This study has analyzed ozone pollution in Tianjin from 2013 to 2022, focusing on the relationships between ozone distribution, meteorological conditions, and precursor substances. A method for identifying high-value areas of ozone precursors using the Ozone Sensitivity Factor (FNR) has been introduced. Results show that the average ozone concentration in Tianjin has been 100.608 µg/m³, with an annual growth rate of 2.84 µg·m⁻³·yr⁻¹. Tianjin has ranked among the top provinces and urban agglomerations in China for both ozone concentration and growth rate. Ozone levels have peaked in summer, followed by spring, autumn, and winter, while the growth rate has been highest in spring. This indicates that ozone pollution extends from summer into spring and autumn. An analysis of six ozone pollution events reveals significant regional transmission impacts from northern Hebei and Inner Mongolia, contributing over 30%, with additional significant contributions from southern and southwestern Hebei and western Shandong. In terms of controlling ozone precursors, high-HCHO-value areas have been identified. The correlation between areas of high HCHO values and ground-level ozone concentrations was 0.56339 during the ozone season and 0.2214 during the non-ozone season, both of which improved identification accuracy to varying degrees, suggesting that targeting precursor emissions in these areas could enhance pollution mitigation efforts.

Surface reflectance prior uncertainty remains a major source of error in land aerosol optical depth (AOD) retrieval, particularly over complex surfaces and for newly launched satellite sensors lacking long-term observations for constructing stable surface priors. Here, we develop a hybrid AOD retrieval framework for the DQ-1 Wide Swath Imager (WSI) that integrates a static surface reflectance prior, machine-learning-based blue-band surface residual correction, and a physically based lookup-table (LUT) inversion. A multi-band static surface prior is first constructed from quality-controlled clear-sky observations under low-aerosol-loading conditions. Rather than directly predicting AOD, a random forest model is used to estimate the residual between the static and reference surface terms at 0.443 μm from the static blue-band prior, top-of-atmosphere reflectance, NDVI, viewing geometry, and elevation. The corrected blue-band surface term is then propagated through the conventional LUT retrieval, thereby retaining the physical aerosol inversion framework while using machine learning only to mitigate surface-prior mismatch. MCD19A2 AOD is used exclusively in the offline stage to support the construction of LUT-derived reference surface terms and for independent cross-product spatial consistency assessment, whereas AERONET observations provide the primary ground-based validation. Relative to the static-prior LUT retrieval, residual correction reduces the AERONET-based RMSE from 0.256 to 0.107 and mean bias error from 0.187 to 0.059, while increasing the fraction of retrievals within the expected error envelope from 14.8 % to 55.6 %. Evaluation on a held-out scene further demonstrates that the model generalizes the relationship between static-prior error and the LUT-derived reference surface term, reducing the blue-band surface-term MAE from 0.0088 to 0.0044. On dates excluded from model training and internal validation, the retrieved DQ-1 WSI AOD also exhibits strong regional spatial consistency with MCD19A2 across 200,507 collocated pixels (R = 0.863, RMSE = 0.131, MAE = 0.093, MBE = 0.012, and regression slope = 0.99). These results indicate that the performance improvement primarily arises from alleviating blue-band surface-prior mismatch within the physical LUT inversion rather than from direct empirical fitting of AOD. The proposed framework therefore provides a physically interpretable strategy for improving land AOD retrieval from DQ-1 WSI over springtime complex surfaces and offers a transferable solution for new wide-swath sensors for which robust long-term surface reflectance priors are not yet available.

