Fine particulate matter (PM 2.5) in China at a city level

Differential transcriptional changes in human alveolar epithelial A549 cells exposed to airborne PM2.5 collected from Shanghai, China

Fine particulate matters (PM_2.5) are the core pollutants of haze episode, which pose a serious threat to the human health of developing countries. However, the mechanisms involved in PM_2.5-induced hazard influence are not to fully elucidated. In the present study, human lung epithelial cells (A549) were exposed to various concentrations of PM_2.5 samples collected from Shanghai, China. Illumina RNA-Seq method with transcriptome, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were further employed to identify the detrimental effects of PM_2.5 on A549 cells in vitro. A total of 712 differentially expressed genes were obtained from global transcriptome profiling of A549 cells after PM_2.5 exposure. In addition, GO function enrichment analysis revealed that major differentially expressed genes (DEGs) involved in the biological process of the immune system and the response to the stress. KEGG pathway analysis further proposes that infectious disease, cancers, cardiovascular disease, and immune disease pathway were the key human disease events that occur in A549 cells under PM_2.5 stress. The data obtained here shed light on the related biological process and gene signaling pathways affected by PM_2.5 exposure. This study aids our understanding of the complicated mechanisms related to PM_2.5-induced health effects and is informative for the prevention and treatment of PM_2.5-induced systemic diseases.

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.

Ambient PM2.5 Human Health Effects—Findings in China and Research Directions

Exposure to fine particulate matter (PM) results in adverse health outcomes. Although this is a global concern, residents of China may be particularly vulnerable due to frequent severe air pollution episodes associated with economic growth, industrialization, and urbanization. Until 2012, PM2.5 was not regulated and monitored in China and annual average concentrations far exceeded the World Health Organizations guidelines of 10 μg/m3. Since the establishment of PM2.5 Ambient Air Quality Criteria in 2012, concentrations have decreased, but still pose significant health risks. A review of ambient PM2.5 health effect studies is warranted to evaluate the current state of knowledge and to prioritize future research efforts. Our review found that recent literature has confirmed associations between PM2.5 exposure and total mortality, cardiovascular mortality, respiratory mortality, hypertension, lung cancer, influenza and other adverse health outcomes. Future studies should take a long-term approach to verify associations between exposure to PM2.5 and health effects. In order to obtain adequate exposure assessment at finer spatial resolutions, high density sampling, satellite remote sensing, or models should be employed. Personal monitoring should also be conducted to validate the use of outdoor concentrations as proxies for exposure. More research efforts should be devoted to seasonal patterns, sub-population susceptibility, and the mechanism by which exposure causes health effects. Submicron and ultrafine PM should also be monitored and regulated.

Preliminary monitoring of concentration of particulate matter (PM2.5) in seven townships of Yangon City, Myanmar

Background

Airborne particulate pollution is more critical in the developing world than in the developed countries in which industrialization and urbanization are rapidly increased. Yangon, a second capital of Myanmar, is a highly congested and densely populated city. Yet, there is limited study which assesses particulate matter (PM_2.5) in Yangon currently. Few previous local studies were performed to assess particulate air pollution but most results were concerned PM₁₀ alone using fixed monitoring. Therefore, the present study aimed to assess distribution of PM_2.5 in different townships of Yangon, Myanmar. This is the first study to quantify the regional distribution of PM_2.5 in Yangon City.

Methods

The concentration of PM_2.5 was measured using Pocket PM_2.5 Sensor (Yaguchi Electric Co., Ltd., Miyagi, Japan) three times (7:00 h, 13:00 h, 19:00 h) for 15 min per day for 5 days from January 25^th to 29^th in seven townships. Detailed information of eight tracks for PM_2.5 pollution status in different areas with different conditions within Kamayut Township were also collected.

Results

The results showed that in all townships, the highest PM_2.5 concentrations in the morning followed by the evening and the lowest concentrations in the afternoon were observed. Among the seven townships, Hlaingtharyar Township had the highest concentrations (164 ± 52 μg/m³) in the morning and (100 ± 35 μg/m³) in the evening. Data from eight tracks in Kamayut Township also indicated that PM_2.5 concentrations varied between different areas and conditions of the same township at the same time.

Conclusion

Myanmar is one of the few countries that still have to establish national air quality standards. The results obtained from this study are useful for the better understanding of the nature of air pollution linked to PM_2.5. Moreover, the sensor which was used in this study can provide real-time exposure, and this could give more accurate exposure data of the population especially those subpopulations that are highly exposed than fixed station monitoring.

The frequency of ANCA-associated vasculitis in a national database of hospitalized patients in China

BACKGROUND

Anti-neutrophil cytoplasmic autoantibody (ANCA)-associated vasculitis (AAV) is a group of life-threatening autoimmune diseases. The epidemiological data on AAV in China are limited. The aim of the present study is to investigate the frequency, geographical distribution, and ethnic distribution of AAV in hospitalized patients in China, and its association with environmental pollution.

METHODS

We investigated the hospitalized patients in a national inpatient database covering 54.1% tertiary hospitals in China from 2010 to 2015. Diagnosis of AAV was extracted according to the definition of International Classification of Diseases (ICD)-10 codes and free text. Variables from the front page of inpatient records were collected and analyzed, including frequency, geographic distribution, demographic characteristics and seasonal variations of AAV. The association between various environmental pollutants and frequency of AAV was further analyzed.

RESULTS

Among 43.7 million inpatients included in the study period, 0.25‰ (10,943) were diagnosed as having AAV. The frequency of AAV was relatively stable during the study period (from 0.34‰ in 2010 to 0.27‰ in 2015). The proportion of AAV increased with latitude (0.44‰ in Northern China and 0.27‰ in Southern China in 2015). Hospitalizations were mostly observed in winter (30.2%). The Dong population, an ethnic minority of the Chinese population, had the highest frequency of patients with AAV (0.67‰). We also found a positive association between the exposure to carbon monoxide and the frequency of AAV (R² = 0.172, p = 0.025). In Yunnan province, the frequency of AAV increased 1.37-fold after the Zhaotong earthquake, which took place in 2014.

CONCLUSIONS

Our present investigation of hospitalized patients provided epidemiological information on AAV in China for the first time. A spatial and ethnic clustering trend and an association between pollution and the frequency of AAV were observed.

Elucidating the relationship between aerosol concentration and summertime boundary layer structure in central China

Wuhan, a megacity in central China, suffers from frequent aerosol pollution and is accompanied by meteorological factors at both synoptic and local scales. Partly due to the lack of appropriate observations of planetary boundary layer (PBL), the associations between synoptic conditions, PBL, and pollution there are not yet fully understood. Thus, systematic analyses were conducted using the fine-resolution soundings, surface meteorological measurements, and aerosol observations in Wuhan during summer for the period 2013-2016, in combination with T-mode principal component analysis and simulations of backward trajectory. The results showed that the variations of boundary layer height (BLH) not only modulated the diurnal variation of PM_2.5 concentration in Wuhan, but also the daily pollution level. Five different synoptic patterns during summer in Wuhan were identified from reanalysis geopotential height fields. Among these synoptic patterns, two types characterized by northeasterly prevailing winds, were found to be associated with heavy pollution in Wuhan. Driven by the northeasterly winds, the polluted air mass from the heavily polluted regions could be easily transported to Wuhan, such as North China Plain and Yangtze River Delta. Such regional transports of pollutants must be partly responsible for the aerosol pollution in Wuhan. In addition, these two synoptic patterns were also featured by the relatively high cloud cover and low boundary layer height in Wuhan, which would favor the occurrence of pollution there. Overall, this study has important implications for understanding the important roles of meteorological factors in modulating aerosol pollution in central China.

