Fine Particulate Air Pollution and Hospital Emergency Room Visits for Respiratory Disease in Urban Areas in Beijing,China, in 2013

BackgroundHeavy fine particulate matter (PM_2.5) air pollution occurs frequently in China. However, epidemiological research on the association between short-term exposure to PM_2.5 pollution and respiratory disease morbidity is still limited. This study aimed to explore the association between PM_2.5 pollution and hospital emergency room visits (ERV) for total and cause-specific respiratory diseases in urban areas in Beijing.MethodsDaily counts of respiratory ERV from Jan 1 to Dec 31, 2013, were obtained from ten general hospitals located in urban areas in Beijing. Concurrently, data on PM_2.5 were collected from the Beijing Environmental Protection Bureau, including 17 ambient air quality monitoring stations. A generalized-additive model was used to explore the respiratory effects of PM_2.5, after controlling for confounding variables. Subgroup analyses were also conducted by age and gender.ResultsA total of 92,464 respiratory emergency visits were recorded during the study period. The mean daily PM_2.5 concentration was 102.1±73.6 μg/m³. Every 10 μg/m³ increase in PM_2.5 concentration at lag₀ was associated with an increase in ERV, as follows: 0.23% for total respiratory disease (95% confidence interval [CI]: 0.11%-0.34%), 0.19% for upper respiratory tract infection (URTI) (95%CI: 0.04%-0.35%), 0.34% for lower respiratory tract infection (LRTI) (95%CI: 0.14%-0.53%) and 1.46% for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) (95%CI: 0.13%-2.79%). The strongest association was identified between AECOPD and PM_2.5 concentration at lag_0-3 (3.15%, 95%CI: 1.39%-4.91%). The estimated effects were robust after adjusting for SO₂, O₃, CO and NO₂. Females and people 60 years of age and older demonstrated a higher risk of respiratory disease after PM_2.5 exposure.ConclusionPM_2.5 was significantly associated with respiratory ERV, particularly for URTI, LRTI and AECOPD in Beijing. The susceptibility to PM_2.5 pollution varied by gender and age.     

PM2.5 Spatiotemporal Variations and the Relationship with Meteorological Factors during 2013-2014 in Beijing,China

Objective

Limited information is available regarding spatiotemporal variations of particles with median aerodynamic diameter < 2.5 μm (PM_2.5) at high resolutions, and their relationships with meteorological factors in Beijing, China. This study aimed to detect spatiotemporal change patterns of PM_2.5 from August 2013 to July 2014 in Beijing, and to assess the relationship between PM_2.5 and meteorological factors.

Methods

Daily and hourly PM_2.5 data from the Beijing Environmental Protection Bureau (BJEPB) were analyzed separately. Ordinary kriging (OK) interpolation, time-series graphs, Spearman correlation coefficient and coefficient of divergence (COD) were used to describe the spatiotemporal variations of PM_2.5. The Kruskal-Wallis H test, Bonferroni correction, and Mann-Whitney U test were used to assess differences in PM_2.5 levels associated with spatial and temporal factors including season, region, daytime and day of week. Relationships between daily PM_2.5 and meteorological variables were analyzed using the generalized additive mixed model (GAMM).

Results

Annual mean and median of PM_2.5 concentrations were 88.07 μg/m³ and 71.00 μg/m³, respectively, from August 2013 to July 2014. PM_2.5 concentration was significantly higher in winter (P < 0.0083) and in the southern part of the city (P < 0.0167). Day to day variation of PM_2.5 showed a long-term trend of fluctuations, with 2–6 peaks each month. PM_2.5 concentration was significantly higher in the night than day (P < 0.0167). Meteorological factors were associated with daily PM_2.5 concentration using the GAMM model (R ² = 0.59, AIC = 7373.84).

Conclusion

PM_2.5 pollution in Beijing shows strong spatiotemporal variations. Meteorological factors influence the PM_2.5 concentration with certain patterns. Generally, prior day wind speed, sunlight hours and precipitation are negatively correlated with PM_2.5, whereas relative humidity and air pressure three days earlier are positively correlated with PM_2.5.     

