Based on multiple linear regression (MLR) models, we estimated the PM_2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM_2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM_2.5 estimation. We also found that the quality of estimated PM_2.5 shows some seasonal variation; the estimated PM_2.5 values consistently have the highest correlation with in situ PM_2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM_2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM_2.5 estimation, our analyses of the difference in estimated PM_2.5 reveal the importance of utilizing the proper AOD for the best quality of PM_2.5 estimation.

This study proposes an improved approach for monitoring the spatial concentrations of hourly particulate matter less than 2.5 μm in diameter (PM_2.5) via a deep neural network (DNN) using geostationary ocean color imager (GOCI) images and unified model (UM) reanalysis data over the Korean Peninsula. The DNN performance was optimized to determine the appropriate training model structures, incorporating hyperparameter tuning, regularization, early stopping, and input and output variable normalization to prevent training dataset overfitting. Near-surface atmospheric information from the UM was also used as an input variable to spatially generalize the DNN model. The retrieved PM_2.5 from the DNN was compared with estimates from random forest, multiple linear regression, and the Community Multiscale Air Quality model. The DNN demonstrated the highest accuracy compared to that of the conventional methods for the hold-out validation (root mean square error (RMSE) = 7.042 μg/m³, mean bias error (MBE) = −0.340 μg/m³, and coefficient of determination (R²) = 0.698) and the cross-validation (RMSE = 9.166 μg/m³, MBE = 0.293 μg/m³, and R² = 0.49). Although the R² was low due to underestimated high PM_2.5 concentration patterns, the RMSE and MBE demonstrated reliable accuracy values (<10 μg/m³ and 1 μg/m³, respectively) for the hold-out validation and cross-validation.

Wildfire smoke visualization using geostationary satellite imagery is essential for real-time monitoring and atmospheric analysis; however, inconsistencies in color tone across Geostationary Environment Monitoring Spectrometer (GEMS) images hinder reliable interpretation and model training. This study proposes a Standardized False Color Composite (SFCC) framework based on deep learning style transfer to enhance the visual consistency and interpretability of wildfire smoke scenes. Four tone-standardization methods were compared: the statistical Empirical Cumulative Distribution Function (ECDF) correction and three neural approaches—ReHistoGAN, StyTr², and Style Injection Diffusion Model (SI-DM). Each model was evaluated visually and quantitatively using six metrics (SSIM, LPIPS, FID, histogram similarity, ArtFID, and LSCI) and validated on three major wildfire events in Korea (2022–2025). Among the tested models, SI-DM achieved the most balanced performance, preserving structural features while ensuring consistent color-tone alignment (ArtFID = 1.620; LSCI mean = 0.894). Qualitative assessments further confirmed that SI-DM effectively delineated smoke boundaries and maintained natural background tones under complex atmospheric conditions. Additional analysis using GEMS UVAI, VISAI, and CHOCHO demonstrated that the styled composites partially reflect the optical and chemical characteristics distinguishing wildfire smoke from dust aerosols. The proposed SFCC framework establishes a foundation for visually standardized satellite smoke imagery and provides potential for future aerosol-type classification and automated detection applications.