants above and below the PBL. Additionally, a residual high O₃ layer (79.9 ± 2.5 ppb, 500-1000 m) was observed above the PBL, and it contributed to the surface peak O₃ level at noon through downward transport along with the opening up of the PBL. These results indicate that coupled effects of horizontal and vertical transport should be investigated in future studies to improve the chemical transport models used to study the vertical distribution and regional transport over the BTH region.

Estimating Particulate Matter Emission from Dust Sources Using ZY-3 Data and GIS Technology—A Case Study in Zhengzhou City, China

With the rapid development of the social economy in China, numerous Chinese cities are facing high levels of particulate matter (PM) pollution problems. In this study, high-resolution ZY-3 images and GIS techniques were used to establish the emission inventory of total suspended particle (TSP), particulate matter 10 (PM10) and particulate matter 2.5 (PM2.5) from fugitive dust sources in May 2016, and a spatial grid of 3 km × 3 km resolution was established to demonstrate the spatial distribution of PM emission. Results showed that the total emissions of TSP, PM10 and PM2.5 in Zhengzhou city were 237.5 kt·a−1, 103.7 kt·a−1 and 22.4 kt·a−1, respectively. Construction dust source was the main fugitive dust emission source in Zhengzhou city—the TSP, PM10 and PM2.5 emission of which account for 76.42%, 89.68% and 88.39%, respectively, of the total emission, followed by road dust source and soil dust source. PM emission was higher in Zhongyuan, Huiji, Jinshui and Zhengdong New District, while Zhongmou, Xingyang, Dengfeng and other remote areas had low PM emissions. Compared to other Chinese cities or regions, the PM emission from the construction dust source was at a high level in Zhengzhou city, while the PM emissions from the soil dust source and road dust source were at moderate levels.

Characterization of airborne particles and cytotoxicity to a human lung cancer cell line in Guangzhou, China

Air pollution by airborne particles is a serious health problem worldwide. The present study was aimed at investigating the concentrations and composition of total suspended particles (TSPs) and PM_2.5 at various industrial/commercial sites of Guangzhou, a megacity of Southern China. Major and trace elements, ions and carbonaceous fraction were determined and main components were calculated. In addition, in order to assess the potential toxic on the respiratory system of these PM, cytotoxicity of size-fractionated particles (PM_10-5.6, PM_5.6-3.3, PM_3.3-1.1, PM_1.1-0.43) for a human lung cancer cell line (A549) was also investigated. Correlations between PM constituents and toxicity were assessed. Median levels of TSPs and PM_2.5 in industrial/commercial sites were 206 and 57.7 μg/m³, respectively. Nickel, Cu, Mo, Mn, Pb, and Ti were the most abundant metals in TSPs and PM_2.5. Industrial activities and coal combustion were the most important sources of carbonaceous particles in the zone. MTT assays showed that PM_10-5.6 and PM_1.1-0.43 had the highest and the lowest cytotoxicity to A549 cell lines, respectively. Inhalable particles around the manufacturing of metal facilities and formal waste treatment plants showed a high cytotoxicity to A549 cell lines. In general terms, no significant correlations were found between main components of PM and toxicity. However, W showed a significant correlation with cell viability.

Distribution characteristics of microbial community structure in atmospheric particulates of the typical industrial city in Jiangsu province, China

In this study, Xuzhou, a typical industrial city in the north of Jiangsu Province, was chosen to investigate the pollution level of atmospheric particulates. The proportion of fine particles (PM_2.5) in PM₁₀ is larger than that of coarse particles (about 58%). The physicochemical properties of PM_2.5 were analyzed by SEM and EDS. DGGE was used to study the distribution characteristics of bacterial community structure on atmospheric particulates (TSP, PM_2.5 and PM₁₀) in different functional areas of Xuzhou city during the winter haze. It was found that the microbial populations of atmospheric particles were mainly divided into three groups: Proteobacteria, Bacteroidetes, and Pachytenella. The community structure of bacteria in fine particle size was more abundant than that in coarse particle size. When haze occurs, the concentration of all kinds of pathogens in fine particle size will increase. Therefore, it is necessary to focus on the monitoring and management of fine particles.

