QUANTIFYING PM INFILTRATION IN KAZAKH HOMES UNDER EXTREME WEATHER CONDITIONS

Abstract

Since people spend most of their time indoors, indoor air quality has emerged as a serious concern. Particulate matter with a diameter of less than 2.5 micrometers (PM2.5) is widely studied due to its serious health impacts. The PM2.5 concentration inside a building depends on indoor pollution sources and outdoor concentration. This study focuses on PM2.5 infiltration modeling in Astana city households. Eight buildings across Astana were analyzed. Data collection involved indoor and outdoor air monitoring in each household. The effect of indoor pollution sources was minimized by filtering the indoor concentration data. An increase in PM2.5 concentration indoors should appear following an increase in outdoor concentration. Multiple linear regression was performed to identify the relationship between indoor PM2.5 concentration and both indoor and outdoor parameters. As a result, the model accuracy for two households reached almost 90%, while the lowest value was 47%. The results could be improved with better separation of PM2.5 data based on its origin. More efficient removal of indoor-related PM2.5 concentration changes could result in better model performance. Additionally, the relationships between indoor PM2.5 and other parameters, such as temperature and relative humidity, are discussed, as well as the seasonal characteristics of infiltration.