Search Results (7,154)

Identifying habitat characteristics is essential for conserving critically endangered species. When quantifying species habitat characteristics, ignoring data types may lead to misunderstandings about species’ specific habitat requirements. This study focused on the critically endangered Chinese pangolin in Guangdong Province, China, and divided the study area into 600 m × 600 m grids based on its average home range. The burrow number within each grid was obtained through line transect surveys, with burrow numbers/line transect lengths used as direct indicators of habitat utilization intensity. The relationships with sixteen environmental variables, which could be divided into three categories, including topographic, human disturbance and land cover composition, were quantified using the GAM method. We also converted continuous data into binary data (0, 1), constructed GAMs and compared them with habitat utilization intensity models. Our results indicate that the habitat utilization intensity model identified profile curvature and slope as primary factors, showing a nonlinear response to profile curvature (Edf = 5.610, p = 0.014) and a positive relationship with slope (Edf = 1.000, p = 0.006). The presence–absence model emphasized distance to water (Edf = 1.000, p = 0.014), slope (Edf = 1.709, p = 0.043) and aspect (Edf = 2.000, p = 0.026). The intensity model explained significantly more deviance, captured complex nonlinear relationships and supported higher model complexity without overfitting. This study demonstrates that habitat utilization intensity data provides a more ecologically informative basis for in situ conservation (e.g., identifying core habitats), and the process from habitat selection to habitat utilization should be integrated to reveal species’ habitat characteristics. Full article

Water infiltration into soil is a key process in regulating the hydrological cycle and sustaining ecosystem services in high-Andean environments. However, limited information is available regarding its dynamics in these ecosystems. This study evaluated the influence of three types of vegetation cover and soil properties on water infiltration in a high-Andean environment. A double-ring infiltrometer, the Water Drop Penetration Time (WDPT, s) method, and laboratory physicochemical characterization were employed. Soils under forest cover exhibited significantly higher quasi-steady infiltration rates (i_s, 0.248 ± 0.028 cm·min⁻¹) compared to grazing areas (0.051 ± 0.016 cm·min⁻¹) and agricultural lands (0.032 ± 0.013 cm·min⁻¹). Soil organic matter content was positively correlated with i_s. The modified Kostiakov infiltration model provided the best overall fit, while the Horton model better described infiltration rates approaching i_s. Sand and clay fractions, along with K⁺, Ca²⁺, and Mg²⁺, were particularly significant during the soil’s wet stages. In drier stages, increased Na⁺ concentrations and decreased silt content were associated with higher water repellency. Based on WDPT, agricultural soils exhibited persistent hydrophilic behavior even after drying (median [IQR] from 0.61 [0.38] s to 1.24 [0.46] s), whereas forest (from 2.84 [3.73] s to 3.53 [24.17] s) and grazing soils (from 4.37 [1.95] s to 19.83 [109.33] s) transitioned to weakly or moderately hydrophobic patterns. These findings demonstrate that native Andean forest soils exhibit a higher infiltration capacity than soils under anthropogenic management (agriculture and grazing), highlighting the need to conserve and restore native vegetation cover to strengthen water resilience and mitigate the impacts of land-use change. Full article

This paper examines the farming systems and operational structures in the Region of Western Macedonia (RWM), Greece and constructs a typology of farms based on structural, operational, and socio-economic characteristics. Agriculture remains a vital pillar of the regional economy, particularly in the context of RWM’s ongoing transition to a post-lignite development model. Using farm-level data from the 2018 Farm Accountancy Data Network (FADN), Principal Component Analysis (PCA) identified four latent dimensions of farm heterogeneity—income and productivity, asset base, land size, and labour structure. Hierarchical and K-means cluster analysis revealed three distinct farm types: (1) medium-sized, high-efficiency farms with moderate reliance on subsidies (30% of the sample); (2) small-scale, family farms with modest productivity and limited capitalisation (48%); and (3) large, asset-rich farms exhibiting structural inefficiencies and lower output per hectare (22%). These findings highlight structural vulnerabilities, particularly the predominance of undercapitalised smallholdings, and provide a data-driven foundation for Thdesigning differentiated policies that support farm resilience, generational renewal, and sustainable rural development. Full article

