Long-term exposure to particulate matter (PM) with aerodynamic diameters < 10 (PM 10 ) and 2.5 μm (PM 2.5 ) has negative effects on human health. Although station-based PM monitoring has been conducted around the world, it is still challenging to provide spatially continuous PM information for vast areas at high spatial resolution. Satellite-derived aerosol information such as aerosol optical depth (AOD) has been frequently used to investigate ground-level PM concentrations. In this study, we combined multiple satellite-derived products including AOD with model-based meteorological parameters (i.e., dew-point temperature, wind speed, surface pressure, planetary boundary layer height, and relative humidity) and emission parameters (i.e., NO, NH 3 , SO 2 , primary organic aerosol (POA), and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM 10 and PM 2.5 concentrations with a total of 32 parameters for 2015-2016. The results show that the RF-based models produced good performance resulting in R2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 μgm -3 for PM 10 and PM 2.5 , respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed, solar radiation, and dew-point temperature. The use of aerosol information derived from a geostationary satellite sensor (i.e., Geostationary Ocean Color Imager, GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., the Moderate Resolution Imaging Spectroradiometer, MODIS). The proposed RF models yielded better performance than the process-based approaches, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and the Community Multiscale Air Quality Modeling System, CMAQ).
Bibliographical noteFunding Information:
Acknowledgements. This study was supported by a grant from the National Institute of Environmental Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-2017-01-02-063), the Space Technology Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning (NRF-2017M1A3A3A02015981), and the National Strategic Project-Fine Particle of the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT), the Ministry of Environment (ME), and the Ministry of Health and Welfare (MOHW) (NRF-2017M3D8A1092021). MC’s work was undertaken as a private enterprise and not in the author’s capacity as an employee of the Jet Propulsion Laboratory, California Institute of Technology.
© Author(s) 2019.
All Science Journal Classification (ASJC) codes
- Atmospheric Science