The northern slope of the Tianshan Mountains city cluster (NSTM), as a key urban agglomeration for the development of western China, has experienced rapid regional economic development and high population concentration since the twenty-first century. Accompanied by the increase in human activities in the NSTM, it has significantly altered the land use structure, leading to varying levels of habitat disturbance and degradation. In this paper, based on the land use and land cover (LULC) of NSTM from 2000 to 2020. The InVEST model was employed to assess habitat quality, revealing notable spatial and temporal variations. A geoprobe was further employed to explore the key drivers of the spatially distributed pattern of habitat quality in the research region. The results show that (1) from 2000 to 2020, the NSTM was largely characterized by grassland, unused land, and cropland in terms of land use, with a notable expansion of cropland and construction land; (2) the overall habitat quality in the study area is poor, with a clear spatial distribution pattern of high in the south and low in the north, with a predominance of low grades, and a trend of decreasing and then increasing is shown in the temporal direction; (3) under the influence of rapid urbanization in the region, the degradation degree of habitat quality on the NSTM shows a distinct radial structure, with high degradation in the middle and low degradation at the edges, and shows the trend of “increase-decrease-increase” over time; and (4) the results of the geodetector show that altitude and land use type have the greatest influence on habitat quality on the NSTM, indicating that the habitat quality of the research region is primarily influenced by the type of land use.

Methane (CH₄), the second most important anthropogenic greenhouse gas, significantly impacts global warming. As the world's largest anthropogenic CH₄ emitter, China faces challenges in accurately estimating its emissions. Top-down methods often suffer from coarse resolution, limited data constraints, and result discrepancies. Here, we developed the Regional Methane Assimilation System (RegGCAS-CH₄) based on the WRF-CMAQ model and the EnKF algorithm. By assimilating extensive TROPOMI column-averaged dry CH₄ mixing ratio (XCH₄) retrievals, we conducted high-resolution nested inversions to quantify daily CH₄ emissions across China, with a focus on Shanxi Province in 2022. Nationally, posterior CH₄ emissions were 45.1 ± 3.8 TgCH₄ yr⁻¹, 36.5 % lower than the EDGAR estimates, with the largest reductions in the coal and waste sectors. In North China, emissions decreased most significantly, mainly attributed to the coal and enteric fermentation sectors. Posterior emissions in coal-reliant Shanxi Province decreased by 46.3 %. Sporadic emission increases were detected in major coal-producing cities but were missed by the coarse-resolution inversion. Monthly emissions exhibited a winter-low, summer-high pattern, with the rice cultivation and waste sectors showing higher seasonal increases than those in EDGAR. The inversion significantly improved XCH₄ and surface CH₄ concentration simulations, reducing emission uncertainty. Compared to other bottom-up/top-down estimates, our results were the lowest, primarily because the high-resolution inversion better captured local emission hotspots. Sensitivity tests underscored the importance of nested inversions in reducing the influence of boundary condition uncertainties on emission estimates. This study provides robust CH₄ emission estimates for China, crucial for understanding the CH₄ budget and informing climate mitigation strategies.

Methane (CH₄), the second most important anthropogenic greenhouse gas, significantly impacts global warming. As the world's largest anthropogenic CH₄ emitter, China faces challenges in accurately estimating its emissions. Top-down methods often suffer from coarse resolution, limited data constraints, and result discrepancies. Here, we developed the Regional Methane Assimilation System (RegGCAS-CH₄) based on the WRF-CMAQ model and the EnKF algorithm. By assimilating extensive TROPOMI column-averaged dry CH₄ mixing ratio (XCH₄) retrievals, we conducted high-resolution nested inversions to quantify daily CH₄ emissions across China and Shanxi Province in 2022. Nationally, posterior CH₄ emissions were 45.1 TgCH₄·yr^⁻1, 36.5 % lower than the EDGAR estimates, with the largest reductions in the coal and waste sectors. In North China, emissions decreased most significantly, mainly attributed to the coal and enteric fermentation sectors. Posterior emissions in coal-reliant Shanxi Province decreased by 46.3 %. Sporadic emission increases were detected in major coal-producing cities but were missed by the coarse-resolution inversion. Monthly emissions exhibited a winter-low, summer-high pattern, with the rice cultivation and waste sectors showing higher seasonal increases than those in EDGAR. The inversion significantly improved XCH₄ and surface CH₄ concentration simulations, reducing emission uncertainty. Compared to other bottom-up/top-down estimates, our results were the lowest, primarily because the high-resolution inversion better captured local emission hotspots. Sensitivity tests underscored the importance of nested inversions in reducing the influence of boundary condition uncertainties on emission estimates. This study provides robust CH₄ emission estimates for China, crucial for understanding the CH₄ budget and informing climate mitigation strategies.

