A novel model for predicting the semivolatile organic compound partition coefficient of multicomponent airborne particles. (January 2020)

• Record Type: Journal Article
• Authors: Zhou, Xiaojun; Luo, Changgui; Liu, Ke; Wang, Xinke
• Is Part Of: Building and environment. Volume 167(2020)
• Publication Date: 2020-01
• Subjects: Indoor air quality -- Semivolatile organic compounds -- Partition coefficient -- Sorption -- Airborne particles; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2019.106446
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Semivolatile organic compounds (SVOCs) are highly susceptible to sorption by particles, so the presence of particles will have a significant impact on indoor SVOC distribution. The partition coefficient of SVOCs between the air and particles was closely related to indoor environmental parameters, particle composition, and physical structure. However, owing to the complexity of indoor particle composition and the variety of SVOCs, theoretical models of the particle partition coefficient are still not comprehensive in characterizing the SVOC sorption process by different components, and most only consider the sorption of elemental carbon and organic carbon. Studies have demonstrated that inorganic particles have nonnegligible sorption capacity on SVOCs. In this study, experimental results in the literature were compared with the theoretical values predicted by the widely used equilibrium model of the SVOC particle/gas partition coefficient. It was found that neglecting the inorganic components in particles led to a low predictive value of the partition coefficient. Therefore, based on the two-parameter equilibrium model, a novel model considering the sorption of SVOCs on inorganic substances was proposed. This model could describe the formation mechanism of the particle/air partition coefficient more comprehensively, and the experimental data in many relevant studies were used to verify the model. In addition, based on this model, the quantitative relationship between particle property and its sorption of SVOCs was calculated and analyzed. This study aimed to improve the predictive accuracy of indoor SVOC concentrations and help to make more accurate estimates of the risk of human health exposure in further research. Highlights: Sorption of SVOCs by inorganic components in particles cannot be neglected. An improved model of the SVOC particle/gas partition coefficient was proposed. The accuracy of the improved model was comprehensively verified by literature data. The correlation between particle property and partition coefficient was analyzed.