Development of Hourly Indoor PM2.5 Concentration Prediction Model
The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity
Bibliographic Data
| ID | 17801153 |
|---|---|
| Authors | Chien‐Cheng Jung (0000-0003-0124-689X, China Medical University), Chien-Cheng Jung (China Medical University), Wan-Yi Lin (National Cheng Kung University), Nai‐Yun Hsu (0000-0003-3321-6388, National Cheng Kung University), Nai-Yun Hsu (National Cheng Kung University), Chih‐Da Wu (0000-0001-9720-7937, National Health Research Institutes), Hao-Ting Chang (0000-0001-8254-8923, National Cheng Kung University), Huey‐Jen Su (0000-0003-3589-9987, National Cheng Kung University, corresponding author), Huey-Jen Su (National Cheng Kung University) |
| Year | 2020 |
| Volume | 17 |
| Issue | 16 |
| Pages | 5906-5906 |
| Publication date | 2020-08-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph17165906 |
| PMID | 32823930 |
| OpenAlex | W3049663047 |
| Language | EN |
| Citations received | 1 |
| References cited | 49 |
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 CO 2 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
Atmospheric sciences · Biology · Geography · Indoor air · Indoor air quality · Meteorology · Particulates · Air Quality and Health Impacts · Air Quality Monitoring and Forecasting · Atmospheric chemistry and aerosols · Environmental Science · Ecology · Environmental Engineering · Geology
| Unique citing works | 1 |
|---|---|
| Citations per year | 0,25 |
| Citation span | 2022 - 2022 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |