A general regression method for accurately determining the key parameters of VOC emissions from building materials/furniture in a ventilated chamber. (15th June 2020) - Wang, Yuanzheng; Yang, Tao; He, Zhangcan; Sun, Lihua; Yu, Xuefei; Zhao, Jing; Hu, Yanjun; Zhang, Shuhua; Xiong, Jianyin
Abstract: Emissions of volatile organic compounds (VOCs) from building materials/furniture can be characterized by three key parameters: the initial emittable concentration ( C 0 ), the diffusion coefficient ( D m ), and the partition coefficient ( K ). These parameters provide the basis for realizing effective source control, as well as for evaluating health risks. In this study, we propose a general regression method to accurately and rapidly measure the three key parameters of VOC emissions from building materials/furniture in a ventilated chamber. This method firstly establishes the relationship between the three key parameters and the first root of the analytical solution describing VOC emissions, and this root is then determined by curve regression. Compared with previous regression methods which need to fit two or three parameters simultaneously, the main merit of the present method lies in that it just needs to fit one parameter and thus can get a unique solution. We tested panel furniture in a ventilated chamber to measure the three key parameters of some common VOCs. Results indicate that the model predictions based on the parameters determined via this new method agree well with experimental data, which validates the reliability of this proposed method. Analyzing data from the literature further demonstrates the accuracy of this method. The present method involves chamber testing under ventilated conditions only, which is consistent with the testing conditions for many standards, thus will benefit routine laboratory testing. Highlights: A general regression method is proposed to accurately measure the key parameters. Seven VOCs from panel furniture were tested and the key parameters were determined. The method is validated by comparing predicted VOC concentrations with test data. The uniqueness of the present method on the determined key parameters is examined.