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In response to the impacts of climate change and the intensity of human activities in the alpine meadow region, there is an urgent need to determine the ecological quality and its drivers in alpine meadow areas. In this paper, Shangri-La was adopted as an example, the spatial and temporal evolution patterns of the ecological quality in Shangri-La were determined in both natural and social dimensions, and the contributions of various driving factors were analyzed. The conclusions are as follows: (1) the natural status index of Shangri-La from 2000 to 2020 generally showed a spatial distribution pattern that decreased from the central townships toward the north and south, and the social pressure index was irregularly distributed in high-value areas and continuously distributed in low-value areas. (2) From 2000 to 2020, the areas with high values of the ecological quality index were mainly distributed in central Shangri-La, with a maximum value of 0.91, while the low values were largely distributed in some townships in the north and south, with a minimum value of 0.26. (3) In the driving factors, the influences of the normalized difference vegetation index (NDVI) and net primary productivity (NPP) were greater than those of the other factors, among which the NDVI attained the largest mean value of 0.452, while the relative humidity (RHU) attained the lowest value of 0.036. (4) In terms of relative contributions, evapotranspiration (EVP) and precipitation (TEM) shifted from a positive drive to a negative drive from south to north. The contribution of the temperature to the ecological quality was the highest, at 64%. The spatial heterogeneity in the contributions of human disturbance activity factors to the ecological quality varied significantly, with the largest negative driving contribution of the NPP, at − 42.36%. The results could provide a basis for regional ecological quality protection and restoration. [ABSTRACT FROM AUTHOR]

Analysis of the relationship between future land use change and agricultural non-point source pollution (ANPSP) evolution is vital to promoting sustainable regional development. By simulating future land use types, we can identify and analyze the evolution trend of ANPSP. This study takes Chongqing as a case study to establish an integrated solution based on the PLUS model, output coefficient model, and GIS technology. The solution can simulate data, identify trends, and identify key control areas under future development scenarios. The results show that the PLUS model can simulate land use types at the provincial scale with high accuracy, with a Kappa coefficient of around 0.9. The land use type changes show that urban expansion has occupied a large amount of cultivated land. From 2000 to 2020, the proportion of high-load areas with TN pollution load levels was 4.93%, 5.02%, and 4.73%, respectively. Under the two scenarios in 2030–2050, the number of high-load areas decreased, and the average load level decreased from west to east. Sensitivity analysis found that risk changes are more sensitive to the increase in fertilizer application. When the TN and TP output coefficients are increased, the number of towns with increased levels is greater than those with decreased levels when the output coefficients are decreased. Sensitivity analysis can better identify key pollution control areas. The areas sensitive to changes in farmers' behavior are mainly the Hechuan District, Nanchuan District, Qijiang District, Jiangjin District, and Bishan District. This study provides data and decision-making support for rural green development and water environment improvement. [ABSTRACT FROM AUTHOR]

According to China's second national survey of pollution sources, the contribution of agricultural non-point sources (ANS) to water pollution is still high. Risk prevention and control are the main means to control costs and improve the efficiency of ANS, but most studies directly take pollution load as the risk standard, leading to a considerable misjudgment of the actual pollution risk. To objectively reflect the risk of agricultural non-point source pollution (ANSP) in Chongqing, China, we investigated the influences of initial source input, intermediate transformation, and terminal absorption of pollutants via literature research and the Delphi method and built a PTA (pressure kinetic energy, transformation kinetic energy, and absorption kinetic energy) model that covers 12 factors, with the support of geographical information system (GIS) technology. The terrain factor calculation results and the calculation results of other factors were optimized by Python tools to reduce human error and workload. Via centroid migration analysis and Kernel density analysis, the risk level, spatial aggregation degree, and key prevention and control regions could be accurately determined. There was a positive correlation between the water quality of the rivers in Chongqing and the risk assessment results of different periods, indirectly reflecting the reliability of the assessment results by the proposed model. There was an obvious tendency for the low-risk regions transforming into high-risk regions. The proportion of high-risk regions and extremely high-risk regions increased from 17.82% and 16.63% in 2000 to 18.10% and 16.76% in 2015, respectively. And the risk level in the main urban areas was significantly higher than that in the southeastern and northeastern areas of Chongqing. The centroids of all grades of risky areas presented a successive distribution from west to east, and the centroids of high-risk and extremely high-risk regions shifted eastward. From 2000 to 2015, the centroids of high-risk and extremely high-risk regions moved 4.63 km (1.68°) and 4.48 km (12.08°) east by north, respectively. The kernel density analysis results showed that the high-risk regions were mainly concentrated in the main urban areas and that the distribution of agglomeration areas overall displayed a transition trend from contiguous distribution to decentralized concentration. The risk levels of the regions with a high proportion of cultivated land and artificial surface were significantly increased, and the occupation of cultivated land in the process of urbanization promoted the movement of the centroids of high-risk and extremely high-risk regions. The identification of key areas for risk prevention and control provides data scientific basis for the development of prevention and control strategies. [ABSTRACT FROM AUTHOR]

