Estimating effects of ambient PM(2.5) exposure on health using PM(2.5) component measurements and regression calibration

Most air pollution and health studies conducted in recent years have examined how a health outcome is related to pollution concentrations from a fixed outdoor monitor. The pollutant effect estimate in the health model used indicates how ambient pollution concentrations are associated with the health outcome, but not how actual exposure to ambient pollution is related to health. In this article, we propose a method of estimating personal exposures to ambient PM(2.5) (particulate matter less than 2.5 microm in diameter) using sulfate, a component of PM(2.5) that is derived primarily from ambient sources. We demonstrate how to use regression calibration in conjunction with these derived values to estimate the effects of personal ambient PM(2.5) exposure on a continuous health outcome, forced expiratory volume in 1 s (FEV(1)), using repeated measures data. Through simulation, we show that a confidence interval (CI) for the calibrated estimator based on large sample theory methods has an appropriate coverage rate. In an application using data from our health study involving children with moderate to severe asthma, we found that a 10 microg/m3 increase in PM(2.5) was associated with a 2.2% decrease in FEV(1) at a 1-day lag of the pollutant (95% CI: 0.0-4.3% decrease). Regressing FEV(1) directly on ambient PM(2.5) concentrations from a fixed monitor yielded a much weaker estimate of 1.0% (95% CI: 0.0-2.0% decrease). Relatively small amounts of personal monitor data were needed to calibrate the estimate based on fixed outdoor concentrations.     

Examining the representativeness of home outdoor PM2.5, EC, and OC estimates for daily personal exposures in Southern California

Recent studies have linked acute respiratory and cardiovascular outcomes to measurements or estimates of traffic-related air pollutants at homes or schools. However, few studies have evaluated these outdoor measurements and estimates against personal exposure measurements. We compared measured and modeled home outdoor concentrations with personal measurements of traffic-related air pollutants in the Los Angeles air basin (Whittier and Riverside). Personal exposure of 63 children with asthma and 15 homes were assessed for particulate matter with an aerodynamic diameter less than 2.5???m (PM_2.5), elemental carbon (EC), and organic carbon (OC) during sixteen 10-day monitoring runs. Regression models to predict daily home outdoor PM_2.5, EC, and OC were constructed using home outdoor measurements, geographical and meteorological parameters, as well as CALINE4 estimates at outdoor home sites, which represent the concentrations from local traffic sources. These home outdoor models showed the variance explained (R ²) was 0.97 and 0.94 for PM_2.5, 0.91 and 0.83 for OC, and 0.76 and 0.87 for EC in Riverside and Whittier, respectively. The PM_2.5 outdoor estimates correlated well with the personal measurements (Riverside R ²?=?0.65 and Whittier R ²?=?0.69). However, excluding potentially inaccurate samples from Riverside, the correlation between personal exposure to carbonaceous species and home outdoor estimates in Whittier was moderate for EC (R ²?=?0.37) and poor for OC (R ²?=?0.08). The CALINE4 estimates alone were not correlated with personal measurements of EC or other pollutants. While home outdoor estimates provide good approximations for daily personal PM_2.5 exposure, they may not be adequate for estimating daily personal exposure to EC and OC.     

Assessing personal travel exposure to on-road PM2.5 using cellphone positioning data and mobile sensors

PM_2.5 pollution imposes substantial health risks on urban residents. Previous studies mainly focused on assessing peoples' exposures at static locations, such as homes or workplaces. There has been a scarcity of research that quantifies the dynamic PM_2.5 exposures of people when they travel in cities. To address this gap, we use cellphone positioning data and PM_2.5 concentration data collected from smart sensors along roads in Guangzhou, China, to assess personal travel exposure to on-road PM_2.5. First, we extract the trips of cellphone users from their trajectories and use the shortest path algorithm to calculate their travel routes on the road network. Second, the travel exposure of each user is estimated by associating their movement patterns with PM_2.5 concentrations on roads. The result shows that most users’ average travel exposures per hour fall within the range of 20 ug/m³ to 75 ug/m³. Travel exposure varies across users, and 54.0% of users experience low travel exposure throughout the day, 25.5% of users experience high travel exposure in the evening, and 20.5% of users experience high travel exposure in the afternoon. Furthermore, the impacts of on-road PM_2.5 on urban populations are uneven across roads. More attention should be given to roads with high PM_2.5 concentrations and traffic flows in each period, such as Huan Shi Middle Road in the morning, Inner Ring Road in the afternoon, and Xinjiao Middle Road in the evening. The findings in this study can contribute to a more in-depth understanding of the relationship between air pollution and the travel activities of urban populations.     

