Predictors of concentrations of nitrogen dioxide, ne particulate matter, and particle constituents inside of lower socioeconomic status urban homes
Predictors of concentrations of nitrogen dioxide, ne particulate matter, and particle constituents inside of lower socioeconomic status urban homes
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DOI：
10.1038/sj.jes.7500532
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发表时间：
2007-08-01
影响因子：
4.5
通讯作者：
Levy, Jonathan I.
中科院分区：
文献类型：
--
作者：
Baxter, Lisa K.;Clougherty, Jane E.;Levy, Jonathan I.
空气污染暴露模式可能有助于城市地区哮喘发病率的已知空间模式。虽然研究已经评估了交通和室外浓度之间的关系，但很少有人考虑低社会经济地位（SES）城市社区的室内暴露模式。在这项研究中，一个前瞻性的出生队列研究的一部分，评估哮喘病因在城市波士顿，我们收集了室内和室外3-4天的二氧化氮（NO2）和。2003 - 2005年，在43个住宅区的多个季节的空气颗粒物（PM2.5）。选择的住宅代表低社会经济地位的家庭，包括在类似社区的队列和非队列住宅，几乎完全由多单元住宅组成。对颗粒过滤器进行反射率分析和X射线荧光光谱分析，以分别确定元素碳（EC）和微量元素的浓度。此外，关于家庭特征的信息（e）。G.类型、年龄、炉灶燃料）和居住者行为（e. G.吸烟、烹饪、清洁）通过标准化问卷收集。室外和室内源的室内浓度的贡献进行了量化与回归分析，使用质量平衡原理。对于NO2和大多数颗粒成分（除了室外占主导地位的成分，如硫和钒），除了选定的室内源项提高了模型的预测能力。烹饪时间、燃气灶使用、居住密度和加湿器被认为是室内各种污染物水平的重要影响因素。队列和非队列参与者之间的比较提供了另一种方法来确定居住者活动模式对室内-室外比率的影响。虽然这些群体具有相似的住房特征，并且位于相似的社区，但队列成员的室内PM2.5和NO2浓度显着较高，与室内活动有关。我们的结论是，室内源的影响可能会更明显，在高密度的多单元住宅，未来的流行病学研究在这些人群中应明确考虑这些来源分配暴露。
Air pollution exposure patterns may contribute to known spatial patterning of asthma morbidity within urban areas. While studies have evaluated the relationship between traffic and outdoor concentrations, few have considered indoor exposure patterns within low socioeconomic status (SES) urban communities. In this study, part of a prospective birth cohort study assessing asthma etiology in urban Boston, we collected indoor and outdoor 3-4 day samples of nitrogen dioxide (NO2) and. ne particulate matter (PM2.5) in 43 residences across multiple seasons from 2003 to 2005. Homes were chosen to represent low SES households, including both cohort and non-cohort residences in similar neighborhoods, and consisted almost entirely of multiunit residences. Reflectance analysis and X-ray fluorescence spectroscopy were performed on the particle filters to determine elemental carbon (EC) and trace element concentrations, respectively. Additionally, information on home characteristics (e. g. type, age, stove fuel) and occupant behaviors (e. g. smoking, cooking, cleaning) were collected via a standardized questionnaire. The contributions of outdoor and indoor sources to indoor concentrations were quantified with regression analyses using mass balance principles. For NO2 and most particle constituents (except outdoor-dominated constituents like sulfur and vanadium), the addition of selected indoor source terms improved the model's predictive power. Cooking time, gas stove usage, occupant density, and humidifiers were identified as important contributors to indoor levels of various pollutants. A comparison between cohort and non-cohort participants provided another means to determine the influence of occupant activity patterns on indoor-outdoor ratios. Although the groups had similar housing characteristics and were located in similar neighborhoods, cohort members had significantly higher indoor concentrations of PM2.5 and NO2, associated with indoor activities. We conclude that the effect of indoor sources may be more pronounced in high-density multiunit dwellings, and that future epidemiological studies in these populations should explicitly consider these sources in assigning exposures.