北京和阿拉善盟沙尘天气PM10和PM2.5中化学元素含量变化的研究

目的探讨北京地区沙尘暴期间PM10、PM2.5中所含化学元素含量变化的规律和特点。方法 2007年5—6月连续30d监测PM10、PM2.5浓度,每天24h连续采样,利用采样前后质量差值的差重法计算每日PM10、PM2.5的日均质量浓度。采用原子吸收光谱法,X-荧光分析法和电感耦合等离子体质谱法(ICP-MS)测量颗粒物的金属元素。气象数据由当地气象局提供。结果北京市大气PM2.5中铝、锰、锶、钛、镍、钙、硅、铁、镁元素含量,PM10中铝、锰、钛、钙、硅、铁、镁元素含量低于阿拉善盟。北京市大气PM2.5中铜、锌、铅、砷、镐、钼、镉、钾、硼元素含量,PM10中铜、锌、铅、砷、镐、钼、镉、钾、钡、汞元素含量高于阿拉善盟。阿拉善盟地区沙尘天气大气PM2.5中镐、钼、镉、汞、铅、铜、锌元素含量,PM10中镐、钼、镉、汞、铅、硼元素含量,与非沙尘天气比较,差异没有统计学意义。其他元素含量在沙尘天气时出现浓度增高的现象,差异有统计学意义。北京地区沙尘天气PM2.5中铝、磷、锰、锶、钡、钛、镍、钙、硅、铁、镁、钠、镐、硼元素含量升高;沙尘天气PM10中铝、磷、锰、锶、钡、镍、钙、硅、铁、镁、钠、镐、硼、钛元素含量升高。沙尘天气时PM2.5和PM10中锌、铅、砷、镉、汞、铜元素含量降低。沙尘天气PM2.5和PM10中钼、钾元素含量与非沙尘天气比较,变化并不明显。结论北京出现沙尘天气时,对汽车尾气带来的锌、铅、铜3种元素的污染有一定的稀释和缓解作用。     

新疆春季两次沙尘暴过程中大气PM2.5元素组成特征分析

目的研究新疆沙尘天气发生频次最高的中心区和田市和沙尘天气发生频次较低的地区库尔勒市大气PM2.5表面和整体元素组成特征。方法于2008年4—5月对和田、库尔勒市的大气PM2.5进行同时段采样,分别用X射线光电子能谱(XPS)分析两城市大气PM2.5表面元素组成及价态特征,用X-射线荧光法(XRF)分析两城市大气PM2.5整体各元素的质量分数。结果和田大气PM2.5整体5种元素(Si、Al、Na、Ca、Mg)的质量分数超过1%,库尔勒大气PM2.5整体3种元素(Si、Al、Na)的质量分数超过1%,两城市大气PM2.5整体元素质量分数的差异均没有统计学意义;库尔勒市大气PM2.5表面Zn、S的质量分数较高,和田市大气PM2.5表面K、Al、Mg的质量分数较高,其差异有统计学意义(P0.05)。在两城市大气PM2.5整体和表面都检测到的元素中,元素Fe、Mg、K在PM2.5表面发生了富集。沙尘天气发生时,大气PM2.5整体元素,除Si外,其他元素的质量分数均升高;大气PM2.5表面元素Na、Ca、K、Al、Mg、Fe的质量分数升高,脂肪族碳、和氮结合的碳、羰基碳、铵盐、硫酸盐、Cl的质量分数降低。结论和田大气PM2.5整体主要由5种元素(Si、Al、Na、Ca、Mg)组成,库尔勒大气PM2.5整体主要由3种元素(Si、Al、Na)组成。两城市大气PM2.5表面的主要元素相同,均为O、C、N、Si、Al、Mg。沙尘天气多发区和田市大气PM2.5整体和表面的自然源的元素质量分数较高;沙尘天气发生频次较低的库尔勒大气PM2.5整体和表面来自污染源的元素质量分数较高。沙尘天气时,大气PM2.5整体和表面的自然源元素质量分数升高;大气PM2.5表面污染元素(脂肪族碳、和氮结合的碳、羰基碳、铵盐、S、Cl)质量分数降低。     

