Estimation of Ground-level Nitrogen Dioxide and Ozone Concentrations Using Satellite Data and Numerical Model Output

Long exposure to high concentrations of nitrogen dioxide (NO2) and ozone (O3) at ground level could be harmful to human health. Air pollutant concentrations including NO2 and O3 have been measured at monitoring stations, which has a major limitation that it is difficult to provide spatially continuous air quality information. In this study, machine learning based models were developed to estimate ground-level NO2 and O3 concentrations using satellite-based remote sensing data and numerical model output over East Asia to overcome such a limitation. NO2 and O3 vertical column density products from the Aura Ozone Monitoring Instrument (OMI) play an important role in monitoring of the spatial and temporal patterns of the gases, although one third to one half of the OMI products have been missing due to row anomalies. In this study, missing pixels of OMI products were filled using an interpolation approach to generate spatio-temporally continuous distribution of NO2 and O3 concentrations. In addition to satellite-derived data, model-based meteorological parameters and emission information during 2015-2016 were used to estimate surface air quality concentrations over East Asia. Random forest (RF) was used to develop the estimation models for NO2 and O3 concentrations. Over South Korea, the RF-based models showed good performance resulting in R2 values of 0.78 and 0.73, and RMSEs of 8.88 ppb and 10.50 ppb for NO2 and O3, respectively. The NO2 vertical column density was identified most important variable in both models. The model-based meteorological variables such as max wind speed, planetary boundary layer height (PBLH), frictional velocity, and solar radiation were also considered significant for estimation. Spatial distribution of ground-level NO2 and O3 concentrations were also examined over South Korea. Relatively high concentrations were shown around large cities including Seoul metropolitan area