Fine Particulate Matter and Incident Cognitive Impairment in the REasons for Geographic and Racial Differences in Stroke (REGARDS) Cohort

Studies of the effect of air pollution on cognitive health are often limited to populations living near cities that have air monitoring stations. Little is known about whether the estimates from such studies can be generalized to the U.S. population, or whether the relationship differs between urban and rural areas. To address these questions, we used a satellite-derived estimate of fine particulate matter (PM_2.5) concentration to determine whether PM_2.5 was associated with incident cognitive impairment in a geographically diverse, biracial US cohort of men and women (n = 20,150). A 1-year mean baseline PM_2.5 concentration was estimated for each participant, and cognitive status at the most recent follow-up was assessed over the telephone using the Six-Item Screener (SIS) in a subsample that was cognitively intact at baseline. Logistic regression was used to determine whether PM_2.5 was related to the odds of incident cognitive impairment. A 10 µg/m³ increase in PM_2.5 concentration was not reliably associated with an increased odds of incident impairment, after adjusting for temperature, season, incident stroke, and length of follow-up [OR (95% CI): 1.26 (0.97, 1.64)]. The odds ratio was attenuated towards 1 after adding demographic covariates, behavioral factors, and known comorbidities of cognitive impairment. A 10 µg/m³ increase in PM_2.5 concentration was slightly associated with incident impairment in urban areas (1.40 [1.06–1.85]), but this relationship was also attenuated after including additional covariates in the model. Evidence is lacking that the effect of PM_2.5 on incident cognitive impairment is robust in a heterogeneous US cohort, even in urban areas.     

Air Pollution and Stillbirth Risk: Exposure to Airborne Particulate Matter during Pregnancy Is Associated with Fetal Death

Objective

To test the hypothesis that exposure to fine particulate air pollution (PM_2.5) is associated with stillbirth.

Study Design

Geo-spatial population-based cohort study using Ohio birth records (2006-2010) and local measures of PM_2.5, recorded by the EPA (2005-2010) via 57 monitoring stations across Ohio. Geographic coordinates of the mother’s residence for each birth were linked to the nearest PM_2.5 monitoring station and monthly exposure averages calculated. The association between stillbirth and increased PM_2.5 levels was estimated, with adjustment for maternal age, race, education level, quantity of prenatal care, smoking, and season of conception.

Results

There were 349,188 live births and 1,848 stillbirths of non-anomalous singletons (20-42 weeks) with residence ≤10 km of a monitor station in Ohio during the study period. The mean PM_2.5 level in Ohio was 13.3 μg/m³ [±1.8 SD, IQR(Q1: 12.1, Q3: 14.4, IQR: 2.3)], higher than the current EPA standard of 12 μg/m³. High average PM_2.5 exposure through pregnancy was not associated with a significant increase in stillbirth risk, _adjOR 1.21(95% CI 0.96,1.53), nor was it increased with high exposure in the 1^st or 2^nd trimester. However, exposure to high levels of PM_2.5 in the third trimester of pregnancy was associated with 42% increased stillbirth risk, _adjOR 1.42(1.06,1.91).

Conclusions

Exposure to high levels of fine particulate air pollution in the third trimester of pregnancy is associated with increased stillbirth risk. Although the risk increase associated with high PM_2.5 levels is modest, the potential impact on overall stillbirth rates could be robust as all pregnant women are potentially at risk.     

Prenatal Air Pollution Exposure and Early Cardiovascular Phenotypes in Young Adults

Exposure to ambient air pollutants increases risk for adverse cardiovascular health outcomes in adults. We aimed to evaluate the contribution of prenatal air pollutant exposure to cardiovascular health, which has not been thoroughly evaluated. The Testing Responses on Youth (TROY) study consists of 768 college students recruited from the University of Southern California in 2007–2009. Participants attended one study visit during which blood pressure, heart rate and carotid artery arterial stiffness (CAS) and carotid artery intima-media thickness (CIMT) were assessed. Prenatal residential addresses were geocoded and used to assign prenatal and postnatal air pollutant exposure estimates using the U.S. Environmental Protection Agency’s Air Quality System (AQS) database. The associations between CAS, CIMT and air pollutants were assessed using linear regression analysis. Prenatal PM₁₀ and PM_2.5 exposures were associated with increased CAS. For example, a 2 SD increase in prenatal PM_2.5 was associated with CAS indices, including a 5% increase (β = 1.05, 95% CI 1.00–1.10) in carotid stiffness index beta, a 5% increase (β = 1.05, 95% CI 1.01–1.10) in Young’s elastic modulus and a 5% decrease (β = 0.95, 95% CI 0.91–0.99) in distensibility. Mutually adjusted models of pre- and postnatal PM_2.5 further suggested the prenatal exposure was most relevant exposure period for CAS. No associations were observed for CIMT. In conclusion, prenatal exposure to elevated air pollutants may increase carotid arterial stiffness in a young adult population of college students. Efforts aimed at limiting prenatal exposures are important public health goals.     

