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At present, many researchers are increasingly aware of the importance of using models to identify heavy metal (HM) pollution sources. However, on the performance and application of different source identification models to HMs under different land use types had been studied little. In this study, comparison of absolute principal component scores-multiple linear regression (APCS-MLR) and positive matrix factorization (PMF) models and their application characteristics in identifying pollution sources were carried out by using 11 HMs in Zhongwei City farmland and Shizuishan industrial park, Ningxia. The results indicated that HM pollution in farmland mainly came from pesticides, fertilizers, and deposition of the Yellow River, while the pollution in industrial park mainly originated from atmospheric deposition and various industrial productions. The APCS-MLR model had the problem of less identification sources and the difficulty to explain the complex pollution, while the PMF model not only identified more pollution sources, but also distinguished heavy metal–related sources for two different land use types and different industrial production conditions. It is of great significance the formulation of agricultural-related pesticides’ and chemical fertilizers’ rational use and various industrial production–related raw materials put in and emission control strategies.

With the increasing impact of human activities on the environment, evapotranspiration (ET) has changed in arid areas, which further affects the water resources availability in the region. Therefore, understanding the impact of human activities on ET and its components is helpful to the management of water resources in arid areas. This study verified the accuracy of Fisher’s model (PT-JPL model) for ET estimation in southern Xinjiang, China by using the evaporation complementarity theory dataset (AET dataset). The ET and the evapotranspiration components (T:E) of six land-use types were estimated in southern Xinjiang from 1982 to 2015, and the impact of human activities on ET was analyzed. In addition, the impact of four environmental factors (temperature (Temp), net radiation (Rn), relative humidity (RH), and NDVI) on ET were evaluated. The results showed that the calculated ET values of the PT-JPL model were close to the ET values of the AET dataset. The correlation coefficient (R2) was more than 0.8, and the NSE was close to 1. In grassland, water area, urban industrial and mining land, forest land, and cultivated land, the ET values were high, and in unused land types, the ET values were the lowest. The T:E values varied greatly in urban industrial and mining land, forest land, and cultivated land, which was due to the intensification of human activities, and the values were close to 1 in summer in recent years. Among the four environmental factors, temperature largely influenced the monthly ET. These findings suggest that human activities have significantly reduced soil evaporation and improved water use efficiency. The impact of human activities on environmental factors has caused changes in ET and its components, and appropriate oasis expansion is more conducive to regional sustainable development.

Soil carbon and nitrogen levels are key indicators of soil fertility and are used to assess ecological value and safeguard the environment. Previous studies have focused on the contributions of vegetation, topography, physical and chemical qualities, and meteorology to soil carbon and nitrogen change, but there has been little consideration of landscape and ecological environment types as potential driving forces. The study investigated the horizontal and vertical distribution and influencing factors of total carbon and total nitrogen in soil at 0–20 and 20–50 cm depths in the source region of the Heihe River. A total of 16 influencing factors related to soil, vegetation, landscape, and ecological environment were selected, and their individual and synergistic effects on the distributions of total carbon and total nitrogen in soil were assessed. The results show gradually decreasing average values of soil total carbon and total nitrogen from the surface layer to the bottom layer, with larger values in the southeast part of the sampling region and smaller values in the northwest. Larger values of soil total carbon and total nitrogen at sampling points are distributed in areas with higher clay and silt and lower soil bulk density, pH, and sand. For environmental factors, larger values of soil total carbon and total nitrogen are distributed in areas with higher annual rainfall, net primary productivity, vegetation index, and urban building index, and lower surface moisture, maximum patch index, boundary density, and bare soil index. Among soil factors, soil bulk density and silt are most closely associated with soil total carbon and total nitrogen. Among surface factors, vegetation index, soil erosion, and urban building index have the greatest influence on vertical distribution, and maximum patch index, surface moisture, and net primary productivity have the greatest influence on horizontal distribution. In conclusion, vegetation, landscape, and soil physical properties all have a significant impact on the distribution of soil carbon and nitrogen, suggesting better strategies to improve soil fertility.

The diversity and community distribution of soil bacteria in different land use types in Yangtze River Basin, Chongqing Municipality were studied by using Illumina MiSeq analysis methods. Soil physical and chemical properties were determined, and correlation analyses were performed to identify the key factors affecting bacterial numbers and α-diversity in these soils. The results showed that the soil physical and chemical properties of different land use types decrease in the order: mixed forest (M2) > pure forest (P1) > grassland (G3) > bare land (B4). There were significant differences in bacterial diversity and communities of different land use types. The diversity of different land use types showed the same sequence with the soil physical and chemical properties. The abundance and diversity of bacterial in M2 and P1 soils was significantly higher than that in G3 and B4 soils. At phylum level, G3 and B4 soils were rich in only Proteobacteria and Actinobacteria, whereas M2 and P1 soils were rich in Proteobacteria, Actinobacteria and Firmicutes. At genus level, Faecalibacterium and Agathobacter were the most abundant populations in M2 soil and were not found in other soils. Pearson correlation analysis showed that soil moisture content, pH, AN, AP, AK and soil enzyme activity were significantly related to bacterial numbers, diversity and community distribution.

