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。     

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...     

哈密露天煤矿不同环境介质微生物群落特征分析

【背景】干旱区露天煤矿开采过程中产生的粉尘颗粒物加剧了土壤生态环境的恶化和矿区空气质量的下降,针对煤矿区土壤和粉尘颗粒物的微生物群落组成的研究鲜有报道。【目的】研究新疆哈密南湖乡露天煤矿土壤、粉尘及大气PM_2.5颗粒物中的微生物群落结构和多样性特征,并预测潜在的功能类群。【方法】采用高通量测序技术,对煤矿露天采坑区和电厂粉煤灰堆放区的土壤、粉尘及大气PM_2.5颗粒物的微生物真菌及细菌群落组成进行比对分析。【结果】矿区优势真菌类群来自子囊菌门(Ascomycota)和担子菌门(Basidiomycota),优势细菌类群来自变形菌门(Proteobacteria)和放线菌门(Actinobacteria)。真菌和细菌群落的丰富度及α多样性在整个矿区内无显著性差异,大气PM_2.5颗粒物的细菌群落生态位宽度显著大于露天采坑区和粉煤灰区。矿区内的土壤和PM_2.5颗粒物样本中均发现了一些丰度差异显著的功能类群,真菌特征功能类群为腐生营养型类群,细菌特征功能类群主要包括甲烷营养型类群、几丁质酶类细菌类群等。【结论】露天煤矿区粉尘可能对区域内土壤和PM_2.5颗粒物的微生物群落结构产生重要影响,具有煤组分降解功能的特定微生物类群可能是维持矿区土壤生态安全的重要微生物学机制之一。     

长江中游城市群建成区绿色基础设施对PM2.5的消减效应

研究城市群建成区绿色基础设施对PM_2.5的消减效应,有助于为城市群应对气候变化采取的可持续发展战略提供理论支撑。以长江中游城市群建成区为例,基于2000—2020年建成区面积数据、土地覆盖数据以及PM_2.5数据系统分析城市群PM_2.5浓度的时空演变特征,以林地、草地、耕地、湿地、水体等5种绿色基础设施为驱动因子,采用地理探测器模型中的因子探测与交互作用探测,探索城市群建成区绿色基础设施对PM_2.5浓度的削减效应。同时,结合夜间灯光数据以及约束线方法,进一步剖析城市化水平对建成区绿色基础设施的约束效应。结果表明：(1)2000—2020年期间,长江中游城市群年均PM_2.5浓度在时序上呈现先升后降的“倒U型”趋势,在空间上呈现由西北向东南级差化递减的分异特征。(2)2000—2020年期间,长江中游城市群建成区绿色基础设施对PM_2.5存在削减效应,但历年削减率均不超过4%,其中扩张区的削减效应显著高于老城区。(3)因子探测结果表明,长江中游城市群各绿色基础设施...     

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。     

Estimation of Citywide Air Pollution in Beijing

There has been discrepancies between the daily air quality reports of the Beijing municipal government, observations recorded at the U.S. Embassy in Beijing, and Beijing residents’ perceptions of air quality. This study estimates Beijing’s daily area PM_2.5 mass concentration by means of a novel technique SPA (Single Point Areal Estimation) that uses data from the single PM_2.5 observation station of the U.S Embassy and the 18 PM₁₀ observation stations of the Beijing Municipal Environmental Protection Bureau. The proposed technique accounts for empirical relationships between different types of observations, and generates best linear unbiased pollution estimates (in a statistical sense). The technique extends the daily PM_2.5 mass concentrations obtained at a single station (U.S. Embassy) to a citywide scale using physical relations between pollutant concentrations at the embassy PM_2.5 monitoring station and at the 18 official PM₁₀ stations that are evenly distributed across the city. Insight about the technique’s spatial estimation accuracy (uncertainty) is gained by means of theoretical considerations and numerical validations involving real data. The technique was used to study citywide PM_2.5 pollution during the 423-day period of interest (May 10, 2010 to December 6, 2011). Finally, a freely downloadable software library is provided that performs all relevant calculations of pollution estimation.     

