Department of Public Health Sciences, University of California, Davis, CA, USA.
Environmental Energy Technologies Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA; School of Public Health, University of California, Berkeley, CA, USA.
Environ Int. 2014 Sep;70:183-91. doi: 10.1016/j.envint.2014.05.020. Epub 2014 Jun 14.
Information about the distribution of chemical-production mass with respect to use and release is a major and unavailable input for calculating population-scale exposure estimates. Based on exposure models and biomonitoring data, this study evaluates the distribution of total production volumes (and environmental releases if applicable) for a suite of organic compounds. We used Bayesian approaches that take the total intake from our exposure models as the prior intake distribution and the intake inferred from measured biomarker concentrations in the NHANES survey as the basis for updating. By carrying out a generalized sensitivity analysis, we separated the input parameters for which the modeled range of the total intake is within a factor of 2 of the intake inferred from biomonitoring data and those that result in a range greater than a factor of 2 of the intake. This analysis allows us to find the most sensitive (or important) parameters and the likelihood of emission rates for various source emission categories. Pie charts of contribution from each exposure pathway indicate that chemical properties are a primary determinant of the relative contribution of each exposure pathway within a given class of compounds. For compounds with relatively high octanol-water partition coefficients (Kow) such as di-2-ethylhexyl phthalate (DEHP), pyrene, 2,2',4,4'-tetrabromodiphenyl ether (PBDE-47), and 2,2',4,4',5,5'-hexabromodiphenyl ether (PBDE-153), more than 80% of exposure derives from outdoor food ingestion and/or indoor dust ingestion. In contrast, for diethyl phthalate (DEP), di-iso-butyl phthalate (DiBP), di-n-butyl phthalate (DnBP), butylbenzyl phthalate (BBP), and naphthalene, all relatively volatile compounds, either inhalation (indoor and outdoor) or dermal uptake from direct consumer use is the dominant exposure pathway. The approach of this study provides insights on confronting data gaps to improve population-scale exposure estimates used for high-throughput chemical prioritization.
有关使用和释放的化学制品生产总量分布的信息是计算人群暴露估计的主要且缺失的输入。本研究基于暴露模型和生物监测数据，评估了一系列有机化合物的总产量（如适用，还有环境排放）的分布情况。我们使用贝叶斯方法，将暴露模型中的总摄入量作为先验摄入量分布，将 NHANES 调查中测量的生物标志物浓度推断出的摄入量作为更新的基础。通过进行广义敏感性分析，我们将模型中总摄入量的范围在 2 倍于生物监测数据推断出的摄入量的输入参数与导致范围大于 2 倍的摄入量的输入参数区分开来。这种分析使我们能够找到最敏感（或重要）的参数以及各种源排放类别的排放率的可能性。每个暴露途径贡献的饼图表明，化学性质是给定化合物类别的每个暴露途径相对贡献的主要决定因素。对于具有相对较高辛醇-水分配系数（Kow）的化合物，如邻苯二甲酸二（2-乙基己基）酯（DEHP）、芘、2,2',4,4'-四溴二苯醚（PBDE-47）和 2,2',4,4',5,5'-六溴二苯醚（PBDE-153），超过 80%的暴露来自户外食物摄入和/或室内灰尘摄入。相比之下，对于邻苯二甲酸二乙酯（DEP）、邻苯二甲酸二异丁酯（DiBP）、邻苯二甲酸二正丁酯（DnBP）、邻苯二甲酸丁基苄基酯（BBP）和萘，所有这些都是相对挥发性的化合物，无论是室内和室外的吸入还是直接消费者使用的皮肤吸收，都是主要的暴露途径。本研究的方法提供了关于如何应对数据差距的见解，以改善用于高通量化学物质优先级排序的人群暴露估计。
