Table_1_A generic approach to estimate airborne concentrations of substances released by indoor spray processes using a deterministic 2-box model.xlsx

Sprays are used both in workplace and consumer settings. Although spraying has advantages, such as uniform distribution of substances on surfaces in a highly efficient manner, it is often associated with a high inhalation burden. For an adequate risk assessment, this exposure has to be reliably quantified. Exposure models of varying complexity are available, which are applicable to spray applications. However, a need for improvement has been identified. In this contribution, a simple 2-box approach is suggested for the assessment of the time-weighted averaged exposure concentration (TWA) using a minimum of input data. At the moment, the model is restricted to binary spray liquids composed of a non-volatile fraction and volatile solvents. The model output can be refined by introducing correction factors based on the classification and categorization of two key parameters, the droplet size class and the vapor pressure class of the solvent, or by using a data set of experimentally determined airborne release fractions related to the used spray equipment. A comparison of model results with measured data collected at real workplaces showed that this simple model based on readily available input parameters is very useful for screening purposes. The generic 2-box spray model without refinement overestimates the measurements of the considered scenarios in approximately 50% of the cases by more than a factor of 100. The generic 2-box model performs better for room spraying than for surface spraying, as the airborne fraction in the latter case is clearly overestimated. This conservatism of the prediction was significantly reduced when correction factors or experimentally determined airborne release fractions were used in addition to the generic input parameters. The resulting predictions still overestimate the exposure (ratio tool estimate to measured TWA > 10) or they are accurate (ratio 0.5–10). If the available information on boundary conditions (application type, equipment) does not justify the usage of airborne release fraction, room spraying should be used resulting in the highest exposure estimate. The model scope may be extended to (semi)volatile substances. However, acceptance may be compromised by the limited availability of measured data for this group of substances and thus may have limited potency to evaluate the model prediction.喷雾作业广泛应用于工作场所与消费场景。尽管喷雾作业具备诸多优势，例如可高效实现物质在物体表面的均匀分布，但该作业往往伴随较高的吸入负荷。为开展充分的风险评估，需对这类暴露量进行可靠量化。当前已有多种复杂度各异的暴露模型可适配喷雾作业场景，但现有模型仍存在改进空间。本文提出一种简易双箱模型法（2-box approach），仅需少量输入数据即可估算时间加权平均暴露浓度（TWA）。目前该模型仅适用于由非挥发性组分与挥发性溶剂构成的二元喷雾液。可通过两种方式优化模型输出：一是基于两个关键参数——溶剂的液滴粒径等级（droplet size class）与蒸气压等级（vapor pressure class）的分类分级结果引入校正因子；二是采用与所用喷雾设备相关的实测空气中释放分数数据集。将模型结果与真实工作场所采集的实测数据进行对比后发现，基于易得输入参数的简易模型非常适用于筛查工作。未经过优化的通用双箱喷雾模型在约50%的研究场景中，对实测值的高估幅度超过100倍。相较于表面喷雾，该通用模型在空间喷雾场景中的表现更优，因为其对表面喷雾的空气中物质占比存在显著高估。若在通用输入参数基础上引入校正因子或实测空气中释放分数，该预测的保守性可得到显著降低。优化后的预测结果要么仍存在一定高估（模型估算值与实测TWA之比＞10），要么预测精度达标（比值处于0.5~10区间）。若关于边界条件（作业类型、所用设备）的现有信息不足以支撑使用空气中释放分数数据，则应采用空间喷雾场景的估算方式，以得到最高的暴露量预估结果。该模型的应用范围可拓展至（半）挥发性物质，但由于这类物质的实测数据较为匮乏，模型的认可度可能受到影响，进而限制其对预测结果的评估效能。