大气颗粒物PM10和PM2.5中水溶性离子及元素分析

目的了解大气颗粒物PM10与PM2.5中水溶性离子及元素的主要组成及其浓度。方法在北京市城区设置1个采样点,于2006年6月16—18日和6月20—22日采集大气颗粒物PM10和PM2.5。采用离子色谱法测定PM10和PM2.5中8种水溶性离子(SO42-、NO3-、Cl-、NH4 、K 、Na 、Ca2 和Mg2 )的浓度;采用"酸提"法测定其中Ca、Mg、Al、As、Zn、Pb、Cu、V、Mn、Co、Fe、Se、Mo、Ni、Cr和Cd的浓度;采用"水提"法测定其中Zn、Pb、Cu、V、Mn、Co、Fe、Ni、Cr和Cd的浓度。结果PM2.5和PM10中8种水溶性离子平均质量浓度范围分别为0.44～9.16μg/m3和0.69～12.61μg/m3。PM2.5中SO42-和NO3-浓度分别占离子总浓度的30.2%和26.5%。PM10中SO42-和NO3-浓度分别占离子总浓度的29.7%和25.6%。PM2.5和PM10中,"酸提"元素平均质量浓度范围分别为1.06～6607.30ng/m3和1.92～12455.50ng/m3;"水提"元素平均质量浓度范围分别为0.31～189.80ng/m3和0.48～187.45ng/m3。结论水溶性离子是大气颗粒物的主要成分之一,值得关注。     

上海金山化学工业区周边PM2.5金属元素特征分析

目的掌握上海市金山化学工业区周边大气细颗粒物(PM2.5)金属元素的富集特征和周期规律,并探讨PM2.5来源。方法使用石英滤膜采集2015年7月-2016年6月金山化学工业区周边大气PM2.5样品,滤膜经5%硝酸超声浸提后,采用ICP-MS测定滤膜中14种金属元素(Na、Mg、Al、K、Ca、Ti、Mn、Fe、Ni、Cu、Zn、Pb、V、As)的质量浓度。结果As、Cu、Ni、Pb、V、Zn、Mn、Ca、Na、K富集因子10。14种金属元素的质量浓度整体呈现"单峰"分布,其中Ni、V的峰值出现在3月-5月,其余均出现在10月-次年4月。主成分分析法将金山化学工业区周边污染来源分为3类。结论大气PM2.5中的As、Cu、Ni、Pb、V、Zn、Mn、Ca、Na、K主要是人为来源,Al、Fe、Mg可能有自然来源,也可能有人为来源。金山化学工业区周边空气金属污染来源分为3类:Al、Cu、Fe、Mg、Ti、Ca、Na主要来源于地壳物质、土壤元素;Mn、As、Pb、Zn、K主要来源于工业和机动车排放;V、Ni主要来源于石油加工。     

某市秋冬季大气PM2.5中6种有毒元素的生态风险评价

目的 对某市秋冬季大气PM2.5中镉(Cd)、铬(Cr)、汞(Hg)、铅(Pb)、镍(Ni)、砷(As)的污染特征进行分析,并对其生态风险进行评价,为政府制定防治对策提供科学依据。方法 以该市光明实小、钓鱼台小学为采样点,分别在秋季冬季对PM25进行采样。测定Cd、Cr、Hg、Pb、Ni、As的含量,并利用富集因子(EF)法和潜在生态危害指数法对其生态风险进行评价。结果 除Ni外,其他元素的浓度均为冬季均高于秋季。秋季细颗粒物中As、Cd、Cr、Ni的超标率分别为2619%、2619%、10000%、238%;冬季细颗粒物中As、Cd、Cr的超标率分别为4091%、2728%、10000%。Hg的富集因子大于2,其他元素的富集因子均小于1。所有元素均属于低生态风险。结论 该市大气PM2.5中秋季Cr、Ni、Cd、As浓度超标,冬季Cr、As、Cd超标。除Ni外,其他元素的浓度冬季均高于秋季。Hg为中度富集,而其他元素则没有富集。该市秋冬季大气PM2.5中6种有毒元素均属于低生态风险。     

