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.     

Direct effects of airborne PM2.5 exposure on macrophage polarizations

BackgroundExposure of atmospheric particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) is epidemiologically associated with illnesses. Potential effects of air pollutants on innate immunity have raised concerns. As the first defense line, macrophages are able to induce inflammatory response. However, whether PM_2.5 exposure affects macrophage polarizations remains unclear.MethodsWe used freshly isolated macrophages as a model system to demonstrate effects of PM_2.5 on macrophage polarizations. The expressions of cytokines and key molecular markers were detected by real-time PCR, and flow cytometry. The specific inhibitors and gene deletion technologies were used to address the molecular mechanisms.ResultsPM_2.5 increased the expression of pro-inflammatory cytokines granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin-6 (IL-6), interleukin-1β (IL-1β), tumor necrosis factor alpha (TNFα). PM_2.5 also enhanced the lipopolysaccharide (LPS)-induced M1 polarization even though there was no evidence in the change of cell viability. However, PM_2.5 significantly decreased the number of mitochondria in a dose dependent manner. Pre-treatment with NAC, a scavenger of reactive oxygen species (ROS), prevented the increase of ROS and rescued the PM_2.5-impacted M1 but not M2 response. However, mTOR deletion partially rescued the effects of PM_2.5 to reduce M2 polarization.ConclusionsPM_2.5 exposure significantly enhanced inflammatory M1 polarization through ROS pathway, whereas PM_2.5 exposure inhibited anti-inflammatory M2 polarization through mTOR-dependent pathway.General significanceThe present studies suggested that short-term exposure of PM_2.5 acts on the balance of inflammatory M1 and anti-inflammatory M2 macrophage polarizations, which may be involved in air pollution-induced immune disorders and diseases. This article is part of a Special Issue entitled Air Pollution, edited by Wenjun Ding, Andrew J. Ghio and Weidong Wu.     

Acute effects of smoking and high experimental exposure to environmental tobacco smoke (ETS) on the immune system

Controversial results have been published on the immune response to cigarette smoking while the effects of exposure to environmental tobacco smoke (ETS) have not yet been reported. In a controlled study, acute effects of smoking and of a high environmental exposure to ETS on immunological parameters have been investigated. The study consisted of four experimental days, two control and two exposure days. On control days, 1 and 3, smokers (n=5) and nonsmokers (n=5) sat in an unventilated 45 m³ room for 8 h. On the exposure days, 2 and 4, each of the smokers smoked 24 cigarettes in 8 h, while the nonsmokers were exposed to the ETS generated by the smoking volunteers. Blood was drawn before and after each exposure session on all four experimental days for dosimetry of tobacco smoke exposure and determination of the immune response. Flow cytometry using monoclonal antibodies was used to determine CD3⁺ cells (whole T cells), CD19⁺ cells (B lymphocytes), CD16⁺ and CD56⁺ cells (natural killer cells), CD4⁺ cells (T-helper cells), CD8⁺ cells (T-suppressor cells), the CD4⁺/CD8⁺ (helper/supressor ratio), and Fc receptors on granulocytes. Serum was analyzed for soluble CD14 receptors (scD14), interleukin 1, interleukin 6 and prostaglandin E₂ (PGE₂). Functional stimulation assays were performed to determine the basal and induced level of reactive oxygen intermediate (ROI) production by polymorphic neutrophils. Exposure to tobacco smoke in both groups was confirmed by dosimetry of carboxyhemoglobin, plasma nicotine, and cotinine levels. In comparison to nonsmokers, smokers had elevated granulocyte cell counts, increased CD16⁺ and CD56⁺ cell levels and decreased CD3⁺ and CD19⁺ levels. Acute smoking, but not exposure to ETS, resulted in a slight decrease in the number of CD19⁺ cells and an increase in the number of granulocytes; the latter was restricted to one subject. Acute smoking and exposure to high experimental concentrations of ETS resulted in a slight increase in CD16⁺ and CD56⁺ cells. None of the changes determined in immunological parameters after either acute smoking or exposure to ETS reached statistical significance. Serum sCD14, cytokine and PGE₂, functional stimulation of in vitro ROI production, and changes in Fc receptors were not affected by acute smoking or exposure to ETS. Although no clear guidelines exist to assess immunotoxicity in man, our data do not favor immunosuppression and the possibility of increased risk of infection in nonsmokers exposed to ETS under real-life conditions.Abbreviations AM alveolar macrophage - BALF bronchoalveolar lavage fluid - CO carbon monoxide - CO₂ carbon dioxide - COHb carboxyhemoglobin - ELISA enzyme linked immunoassay - ETS environmental tobacco smoke - FITC fluorescein isothiocyanate - IL interleukin - MHC major histocompatibility complex - NK natural killer cell - NO nitrogen oxide - NO₂ nitrogen dioxide - PBS phosphate-buffered saline - PE phycoerythrin - PGE₂ prostaglandin E₂ - PMA phorbol-12-myristate-13-acetate - PMN polymorphic neutrophils - RIA radioimmunoassay - ROI reactive oxygen intermediates - RSP respirable suspended particles - sCD14 soluble CD14 receptor     