不同出行方式PM_(2.5)个体暴露水平的比较研究

目的在不同浓度大气PM_(2.5)水平下,比较不同出行方式的PM_(2.5)个体暴露水平,并进一步比较地铁站不同位置的PM_(2.5)浓度,以期为城市居民选择适宜的出行方式提供科学依据。方法于2015年12月30日至2016年1月4日,选择北京市城区的某条路线作为研究地点,使用便携式PM_(2.5)监测仪同时测量步行、乘坐公交车和地铁3种出行方式的PM_(2.5)个体暴露水平;对地铁站台、安检处和地铁车厢内的PM_(2.5)浓度进行同步监测并比较。结果在同一研究时期,步行、乘坐公交车和地铁测得的PM_(2.5)个体暴露浓度均值分别为219.34、209.61、167.56μg/m3。当大气PM_(2.5)浓度较低时(≤60μg/m3),乘坐地铁的PM_(2.5)个体暴露水平高于步行和乘坐公交车;当大气PM_(2.5)浓度较高时(100μg/m3),乘坐地铁的个体PM_(2.5)暴露水平明显低于步行和公交车,差异均有统计学意义(P0.05)。监测期间地铁站台、安检处和车厢内的PM_(2.5)浓度均值分别为196.90、170.20、136.82μg/m3。结论在不同的大气PM_(2.5)污染水平下,不同出行方式的人群PM_(2.5)个体暴露水平不同。     

大气PM10浓度与健康暴露-反应关系门槛效应模型分析

目的 探讨大气可吸入颗粒物（PM₁₀）污染浓度与健康的暴露-反应关系是否存在拐点及拐点的个数,为相关政策的制定提供参考。方法 收集2011年和2013年中国统计年鉴以及各城市环境公报中的PM₁₀数据,利用雷达图作出2011和2013年各省份PM₁₀平均浓度分布图;利用2011和2013年中国健康与养老追踪调查数据,通过门槛效应模型研究各城市PM₁₀污染与自评健康、过去1个月是否患病、过去1年是否有住院需要等3个指标的暴露-反应曲线。结果 雷达图显示,2011和2013年全国27个省市的PM₁₀浓度分布情况相似,中部和北部地区的污染物浓度高于东南沿海地区。门槛效应模型回归分析结果显示,自评健康、过去1个月是否患病、过去1年是否有住院需要3个健康指标的暴露-反应曲线均存在2个拐点（门槛效应的门槛值）;当PM₁₀浓度分别为<96、96~199、>199 μg/m³时,PM₁₀浓度每升高1 μg/m³,被调查者自评健康较好的概率分别下降0.092%（P<0.01）、0.041%（P>0.05）、0.010%（P>0.05）;当PM₁₀浓度分别为<52、52~60、>60 μg/m³时,PM₁₀浓度每升高1 μg/m³,被调查者报告过去1个月患病的概率分别升高0.131%（P<0.05）、下降0.070%（P>0.05）、升高0.023%（P<0.05）;当PM₁₀浓度分别为<45、45~56、>56 μg/m³时,PM₁₀浓度每升高1 μg/m³,过去1年有住院需要的概率分别升高0.061%（P>0.05）、下降0.110%（P<0.05）、升高0.019%（P>0.05）。结论 PM₁₀浓度与健康的暴露-反应关系曲线存在拐点,政府在制定卫生政策时应充分考虑到不同污染物浓度对居民健康影响的差异性,以减少不必要的卫生费用支出和卫生资源投入。     