PM10和PM2.5与人类健康效应关系的研究进展

悬浮在空气中的颗粒物,按其空气动力学直径的大小,可分为PM10和PM2.5。2006年,WHO推荐用PM2.5代替PM10作为空气颗粒物浓度的指标。大气颗粒物（PM2.5）中主要包含有机碳、元素碳及碳酸盐碳。建筑扬尘、土壤尘、民用污染（燃煤）和交通污染（机动车尾气排放）为主要来源。北京、上海、西安日PM2.5和PM10日超标浓度皆较高。风速与春季和冬季的PM2.5质量浓度之间呈负相关,PM2.5质量浓度随空气相对湿度增加而增大,相对湿度与PM2.5质量浓度之间有正相关;温度与PM2.5质量浓度之间则无明显相关性。大气PM2.5浓度的升高会引起全死因疾病死亡率、心血管疾病死亡率的增加。大气PM2.5浓度的升高与心血管疾病有关。建议采取加大环境污染企业的治理力度,此外应该降低大城市汽车数量。     

北方沙尘天气过程对遂宁市环境空气质量的影响

目的探讨北方沙尘天气过程对遂宁市环境空气质量的影响。方法利用遂宁市国控空气质量自动监测站监测数据和气象资料,分析2013年3月外来沙尘对该市大气颗粒物粒径分布及其浓度和空气质量的影响。结果外来沙尘的输入导致遂宁市出现11 d污染天气,空气污染指数(API)最高达187。沙尘天气时段大气中PM10浓度为非沙尘时段的1.98倍,小时浓度最高达0.772 mg/m3,是沙尘到来前的5.4倍,而气态污染物SO2和NO2浓度不受外来沙尘的影响。在沙尘时段,大气PM2.5在PM10中所占比例下降,PM2.5-10浓度及其在PM10中所占比例升高。结论此次沙尘天气过程导致遂宁市大气呈现以PM2.5-10为主要特征的空气污染。     

中国三城市春季大气可吸入颗粒物和细颗粒物来源调查

目的 对北京、乌鲁木齐、青岛市大气可吸入颗粒物(PM10)和细颗粒物(PM2.5)的来源进行探讨.方法 利用2007年5月17日-6月18日采自北京市、2006年4月20日-6月1日采自乌鲁木齐市、2005年4月4日-5月15日采自青岛市的大气PM10和PM2.5中化学成分的检测数据,应用因子分析法和富集因子法进行颗粒物来源分析.结果 北京市大气PM10中主要污染源元素[中位数(最小值～最大值)]为Pb[0.216(0.040～0.795)μg/m3],主要来源于建筑水泥尘、土壤风沙尘(对PM10的方差贡献率为45.35％),工业粉尘、燃煤尘、机动车尾气(31.83％),生物质燃烧尘(13.57％);PM2.5主要污染源元素为Zn[0.365(0.126 ～0.808) μg/m3],污染来源于建筑水泥尘、土壤风沙尘(对PM2.5的方差贡献率为38.86％),工业粉尘、燃煤尘、机动车尾气(25.73％),生物质燃烧尘(13.10％),燃油尘(11.92％).乌鲁木齐大气PM10主要特征污染源元素为Cd[0.463(0.033 ～1.351) ng/m3],污染来源于土壤风沙尘、燃煤尘(对PM10的方差贡献率为49.75％),工业粉尘、机动车尾气、二次粒子尘(30.65％);PM25主要特征污染源元素为As[14.599(1.696 ～36.741) μg/m3],污染来源于土壤风沙尘、燃煤尘(对PM2.5的方差贡献率为43.26％),二次粒子尘(22.29％),工业粉尘、机动车尾气(20.50％).青岛大气PM10主要特征污染源元素为Pb[64.071(5.846～346.831) μg/m3],污染来源于建筑水泥尘(对PM10的方差贡献率为30.91％),机动车尾气、工业粉尘(29.65％),二次粒子尘(28.99％);PM2.5主要特征污染源元素为Pb[57.340(5.004 ～241.559) μg/m3],污染来源于二次粒子尘、工业粉尘、机动车尾气(对PM2.5的方差贡献率为49.82％),建筑水泥尘(33.71％).北京和乌鲁木齐大气PM2.5中重金属元素Zn、Pb、As、Cd的富集因子均高于PM10.结论 北京大气PM10和PM2.5主要来自于建筑水泥尘和土壤风沙尘;乌鲁木齐市则为煤烟沙尘型污染;青岛市大气PM10主要来自于建筑水泥尘,而PM2.5主要来自于二次粒子尘、工业粉尘、机动车尾气污染.重金属元素更易在PM2.5中富集.     

