Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH₄) in the atmosphere is important for greenhouse gas emission management. Traditional XCH₄ retrieval methods are complex, while machine learning can be used to model nonlinear relationships by analyzing large datasets, providing an efficient alternative. This study proposes an XGBoost algorithm-based retrieval method to improve the efficiency of atmospheric XCH₄ retrieval. First, the key wavelengths affecting XCH₄ retrieval were determined using a radiative transfer model. The TROPOspheric Monitoring Instrument (TROPOMI) L1B satellite data, L2 XCH₄ products, and auxiliary data were matched to construct the dataset. The dataset constructed was used to train the XGBoost model and obtain the TRO_XGB_XCH₄ model. Finally, the accuracy of the proposed model was evaluated using various parameter values and validated against XCH₄ products and Total Carbon Column Observing Network (TCCON) ground-based observations. The results showed that the proposed TRO_XGB_XCH₄ model had a tenfold cross-validation accuracy R of 0.978, a ground-based validation R of 0.749, and a temporal extension accuracy R of 0.863. Therefore, the accuracy of the TRO_XGB_XCH₄ retrieval model is comparable to that of the official TROPOMI L2 product.

As carbon dioxide (CO₂) concentrations continue to rise, climate change, characterized by global warming, presents a significant challenge to global sustainable development. Currently, most global shortwave infrared CO₂ retrievals rely on fully physical retrieval algorithms, for which complex calculations are necessary. This paper proposes a method to predict the concentration of column-averaged CO₂ (XCO₂) from shortwave infrared hyperspectral satellite data, using machine learning to avoid the iterative computations of the physical method. The training dataset is constructed using the Orbiting Carbon Observatory-2 (OCO-2) spectral data, XCO₂ retrievals from OCO-2, surface albedo data, and aerosol optical depth (AOD) measurements for 2019. This study employed a variety of machine learning algorithms, including Random Forest, XGBoost, and LightGBM, for the analysis. The results showed that Random Forest outperforms the other models, achieving a correlation of 0.933 with satellite products, a mean absolute error (MAE) of 0.713 ppm, and a root mean square error (RMSE) of 1.147 ppm. This model was then applied to retrieve CO₂ column concentrations for 2020. The results showed a correlation of 0.760 with Total Carbon Column Observing Network (TCCON) measurements, which is higher than the correlation of 0.739 with satellite product data, verifying the effectiveness of the retrieval method.

The FY-4A satellite represents a new generation of geostationary platforms, providing high-temporal-resolution observations over China. However, challenges remain in effectively leveraging the FY-4A satellite data for high-temporal-resolution PM_2.5 concentration estimation, particularly regarding the unclear key parameters required for accurate estimation and the limited interpretability of models. This study utilizes an interpretable deep learning framework that integrates FY-4A Top-of-Atmosphere (TOA) reflectance data, meteorological variables, and auxiliary data to estimate surface high-temporal-resolution PM_2.5 concentrations from 2019 to 2023. A multicollinearity test was applied to optimize feature selection, while the SHapley Additive exPlanations (SHAP) method was used to enhance model interpretability. The results indicate that parameters such as TOA02, TOA03, TOA04, and boundary layer height (BLH) significantly influence model performance across years. The model demonstrates strong predictive ability in the Beijing–Tianjin–Hebei (BTH) region, achieving an average R² of 0.83. Root mean square error (RMSE) values remained below 15 µg/m³, aligning well with ground-based monitoring data. These findings demonstrate that combining high temporal satellite data with interpretable deep learning provides a reliable approach for long-term, high-temporal-resolution PM_2.5 monitoring in regions.

The Beijing–Tianjin–Hebei (BTH) region is a critical political and economic hub in China, which has long faced challenges related to atmospheric conditions. Traditional aerosol optical depth (AOD) monitoring methods suffer from issues of data discontinuity and gaps, limiting the ability for continuous long-term observation of aerosols. Aerosols have significant impacts on climate change and air quality, with AOD serving as a key indicator for characterizing atmospheric particulate concentration. Therefore, this study applied a machine learning model to improve all-day AOD estimation based on ground-level air quality and meteorological data, generating a long-term dataset spanning from 2018 to 2023. The results of the all-day AOD estimation method were evaluated through comparisons with Himawari-8, the Aerosol Robotic Network (AERONET), and the Copernicus Atmosphere Monitoring Service (CAMS). The estimated AOD demonstrated good agreement with AHI data, achieving an annual R² greater than 0.96 and RMSE less than 0.1. Spatially, the estimated AOD also showed strong consistency with AHI, AERONET, and CAMS. Additionally, the annual, seasonal, and hourly distribution characteristics of AOD from 2018 to 2023 were analyzed. Two typical cases of aerosol variation in the BTH region were selected and examined: a dust storm event in 2023 and changes during the Spring Festival in 2021. This method provides continuous data support for air pollution monitoring and control in the BTH region and offers valuable references for pollution prevention efforts.