北京市冬季住宅内PM_(2.5)暴露水平及室内外关系的研究

目的研究自然通风条件下大规模人群的住宅内PM_(2.5)浓度水平,探讨PM_(2.5)浓度的室内外关系,为评估室内PM_(2.5)暴露提供重要数据支撑和新的研究思路。方法于2013年12月1日—2014年2月28日(2013—2014冬季)在北京市某区开展大规模人群的时间-活动模式和空气污染暴露影响因素调查,基于调查数据及PM_(2.5)空气动力学特性建立住宅内PM_(2.5)的质量平衡模型,利用环境监测站点PM_(2.5)监测数据模拟住宅内PM_(2.5)浓度,计算室内外PM_(2.5)浓度比(I/O),并探讨PM_(2.5)室内外关系。结果本研究1 092个样本2013—2014冬季住宅内PM_(2.5)浓度范围为26~167μg/m~3,PM_(2.5)浓度的中位数为73μg/m~3,四分位数间距为34μg/m~3。室外PM_(2.5)浓度范围分别为0~33μg/m~3、34~65μg/m~3、66~129μg/m~3、≥130μg/m~3时,PM_(2.5)浓度I/O分别为1.75、1.05、0.76和0.63;随着室外PM_(2.5)浓度的增加,I/O呈减小趋势,且分布趋于集中。结论基于大规模人群的时间-活动模式和空气污染暴露影响因素调查建立质量平衡模型,可实现大规模人群室内PM_(2.5)浓度的连续模拟。     

2010年北京市归因于大气PM2.5污染的过早死亡研究

目的 评估大气PM2.5长期暴露导致的过早死亡,为科学制定政策保护人群健康提供科学依据.方法 收集卫星遥感反演的北京市2010年的PM2.5浓度数据、人口数据、基线死亡率等基础数据,利用GIS空间统计工具将网格化的PM2.5污染数据转换为区县尺度数据,基于WHO的经典全球疾病负担评估模型和PM25全暴露范围RR函数表,评估北京市各区县归因于大气PM2.5污染的过早死亡,并探讨其空间分布特征及分疾病种类的特点.结果 2010年北京市各区县的大气PM25年均浓度均超过GB 3095-2012《环境空气质量标准》二级浓度限值(35 μg/m3);北京市归因于大气PM:5污染的过早死亡数为16 527人,归因死亡率为0.843‰,中心城区和东南部郊区的过早死亡风险较高,过早死亡数最多的为朝阳区(5 648人,归因死亡率为1.542‰)和海淀区(4 21 1人,归因死亡率为1.284‰),过早死亡数最少的为西北部郊区.结论北京市2010年归因于大气PM2.5污染的过早死亡人数较多,应引起相关部门的重视;在制定政策减少归因于大气PM2.5污染的过早死亡时,应重点关注人口密集的中心城区和发展较快的东南部郊区,并基于各区县过早死亡的疾病分布特点开展针对性的宣教和防控.     

Algorithms for estimating personal exposures to chemical agents in the semiconductor health study

For exposure assessment in a series of epidemiologic studies of semiconductor industry workers, estimates of potential personal exposures to selected chemical agents were developed using a set of algorithms. A worker's total exposure to each agent was defined as the sum of task scores for each task using that agent. Task scores were calculated as the intensity and frequency score for each task, modified by a factor indicating degree of presence of the target agent and a process-specific emission factor representing degree of emission control present for that process. The algorithms used multiple sources of information to generate an exposure score for each worker-process-agent combination. Exposure scores were then placed into ordinal categories of exposure (0–3) for use in the epidemiologic analysis.     

冬季重污染天气大气PM_(2.5)致突变性的初步研究

目的利用鼠伤寒沙门菌回复突变试验(Ames试验)测定冬季重污染天气下PM_(2.5)的致突变性。方法利用大流量采样器收集冬季优良天气及重污染天气PM_(2.5),PM_(2.5)全颗粒物剂量为100、250、500和1 000μg/皿,选用TA98菌株,采用平板掺入法进行Ames试验。结果优良天气下收集的PM_(2.5)在500、1 000μg/皿-S9剂量组及1 000μg/皿+S9剂量组致突变率(MR)2且有剂量-效应关系;严重污染天气下收集的PM_(2.5)在250、500和1 000μg/皿±S9剂量组MR2且有剂量-效应关系。结论 PM_(2.5)全颗粒物对TA98具有一定的致突变效应。     