住宅室内外空气中颗粒物及无机元素的分布特征及关系

目的探讨住宅室内外空气PM10及元素组分的污染特征和相互关系。方法于2009年夏季选择某居住区内55户家庭,采用个体暴露撞击式采样器采集居室内外PM10样品,使用大气采样器采集居住区中心点位PM10、PM2.5样品。运用电感耦合等离子体-质谱(ICP-MS)和电感耦合等离子-光谱(ICP-OES)检测39种元素。使用富集因子法分析元素的富集特征,通过元素的室内外浓度回归分析各种元素的室内外来源贡献及相互关系。结果 39种元素在室内、室外和居住区点位PM10样品中的总含量分别为(18.5±11.5)%,(21.7±14.9)%,(18.8±7.3)%。其中7种常量元素(Si、Ca、Al、Fe、K、Na、Mg)分别占到室内、室外和居住区点位的PM10元素总量的89.7%,91.4%,91.5%。在居住区点位中W、Mo、Cr、K、Rb、Zn、Ni、As、Cs、Sb、Pb、Bi、Cd、Sn、Cu、Tl较多分布于PM2.5中。K、V、Mn、Cu、Zn、As、Cd、Pb的穿透因子较高,分别为0.51、0.40、0.44、0.85、0.58、0.67、0.58和0.77。结论回归分析表明除V、Zn、Cu、As、Cd、Pb之外,PM10中各种元素的室内外浓度相关性较差,仅适宜用室外PM10中V、Zn、Cu、As、Cd、Pb浓度来指示其室内浓度。Cr、Ni、Cu、Zn、As等13种元素在室内、室外和社区中心点位PM10中显著富集,表明其在环境中的人为源十分广泛。     

沙尘天气颗粒物对呼吸与心血管系统疾病日入院人数的影响

[目的]研究沙尘天气及沙尘颗粒物(PM2.5、PM10)与呼吸、心血管系统疾病日入院人数的关系.[方法]采用时问序列的半参数广义相加模型(Semi-parametric generalized additive model,GAM),在控制了长期趋势、星期几效应(day of the week,DOW)以及气象因子等混杂因素的影响后,分析2004年春季沙尘PM2.5和PM10及其大气污染物(SO2和NO2)与呼吸、心血管系统疾病日入院人数的关系,并将沙尘PM2.5和PM10按浓度分类探讨不同沙尘天气对人体健康的影响.[结果]①沙尘PM2.5和PM10在单污染模型中对居民呼吸和心血管系统疾病日入院人数的影响在滞后第2天和第3天有统计学意义.多污染模型分析表明,引入SO2或(和)NO2后,PM10对呼吸和心血管系统疾病日入院人数的影响有统计学意义,PM2.5对心血管系统疾病日入院人数的影响有统计学意义.在模型中引入其他污染物后,SO2和NO2对呼吸和心血管系统日入院人数的影响均无统计学意义.②颗粒物浓度分类模型分析表明,沙尘天气PM2.5只有达到沙尘暴颗粒物水平(＞167.5μg/m3)对男性呼吸和心血管系统疾病日入院人数的影响才有意义,而对女性的影响PM2.5在扬沙天气水平(95.4～167.5μg/m3)就有意义.沙尘天气PM10对男、女性呼吸和心血管系统健康的影响在扬沙天气水平(150～250μg/m3)就有意义,达到沙尘暴水平时,其影响达到最大.[结论]①在几乎没有工业污染的沙尘发生源区或近源区,其沙尘PM2.5、PM10只有达到较高浓度(扬沙天气水平)时,才会对居民呼吸和心血管系统疾病日入院人数有影响,且为滞后效应,并呈一定剂量效应关系,即:沙尘暴天＞扬沙天＞沙尘轻度污染天、清洁天;②本研究根据大气PM10和PM2.5水平划分沙尘天气类型,比以能见度划分更为科学且更易操作,同时也为我国制定PM10和PM2.5大气标准提出了科学依据.     

