High-purity quartz is an emerging strategic material that has been extensively used in the semiconductor and photovoltaic fields. Taking vein quartz from southern Anhui Province as an example, raw materials were processed by ultrasonic scrubbing-desliming, magnetic separation, flotation, high-temperature calcination, water quenching, hot-press acid leaching, and deionized water cleaning to prepare high-purity quartz sand. At the same time, the microscopic structure, inclusions, phase, mineral morphology, water content in inclusions, and trace impurities of the gangue samples were analyzed using an optical microscope, a laser Raman spectrometer, an X-ray diffractometer, a scanning electron microscope, an infrared spectrum analyzer, and an inductively coupled plasma mass spectrometer. The results showed that feldspar and muscovite were the main impurities. After purification, the total amount of 13 impurities in quartz sand was reduced to 28.66 μg/g, and the contents of the main impurity elements Al, Na, and Fe were 12.81 μg/g, 12.80 μg/g, and 0.52 μg/g, respectively. The mass fraction of SiO₂ increased from 99.06% to 99.9972%. This shows that flotation, high-temperature calcination, and hot-pressing acid leaching are the keys to obtaining high-purity quartz sand. Fluoride-free flotation with the new collector XK02 can effectively realize the deep separation of quartz and mineral impurities. High-temperature calcination can form more cracks on the surface of quartz sand particles, and the mixed acid enters the open crack channels to effectively remove impurities from the inclusions. This method provides technical support for the preparation of high-purity quartz sand with high value and for the efficient utilization of quartz ore.

Good urban design helps mitigate carbon dioxide emissions and is important for achieving global low-carbon goals. Previous studies have mostly focused on the two-dimensional level of urban socio-economic activities, urban land use changes, and urban morphology, neglecting the importance of the three-dimensional spatial structure of cities. This study takes 30 cities in East China as an example. By using urban building data and carbon emission datasets, four machine learning algorithms, BP, RF, CNN, and CNN-RF, are established to build a CO₂ emission prediction model based on three-dimensional spatial structure, and the main influencing factors are further studied. The results show that the CNN-RF model performed optimally in both the testing and validation phases, with the coefficient of determination (R²), root mean square error (RMSE), and residual prediction deviation (RPD) of 0.85, 0.82; 10.60, 22.32; and 2.53, 1.92, respectively. Meanwhile, in the study unit, S, V, NHB, AN, BCR, SCD, and FAR have a greater impact on CO₂ emissions. This indicates a strong correlation between urban three-dimensional spatial structure and carbon emissions. The CNN-RF model can effectively evaluate the relationship between them, providing strategic support for spatial optimization of low-carbon cities.

Land use patterns are a critical driver of changes in carbon emissions, making it essential to elucidate the relationship between regional carbon emissions and land use types. As a nationally designated economic strategic zone, the Yangtze River Basin encompasses megacities, rapidly developing medium-sized cities, and relatively underdeveloped regions. However, the mechanism underlying the interaction between landscape patterns and carbon emissions across such gradients remains inadequately understood. This study utilizes nighttime light, land use and carbon emissions datasets, employing XGBoost, CatBoost, LightGBM and a stacking ensemble model to analyze the impacts and driving factors of land use changes on carbon emissions in the Yangtze River Basin from 2002 to 2022. The results showed: (1) The stacking ensemble learning model demonstrated the best predictive performance, with a coefficient of determination (R²) of 0.80, a residual prediction deviation (RPD) of 2.22, and a root mean square error (RMSE) of 4.46. Compared with the next-best models, these performance metrics represent improvements of 19.40% in R² and 28.32% in RPD, and a 22.16% reduction in RMSE. (2) Based on SHAP feature importance and Pearson correlation analysis, the primary drivers influencing CO₂ net emissions in the Yangtze River Basin are GDP per capita (GDPpc), population density (POD), Tertiary industry share (TI), land use degree comprehensive index (LUI), dynamic degree of water-body land use (K_water), Largest patch index (LPI), and number of patches (NP). These findings indicate that changes in regional landscape patterns exert a significant effect on carbon emissions in strategic economic regions, and that stacked ensemble models can effectively simulate and interpret this relationship with high predictive accuracy, thereby providing decision support for regional low-carbon development planning.

