Wan-Yi Lin

Exposure to indoor particulate matter less than 2.5 µm in diameter (PM_2.5) is a critical health risk factor. Therefore, measuring indoor PM_2.5 concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM_2.5 data is difficult and expensive. Therefore, several indoor PM_2.5 concentration prediction models have been developed. However, these prediction models only assess the daily average PM_2.5 concentrations in cold or temperate regions. The factors that influence PM_2.5 concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM_2.5 concentrations in Taiwan (tropical and subtropical region) by using a multiple linear regression model and investigated the impact factor. The sample comprised 93 study cases (1979 measurements) and 25 potential predictor variables. Cross-validation was performed to assess performance. The prediction model explained 74% of the variation, and outdoor PM_2.5 concentrations, the difference between indoor and outdoor CO₂ levels, building type, building floor level, bed sheet cleaning, bed sheet replacement, and mosquito coil burning were included in the prediction model. Cross-validation explained 75% of variation on average. The results also confirm that the prediction model can be used to estimate indoor PM_2.5 concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM_2.5 concentrations and suggested that outdoor PM_2.5 concentrations, ventilation, building characteristics, and human activities should be considered. Moreover, it is important to consider outdoor air quality while occupants open or close windows or doors for regulating ventilation rate and human activities changing also can reduce indoor PM_2.5 concentrations.