Arnold Susan F, Shao Yuan, Ramachandran Gurumurthy
a Division of Environmental Health Sciences, School of Public Health , University of Minnesota , Minneapolis , Minnesota.
b Department of Environmental Health and Engineering, Bloomberg School of Public Health , Johns Hopkins University , Baltimore , Maryland.
J Occup Environ Hyg. 2017 Sep;14(9):694-702. doi: 10.1080/15459624.2017.1321843.
Drawing appropriate conclusions about a scenario for which the exposure is truly unacceptable drives appropriate exposure and risk management, and protects the health and safety of those individuals. To ensure the vast majority of these decisions are accurate, these decisions must be based upon proven approaches and tools. When these decisions are based solely on professional judgment guided by subjective inputs, however, they are more than likely wrong, and biased, underestimating the true exposure. Models have been shown anecdotally to be useful in accurately predicting exposure but their use in occupational hygiene has been limited. Possible reasons are a general lack of guidance on model selection and use and scant model input data. The lack of systematic evaluation of the models is also an important factor. This research is the second phase of work building upon the robust evaluation of the Well Mixed Room (WMR) and Near Field Far Field (NF-FF) models under controlled conditions in an exposure chamber, in which good concordance between measured and modeled airborne concentrations of three solvents under a range of conditions was observed. In real world environments, the opportunity to control environmental conditions is limited and measuring the model inputs directly can be challenging; in many cases, model inputs must be estimated indirectly without measurement. These circumstances contribute to increased model input uncertainty and consequent uncertainty in the output. Field studies of model performance directly inform us about how well models predict exposures given these practical limitations, and are, therefore, an important component of model evaluation. The evaluation included ten diverse contaminant-exposure scenarios at five workplaces involving six different contaminants. A database of parameter values and measured and modeled exposures was developed and will be useful for modeling similar scenarios in the future.
针对真正不可接受的暴露场景得出恰当结论，有助于推动适当的暴露和风险管理，并保护这些个体的健康与安全。为确保这些决策中的绝大多数准确无误，必须基于经过验证的方法和工具。然而，当这些决策仅基于由主观因素引导的专业判断时，它们很可能是错误且有偏差的，会低估真实暴露情况。轶事表明模型在准确预测暴露方面很有用，但它们在职业卫生中的应用一直有限。可能的原因包括普遍缺乏关于模型选择和使用的指导，以及模型输入数据匮乏。对模型缺乏系统评估也是一个重要因素。本研究是在暴露室的受控条件下对充分混合室（WMR）和近场远场（NF - FF）模型进行有力评估的基础上开展的第二阶段工作，在该阶段观察到在一系列条件下三种溶剂的实测和模型预测空气浓度之间具有良好的一致性。在实际环境中，控制环境条件的机会有限，直接测量模型输入可能具有挑战性；在许多情况下，必须间接估计模型输入而不进行测量。这些情况导致模型输入不确定性增加，进而导致输出结果的不确定性增加。模型性能的现场研究直接告知我们在这些实际限制条件下模型预测暴露情况的能力如何，因此是模型评估的重要组成部分。该评估包括五个工作场所涉及六种不同污染物的十种不同污染物 - 暴露场景。开发了一个参数值以及实测和模型预测暴露的数据库，该数据库将有助于未来对类似场景进行建模。