Effects of Urban Landscape Pattern on PM2.5 Pollution—A Beijing Case Study

PM_2.5 refers to particulate matter (PM) in air that is less than 2.5μm in aerodynamic diameter, which has negative effects on air quality and human health. PM_2.5 is the main pollutant source in haze occurring in Beijing, and it also has caused many problems in other cities. Previous studies have focused mostly on the relationship between land use and air quality, but less research has specifically explored the effects of urban landscape patterns on PM_2.5. This study considered the rapidly growing and heavily polluted Beijing, China. To better understand the impact of urban landscape pattern on PM_2.5 pollution, five landscape metrics including PLAND, PD, ED, SHEI, and CONTAG were applied in the study. Further, other data, such as street networks, population density, and elevation considered as factors influencing PM_2.5, were obtained through RS and GIS. By means of correlation analysis and stepwise multiple regression, the effects of landscape pattern on PM_2.5 concentration was explored. The results showed that (1) at class-level, vegetation and water were significant landscape components in reducing PM_2.5 concentration, while cropland played a special role in PM_2.5 concentration; (2) landscape configuration (ED and PD) features at class-level had obvious effects on particulate matter; and (3) at the landscape-level, the evenness (SHEI) and fragmentation (CONTAG) of the whole landscape related closely with PM_2.5 concentration. Results of this study could expand our understanding of the role of urban landscape pattern on PM_2.5 and provide useful information for urban planning.     

Air Pollution in China: Mapping of Concentrations and Sources

China has recently made available hourly air pollution data from over 1500 sites, including airborne particulate matter (PM), SO₂, NO₂, and O₃. We apply Kriging interpolation to four months of data to derive pollution maps for eastern China. Consistent with prior findings, the greatest pollution occurs in the east, but significant levels are widespread across northern and central China and are not limited to major cities or geologic basins. Sources of pollution are widespread, but are particularly intense in a northeast corridor that extends from near Shanghai to north of Beijing. During our analysis period, 92% of the population of China experienced >120 hours of unhealthy air (US EPA standard), and 38% experienced average concentrations that were unhealthy. China’s population-weighted average exposure to PM_2.5 was 52 μg/m³. The observed air pollution is calculated to contribute to 1.6 million deaths/year in China [0.7–2.2 million deaths/year at 95% confidence], roughly 17% of all deaths in China.     

Characterizing the Indoor-Outdoor Relationship of Fine Particulate Matter in Non-Heating Season for Urban Residences in Beijing

Objective

Ambient fine particulate matter (PM_2.5) pollution is currently a major public health concern in Chinese urban areas. However, PM_2.5 exposure primarily occurs indoors. Given such, we conducted this study to characterize the indoor-outdoor relationship of PM_2.5 mass concentrations for urban residences in Beijing.

Methods

In this study, 24-h real-time indoor and ambient PM_2.5 mass concentrations were concurrently collected for 41 urban residences in the non-heating season. The diurnal variation of pollutant concentrations was characterized. Pearson correlation analysis was used to examine the correlation between indoor and ambient PM_2.5 mass concentrations. Regression analysis with ordinary least square was employed to characterize the influences of a variety of factors on PM_2.5 mass concentration.

Results

Hourly ambient PM_2.5 mass concentrations were 3–280 μg/m³ with a median of 58 μg/m³, and hourly indoor counterpart were 4–193 μg/m³ with a median of 34 μg/m³. The median indoor/ambient ratio of PM_2.5 mass concentration was 0.62. The diurnal variation of residential indoor and ambient PM_2.5 mass concentrations tracked with each other well. Strong correlation was found between indoor and ambient PM_2.5 mass concentrations on the community basis (coefficients: r≥0.90, p<0.0001), and the ambient data explained ≥84% variance of the indoor data. Regression analysis suggested that the variables, such as traffic conditions, indoor smoking activities, indoor cleaning activities, indoor plants and number of occupants, had significant influences on the indoor PM_2.5 mass concentrations.