Surface reflectance prior uncertainty remains a major source of error in land aerosol optical depth (AOD) retrieval, particularly over complex surfaces and for newly launched satellite sensors lacking long-term observations for constructing stable surface priors. Here, we develop a hybrid AOD retrieval framework for the DQ-1 Wide Swath Imager (WSI) that integrates a static surface reflectance prior, machine-learning-based blue-band surface residual correction, and a physically based lookup-table (LUT) inversion. A multi-band static surface prior is first constructed from quality-controlled clear-sky observations under low-to-moderate aerosol loading. Rather than directly predicting AOD, a random forest model is used to estimate the residual between the static and reference surface terms at 0.443 µm using the static blue-band prior, top-of-atmosphere reflectance, normalized difference vegetation index (NDVI), viewing geometry, and elevation. The corrected blue-band surface term is then used in the conventional LUT retrieval, retaining the physical aerosol inversion while using machine learning only to correct surface-prior mismatch. MCD19A2 AOD is used as an external atmospheric constraint in the offline construction of LUT-derived reference surface terms and as a reference for regional spatial and hold-out-date consistency analyses, whereas AERONET observations provide independent ground-based validation. Relative to the static-prior LUT retrieval, the residual-corrected retrieval reduced the AERONET-based RMSE from 0.256 to 0.107 and the mean bias error from 0.187 to 0.059 in spring 2025 (March–May), while increasing the fraction within the expected-error envelope from 14.8 % to 55.6 %. Improvement was also observed for autumn–winter 2025 (October–December) using a season-specific static prior together with a seasonally adjusted LUT and RF model. When the spring-2025 configuration was applied to spring 2026 (March–May) without retraining or recalibration, the RMSE decreased from 0.330 to 0.153. In a held-out-scene evaluation, the blue-band surface-term MAE relative to the LUT-derived reference surface term decreased from 0.0088 to 0.0044, and hold-out-date comparisons showed regional consistency with MCD19A2. Together, these evaluations show that correcting blue-band surface-prior mismatch improves DQ-1 WSI land AOD retrieval while retaining the LUT-based physical inversion under the BTH conditions examined here.

With the acceleration of global climate change and urbanization, air pollution, particularly ozone pollution, has become a critical environmental issue, especially in the Beijing–Tianjin–Hebei region of China. This study investigates the spatiotemporal distribution of ozone pollution and its precursors, focusing on formaldehyde as a key indicator of volatile organic compounds. Utilizing high-resolution remote sensing data from the China High-Resolution Air Pollutants dataset and TROPOMI HCHO observations from 2013 to 2022, we employed advanced techniques such as the Kolmogorov–Zurbenko filter and high-value area identification to analyze ozone pollution trends, meteorological influences, and the spatial distribution of HCHO concentrations. Our findings reveal a significant increase in ozone concentrations across BTH, with an annual growth rate of 2.51 μg/m³, peaking during the summer months. The KZ filter decomposition highlighted that short-term and seasonal variations dominate ozone fluctuations, driven by meteorological factors such as solar radiation and temperature. Furthermore, the identification of HCHO HVAs demonstrated that urban agglomeration and expansion zones exhibit higher HCHO concentrations, with VOCs-limited zones showing the most pronounced HCHO levels. The study also introduced the PHV (Percentage Higher than Vicinity) index to quantify anomalous HCHO emissions, providing a robust tool for pinpointing pollution hotspots. Based on these insights, we propose targeted emission control strategies for key regions, including urban expansion zones in Zhangjiakou and non-urban zones in Qinhuangdao, to mitigate ozone pollution effectively. This research offers valuable scientific support for regional air quality management and the formulation of precise pollution control measures in the Beijing–Tianjin–Hebei region.

