Uncertainty in exposure estimates made by modeling versus monitoring
Uncertainty in exposure estimates made by modeling versus monitoring
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DOI：
10.1080/15428110208984714
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发表时间：
2002-05-01
期刊：
影响因子：
--
通讯作者：
Jayjock, M
中科院分区：
文献类型：
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作者：
Nicas, M;Jayjock, M
为了对空气中的有毒物质进行初步暴露评估，工业卫生员通常更喜欢空气监测而不是数学建模，即使只测量一个暴露值。本文认为，如果只采集几个空气样本，如果预期暴露变异性很大，并且关于暴露决定因素的信息不太不确定，那么数学建模可能会提供比监测更准确(不太确定)的暴露估计。为了探索这一想法，假设了8小时时间加权平均空气曝光值C的“真实”分布，该分布基于核辐射暴露模型。C分布近似为对数正态分布。考虑了平均值u(C)(长期平均暴露水平)的估计。基于工作日的简单随机抽样和使用bar上的样本平均值(C)来估计µ(C)，估计中的精度(不确定度)由均方误差MSE((C)over bar)来衡量。在替代方案中，可以使用平均化学排放率Mu(G)、平均房间稀释送风率Mu(Q)和源Mu的平均稀释通风率(β)的Nf的估计来进行建模估计。通过假定估计(μ)在帽(G)上、(μ)在帽(Q)上和(μ)在帽(β)上的均匀分布，给出了帽(C)上的建模均方误差均方误差(μ)的方程。结果表明，对于三个工作日或更少的样本量，如果C分布的预期几何标准差超过2.3，则数学建模而不是空气监测应该提供更准确的u(C)估计。
To conduct an initial exposure assessment for an airborne toxicant, industrial hygienists usually prefer air monitoring to mathematical modeling, even if only one exposure value is to be measured. This article argues that mathematical modeling may provide a more accurate (less uncertain) exposure estimate than monitoring if only a few air samples are to be collected, if anticipated exposure variability is high, and if information on exposure determinants is not too uncertain. To explore this idea, a hypothetical "true" distribution of 8-hour time-weighted average airborne exposure values, C, is posited based on an NF exposure model. The C distribution is approximately lognormal. Estimation of the mean value, mu(C) (the long-term average exposure level), is considered. Based on simple random sampling of workdays and use of the sample mean (C) over bar to estimate mu(C), accuracy (uncertainty) in the estimate is measured by the mean square error, MSE((C) over bar). In the alternative, a modeling estimate can be made using estimates of the mean chemical emission rate mu(G), the mean room dilution supply air rate mu(Q), and the mean dilution ventilation rate in the NF of the source mu(beta). By positing uniform distributions for the estimates (μ) over cap (G), (μ) over cap (Q) and (μ) over cap (beta), an equation for the modeling mean square error MSE((μ) over cap (C)) is presented. It is shown that for a sample size of three or fewer workdays, mathematical modeling rather than air monitoring should provide a more accurate estimate of mu(C) if the anticipated geometric standard deviation for the C distribution exceeds 2.3.