Division of Environmental Health Sciences, School of Public Health, University of Minnesota, MMC 807, Room 1239, 420 Delaware Street SE, Minneapolis, MN, 55455, USA.
Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA.
J Expo Sci Environ Epidemiol. 2020 Mar;30(2):374-382. doi: 10.1038/s41370-019-0142-5. Epub 2019 May 14.
Understanding the relationship between consumer product use and risk of adverse health outcomes facilitates appropriate risk management and product stewardship. A preferred method for estimating the systemic and respiratory tract exposure and dose tailored to cleaning products use has been proposed, refining previously issued exposure guidance. Consistent with other exposure and risk-assessment frameworks, it is dependent upon high-quality exposure determinant data that also serve as model inputs. However, as publicly available exposure determinant data are scarce, the risk assessor is left with the option of estimating determinants such as the generation rate or employing empirical methods to estimate them. When the exposure scenario involves a chemical mixture, estimating the generation rate may not be feasible. We present an approach for estimating the time-varying generation rate of an aqueous acetic acid mixture representative of the base formulation for many consumer and DIY cleaning products that was previously assessed in a screening-level assessment. The approach involved measuring the evaporation rate for a reasonable worst-case scenario under controlled conditions. Knowing the mass applied, a time-varying generation rate was estimated. To evaluate its portability, a field study was conducted in a home where measurements were collected in an all-purpose room with the exterior door open (Room 1) and closed (Room 2), and a bathroom (Room 3) using a portable Fourier Transform Infrared (FTIR) spectrophotometer. Acetic acid concentrations were modeled using two common indoor air models, the Well Mixed Room model. Measured and modeled acetic acid concentrations were compared, with the WMR 95% confidence intervals encompassing measured concentrations for all three rooms, supporting the utility of the approach used and portability of the generation rate derived from it.
了解消费品使用与不良健康结果风险之间的关系有助于进行适当的风险管理和产品管理。已经提出了一种估计系统性和呼吸道暴露以及针对清洁产品使用量身定制的剂量的首选方法，对先前发布的暴露指南进行了改进。与其他暴露和风险评估框架一致，它依赖于高质量的暴露决定因素数据，这些数据也是模型输入。然而，由于公开可用的暴露决定因素数据稀缺，风险评估人员只能选择估计生成率或采用经验方法来估计它们。当暴露情况涉及化学混合物时，估计生成率可能不可行。我们提出了一种方法来估计代表许多消费者和 DIY 清洁产品基础配方的水性乙酸混合物的时变生成率，该方法之前在筛选水平评估中进行了评估。该方法涉及在受控条件下测量合理最坏情况下的蒸发率。知道施加的质量，就可以估计时变生成率。为了评估其可移植性，在一个家庭中进行了现场研究，在一个外部门打开（房间 1）和关闭（房间 2）的多功能房间和浴室（房间 3）中使用便携式傅里叶变换红外（FTIR）分光光度计收集测量值。使用两种常见的室内空气模型，即完全混合室模型，对乙酸浓度进行建模。比较了实测和模拟的乙酸浓度，WMR 的 95%置信区间包含了所有三个房间的实测浓度，这支持了所使用的方法的实用性和由此衍生的生成率的可移植性。