Conclusions

PM_2.5 of ambient origin made dominant contribution to residential indoor PM_2.5 exposure in the non-heating season under the high ambient fine particle pollution condition. Nonetheless, the large inter-residence variability of infiltration factor of ambient PM_2.5 raised the concern of exposure misclassification when using ambient PM_2.5 mass concentrations as exposure surrogates. PM_2.5 of indoor origin still had minor influence on indoor PM_2.5 mass concentrations, particularly at 11:00–13:00 and 22:00–0:00. The predictive models suggested that particles from traffic emission, secondary aerosols, particles from indoor smoking, resuspended particles due to indoor cleaning and particles related to indoor plants contributed to indoor PM_2.5 mass concentrations in this study. Real-time ventilation measurements and improvement of questionnaire design to involve more variables subject to built environment were recommended to enhance the performance of the predictive models.     

Chemical Characteristics of Water-Soluble Ions in Particulate Matter in Three Metropolitan Areas in the North China Plain

PM_2.5 and PM₁₀ samples were collected simultaneously in each season in Beijing, Tianjin and Shijiazhuang to identify the characteristics of water-soluble ion compositions in the North China Plain. The water-soluble ions displayed significant seasonal variation. The dominant ions were NO₃ ⁻, SO₄ ²⁻, NH₄ ⁺ and Cl⁻, accounting for more than 90% and 86% to the mass of total water-soluble ions in PM_2.5 and PM₁₀, respectively. The anion/cation ratio indicated that the ion acidity of each city varied both between sites and seasonally. Over 50% of the ion species were enriched in small particles ≤1 µm in diameter. The [NO₃ ⁻]/[SO₄ ²⁻] ratio indicated that vehicles accounted for the majority of the particulate pollution in Beijing. Shijiazhuang, a city highly reliant on coal combustion, had a higher SO₄ ²⁻ concentration.     

植物吸滞PM2.5研究进展——从宏观到微观

PM_2.5严重危害环境安全和人体健康,虽然国内外大气PM_2.5状况已有好转,但雾霾天气仍然时有发生。植物能有效吸附和净化大气中PM_2.5,其净化作用受到生态学广泛关注。随着研究内容的深入,该领域研究尺度由宏观尺度转向微观尺度,研究对象由植被区转向植物个体,研究方法由野外监测转向人工控制法。因此在区域尺度上对比了植被、非植被区PM_2.5浓度差异及不同树种单位叶面积PM_2.5吸滞量,以风洞、熏气法两种研究方法归纳了人工控制条件下植物净化PM_2.5的研究成果,在大气PM_2.5浓度和气象因素两方面探讨了环境因素对植物净化PM_2.5的影响机制。得出宏观研究方面很多城市缺乏植被区与非植被区PM_2.5浓度监测数据,微观方面植物个体吸滞PM_2.5机理研究不够深入,缺乏植物吸滞PM_2.5过程与机理的室内模拟外界环境的高精度对比试验,更缺乏环境因素直接影响植物吸收、分...     

北京西山典型城市森林内PM_(2.5)动态变化规律

城市森林内PM2.5浓度的状况可以直接反映城市森林对PM2.5的净化效果,也是居民休闲游憩关心的森林环境问题。选择北京西山3种典型的游憩型城市森林,通过对林内PM2.5浓度一年四季昼夜24h内变化的同步观测,分析了不同类型城市森林内PM2.5浓度的季节变化、日变化以及影响因素,结果表明:(1)北京西山3种游憩林内PM2.5浓度多数时候远低于城区对照值,在春、夏、秋三季都达到了国家城市化地区的标准,甚至在春季、秋季还达到了国家一类地区的标准。(2)城市森林在不同季节对PM2.5的净化效果存在差异,林内PM2.5浓度总体上呈现冬季夏季秋季春季的规律。(3)林内PM2.5浓度在一天24h内有很大变化波动,夜间浓度总体上高于白天,日变化曲线近似呈"双峰双谷"型,两个高峰出现在夜晚和早上,两个低谷出现在凌晨和中午前后。一年四季白天低谷出现时间有所不同,春季15:00左右、夏季13:00—17:00、秋季13:00—15:00、冬季9:00—11:00。(4)PM2.5在不同类型游憩林内的变化趋势和浓度值存在一定差异。郁闭度较大的侧柏林夜间PM2.5浓度总体上高于其它两种林型,其高峰和低谷出现时间延迟,高峰值大,高峰期持续时间长,且这种规律在秋季表现得更明显。(5)基于上述研究认为,北京西山城市森林为居民在PM2.5污染比较突出的都市背景下提供了一个相对清洁、健康的森林游憩环境,春季、夏季、秋季全天以及冬季9:00—11:00均是森林中PM2.5状况健康而适宜外出游憩的时段。     

