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Steady-state model for predicting size-resolved gas-particle partitioning of semi-volatile organic compounds (SVOCs) in indoor environments
Semi-volatile organic compounds (SVOCs) are ubiquitous and important pollutants in indoor environments. The strong partition between gas phase and suspended particles has significant effects on the transport, human exposure via inhalation, and control strategies of indoor SVOCs. Several models have been developed to simulate the gas-particle partitioning of indoor SVOCs, including a steady-state model by expanding the steady-state model suitable for the outdoor environment to indoor environments. However, the effects of two important indoor environment-specific parameters, i.e., the particle size distribution (PSD) and the air-change rate (ACH), were not considered in the existing steady-state model, leading to the inaccurate predictions among buildings. To solve this problem, this study developed a novel steady-state model to more comprehensively simulate the gas-particle partitioning of indoor SVOCs by incorporating the effects of PSD and ACH. Better agreement between the predictions of the novel model and the results collected via both field tests and laboratory tests (retrieved from two different studies) supported the effectiveness of the improvements in the novel model. Sensitivity analysis further supported the necessity of involving PSD and ACH. Further implications of the novel model were also discussed. This study should be helpful for deepening the understanding and accurate simulation of the gas-particle partitioning, as well as the transport and human exposure via inhalation, of indoor SVOCs.
Steady-state model for predicting size-resolved gas-particle partitioning of semi-volatile organic compounds (SVOCs) in indoor environments
Semi-volatile organic compounds (SVOCs) are ubiquitous and important pollutants in indoor environments. The strong partition between gas phase and suspended particles has significant effects on the transport, human exposure via inhalation, and control strategies of indoor SVOCs. Several models have been developed to simulate the gas-particle partitioning of indoor SVOCs, including a steady-state model by expanding the steady-state model suitable for the outdoor environment to indoor environments. However, the effects of two important indoor environment-specific parameters, i.e., the particle size distribution (PSD) and the air-change rate (ACH), were not considered in the existing steady-state model, leading to the inaccurate predictions among buildings. To solve this problem, this study developed a novel steady-state model to more comprehensively simulate the gas-particle partitioning of indoor SVOCs by incorporating the effects of PSD and ACH. Better agreement between the predictions of the novel model and the results collected via both field tests and laboratory tests (retrieved from two different studies) supported the effectiveness of the improvements in the novel model. Sensitivity analysis further supported the necessity of involving PSD and ACH. Further implications of the novel model were also discussed. This study should be helpful for deepening the understanding and accurate simulation of the gas-particle partitioning, as well as the transport and human exposure via inhalation, of indoor SVOCs.
Steady-state model for predicting size-resolved gas-particle partitioning of semi-volatile organic compounds (SVOCs) in indoor environments
Build. Simul.
Cao, Jianping (author) / Han, Yu (author) / Zhu, Yujie (author) / Duan, Xingyu (author) / Wang, Luyang (author) / Huang, Haibao (author)
Building Simulation ; 16 ; 443-460
2023-03-01
18 pages
Article (Journal)
Electronic Resource
English
indoor air quality , airborne particles , particle size distribution , ventilation , mass transfer analysis Engineering , Building Construction and Design , Engineering Thermodynamics, Heat and Mass Transfer , Atmospheric Protection/Air Quality Control/Air Pollution , Monitoring/Environmental Analysis
| British Library Online Contents | 2017
| British Library Online Contents | 2017
| DOAJ | 2019