The modeling and health risk assessment of PM2.5 from Tema Oil Refinery

Fine particulate matters (PM_2.5) are known to pose serious health problems compared to other air pollutants. The current study employed air dispersion modeling system (AERMOD) to simulate the concentration of PM_2.5 from Tema Oil Refinery (TOR) and to assess the non-cancer risk and mortalities of the exposed population. In addition, the effects of local climatic factors on the distribution and concentration of PM_2.5 within the three main seasons (Major Raining Season (MRS), Low Raining Season (LRS) and Dry Season (DS)) were investigated. The AERMOD results showed that both 24-h (38.8 µg m^?3) and annual (12.6 µg m^?3) PM_2.5 concentration levels were in exceedance of the international limits. However, a decreasing trend in seasonal PM_2.5 concentrations was observed. Health risk assessment (HRA), indicated by hazard index (HI), revealed that the amount of Al₂O₃ present in the PM_2.5 caused a significant non-carcinogenic health risk to the exposed population (both adults and children) within the Metropolis (HI = 2.4 for adults and HI = 1.5 for children). Additionally, cardiopulmonary disease related mortalities due to PM_2.5 exposure (181 deaths for adults and 24 deaths for children) were found high compared to deaths caused by lung cancer (137 deaths for adults and 16 deaths for children).     

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。     

大气颗粒物(PM2.5、PM10)对地表景观结构的响应研究进展

颗粒物PM_(2.5)、PM_(10)是近年来我国大气首要污染物,威胁环境和人类健康。地表景观结构直接或间接影响PM_(2.5)、PM_(10)浓度,了解其影响过程和机理对于改善生态环境具有重要意义。系统总结了国内外关于PM_(2.5)、PM_(10)对地表景观结构响应的研究成果,指出研究中出现不确定性的可能影响因素,并对今后的发展方向进行展望。得出基本结论:(1)地表景观类型的构成及其格局显著影响大气颗粒物浓度,对PM_(2.5)、PM_(10)起到"源"和"汇"的作用。(2)地表景观结构引起局地气候变化并影响颗粒物的迁移转化,但其影响过程和机理复杂,研究结论并不明确。(3)颗粒物浓度和地表景观数据主要通过实际监测或遥感处理方法获得,但因为获取方法、监测点微观环境及遥感影像等因素影响,导致数据具有不确定性,加上时空尺度相对应的复杂性,大大限制了地表景观结构与PM_(2.5)、PM_(10)响应关系的研究进展,是未来要突破的难点。(4)PM_(2.5)、PM_(10)对地表景观结构响应的区域时空差异及过程,局地小气候变化对颗粒物浓度的影响过程和强度,主要景观类型尤其是水体、湿地景观对大气颗粒物浓度的影响过程、机理与贡献程度等是未来需要关注的方向。     

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.     

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.     

Is the Current Fine Particulate Standard Protective of Public Health?

The new annual PM_2.5 standard will be the most difficult particulate matter (PM) standard to satisfy. At issue is whether the extant health evidence supports the rationale for this standard being the controlling standard. Indeed the key issue is whether this standard will result in the most cost-effective way to protect public health. This paper examines the health literature and concludes that the evidence for the annual PM_2.5 standard is weak. The bulk of the health evidence is related to daily exposures to PM₁₀ and larger particle sizes, and there is no rational way to decide upon the correct level for this standard. It is unclear whether the most restrictive PM_2.5 standard will be protective of public health. Clearly research is needed to determine the correct PM metric, averaging time, and level for a standard. To date such research has been limited.     

