| dc.description.abstract | Recently, air pollution has become a serious problem in developing countries. Among them, people are most concerned about the impact of PM2.5 on health. In Taiwan, about 80 stations provide PM2.5 concentrations monitoring, which is mainly concentrated in the west. The location of the monitoring station is mainly set up at various agencies or school sites and roofs. In addition, the measurements from a central point monitor often lack sufficient spatial and temporal resolution to capture the exposure variability of the study. In this study, we collected the meteorology, air pollution, traffic-related and land use data. After data screening and processing, we estimated hourly PM2.5 concentrations in ungauged sites in Taiwan by using linear regression, decision tree and random forest. Random forest has the best performance in estimating PM2.5 concentrations. We achieve the value of 10 cross-validation coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE) are 0.651, 11.05μg/m3 and 7.84μg/m3 respectively. Compared with previous studies using neural networks or regulatory models, the error between our estimation and the actual measured value is not large. We also applied the model to the real time PM2.5 concentrations estimation and showed it on the website, which can provide the query of PM2.5 concentrations in the region and the distribution of PM2.5 concentrations in Taiwan every hour. | en_US |