Citation:

Zhang, X., B. Xu, Y. Wang, Z. He, H. Wang, T. Yang, Y. Tan, J. Xiong, AND X. Liu. Predicting the emissions of VOCs/SVOCs in source and sink materials: Development of analytical model and determination of the key parameters. ENVIRONMENT INTERNATIONAL. Elsevier B.V., Amsterdam, Netherlands, 160:NA, (2022). https://doi.org/10.1016/j.envint.2021.107064

Impact/Purpose:

United States Environmental Protection Agency (U.S. EPA) is rapidly expanding the scientific foundation for understanding and managing risk from chemicals, including volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs), under the Toxic Substances Control Act (TSCA) amended by the Frank R. Lautenberg Chemical Safety for the 21st Century Act. Human exposure to VOCs and SVOCs emitted from building materials and consumer products can result in severely adverse health effects. Exposure models used for risk-based chemical evaluation require measurement data to enable accurate and precise predictions. Under EPA Office of Research and Development (ORD)’s Chemical Safety and Suitability (CSS) research program, we are conducting research and collecting measurement data for high priority chemicals to characterize their properties and behaviors in support of risk-based prioritization. These data include parameters for model inputs and monitoring data for model calibration and evaluation, specifically, source, emission, diffusion, and partitioning data for select VOCs and SVOCs, such as flame retardants, PCBs, phthalates, and other emerging contaminants. In this research, a fully-analytical model that could characterize the emissions of both VOCs and SVOCs from indoor materials and products was developed. Based on this model, a boost intelligence algorithm (BIA) method was proposed to accurately determine the three key emission parameters in the model: the initial emittable concentration (C0), the material diffusion coefficient (Dm), and the material-air partition coefficient (Kma). Experiments by collecting concentrations of VOCs and SVOCs emitted from materials in test chambers were performed to evaluate the effectiveness of the fully-analytical model and the developed BIA method. The predictions of the analytical model are consistent with that of previous numerical models and experimental data. The effect of temperature on these determined parameters were also investigated. In addition, the BIA method for determining the three key emission parameters were applied for VOCs sorption analysis and compared with literature data. The present study provides a unified modelling and methodology analysis for both VOCs and SVOCs, which will help fill the data gap for source/sink characterization and rapid exposure modeling.

Description:

The emissions of volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) from indoor materials pose an adverse effect on people’s health. In this study, a new analytical model was developed to simulate emission behaviors for both VOCs and SVOCs. Based on this model, we further proposed a boost intelligence algorithm (BIA) method to accurately determine the three key emission parameters in the model: the initial emittable concentration, the diffusion coefficient, and the partition coefficient. Experiments for VOC emissions from solid wood furniture were performed to determine the key parameters. We also used the data of SVOC emissions from polyisocyanurate rigid foam in the literature for SVOC parameter determination with the BIA method. The correlation coefficients are high during the fitting process (R2=0.92-0.99), demonstrating effectiveness of this method. In addition, we observed that chemical properties could transfer from SVOC-type to VOC-type with the increase of temperature. The transition temperatures from SVOC-type to VOC-type for the emissions of tris(2-chloroethyl) phosphate (TCEP) and tris(1-chloro-2-propyl) phosphate (TCIPP) were determined to be about 45 ¿ and 35 ¿, respectively. The present study provides a unified modelling and methodology analysis for both VOCs and SVOCs, which should be very useful for source/sink characterization and control.

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