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。     

Seasonal Variation in Schools’ Indoor Air Environments and Health Symptoms among Students in an Eastern Mediterranean Climate

The indoor air quality (IAQ) in classrooms highly affects the health and productivity of students. This article aims to clarify seasonal variation in indoor environment and sick building syndromes (SBS) symptoms in an Eastern Mediterranean climate. A series of field measurements were conducted during the fall and winter seasons from October 2011 to March 2012 in 12 naturally ventilated schools located in the Gaza Strip. Data on environmental perception and health symptoms were obtained from 724 students by using a validated questionnaire. The results showed that indoor PM₁₀ and PM_2.5 concentrations were 426.3 ± 187.6 μg/m³ and 126.6 ± 94.8 μg/m³, respectively. The CO₂ concentrations and ventilation rate widely exceeded their reference values during the winter season. The prevalence rates of general symptoms were relatively high at baseline assessment in the fall season and increased significantly during follow-up in the winter season. Significant increases in disease symptoms such as mucosal irritation and pre-existing asthma symptoms among students could be related to poor indoor air quality. Five distinct groups of SBS symptoms from factor analysis of students’ related symptoms were significantly correlated with PM₁₀ and PM_2.5, CO₂, ventilation rate, and indoor temperature. As vulnerable children, this situation negatively affects their school performance and health.     

深圳市大气污染时空分布及其与景观格局的关系

气溶胶和臭氧是我国大部分地区的两种主要大气污染物,对城市生态环境和人类身体健康影响严重。以深圳市为研究区,利用大气监测站点提供的监测数据和普通kriging空间插值法,从年、季、月尺度上开展近5年（2015-2019）深圳全市PM_2.5和臭氧的时空分布研究,通过皮尔逊分析和显著性检验从月尺度上分析土地覆盖类型及其景观格局对大气污染的影响。结果表明：（1）近5年内深圳市PM_2.5浓度总体呈现下降趋势,臭氧浓度则先降后升,二者季节性和月度差异显著。（2）PM_2.5和臭氧在年、季、月尺度上空间差异明显,全市PM_2.5浓度整体为西北高、东南低;臭氧浓度西北、东南较高,其余区域相对较低。（3）植被占比的增多对PM_2.5浓度有明显的抑制和调控作用,植被斑块间距离越近,对PM_2.5浓度的调控作用越明显;水体占比的增高会导致臭氧浓度增高和变化幅度增大。研究结果可为城市大气污染防治和景观格局规划管理提供参考。     

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.     

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).     

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

Particulate matter with an aerodynamic diameter <2.5 μm (PM_2.5) represents a severe environmental problem and is of negative impact on human health. Xi''an City, with a population of 6.5 million, is among the highest concentrations of PM_2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM_2.5 concentrations were greater than 100 μg/m³. Recently, a few studies have explored the potential causes of high PM_2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM_2.5 concentrations and other pollutants, including CO, NO₂, SO₂, and O₃, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM_2.5 concentrations. This model contains linear functions of SO₂ and CO, univariate smoothing non-linear functions of NO₂, O₃, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM_2.5 concentrations, with R² = 0.691, which improves the result of a stepwise linear regression (R² = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO₂, NO₂, and O₃ account for 10.88% of the total deviance. These results show that in Xi''an City, the traffic and other industrial emissions are the primary source of PM_2.5. Temperature, location, and wind variables also non-linearly related with PM_2.5.     

广州大夫山雨季林内外空气TSP和PM2.5浓度及水溶性离子特征

采用平行同步采样法,于2012年雨季,对广州市大夫山森林公园林内外空气的总悬浮颗粒物(TSP)和细颗粒物(PM_2.5)样品进行了24 h收集,测定了TSP和PM_2.5的质量浓度并分析了样品中水溶性无机离子成分。结果表明:林内外PM_2.5的质量浓度平均值分别为(40.18±10.47)和(55.79±13.01) g/cm³;林内外TSP的质量浓度分别为(101.32 ± 33.19)和(116.61±35.36) g/cm³。林内与林外比,PM_2.5和TSP平均质量浓度都显著减少(P < 0.05),表明森林能显著改善空气环境质量。TSP和PM_2.5中SO₄^2-、Na⁺、NH₄⁺和NO₃^-为水溶性无机离子主要成分,占总离子质量的80%以上,林外这些离子的浓度高于林内(NH₄⁺除外)。这4种离子雨季在空气中的主要存在方式为NaCl、Na₂SO₄、NH₄HSO₄和NH₄NO₃。计算表明,采样期间海盐对大夫山空气TSP和PM_2.5的水溶性组分中Na⁺和Cl^-贡献最大,其它元素主要源自陆地源。林内外TSP和PM_2.5的c(NO₃^-)/c(SO₄^2-)比值在0.3以下,表明固定源是大夫山森林公园空气主要污染贡献者,TSP中c(NO₃^-)/c(SO₄^2-)的比值大于PM_2.5的比值,说明移动源对TSP的贡献大于PM_2.5。     

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.     