Quantifying the influence of natural and socioeconomic factors and their interactive impact on PM2.5 pollution in China

PM_2.5 pollution is an environmental issue caused by multiple natural and socioeconomic factors, presenting with significant spatial disparities across mainland China. However, the determinant power of natural and socioeconomic factors and their interactive impact on PM_2.5 pollution is still unclear. In the study, the GeogDetector method was used to quantify nonlinear associations between PM_2.5 and potential factors. This study found that natural factors, including ecological environments and climate, were more influential than socioeconomic factors, and climate was the predominant factor (q = 0.56) in influencing PM_2.5 pollution. Among all interactions of the six influencing factors, the interaction of industry and climate had the largest influence (q = 0.66). Two recognized major contaminated areas were the Tarim Basin in the northwest region and the eastern plain region; the former was mainly influenced by the warm temperate arid climate and desert, and the latter was mainly influenced by the warm temperate semi-humid climate and multiple socioeconomic factors. The findings provided an interpretation of the influencing mechanisms of PM_2.5 pollution, which can contribute to more specific policies aimed at successful PM_2.5 pollution control and abatement.

Estimating hourly PM1 concentrations from Himawari-8 aerosol optical depth in China

Particulate matter with diameter less than 1 μm (PM₁) has been found to be closely associated with air quality, climate changes, and even adverse human health. However, a large gap in our knowledge concerning the large-scale distribution and variability of PM₁ remains, which is expected to be bridged with advanced remote-sensing techniques. In this study, a hybrid model called principal component analysis-general regression neural network (PCA-GRNN) is developed to estimate hourly PM₁ concentrations from Himawari-8 aerosol optical depth in combination with coincident ground-based PM₁ measurements in China. Results indicate that the hourly estimated PM₁ concentrations from satellite agree well with the measured values at national scale, with R² of 0.65, root-mean-square error (RMSE) of 22.0 μg/m³ and mean absolute error (MAE) of 13.8 μg/m³. On daily and monthly time scales, R² increases to 0.70 and 0.81, respectively. Spatially, highly polluted regions of PM₁ are largely located in the North China Plain and Northeast China, in accordance with the distribution of industrialisation and urbanisation. In terms of diurnal variability, PM₁ concentration tends to peak in rush hours during the daytime. PM₁ exhibits distinct seasonality with winter having the largest concentration (31.5±3.5 μg/m³), largely due to peak combustion emissions. We further attempt to estimate PM_2.5 and PM₁₀ with the proposed method and find that the accuracies of the proposed model for PM₁ and PM_2.5 estimation are significantly higher than that of PM₁₀. Our findings suggest that geostationary data is one of the promising data to estimate fine particle concentration on large spatial scale.

Non-Target and Suspect Screening of Per- and Polyfluoroalkyl Substances in Airborne Particulate Matter in China

Airborne particulate matter (APM) has an important role in inhalation exposure, especially in China. The environmental occurrence of conventional and unknown per- and polyfluoroalkyl substances (PFASs) in APM remains unclear. Therefore, in this study, a two-stage experiment was designed to identify potential PFASs and to investigate their distribution in APM. Indoor and outdoor APM samples were collected from five selected cities in China. Through PFAS homologue analysis and suspect screening, 50 peaks were identified with different confidence levels (levels 1-3). Among the identified PFASs, 34 emerging PFASs including p-perfluorous nonenoxybenzenesulfonate, 6:2 polyfluoroalkyl phosphate diester, n:2 fluorotelomer sulfonates, n:2 fluorinated telomer acids, n:2 chlorinated polyfluoroalkyl ether sulfonic acids, 1:n polyfluoroalkyl ether carboxylic acids (1:n PFECAs), perfluoroalkyl dioic acids (PFdiOAs), hydro-substituted perfluoroalkyl dioic acids (H-PFdiOAs), and unsaturated perfluorinated alcohols (UPFAs) were identified in APM. In particular, 1:n PFECAs, PFdiOAs, H-PFdiOAs, and UPFAs were first detected in APM. Although human exposure to perfluorooctanoic acid via inhaled APM was noted to not be a risk (hazard quotient <0.1) in this study, the expansion of the PFASs screened in APM implies that human exposure to PFASs might be much more serious and should be considered in future risk assessments in China.

Impacts of meteorological conditions on wintertime PM2.5 pollution in Taiyuan, North China

Taiyuan frequently experiences heavy PM_2.5 pollution in winter under unfavorable meteorological conditions. To understand how the meteorological factors influence the pollution in Taiyuan, this study involved a systematic analysis for a continuous period from November 2016 to January 2017, using near-surface meteorological observations, radiosonde soundings, PM_2.5 measurements, and three-dimension numerical simulation, in combination with backward trajectory calculations. The results show that PM_2.5 concentration positively correlates with surface temperature and relative humidity and anti-correlates with near-surface wind speed and boundary layer height (BLH). The low BLH is often associated with a strong thermal inversion layer capping over. In addition to the high local emissions, it is found that under certain synoptic conditions, the southwesterly and southerly winds could bring pollutants from Linfen to Taiyuan, leading to a near-surface PM_2.5 concentration higher than 200 μg m^-3. Another pollution enhancing issue is due to the semi-closed basin of Taiyuan affecting the planetary boundary layer (PBL): the surrounding mountains favor the formation of a cold air pool in the basin, which inhibits vertical exchanges of heat, flux, and momentum between PBL and the free troposphere, resulting in stagnant conditions and poor air quality in Taiyuan. These findings can be utilized to improve the understanding of PM_2.5 pollution in Taiyuan, to enhance the accuracy of forecasting pollution, and to provide scientific support for policy makers to mitigate the pollution.

Ambient air pollution of particles and gas pollutants, and the predicted health risks from long-term exposure to PM2.5 in Zhejiang province, China

In recent years, ambient air has been severely contaminated by particulate matters (PMs) and some gas pollutants (nitrogen dioxide (NO₂) and sulfur dioxide (SO₂)) in China, and many studies have demonstrated that exposure to these pollutants can induce great adverse impacts on human health. The concentrations of the pollutants were much higher in winter than those in summer, and the average concentrations in this studied area were lower than those in northern China. In the comparison between high-resolution emission inventory and spatial distribution of PM_2.5, significant positive linear correlation was found. Though the pollutants had similar trends, NO₂ and SO₂ delayed with 1 h to PM_2.5. Besides, PM_2.5 had a lag time of 1 h to temperature and relative humidity. Significant linear correlation was found among pollutants and meteorological conditions, suggesting the impact of meteorological conditions on ambient air pollution other than emission. For the 24-h trend, lowest concentrations of PM_2.5, NO₂, and SO₂ were found around 15:00-18:00. In 2015, the population attributable fractions (PAFs) for ischemic heart disease (IHD), cerebrovascular disease (stroke), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and acute lower respiratory infection (ALRI) due to the exposure to PM_2.5 in Zhejiang province were 25.82, 38.94, 17.73, 22.32, and 31.14%, respectively. The population-weighted mortality due to PM_2.5 exposure in Zhejiang province was lower than the average level of the whole country-China.

Characterizing the Influences of Economic Development, Energy Consumption, Urbanization, Industrialization, and Vehicles Amount on PM2.5 Concentrations of China

The speeding-up of economic development and industrialization processes in China have brought about serious atmospheric pollution issues, especially in terms of particulate matter harmful to health. However, impact mechanisms of socio-economic forces on PM2.5 (the particle matter with diameter less than 2.5 μm) have rarely been further investigated. This paper selected GDP (gross domestic product) per capita, energy consumption, urbanization process, industrialization structure, and the amount of possession of civil vehicles as the significant factors, and researched the relationship between these factors and PM2.5 concentrations from 1998 to 2016, employing auto-regressive distributed lag (ARDL) methodology and environmental Kuznets curve (EKC) theory. Empirical results illustrated that a long-term equilibrium nexus exists among these variables. Granger causality results indicate that bi-directional causality exist between PM2.5 concentrations and GDP per capita, the squared component of GDP per capita, energy consumption and urbanization process. An inverse U-shape nexus exists between PM2.5 concentrations and GDP per capita. When the real GDP per capita reaches 5942.44 dollars, PM2.5 concentrations achieve the peak. Results indicate that Chinese governments should explore a novel pathway to resolve the close relationship between socio-economic factors and PM2.5, such as accelerating the adjustment of economic development mode, converting the critical industrial development driving forces, and adjusting the economic structure.

