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Im, Jungho
Intelligent Remote sensing and geospatial Information Science Lab.
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Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Author(s)
Park, SeohuiShin, MinsoIm, JunghoSong, Chang-KeunChoi, MyungjeKim, JhoonLee, SeungunPark, RokjinKim, JiyoungLee, Dong-WonKim, Sang-Kyun
Issued Date
2019-01
DOI
10.5194/acp-19-1097-2019
URI
https://scholarworks.unist.ac.kr/handle/201301/26230
Fulltext
https://www.atmos-chem-phys.net/19/1097/2019/
Citation
ATMOSPHERIC CHEMISTRY AND PHYSICS, v.19, no.2, pp.1097 - 1113
Abstract
Long-term exposure to particulate matter (PM) with aerodynamic diameters < 10 (PM10) and 2.5 mu m (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, primary organic aerosol (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 R-2 values of 0.78 and 0.73 and root mean square errors (RMSEs) of 17.08 and 8.25 mu gm(-3) for PM10 and PM2.5, respectively. In particular, the proposed models successfully estimated high PM concentrations. AOD was identified as the most significant for esti-mating 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).
Publisher
COPERNICUS GESELLSCHAFT MBH
ISSN
1680-7316
Keyword
AEROSOL OPTICAL DEPTHPM2.5 CONCENTRATIONSPM10 CONCENTRATIONSOLAR-RADIATIONAIR-POLLUTIONKM RESOLUTIONRANDOM FORESTLAND-USECHINAPRODUCTS

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