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...     

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。     

Particulate matter exposure predicts residence in high-risk areas for community acquired pneumonia among hospitalized children

Particulate matter exposure is a risk factor for lower respiratory tract infection in children. Here, we investigated the geospatial patterns of community-acquired pneumonia and the impact of PM_2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) on geospatial variability of pneumonia in children. We performed a retrospective analysis of prospectively collected population-based surveillance study data of community-acquired pneumonia hospitalizations among children <18 years residing in the Memphis metropolitan area, who were enrolled in the Centers for Disease Control and Prevention sponsored Etiology of Pneumonia in the Community (EPIC) study from January 2010 to June 2012. The outcome measure, residence in high- and low-risk areas for community-acquired pneumonia, was determined by calculating pneumonia incidence rates and performing cluster analysis to identify areas with higher/lower than expected rates of community-acquired pneumonia for the population at risk. High PM_2.5 was defined as exposure to PM_2.5 concentrations greater than the mean value (>10.75 μg/m³), and low PM_2.5 is defined as exposure to PM_2.5 concentrations less than or equal to the mean value (≤10.75 μg/m³). We also assessed the effects of age, sex, race/ethnicity, history of wheezing, insurance type, tobacco smoke exposure, bacterial etiology, and viral etiology of infection. Of 810 (96.1%) subjects with radiographic community-acquired pneumonia, who resided in the Memphis metropolitan area and had addresses which were successfully geocoded (Supplementary Figure F2), 220 (27.2%) patients were identified to be from high- (n = 126) or low-risk (n = 94) community-acquired pneumonia areas. Community-acquired pneumonia in Memphis metropolitan area had a non-homogenous geospatial pattern. PM_2.5 was associated with residence in high-risk areas for community-acquired pneumonia. In addition, children with private insurance and bacterial, as opposed to viral, etiology of infection had a decreased risk of residence in a high-risk area for community-acquired pneumonia. The results from this paper suggest that environmental exposures as well as social risk factors are associated with childhood pneumonia.     

The Impact of Winter Heating on Air Pollution in China

Fossil-fuel combustion related winter heating has become a major air quality and public health concern in northern China recently. We analyzed the impact of winter heating on aerosol loadings over China using the MODIS-Aqua Collection 6 aerosol product from 2004–2012. Absolute humidity (AH) and planetary boundary layer height (PBL) -adjusted aerosol optical depth (AOD^*) was constructed to reflect ground-level PM_2.5 concentrations. GIS analysis, standard statistical tests, and statistical modeling indicate that winter heating is an important factor causing increased PM_2.5 levels in more than three-quarters of central and eastern China. The heating season AOD^* was more than five times higher as the non-heating season AOD^*, and the increase in AOD^* in the heating areas was greater than in the non-heating areas. Finally, central heating tend to contribute less to air pollution relative to other means of household heating.     

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.     

Issues in Human Particulate Exposure Assessment: Relationship between Outdoor,Indoor, and Personal Exposures

The recent review of the National Ambient Air Quality Standard for particuslate matter and the resultant new health-based PM_2.5 standard was in part motivated by findings from epidemiological studies. These studies reported significant associations between adverse health effects and concentrations of ambient particulate matter at levels below the previously existing PM₁₀ standard. Interpretation of these results has been hindered by our relatively poor understanding of the relationship between personal exposures and concentrations in the indoor and outdoor environments. Individuals spend the majority of their time in indoor environments. Therefore, it is important to understand where and how they may be exposed to the contaminants which may be causing the health effects, and which activities place them at a higher risk of exposure to these agents. In addition, since particulate matter is a complex mixture of contaminants, further research is required to examine its formation process, sources, composition, and health effects. Without an improved scientific understanding of these issues, it is difficult to assess whether the new PM_2.5 standard will be implemented, and if so, whether it can be adequately protective of public health.