The exposure prediction component of the Control of Substances Hazardous to Health (COSHH) Essentials model (paper version) was evaluated using field measurements from National Institute of Occupational Safety and Health (NIOSH) Health Hazard Evaluation (HHE) reports. Overall 757 measured exposures for 94 similar exposure groups (SEGs) were compared with the COSHH Essentials predicted exposure range (PER). The SEGs were stratified based on the magnitude of measured exposures (high, medium, or low) and physical state of the substance (vapor or particulate). The majority of measured exposures observed involved low-level exposure to vapors; thus, overall findings from the current study are limited to low-level vapor exposure scenarios. Overall, the exposure prediction component of COSHH Essentials vastly overestimated low-level exposures to vapors. This study went beyond the scope of previous studies and investigated which model components led to the overestimation. It was concluded that COSHH Essential's tendency to overestimate was due to multiple complex interactions among model components. Overall, the magnitude of overestimation seems to increase exponentially as values for predictor variables increase. This is likely due to the log-based scale used by the model to allocate concentration ranges. In addition, the current banding scheme used to allocate volatility appears to play a role in the overestimation of low-level exposures to vapors.

中文翻译：

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评估危害健康必需品的物质的接触预测组成部分。

使用美国国家职业安全与健康研究所（NIOSH）健康危害评估（HHE）报告的现场测量，评估了对健康有害物质控制（COSHH）基本模型（纸版）的暴露预测组成部分。将94个相似暴露组（SEG）的757次测量暴露与COSHH Essentials预测暴露范围（PER）进行了比较。根据测得的暴露量（高，中或低）和物质的物理状态（蒸气或颗粒）对SEG进行分层。观察到的大多数测得的暴露量都涉及对蒸气的低水平暴露。因此，当前研究的总体发现仅限于低水平的蒸汽暴露场景。总体而言，COSHH Essentials的暴露预测成分大大高估了低水平的蒸汽暴露量。这项研究超出了先前的研究范围，并调查了哪些模型成分导致了高估。结论是，COSHH Essential的高估趋势是由于模型组件之间存在多个复杂的交互作用。总体而言，高估的幅度似乎随着预测变量的值增加而呈指数增加。这很可能是由于模型使用基于对数的比例来分配浓度范围。此外，目前用于分配波动率的条带化方案似乎在高估低水平的蒸气暴露中发挥了作用。高估的趋势是由于模型组件之间存在多种复杂的相互作用。总体而言，高估的幅度似乎随着预测变量的值增加而呈指数增加。这很可能是由于模型使用基于对数的比例来分配浓度范围。此外，目前用于分配波动率的条带化方案似乎在高估低水平的蒸气暴露中发挥了作用。高估的趋势是由于模型组件之间存在多种复杂的相互作用。总体而言，高估的幅度似乎随着预测变量的值增加而呈指数增加。这很可能是由于模型使用基于对数的比例来分配浓度范围。此外，目前用于分配波动率的条带化方案似乎在高估低水平的蒸气暴露中发挥了作用。