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用于健康效应研究的特定空气有毒物质暴露的测量与建模以及生物标志物验证。

Measurement and modeling of exposure to selected air toxics for health effects studies and verification by biomarkers.

作者信息

Harrison Roy M, Delgado-Saborit Juana Maria, Baker Stephen J, Aquilina Noel, Meddings Claire, Harrad Stuart, Matthews Ian, Vardoulakis Sotiris, Anderson H Ross

机构信息

University of Birmingham, Division of Environmental Health and Risk Management, Edgbaston Park Road, School of Geography, Earth, and Environmental Sciences, Birmingham B15 2TT, United Kingdom.

出版信息

Res Rep Health Eff Inst. 2009 Jun(143):3-96; discussion 97-100.

Abstract

The overall aim of our investigation was to quantify the magnitude and range of individual personal exposures to a variety of air toxics and to develop models for exposure prediction on the basis of time-activity diaries. The specific research goals were (1) to use personal monitoring of non-smokers at a range of residential locations and exposures to non-traffic sources to assess daily exposures to a range of air toxics, especially volatile organic compounds (VOCs) including 1,3-butadiene and particulate polycyclic aromatic hydrocarbons (PAHs); (2) to determine microenvironmental concentrations of the same air toxics, taking account of spatial and temporal variations and hot spots; (3) to optimize a model of personal exposure using microenvironmental concentration data and time-activity diaries and to compare modeled exposures with exposures independently estimated from personal monitoring data; (4) to determine the relationships of urinary biomarkers with the environmental exposures to the corresponding air toxic. Personal exposure measurements were made using an actively pumped personal sampler enclosed in a briefcase. Five 24-hour integrated personal samples were collected from 100 volunteers with a range of exposure patterns for analysis of VOCs and 1,3-butadiene concentrations of ambient air. One 24-hour integrated PAH personal exposure sample was collected by each subject concurrently with 24 hours of the personal sampling for VOCs. During the period when personal exposures were being measured, workplace and home concentrations of the same air toxics were being measured simultaneously, as were seasonal levels in other microenvironments that the subjects visit during their daily activities, including street microenvironments, transport microenvironments, indoor environments, and other home environments. Information about subjects' lifestyles and daily activities were recorded by means of questionnaires and activity diaries. VOCs were collected in tubes packed with the adsorbent resins Tenax GR and Carbotrap, and separate tubes for the collection of 1,3-butadiene were packed with Carbopack B and Carbosieve S-III. After sampling, the tubes were analyzed by means of a thermal desorber interfaced with a gas chromatograph-mass spectrometer (GC-MS). Particle-phase PAHs collected onto a quartz-fiber filter were extracted with solvent, purified, and concentrated before being analyzed with a GC-MS. Urinary biomarkers were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS-MS). Both the environmental concentrations and personal exposure concentrations measured in this study are lower than those in the majority of earlier published work, which is consistent with the reported application of abatement measures to the control of air toxics emissions. The environmental concentration data clearly demonstrate the influence of traffic sources and meteorologic conditions leading to higher air toxics concentrations in the winter and during peak-traffic hours. The seasonal effect was also observed in indoor environments, where indoor sources add to the effects of the previously identified outdoor sources. The variability of personal exposure concentrations of VOCs and PAHs mainly reflects the range of activities the subjects engaged in during the five-day period of sampling. A number of generic factors have been identified to influence personal exposure concentrations to VOCs, such as the presence of an integral garage (attached to the home), exposure to environmental tobacco smoke (ETS), use of solvents, and commuting. In the case of the medium- and high-molecular-weight PAHs, traffic and ETS are important contributions to personal exposure. Personal exposure concentrations generally exceed home indoor concentrations, which in turn exceed outdoor concentrations. The home microenvironment is the dominant individual contributor to personal exposure. However, for those subjects with particularly high personal exposures, activities within the home and exposure to ETS play a major role in determining exposure. Correlation analysis and principal components analysis (PCA) have been performed to identify groups of compounds that share common sources, common chemistry, or common transport or meteorologic patterns. We used these methods to identify four main factors determining the makeup of personal exposures: fossil fuel combustion, use of solvents, ETS exposure, and use of consumer products. Concurrent with sampling of the selected air toxics, a total of 500 urine samples were collected, one for each of the 100 subjects on the day after each of the five days on which the briefcases were carried for personal exposure data collection. From the 500 samples, 100 were selected to be analyzed for PAHs and ETS-related urinary biomarkers. Results showed that urinary biomarkers of ETS exposure correlated strongly with the gas-phase markers of ETS and 1,3-butadiene. The urinary ETS biomarkers also correlated strongly with high-molecular-weight PAHs in the personal exposure samples. Five different approaches have been taken to model personal exposure to VOCs and PAHs, using 75% of the measured personal exposure data set to develop the models and 25% as an independent check on the model performance. The best personal exposure model, based on measured microenvironmental concentrations and lifestyle factors, is able to account for about 50% of the variance in measured personal exposure to benzene and a higher proportion of the variance for some other compounds (e.g., 75% of the variance in 3-ethenylpyridine exposure). In the case of the PAHs, the best model for benzo[a]pyrene is able to account for about 35% of the variance among exposures, with a similar result for the rest of the PAH compounds. The models developed were validated by the independent data set for almost all the VOC compounds. The models developed for PAHs explain some of the variance in the independent data set and are good indicators of the sources affecting PAH concentrations but could not be validated statistically, with the exception of the model for pyrene. A proposal for categorizing personal exposures as low or high is also presented, according to exposure thresholds. For both VOCs and PAHs, low exposures are correctly classified for the concentrations predicted by the proposed models, but higher exposures were less successfully classified.