The impacts of land use transition on ecological environment quality (EEQ) during China’s rapid urbanization have attracted growing concern. However, existing studies predominantly focus on single-scale analyses, neglecting scale effects and driving mechanisms of EEQ changes under the coupling of administrative units and grid scales. Therefore, this study selects Zhejiang Province—a representative rapidly transforming region in China—to establish a “type-process-ecological effect” analytical framework. Utilizing four-period (2005–2020) 30 m resolution land use data alongside natural and socio-economic factors, four spatial scales (city, county, township, and 5 km grid) were selected to systematically evaluate multi-scale impacts of land use transition on EEQ and their driving mechanisms. The research reveals that the spatial distribution, changing trends, and driving factors of EEQ all exhibit significant scale dependence. The county scale demonstrates the strongest spatial agglomeration and heterogeneity, making it the most appropriate core unit for EEQ management and planning. City and county scales generally show degradation trends, while township and grid scales reveal heterogeneous patterns of local improvement, reflecting micro-scale changes obscured at coarse resolutions. Expansive land transition including conversions of forest ecological land (FEL), water ecological land (WEL), and agricultural production land (APL) to industrial and mining land (IML) primarily drove EEQ degradation, whereas restorative ecological transition such as transformation of WEL and IML to grassland ecological land (GEL) significantly enhanced EEQ. Regarding driving mechanisms, natural factors (particularly NDVI and precipitation) dominate across all scales with significant interactive effects, while socio-economic factors primarily operate at macro scales. This study elucidates the scale complexity of land use transition impacts on ecological environments, providing theoretical and empirical support for developing scale-specific, typology-differentiated ecological governance and spatial planning policies. Full article

Per- and polyfluoroalkyl substances (PFAS) are synthetically produced chemicals that are causing a major One Health crisis. These “forever chemicals” are widely distributed globally in air, water, and soil, and because they are highly mobile and extremely difficult to degrade in the environment. They cause additional health concerns in a circular bioeconomy and food system that recycles and reuses by-products and numerous types of waste materials. Uptake of PFAS by plants and food-producing animals ultimately leads to the consumption of PFAS-contaminated food that is associated with numerous adverse health and developmental effects in humans. Contaminated meat, milk, and eggs are some of the main sources of human PFAS exposure. Although there is no safe level of PFAS exposure, maximum tolerable PFAS consumption guidelines have been established for some countries. However, there is no international PFAS monitoring system, and there are no standardized international guidelines and mechanisms to prevent the consumption of PFAS-contaminated foods. Urgent action is needed to stop PFAS production except for critical uses, implementing effective water-purification treatments, preventing spreading sewage sludge on land and pastures used to produce food, and requiring marketers and manufacturers to use packaging that is free of PFAS. Full article

Urban air pollution remains a significant environmental and public health issue, especially in European coastal cities such as Klaipėda. However, there is still a lack of local-scale knowledge on how land use structure influences pollutant distribution, highlighting the need to address this gap. This study addresses this by examining the spatial distribution of nitrogen dioxide (NO₂) concentrations in Klaipėda’s seaport city and several inland and coastal resort towns in Lithuania. The research specifically asks how different land cover types and demographic factors affect NO₂ variability and population exposure risk. Data were collected using passive sampling methods and analyzed within a GIS environment. The results revealed clear air quality differences between industrial/port zones and greener resort areas, confirmed by statistically significant associations between land cover types and pollutant levels. Based on these findings, a Land Use Pollution Pressure index (LUPP) and its population-weighted variant (PLUPP) were developed to capture demographic sensitivity. These indices provide a practical decision-support tool for sustainable urban planning, enabling the assessment of pollution risks and the forecasting of air quality changes under different land use scenarios, while contributing to local climate adaptation and urban environmental governance. Full article

