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To quantify the impact of the direct aerosol effect accurately, this study incorporated the Geostationary Ocean Color Imager (GOCI) aerosol optical depth (AOD) into a coupled meteorology‐chemistry model. We designed three model simulations to observe the impact of AOD assimilation and aerosol feedback during the KORUS‐AQ campaign (May–June 2016). By assimilating the GOCI AOD with high temporal and spatial resolutions, we improve the statistics from the comparison AOD and AERONET data (root‐mean‐square error: 0.12, R: 0.77, index of agreement: 0.69, mean‐absolute error: 0.08). The inclusion of the direct effect of aerosols produces the best model performance (root‐mean‐square error: 0.10, R: 0.86, index of agreement: 0.72, mean‐absolute error: 0.07). AOD values increased as much as 0.15, which is associated with an average reduction in solar radiation of ‐31.39 W/m2, a planetary boundary layer height (‐104.70 m), an air temperature (‐0.58 °C), and a surface wind speed (‐0.07 m/s) over land. In addition, concentrations of major gaseous and particulate pollutants at the surface (SO2, NO2, NH3, normalSO42−, normalNO3−, normalNH4+, and PM2.5) increase by 7.87–34%, while OH concentration decreases by ‐4.58%. Changes in meteorology and air quality appear to be more significant in high‐aerosol loading areas. The integrated process rate analysis shows decelerated vertical transport, resulting in an accumulation of air pollutants near the surface and the amount of nitrate, which is higher than that of sulfate because of its response to reduced temperature. We conclude that constraining aerosol concentrations using geostationary satellite data is a prerequisite for quantifying the impact of aerosols on meteorology and air quality.

Issues regarding air quality and related health concerns have prompted this study, which develops an accurate and computationally fast, efficient hybrid modeling system that combines numerical modeling and machine learning for forecasting concentrations of surface ozone. Currently available numerical modeling systems for air quality predictions (e.g., CMAQ) can forecast 24 to 48 h in advance. In this study, we develop a modeling system based on a convolutional neural network (CNN) model that is not only fast but covers a temporal period of two weeks with a resolution as small as a single hour for 255 stations. The CNN model uses meteorology from the Weather Research and Forecasting model (processed by the Meteorology-Chemistry Interface Processor), forecasted air quality from the Community Multi-scale Air Quality Model (CMAQ), and previous 24-h concentrations of various measurable air quality parameters as inputs and predicts the following 14-day hourly surface ozone concentrations. The model achieves an average accuracy of 0.91 in terms of the index of agreement for the first day and 0.78 for the fourteenth day, while the average index of agreement for one day ahead prediction from the CMAQ is 0.77. Through this study, we intend to amalgamate the best features of numerical modeling (i.e., fine spatial resolution) and a deep neural network (i.e., computation speed and accuracy) to achieve more accurate spatio-temporal predictions of hourly ozone concentrations. Although the primary purpose of this study is the prediction of hourly ozone concentrations, the system can be extended to various other pollutants.

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