摘要

我们调查的总体目标是量化个体对各种空气有毒物质的暴露程度和范围,并基于时间 - 活动日记建立暴露预测模型。具体研究目标如下:(1)通过对一系列居住场所的非吸烟者进行个人监测以及对非交通源暴露情况的监测,来评估对一系列空气有毒物质的每日暴露情况,尤其是挥发性有机化合物(VOCs),包括1,3 - 丁二烯和颗粒态多环芳烃(PAHs);(2)考虑空间和时间变化以及热点情况,确定相同空气有毒物质的微环境浓度;(3)利用微环境浓度数据和时间 - 活动日记优化个人暴露模型,并将模型预测的暴露情况与根据个人监测数据独立估算的暴露情况进行比较;(4)确定尿生物标志物与相应空气有毒物质环境暴露之间的关系。个人暴露测量使用置于公文包内的主动泵吸式个人采样器进行。从100名具有不同暴露模式的志愿者中采集了五个24小时综合个人样本,用于分析VOCs和环境空气中1,3 - 丁二烯的浓度。每个受试者在采集24小时VOCs个人样本的同时,采集一个24小时综合PAH个人暴露样本。在测量个人暴露期间,同时测量相同空气有毒物质在工作场所和家庭中的浓度,以及受试者日常活动中所到访的其他微环境(包括街道微环境、交通微环境、室内环境和其他家庭环境)中的季节性水平。通过问卷调查和活动日记记录受试者的生活方式和日常活动信息。VOCs收集于填充有吸附树脂Tenax GR和Carbotrap的管中,用于收集1,3 - 丁二烯的单独管中填充有Carbopack B和Carbosieve S - III。采样后,通过与气相色谱 - 质谱仪(GC - MS)联用的热解吸仪对管进行分析。收集在石英纤维滤纸上的颗粒态PAHs先用溶剂萃取、纯化和浓缩,然后用GC - MS分析。尿生物标志物通过液相色谱 - 串联质谱法(LC - MS - MS)分析。本研究中测量的环境浓度和个人暴露浓度均低于大多数早期发表的研究结果,这与报道的采用减排措施控制空气有毒物质排放的情况一致。环境浓度数据清楚地表明了交通源和气象条件的影响,导致冬季和交通高峰时段空气有毒物质浓度更高。在室内环境中也观察到了季节性影响,室内源加剧了先前确定的室外源的影响。VOCs和PAHs个人暴露浓度的变异性主要反映了受试者在五天采样期间所从事活动的范围。已确定一些一般因素会影响个人对VOCs的暴露浓度,例如是否有整体车库(附属于住宅)、接触环境烟草烟雾(ETS)、使用溶剂和通勤等。对于中高分子量PAHs而言,交通和ETS是个人暴露的重要来源。个人暴露浓度通常超过家庭室内浓度,而家庭室内浓度又超过室外浓度。家庭微环境是个人暴露的主要个体贡献源。然而,对于那些个人暴露特别高的受试者,家庭内活动和接触ETS在决定暴露方面起主要作用。已进行相关分析和主成分分析(PCA)以识别具有共同来源、共同化学性质、共同传输或气象模式的化合物组。我们使用这些方法确定了决定个人暴露构成的四个主要因素:化石燃料燃烧、溶剂使用、ETS暴露和消费品使用。在对选定的空气有毒物质进行采样的同时,总共收集了500份尿液样本,在携带公文包进行个人暴露数据收集的五天中的每一天之后,为100名受试者中的每一人收集一份。从这500个样本中,选择100个样本分析PAHs和与ETS相关的尿生物标志物。结果表明,ETS暴露的尿生物标志物与ETS和1,3 - 丁二烯的气相标志物密切相关。尿ETS生物标志物与个人暴露样本中的高分子量PAHs也密切相关。已采用五种不同方法对VOCs和PAHs的个人暴露进行建模,使用75%的实测个人暴露数据集来建立模型,并将25%作为对模型性能的独立检验。基于实测微环境浓度和生活方式因素的最佳个人暴露模型能够解释实测个人对苯暴露方差的约50%,对其他一些化合物(例如3 - 乙烯基吡啶暴露方差的75%)的方差解释比例更高。对于PAHs,苯并[a]芘的最佳模型能够解释暴露之间约35%的方差,其他PAH化合物的情况类似。几乎所有VOC化合物的模型都通过独立数据集进行了验证。为PAHs开发的模型解释了独立数据集中的一些方差,是影响PAH浓度来源的良好指标,但除芘的模型外,无法进行统计学验证。还根据暴露阈值提出了将个人暴露分类为低或高的建议。对于VOCs和PAHs,所提出模型预测的低暴露浓度分类正确,但高暴露浓度的分类不太成功。

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