LAUSR

Abstract To effectively control indoor PM2.5 (particulate matter with diameter less than 2.5 μm) in residential buildings, it is essential to differentiate between the contributions of outdoor PM2.5 infiltration and indoor PM2.5 emissions to the total indoor exposure. This study developed a method for automatically differentiating between indoor exposure to PM2.5 of indoor and outdoor origin using only the time-resolved indoor and outdoor PM2.5 concentrations and information about window opening/closing behavior. This investigation focused on naturally ventilated buildings without the use of portable air cleaners. The proposed approach combines change point analysis, a statistical method; the mass balance for PM2.5, a physical model; and window behavior characteristics to analyze the data and identify the indoor PM2.5 emissions. A series of experiments in a small-scale laboratory setup were conducted to validate the proposed method. The results show that the proposed method can automatically and successfully identify the indoor PM2.5 emissions for all of the 17 cases. Also, the proposed method accurately estimated the indoor exposure to PM2.5 of indoor and outdoor origin as a percentage of the total indoor exposure for all 17 cases with an average absolute error of 0.32%.