High-Resolution Inversion, Driving Mechanisms, and Source Apportionment of Near-Surface Ozone in Arid Urban Clusters: A Case Study of the Tianshan North Slope Urban Agglomeration.
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| Title: | High-Resolution Inversion, Driving Mechanisms, and Source Apportionment of Near-Surface Ozone in Arid Urban Clusters: A Case Study of the Tianshan North Slope Urban Agglomeration. |
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| Authors: | Pan, Guangrui1,2 (AUTHOR), Xi, Yunyun1,2 (AUTHOR), Wang, Tuodi3 (AUTHOR), Shen, Liqiang1,2,4 (AUTHOR), Luo, Yutian1,2,5 (AUTHOR), Li, Zhijun1,4,6 (AUTHOR), Wang, Lihong1,2,7 (AUTHOR), Xu, Liping1,2 (AUTHOR) xlp_tea@shzu.edu.cn, Cui, Linlin1,2 (AUTHOR), Zhang, Shuliang3,5 (AUTHOR), Lu, Xiangjun4,6 (AUTHOR), Tong, Yongpeng5,7 (AUTHOR) |
| Source: | Remote Sensing. Jul2026, Vol. 18 Issue 13, p2191. 25p. |
| Subjects: | Atmospheric ozone, Pollution source apportionment, Air quality monitoring, Tropospheric ozone, Arid regions, Image reconstruction, Machine learning |
| Geographic Terms: | Urumqi (China), Tien Shan |
| Abstract: | Highlights: What are the main findings? Developed a near-surface O3 spatial inversion model suitable for arid urban clusters, achieving high-precision daily-scale spatial reconstruction of O3 in the Tianshan North Slope Urban Agglomeration (TNSUA). Revealed the spatiotemporal distribution characteristics of near-surface O3, its transport pathways, and potential source regions in the TNSUA. Downward shortwave radiation and air temperature are the dominant factors controlling the spatial variation in near-surface O3, exhibiting significant threshold effects. What are the implications of the main findings? Provides high-precision spatial distribution of near-surface O3, offering scientific support for air quality monitoring in urban clusters. Quantitatively elucidates the regulatory effects of meteorological conditions on near-surface O3 formation, providing a reference for precise pollution control. Accurately identifies transport pathways and potential source regions, supplying a scientific basis for cross-regional coordinated emission reduction strategies in arid urban clusters. Ozone (O3), as a key secondary pollutant, exhibits pronounced spatiotemporal heterogeneity, posing significant challenges to coordinated regional air pollution control. However, systematic understanding of high-resolution O3 spatial inversion and its driving mechanisms in arid urban agglomerations remains limited. In this study, the Tianshan North Slope Urban Agglomeration (TNSUA) was selected as the study area, and a multi-model comparative framework was established to comprehensively evaluate the O3 inversion performance of 16 machine learning and deep learning models, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Extremely Randomized Trees (ET), and Gradient Boosting Decision Tree (GBDT). Based on the optimal model performance, high-precision daily O3 spatial reconstruction for the year 2023 was achieved across the study region. The contributions of individual driving factors and their nonlinear response relationships were quantitatively interpreted using Shapley Additive Explanations (SHAP). Furthermore, a backward trajectory model combined with the Weighted Potential Source Contribution Function (WPSCF) and Weighted Concentration Weighted Trajectory (WCWT) methods was employed to identify potential source regions and transport pathways of O3. The results indicate that: (1) The XGBoost model exhibited the best performance (R2 = 0.93, RPD > 3). The reconstructed results reveal that high O3 concentrations in 2023 were primarily distributed in southern Urumqi, southern Changji, and southern Tacheng, with southern Urumqi identified as the most prominent hotspot. (2) The spatial variability of O3 was predominantly driven by downward shortwave radiation (DSR) and air temperature (TEM), both of which showed significant nonlinear responses and threshold effects on O3 formation. (3) Source apportionment analysis indicates that westerly transport serves as a major exogenous contribution pathway, with potential source regions mainly located in the surrounding areas of the northern Tianshan slope as well as Central Asia, particularly eastern Kazakhstan and northern Kyrgyzstan. This study systematically elucidates the formation mechanisms of O3 pollution in arid urban agglomerations from three aspects—high-precision inversion, driving mechanism analysis, and cross-regional transport identification—thereby providing a scientific basis for precise air pollution control strategies. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? Developed a near-surface O3 spatial inversion model suitable for arid urban clusters, achieving high-precision daily-scale spatial reconstruction of O3 in the Tianshan North Slope Urban Agglomeration (TNSUA). Revealed the spatiotemporal distribution characteristics of near-surface O3, its transport pathways, and potential source regions in the TNSUA. Downward shortwave radiation and air temperature are the dominant factors controlling the spatial variation in near-surface O3, exhibiting significant threshold effects. What are the implications of the main findings? Provides high-precision spatial distribution of near-surface O3, offering scientific support for air quality monitoring in urban clusters. Quantitatively elucidates the regulatory effects of meteorological conditions on near-surface O3 formation, providing a reference for precise pollution control. Accurately identifies transport pathways and potential source regions, supplying a scientific basis for cross-regional coordinated emission reduction strategies in arid urban clusters. Ozone (O3), as a key secondary pollutant, exhibits pronounced spatiotemporal heterogeneity, posing significant challenges to coordinated regional air pollution control. However, systematic understanding of high-resolution O3 spatial inversion and its driving mechanisms in arid urban agglomerations remains limited. In this study, the Tianshan North Slope Urban Agglomeration (TNSUA) was selected as the study area, and a multi-model comparative framework was established to comprehensively evaluate the O3 inversion performance of 16 machine learning and deep learning models, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Extremely Randomized Trees (ET), and Gradient Boosting Decision Tree (GBDT). Based on the optimal model performance, high-precision daily O3 spatial reconstruction for the year 2023 was achieved across the study region. The contributions of individual driving factors and their nonlinear response relationships were quantitatively interpreted using Shapley Additive Explanations (SHAP). Furthermore, a backward trajectory model combined with the Weighted Potential Source Contribution Function (WPSCF) and Weighted Concentration Weighted Trajectory (WCWT) methods was employed to identify potential source regions and transport pathways of O3. The results indicate that: (1) The XGBoost model exhibited the best performance (R2 = 0.93, RPD > 3). The reconstructed results reveal that high O3 concentrations in 2023 were primarily distributed in southern Urumqi, southern Changji, and southern Tacheng, with southern Urumqi identified as the most prominent hotspot. (2) The spatial variability of O3 was predominantly driven by downward shortwave radiation (DSR) and air temperature (TEM), both of which showed significant nonlinear responses and threshold effects on O3 formation. (3) Source apportionment analysis indicates that westerly transport serves as a major exogenous contribution pathway, with potential source regions mainly located in the surrounding areas of the northern Tianshan slope as well as Central Asia, particularly eastern Kazakhstan and northern Kyrgyzstan. This study systematically elucidates the formation mechanisms of O3 pollution in arid urban agglomerations from three aspects—high-precision inversion, driving mechanism analysis, and cross-regional transport identification—thereby providing a scientific basis for precise air pollution control strategies. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18132191 |