This study comprehensively examined the accumulation of cadmium (Cd) in soybeans grown in low- and high-Cd soils around the high geological background areas in Guizhou province. The aim was to analyze the relationship between soybean Cd and soil pH and soil Cd, alongside assessing the potential carcinogenic and non-carcinogenic risks associated with Cd in soybeans. Cd content of soybeans cultivated in the high-Cd area (0.430 mg/kg) was significantly higher than that in low-Cd areas (0.156 mg/kg) (P < 0.05). Biological concentration factors (BCFs) of soybean for Cd in low- and high-Cd areas were 0.282 and 0.314, respectively, with no significant differences (P > 0.05). Multiple linear regression results indicated that soil pH was a determining factor for Cd accumulation in soybeans in both areas. Furthermore, soil pH and soil Cd could accurately predict Cd accumulation in soybeans according to the neural network model. These findings suggest that regulating soil pH could reduce Cd accumulation in soybeans in areas with high geological background. In both areas, there was no significant non-carcinogenic risk for the adult population (HQ value < 1) through soybean consumption. However, according to the Monte Carlo model, the percentage of Cd in soybeans exceeding the acceptable range (CR value > 1.00 × 10 −04) in areas was 99.18%, indicating an unacceptable carcinogenic risk for the adult population. Our discussion revealed that reducing the soybean intake and increasing soil pH did not effectively lower the carcinogenic risk of Cd in soybeans to an acceptable range (CR value ≤ 1.00 × 10 −04). These findings necessitate further exploration of alternative remediation strategies to ensure the safe production of soybeans, such as screening for low-Cd accumulation soybean varieties and implementing the combined remediation strategies. [ABSTRACT FROM AUTHOR]

Deep neural networks, despite their exceptional performance on individual tasks, face the challenge of catastrophic forgetting when incrementally learning new scenarios. Existing class-incremental learning methods have shown promising results by rehearsing past samples from auxiliary memory. However, preserving only a limited subset of previous samples fails to capture the complete distribution, leading to a decline in memory quality as new data is added. Additionally, issues like exemplar distribution collapse and task-recency bias impede the effective transfer of knowledge from teacher to student during distillation. To address these problems, we propose a novel replay-based learning framework called K nowledge fusion D istillation and gradient-based D ata D istillation (K3D). K3D simultaneously optimizes both the classifier and exemplars during the learning phase. By parameterizing exemplars, we enable their optimization through gradient-based data distillation, enhancing their representativeness instead of discarding them, as done in other replay strategies. Furthermore, we enhance the classifier using knowledge fusion distillation, ensuring that decision boundaries are maintained across all classes. Extensive experiments on the CIFAR100 and ImageNet100 benchmarks show that synthetic exemplars optimized by K3D are more representative than selected ones. By combining optimizable exemplars and knowledge fusion distillation, K3D outperforms several state-of-the-art methods and effectively mitigates catastrophic forgetting in class-incremental learning scenarios. • Gradient-based data distillation eliminates constraints on fixed examples, optimizing exemplars. • Auxiliary knowledge distillation enhances knowledge transferred in feature representation. • Knowledge and data distillation are complementary in class-incremental learning. [ABSTRACT FROM AUTHOR]

Exploring the mismatch between supply and demand (SD) for carbon sequestration services (CSS) is essential for achieving the "double carbon" goal. However, more studies are needed on the traits of the spatial mismatch between SD in mountainous cities. We used the CASA model and the IPCC emission factor approach to address this issue and quantify the SD of CSS in Chongqing. Second, we established a matching relationship model for the SD of CSS in Chongqing. Finally, we applied the Structural Equation Model with the Partial Dependence Plots model to reveal the influencing factors and internal mechanisms of spatial mismatch between the SD of CSS in Chongqing. The outcomes confirmed a decrease in fashion in the total supply of CSS in Chongqing and growth in fashion in general demand from 2000 to 2020. The SD mismatch was mainly concentrated inside the central city and other built-up areas. The SD mismatch area had increased by 390%, indicating a continuous upward trend. In exploring the factors influencing the mismatch between the SD of CSS in Chongqing, supply is mainly positively influenced by NDVI, and demand and supply-demand relationships are influenced by population density and LUCC. We proposed policy suggestions to alleviate the spatial mismatch and practical significance for achieving the "double carbon" goal and promoting sustainable development. [Display omitted] • The CSS deficit was concentrated in the West and continued to worsen. • The spatial mismatch between CSS supply and demand was predominantly banded. • The mismatch of CSS was mainly affected by human activities. • LUCC pathway coefficient was −0.37, and night lights was −0.21 in 2020. [ABSTRACT FROM AUTHOR]

A novel H+ and Cl− intercalated montmorillonite@MgAl LDH (MMT-N) nanocomposite was prepared by a one-step hydrothermal method following acid-salt treatment. The MMT-N prepared by the advanced modification method contains low CO2–3contamination, high specific surface area, and high porosity. By enhancing the ion exchange of montmorillonite and LDH, efficient removal of anionic and anionic dyes was achieved without the introduction of another cation contamination. The adsorption kinetics of methyl orange (MO) and methylene blue (MB) are in accordance with the pseudo-second-order model, and the maximum adsorption amounts of methylene blue and methyl orange reached 815.998 mg g−1 and 399.836 mg g−1, respectively. The coefficient of determination (R2) and the corrected Akaike Information Criterion (AICc) revealed that the adsorption isotherm data of MO and MB on MMT-80 fitted well with Freundlich isotherm, signifying that the adsorption process was dominated by chemisorption. And the adsorption of MO and MB by MMA-80 was endothermic and exothermic, respectively. The "intercalation-split mechanism" and "ion exchange" mechanisms of MO and MB in MMA-80 were also investigated. This study provides an efficient method for the synthesis of high-performance bifunctional adsorbents for cationic and anionic pollutants and offers potential applications for practical wastewater purification. [Display omitted] • The material after acid salt treatment exhibits superior adsorption performance for anionic and cationic dyes. • The cation exchange of montmorillonite and the anion exchange characteristics of LDH are skillfully utilized without introducing other cation pollutants. • The adsorption mechanism including the "intercalation-split mechanism" and "ion exchange mechanism" mechanism was also investigated. [ABSTRACT FROM AUTHOR]