Ambient and household PM2.5 pollution and adverse perinatal outcomes: A meta-regression and analysis of attributable global burden for 204 countries and territories

BackgroundParticulate matter <2.5 micrometer (PM_2.5) is associated with adverse perinatal outcomes, but the impact on disease burden mediated by this pathway has not previously been included in the Global Burden of Disease (GBD), Mortality, Injuries, and Risk Factors studies. We estimated the global burden of low birth weight (LBW) and preterm birth (PTB) and impacts on reduced birth weight and gestational age (GA), attributable to ambient and household PM_2.5 pollution in 2019.Methods and findingsWe searched PubMed, Embase, and Web of Science for peer-reviewed articles in English. Study quality was assessed using 2 tools: (1) Agency for Healthcare Research and Quality checklist; and (2) National Institute of Environmental Health Sciences (NIEHS) risk of bias questions. We conducted a meta-regression (MR) to quantify the risk of PM_2.5 on birth weight and GA. The MR, based on a systematic review (SR) of articles published through April 4, 2021, and resulting uncertainty intervals (UIs) accounted for unexplained between-study heterogeneity. Separate nonlinear relationships relating exposure to risk were generated for each outcome and applied in the burden estimation.The MR included 44, 40, and 40 birth weight, LBW, and PTB studies, respectively. Majority of the studies were of retrospective cohort design and primarily from North America, Europe, and Australia. A few recent studies were from China, India, sub-Saharan Africa, and South America. Pooled estimates indicated 22 grams (95% UI: 12, 32) lower birth weight, 11% greater risk of LBW (1.11, 95% UI: 1.07, 1.16), and 12% greater risk of PTB (1.12, 95% UI: 1.06, 1.19), per 10 μg/m³ increment in ambient PM_2.5. We estimated a global population–weighted mean lowering of 89 grams (95% UI: 88, 89) of birth weight and 3.4 weeks (95% UI: 3.4, 3.4) of GA in 2019, attributable to total PM_2.5. Globally, an estimated 15.6% (95% UI: 15.6, 15.7) of all LBW and 35.7% (95% UI: 35.6, 35.9) of all PTB infants were attributable to total PM_2.5, equivalent to 2,761,720 (95% UI: 2,746,713 to 2,776,722) and 5,870,103 (95% UI: 5,848,046 to 5,892,166) infants in 2019, respectively. About one-third of the total PM_2.5 burden for LBW and PTB could be attributable to ambient exposure, with household air pollution (HAP) dominating in low-income countries. The findings should be viewed in light of some limitations such as heterogeneity between studies including size, exposure levels, exposure assessment method, and adjustment for confounding. Furthermore, studies did not separate the direct effect of PM_2.5 on birth weight from that mediated through GA. As a consequence, the pooled risk estimates in the MR and likewise the global burden may have been underestimated.ConclusionsAmbient and household PM_2.5 were associated with reduced birth weight and GA, which are, in turn, associated with neonatal and infant mortality, particularly in low- and middle-income countries.

Rakesh Ghosh and co-workers report on associations between particulate matter air pollution and adverse perinatal health outcomes.     

The Association between Ambient Air Pollution and Daily Mortality in Beijing after the 2008 Olympics: A Time Series Study

In recent decades, ambient air pollution has been an important public health issue in Beijing, but little is known about air pollution and health effects after the 2008 Beijing Olympics. We conduct a time-series analysis to evaluate associations between daily mortality (nonaccidental, cardiovascular and respiratory mortality) and the major air pollutants (carbon monoxide, nitrogen dioxide and particulate matter less than 10 µm in aerodynamic diameter) in Beijing during the two years (2009∼2010) after the 2008 Beijing Olympics. We used generalized additive model to analyze relationship between daily mortality and air pollution. In single air pollutant model with two-day moving average concentrations of the air pollutants, increase in their interquartile range (IQR) associated with percent increase in nonaccidental mortality, 2.55 percent [95% confidence interval (CI): 1.99, 3.11] for CO, 2.54 percent (95% CI: 2.00, 3.08) for NO₂ and 1.80 percent (95% CI: 1.21, 2.40) for PM₁₀, respectively; increases in the IQR of air pollutant concentrations associated with percent increase in cardiovascular mortality, 2.88 percent (95% CI: 2.10,3.65) for CO, 2.63 percent (95% CI: 1.87, 3.39) for NO₂ and 1.72 percent (95% CI: 0.88, 2.55) for PM₁₀, respectively; and increase in IQR of air pollutant concentrations associated with respiratory mortality, 2.39 percent (95% CI: 0.68, 4.09) for CO, 1.79 percent (95% CI: 0.11, 3.47) for NO₂ and 2.07 percent (95% CI: 0.21, 3.92) for PM₁₀, respectively. We used the principal component analysis to avoid collinearity of varied air pollutants. In addition, the association stratified by sex and age was also examined. Ambient air pollution remained a significant contributor to nonaccidental and cardiopulmonary mortalities in Beijing during 2009∼2010.     

