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Near-surface ozone is a secondary pollutant, and its high concentrations pose significant risks to human and plant health. Based on an Extra Tree (ET) model, this study estimated near-surface ozone concentrations with the high spatiotemporal resolution based on Himawari-8 aerosol optical depth (AOD) data and meteorological variables from 1 January 2016 to 31 December 2020. The SHapley Additive exPlanation (SHAP) method was employed to evaluate the contribution of AOD and meteorological factors on ozone concentration. The results indicate that (1) the ET model achieves a sample-based cross-validation R2 of 0.75–0.87 and an RMSE (μg/m3) of 17.96–20.30. The coefficient of determination (R2) values of the model in spring, summer, autumn, and winter are 0.81, 0.80, 0.87, and 0.75, respectively. (2) Higher temperature and boundary layer heights were found to positively contribute to ozone concentration, whereas higher relative humidity exerted a negative influence. (3) From 11:00 to 15:00 (Beijing time, UTC+08:00), ozone concentration increases gradually, with the highest occurring in the summer, followed by spring. This study has obtained high spatial and temporal resolution ozone concentration data, offering valuable insights for the development of fine-scale ozone pollution prevention and control strategies.

Aerosol optical depths (AODs) from the new Himawari‐8 satellite instrument have been assimilated in a dust simulation model over East Asia. This advanced geostationary instrument is capable of monitoring the East Asian dust storms which usually have great spatial and temporal variability. The quality of the data has been verified through a comparison with AErosol RObotic NETwork AODs. This study focuses on extreme dust events only when dust aerosols are dominant; promising results are obtained in AOD assimilation experiments during a case in May 2017. The dust emission fields that drive the simulation model are strongly improved by the inverse modeling, and consequently, the simulated dust concentrations are in better agreements with the observed AOD as well as ground‐based observations of PM10. However, some satellite AODs show significant inconsistence with the simulations and the PM10 and AErosol RObotic NETwork observations, which might arise from retrieval errors over a partially clouded scene. The data assimilation procedure therefore includes a screening method to exclude these observations in order to avoid unrealistic results. A dust mask screening method is designed, which is based on selecting only those observations where the deterministic model produces a substantial amount of dust. This screen algorithm is tested to give more accurate result compared to the traditional method based on background covariance in the case study. Note that our screen method would exclude valuable information in case the model is not able to simulate the dust plume shape correctly; hence, applications in related studies require inspections of simulations and observations by user. Key Points The dust storm emission inversion is conducted by assimilating the new Himawari‐8 AODs with high spatiotemporal resolutions A novel dust mask screen‐based selection is designed to exclude those less representative AODs The estimated dust forecast shows better agreements with independent PM10 data

The estimation of the PM2.5 concentration based on satellite remote sensing is currently a hot topic for research, with machine learning algorithms starting to be applied to the estimation model. However, there are few comparisons between different machine learning algorithms, and few scholars objectively evaluate the estimation performance of machine learning algorithms under different weather conditions. In this study, PM2.5 concentration was estimated by six commonly used machine learning algorithms, and their performances were compared in four different weather conditions in the Beijing–Tianjin–Hebei (BTH) region. The results showed that decision tree and random forest consistently performed well in different weather conditions, while SVM performed poorly. When the PM2.5 concentration was greater than 150 μg/m3, the R and RMSE values for decision tree were 0.854 and 31.53 μg/m3, respectively, while the evaluation coefficient of SVM algorithm was only 0.597 and 49.31 μg/m3, it was worth noting that all algorithms performed better in this interval than in others. This study also focused on the development of an optimal combination algorithm to estimate PM2.5 concentration under different weather conditions and got a good application effect. The results of this research may provide a theoretical basis and an important reference for the application of machine learning algorithms in the field of remote sensing.

