Predicting particulate (PM10) personal exposure distributions using a random component superposition statistical model
Predicting particulate (PM10) personal exposure distributions using a random component superposition statistical model
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DOI：
10.1080/10473289.2000.10464169
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发表时间：
2000-08-01
影响因子：
2.7
通讯作者：
Mage, D
中科院分区：
文献类型：
--
作者：
Ott, W;Wallace, L;Mage, D
本文提出了一个新的统计模型，旨在将我们对城市人口先前个人暴露场测量的理解扩展到存在环境监测数据但没有个人暴露测量的其他城市。该模型将个人暴露分为两个不同的部分：环境浓度和非环境浓度。假设环境和非环境浓度成分不相关，并加在一起；因此，该模型称为随机分量叠加（RCS）模型。24小时的室外环境浓度乘以0到1之间的无量纲“衰减因子”，以考虑环境空气渗入室内时颗粒的沉积。将RCS模型应用于现场PM10测量。数据来自三个大规模的个人暴露现场研究：新泽西州菲利普斯堡的THEES（人类环境总暴露研究）；加州河滨市粒子总暴露评估方法（PTEAM）；和乙酯公司在加拿大多伦多的研究。由于室内源和活动（吸烟、烹饪、清洁、个人云等）在相似人群中可能相似，因此假设非环境个人暴露的统计分布在城市之间是不变的。利用固定的24小时衰减因子作为回归分析得出的第一近似，获得了每个城市非环境PM10个人暴露的分布。虽然这三个城市的平均环境PM10浓度从多伦多的27.9 μ g/m(3)到菲利斯堡的60.9 μ g/m(3)到里弗赛德的94.1 μ g/m(3)不等，但发现个人暴露的平均非环境成分更接近：多伦多的52.6 μ g/m(3)；菲力蒲52.4 μ g/m(3)；河滨市59.2 μ g/m(3)。暴露的非环境成分的三个频率分布在形状上也相似，这支持了非环境浓度在不同城市和人群中相似的假设。这些结果表明，如果这三个城市的环境浓度完全控制并设置为零，则剩余个人暴露于PM10的中位数将从32.0 μ g/m（多伦多）到34.4 μ g/m(3)（菲利普斯堡）到48.8 μ g/m(3)（河滨）。三个城市中暴露程度最高的30%的人口仍将暴露在24小时平均PM10浓度为47-74 μ g/m(3)的环境中；最高的20%将暴露于56-92 μ g/m(3)的浓度；最高10%至浓度为88 ~ 131 μ g/m(3)；最高：5%至133-175 μ g/m(3)，仅受室内源和活动的影响。在这三个城市，个人暴露量和室内浓度（或“个人云”）之间的差异分布也相似，平均值为30-35 μ g/m(3)，这表明个人云占三个城市PM10个人暴露的非环境成分的一半以上。仅使用多伦多的环境测量数据，菲利普斯堡的非环境数据用于预测多伦多的整个个人暴露分布。模型预测的PM10暴露分布与多伦多实测的PM10个人暴露分布基本一致。这些初步结果表明，RCS模型可能是预测其他只有环境颗粒数据的城市的个人暴露分布和统计数据的有力工具。
This paper presents a new statistical model designed to extend our understanding from prior personal exposure field measurements of urban populations to other cities where ambient monitoring data, but no personal expo sure measurements, exist. The model partitions personal exposure into two distinct components: ambient concentration and nonambient concentration. It is assumed the ambient and nonambient concentration components are uncorrelated and add together; therefore, the model is called a random component superposition (RCS) model. The 24-hr ambient outdoor concentration is multiplied by a dimensionless "attenuation factor" between 0 and 1 to account for deposition of particles as the ambient air infiltrates indoors. The RCS model is applied to field PM10 measurement. data from three large-scale personal exposure field studies: THEES (Total Human Environmental Exposure Study) in Phillipsburg, NJ; PTEAM (Particle Total Exposure Assessment Methodology) in Riverside, CA; and the Ethyl Corporation study in Toronto, Canada. Because indoor sources and activities (smoking, cooking, cleaning, the personal cloud, etc.) may be similar in similar populations, it was hypothesized that the statistical distribution of nonambient personal exposure is invariant across cities.Using a fixed 24-hr attenuation factor as a first approximation derived from regression analysis for the respondents, the distributions of nonambient PM10 personal exposures were obtained for each city. Although the mean ambient PM10 concentrations in the three cities varied from 27.9 mu g/m(3) in Toronto to 60.9 mu g/m(3) in Philligsburg to 94.1 mu g/m(3) in Riverside, the mean nonambient components of personal exposures were found to be closer: 52.6 mu g/m(3) in Toronto; 52.4 mu g/m(3) in Phillipsbug; and 59.2 mu g/m(3) in Riverside. The three frequency distributions of the nonambient components of exposure also were similar in shape, giving support to the hypothesis that nonambient concentrations are similar across different cities and populations. These results indicate that, if the ambient concentrations were completely controlled and set to zero in all three cities, the median of the remaining personal exposures to PM10 would range from 32.0 mu g/m(3) (Toronto) to 34.4 mu g/m(3) (Phillipsburg) to 48.8 mu g/m(3) (Riverside). The highest-exposed 30% of the population in the three cities would still be exposed to 24-hr average PM10 concentrations of 47-74 mu g/m(3); the highest 20% would be exposed to concentrations of 56-92 mu g/m(3); the highest 10% to concentrations of 88-131 mu g/m(3); and the highest: 5% to 133-175 mu g/m(3), due only to indoor sources and activities. The distribution for the difference between personal exposures and indoor concentrations, or the "personal cloud," also was similar in the three cities, with a mean of 30-35 mu g/m(3), suggesting that the personal cloud accounts for more than half of the nonambient component of PM10 personal exposure in the three cities. Using only the ambient measurements in Toronto, the nonambient data from THEES in Phillipsburg was used to predict the entire personal exposure distribution in Toronto. The PM10 exposure distribution predicted by the model showed reasonable agreement with the PM10 personal exposure distribution measured in Toronto. These initial results suggest that the RCS model may be a powerful tool for predicting personal exposure distributions and statistics in other cities where only ambient particle data are available.