Comparison of PM2.5 and PM10 monitors

An extensive PM monitoring study was conducted during the 1998 Baltimore PM Epidemiology-Exposure Study of the Elderly. One goal was to investigate the mass concentration comparability between various monitoring instrumentation located across residential indoor, residential outdoor, and ambient sites. Filter-based (24-h integrated) samplers included Federal Reference Method Monitors (PM2.5-FRMs), Personal Environmental Monitors (PEMs), Versatile Air Pollution Samplers (VAPS), and cyclone-based instruments. Tapered element oscillating microbalances (TEOMs) collected real-time data. Measurements were collected on a near-daily basis over a 28-day period during July-August, 1998. The selected monitors had individual sampling completeness percentages ranging from 64% to 100%. Quantitation limits varied from 0.2 to 5.0 microg/m3. Results from matched days indicated that mean individual PM10 and PM2.5 mass concentrations differed by less than 3 microg/m3 across the instrumentation and within each respective size fraction. PM10 and PM2.5 mass concentration regression coefficients of determination between the monitors often exceeded 0.90 with coarse (PM10-2.5) comparisons revealing coefficients typically well below 0.40. Only one of the outdoor collocated PM2.5 monitors (PEM) provided mass concentration data that were statistically different from that produced by a protoype PM2.5 FRM sampler. The PEM had a positive mass concentration bias ranging up to 18% relative to the FRM prototype.     

深圳市部分幼儿园室内空气PM10、PM2.5、PM1.0浓度调查

目的了解深圳市部分幼儿园室内空气中可吸入颗粒物浓度。方法于2011年3—7月采用分层抽样抽取深圳市某区11所幼儿园共89间教室,采用DUSTMATE环境粉尘仪对空气中PM10、PM2.5、PM1.0浓度进行测定。结果PM10最大值为0.396 mg/m3,超出GB/T 18883—2002《室内空气质量标准》限值(<0.15 mg/m3)1.64倍;PM2.5最大值为0.137 mg/m3,超出美国EPA标准(<0.035 mg/m3)2.91倍;PM1.0最大值为0.063 mg/m3。工业区幼儿园教室空气中的PM10、PM2.5、PM1.0浓度均高于商业区和居民区,且差异有统计学意义(P<0.05)。结论本次调查的深圳市部分幼儿园室内空气中PM10、PM2.5浓度较高,尤其是工业区幼儿园,应引起重视并采取综合控制措施。     

PM2.5

＂PM2,5＂有一个容易理解的中文名——细颗粒物,是对空气中直径小于或等于2.5微米的固体颗粒或液滴的总称。这些颗粒如此细小,肉眼是看不到的,它们可以在空气中漂浮数天。人类纤细的头发直径大约是70微米,这就比最大的PM2.5还大了近三十倍。虽然自然过程也会产生PM2.5,但其主要来源还是人为排放。人类既直接排放PM2....     

深圳市某区中小学教室可吸入颗粒物现况评价

目的了解深圳市某区中小学教室空气可吸入颗粒物状况,为改善中小学教学环境卫生状况提供科学依据。方法分层抽取深圳市某区19所中小学114个教室,采用DUSTMATE环境粉尘仪对PM 10,PM 2.5,PM 1.0进行检测。结果 PM 10最大值为0.418mg/m3,超出国家标准1.79倍;PM 2.5最大值为0.142 mg/m3,超出美国EPA新标准3.06倍;PM 1.0最大值为0.056 mg/m3。工业区中小学教室PM 10,PM 2.5,PM 1.0含量高于商业区和居民区,差异均有统计学意义(P值均<0.05)。小学教室PM 10、PM 2.5合格率均高于中学教室;教室PM 10,PM 2.5含量随着楼层增加而下降,差异均有统计学意义(P值均<0.05)。结论深圳市某区中小学教室颗粒物PM 10,PM 2.5,PM 1.0污染情况严重,应引起高度重视并采取综合控制措施。     

Winter Mass Concentrations of Carbon Species in PM10, PM2.5 and PM1 in Zagreb Air,Croatia

The purpose of our investigation was to examine the mass concentrations of EC, OC and TC (EC + OC) in PM₁₀, PM_2.5 and PM₁ particle fractions. Daily PM₁₀, PM_2.5 and PM₁ samples were collected at an urban background monitoring site in Zagreb during winter 2009. Average OC and EC mass concentrations were 11.9 and 1.8 μg m⁻³ in PM₁₀, 9.0 and 1.4 μg m⁻³ in PM_2.5, and 5.5 and 1.1 μg m⁻³ in PM₁. Average OC/EC ratios in PM₁₀, PM_2.5, and PM₁ were 7.4, 6.9 and 5.4, respectively.     

