Abstract
Exposure to indoor particulate matter less than 2.5 µm in diameter (PM2.5) is a critical health risk factor. Therefore, measuring indoor PM2.5 concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM2.5 data is difficult and expensive. Therefore, several indoor PM2.5 concentration prediction models have been developed. However, these prediction models only assess the daily average PM2.5 concentrations in cold or temperate regions. The factors that influence PM2.5 concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM2.5 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 PM2.5 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 PM2.5 concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM2.5 concentrations and suggested that outdoor PM2.5 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 PM2.5 concentrations.
Highlights
Particulate matter with an aerodynamic diameter of fewer than 2.5 μm (PM2.5 ) is a critical risk factor for hospital admission for respiratory [1,2] and cardiovascular diseases [1,3]
PM2.5 concentrations (Kriging), difference in indoor and outdoor CO2 levels, building types, building floor levels, the frequencies of bed sheet cleaning and replacing, and mosquito burning behavior were associated with the indoor PM2.5 concentrations
This study used the data on household indoor and outdoor pollutants, building characteristics, and human indoor activity from the Dampness in Buildings and Health (DBH) study in Taiwan to develop a prediction model for hourly indoor PM2.5 concentrations and further investigate the impact factor
Summary
Particulate matter with an aerodynamic diameter of fewer than 2.5 μm (PM2.5 ) is a critical risk factor for hospital admission for respiratory [1,2] and cardiovascular diseases [1,3]. Elbayoumi used an MLR model to estimate indoor PM2.5 levels in schools [16] These studies were conducted in temperate or cold regions, and air conditioning use or the influence of outdoor air on indoor air may differ by climate region. We used data from the DBH study to (1) create a prediction model for hourly average indoor PM2.5 concentrations and (2) analyze the factors influencing indoor PM2.5 concentrations. These results can prove beneficial for studying the relationship between PM2.5 exposure and health effects and can help the government in formulating policies for reducing indoor PM2.5 concentrations
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