PM2.5 10-km prediction model > TAP developed a two-stage machine learning model to predict daily PM2.5 concentrations with complete spatial coverage. The structure of our model is shown below. The data sources include PM2.5 measurements, satellite AOD (aerosol optical depth) retrievals, online CMAQ simulations, meteorological reanalysis data, land use information, and population distribution. The first-stage model predicts…
If you use the PM2.5 concentration data from TAP, please cite this website (http://tapdata.org.cn) and the following papers: Geng, G., Xiao, Q., Liu, S., Liu, X., Cheng, J., Zheng, Y., Xue, T., Tong, D., Zheng, B., Peng, Y., Huang, X., He, K., & Zhang, Q. (2021). Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals…
Datasets > Geng, G., Xiao, Q., Liu, S., Liu, X., Cheng, J., Zheng, Y., Xue, T., Tong, D., Zheng, B., Peng, Y., Huang, X., He, K., & Zhang, Q. (2021). Tracking Air Pollution in China: Near Real-Time PM2.5 Retrievals from Multisource Data Fusion. Environ Sci Technol, 55, 12106-12115. [Link] [PDF] Xiao, Q., Geng G., Liu,…