The use of the Soil and Water Assessment Tool (SWAT) model driven by China Meteorological Assimilation Driving Datasets (CMADS) for runoff simulation research is of great significance for regional flood prevention and control. Therefore, from the perspective of production-living-ecological space, this article combined multi-source data such as DEM, soil texture and land use type, in order to construct scenarios of territorial spatial change (TSC) across distinct periods. Based on the CMADS-L40 data and the SWAT model, it simulated the runoff dynamics in the Xitiaoxi River Basin, and analyzed the hydrological response characteristics under different TSCs. The results showed that The SWAT model, driven by CMADS-L40 data, demonstrated robust performance in monthly runoff simulation. The coefficient of determination (R²), Nash–Sutcliffe efficiency coefficient (NSE), and the absolute value of percentage bias (|PBIAS|) during the calibration and validation period all met the accuracy requirements of the model, which validated the applicability of CMADS-L40 data and the SWAT model for runoff simulation at the watershed scale. Changes in territorial spatial patterns are closely correlated with runoff variation. Changes in agricultural production space and forest ecological space show statistically significant negative correlation with runoff change, while industrial production space change exhibits a significant positive correlation with runoff change. The expansion of production space, particularly industrial production space, leads to increased runoff, whereas the enlargement of agricultural production space and forest ecological space can reduce runoff. This article contributes to highlighting the role of land use policy in hydrological regulation, providing a scientific basis for optimizing territorial spatial planning to mitigate flood risks and protect water resources. Full article

Small towns in mountainous regions face significant challenges in formulating effective landscape zoning strategies due to pronounced landscape fragmentation, which is driven by both the dominance of large-scale forest resources and the lack of coordination between administrative planning departments. To tackle this problem, this study focused on Yuqian, a quintessential small mountainous town in Hangzhou, Zhejiang Province. The town’s layout was divided into a grid network measuring 70 m × 70 m. A two-step cluster process was employed using ArcGIS and SPSS software to analyze five landscape variables: altitude, slope, land use, heritage density, and visual visibility. Further, eCognition software’s semi-automated segmentation technique, complemented by manual adjustments, helped delineate landscape character types and areas. The overlay analysis integrated these areas with administrative village units, identifying four landscape character types across 35 character areas, which were recategorized into four planning and management zones: urban comprehensive service areas, agricultural and cultural tourism development areas, industrial development growth areas, and mountain forest ecological conservation areas. This result optimizes the current zoning types. These zones closely match governmental sustainable development zoning requirements. Based on these findings, we propose integrated landscape management and conservation strategies, including the cautious expansion of urban areas, leveraging agricultural and cultural tourism, ensuring industrial activities do not impact the natural and village environment adversely, and prioritizing ecological conservation in sensitive areas. This approach integrates spatial and administrative dimensions to enhance landscape connectivity and resource sustainability, providing key guidance for small town development in mountainous regions with unique environmental and cultural contexts. Full article

Cultivated land quality is a key factor in ensuring sustainable agricultural development. Exploring differences in cultivated land quality under distinct cropping systems is essential for developing targeted improvement strategies. This study takes place in Shenyang City—located in the typical black soil region of Northeast China—as a case area to construct a cultivated land quality evaluation system comprising 13 indicators, including organic matter, effective soil layer thickness, and texture configuration. A minimum data set (MDS) was separately extracted for paddy and upland fields using principal component analysis (PCA) to conduct a comprehensive evaluation of cultivated land quality. Additionally, an obstacle degree model was employed to identify the limiting factors and quantify their impact. The results indicated the following. (1) Both MDSs consisted of seven indicators, among which five were common: ≥10 °C accumulated temperature, available phosphorus, arable layer thickness, irrigation capacity, and organic matter. Parent material and effective soil layer thickness were unique to paddy fields, while landform type and soil texture were unique to upland fields. (2) The cultivated land quality index (CQI) values at the sampling point level showed no significant difference between paddy (0.603) and upland (0.608) fields. However, their spatial distributions diverged significantly; paddy fields were dominated by high-grade land (Grades I and II) clustered in southern areas, whereas uplands were primarily of medium quality (Grades III and IV), with broader spatial coverage. (3) Major constraint factors for paddy fields were effective soil layer thickness (21.07%) and arable layer thickness (22.29%). For upland fields, the dominant constraints were arable layer thickness (27.57%), organic matter (25.40%), and ≥10 °C accumulated temperature (23.28%). Available phosphorus and ≥10 °C accumulated temperature were identified as shared constraint factors affecting quality classification in both systems. In summary, cultivated land quality under different cropping systems is influenced by distinct limiting factors. The construction of cropping-system-specific MDSs effectively improves the efficiency and accuracy of cultivated land quality assessment, offering theoretical and methodological support for land resource management in the black soil regions of China. Full article

