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Discussion papers | Copyright
https://doi.org/10.5194/acp-2018-647
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 13 Sep 2018

Research article | 13 Sep 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Estimation of ground level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Seohui Park1, Minso Shin1, Jungho Im1, Chang-Keun Song1, Myungje Choi2, Jhoon Kim2, Seungun Lee3, Rokjin Park3, Jiyoung Kim4, Dong-Won Lee5, and Sang-Kyun Kim5 Seohui Park et al.
  • 1School of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 44919, Republic of Korea
  • 2Department of Atmospheric Sciences, Yonsei University, Seoul, 03722, Republic of Korea
  • 3School of Earth and Environmental Sciences, Seoul National University, Seoul, 08826, Republic of Korea
  • 4Global Environment Research Division, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon, 22689, Republic of Korea
  • 5Environmental Satellite Centre, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon, 22689, Republic of Korea

Abstract. The long exposure to particulate matter (PM) with aerodynamic diameters <10µm (PM10) and 2.5µm (PM2.5) 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, NH3, SO2, POA, and HCHO) to estimate surface PM concentrations over South Korea. Random forest (RF) machine learning was used to estimate both PM10 and PM2.5 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 RMSEs of 17.08µg/m3 and 8.25µg/m3 for PM10 and PM2.5, 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., GOCI) resulted in slightly higher accuracy for estimating PM concentrations than that from a polar-orbiting sensor system (i.e., MODIS). The proposed RF models yielded better performance, particularly in improving on the underestimation of the process-based models (i.e., GEOS-Chem and CMAQ).

Seohui Park et al.
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Short summary
This study proposed machine learning-based models to estimate ground level particulate matter concentrations using satellite observations and numerical model-derived data. Aerosol optical depth was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed and solar radiation. The results show that the proposed models produced better performance than the existing approaches, particularly in improving on the biases of the process-based models.
This study proposed machine learning-based models to estimate ground level particulate matter...
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