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| A study of multiple regression method for estimating concentration of fine particulate matter using satellite remote sensing |
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Abstract In comparison with the simple linear model (Model 1), we developed two multiple regression models- linearmodel (Model 2) and nonlinear model (Model 3)-to estimate the ground PM2.5concentration using satellite observationsover Beijing and its surrounding area based on the analysis of the PM2.5data, the meteorological data, the MODIS AOD dataand the NCEP FNL data. Results showed that Model 1, Model 2 and Model 3 could explain 32.5%, 56.1%, 62.7% of the variability in ground-level PM2.5 concentration respectively. Correlation coefficients (R) of the three model estimated values of PM2.5 mass concentration with the actual observations were 0.5488, 0.7449, 0.7431 respectively. With an average PM2.5 concentration of 63.1652 μg/m3, their RMSEs were 43.5562, 35.3321,36.8450μg/m3 respectively. Meteorological factors in Model 2 and Model 3 could separatelyexplain 23.6%, 12.6% of the variability in ground-level PM2.5 concentration, which indicatedtheir significant influenceson the PM2.5-AOD relationship. In addition, there were low-value overestimation and high-value underestimation phenomenon in the three models.
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Received: 28 June 2013
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