Arid regions, while providing essential ecosystem services, are among the most ecologically vulnerable worldwide. Understanding and monitoring their long-term vegetation dynamics is essential for accurate environmental assessment and climate adaptation strategies. This study examined the spatiotemporal variations and driving forces of the vegetation dynamics in arid Northwestern China during 2000 to 2020, using the annual peak fractional vegetation cover (FVC) as the primary indicator. The Sen’s slope estimator with the Mann–Kendall test and the coefficient of variation were employed to assess the spatiotemporal variations in FVC, while the Pearson correlation, geographic detector model and random forest model were applied to identify the dominant driving factors for FVC. The results indicated that (1) overall vegetation cover was low (averaged peak FVC = 0.191), showing a spatial pattern of higher values in the northwest and lower values in the southeast; high FVC values were primarily observed in mountainous areas and river corridors; (2) the annual peak FVC increased significantly at a rate of 0.0508 yr⁻¹, with 33.72% of the region showing significant improvements and 5.49% degradation; (3) the spatial pattern of FVC was shaped by the distribution of land use types (59.46%), while the temporal dynamics of FVC were driven by land use changes (16.37%) and the land use intensity (37.56%); (4) both the spatial pattern and the temporal dynamics were limited by the environmental conditions. These findings highlight the critical role of anthropogenic activities in shaping the spatiotemporal variations in FVC, particularly emphasizing the distinct contributions of changes in land use types and land use intensity. This study could provide a scientific basis for sustainable land management and restoration strategies in arid regions facing global changes. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

Zoning policies play a critical role in shaping the geography of urban and suburban development in the United States. Using data from the National Zoning and Land-Use Database and tract-level census data from 42 Metropolitan Statistical Areas, we classify metros into four zoning regime types based on the relative restrictiveness of urban and suburban land-use policies and compare trends in population growth and neighborhood change across these regimes. Our findings show that suburban areas have outpaced urban cores in population growth across all zoning configurations, with the most pronounced growth occurring in metros where restrictive urban zoning coexists with permissive suburban regulation. This growth is disproportionately concentrated in affluent suburban neighborhoods, suggesting a spatial sorting of access to resources and amenities. We also find that urban–suburban gentrification gaps are the smallest in these asymmetrical zoning regimes, suggesting that permissive suburban land use may facilitate spillover effects from constrained cores. These findings suggest that zoning asymmetries shape not only the geography of growth but also the spatial dynamics of gentrification. We argue for a metropolitan perspective on land-use governance to better understand the interconnected nature of suburbanization and the spatial expansion of gentrification. Full article

Eco-enzymatic stoichiometry provides a basis for understanding soil ecosystem functions, with implications for land management and ecological protection. Long-term climatic factors and human interferences have caused significant land-use transformations in the Qinghai–Tibet Plateau region, affecting various ecological functions, such as soil nutrient cycling and chemical element balance. It is currently unclear how large-scale land-use conversion affects soil ecological stoichiometry. In this study, 763 soil samples were collected across three land-use types: farmland, grassland, and forest land. In addition, changes in soil physicochemical properties and enzyme activity and stoichiometry were determined. The soil available phosphorus (SAP) and total phosphorus (TP) concentrations were the highest in farmland soil. Bulk density, pH, SAP, TP, and NO₃⁻-N were lower in forest soil, whereas NH₄⁺-N, available nitrogen, soil organic carbon (SOC), available potassium, and the soil nutrient ratio increased. Land-use conversion promoted soil β-1,4-glucosidase, N-acetyl-β-glucosaminidase, and alkaline phosphatase activities, mostly in forest soil. The eco-enzymatic C:N ratio was higher in farmland soils but grassland soils had a higher enzymatic C:P and N:P. Soil microorganisms were limited by P nutrients in all land-use patterns. C limitation was the highest in farmland soil. The redundancy analysis indicated that the ecological stoichiometry in farmland was influenced by TN, whereas grass and forest soils were influenced by SOC. Overall, the conversion of cropland or grassland to complex land-use types can effectively enhance soil nutrients, enzyme activities, and ecosystem functions, providing valuable insights for ecological restoration and sustainable land management in alpine regions. Full article

