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

Seohui Park, Minso Shin, Jungho Im, Chang Keun Song, Myungje Choi, Jhoon Kim, Seungun Lee, Rokjin Park, Jiyoung Kim, Dong Won Lee, Sang Kyun Kim

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

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).

Original languageEnglish
Pages (from-to)1097-1113
Number of pages17
JournalAtmospheric Chemistry and Physics
Volume19
Issue number2
DOIs
Publication statusPublished - 2019 Jan 28

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particulate matter
aerosol
optical depth
dew point
observation satellite
wind velocity
geostationary satellite
satellite sensor
surface pressure
EOS
MODIS
aerodynamics
relative humidity
solar radiation
air quality
spatial resolution
boundary layer
temperature
sensor

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

Park, Seohui ; Shin, Minso ; Im, Jungho ; Song, Chang Keun ; Choi, Myungje ; Kim, Jhoon ; Lee, Seungun ; Park, Rokjin ; Kim, Jiyoung ; Lee, Dong Won ; Kim, Sang Kyun. / Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea. In: Atmospheric Chemistry and Physics. 2019 ; Vol. 19, No. 2. pp. 1097-1113.
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abstract = "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).",
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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. / Park, Seohui; Shin, Minso; Im, Jungho; Song, Chang Keun; Choi, Myungje; Kim, Jhoon; Lee, Seungun; Park, Rokjin; Kim, Jiyoung; Lee, Dong Won; Kim, Sang Kyun.

In: Atmospheric Chemistry and Physics, Vol. 19, No. 2, 28.01.2019, p. 1097-1113.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

AU - Park, Seohui

AU - Shin, Minso

AU - Im, Jungho

AU - Song, Chang Keun

AU - Choi, Myungje

AU - Kim, Jhoon

AU - Lee, Seungun

AU - Park, Rokjin

AU - Kim, Jiyoung

AU - Lee, Dong Won

AU - Kim, Sang Kyun

PY - 2019/1/28

Y1 - 2019/1/28

N2 - 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).

AB - 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).

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