A population exposure model for particulate matter:: case study results for PM2.5 in Philadelphia, PA
A population exposure model for particulate matter:: case study results for PM2.5 in Philadelphia, PA
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DOI：
10.1038/sj.jea.7500188
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发表时间：
2001-11-01
期刊：
影响因子：
--
通讯作者：
Özkaynak, H
中科院分区：
文献类型：
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作者：
Burke, JM;Zufall, MJ;Özkaynak, H
一个人口暴露模型的颗粒物（PM），称为随机人体暴露和剂量模拟（SHEDS-PM）模型，已开发和应用在案例研究中的每日PM2.5暴露的人口生活在费城，PA。SHEDS-PM是一个概率模型，通过从各种输入分布中随机抽样来估计总PM暴露的人口分布。质量平衡方程用于从环境室外PM浓度和物理因素数据（例如，空气交换、渗透、沉积），以及室内PM源的排放强度（例如，吸烟、烹饪）。非住宅微环境中的PM浓度是使用从车辆、办公室、学校、商店和餐馆/酒吧的可用室内和室外测量数据的回归分析中开发的方程计算的。额外的模型输入包括被建模的人口的人口统计数据和来自EPA的综合人类活动数据库（CHAD）的人类活动模式数据。模型输出包括在各种微环境（室内，车辆，室外）的每日总PM暴露量的分布，以及在这些微环境中的每日总PM暴露量的贡献泡沫PM的周围来源。SHEDS-PM已被应用到人口的费城使用空间和时间内插的环境PM2.5测量从1992 - 1993年和1990年美国人口普查数据为每个人口普查区在费城。由此产生的分布显示，费城人口每日总PM2.5暴露量存在很大差异（中位数= 20 mug/m（3）;第90百分位数= 59 mug/m（3））。人类活动的变化，特别是室内住宅源的存在，导致了所观察到的PM2.5总暴露量的变化。总PM2.5暴露量的估计人口分布的不确定性在分布的上端最高，揭示了在人口暴露模型中包括输入不确定性估计的重要性。每日微环境PM2.5暴露量分布（由于在各种微环境中花费的时间而导致的暴露）表明，室内住宅PM2.5暴露（中位数= 13 mug/m3）与其他微环境相比，对总PM2.5暴露的影响最大，与每日总PM2.5暴露量的分布相比，每日暴露于环境源PM2.5的分布在人群中的变化较小（中位数= 7 mug/m（3）;第90百分位数= 18 mug/m（3）），与室外环境PM2.5浓度分布相似。这一结果表明，人类活动模式对环境PM2.5暴露的影响并不像对其他PM2.5来源的暴露所观察到的那样强烈。对于大多数模拟人群，暴露于环境来源的PM2.5占每日总PM2.5暴露量的显著百分比（中位数= 37.5%），特别是对于没有暴露于住宅环境烟草烟雾的人群（中位数= 46.4%）。使用费城PM2.5案例研究开发的SHEDS-PM模型也为当前可用数据用于人口暴露模型的局限性提供了有用的见解。此外，还确定了改进SHEDS-PM模型输入、减少不确定性和进一步完善模型结构的数据需求。
A population exposure model for particulate matter (PM), called the Stochastic Human Exposure and Dose Simulation (SHEDS-PM) model, has been developed and applied in a case study of daily PM2.5 exposures for the population living in Philadelphia, PA. SHEDS-PM is a probabilistic model that estimates the population distribution of total PM exposures by randomly sampling from various input distributions. A mass balance equation is used to calculate indoor PM concentrations for the residential microenvironment from ambient outdoor PM concentrations and physical factor data (e.g., air exchange, penetration, deposition), as well as emission strengths for indoor PM sources (e.g., smoking, cooking). PM concentrations in nonresidential microenvironments are calculated using equations developed from regression analysis of available indoor and outdoor measurement data for vehicles, offices, schools, stores, and restaurants/bars. Additional model inputs include demographic data for the population being modeled and human activity pattern data from EPA's Consolidated Human Activity Database (CHAD). Model outputs include distributions of daily total PM exposures in various microenvironments (indoors, in vehicles, outdoors), and the contribution froth PM of ambient origin to daily total PM exposures in these microenvironments. SHEDS-PM has been applied to the population of Philadelphia using spatially and temporally interpolated ambient PM2.5 measurements from 1992 - 1993 and 1990 US Census data for each census tract in Philadelphia. The resulting distributions showed substantial variability in daily total PM2.5 exposures for the population of Philadelphia (median = 20 mug/m(3); 90th percentile = 59 mug/m(3)). Variability in human activities, and the presence of indoor-residential sources in particular, contributed to the observed variability in total PM2.5 exposures. The uncertainty in the estimated population distribution for total PM2.5 exposures was highest at the upper end of the distribution and revealed the importance of including estimates of input uncertainty in population exposure models. The distributions of daily microenvironmental PM2.5 exposures (exposures due to time spent in various microenvironments) indicated that indoor-residential PM2.5 exposures (median = 13 mug/m(3)) had the greatest influence on total PM2.5 exposures compared to the other microenvironments, The distribution of daily exposures to PM2.5 of ambient origin was less variable across the population than the distribution of daily total PM2.5 exposures (median = 7 mug/m(3); 90th percentile = 18 mug/m(3)) and similar to the distribution of ambient outdoor PM2.5 concentrations. This result suggests that human activity patterns did not have as strong an influence on ambient PM2.5 exposures as was observed for exposure to other PM2.5 sources. For most of the simulated population, exposure to PM2.5 of ambient origin contributed a significant percent of the daily total PM2.5 exposures (median = 37.5%), especially for die segment of the population without exposure to environmental tobacco smoke in the residence (median = 46.4%). Development of the SHEDS-PM model using the Philadelphia PM2.5 case study also provided useful insights into the limitations of currently available data for use in population exposure models.In addition, data needs for improving inputs to the SHEDS-PM model, reducing uncertainty and further refinement of the model structure, were identified.