To enhance agricultural soil health and soil organic carbon (SOC) sequestration, it is important to accurately measure SOC. The aim of this study was to compare common methods for measuring SOC in soils in order to determine the most effective approach among different agricultural land types. The measurement methods of loss-on-ignition (LOI), automated dry combustion (Dumas), and real-time near-infrared spectroscopy (NIRS) were compared. A total of 95 soil core samples, ranging in clay and calcareous content, were collected across a range of agricultural land types from forty-eight fields across five farms in the Southwest of England. There were similar and positive correlations between all three methods for measuring SOC (ranging from r = 0.549 to 0.579; all p < 0.001). On average, permanent grass fields had higher SOC content (6.6%) than arable and temporary ley fields (4.6% and 4.5%, respectively), with the difference of 2% indicating a higher carbon storage potential in permanent grassland fields. Newly predicted conversion equations of linear regression were developed among the three measurement methods according to all the fields and land types. The correlation of the conversation equations among the three methods in permanent grass fields was strong and significant compared to those in both arable and temporary ley fields. The analysed results could help understand soil carbon management and maximise sequestration. Moreover, the approach of using real-time NIRS analysis with a rechargeable portable NIRS soil device can offer a convenient and cost-saving alternative for monitoring preliminary SOC changes timely on or offsite without personnel risks from the high-temperature furnace and chemical reagent adopted in the LOI and Dumas processes, respectively, at the laboratory. Therefore, the study suggests that faster, lower-cost, and safer methods like NIRS for analysing initial SOC measurements are now available to provide similar SOC results as traditional soil analysis methods of the LOI and Dumas. Further studies on assessing SOC levels in different farm locations, land, and soil types across seasons using NIRS will improve benchmarked SOC data for farm stakeholders in making evidence-informed agricultural practices. Full article

Rapid socio-economic development and the impact of human activities have exerted tremendous pressure on the groundwater system of the Dawen River Basin (DRB), the largest tributary in the middle and lower reaches of the Yellow River. Hydrochemical studies on the DRB have largely centered on the upstream Muwen River catchment and downstream Dongping Lake, with some focusing solely on karst groundwater. Basin-wide evaluations suggest good overall groundwater quality, but moderate to severe contamination is confined to the lower Dongping Lake area. The hydrogeologically complex mid-reach, where the Muwen and Chaiwen rivers merge, warrants specific focus. This region, adjacent to populous areas and industrial/agricultural zones, features diverse aquifer systems, necessitating a thorough analysis of its hydrochemistry and origins. This study presents an integrated hydrochemical, isotopic investigation and EWQI evaluation of groundwater quality and formation mechanisms within the multiple groundwater types of the central DRB. Central DRB groundwater has a pH of 7.5–8.2 (avg. 7.8) and TDSs at 450–2420 mg/L (avg. 1075.4 mg/L) and is mainly brackish, with Ca²⁺ as the primary cation (68.3% of total cations) and SO₄²⁻ (33.6%) and NO₃⁻ (28.4%) as key anions. The Piper diagram reveals complex hydrochemical types, primarily HCO₃·SO₄-Ca and SO₄·Cl-Ca. Isotopic analysis (δ²H, δ¹⁸O) confirms atmospheric precipitation as the principal recharge source, with pore water showing evaporative enrichment due to shallow depths. The Gibbs diagram and ion ratios demonstrate that hydrochemistry is primarily controlled by silicate and carbonate weathering (especially calcite dissolution), active cation exchange, and anthropogenic influences. EWQI assessment (avg. 156.2) indicates generally “good” overall quality but significant spatial variability. Pore water exhibits the highest exceedance rates (50% > Class III), driven by nitrate pollution from intensive vegetable cultivation in eastern areas (Xiyangzhuang–Liangzhuang) and sulfate contamination from gypsum mining (Guojialou–Nanxiyao). Karst water (26.7% > Class III) shows localized pollution belts (Huafeng–Dongzhuang) linked to coal mining and industrial discharges. Compared to basin-wide studies suggesting good quality in mid-upper reaches, this intensive mid-reach sampling identifies critical localized pollution zones within an overall low-EWQI background. The findings highlight the necessity for aquifer-specific and land-use-targeted groundwater protection strategies in this hydrogeologically complex region. Full article