大气PM2.5长期暴露与中国全因死亡风险的关联研究

继往研究表明,长期PM2.5暴露与死亡风险的增加有关,但在较高的大气浓度下这种关联性很少被量化。本研究基于中国健康长寿纵向调查(the Chinese Longitudinal Healthy Longevity Survey,CLHLS),估算了PM2.5长期暴露的死亡风险比(HR)。CLHLS是一项针对中国65岁及以上老年人的前瞻性队列研究,受访者2008年入组并持续跟踪死亡情况至2014年。控制年龄、性别、吸烟状况、饮酒状况、身体活动、体质指数、家庭收入、婚姻状况和教育程度,使用Cox比例风险模型分析大气PM2.5长期暴露对全因死亡率的影响。结果显示,CLHLS队列中共有13344人具有全时间点的数据,产生了49440人-年的随访数据。调查对象暴露于PM2.5浓度的中位数为50.7μg/m3(95%CI:1.06~113.3),PM2.5浓度每增加10μg/m3的总风险比为1.08(95%CI:1.06~1.09)。分层分析显示,农村、南部地区和较低浓度的PM2.5暴露下HR结果分别高于城市、北部地区和较高浓度的PM2.5暴露。2010年中国65岁及以上人群中估计有1765820人的过早死亡与PM2.5暴露有关。研究表明,PM2.5长期暴露与中国65岁及以上老年人全因死亡风险的增加有关,但随着PM2.5浓度的增加,风险程度有所降低。     

Influence of ambient (outdoor) sources on residential indoor and personal PM2.5 concentrations: analyses of RIOPA data

The Relationship of Indoor, Outdoor and Personal Air (RIOPA) study was designed to investigate residential indoor, outdoor and personal exposures to several classes of air pollutants, including volatile organic compounds, carbonyls and fine particles (PM2.5). Samples were collected from summer, 1999 to spring, 2001 in Houston (TX), Los Angeles (CA) and Elizabeth (NJ). Indoor, outdoor and personal PM2.5 samples were collected at 212 nonsmoking residences, 162 of which were sampled twice. Some homes were chosen due to close proximity to ambient sources of one or more target analytes, while others were farther from sources. Median indoor, outdoor and personal PM2.5 mass concentrations for these three sites were 14.4, 15.5 and 31.4 microg/m3, respectively. The contributions of ambient (outdoor) and nonambient sources to indoor and personal concentrations were quantified using a single compartment box model with measured air exchange rate and a random component superposition (RCS) statistical model. The median contribution of ambient sources to indoor PM2.5 concentrations using the mass balance approach was estimated to be 56% for all study homes (63%, 52% and 33% for California, New Jersey and Texas study homes, respectively). Reasonable variations in model assumptions alter median ambient contributions by less than 20%. The mean of the distribution of ambient contributions across study homes agreed well for the mass balance and RCS models, but the distribution was somewhat broader when calculated using the mass balance model with measured air exchange rates.     

基于支付意愿的大气PM2.5健康经济学损失评价

目的采用支付意愿(WTP)法对北京市PM2.5的健康经济学损失进行初步评价,将以调查结果的WTP值为基础计算的健康经济学损失与已有数据进行对比和讨论。方法于2014年采用条件价值评估法(contingent valuation method,CVM),调查北京市海淀区、朝阳区、西城区722名居民对大气污染健康风险的WTP,选取死亡为健康终点,核算北京市居民统计学意义上的生命价值(VOSL)和大气PM2.5的健康损害成本。结果有65.4%(472/722)的调查对象愿意为降低大气环境污染健康风险支付费用,其WTP值为100~200元/人,居民VOSL为66.7万元~133.3万元。2011年北京市大气PM2.5污染对非意外总死亡、呼吸系统及循环系统疾病死亡的环境损害成本分别为27.11亿元~54.19亿元、3.22亿元~6.43亿元、8.11亿元~16.21亿元,分别占该市该年GDP总量的0.17%~0.33%,0.02%~0.04%和0.05%~0.10%。结论可采用CVM调查大气污染健康风险的WTP,并评估大气污染的健康损失成本。     

Factors affecting the association between ambient concentrations and personal exposures to particles and gases

