Abstract Characterizing the emission behaviors of the gaseous, particulate, and adsorbed semivolatile organic compounds (SVOCs) in indoor environments is critical for exposure assessments and control strategies. A long-term multiscale model was developed to predict the emission profiles of the SVOC concentrations in different phases with the macroscale model, and the dynamic gas/particle partitioning process via the mesoscale model. The mesoscopic method can fully consider the detailed microstructures of the indoor airborne particles, and the macroscale model considers their residence time. The dynamic SVOC concentration in a particle predicted with the mesoscale model was upscaled to the macroscale model. Results show that the difference of the critical equilibrium times predicted with the simulated and the existing dynamic partitioning model is attributed to the surface area per unit volume of the particle. The critical equilibrium time increases with the partition coefficient, while decreases with the mass transfer coefficient at gas/particle surfaces. If the residence time is far lower than the critical equilibrium time, the difference of the gaseous, particulate and adsorbed SVOC concentrations of low volatility obtained with the present model and the existing models becomes more significant. Parameter sensitivity analyses of the relative deviations of different SVOC phases predicted with the present model and the existing models on several critical model parameters such as the ventilation rate, the residence time, the total suspended mass concentration particles and the partition coefficient, demonstrate the possible large errors that may be introduced.

中文翻译：

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一种预测半挥发性有机化合物长期排放行为的多尺度方法

摘要 表征室内环境中气态、颗粒和吸附的半挥发性有机化合物 (SVOC) 的排放行为对于暴露评估和控制策略至关重要。开发了一个长期多尺度模型，通过宏观模型预测不同阶段 SVOC 浓度的排放曲线，并通过中尺度模型预测动态气体/颗粒分配过程。细观方法可以充分考虑室内空气传播颗粒的详细微观结构，宏观模型考虑其停留时间。用中尺度模型预测的粒子中的动态 SVOC 浓度被放大到宏观模型。结果表明，模拟和现有动态分配模型预测的临界平衡时间的差异归因于颗粒单位体积的表面积。临界平衡时间随着分配系数的增加而增加，而随着气体/颗粒表面的传质系数而减少。如果停留时间远低于临界平衡时间，则本模型与现有模型得到的低挥发性气体、颗粒和吸附SVOC浓度的差异变得更加显着。使用本模型和现有模型预测的不同 SVOC 相的相对偏差的参数敏感性分析对几个关键模型参数，如通风率、停留时间、