Changes in Haze Trends in the Sichuan-Chongqing Region, China, 1980 to 2016

This study analyzed the long-term variations and trends of haze pollution and its relationships with emission and meteorological factors using the haze days (HDs) data derived from surface observation stations in Sichuan-Chongqing (SCC) region during 1980–2016. The results showed that the multi-year mean number of HDs were 68.7 and 4.9 days for the Sichuan-Basin (SCB) and the rest of SCC region, respectively. The seasonally averaged HDs over SCB reached its maximum in winter (34.7 days), followed by autumn (17.0 days) and spring (11.6 days), and with the minimum observed in summer (5.5 days). The inter-annual variations of HDs in 18 main cities revealed that Zigong, Neijiang, and Yibin, which are located in the southern of SCB, have been the most polluted areas over the SCC region in the past decades. A notable increasing trend in annual HDs over the majority of SCC region was found during 1980–1995, then the trend sharply reversed during 1996–2005, while it increased, fluctuating at some cities after 2006. Seasonally, the increased trend in spring and autumn seems to be the strongest during 1980–1995, whereas the decreased trend in spring and winter was stronger than other seasons during 1996–2005. In addition, a remarkable increasing trend was found in winter since 2006. Using correlation analysis between HDs and emission and meteorological factors during different periods, we found that the variability of local precipitation days (PDs), planetary boundary layer height (PBLH), near-surface wind speed (WS), and relatively humidity (RH) play different roles in influencing the haze pollution change during different historical periods. The joint effect of sharp increase of anthropogenic emissions, reduced PDs and WS intensified the haze pollution in SCB during 1980–1995. In contrast, decreased HDs during 1996–2005 are mainly attributable to the reduction of PM2.5 emission and the increase of PDs (especially in winter). In addition, the decrease of PDs is likely to be responsible for the unexpected increase in winter HDs over SCB in the last decade.

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.

Quantitative association analysis between PM2.5 concentration and factors on industry, energy, agriculture, and transportation

Rapid urbanization is causing serious PM_2.5 (particulate matter ≤2.5 μm) pollution in China. However, the impacts of human activities (including industrial production, energy production, agriculture, and transportation) on PM_2.5 concentrations have not been thoroughly studied. In this study, we obtained a regression formula for PM_2.5 concentration based on more than 1 million PM_2.5 recorded values and data from meteorology, industrial production, energy production, agriculture, and transportation for 31 provinces of mainland China between January 2013 and May 2017. We used stepwise regression to process 49 factors that influence PM_2.5 concentration, and obtained the 10 primary influencing factors. Data of PM_2.5 concentration and 10 factors from June to December, 2017 was used to verify the robustness of the model. Excluding meteorological factors, production of natural gas, industrial boilers, and ore production have the highest association with PM_2.5 concentration, while nuclear power generation is the most positive factor in decreasing PM_2.5 concentration. Tianjin, Beijing, and Hebei provinces are the most vulnerable to high PM_2.5 concentrations caused by industrial production, energy production, agriculture, and transportation (IEAT).

Application of Positive Matrix Factorization in the Identification of the Sources of PM2.5 in Taipei City

Fine particulate matter (PM2.5) has a small particle size, which allows it to directly enter the respiratory mucosa and reach the alveoli and even the blood. Many countries are already aware of the adverse effects of PM2.5, and determination of the sources of PM2.5 is a critical step in reducing its concentration to protect public health. This study monitored PM2.5 in the summer (during the southwest monsoon season) of 2017. Three online monitoring systems were used to continuously collect hourly concentrations of key chemical components of PM2.5, including anions, cations, carbon, heavy metals, and precursor gases, for 24 h per day. The sum of the concentrations of each compound obtained from the online monitoring systems is similar to the actual PM2.5 concentration (98.75%). This result suggests that the on-line monitoring system of this study covers relatively complete chemical compounds. Positive matrix factorization (PMF) was adopted to explore and examine the proportion of each source that contributed to the total PM2.5 concentration. According to the source contribution analysis, 55% of PM2.5 can be attributed to local pollutant sources, and the remaining 45% can be attributed to pollutants emitted outside Taipei City. During the high-PM2.5-concentration (episode) period, the pollutant conversion rates were higher than usual due to the occurrence of vigorous photochemical reactions. Moreover, once pollutants are emitted by external stationary pollutant sources, they move with pollution air masses and undergo photochemical reactions, resulting in increases in the secondary pollutant concentrations of PM2.5. The vertical monitoring data indicate that there is a significant increase in PM2.5 concentration at high altitudes. High-altitude PM2.5 will descend to the ground and thereby affect the ground-level PM2.5 concentration.

Particulate Matter and Gaseous Pollutions in Three Metropolises along the Chinese Yangtze River: Situation and Implications

The situation of criteria atmospheric pollutants, including particulate matter and trace gases (SO₂, NO₂, CO and O₃), over three metropolises (Chongqing, Wuhan, and Nanjing), representing the upstream, midstream and downstream portions of the Yangtze River Basin from September 2015 to August 2016 were analyzed. The maximum annual mean PM2.5 and PM10 concentrations were 61.3 and 102.7 μg/m³ in Wuhan, while highest annual average gaseous pollutions occurred in Nanjing, with 49.6 and 22.9 ppb for 8 h O₃ and NO₂, respectively. Compared to a few years ago, SO₂ and CO mass concentrations have dropped to well below the qualification standards, and the O₃ and NO₂ concentrations basically meet the requirements though occasionally is still high. In contrary, about 13%, 25%, 22% for PM2.5, and 4%, 17%, 15% for PM10 exceed the Chinese Ambient Air Quality Standard (CAAQS) Grade II. Particulate matter, especially PM2.5, is the most frequent major pollutant to poor air quality with 73%, 64% and 88% accounting for substandard days. Mean PM2.5 concentrations on PM2.5 episode days are 2⁻3 times greater than non-episode days. On the basis of calculation of PM2.5/PM10 and PM2.5/CO ratios, the enhanced particulate matter pollution on episode days is closely related to secondary aerosol production. Except for O₃, the remaining five pollutants exhibit analogous seasonal patterns, with the highest magnitude in winter and lowest in summer. The results of back trajectories show that air pollution displays synergistic effects on local emissions and long range transport. O₃ commonly demonstrated negative correlations with other pollutants, especially during winter, while moderate to strong positive correlation between particulate matter and NO₂, SO₂, CO were seen. Compared to pollutant substandard ratios over three megacities in eastern China (Beijing, Shanghai, and Guangzhou), the situation in our studied second-tier cities are also severe. The results in this paper provide basic knowledge for pollution status of three cities along Chinese Yangtze River and are conductive to mitigating future negative air quality levels.

PM2.5 Exposure Elicits Oxidative Stress Responses and Mitochondrial Apoptosis Pathway Activation in HaCaT Keratinocytes

Background:

PM_2.5 (aerodynamic diameter ≤ 2.5 μm) is a dominant and ubiquitous air pollutant that has become a global concern as PM_2.5 exposure has been linked to many adverse health effects including cardiovascular and pulmonary diseases. Emerging evidence supports a correlation between increased air PM_2.5 levels and skin disorders although reports on the underlying pathophysiological mechanisms are limited. Oxidative stress is the most common mechanism of PM_2.5-induced adverse health effects. This study aimed to investigate PM_2.5-induced oxidative damage and apoptosis in immortalized human keratinocyte (HaCaT) cells.