Results from air pollution exposure assessment studies suggest that ambient fine particles [particulate matter with aerodynamic diameter相似文献   

Traffic-related occupational exposures to PM2.5, CO, and VOCs in Trujillo, Peru

A traffic-related exposure study was conducted among 58 workers (drivers, vendors, traffic police, and gas station attendants) and 10 office workers as controls in Trujillo, Peru, in July 2002. PM2.5 was collected, carbon monoxide (CO) was measured, volatile organic compounds (VOCs) were sampled and analyzed. Newspaper vendors had the highest full-shift CO exposures (mean +/- SD: 11.4 +/- 8.9 ppm), while office workers had the lowest (2.0 +/- 1.7 ppm). Bus drivers had the highest full-shift PM2.5 exposures (161 +/- 8.9 microg/m3), while gas station attendants (64 +/- 26.5 microg/m3) and office workers (65 +/- 8.5 microg/m3) were the lowest. Full-shift benzene/toluene/ethylbenzene/xylene exposures (BTEX) among gas station attendants (111/254/43/214 microg/m3) were much higher than those among van and taxi drivers. Several of the traffic-related occupational exposures studied were elevated and are of occupational health concern.     

Design and validation of a high-flow personal sampler for PM2.5

A high-flow personal sampler (HFPS) for airborne particulate matter has been developed and fully characterised, and validation tests have been carried out. The sampler is a low-cost gravimetric instrument designed to collect particulate matter with a 50% cut point at 2.5 microm aerodynamic equivalent diameter (PM2.5), where size selection is achieved by the use of porous polyurethane foam. Development of a porous foam selector was chosen over a cyclone or impactor due to the lightweight, low-cost, and compact design that could be achieved. The sampler flow rate of 16 1/min is achieved using a portable, flow-controlled pump; this flow rate is far higher than that of conventional personal samplers and the HFPS can therefore be used for personal sampling in the ambient environment over short sampling periods of much less than 24 h. The HFPS is currently being used in a study of particle exposure of urban transport users (cyclists, car drivers, bus and Underground rail passengers) where personal sampling over short time periods representing typical commuter journey times is required. The HFPS was fully characterised in chamber studies with a TSI aerodynamic particle sizer (APS). The sampler was then validated against a co-located U.S. EPA Federal Reference PM2.5 Well Impactor Ninety Six (WINS) and a KTL cyclone, and parallel testing was performed. Initial testing showed some penetration of particles through the porous foam structure; applying an oil coating to the foam eliminated this problem. Chamber testing was carried out on a number of different selector prototypes, with the final design giving a 50% penetration diameter (i.e., d50) of 2.4 microm at 16 1/min. The new sampler exhibited good agreement in three sets of co-located tests with established samplers, and parallel testing showed excellent agreement between paired HFPS samplers.     

哈尔滨市部分地区大气PM_(2.5)中多环芳烃的污染特征分析

目的分析哈尔滨市2013—2014年大气PM_(2.5)中多环芳烃的污染水平及污染特征。方法于2013-2014年在哈尔滨市道外区(污染区)和道里区(对照区)采集大气PM_(2.5)共336个样品,用气相色谱-质谱联用仪测定样品中多环芳烃浓度,分析不同时期、不同地区的多环芳烃污染特征。结果哈尔滨市大气PM_(2.5)中可检出17种多环芳烃,其中检出7种致癌PAHs。2013、2014年大气PM_(2.5)中PAHs在冬、春、夏、秋季的平均浓度分别为(79.37±23.36)、(8.66±4.34)、(5.79±2.66)、(6.54±1.42)ng/m3和(57.08±11.94)、(12.36±5.79)、(3.33±1.68)、(18.00±9.39)ng/m3,冬季浓度值均高于春、夏、秋季(P0.05)。2013、2014年采暖期污染区大气PAHs总浓度分别为(24.55±6.20)、(49.09±6.22)ng/m3,分别高于对照区[(9.50±5.20)、(19.02±5.22)ng/m~3],差异有统计学意义(P0.01)。2013、2014年大气PM_(2.5)中PAHs的各环构成比均以4环最高。PAHs总浓度与BaP、PM_(2.5)浓度之间均呈正相关。结论哈尔滨市大气PM_(2.5)中可检出具有致癌性的多环芳烃,尤其是采暖期。     