Surface reflectance prior uncertainty remains a major source of error in land aerosol optical depth (AOD) retrieval, particularly over complex surfaces and for newly launched satellite sensors lacking long-term observations for constructing stable surface priors. Here, we develop a hybrid AOD retrieval framework for the DQ-1 Wide Swath Imager (WSI) that integrates a static surface reflectance prior, machine-learning-based blue-band surface residual correction, and a physically based lookup-table (LUT) inversion. A multi-band static surface prior is first constructed from quality-controlled clear-sky observations under low-aerosol-loading conditions. Rather than directly predicting AOD, a random forest model is used to estimate the residual between the static and reference surface terms at 0.443 μm from the static blue-band prior, top-of-atmosphere reflectance, NDVI, viewing geometry, and elevation. The corrected blue-band surface term is then propagated through the conventional LUT retrieval, thereby retaining the physical aerosol inversion framework while using machine learning only to mitigate surface-prior mismatch. MCD19A2 AOD is used exclusively in the offline stage to support the construction of LUT-derived reference surface terms and for independent cross-product spatial consistency assessment, whereas AERONET observations provide the primary ground-based validation. Relative to the static-prior LUT retrieval, residual correction reduces the AERONET-based RMSE from 0.256 to 0.107 and mean bias error from 0.187 to 0.059, while increasing the fraction of retrievals within the expected error envelope from 14.8 % to 55.6 %. Evaluation on a held-out scene further demonstrates that the model generalizes the relationship between static-prior error and the LUT-derived reference surface term, reducing the blue-band surface-term MAE from 0.0088 to 0.0044. On dates excluded from model training and internal validation, the retrieved DQ-1 WSI AOD also exhibits strong regional spatial consistency with MCD19A2 across 200,507 collocated pixels (R = 0.863, RMSE = 0.131, MAE = 0.093, MBE = 0.012, and regression slope = 0.99). These results indicate that the performance improvement primarily arises from alleviating blue-band surface-prior mismatch within the physical LUT inversion rather than from direct empirical fitting of AOD. The proposed framework therefore provides a physically interpretable strategy for improving land AOD retrieval from DQ-1 WSI over springtime complex surfaces and offers a transferable solution for new wide-swath sensors for which robust long-term surface reflectance priors are not yet available.

Surface reflectance prior uncertainty remains a major source of error in land aerosol optical depth (AOD) retrieval, particularly over complex surfaces and for newly launched satellite sensors lacking long-term observations for constructing stable surface priors. Here, we develop a hybrid AOD retrieval framework for the DQ-1 Wide Swath Imager (WSI) that integrates a static surface reflectance prior, machine-learning-based blue-band surface residual correction, and a physically based lookup-table (LUT) inversion. A multi-band static surface prior is first constructed from quality-controlled clear-sky observations under low-aerosol-loading conditions. Rather than directly predicting AOD, a random forest model is used to estimate the residual between the static and reference surface terms at 0.443 μm from the static blue-band prior, top-of-atmosphere reflectance, NDVI, viewing geometry, and elevation. The corrected blue-band surface term is then propagated through the conventional LUT retrieval, thereby retaining the physical aerosol inversion framework while using machine learning only to mitigate surface-prior mismatch. MCD19A2 AOD is used exclusively in the offline stage to support the construction of LUT-derived reference surface terms and for independent cross-product spatial consistency assessment, whereas AERONET observations provide the primary ground-based validation. Relative to the static-prior LUT retrieval, residual correction reduces the AERONET-based RMSE from 0.256 to 0.107 and mean bias error from 0.187 to 0.059, while increasing the fraction of retrievals within the expected error envelope from 14.8 % to 55.6 %. Evaluation on a held-out scene further demonstrates that the model generalizes the relationship between static-prior error and the LUT-derived reference surface term, reducing the blue-band surface-term MAE from 0.0088 to 0.0044. On dates excluded from model training and internal validation, the retrieved DQ-1 WSI AOD also exhibits strong regional spatial consistency with MCD19A2 across 200,507 collocated pixels (R = 0.863, RMSE = 0.131, MAE = 0.093, MBE = 0.012, and regression slope = 0.99). These results indicate that the performance improvement primarily arises from alleviating blue-band surface-prior mismatch within the physical LUT inversion rather than from direct empirical fitting of AOD. The proposed framework therefore provides a physically interpretable strategy for improving land AOD retrieval from DQ-1 WSI over springtime complex surfaces and offers a transferable solution for new wide-swath sensors for which robust long-term surface reflectance priors are not yet available.