Methods:

HaCaT cells were exposed to 0, 25, 50, 100, or 200 μg/ml PM_2.5 for 24 h. Reactive oxygen species (ROS) generation, lipid peroxidation products, antioxidant activity, DNA damage, apoptotic protein expression, and cell apoptosis were measured.

Results:

PM_2.5 exposure (0-200 μg/ml) for 24 h resulted in increased ROS levels (arbitrary unit: 201.00 ± 19.28, 264.50 ± 17.91, 305.05 ± 19.57, 427.95 ± 18.32, and 436.70 ± 17.77) and malondialdehyde production (0.54 ± 0.05 nmol/mg prot, 0.61 ± 0.06 nmol/mg prot, 0.68 ± 0.05 nmol/mg prot, 0.70 ± 0.05 nmol/mg prot, and 0.76 ± 0.05 nmol/mg prot), diminished superoxide dismutase activity (6.47 ± 0.28 NU/mg prot, 5.97 ± 0.30 NU/mg prot, 5.15 ± 0.42 NU/mg prot, 4.08 ± 0.20 NU/mg prot, and 3.76 ± 0.37 NU/mg prot), and increased DNA damage and apoptosis in a dose-dependent manner in HaCaT cells. Moreover, cytochrome-c, caspase-3, and caspase-9 expression also increased proportionately with PM_2.5 dosing.

Conclusion:

PM_2.5 might elicit oxidative stress and mitochondria-dependent apoptosis that likely manifests as skin irritation and damage.

Exposure to concentrated ambient PM2.5 alters the composition of gut microbiota in a murine model

Background

Exposure to ambient fine particulate matter (PM_2.5) correlates with abnormal glucose homeostasis, but the underlying biological mechanism has not been fully understood. The gut microbiota is an emerging crucial player in the homeostatic regulation of glucose metabolism. Few studies have investigated its role in the PM_2.5 exposure-induced abnormalities in glucose homeostasis.

Methods

C57Bl/6J mice were exposed to filtered air (FA) or concentrated ambient PM_2.5 (CAP) for 12 months using a versatile aerosol concentration enrichment system (VACES) that was modified for long-term whole-body exposures. Their glucose homeostasis and gut microbiota were examined and analysed by correlation and mediation analysis.

Results

Intraperitoneal glucose tolerance test (IPGTT) and insulin tolerance test (ITT) showed that CAP exposure markedly impaired their glucose and insulin tolerance. Faecal microbiota analysis demonstrated that the impairment in glucose homeostasis was coincided with decreased faecal bacterial ACE and Chao-1 estimators (the indexes of community richness), while there was no significant change in all faecal fungal alpha diversity estimators. The Pearson’s correlation analyses showed that the bacterial richness estimators were correlated with glucose and insulin tolerance, and the mediation analyses displayed a significant mediation of CAP exposure-induced glucose intolerance by the alteration in the bacterial Chao-1 estimator. LEfSe analyses revealed 24 bacterial and 21 fungal taxa differential between CAP- and FA-exposed animals. Of these, 14 and 20 bacterial taxa were correlated with IPGTT AUC and ITT AUC, respectively, and 5 fungal taxa were correlated with abnormalities in glucose metabolism.

Conclusions

Chronic exposure to PM_2.5 causes gut dysbiosis and may subsequently contribute to the development of abnormalities in glucose metabolism.

Temporal trends in respiratory mortality and short-term effects of air pollutants in Shenyang, China

Short-term exposures to air pollution are associated with acute effects on respiratory health. This study aimed to describe 10-year temporal trends in respiratory mortality in the urban areas of Shenyang, China, according to gender and age and estimate the effects of air pollution on respiratory diseases (ICD-10J00-J99) and lung cancer (ICD-10 C33-C34) using a case-crossover design. During the study period 2013-2015, the exposure-response relationship between ambient air pollutants and mortality data was fitted by a quasi-Poisson model. Age-standardized mortality rates for a combined number of respiratory diseases and for lung cancer declined in Shenyang; however, death counts increased with aging. Deaths from respiratory diseases increased by 4.7% (95% CI, 0.00-9.9), and lung cancer mortality increased by 6.5% (95% CI, 1.2-12.0), both associated with a 10 μg/m³ increase in exposure to particulate matter < 2.5 μg in diameter (PM2.5). Moreover, males in Shenyang's urban areas were more susceptible to the acute effects of PM2.5 and SO₂ exposure; people aged ≥ 65 years had a high susceptibility to ozone, and those aged < 65 years were more susceptible to other air pollutants. These results provided an updated estimate of the short-term effects of air pollution in Shenyang. Since population aging is also associated with increasing mortality from respiratory diseases and lung cancer, reinforcing air quality control measures and health-promoting behaviors is urgent and necessary in Shenyang.

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.

Characterization of Fine Particulate Matter and Associated Health Burden in Nanjing

Particulate matter (PM) air pollution has become a serious environmental problem in Nanjing and poses great health risks to local residents. In this study, characteristics of particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) over Nanjing were analyzed using hourly and daily averaged PM_2.5 concentrations and meteorological parameters collected from nine national monitoring sites during the period of March 2014 to February 2017. Then, the integrated exposure-response (IER) model was applied to assess premature mortality, years of life lost (YLL) attributable to PM_2.5, and mortality benefits due to PM_2.5 reductions. The concentrations of PM_2.5 varied among hours, seasons and years, which can be explained by differences in emission sources, secondary formations and meteorological conditions. The decreased ratio of PM_2.5 to CO suggested that secondary contributions decreased while the relative contributions of vehicle exhaust increased from increased CO data. According to the values of attributable fractions (AF), stroke was the major cause of death, followed by ischemic heart disease (IHD), lung cancer (LC) and chronic obstructive pulmonary disease (COPD). The estimated total deaths in Nanjing due to PM_2.5 were 12,055 and 10,771, leading to 98,802 and 87,647 years of life lost in 2014 and 2015, respectively. The elderly and males had higher health risks than youngsters and females. When the PM_2.5 concentrations meet the World Health Organization (WHO) Air Quality Guidelines (AQG) of 10 μg/m³, 84% of the premature deaths would be avoided, indicating that the Nanjing government needs to adopt more stringent measure to reduce PM pollution and enhance the health benefits.

A multidimensional comparison between MODIS and VIIRS AOD in estimating ground-level PM2.5 concentrations over a heavily polluted region in China

Satellite-derived aerosol optical depth (AOD) has been proven effective for estimating ground-level particles with an aerodynamic diameter <2.5μm (PM_2.5) concentrations. Using a time fixed effects regression model, we compared the capacity of two AOD sources, Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), to estimate ground-level PM_2.5 concentrations over a heavily polluted region in China. Regarding high-quality AOD data, the results show that the VIIRS model performs better than the MODIS model with respect to all model accuracy evaluation indexes (e.g., the coefficient of determination, R², of the VIIRS and MODIS models are 0.76 and 0.71 during model fitting and 0.72 and 0.66 in cross validation, respectively), the potential for capturing high PM_2.5 concentrations, and the precision of annual and seasonal PM_2.5 estimates. However, the spatiotemporal coverage of the high-quality VIIRS AOD is inferior to that of the MODIS AOD. We attempted to include medium-quality VIIRS AOD data to eliminate this, while exploring its influence on the performance of the VIIRS model. The results show that it improves the spatiotemporal coverage of the VIIRS AOD dramatically especially in winter, although a decline in model accuracy occurred. Compared to the MODIS model, the VIIRS model with both high-quality and medium-quality AOD data performs comparably or even better with respect to some model accuracy evaluation indexes (e.g., the model overfitting degree of the VIIRS and MODIS models are 7.46% and 5.82%, respectively), the potential for capturing high PM_2.5 concentrations, and the precision of annual and seasonal PM_2.5 estimates. Nevertheless, the VIIRS models did not perform as well as the MODIS model in summer. This study reveals the advantages and disadvantages of the MODIS and VIIRS AOD in simulating ground-level PM_2.5 concentrations, promoting research on satellite-based PM_2.5 estimates.