Lacrimal Cytokines Assessment in Subjects Exposed to Different Levels of Ambient Air Pollution in a Large Metropolitan Area

Background

Air pollution is one of the most environmental health concerns in the world and has serious impact on human health, particularly in the mucous membranes of the respiratory tract and eyes. However, ocular hazardous effects to air pollutants are scarcely found in the literature.

Design

Panel study to evaluate the effect of different levels of ambient air pollution on lacrimal film cytokine levels of outdoor workers from a large metropolitan area.

Methods

Thirty healthy male workers, among them nineteen professionals who work on streets (taxi drivers and traffic controllers, high pollutants exposure, Group 1) and eleven workers of a Forest Institute (Group 2, lower pollutants exposure compared to group 1) were evaluated twice, 15 days apart. Exposure to ambient PM_2.5 (particulate matter equal or smaller than 2.5 μm) was 24 hour individually collected and the collection of tears was performed to measure interleukins (IL) 2, 4, 5 and 10 and interferon gamma (IFN-γ) levels. Data from both groups were compared using Student’s t test or Mann- Whitney test for cytokines. Individual PM_2.5 levels were categorized in tertiles (lower, middle and upper) and compared using one-way ANOVA. Relationship between PM_2.5 and cytokine levels was evaluated using generalized estimating equations (GEE).

Results

PM_2.5 levels in the three categories differed significantly (lower: ≤22 μg/m³; middle: 23–37.5 μg/m³; upper: >37.5 μg/m³; p<0.001). The subjects from the two groups were distributed unevenly in the lower category (Group 1 = 8%; Group 2 = 92%), the middle category (Group 1 = 89%; Group 2 = 11%) and the upper category (Group 1 = 100%). A significant relationship was found between IL-5 and IL-10 and PM_2.5 levels of the group 1, with an average decrease of 1.65 pg/mL of IL-5 level and of 0.78 pg/mL of IL-10 level in tear samples for each increment of 50 μg/m3 of PM_2.5 (p = 0.01 and p = 0.003, respectively).

Conclusion

High levels of PM_2.5 exposure is associated with decrease of IL-5 and IL-10 levels suggesting a possible modulatory action of ambient air pollution on ocular surface immune response.     

Fine Particulate Air Pollution and the Progression of Carotid Intima-Medial Thickness: A Prospective Cohort Study from the Multi-Ethnic Study of Atherosclerosis and Air Pollution

Background

Fine particulate matter (PM_2.5) has been linked to cardiovascular disease, possibly via accelerated atherosclerosis. We examined associations between the progression of the intima-medial thickness (IMT) of the common carotid artery, as an indicator of atherosclerosis, and long-term PM_2.5 concentrations in participants from the Multi-Ethnic Study of Atherosclerosis (MESA).

Methods and Results

MESA, a prospective cohort study, enrolled 6,814 participants at the baseline exam (2000–2002), with 5,660 (83%) of those participants completing two ultrasound examinations between 2000 and 2005 (mean follow-up: 2.5 years). PM_2.5 was estimated over the year preceding baseline and between ultrasounds using a spatio-temporal model. Cross-sectional and longitudinal associations were examined using mixed models adjusted for confounders including age, sex, race/ethnicity, smoking, and socio-economic indicators. Among 5,362 participants (5% of participants had missing data) with a mean annual progression of 14 µm/y, 2.5 µg/m³ higher levels of residential PM_2.5 during the follow-up period were associated with 5.0 µm/y (95% CI 2.6 to 7.4 µm/y) greater IMT progressions among persons in the same metropolitan area. Although significant associations were not found with IMT progression without adjustment for metropolitan area (0.4 µm/y [95% CI −0.4 to 1.2 µm/y] per 2.5 µg/m³), all of the six areas showed positive associations. Greater reductions in PM_2.5 over follow-up for a fixed baseline PM_2.5 were also associated with slowed IMT progression (−2.8 µm/y [95% CI −1.6 to −3.9 µm/y] per 1 µg/m³ reduction). Study limitations include the use of a surrogate measure of atherosclerosis, some loss to follow-up, and the lack of estimates for air pollution concentrations prior to 1999.