Light absorption enhancement of particulate matters and their source apportionment over the Asian continental outflow site and South Yellow Sea

Light absorption enhancement of black carbon due to the aerosol mixing states is an important parameterization for climate modeling, while emission source contributions to the enhancement factor are unclear. An intensive campaign was conducted simultaneously at a China coastal site (Qingdao city) and maritime sites (South Yellow Sea, SYS) in August and Nov to Dec 2018. The absorption enhancement (E_MAC) of the black carbon was calculated using a two-step solvent dissolution protocol and found 1.96 ± 0.68, 1.64 ± 0.38, and 2.40 ± 0.76 for Qingdao summer (QS), Qingdao autumn (QA), and SYS, respectively. Positive matrix factorization (PMF) model identified six sources of PM_2.5 and E_MAC, which were secondary aerosol (with contribution 27.9% and 29.2%), coal combustion (24.9% and 20.2%), industrial emissions (15.2% and 25.4%), sea salt (6.9% and 9.6%), vehicle emissions (12.1% and 10.9%), and soil dust (13.0% and 4.7%), respectively. These sources increased the absorption of black carbon by a factor of 1.25 ± 0.11 (secondary aerosol), 1.21 ± 0.20 (industrial emissions), 1.17 ± 0.08 (coal combustion), 1.09 ± 0.07 (vehicle emissions), 1.08 ± 0.17 (sea salt), and 1.04 ± 0.10 (soil dust). Based on the correlation between PM and E_MAC source contributions, we estimated that secondary aerosols, industrial emissions, and coal combustion contributed to 74.8% of absorption enhancement at a regional scale in China. The source apportionment for E_MAC offers a new diagnosis for each source regarding aerosol forcing simulation which inputs from the individual emission sector.

Chemical characterization and source apportionment of PM1 and PM2.5 in Tianjin, China: Impacts of biomass burning and primary biogenic sources

The submicron particulate matter (PM₁) and fine particulate matter (PM_2.5) are very important due to their greater adverse impacts on the natural environment and human health. In this study, the daily PM₁ and PM_2.5 samples were collected during early summer 2018 at a sub-urban site in the urban-industrial port city of Tianjin, China. The collected samples were analyzed for the carbonaceous fractions, inorganic ions, elemental species, and specific marker sugar species. The chemical characterization of PM₁ and PM_2.5 was based on their concentrations, compositions, and characteristic ratios (PM₁/PM_2.5, AE/CE, NO₃^-/SO₄^2-, OC/EC, SOC/OC, OM/TCA, K+/EC, levoglucosan/K⁺, V/Cu, and V/Ni). The average concentrations of PM₁ and PM_2.5 were 32.4 µg/m³ and 53.3 µg/m³, and PM₁ constituted 63% of PM_2.5 on average. The source apportionment of PM₁ and PM_2.5 by positive matrix factorization (PMF) model indicated the main sources of secondary aerosols (25% and 34%), biomass burning (17% and 20%), traffic emission (20% and 14%), and coal combustion (17% and 14%). The biomass burning factor involved agricultural fertilization and waste incineration. The biomass burning and primary biogenic contributions were determined by specific marker sugar species. The anthropogenic sources (combustion, secondary particle formation, etc) contributed significantly to PM₁ and PM_2.5, and the natural sources were more evident in PM_2.5. This work significantly contributes to the chemical characterization and source apportionment of PM₁ and PM_2.5 in near-port cities influenced by the diverse sources.

Depression and Anxiety Associated with Exposure to Fine Particulate Matter Constituents: A Cross-Sectional Study in North China

The association between fine particulate matter (PM_2.5) exposure and mental disorders is attracting increasing attention, but the roles of specific PM_2.5 chemical constituents have yet to be explored. We conducted a multicenter cross-sectional study in nine cities located in the Beijing-Tianjin-Hebei region in China to assess the effects of PM_2.5 and chemical constituents on depression and anxiety. The Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder (GAD-7) scale were used to quantify the depression and anxiety status, atmospheric monitoring data from fixed stations was used to calculate exposure concentrations. We performed multiple logistic regression models to assess the associations of PM_2.5 chemical constituents exposure over the preceding 2 weeks with depression and anxiety. Overall, anxiety and depression were significantly associated with organic carbon (OC), elemental carbon (EC), copper (Cu), cadmium (Cd), nickel (Ni), and zinc (Zn). Subgroup analysis showed a stronger effect of PM_2.5 constituents on depression during the heating period. This study provide evidence for the possible link between PM_2.5 constituents and mental disorders among middle-aged and elderly Chinese adults, which requires further validation of the causal correlation. Our findings support the need for a stricter regulation on emissions of certain specific constituents, in addition to targeting control of total PM_2.5 emission concentration.