Development of a continuous monitoring system for PM10 and components of PM2.5

While particulate matter with aerodynamic diameters below 10 and 2.5 microns (PM10 and PM2.5) correlate with excess mortality and morbidity, there is evidence for still closer epidemiological associations with sulfate ion, and experimental exposure-response studies suggest that the hydrogen ion and ultrafine (PM0.15) concentrations may be important risk factors. Also, there are measurement artifacts in current methods used to measure ambient PM10 and PM2.5, including negative artifacts because of losses of sampled semivolatile components (ammonium nitrate and some organics) and positive artifacts due to particle-bound water. To study such issues, we are developing a semi-continuous monitoring system for PM10, PM2.5, semivolatiles (organic compounds and NH4NO3), particle-bound water, and other PM2.5 constituents that may be causal factors. PM10 is aerodynamically sorted into three size-fractions: (1) coarse (PM10-PM2.5); (2) accumulation mode (PM2.5-PM0.15); and (3) ultrafine (PM0.15). The mass concentration of each fraction is measured in terms of the linear relation between accumulated mass and pressure drop on polycarbonate pore filters. The PM0.15 mass, being highly correlated with the ultrafine number concentration, provides a good index of the total number concentration in ambient air. For the accumulation mode (PM2.5-PM0.15), which contains nearly all of the semivolatiles and particle-bound water by mass, aliquots of the aerosol stream flow into system components that continuously monitor sulfur (by flame photometry), ammonium and nitrate (by chemiluminescence following catalytic transformations to NO), organics (by thermal-optical analysis) and particle-bound water (by electrolytic hygrometer after vacuum evaporation of sampled particles). The concentration of H+ can be calculated (by ion balance using the monitoring data on NO3-, NH4+, and SO4=).     

Concentrations of PM10 and PM2.5 particulate material in the atmosphere of Rome

Starting from 1993, various monitoring campaigns were carried out in Rome to determine PM10 and PM2.5. Their results are presented here cumulatively, with the aim of obtaining preliminary information on relationships among these size fractions, in various seasonal periods and in two sites with different characteristics (a road site and an urban background site in a public park). Particles were collected on filter and gravimetrically determined. Both PM10 and PM2.5 concentrations show temporal fluctuations with higher values during winter months. Background concentrations are lower than those contemporaneously measured at the road site only to a limited extent (10-17%). The contribution of PM2.5 to PM10 during the winter semester is higher than during the summer one (67 vs. 52%), with no substantial intersite differences.     

大气PM10与PM2.5的健康效应比较

可吸入颗粒物（IP）不仅影响气候和空气质量、破坏生态环境和历史文物，而且严重危害人体呼吸系统，是大气颗粒物中对人体健康威胁最大的一类。北京市环保监测中心监测结果显示，2000—2002年，市区PM10的年日均质量浓度均超过国家二级标准60％以上，在空气质量超标日中，首要污染物为PM10的天数最多达到96％。可吸入颗粒物可分为粗粒（空气动力学直径介于2．5～10μm）和细粒（空气动力学直径小于或等于2.5μm，也称为PM2．5）。     

Indoor/outdoor PM10 and PM2.5 in Bangkok, Thailand

Twenty-four-hour averaged PM10 and PM2.5 concentrations were obtained by using 4-liter-per-minute-pumps and impactors in microenvironments of a busy shopping district and a university hospital campus. In both areas, most people live directly adjacent to their worksites--minimizing the need to measure commuting exposure as part of total daily exposure. Co-located samplers were set in indoor microenvironments, the near-ambient zone of the households, and at nearby streetside central ambient monitoring stations. Smoking and use of other indoor PM sources were recorded daily via questionnaires. Consistent with previous studies, smoking and the use of charcoal stoves increased indoor particulate matter levels. The sampled air-conditioned hospital area had substantially lower particle concentrations than outdoors. A simple total exposure model was used to estimate the human exposure. The averaged ratios of co-located PM2.5/PM10 concentrations in various microenvironments are reported for each location. A single daily indoor average PM10 concentration for all households measured in a given sampling day is calculated for correlation analysis. Results showed that day-to-day fluctuations of these calculated indoor PM10 levels correlated well with near-ambient data and moderately well with ambient data collected at the nearby central monitoring site. This implies that ambient monitors are able to capture the daily variations of indoor PM levels or even personal exposure and may help explain the robust association of ambient PM levels and health effects found in many epidemiological studies. Absolute PM exposures, however, were substantially underestimated by ambient monitors in the shopping district, probably because of strong local sources.