Estimating PM2.5 concentrations with statistical distribution techniques for health risk assessment in Bangkok

Abstract

Ambient PM_2.5 data in the Central Business District (CBD) of Bangkok monitored by Pollution Control Department and Bangkok Metropolitan Administration were collected over three years in Bangkok from 2015 to 2017. The other air pollutions data were used as the dependent variables to develop mathematic models with statistical distribution technique. Multiple linear regression technique was selected as the main statistical distribution methodology for estimating PM_2.5 concentrations in non-monitored areas. The predicted PM_2.5 concentrations were validated against the measured PM_2.5 concentrations by various statistical techniques. The validation found that the model had strong significant correlations for ambient and roadside area with R ²?=?0.88 and 0.96, respectively. The non-carcinogenic health risk assessment of PM_2.5 was quantified as the hazard quotient (HQ) from both the measured and predicted data. The risk areas and HQ were compared using the inverse distance weighting interpolation technique and illustrated as GIS-based maps. During December to February, the HQ values of PM_2.5 were exceed 1 (HQs?>?1) at all area of CBD; however, the highest HQ was found in the southern part of CBD. The finding could be used for residential health awareness in that area.     

粤港澳大湾区PM2.5时空分布特征及其与气象要素的关系

细颗粒物（PM_2.5）污染不仅是现代社会城市化进程中的痛点,也是城市大气环境研究不可忽略的重要焦点。粤港澳大湾区作为世界级城市群,既是城市区域经济社会文化发展的重要体现,更是国家区域发展战略的重要构成与政策实施落脚点,其生态环境的优劣尤其受瞩目。对1999-2016年大湾区地表PM_2.5浓度栅格数据集进行了时空分布特征分析,其中空间自相关分析选取莫兰指数（Moran''I指数）作为度量;并利用多元线性回归模型探讨研究区内PM_2.5与气象要素之间关系。结果表明：粤港澳大湾区1999-2016年历年PM_2.5浓度呈先增加后减小的趋势,2008年为时间拐点,该时间节点之后空气质量显著提高,且1999、2009、2016三年,年平均PM_2.5浓度相似值趋于聚集分布。冷热点分析结果表明：热点区域集中于湾区行政核心区域范围内;冷点集中于核心边缘区域,空气质量较优。利用皮尔森相关分析最终筛选出实际蒸散量（aet）、太阳辐射（srad）、最低温度（tmmn）、蒸汽压（vap）、饱和水汽压差（vpd）、风速（ws）等6个气象因子,利用回归分析判断影响PM_2.5浓度时空分布的显著因子。结果表明：本研究区太阳辐射与PM_2.5浓度关系呈负相关,该结果与其他城市相关研究有较大差异,最小温度与PM_2.5浓度呈正相关,风速与PM_2.5浓度呈负相关,饱和水气压差与PM_2.5浓度呈正相关。     

基于ENVI-met的城市道路绿地植物配置对PM2.5的影响研究

随着城市化的快速发展,大气污染尤其细颗粒物(PM_(2.5))已成为制约城市环境的重要因素,相关研究表明,在众多的PM_(2.5)来源中道路交通是在其中的重要来源之一,而道路绿地植物群落能够消减来自道路的PM_(2.5)。运用场地观测和ENVI-met模拟相结合的方法探讨城市道路绿地植物群落对PM_(2.5)的影响,分析仅有乔木(i)、乔木+树篱(ii)、乔木+树篱+灌木(iii)3种模式下的植物群落配置对PM_(2.5)的影响,揭示绿地植物群落的长度、宽度、高度和LAI对PM_(2.5)的影响。结果表明:(1)场地观测中的道路绿地植物群落的3种配置模式对PM_(2.5)的消减率分别是iii(14.2%)ii(12.9%)i(9.2%)。(2)绿地中植物的种类、绿地宽度、绿地植物的叶面指数等要素对消减作用起正面作用,高度和长度起负面作用。(3)绿地的长度的一定量的减少可以在绿地后方形成一个宽度约为绿地长度80%的、随着与绿地的距离的增加而宽度递减的较绿地长度更长环境PM_(2.5)浓度更低的低谷区,但其他没有绿地的空间的PM_(2.5)浓度会加重。(4)绿地高度的增加会迫使PM_(2.5)向更高的空间运动。     

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。     

Fine particulate matter air pollution and atherosclerosis: Mechanistic insights

BackgroundAtherosclerosis is a progressive disease characterized by the accumulation of lipids and fibrous plaque in the arteries. Its etiology is very complicated and its risk factors primarily include genetic defects, smoking, hyperlipidemia, hypertension, lack of exercise, and infection. Recent studies suggest that fine particulate matter (PM_2.5) air pollution may also contribute to the development of atherosclerosis.Scope of reviewThe present review integrates current experimental evidence with mechanistic pathways whereby PM_2.5 exposure can promote the development of atherosclerosis.Major conclusionsPM_2.5-mediated enhancement of atherosclerosis is likely due to its pro-oxidant and pro-inflammatory effects, involving multiple organs, different cell types, and various molecular mediators.General significanceStudies about the effects of PM_2.5inhalation on atherosclerosis may yield a better understanding of the link between air pollution and major cardiovascular diseases, and provide useful information for policy makers to determine acceptable levels of PM_2.5 air quality. This article is part of a Special Issue entitled Air Pollution, edited by Wenjun Ding, Andrew J. Ghio and Weidong Wu.     