Litter decomposition is a prominent pathway for nutrient availability and management in terrestrial ecosystems. An in-situ litter decomposition experiment was carried out for different land use types along an elevation gradient in the Taihang Mountain area restored after heavy forest degradation in the past. Four land use types, i.e., cropland, shrubland, grassland, and forest, selected randomly from a 300–700 m elevation were investigated for the experiment using the litter bag technique. Litter mass loss ranged from 26.9% (forest) to 44.3% (cropland) varying significantly among land use types. The initial litter quality, mainly N and C/N, had a significant effect on the litter loss rate. The interaction of elevation × land use types × time was significant (p < 0.001). Litter nutrient mobility (K > P ≈ N > C) of the decomposing litter was sporadic with substantial stoichiometric effects of C/N, N/P, and C/P. The residual litters were enriched in 15N and depleted in 13C as compared to the initial litter. Increment of N, P, and δ15N values in residual litter indicates that, even in the highly weathered substrate, plant litter plays a crucial role in conserving nutrients. This study is a strong baseline for monitoring the functioning of the Taihang Mountain ecosystem restored after the complete destruction in the early 1990s.

(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective of topography differentiation. (2) Methods: This paper selects three counties (Chengkou, Wushan and Wuxi counties) in northeastern Chongqing, delineated as the corrosion layered high and middle mountain region (Zone I), and three counties (Wulong, Pengshui and Shizhu counties) in southeastern Chongqing, delineated as the middle mountainous region of strong karst gorges (Zone II), as the study area. This study used a Bayesian optimization algorithm to optimize the parameters of the LightGBM and XGBoost models and construct evaluation models for each of the two regions. The model with high accuracy was selected according to the accuracy of the evaluation indicators in order to establish the landslide susceptibility mapping. The SHAP algorithm was then used to explore the landslide formation mechanisms of different landforms from both a global and local perspective. (3) Results: The AUC values for the test set in the LightGBM mode for Zones I and II are 0.8525 and 0.8859, respectively, and those for the test set in the XGBoost model are 0.8214 and 0.8375, respectively. This shows that LightGBM has a high prediction accuracy with regard to both landforms. Under the two different landform types, the elevation, land use, incision depth, distance from road and the average annual rainfall were the common dominant factors contributing most to decision making at both sites; the distance from a fault and the distance from the river have different degrees of influence under different landform types. (4) Conclusions: the optimized LightGBM-SHAP model is suitable for the analysis of landslide susceptibility in two types of landscapes, namely the corrosion layered high and middle mountain region, and the middle mountainous region of strong karst gorges, and can be used to explore the internal decision-making mechanism of the model at both the global and local levels, which makes the landslide susceptibility prediction results more realistic and transparent. This is beneficial to the selection of a landslide susceptibility index system and the early prevention and control of landslide hazards, and can provide a reference for the prediction of potential landslide hazard-prone areas and interpretable machine learning research.

Analyzing cultivated land input behavior (CLIB) at the scale of rural households links with cultivated land-use efficiency (CLUE), this study examined the Yimeng Mountain area in northern China, supported by field survey data from 737 rural households. This research systematically analyzed the characteristics of CLIB of different types of rural households, measured the CLUE of different types of rural households by using a data envelopment analysis (DEA) model, and explored the influence of CLIB on CLUE based on the Tobit regression model. The results show (1) significant differences in the characteristics of the CLIB of different types of rural households in the Yimeng Mountain area. Among them, the highest land, labor, and capital inputs were I part-time rural households (I PTRH), followed by full-time rural households (FTRH). In contrast, II part-time rural households (II PTRH) and non-agricultural rural households (NARH) had higher levels of non-agricultural employment; however, their input levels gradually declined. (2) The CLUE of the sample rural households was generally low and had considerable potential for improvement. Regarding the types of rural households, as the degree of part-time employment increased, the CLUE showed an inverted U-shaped trend of first increased and then decreased, namely, I PTRH > FTRH > II PTRH > NARH. This finding indicates that appropriate part-time employment could help to promote investment in agricultural production and improve the CLUE. (3) The CLIB of rural households had significant effects on CLUE; the literacy of the agricultural labor force, yield-increasing input per unit area, per capita household income, share of agricultural income, operation scale of cultivated land, effective irrigation rate of cultivated land, and soil and water conservation rate of cultivated land had positive effects on improving CLUE. Even so, there was still significant heterogeneity in the degree of influence of different rural household types. The study concluded with some policy recommendations from the perspective of different rural household types to provide references for optimizing farming inputs and improving CLUE.