A Review of Recent Advances in Research on PM2.5 in China

PM_2.5 pollution has become a severe problem in China due to rapid industrialization and high energy consumption. It can cause increases in the incidence of various respiratory diseases and resident mortality rates, as well as increase in the energy consumption in heating, ventilation, and air conditioning (HVAC) systems due to the need for air purification. This paper reviews and studies the sources of indoor and outdoor PM_2.5, the impact of PM_2.5 pollution on atmospheric visibility, occupational health, and occupants’ behaviors. This paper also presents current pollution status in China, the relationship between indoor and outdoor PM_2.5, and control of indoor PM_2.5, and finally presents analysis and suggestions for future research.

Associations between daily cooking duration and the prevalence of diabetes and prediabetes in a middle-aged and elderly Chinese population: A cross-sectional study

Experimental and epidemiological studies indicated that ambient air pollution was positively associated with diabetes. Few studies investigated the associations between household air pollution, for example, daily cooking duration and diabetes or prediabetes. We conducted a cross-sectional study to investigate the associations of daily cooking duration with the prevalence of diabetes and prediabetes among a middle-aged and elderly population. A total of 26 089 individuals (11 250 males and 14 839 females) derived from the Dongfeng-Tongji cohort study were included. Daily cooking duration was assessed by questionnaire. Diabetes and prediabetes were identified according to the criterion of American Diabetes Association. No significant association was observed between daily cooking duration and the prevalence risk of diabetes (odds ratio[OR] = 0.97, 95% confidence interval[CI]: [0.81-1.16], P for trend = .74); however, longer daily cooking duration was associated with higher prevalence risk of prediabetes (OR = 1.26, 95% CI: 1.07-1.47; P for trend = .003) and hyperglycemia (OR = 1.21, 95% CI: 1.05-1.41; P for trend = .005). Our study suggested that daily cooking duration was not associated with diabetes but with higher prevalence risk of prediabetes/hyperglycemia in a middle-aged and elderly Chinese population.

Long-term high air pollution exposure induced metabolic adaptations in traffic policemen

OBJECTIVE

To assess the adverse physiological changes induced by long-term exposure to PM2.5.

METHODS

Totally 183 traffic policemen and 88 office policemen as the control group, were enrolled in this study. The concentrations of PM2.5 in both the working places of traffic and office policemen were obtained. Detailed personal questionnaires and conventional laboratory tests including hematology, fasting blood glucose, blood lipids, liver, kidney, immunity and tumor-related markers were conducted on all participants of this study.

RESULTS

A dose-response relationship between the FBG, HDL-c and CEA values and the PM2.5 exposure duration was observed. Multivariate analysis confirmed that one hour on duty outdoor per day for one year was associated with an increase in FBG of 0.005% (95% CI: 0.0004% to 0.009%), CEA of 0.012% (95% CI: 0.006% to 0.017%), and a decrease in HDL-C of 0.001% (95% CI: 0.00034% to 0.002%).

CONCLUSION

Long-term high air pollution exposure may lead to metabolism adaptation and it is likely involved in the development of cardiovascular disease and diabetes mellitus.

Seasonal variation of window opening behaviors in two naturally ventilated hospital wards

Natural ventilation enables personal control, and occupant behaviors in window opening play a decisive role on natural ventilation performance, indoor air quality (IAQ), and/or airborne infection risk in a hospital setting. The occupant behaviors differ significantly from different building types with different functions and living habits. Based on a one-year field measurement in two general hospital wards in Nanjing, China, the effects of air quality (i.e. indoor CO2 concentration and outdoor PM2.5 concentration) and the climatic parameters (i.e. indoor/outdoor temperature, relative humidity, and outdoor wind speed, wind direction and rainfall) on window opening/closing behaviors are analyzed. Indoor air temperature or relative humidity is found to be a dominant factor for window opening behaviors. Seasonal differences are observed for the different influences of physical factors. The outdoor temperature is found to be associated with the window opening probability negatively during the cooling season, but positively during the transition and heating seasons. The indoor relative humidity positively affects the window opening probability during the transition season while a negative impact appears during the cooling and heating seasons. Based on the seasonal variation of window opening behaviors, Logistic regression models in different seasons (cooling, transition and heating seasons) are developed to predict the window opening/closing state and are verified to be promisingly adaptable with results of accuracy bigger than 70%.

Long-term trends and spatial patterns of satellite-retrieved PM2.5 concentrations in South and Southeast Asia from 1999 to 2014

Fine particulate matter, or PM_2.5, is a serious air pollutant and has significant effects on human health, including premature death. Based on a long-term series of satellite-retrieved PM_2.5 concentrations, this study analyzed the spatial and temporal characteristics of PM_2.5 in South and Southeast Asia (SSEA) from 1999 to 2014 using standard deviation ellipse and trend analyses. A health risk assessment of human exposure to PM_2.5 between 1999 and 2014 was then undertaken. The results show that PM_2.5 concentrations increased in most areas of SSEA from 1999 to 2014 and exceeded the World Health Organization average annual limit of primary PM_2.5 standards. Bangladesh, Pakistan and India experienced average PM_2.5 values higher than the total average for SSEA. From 1999 to 2014, the entirety of SSEA exhibited an increased rate of 0.02μg/m³/year on average. Bangladesh and Myanmar witnessed greater incremental rates of PM_2.5 than India. Correspondingly, the center of the average regional PM_2.5 concentration gradually shifted to the southeast during the study period. The proportion of areas with PM_2.5 concentrations exceeding 35μg/m³ increased consistently, and the areas with PM_2.5 concentrations below 15μg/m³ decreased continuously. The proportion of the population exposed to high PM_2.5 (above 35μg/m³) increased annually. The extent of high-health-risk areas in SSEA expanded in size and extent between 1999 and 2014, particularly in North India, Bangladesh and East Pakistan. Therefore, all of SSEA should receive special attention, and strict controls on PM_2.5 concentrations in SSEA countries are urgently required.

Assessment of ambient dust pollution status at selected point sources (residential and commercial) of Mingaladon area, Yangon region, Myanmar

Purpose

Dust (particulate matters) is very dangerous to our health as it is not visible with our naked eyes. Emissions of dust concentrations in the natural environment can occur mainly by road traffic, constructions and dust generating working environments. The purpose of this paper is to assess the ambient dust pollution status and to find out the association between PM concentrations and other determinant factors such as wind speed, ambient temperature, relative humidity and traffic congestion.

Design/methodology/approach

A cross-sectional study was conducted for two consecutive months (June and July, 2016) at a residential site (Defence Services Liver Hospital, Mingaladon) and a commercial site (Htouk-kyant Junction, Mingaladon) based on WHO Air Quality Reference Guideline Value (24-hour average). Hourly monitoring of PM2.5 and PM10 concentration and determinant factors such as traffic congestion, wind speed, ambient temperature and relative humidity for 24 hours a day was performed in both study sites. CW-HAT200 handheld particulate matters monitoring device was used to assess PM concentrations, temperature and humidity while traffic congestion was monitored by CCTV cameras.

Findings

The baseline PM2.5 and PM10 concentrations of Mingaladon area were (28.50±11.49)µg/m3 and (52.69±23.53)µg/m3, means 61.48 percent of PM2.5 concentration and 54.92 percent of PM10 concentration exceeded than the WHO reference value during the study period. PM concentration usually reached a peak during early morning (within 3:00 a.m.-5:00 a.m.) and at night (after 9:00 p.m.). PM2.5 concentration mainly depends on traffic congestion and temperature (adjusted R2=0.286), while PM10 concentration depends on traffic congestion and relative humidity (adjusted R2=0.292). Wind speed played a negative role in both PM2.5 and PM10 concentration with r=−0.228 and r=−0.266.