Conclusions

This early analysis from MESA suggests that higher long-term PM_2.5 concentrations are associated with increased IMT progression and that greater reductions in PM_2.5 are related to slower IMT progression. These findings, even over a relatively short follow-up period, add to the limited literature on air pollution and the progression of atherosclerotic processes in humans. If confirmed by future analyses of the full 10 years of follow-up in this cohort, these findings will help to explain associations between long-term PM_2.5 concentrations and clinical cardiovascular events. Please see later in the article for the Editors'' Summary     

Effects of particulate matter exposure on blood 5-hydroxymethylation: results from the Beijing truck driver air pollution study

Previous studies have reported epigenetic changes induced by environmental exposures. However, previous investigations did not distinguish 5-methylcytosine (5mC) from a similar oxidative form with opposite functions, 5-hydroxymethylcytosine (5hmC). Here, we measured blood DNA global 5mC and 5hmC by ELISA and used adjusted mixed-effects regression models to evaluate the effects of ambient PM₁₀ and personal PM_2.5 and its elemental components—black carbon (BC), aluminum (Al), calcium (Ca), potassium (K), iron (Fe), sulfur (S), silicon (Si), titanium (Ti), and zinc (Zn)—on blood global 5mC and 5hmC levels. The study was conducted in 60 truck drivers and 60 office workers in Beijing, China from The Beijing Truck Driver Air Pollution Study at 2 exams separated by one to 2 weeks. Blood 5hmC level (0.08%) was ∼83-fold lower than 5mC (6.61%). An inter-quartile range (IQR) increase in same-day PM₁₀ was associated with increases in 5hmC of 26.1% in office workers (P = 0.004), 20.2% in truck drivers (P = 0.014), and 21.9% in all participants combined (P < 0.001). PM₁₀ effects on 5hmC were increasingly stronger when averaged over 4, 7, and 14 d preceding assessment (up to 132.6% for the 14-d average in all participants, P < 0.001). PM₁₀ effects were also significant after controlling for multiple testing (family-wise error rate; FWER < 0.05). 5hmC was not correlated with personal measures of PM_2.5 and elemental components (FWER > 0.05). 5mC showed no correlations with PM₁₀, PM_2.5, and elemental components measures (FWER > 0.05). Our study suggests that exposure to ambient PM₁₀ affects 5hmC over time, but not 5mC. This finding demonstrates the need to differentiate 5hmC and 5mC in environmental studies of DNA methylation.     

不同污染程度下毛白杨叶表面PM2.5颗粒的数量及性质和叶片气孔形态的比较研究

选择了北京市环境PM_(2.5)浓度不同的两个采样点的毛白杨(Populus tomentosa Carr.)作为研究对象,利用环境扫描电镜及X-射线能谱仪对杨树叶片表面滞留的PM_(2.5)颗粒进行了观察、统计和成分分析,并研究了叶片气孔对环境颗粒物污染的适应性变化。结果表明:夏秋两季西直门叶片样品上下表面的PM_(2.5)数量均多于森林公园样品这说明环境PM_(2.5)浓度是影响叶片表面滞留颗粒物数量的主要原因;其中叶片上表面是滞留PM_(2.5)颗粒的主要区域。森林公园样品中PM_(2.5)颗粒性质比较单一,硅铝酸盐颗粒和石英颗粒占很大比例,二者的主要来源均为天然源,如土壤扬尘、矿物颗粒等;而西直门采样点叶片样品滞留的PM_(2.5)颗粒的元素组成更为复杂,其中50%以上的硅铝酸盐颗粒检测出了明显的铜、钾、氯、钠等元素的谱峰其来源主要是工业排放;西直门样品PM_(2.5)的含硫量高于森林公园样品,且夏季明显高于秋季。研究还发现有少数PM_(2.5)颗粒进入了毛白杨叶片的气孔而且不同污染程度下气孔的形态特征存在差异。与森林公园毛白杨叶片的气孔相比,西直门处的毛白杨叶片气孔的长度、宽度、面积和气孔密度均较小,说明较高的PM_(2.5)污染程度对毛白杨叶片的形态发育有一定影响。研究结果可以为揭示植物叶片阻滞、吸收大气颗粒污染物的机制、合理选择和优化城市绿化树种从而改善空气质量提供一定的科学理论依据。     

