Estimation of Surface NO2 Volume Mixing Ratio in Four Metropolitan Cities in Korea Using Multiple Regression Models with OMI and AIRS Data

Abstract

Surface NO₂ volume mixing ratio (VMR) at a specific time (13:45 Local time) (NO₂ VMR_ST) and monthly mean surface NO₂ VMR (NO₂ VMR_M) are estimated for the first time using three regression models with Ozone Monitoring Instrument (OMI) data in four metropolitan cities in South Korea: Seoul, Gyeonggi, Daejeon, and Gwangju. Relationships between the surface NO₂ VMR obtained from in situ measurements (NO₂ VMR_In-situ) and tropospheric NO₂ vertical column density obtained from OMI from 2007 to 2013 were developed using regression models that also include boundary layer height (BLH) from Atmospheric Infrared Sounder (AIRS) and surface pressure, temperature, dew point, and wind speed and direction. The performance of the regression models is evaluated via comparison with the NO₂ VMR_In-situ for two validation years (2006 and 2014). Of the three regression models, a multiple regression model shows the best performance in estimating NO₂ VMR_ST and NO₂ VMR_M. In the validation period, the average correlation coefficient (R), slope, mean bias (MB), mean absolute error (MAE), root mean square error (RMSE), and percent difference between NO₂ VMR_In-situ and NO₂ VMR_ST estimated by the multiple regression model are 0.66, 0.41, −1.36 ppbv, 6.89 ppbv, 8.98 ppbv, and 31.50%, respectively, while the average corresponding values for the other two models are 0.75, 0.41, −1.40 ppbv, 3.59 ppbv, 4.72 ppbv, and 16.59%, respectively. All three models have similar performance for NO₂ VMR_M, with average R, slope, MB, MAE, RMSE, and percent difference between NO₂ VMR_In-situ and NO₂ VMR_M of 0.74, 0.49, −1.90 ppbv, 3.93 ppbv, 5.05 ppbv, and 18.76%, respectively.