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。     

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.     

Chemical speciation and health risks of airborne heavy metals around an industrial community in Nigeria

Abstract

Quantification of PM_2.5 (particulate matter <2.5?µm) bound heavy metals and their potential health risks were carried out around a cement manufacturing company in Ewekoro, Nigeria. The PM_2.5 samples were collected using Environtech gravimetric sampler. A four-staged sequential extraction procedure was used to fractionate PM_2.5 bound chromium (Cr), lead (Pb), aluminum (Al), copper (Cu), and silver (Ag), and further analyzed using inductively coupled plasma mass spectrometry. Chemical speciation results reveal bioavailable levels of Pb (4.05?µg/m³), Cr (10.75?µg/m³), Al (16.47?µg/m³), Cu (4.38E-01?µg/m³), and Ag (1.22E-02?µg/m³) in the airborne particulates. Pb and Cr levels exceeded the World Health Organization allowable limit of 0.5 and 2.5E-05?µg/m³, respectively. The labile phases showed strong indication of the presence of Cr and Cu metal. Excess cancer risks exposure for adults, outdoor workers and children were higher than the acceptable risk target level of 1E-06. Non-carcinogenic health risk estimated using hazard quotients (HQs) and hazard indices (HIs) showed ingestion route within the safe level of HI <1 implying no adverse effect while inhalation route exceeded the safe level for all receptors. Enforcement of pollution control by authorized agencies, and screening of greenbelts as sinks for air pollutants is strongly recommended.     

Fine particulate matter (PM2.5) enhances FcεRI-mediated signaling and mast cell function

Persistent exposure to ambient fine particulate matter (PM_2.5) can exacerbate allergic diseases in humans. Mast cells play an important role in allergic inflammation in peripheral tissues, such as skin, mucosa, and lung. Engagement of the high-affinity Fc receptor leads to mast cell degranulation, releasing a variety of highly active mediators including histamine, leukotrienes, and inflammatory cytokines. How PM_2.5 exposure affects mast cell activation and function remains largely unknown. To characterize the effect of PM_2.5 on mast cells, we used bone marrow-derived mast cells (BMMCs) to examine whether PM_2.5 affected FcεRI-mediated signaling, cytokine production, and degranulation. Exposure to high doses of PM_2.5 caused pronounced apoptosis and death of BMMCs. In contrast, exposure to low doses of PM_2.5 enhanced mast cell degranulation and FcεRI-mediated cytokine production. Further analysis showed that PM_2.5 treatment increased Syk activation and subsequently phosphorylation of its substrates including LAT, PLC-γ1, and SLP-76. Moreover, PM_2.5 treatment led to activation of the PI3K and MAPK pathways. Intriguingly, water-soluble fraction of PM_2.5 were found responsible for the enhancement of FcεRI-mediated signaling, mast cell degranulation, and cytokine production. Our data suggest that PM_2.5, mainly water-soluble fraction of PM_2.5, could affect mast cell activation through enhancing FcεRI-mediated signaling.     

基于实地监测的城市林木调控PM2.5能力研究

随着城市的不断扩张,PM_(2.5)污染凸显,引起广泛关注。研究表明,城市林木为城市环境提供了重要的生态保障,在调控、缓解、降低城市PM_(2.5)污染危害等方面发挥极其重要的作用,通过筛选树种、优化配置结构、提高林木质量等方面进行城市林木前瞻性布局。然而,结合前期研究基础如何进一步深入研究并研究突破城市林木调控PM_(2.5)污染机制与机理,实现调控PM_(2.5)效应的最大化、最优化,依然是一个亟待解决的难题,这迫切需要在多尺度、多维度进行调控PM_(2.5)效应研究,并在不同尺度、不同维度进一步进行结合、延伸。对基于实地监测的城市林木调控PM_(2.5)能力研究现状相关文献进行归纳总结,并从林木单位叶面积与形态特征、配置结构特征、气象条件以及其他因素等方面归纳城市林木调控PM_(2.5)机制,同时从城市林木调控PM_(2.5)效应的时间变化特征、水平距离和垂直变化特征、内外变化特征等方面总结城市林木调控PM_(2.5)时空特征。最终提出研究城市林木调控PM_(2.5)效应目前存在的主要问题以及未来研究展望。     

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.