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Assessment of Population and Microenvironmental Exposure to Fine Particulate Matter (PM2.5).

机译:人口和微环境暴露于细颗粒物(PM2.5)的评估。

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摘要

A positive relationship exists between fine particulate matter (PM 2.5) exposure and adverse health effects. PM2.5 concentration-response functions used in the quantitative risk assessment were based on findings from human epidemiological studies that relied on areawide ambient concentrations as surrogate for actual ambient exposure, which cannot capture the spatial and temporal variability in human exposures. The goal of the study is to assess inter-individual, geographic and seasonal variability in population exposures to inform the interpretation of available epidemiological studies, and to improve the understanding of how exposure-related factors in important exposure microenvironments contribute to the variability in individual PM2.5 exposure. Typically, the largest percentage of time in which an individual is exposed to PM2.5 of ambient origin occurs in indoor residence, and the highest ambient PM2.5 concentrations occur in transportation microenvironments because of the proximity to on-road traffic emissions. Therefore, indoor residence and traffic-related transportation microenvironments were selected for further assessment in the study.;Population distributions of individual daily PM2.5 exposures were estimated for the selected regions and seasons using the Stochastic Human Exposure and Dose Simulation Model for Particulate Matter (SHEDS-PM). For the indoor residence, the current practice by assuming the entire residence to be one large single zone for calculating the indoor residential PM 2.5 concentration was evaluated by applying an indoor air quality model, RISK, to compare indoor PM2.5 concentrations between single-zone and multi-zone scenarios. For the transportation microenvironments, one field data collection focused on in-vehicle microenvironment and was conducted to quantify the variability in the in-vehicle PM2.5 concentration with respect to the outside vehicle concentration for a wide range of conditions that affect intra-vehicle variability in exposure concentration, including ventilation air source, window status, fan setting, AC utilization, vehicle speed, road type, travel direction, and time of day. Another field data collection measured PM2.5 exposure concentrations on pre-selected routes across transportation modes of pedestrian, bus, and car to quantify the variability in the transportation mode concentration ratios, and identify factors affecting variability in traffic-related concentrations.;In general, population daily average exposure to ambient PM2.5 is less than the ambient concentration by approximately half. The ratio of PM2.5 ambient exposure to ambient concentration (Ea/C) varies by individual, geographic area and season, as a result of regional differences in housing stock and seasonal differences in air exchange rates (ACH). For the indoor residence, the single-zone assumption is biased when any non-ambient source is presented. Bias correction factors are developed for cooking and smoking scenarios, separately, to improve the concentration estimates. Correction factors are most sensitive to changes in ACH but relatively insensitive to variations in source emission rate and duration. In a SHEDS-PM case study, the population daily average total exposure increased by 17% after applying correction factors. Transportation mode exposure concentrations are sensitive to mode, and are affected by factors such as vehicle ventilation and proximity to on-road emission sources. The in-vehicle to outside vehicle concentration (I/O) ratio is highly sensitive to whether windows are open or, for closed windows, to whether fresh air or recirculating air is used.;Both model simulations and field studies are needed to inform better understanding of human exposure. Exposure, and not just concentration, should be considered in developing risk management strategies to reduce uncertainty in health effect estimates, and to identify highly exposed groups and possible exposure reduction strategies.
机译:细颗粒物(PM 2.5)暴露与不良健康影响之间存在正相关关系。定量风险评估中使用的PM2.5浓度-响应函数基于人类流行病学研究的结果,该研究依赖于整个地区的环境浓度作为实际环境暴露的替代指标,而实际环境暴露无法捕获人类暴露的时空变化。该研究的目的是评估人群暴露的个体间,地理和季节变异性,以为可用的流行病学研究提供解释,并增进对重要暴露微环境中暴露相关因素如何导致个体PM2变异性的理解。 .5暴露。通常,由于接近道路交通排放,个人暴露于环境起源的PM2.5的时间的最大百分比发生在室内住宅中,而最高的环境PM2.5浓度发生在交通微环境中。因此,本研究选择了室内居住和交通相关的运输微环境进行进一步评估。;使用随机人类接触和颗粒物剂量模拟模型估算了所选区域和季节的个人每日PM2.5暴露的人口分布( SHEDS-PM)。对于室内住宅,通过应用室内空气质量模型RISK评估单区之间的室内PM2.5浓度,评估当前做法,即假设整个住宅为一个大的单一区域来计算室内PM2.5浓度。和多区域方案。对于运输微环境,进行了一次现场数据收集,重点是车内微环境,并进行了量化,以分析在影响车内可变性的各种条件下,车内PM2.5浓度相对于车外浓度的变化性。暴露浓度,包括通风空气源,窗户状态,风扇设置,交流利用率,车速,道路类型,行进方向和一天中的时间。另一个现场数据收集人员在跨行人,公共汽车和汽车的运输方式的预选路线上测量了PM2.5暴露浓度,以量化运输方式浓度比的变化,并确定影响交通相关浓度变化的因素。 ,人口每天平均暴露于环境PM2.5的量比环境浓度少大约一半。由于房屋存量的区域差异和空气交换率(ACH)的季节性差异,PM2.5环境暴露与环境浓度的比率(Ea / C)随个人,地理区域和季节而变化。对于室内住宅,当提供任何非环境源时,单区假设是有偏差的。分别针对烹饪和吸烟场景开发了偏差校正因子,以改善浓度估算值。校正因子对ACH的变化最敏感,但对源发射速率和持续时间的变化相对不敏感。在SHEDS-PM案例研究中,应用校正因子后,人口的每日平均总暴露量增加了17%。运输模式下的暴露浓度对模式敏感,并受诸如车辆通风和靠近道路排放源等因素的影响。车内与外界车辆的浓度(I / O)比对于是否打开窗户或对于关闭的窗户而言是否使用新鲜空气或再循环空气高度敏感。需要模型模拟和现场研究以更好地了解对人体暴露的了解。在制定风险管理策略时应考虑接触而不是集中,以减少健康影响估计的不确定性,并确定接触程度高的人群和可能的接触减少策略。

著录项

  • 作者

    Jiao, Wan.;

  • 作者单位

    North Carolina State University.;

  • 授予单位 North Carolina State University.;
  • 学科 Engineering Environmental.;Atmospheric Sciences.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 220 p.
  • 总页数 220
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:41:40

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