Fine particulate matter (PM2.5) has attracted extensive attention due to its harmful effects on humans and the environment. The sparse ground-based air monitoring stations limit their application for scientific research, while aerosol optical depth (AOD) by remote sensing satellite technology retrieval can reflect air quality on a large scale and thus compensate for the shortcomings of ground-based measurements. In this study, the elaborate vertical-humidity method was used to estimate PM2.5 with the spatial resolution 1 km and the temporal resolution 1 hour. For vertical correction, the scale height of aerosols (Ha) was introduced based on the relationship between the visibility data and extinction coefficient of meteorological observations to correct the AOD of the Advance Himawari Imager (AHI) onboard the Himawari-8 satellite. The hygroscopic growth factor (f(RH)) was fitted site-by-site and month by month (1–12 months). Meanwhile, the spatial distribution of the fitted coefficients can be obtained by interpolation assuming that the aerosol properties vary smoothly on a regional scale. The inverse distance weighted (IDW) method was performed to construct the hygroscopic correction factor grid for humidity correction so as to estimate the PM2.5 concentrations in Sichuan and Chongqing from 09:00 to 16:00 in 2017–2018. The results indicate that the correlation between “dry” extinction coefficient and PM2.5 is slightly improved compared to the correlation between AOD and PM2.5, with r coefficient values increasing from 0.12–0.45 to 0.32–0.69. The r of hour-by-hour verification is between 0.69 and 0.85, and the accuracy of the afternoon is higher than that of the morning. Due to the missing rate of AOD in the southwest is very high, this study utilized inverse variance weighting (IVW) gap-filling method combine satellite estimation PM2.5 and the nested air-quality prediction modeling system (NAQPMS) simulation data to obtain the full-coverage hourly PM2.5 concentration and analyze a pollution process in the fall and winter.

Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R2 (0.86) and low RMSE (24.5 μg/m3). The average estimated PM2.5 in the BTH region during the study time range was about 55 μg/m3. The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 μg/m3 (1600 local time) to 65.5 ± 54.6 μg/m3 (1100 local time) at different hours.

Spectral aerosol optical depth (AOD) estimation from satellite-measured top of atmosphere (TOA) reflectances is challenging because of the complicated TOA-AOD relationship and a nexus of land surface and atmospheric state variations. This task is usually undertaken using a physical model to provide a first estimate of the TOA reflectances which are then optimized by comparison with the satellite data. Recently developed deep neural network (DNN) models provide a powerful tool to represent the complicated relationship statistically. This study presents a methodology based on DNN to estimate AOD using Himawari-8 Advanced Himawari Imager (AHI) TOA observations. A year (2017) of AHI TOA observations over the Himawari-8 full disk collocated in space and time with Aerosol Robotic Network (AERONET) AOD data were used to derive a total of 14,154 training and validation samples. The TOA reflectance in all six AHI solar bands, three TOA reflectance ratios derived based on the dark-target assumptions, sun-sensor geometry, and auxiliary data are used as predictors to estimate AOD at 500 nm. The DNN AOD is validated by separating training and validation samples using random k-fold cross-validation and using AERONET site-specific leave-one-station-out validation, and is compared with a random forest regression estimator and Japan Meteorological Agency (JMA) AOD. The DNN AOD shows high accuracy: (1) RMSE = 0.094, R2 = 0.915 for k-fold cross-validation, and (2) RMSE = 0.172, R2 = 0.730 for leave-one-station-out validation. The k-fold cross-validation overestimates the DNN accuracy as the training and validation samples may come from the same AHI pixel location. The leave-one-station-out validation reflects the accuracy for large-area applications where there are no training samples for the pixel location to be estimated. The DNN AOD has better accuracy than the random forest AOD and JMA AOD. In addition, the contribution of the dark-target derived TOA ratio predictors is examined and confirmed, and the sensitivity to the DNN structure is discussed.

Reliable aerosol optical depth (AOD) data with high spatial and temporal resolutions are needed for research on air pollution in China. AOD products from the Advanced Himawari Imager (AHI) onboard the geostationary Himawari-8 satellite and reanalysis datasets make it possible to capture diurnal variations of aerosol loadings. However, due to the different retrieval methods, their applicability may vary with different space and time. Thus, in this study, taking the measured AOD at the Aerosol Robotic NETwork (AERONET) stations as the gold standard, the performance of the latest AHI hourly AOD product (i.e., L3 AOD) was evaluated and then compared with that of two reanalysis AOD datasets offered by Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS), respectively, covering from July 2015 to December 2017 over China. For all the matchups, AHI AOD shows the highest robustness with a high correlation (R) of 0.82, low root-mean-square error (RMSE) of 0.23, and moderate mean absolute relative error (MARE) of 0.56. Although MERRA-2 and CAMS products both have lower R values (0.74, 0.72) and higher RMSE (0.28, 0.26), the former is slightly better than the latter. Accuracy of AOD products could be mainly affected by the pollution level and less affected by particle size distribution. Comparisons among these AOD products imply that AHI AOD is more reliable in regions with high pollution levels, such as central and eastern China, while in the northern and western part, MERRA-2 AOD seems more satisfying. The performance of all the three AOD products presents a significant diurnal variety, as indicated by the highest accuracy in the morning for AHI and at noon for reanalysis data. Moreover, due to various pollution distribution patterns and meteorological conditions, there are distinct seasonal characteristics in the performance of AOD products for different regions.

Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, achieving a root mean square error (RMSE) of 0.064 and a correlation coefficient (CC) of 0.966. Meteorological analysis indicated optimal model performance under cold, dry conditions (RMSE: 0.047, CC: 0.956), compared to humid conditions (RMSE: 0.105, CC: 0.921). Validation against Aerosol Robotic Network (AERONET) ground observations showed that, while the original Himawari-8 data exhibited higher accuracy (RMSE: 0.189, CC: 0.815, n = 346), the gap-filled dataset maintained reasonable precision (RMSE: 0.208, CC: 0.711) and significantly increased the number of valid data points (n = 4149). Furthermore, the gap-filled dataset successfully captured seasonal AOD patterns, with values ranging from 0.245–0.300 in winter to 0.381–0.391 in summer, providing a comprehensive view of aerosol dynamics across South Korea.

The geostationary earth orbit satellite—Himawari-8 loaded with the Advanced Himawari Imager (AHI) has greatly enhanced our capacity of dynamic monitoring in Asia–Pacific area. The Himawari-8/AHI hourly aerosol product is a promising complementary source to the MODerate resolution Imaging Spectroradiometer (MODIS) daily aerosol product for near real-time air pollution observations. However, a comprehensive evaluation of AHI aerosol optical depth (AOD) is still limited, and the difference in performances of AHI and MODIS remains uncertain. In this study, we evaluated the Himawari-8/AHI Level 3 Version 3.0 and MODIS Collection 6.1 Deep Blue AOD products over China against AOD measurements from AErosol RObotic NETwork (AERONET) sites in a spatiotemporal comparison of the products from February 2018 to January 2019. Results showed that AHI AOD achieved a moderate agreement with AERONET with a correlation coefficient of 0.75 and a root-mean-square-error of 0.26, which was slightly inferior to MODIS. The retrieval accuracy was spatially and temporally varied in AHI AOD, with higher accuracies for XiangHe and Lulin sites as well as in the morning and during the summer. The dependency analysis further revealed that the bias in AHI AOD was strongly dependent on aerosol loading and influenced by the Ångström Exponent and NDVI while those for MODIS appeared to be independent of all variables. Fortunately, the biases in AHI AOD could be rectified using a random forest model that contained the appropriate variables to produce sufficiently accurate results with cross-validation R of 0.92 and RMSE of 0.15. With these adjustments, AHI AOD will continue to have great potential in characterizing precise dynamic aerosol variations and air quality at a fine temporal resolution.

In this study, an improved geographically and temporally weighted regression (IGTWR) model for the estimation of hourly PM2.5 concentration data was applied over central and eastern China in 2017, based on Himawari-8 Advanced Himawari Imager (AHI) data. A generalized distance based on the longitude, latitude, day, hour, and land use type was constructed. AHI aerosol optical depth, surface relative humidity, and boundary layer height (BLH) data were used as independent variables to retrieve the hourly PM₂.₅ concentrations at 1:00, 2:00, 3:00, 4:00, 5:00, 6:00, 7:00, and 8:00 UTC (Coordinated Universal Time). The model fitting and cross-validation performance were satisfactory. For the model fitting set, the correlation coefficient of determination (R²) between the measured and predicted PM₂.₅ concentrations was 0.886, and the root-mean-square error (RMSE) of 437,642 samples was only 12.18 µg/m³. The tenfold cross-validation results of the regression model were also acceptable; the correlation coefficient R² of the measured and predicted results was 0.784, and the RMSE was 20.104 µg/m³, which is only 8 µg/m³ higher than that of the model fitting set. The spatial and temporal characteristics of the hourly PM₂.₅ concentration in 2017 were revealed. The model also achieved stable performance under haze and dust conditions.