Methane (CH₄), the second most important anthropogenic greenhouse gas, significantly impacts global warming. As the world's largest anthropogenic CH₄ emitter, China faces challenges in accurately estimating its emissions. Top-down methods often suffer from coarse resolution, limited data constraints, and result discrepancies. Here, we developed the Regional Methane Assimilation System (RegGCAS-CH₄) based on the WRF-CMAQ model and the EnKF algorithm. By assimilating extensive TROPOMI column-averaged dry CH₄ mixing ratio (XCH₄) retrievals, we conducted high-resolution nested inversions to quantify daily CH₄ emissions across China and Shanxi Province in 2022. Nationally, posterior CH₄ emissions were 45.1 TgCH₄·yr^⁻1, 36.5 % lower than the EDGAR estimates, with the largest reductions in the coal and waste sectors. In North China, emissions decreased most significantly, mainly attributed to the coal and enteric fermentation sectors. Posterior emissions in coal-reliant Shanxi Province decreased by 46.3 %. Sporadic emission increases were detected in major coal-producing cities but were missed by the coarse-resolution inversion. Monthly emissions exhibited a winter-low, summer-high pattern, with the rice cultivation and waste sectors showing higher seasonal increases than those in EDGAR. The inversion significantly improved XCH₄ and surface CH₄ concentration simulations, reducing emission uncertainty. Compared to other bottom-up/top-down estimates, our results were the lowest, primarily because the high-resolution inversion better captured local emission hotspots. Sensitivity tests underscored the importance of nested inversions in reducing the influence of boundary condition uncertainties on emission estimates. This study provides robust CH₄ emission estimates for China, crucial for understanding the CH₄ budget and informing climate mitigation strategies.

Methane (CH₄), the second most important anthropogenic greenhouse gas, significantly impacts global warming. As the world's largest anthropogenic CH₄ emitter, China faces challenges in accurately estimating its emissions. Top-down methods often suffer from coarse resolution, limited data constraints, and result discrepancies. Here, we developed the Regional Methane Assimilation System (RegGCAS-CH₄) based on the WRF-CMAQ model and the EnKF algorithm. By assimilating extensive TROPOMI column-averaged dry CH₄ mixing ratio (XCH₄) retrievals, we conducted high-resolution nested inversions to quantify daily CH₄ emissions across China and Shanxi Province in 2022. Nationally, posterior CH₄ emissions were 45.1 TgCH₄·yr^⁻1, 36.5 % lower than the EDGAR estimates, with the largest reductions in the coal and waste sectors. In North China, emissions decreased most significantly, mainly attributed to the coal and enteric fermentation sectors. Posterior emissions in coal-reliant Shanxi Province decreased by 46.3 %. Sporadic emission increases were detected in major coal-producing cities but were missed by the coarse-resolution inversion. Monthly emissions exhibited a winter-low, summer-high pattern, with the rice cultivation and waste sectors showing higher seasonal increases than those in EDGAR. The inversion significantly improved XCH₄ and surface CH₄ concentration simulations, reducing emission uncertainty. Compared to other bottom-up/top-down estimates, our results were the lowest, primarily because the high-resolution inversion better captured local emission hotspots. Sensitivity tests underscored the importance of nested inversions in reducing the influence of boundary condition uncertainties on emission estimates. This study provides robust CH₄ emission estimates for China, crucial for understanding the CH₄ budget and informing climate mitigation strategies.

Accurately quantifying anthropogenic CO₂ emissions is essential for evaluating carbon budget and mitigation strategies. However, traditional "bottom-up" emission inventories suffer from substantial uncertainties and update time lags, urgently requiring top-down constraints from atmospheric observations while accounting for confounding terrestrial biogenic interferences. In this study, we extended RegGCAS, a regional carbon assimilation system based on the WRF-CMAQ atmospheric chemical transport model and the Ensemble Kalman Filter algorithm. By assimilating column-averaged dry-air CO₂ mole fractions (XCO₂) from OCO-2/3 satellite observations, we inverted anthropogenic CO₂ emissions over mainland China during winter 2022–2023. The results revealed that the total national anthropogenic CO₂ emissions amounted to 2808.3 ± 157.0 Tg, 16.1 % higher than the MEIC inventory. For key emission regions, emissions increased by 13.1 % in the Beijing-Tianjin-Hebei region, whereas they decreased by 10.4 % in the Yangtze River Delta. The system captured distinct urban-suburban emission adjustment differences in key regions, with reductions in city centers and increases in surrounding areas. It also reflected short-term emission fluctuations related to anthropogenic activity changes, such as the Spring Festival work stoppages. Evaluation demonstrates that assimilation effectively reduces prior emission errors by 68.0 %. Validation shows that posterior simulation RMSE decrease by 5.8 % against the assimilated OCO-2/3 XCO₂, and by 15.3 %, 7.7 %, and 25.2 % against independent TCCON, ObsPack, and urban site observations, respectively, confirming the enhanced accuracy of the posterior emission estimates. This study provides a reliable inversion framework for tracking regional carbon dynamics and refining bottom-up emission inventories.