Spatiotemporal Big Data for PM2.5 Exposure and Health Risk Assessment during COVID-19

The coronavirus disease 2019 (COVID-19) first identified at the end of 2019, significantly impacts the regional environment and human health. This study assesses PM_2.5 exposure and health risk during COVID-19, and its driving factors have been analyzed using spatiotemporal big data, including Tencent location-based services (LBS) data, place of interest (POI), and PM_2.5 site monitoring data. Specifically, the empirical orthogonal function (EOF) is utilized to analyze the spatiotemporal variation of PM_2.5 concentration firstly. Then, population exposure and health risks of PM_2.5 during the COVID-19 epidemic have been assessed based on LBS data. To further understand the driving factors of PM_2.5 pollution, the relationship between PM_2.5 concentration and POI data has been quantitatively analyzed using geographically weighted regression (GWR). The results show the time series coefficients of monthly PM_2.5 concentrations distributed with a U-shape, i.e., with a decrease followed by an increase from January to December. In terms of spatial distribution, the PM_2.5 concentration shows a noteworthy decline over the Central and North China. The LBS-based population density distribution indicates that the health risk of PM_2.5 in the west is significantly lower than that in the Middle East. Urban gross domestic product (GDP) and urban green area are negatively correlated with PM_2.5; while, road area, urban taxis, urban buses, and urban factories are positive. Among them, the number of urban factories contributes the most to PM_2.5 pollution. In terms of reducing the health risks and PM_2.5 pollution, several pointed suggestions to improve the status has been proposed.

Spatial-temporal pattern evolution and driving factors of China's energy efficiency under low-carbon economy

Improving energy efficiency and building a low-carbon economy are the important ways to resolve the current contradiction between economic growth and the environment in China. In this paper, we use the super-efficiency Slack-Based Measure model (super-efficiency SBM model) to measure the energy efficiency of 30 provinces in China, and then conduct Empirical Orthogonal Function (EOF) to analyze its spatial-temporal evolution. Moreover, we use the Geographically and Temporally Weighted Regression (GTWR) to analyze the spatial-temporal heterogeneity of its driving factors. The results show that: (i) during the sample period, China's energy efficiency shows a rapidly upward trend, accompanied by the gradually strengthening spatial pattern of the "eastern>central>western"; (ii) the spatial pattern of the "southern>northern" exhibited by the annual growth rate of energy efficiency experienced a process of weakening first and then gradually strengthening; (iii) the influencing effects of market openness, relative energy price and industry structure on energy efficiency have no significant heterogeneity as a whole; (iv) the effects of environmental regulation intensity, the marketization level, the technical level, energy consumption structure and economic development level have significant spatial heterogeneity, and the effects of energy conservation and emission reduction policies has significant temporal heterogeneity.

Cause of PM2.5 pollution during the 2016-2017 heating season in Beijing, Tianjin, and Langfang, China

To investigate the cause of fine particulate matter (particles with an aerodynamic diameter less than 2.5 µm, PM_2.5) pollution in the heating season in the North China Plain (specifically Beijing, Tianjin, and Langfang), water-soluble ions and carbonaceous components in PM_2.5 were simultaneously measured by online instruments with 1-hr resolution, from November 15, 2016 to March 15, 2017. The results showed extreme severity of PM_2.5 pollution on a regional scale. Secondary inorganic ions (SNA, i.e., NO₃^-+SO₄²+ NH₄⁺) dominated the water-soluble ions, accounting for 30%-40% of PM_2.5, while the total carbon (TC, i.e., OC + EC) contributed to 26.5%-30.1% of PM_2.5 in the three cities. SNA were mainly responsible for the increasing PM_2.5 pollution compared with organic matter (OM). NO₃^- was the most abundant species among water-soluble ions, but SO₄^2- played a much more important role in driving the elevated PM_2.5 concentrations. The relative humidity (RH) and its precursor SO₂ were the key factors affecting the formation of sulfate. Homogeneous reactions dominated the formation of nitrate which was mainly limited by HNO₃ in ammonia-rich conditions. Secondary formation and regional transport from the heavily polluted region promoted the growth of PM_2.5 concentrations in the formation stage of PM_2.5 pollution in Beijing and Langfang. Regional transport or local emissions, along with secondary formation, made great contributions to the PM_2.5 pollution in the evolution stage of PM_2.5 pollution in Beijing and Langfang. The favourable meteorological conditions and regional transport from a relatively clean region both favored the diffusion of pollutants in all three cities.