Originality/value

The air quality of the study area did not reach the satisfiable condition. The main cause of increased dust pollution in the whole study area was high traffic congestion (R2=0.63 and 0.60 for PM2.5 and PM10 concentration).

Estimating premature mortality attributable to PM2.5 exposure and benefit of air pollution control policies in China for 2020

In past decade of rapid industrial development and urbanization, China has witnessed increasingly persistent severe haze and smog episodes, posing serious health hazards to the Chinese population, especially in densely populated cities. Quantification of health impacts attributable to PM_2.5 (particulates with aerodynamic diameter≤2.5μm) has important policy implications to tackle air pollution. The Chinese national monitoring network has recently included direct measurements of ground level PM_2.5, providing a potentially more reliable source for exposure assessment. This study reports PM_2.5-related long-term mortality of year 2015 in 161 cities of nine regions across China using integrated exposure risk (IER) model for PM_2.5 exposure-response functions (ERF). It further provides an estimate of the potential health benefits by year 2020 with a realization of the goals of Air Pollution Prevention and Control Action Plan (APPCAP) and the three interim targets (ITs) and Air Quality Guidelines (AQG) for PM_2.5 by the World Health Organization (WHO). PM_2.5-related premature mortality in 161 cities was 652 thousand, about 6.92% of total deaths in China during year 2015. Among all premature deaths, contributions of cerebrovascular disease (stroke), ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), lung cancer (LC) and acute lower respiratory infections (ALRIs) were 51.70, 26.26, 11.77, 9.45 and 0.82%, respectively. The premature mortality in densely populated cities is very high, such as Tianjin (12,533/year), Beijing (18,817/year), Baoding (10,932/year), Shanghai (18,679/year), Chongqing (23,561/year), Chengdu (11,809/year), Harbin (9037/year) and Linyi (9141/year). The potential health benefits will be 4.4, 16.2, 34.5, 63.6 and 81.5% of the total present premature mortality when PM_2.5 concentrations in China meet the APPCAP, WHO IT-1, IT-2, IT-3 and AQG respectively, by the year 2020. In the current situation, by the end of year 2030, even if Chines government fulfills its own target to meet national ambient air quality standard of PM_2.5 (35μg/m³), total premature mortality attributable to PM_2.5 will be 574 thousand across 161 cities. The present methodology will greatly help policy makers and pollution control authorities to further analyze cost and benefits of air pollution management programs in China.

Spatiotemporal Patterns of Ground Monitored PM2.5 Concentrations in China in Recent Years

This paper firstly explores the space-time evolution of city-level PM 2.5 concentrations showed a very significant seasonal cycle type fluctuation during the period between 13 May 2014 and 30 May 2017. The period from October to April following each year was a heavy pollution period, whereas the phase from April to October of the current year was part of a light pollution period. The average monthly PM 2.5 concentrations in mainland China based on ground monitoring, employing a descriptive statistics method and a Bayesian spatiotemporal hierarchy model. Daily and weekly average PM 2.5 concentrations in 338 cities in mainland China presented no significant spatial difference during the severe pollution period but a large spatial difference during light pollution periods. The severe PM 2.5 pollution areas were mainly distributed in the Beijing-Tianjin-Hebei urban agglomeration in the North China Plain during the beginning of each autumn-winter season (September), spreading to the Northeast Plains after October, then later continuing to spread to other cities in mainland China, eventually covering most cities. PM 2.5 pollution in China appeared to be a cyclic characteristic of first spreading and then centralizing in the space in two spring-summer seasons, and showed an obvious process of first diffusing then transferring to shrinkage alternation during the spring-summer season of 2015, but showed no obvious diffusion during the spring-summer season of 2016, maintaining a stable spatial structure after the shrinkage in June, as well as being more concentrated. The heavily polluted areas are continuously and steadily concentrated in East China, Central China and Xinjiang Province.

Spatiotemporal Variations and Driving Factors of Air Pollution in China

In recent years, severe and persistent air pollution episodes in China have drawn wide public concern. Based on ground monitoring air quality data collected in 2015 in Chinese cities above the prefectural level, this study identifies the spatiotemporal variations of air pollution and its associated driving factors in China using descriptive statistics and geographical detector methods. The results show that the average air pollution ratio and continuous air pollution ratio across Chinese cities in 2015 were 23.1 ± 16.9% and 16.2 ± 14.8%. The highest levels of air pollution ratio and continuous air pollution ratio were observed in northern China, especially in the Bohai Rim region and Xinjiang province, and the lowest levels were found in southern China. The average and maximum levels of continuous air pollution show distinct spatial variations when compared with those of the continuous air pollution ratio. Monthly changes in both air pollution ratio and continuous air pollution ratio have a U-shaped variation, indicating that the highest levels of air pollution occurred in winter and the lowest levels happened in summer. The results of the geographical detector model further reveal that the effect intensity of natural factors on the spatial disparity of the air pollution ratio is greater than that of human-related factors. Specifically, among natural factors, the annual average temperature, land relief, and relative humidity have the greatest and most significant negative effects on the air pollution ratio, whereas human factors such as population density, the number of vehicles, and Gross Domestic Product (GDP) witness the strongest and most significant positive effects on air pollution ratio.

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

Exposure to PM2.5 results in negative effects on human health. However, PM2.5 exposure at the national scale is poorly known for China owing to limited spatial and temporal PM2.5 concentration data. In this study, we present analyses of PM2.5 exposure throughout China using high-resolution temporal and spatial ground-level PM2.5 data from 2015. Our results indicated that the annual mean PM2.5 concentration was 52.81μg/m³, and that the highest annual mean PM2.5 concentrations primarily appeared in the North China Plain. We also found the lowest and highest monthly mean PM2.5 concentrations appeared in August and January, respectively, while the lowest and highest diurnal mean PM2.5 concentrations occurred at 16:00 and 10:00, respectively. Moreover, comparisons to data from 2013 indicated that the annual mean PM2.5 concentrations decreased by 12.31% from 2013 to 2015, which was likely due to the implementation of environmental protection laws in early 2015. Our findings provide new insights, for not only studies of PM2.5 exposure and human health, but also to inform the implementation of national and regional air pollution reduction policies.

Visibility-Based PM2.5 Concentrations in China: 1957-1964 and 1973-2014

China established ground PM_2.5 monitoring network in late 2012 and hence the long-term and large-scale PM_2.5 data were lacking before 2013. In this work, we developed a national-scale spatiotemporal linear mixed effects model to estimate the long-term PM_2.5 concentrations in China from 1957 to 1964 and from 1973 to 2014 using ground visibility monitoring data as the primary predictor. The overall model-fitting and cross-validation R² is 0.72 and 0.71, suggesting that the model is not overfitted. Validation beyond the model year (2014) indicated that the model could accurately estimate historical PM_2.5 concentrations at the monthly (R² = 0.71) level. The historical PM_2.5 estimates suggest that air pollution is not a new environmental issue that occurs in the recent decades but a problem existing in a longer time before 1980. The PM_2.5 concentrations have reached 60-80 μg/m³ in the north part of North China Plain during 1950s-1960s and increased to generally higher than 90 μg/m³ during 1970s. The results also show that the entire China experienced an overall increasing trend (0.19 μg/m³/yr, P < 0.001) in PM_2.5 concentrations from 1957 to 2014 with fluctuations among different periods. This paper demonstrated visibility data allow us to understand the spatiotemporal characteristics of PM_2.5 pollution in China in a long-term.

Exposure to fine particulate matter during pregnancy and risk of term low birth weight in Jinan, China, 2014-2016

BACKGROUND

Existing studies exploring the association between low birth weight (LBW) and maternal fine particulate matter (aerodynamic diameter<2.5μm, PM_2.5) exposure have presented equivocal results, and one of the possible reasons for this finding might be due to relatively low maternal exposures. In addition, relatively narrow maternal exposure windows to PM_2.5 have not been well established for LBW.