大气PM2.5长期暴露与中国全因死亡风险的关联性研究

多项研究表明,长期接触细颗粒物(PM2.5)与死亡风险的增加有关,但在较高的大气浓度下这种关联性相对较少。基于中国健康长寿纵向调查(CLHLS),本研究估算了PM2.5长期暴露的全因死亡风险比(HR)。CLHLS是一项针对中国65岁及以上老年人的前瞻性队列研究,受访者于2008年入组并随访至2014年。选取可获得2008年居住地址,并能匹配附近1 km内PM2.5浓度的研究对象,调整年龄、性别、吸烟状况、饮酒状况、身体活动、体质指数、家庭收入、婚姻状况和教育程度,采用Cox比例风险回归模型分析大气PM2.5长期暴露对全因死亡率的影响。本研究共纳入13344名研究对象,共计随访49440人年;空气中PM2.5暴露浓度M(IQR)为50.7(6.7~113.3)μg/m3,PM2.5浓度每增加10μg/m3的HR值为1.08(95%CI:1.06~1.09)。分层分析结果显示,农村、南部地区和较低浓度的PM2.5暴露下HR值分别高于城市、北部地区和较高浓度的PM2.5暴露。2010年,中国65岁及以上人群中估计有1765820人的过早死亡与PM2.5暴露有关。     

Maternal exposure to ambient PM2.5 and term low birthweight in the State of Georgia

A growing body of evidence suggests that ambient air pollution could be associated with low birthweight (LBW). In this study, we examined pregnancy exposure to ambient PM_2.5 and the risk of LBW in the State of Georgia. The study population consisted of 48,172 full-term live births between 1 January 2004 and 31 December 2004 in nine counties of Georgia, which was obtained from the national natality dataset. County-level air quality index data obtained from the U.S. Environmental Protection Agency was used to estimate exposure to ambient levels of PM_2.5. Multivariate logistic regression revealed that infants with maternal exposure to PM_2.5 falling within 75 to < 95th percentiles were at increased risk of LBW (OR: 1.36; 95 % CI: 1.03, 1.79), after adjusting for potential confounders. This study provided more evidence on the role of PM_2.5 in LBW. Reducing exposure for pregnant women would be necessary to improve the health of infants.     

Seasonal trends of PM2.5 and PM10 in ambient air and their correlation in ambient air of Lucknow city, India

The PM₁₀ concentration (μg/m³) in Lucknow city at 4 locations in three different seasons ranged between 148.6–210.8 (avg. 187.2 ± 17.1) during summer, 111.8–187.6 (avg. 155.7 ± 22.7) during monsoon and 199.3–308.8 (avg. 269.3 ± 42.9) during winter while PM_2.5 ranged between 32.4–67.2 (avg. 45.6 ± 10.9), 25.6–68.9 (avg. 39.8 ± 4.6) and 99.3–299.3 (avg. 212.4 ± 55.0) during respective seasons. The mass fraction ratio of PM_2.5 ranged between 0.22–0.92 (avg. 0.42 ± 0.26) and was significantly high during winter season indicating their composition.     

A cross-industry assessment of personal exposures to methyl methacrylate

This study investigated personal exposures to methyl methacrylate (MMA) monomer during monomer production and use within the chemical manufacturing industry. The aim was to include multiple companies and sites to provide information regarding differences and similarities among exposure subgroups within the industry during the time period of 1998-2000. To this end, personal exposure data for MMA monomer from three chemical corporations was combined into a single data set. The data was stratified by sample type (full shift or short term), job classification (monomer production, monomer use, maintenance, distribution, and laboratory operations), company, and facility. Only classifications with three samples or more were included in the final data set, yielding 376 samples for analysis (334 full-shift and 42 short-term samples). Results compare well with previously published data on MMA monomer exposures. Although there was significant variability in several exposure subgroups (particularly among companies and facilities), MMA monomer use in the manufacture of other products resulted in the highest exposure distributions, with lower exposures associated with monomer production, maintenance, and distribution operations. The usefulness of these findings is related to the provision of information for discussion of current needs in the industry regarding data sampling, exposure assessment, and standards development.