城市化对空气污染人群暴露贡献的定量方法研究

短期快速城市化引发一系列生态环境问题,尤其是近年来以细颗粒物(PM_(2.5))为代表的城市与区域空气污染问题。人群的污染暴露一方面是因为污染区范围的扩张,另外一方面则归因于城市化引发的人口迁移,目前的研究重点关注于前者的贡献,而忽略了后者的贡献。因此,建立了城市化对空气污染人群暴露贡献的定量方法,并选取我国PM_(2.5)污染最为严重的京津冀城市群开展了实证研究,通过利用2000、2005、2010、2015年PM_(2.5)浓度和人口栅格数据以及人口自然增长率数据,定量评估了城市化引发的人口迁移对空气污染人群暴露的贡献。研究结果显示:(1)京津冀地区受污染影响面积和人口变化显著,造成大量的人口暴露于PM_(2.5)污染。(2)城市化引发的人口迁移与自然增长贡献率方面:总体上,2000—2015年,京津冀城市群总的人口迁移贡献率为48%,北京市和天津市总的人口迁移贡献率分别为94%和88%,而河北省污染总的人口迁移贡献率为-32%。其中在污染保持区,北京市和天津市的人口迁移贡献率均接近100%,而河北省的迁移贡献率为-26%,尤其在2010—2015年,河北省衡水市的人口迁移贡献率达到-6613%;在污染新增区,北京市和天津市的人口迁移贡献率分别为86%和84%,而河北省污染的人口迁移贡献率为-757%。本研究建立了定量化的方法揭示了城市化在空气污染人群暴露中的定量贡献,为科学引导城市化发展提供了定量的手段,为合理规划京津冀城市群地区的人口流动与空气污染奠定了数据基础。     

川渝地区空气质量时空分布格局及影响因素

川渝地区尤其是四川盆地已成为我国空气污染最严重的地区之一，基于2018—2019年川渝地区128个城市站和71个县级站空气质量监测及自然与社会经济数据，采用全局和局部莫兰指数分析了川渝地区空气质量指数(AQI)和不同空气质量分指数(IAQI)的时空格局，并采用偏最小二乘回归(PLSR)从较为宏观的尺度综合分析了川渝地区空气污染的主要驱动因素。研究结果表明：(1)川渝地区空气质量整体为良，主要污染物为O₃,其次为PM₁₀和PM_2.5。盆地区与高原区的主要污染物分别为PM_2.5和O₃;(2)AQI及PM_2.5、PM₁₀、NO₂呈“U”型变化，春冬季最高，夏秋季最低；O₃则在内部两区域都大致呈倒“U”型变化，但峰值分布时间与持续时长明显不同；SO₂和CO年内无明显变化；(3)各污染物具有明显的空间聚集性特征，AQI及PM₁₀、PM_2.5     

Spatio-temporal modelling of particulate matter concentrations using satellite derived aerosol optical depth over coastal region of Chennai in India

The present study primarily focuses on describing aerosol optical depth (AOD), its distribution pattern and seasonal variation, and modelling Particulate Matter Concentrations in Chennai. The frequency distribution of AOD and PM_2.5 demonstrates that AOD can be used as a proxy for estimating PM_2.5 in the study region as the occurrence of AOD almost resonates with that of PM_2.5. The seasonal variation of AOD and PM_2.5 revealed that during the winter (October–January) and summer (February–May) seasons, AOD reasonably followed the trend of PM_2.5. However, during the monsoon period, AOD showed random variations. Different models like linear and non-linear regression models and machine learning models such as random forest (RF) have been developed for PM_2.5 estimation. The model's performance in different stations and seasons has been assessed. The effect of meteorology and other factors in the model has also been assessed. From linear and non-linear model analysis, AOD was a significant parameter in estimating PM_2.5. The Random Forest model was the stable model for the study region, with a model R² of 0.53 and an RMSE of 15.89 μg/m³. The inclusion of meteorological parameters like relative humidity, wind speed, and wind direction decreased the error in prediction by 17.45 μg/m³. The seasonal and spatial analysis indicates that the prediction capability of models varies with stations and seasons. The best performing model was found to be Model RF, and the model could explain about 53.14% of the variability in PM_2.5 concentration occurrence in the study region with a prediction error of 15.89 μg/m³.