Machine Learning Models for Indoor PM2.5 Concentrations in Residential Architecture in Taiwan

Lin Yu Chen, Yaw Shyan Tsay & Chien Chen Jung
People typically spend 80-90% of their time indoors. Therefore, establishing prediction models estimate particulate matter (PM2.5) concentration in indoor environments is of great importance, especially in residential households, in order to allow for accurate assessments of exposure in epidemiological studies. However, installing monitoring instruments to collect indoor PM2.5 data is both labor and budget-intensive. Therefore, indoor PM2.5 concentration prediction models have become critical issues. This study aimed to develop a predictive model for hourly household...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.