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

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. Published by Copernicus Publications on behalf of the European Geosciences Union. 320 S. Seo et al.: Estimation of PM10 concentrations over Seoul Table 1. Previous studies associated with the estimation of PM concentrations using AOD. Method Study area Data R Reference AOD PMx MT1a Northern Italy Daily sun photometer Daily PM10 0.82 Chu et al. (2003) MT1 Alabama MODIS (10 km) PM2.5 0.70 Wang and Christopher (2003) MT1 Southeastern US MODIS (10 km) PM2.5 Daily PM2.5 0.40 0.43 Engel-Cox et al. (2004) MT1 US MODIS PM2.5 Daily PM2.5 0.52 0.62 Gupta and Christopher (2008) MT1 Cabauw Sun photometer MODIS (10 km) PM2.5 0.75 0.72 Schaap et al. (2009) MT2b Europe MODIS (10 km) PM2.5 PM10 0.60 0.50 Koelemeijer et al. (2006) MT2 Alpine region SEVIRI MODIS Daily PM10 0.70 0.60 Emili et al. (2010) MT2 Beijing MODIS (1 km) PM2.5 PM10 0.68 0.68 Wang et al. (2010) MT3c Eastern US MISR Daily PM2.5 0.69 Liu et al. (2005) MT3 St. Louis MISR MODIS Daily PM2.5 0.79 0.71 Liu et al. (2007) MT3 Lille Sun photometer PM10 0.87 Pelletier et al. (2007) MT4d US MISR Yearly PM2.5 0.78 Liu et al. (2004) MT4 East Asia MODIS Seasonal PM10 0.28–0.54 Choi et al. (2009) a MT1 uses the empirical linear relationship between AOD and PMx (PMx = aAOD+ b). b MT2 uses the empirical linear relationship between corrected AOD (vertical distribution, RH) and PMx. c MT3 uses the poly-parameter model. d MT4 uses the 3-D atmospheric chemistry model.