Urban form profoundly influences the effectiveness of coordinated management of carbon emissions and air pollution, which is of great significance for sustainable urban development. This study took 336 Chinese cities as research samples and used carbon emissions, PM_2.5, and nighttime light datasets from 2000 to 2020 to investigate the contribution of urban form to the degree of coordination between urban carbon emissions and PM_2.5. We constructed a multi-dimensional indicator system for urban form and adopted the coupling coordination degree (CCD) model to quantify the coordinated development of these two variables. A novel analytical framework was established using the stacking ensemble learning model. Combined with the SHAP method, this framework provided nonlinear associations of various urban morphological factors with the CCD. The results are as follows: (1) The stacking ensemble model performs best in capturing high-dimensional nonlinear relationships, with test set R², RMSE, and RPD values of 0.75, 0.082, and 1.98, respectively. (2) Urban form exhibits nonlinear threshold effects on the carbon-pollution coordination degree. The top predictors contributing to the model output are the class area (CA), the landscape shape index (LSI), the percentage of like adjacencies (PLADJ), and the number of patches (NP). Among them, CA exhibits a monotonically increasing asymptotic saturation response, while the LSI presents an inverted U-shaped response. (3) The contribution of urban form to the CCD exhibits significant spatial heterogeneity. The developed regions of eastern China constitute the high-sensitivity zone of the CCD, and are mainly constrained by scale expansion, land fragmentation, and excessive agglomeration, while the central and western regions show the low-sensitivity zone of the CCD. These results offer a scientific reference for designing pollution control and carbon mitigation strategies.

Urban Form (UF), as a synthesis of urban functions and socioeconomic elements, is closely associated with Anthropogenic Carbon Emissions (ACE) and has important implications for low-carbon urban planning. As a key national economic strategy region, the Yangtze River Economic Belt (YREB) exhibits pronounced heterogeneity in urban development, highlighting the urgent need to elucidate the interaction mechanisms between UF and ACE to support carbon reduction strategies. This study employs nighttime light data and carbon emission records from 2002 to 2022 in the YREB. By integrating Support Vector Regression (SVR), Random Forest (RF), and Gradient Boosting Decision Tree (GBDT), we developed a neural network ensemble model (RSG-Net) to analyze the impacts and driving mechanisms of UF on ACE. The results indicate the following: (1) Over the past two decades, total ACE in the YREB increased by 196%, displaying a three-phase trajectory of rapid growth, deceleration, and rebound. (2) The RSG-Net model achieved superior predictive performance, with an R² of 0.93, an RMSE of 1.96 × 10⁶ t, an RPD of 3.69, and a PBIAS of 4.53%. (3) Based on Pearson correlation analysis and SHAP (Shapley Additive Explanations) feature importance, beyond economic and demographic indicators, the most influential UF indicators are ranked as Number of Urban Patches (NP), Normalized Difference Vegetation Index (NDVI), and Construction Land Concentration (CLC). These findings demonstrate that the RSG-Net model can not only predict ACE but also identify key UF factors and explain their interrelationships, thereby providing technical support for the formulation of urban carbon reduction strategies.

The partial pressure of carbon dioxide in water (pCO₂) is an important indicator of CO₂ accumulation and potential air–water exchange in lakes. However, remote-sensing retrieval of pCO₂ remains challenging in large, shallow eutrophic lakes due to the interacting biological, physicochemical, and optical processes. This study combined in situ pCO₂ measurements, Landsat imagery, and aquatic environmental variables to develop a heterogeneous stacking ensemble comprising random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) models for Lake Chaohu. Model comparison showed that the stacking model outperformed the individual models, with an R² of 0.80, RMSE of 104.22 μatm, RPD of 2.24 and SMAPE of 17.50%. During the four investigated years, lake-wide pCO₂ increased from 2013 to 2017 and was lower in 2021 and 2025; the maximum value in 2017 exceeded that in 2025 by 209.74 μatm. Autumn generally exhibited higher pCO₂ than winter. Persistent hotspots occurred in the western basin, nearshore waters in the northwest, and the central connecting zone, while lower values characterized the eastern basin and open waters in the southern part. SHAP analysis identified chlorophyll-a (Chl-a) as the leading predictor. However, its moderate Pearson correlation with pCO₂ (r = 0.34) indicates that its predictive importance may reflect nonlinear multivariate associations rather than a direct causal relationship. Water temperature (WT), dissolved oxygen (DO), pH, salinity (Sal), total dissolved solids (TDS), and electrical conductivity (EC) also contributed to the variability of pCO₂. These findings provide a remote sensing approach to monitor lake pCO₂ and characterize carbon dynamics in shallow eutrophic lakes.