Research Progress of HP Characteristics, Hazards, Control Technologies, and Measures in China after 2013

In recent years, hazy weather (hazy weather (HW) has frequently invaded peoples’ lives in China, resulting in the disturbance of social operation, so it is urgent to resolve the haze pollution (HP) problem. A comprehensive understanding of HP is essential to further effectively alleviate or even eliminate it. In this study, HP characteristics in China, after 2013, were presented. It was found that the situation of HP is getting better year by year while it has been a pattern of high levels in the north and low levels in the south. In most regions of China, the contribution of a secondary source for HP is relatively large, and that of traffic is greater in the regions with rapid economic development. Hazards of HP were then summarized. Not only does HP cause harm to human health, but it also has effects on human production and quality of life, furthermore, property and atmospheric environment cannot be ignored. Next, the source and non-source control technologies of HP were first reviewed to recognize the weakness of HP control in China. This review provides more systematic information about HP problems and the future development directions of HP research were proposed to further effectively control HP in China.

Size-segregated emission factors and health risks of PAHs from residential coal flaming/smoldering combustion

Residential coal combustion is one of the main sources of ambient polycyclic aromatic hydrocarbons (PAHs). Updating its emission estimation is limited by the shortages of emission factors, especially for them in different particle sizes and from different combustion conditions. PAH emission factors (EFs) for nine size-segregated particle segments emitted from smoldering and flaming combustion of residential coals (four kinds of raw coals (RCs) and three kinds of honeycomb coal briquettes (HCBs)) were obtained in China, using a dilution sampling system. EFs of PAHs for the flaming and smoldering of HCB ranged from 1.32 to 2.04 mg kg^-1 and 0.35 to 5.36 mg kg^-1, respectively. The EFs of PAHs for RC flaming combustion varied from 0.50 to 218.96 mg kg^-1. About 53.5-96.4% and 47.4-90.9% of PAHs concentrated in PM_2.1 and PM_1.1, respectively. Different fuel types and combustion conditions strongly affected the PAH EFs. The PAH EF for the RC was 0.3 times that for HCB in Guizhou, which implied that PAH EFs for RC combustion were not always higher than those from HCB burning. For different combustion conditions, the PAH EFs from flaming were more than 2.5 times higher than those from smoldering for HCB except in the Anhui region. Results indicated that current PAH EFs may not be universal, which may bias the establishment of control policies for toxic pollutants emitted from domestic coal burning. On average, 73.2 ± 15.5% of total PAH potential toxicity risks were concentrated in submicron particles. More size-segregated PAH EFs for residential coal combustion should be investigated considering combustion conditions with a uniform sampling method in China.

Chemical Composition and Source Apportionment of PM2.5 in Urban Areas of Xiangtan, Central South China

Xiangtan, South China, is characterized by year-round high relative humidity and very low wind speeds. To assess levels of PM_2.5, daily samples were collected from 2016 to 2017 at two urban sites. The mass concentrations of PM_2.5 were in the range of 30–217 µg/m³, with the highest concentrations in winter and the lowest in spring. Major water-soluble ions (WSIIs) and total carbon (TC) accounted for 58–59% and 21–24% of the PM_2.5 mass, respectively. Secondary inorganic ions (SO₄²⁻, NO₃⁻, and NH₄⁺) dominated the WSIIs and accounted for 73% and 74% at the two sites. The concentrations of K, Fe, Al, Sb, Ca, Zn, Mg, Pb, Ba, As, and Mn in the PM_2.5 at the two sites were higher than 40 ng/m³, and decreased in the order of winter > autumn > spring. Enrichment factor analysis indicates that Co, Cu, Zn, As, Se, Cd, Sb, Tl, and Pb mainly originates from anthropogenic sources. Source apportionment analysis showed that secondary inorganic aerosols, vehicle exhaust, coal combustion and secondary aerosols, fugitive dust, industrial emissions, steel industry are the major sources of PM_2.5, contributing 25–27%, 21–22%, 19–21%, 16–18%, 6–9%, and 8–9% to PM_2.5 mass.