Resource-based cities (RBCs) are pivotal regions for China to achieve low-carbon development, and their transition performance is closely linked to territorial space development and protection (TSDP). To scientifically assess their sustainable development levels, this study constructed a comprehensive evaluation system for territorial space development and protection level (TSDPL), encompassing dual dimensions of development and protection. Based on panel data from 110 Chinese RBCs between 2005 and 2020, we systematically measured the spatiotemporal evolution of TSDPL for each city. The findings reveal that the TSDPL increased at an average annual rate of 2.791% during the period. However, the protection level consistently lagged behind the development intensity, reflecting an overall characteristic of prioritizing development over protection. Spatially, TSDPL exhibited a gradient differentiation pattern of being \"higher in the southeast and lower in the northwest.\" Its center of gravity shifted approximately 51 km to the southwest, forming \"high-high\" agglomeration areas represented by Shandong and Jiangsu provinces and \"low-low\" agglomeration areas represented by Gansu Province. The absolute disparity in TSDPL among cities continued to widen, indicating a pronounced polarization trend. The research further uncovered significant heterogeneity in the transition outcomes: regenerative type cities, non-metallic-based cities, and those in the eastern region performed better, while growth type cities, forestry-based cities, and those in the western region lagged relatively behind. This suggests that under similar resource constraints, local development pathways and governance efficacy are key determinants of transition outcomes. By establishing a nationwide comprehensive TSDPL measurement framework, this study systematically identified the differentiated transition trajectories of RBCs across various development stages, dominant resource types, and regional contexts. It provides a scientific basis for implementing categorized guidance and targeted policies for low-carbon transition and spatial governance.

The appropriate resolution has been confirmed to be crucial to the extraction of urban green space and the related research on ecosystem services. However, the factors affecting the differences between various resolutions of data in certain application scenarios are lacking in attention. To fill the gap, this paper made an attempt to analyze the differences of various resolutions of data in green space extraction and to explore where the differences are reflected in the actual land unit, as well as the factors affecting the differences. Further, suggestions for reducing errors and application scenarios of different resolutions of data in related research are proposed. Taking a typical area of Nanjing as an example, data taken by DJI drone (0.1 m), GaoFen-1 (2 m) and Sentinel-2A (10 m) were selected for analysis. The results show that: (1) There were minimal differences in the green space ratio of the study area calculated by different resolutions of data on the whole, but when subdivided into each land use type and block, the differences were obvious; (2) The function, area and shape of the block, as well as the patch density and aggregation degree of the internal green space, had a certain impact on the differences. However, the specific impact varied when the block area was different; and (3) For the selection of the data source, the research purpose and application scenarios need to be comprehensively considered, including the function and attributes of the block, the distribution characteristics of green space, the allowable error limits and the budget. The present study highlighted the reasons of differences and hopefully it can provide a reference for the data selection of urban green space in the practical planning and design.

The accelerating conversion of agricultural land to non-agricultural uses poses critical threats to food security and sustainable land management, particularly in ecologically fragile karst mountainous regions. This study investigated the spatiotemporal patterns and driving mechanisms of cropland non-agricultural conversion (CNAC) in the Qian-Gui karst region (Guangxi and Guizhou, China) from 2000 to 2020, employing land use datasets and socioeconomic indicators through geographically weighted regression (GWR) modeling. The results showed that (1) from 2000 to 2020, the CNAC rate in the Qian-Guizhou karst mountainous region reached 2.03%. The area of CNAC increased by 14.60 × 104 hm2, increasing 1.74 times in 2010–2020 compared to 2000–2010, showing a trend of rapid growth. Specifically, the growth rate of the CNAC area was the highest in apparent mountainous (110.36%) and quasi-mountainous counties (100.5%), followed by semi-mountainous counties (95.28%), while entirely mountainous (40.89%) and pure hilly counties (37.68%) experienced the lowest growth, revealing distinct regional disparities. (2) Spatially, CNAC exhibited a pattern of “high in the north and south, low in the central region”, and the overall level of CNAC displayed significant regional imbalances, with extreme grades distributed in provincial capitals, high and medium grades concentrated in prefecture-level city districts, and light and low grades mainly located in counties and districts (accounting for more than 55.56% of the total number of research units in the two time periods). (3) There was significant spatial heterogeneity in the driving effect of factors influencing CNAC. Agricultural output and population density showed the strongest positive correlations; effectively irrigated areas exhibited a growing influence over time (except for pure hilly counties); rocky desertification areas exerted a strengthened influence on CNAC in pure hilly counties, while their impact was relatively lower in other regions compared to other indicators. Therefore, when formulating policies to protect farmland, it is essential to take into account the specific conditions of different types of counties in mountainous areas and adopt management measures tailored to these regional characteristics.