METHODS

We employed a nested matched case-control design among 43,855 term births in a large maternity and child care hospital in Jinan, China. A total of 369 cases were identified, and four controls per case matched by maternal age were randomly selected among those with normal birth weight (n=1,476) from 2014 to 2016. Ambient air monitoring data on continuous measures of PM_2.5, nitrogen dioxide (NO₂), and sulfur dioxide (SO₂) (24-h average concentrations) from 2013 to 2016 were collected from thirteen local monitoring stations. An inverse distance weighting method based on both home and work addresses was adopted to estimate the individual daily exposures to these air pollutants during pregnancy by weighting the average of the twelve nearest monitoring stations within 30km of each 100m×100m grid cell by an inverse squared distance, and then the average exposure concentrations for gestational months, trimesters and the entire pregnancy were calculated. Adjusted conditional logistic regression models were used to estimate the odds ratios (ORs) per 10μg/m³ increment in PM_2.5 and by PM_2.5 quartiles during different gestational periods.

RESULTS

In this study, the estimated mean values of PM_2.5, NO₂, and SO₂ exposure during the entire pregnancy were 88.0, 54.6, and 63.1μg/m³, respectively. Term low birth weight (TLBW) increased in association with per 10μg/m³ increment in PM_2.5 for the 8th month [OR=1.13, 95% confidence interval (CI): 1.04, 1.22], the 9th month (OR=1.06, 95% CI: 0.99, 1.15), the third trimester (OR=1.17, 95% CI: 1.05, 1.29), and the entire pregnancy (OR=1.38, 95% CI: 1.07, 1.77) in models adjusted for one pollutant (PM_2.5). In models categorizing the PM_2.5 exposure by quartiles, comparing the second, third, and highest with the lowest PM_2.5 exposure quartile, the PM_2.5 was positively associated with TLBW during the 8th month (OR: 1.77, 95% CI: 1.09, 2.88; OR: 1.77, 95% CI: 1.03, 3.04; OR: 1.92, 95% CI: 1.04, 3.55, respectively) and for the 9th month, only association for exposure in the third versus the lowest quartile was significant (OR: 1.91, 95% CI: 1.02, 3.58).

CONCLUSIONS

The study provides evidence that exposure to PM_2.5 during pregnancy might be associated with the risk of TLBW in the context of very high pollution level of PM_2.5, and the 8th and 9th months were identified as potentially relevant exposure windows.

Large-scale transport of PM2.5 in the lower troposphere during winter cold surges in China

A comprehensive investigation using the air quality network and meteorological data of China in 2015 showed that PM_2.5 driven by cold surges from the ground level could travel up to 2000 km from northern to southern China within two days. Air pollution is more severe and prominent during the winter in north China due to seasonal variations in energy usage, trade wind movements, and industrial emissions. In February 2015, two cold surges traveling from north China caused a temporary increase in the concentration of PM_2.5 in Shanghai. Subsequently, the concentration of PM_2.5 in Xiamen increased to a high of 80 µg/m³_, which is double the average PM_2.5 concentration in Xiamen during the winter. This finding is a new long-range transport mechanism comparing to the well-established mechanism, with long-range transport more likely to occur in the upper troposphere than at lower levels. These observations were validated by results from the back trajectory analysis and the RAMS- CMAQ model. While wind speed was found to be a major facilitator in transporting PM_2.5 from Beijing to Xiamen, more investigation is required to understand the complex relationship between wind speed and PM_2.5 and how it moderates air quality in Beijing, Shanghai, and Xiamen.

Higher-order Network Analysis of Fine Particulate Matter (PM 2.5) Transport in China at City Level

Specification of PM 2.5 transmission characteristics is important for pollution control and policymaking. We apply higher-order organization of complex networks to identify major potential PM 2.5 contributors and PM 2.5 transport pathways of a network of 189 cities in China. The network we create in this paper consists of major cities in China and contains information on meteorological conditions of wind speed and wind direction, data on geographic distance, mountains, and PM 2.5 concentrations. We aim to reveal PM 2.5 mobility between cities in China. Two major conclusions are revealed through motif analysis of complex networks. First, major potential PM 2.5 pollution contributors are identified for each cluster by one motif, which reflects movements from source to target. Second, transport pathways of PM 2.5 are revealed by another motif, which reflects transmission routes. To our knowledge, this is the first work to apply higher-order network analysis to study PM 2.5 transport.

Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China

PM_2.5 air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM_2.5 exposure spatiotemporally in China, but all these have limitations. This study was to develop a data fusion approach and compare it with kriging and Chemistry Module. Two techniques were applied to create daily spatial cover of PM_2.5 in grid cells with a resolution of 10km in North China in 2013, respectively, which was kriging with an external drift (KED) and Weather Research and Forecast Model with Chemistry Module (WRF-Chem). A data fusion technique was developed by fusing PM_2.5 concentration predicted by KED and WRF-Chem, accounting for the distance from the central of grid cell to the nearest ground observations and daily spatial correlations between WRF-Chem and observations. Model performances were evaluated by comparing them with ground observations and the spatial prediction errors. KED and data fusion performed better at monitoring sites with a daily model R² of 0.95 and 0.94, respectively and PM_2.5 was overestimated by WRF-Chem (R²=0.51). KED and data fusion performed better around the ground monitors, WRF-Chem performed relative worse with high prediction errors in the central of study domain. In our study, both KED and data fusion technique provided highly accurate PM_2.5. Current monitoring network in North China was dense enough to provide a reliable PM_2.5 prediction by interpolation technique.

Potential Cardiovascular and Total Mortality Benefits of Air Pollution Control in Urban China

BACKGROUND

Outdoor air pollution ranks fourth among preventable causes of China's burden of disease. We hypothesized that the magnitude of health gains from air quality improvement in urban China could compare with achieving recommended blood pressure or smoking control goals.

METHODS

The Cardiovascular Disease Policy Model-China projected coronary heart disease, stroke, and all-cause deaths in urban Chinese adults 35 to 84 years of age from 2017 to 2030 if recent air quality (particulate matter with aerodynamic diameter ≤2.5 µm, PM_2.5) and traditional cardiovascular risk factor trends continue. We projected life-years gained if urban China were to reach 1 of 3 air quality goals: Beijing Olympic Games level (mean PM_2.5, 55 μg/m³), China Class II standard (35 μg/m³), or World Health Organization standard (10 μg/m³). We compared projected air pollution reduction control benefits with potential benefits of reaching World Health Organization hypertension and tobacco control goals.

RESULTS

Mean PM_2.5 reduction to Beijing Olympic levels by 2030 would gain ≈241,000 (95% uncertainty interval, 189 000-293 000) life-years annually. Achieving either the China Class II or World Health Organization PM_2.5 standard would yield greater health benefits (992 000 [95% uncertainty interval, 790 000-1 180 000] or 1 827 000 [95% uncertainty interval, 1 481 00-2 129 000] annual life-years gained, respectively) than World Health Organization-recommended goals of 25% improvement in systolic hypertension control and 30% reduction in smoking combined (928 000 [95% uncertainty interval, 830 000-1 033 000] life-years).

CONCLUSIONS

Air quality improvement in different scenarios could lead to graded health benefits ranging from 241 000 life-years gained to much greater benefits equal to or greater than the combined benefits of 25% improvement in systolic hypertension control and 30% smoking reduction.

Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning

Under rapid urbanization, many cities in China suffer from serious fine particulate matter (PM2.5) pollution. As the emission sources or adsorption sinks, land use and the corresponding landscape pattern unavoidably affect the concentration. However, the correlation varies with different regions and scales, leaving a significant gap for urban planning. This study clarifies the correlation with the aid of in situ and satellite-based spatial datasets over six urban agglomerations in China. Two coverage and four landscape indices are adopted to represent land use and landscape pattern. Specifically, the coverage indices include the area ratios of forest (F_PLAND) and built-up areas (C_PLAND). The landscape indices refer to the perimeter-area fractal dimension index (PAFRAC), interspersion and juxtaposition index (IJI), aggregation index (AI), Shannon’s diversity index (SHDI). Then, the correlation between PM2.5 concentration with the selected indices are evaluated from supporting the potential urban planning. Results show that the correlations are weak with the in situ PM2.5 concentration, which are significant with the regional value. It means that land use coverage and landscape pattern affect PM2.5 at a relatively large scale. Furthermore, regional PM2.5 concentration negatively correlate to F_PLAND and positively to C_PLAND (significance at p < 0.05), indicating that forest helps to improve air quality, while built-up areas worsen the pollution. Finally, the heterogeneous landscape presents positive correlation to the regional PM2.5 concentration in most regions, except for the urban agglomeration with highly-developed urban (i.e., the Jing-Jin-Ji and Chengdu-Chongqing urban agglomerations). It suggests that centralized urbanization would be helpful for PM2.5 pollution controlling by reducing the emission sources in most regions. Based on the results, the potential urban planning is proposed for controlling PM2.5 pollution for each urban agglomeration.

Spatio-temporal variations of PM2.5 emission in China from 2005 to 2014

With the rapid development of economy, air pollution has become increasingly serious nowadays in China, especially for the PM2.5. In this paper, the Spatio-temporal variations of PM2.5 emission over the past decade, from 2005 to 2014, were researched by cartograms. Meanwhile, a complex network technology was adopted to study the spatial auto-correlation of PM2.5 emission. The results showed that every province in China suffered a disparate increment in PM2.5 emission during the past ten years and also indicated that provinces in the same region had a huge influence on each other. There were three sectors including the thermal power, biomass burning and building materials that constituted the major sources of PM2.5 emission and they had different changing trends. There existed a dramatic difference in the east and west of China considering that the amount of PM2.5 was closely related to gross domestic product (GDP) and population. With higher GDP and population, eastern provinces emitted the most amount of PM2.5. Normalization results proposed that most of the provinces were PM2.5 exporting provinces in the southeast of China while most in the northwest were importing provinces. This study can help the policy-makers understand the distribution characteristics of PM2.5 emission and propose the effective strategy to mitigate the pollution of haze.

Inhalation of concentrated PM2.5 from Mexico City acts as an adjuvant in a guinea pig model of allergic asthma

Exposure to Particulate Matter (PM) could function as an adjuvant depending on the city of origin in mice allergic asthma models. Therefore, our aim was to determine whether inhalation of fine particles (PM2.5) from Mexico City could act as an adjuvant inducing allergic sensitization and/or worsening the asthmatic response in guinea pig, as a suitable model of human asthma. Experimental groups were Non-Sensitized (NS group), sensitized with Ovalbumin (OVA) plus Aluminum hydroxide (Al(OH)3) as adjuvant (S + Adj group), and sensitized (OVA) without adjuvant (S group). All the animals were exposed to Filtered Air (FA) or concentrated PM2.5 (5 h/daily/3 days), employing an aerosol concentrator system, PM2.5 composition was characterized. Lung function was evaluated by barometric plethysmography (Penh index). Inflammatory cells present in bronchoalveolar lavage were counted as well as OVA-specific IgG1 and IgE were determined by ELISA assay. Our results showed in sensitized animals without Al(OH)3, that the PM2.5 exposure (609 ± 12.73 μg/m3) acted as an adjuvant, triggering OVA-specific IgG1 and IgE concentration. Penh index increased ∼9-fold after OVA challenge in adjuvant-sensitized animals as well as in S + PM2.5 group (∼6-fold), meanwhile NS + FA and S + FA lacked response. S + Adj + PM2.5 group showed an increase significantly of eosinophils and neutrophils in bronchoalveolar lavage. PM2.5 composition was made up of inorganic elements and Polycyclic Aromatic Hydrocarbons, as well as endotoxins and β-glucan, all these components could act as adjuvant. Our study demonstrated that acute inhalation of PM2.5 acted as an adjuvant, similar to the aluminum hydroxide effect, triggering allergic asthma in a guinea pig model. Furthermore, in sensitized animals with aluminum hydroxide an enhancing influence of PM2.5 exposure was observed as specific-hyperresponsiveness to OVA challenge (quickly response) and eosinophilic and neutrophilic airway inflammation. Fine particles from Mexico City is a complex mix, which play a significant role as adjuvant in allergic asthma.

Comparison of PM2.5 Exposure in Hazy and Non-Hazy Days in Nanjing, China

Fine particulate matter (PM2.5), levels of which are about 6 times the 2014 WHO air quality guidelines for 190 cities in China, has been found to be associated with various adverse health outcomes. In this study, personal PM2.5 exposures were monitored along a fixed routine that included 19 types of non-residential micro-environments (MEs) on 4 hazy days (ambient PM2.5 292 ± 70 μg m-3) and 2 non-hazy days (55 ± 16 μg m-3) in Nanjing, China using miniaturized real-time portable particulate sensors that also collect integrated filters of PM2.5 (MicroPEMs, Research Triangle Institute (RTI), NC). Gravimetric correction is necessary for nephelometer devices in calculating real-time PM levels. During both hazy and non-hazy days, personal PM2.5 levels were generally higher in MEs with noticeable PM2.5 sources than MEs serving as receptor sites, higher in open MEs than indoor MEs, and higher in densely populated MEs than MEs with few people. Personal PM2.5 levels measured during hazy and non-hazy days were 242 ± 91 μg m-3 and 103 ± 147 μg m-3, respectively. The ratio of personal exposure to ambient PM2.5 levels (rp/a ) was less than 1.0 and less variable on hazy days (0.85 ± 0.31); while it was larger than 1.0 and more variable on non-hazy days (1.71 ± 1.93), confirming the importance of local sources other than ambient during non-hazy days. Air handling methods (e.g., ventilation/filtration) impacted personal exposures in enclosed locations on both types of days. Street food vendors with cooking emissions were MEs with the highest personal PM2.5 levels while subway cars in Nanjing were relatively clean due to good air filtration on both hazy and non-hazy days. In summary, on hazy days, personal exposure was mainly affected by the regional ambient levels, while on non-hazy days, local sources together with ambient levels determined personal exposure levels.

Temporal and spatial correlation patterns of air pollutants in Chinese cities

As a huge threat to the public health, China’s air pollution has attracted extensive attention and continues to grow in tandem with the economy. Although the real-time air quality report can be utilized to update our knowledge on air quality, questions about how pollutants evolve across time and how pollutants are spatially correlated still remain a puzzle. In view of this point, we adopt the PMFG network method to analyze the six pollutants’ hourly data in 350 Chinese cities in an attempt to find out how these pollutants are correlated temporally and spatially. In terms of time dimension, the results indicate that, except for O₃, the pollutants have a common feature of the strong intraday patterns of which the daily variations are composed of two contraction periods and two expansion periods. Besides, all the time series of the six pollutants possess strong long-term correlations, and this temporal memory effect helps to explain why smoggy days are always followed by one after another. In terms of space dimension, the correlation structure shows that O₃ is characterized by the highest spatial connections. The PMFGs reveal the relationship between this spatial correlation and provincial administrative divisions by filtering the hierarchical structure in the correlation matrix and refining the cliques as the tinny spatial clusters. Finally, we check the stability of the correlation structure and conclude that, except for PM₁₀ and O₃, the other pollutants have an overall stable correlation, and all pollutants have a slight trend to become more divergent in space. These results not only enhance our understanding of the air pollutants’ evolutionary process, but also shed lights on the application of complex network methods into geographic issues.