Articles | Volume 15, issue 1
Atmos. Chem. Phys., 15, 319–334, 2015

Special issue: Meso-scale aerosol processes, comparison and validation studies...

Atmos. Chem. Phys., 15, 319–334, 2015

Research article 13 Jan 2015

Research article | 13 Jan 2015

Estimation of PM10 concentrations over Seoul using multiple empirical models with AERONET and MODIS data collected during the DRAGON-Asia campaign

S. Seo1,*, J. Kim1, H. Lee1,2, U. Jeong1, W. Kim1, B. N. Holben3, S.-W. Kim4, C. H. Song5, and J. H. Lim6 S. Seo et al.
  • 1Institute of Earth, Astronomy, and Atmosphere, Brain Korea 21 Plus Program, Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
  • 2Department of Spatial Information Engineering, Pukyong National University, Busan, South Korea
  • 3NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 4School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
  • 5School of Environmental Science and Engineering, GIST, Gwangju, South Korea
  • 6Global Environment Research Division, National Institute of Environmental Research, Incheon, South Korea
  • *now at: Korea Polar Research Institute, Incheon, South Korea

Abstract. The performance of various empirical linear models to estimate the concentrations of surface-level particulate matter with a diameter less than 10 μm (PM10) was evaluated using Aerosol Robotic Network (AERONET) sun photometer and Moderate-Resolution Imaging Spectroradiometer (MODIS) data collected in Seoul during the Distributed Regional Aerosol Gridded Observation Network (DRAGON)-Asia campaign from March to May 2012. An observed relationship between the PM10 concentration and the aerosol optical depth (AOD) was accounted for by several parameters in the empirical models, including boundary layer height (BLH), relative humidity (RH), and effective radius of the aerosol size distribution (Reff), which was used here for the first time in empirical modeling. Among various empirical models, the model which incorporates both BLH and Reff showed the highest correlation, which indicates the strong influence of BLH and Reff on the PM10 estimations. Meanwhile, the effect of RH on the relationship between AOD and PM10 appeared to be negligible during the campaign period (spring), when RH is generally low in northeast Asia. A large spatial dependency of the empirical model performance was found by categorizing the locations of the collected data into three different site types, which varied in terms of the distances between instruments and source locations. When both AERONET and MODIS data sets were used in the PM10 estimation, the highest correlations between measured and estimated values (R = 0.76 and 0.76 using AERONET and MODIS data, respectively) were found for the residential area (RA) site type, while the poorest correlations (R = 0.61 and 0.68 using AERONET and MODIS data, respectively) were found for the near-source (NS) site type. Significant seasonal variations of empirical model performances for PM10 estimation were found using the data collected at Yonsei University (one of the DRAGON campaign sites) over a period of 17 months including the DRAGON campaign period. The best correlation between measured and estimated PM10 concentrations (R = 0.81) was found in winter, due to the presence of a stagnant air mass and low BLH conditions, which may have resulted in relatively homogeneous aerosol properties within the BLH. On the other hand, the poorest correlation between measured and estimated PM10 concentrations (R = 0.54) was found in spring, due to the influence of the long-range transport of dust to both within and above the BLH.

Short summary
The estimation of PM10 from optical measurement of AERONET and MODIS by various empirical models was evaluated for the DRAGON-Asia campaign. The results showed the importance of boundary layer height (BLH) and effective radius (Reff) in estimating PM10. The highest correlation between the estimated and measured values was found to be 0.81 in winter due to the stagnant air mass and low BLH, while the poorest values were 0.54 in spring due to the influence of long-range transport above BLH.
Final-revised paper