Search Results (2,063)

Heavy metal (HM) contamination in urban soils is a pressing global issue, particularly in rapidly industrializing regions like Kazakhstan, where anthropogenic activities such as transportation, energy production, and manufacturing exacerbate accumulation in ecosystems. In Almaty, the largest city in Kazakhstan, urban expansion and legacy pollution pose risks to soil functions, biodiversity, and public health through bioaccumulation and migration pathways. This study evaluates the spatial distribution and ecological impacts of total heavy metal concentrations (HMs) (Pb, Cd, As, Zn, Cu, Ni, Co, Mo, Mn) in Almaty’s soils to inform remediation strategies. Soil samples (n = 73) were collected using a systematic grid sampling method across urban, industrial, and peri-urban zones in Almaty. HM concentrations were determined via X-ray fluorescence spectrometry (XRF) following GOST 33850-2016 standards. Pollution indices (contamination factor K_c and integrated pollution index Z_c) were calculated relative to Kazakhstani permissible limits (PDK RK) and Russian approximate permissible concentrations (ODK RF). Statistical analyses included Spearman’s correlation, boxplots, and coefficient of variation. Morphological, physicochemical (pH, humus content), and biological assessments evaluated degradation. Spatial interpolation via GIS mapped the hotspots. HM distributions showed significant variability, with As, Zn, and Ni exceeding norms in >90% of samples (median K_c ≈ 5 for As). Z_c classified >70% of sites as hazardous or extremely hazardous (Z_c > 32), with hotspots in central-eastern districts (Z_c 90–145). Strong correlations (ρ ≥ 0.6) identified a technogenic group (Pb–Zn–Cu–Ni) from traffic and industry, contrasting predominantly geogenic elements with possible anthropogenic contribution (As–Co–Mo–Mn). Pollution induced soil compaction, reduced humus/pH, and disrupting biogeochemical cycles. Local exceedances were noted near TECs, factories, and transport hubs. Almaty’s soils exhibit pervasive technogenic HM pollution, driven by urban sources, leading to ecosystem degradation and health risks. Future research should incorporate vertical profiling and isotopic sourcing for refined risk models. Prioritized monitoring and phytoremediation in hotspots are recommended to enhance resilience, aligning with UN SDGs for sustainable cities and ecosystems. Future research should incorporate vertical profiling and isotopic sourcing for refined risk models. Full article

Dopamine dysregulation is connected to several neurological disorders, including Parkinson’s disease, Huntington’s disease, and addiction. A new, precise, accurate, and specific reversed-phase high-performance liquid chromatographic method was developed for dopamine determination in different biological media (human/mouse plasma and mouse brain homogenate). The chromatographic assay was performed using Avantor ACE^® RP-18 (250 × 4.6 mm, 5 µm) column equipped with a suitable ODS pre-column. The temperature was ambient, and the mobile phase was composed of 10 mM potassium dihydrogen phosphate buffer (pH = 3) with 0.25 g/L sodium octanesulfonate, methanol, and acetonitrile at a volume-to-volume ratio of 75:20:5. Isocratic elution mode, flow rate 1.0 mL/min, and ultraviolet detection (280 nm) were applied. The procedure was validated for linearity, and all calibration curves were linear over the selected range with determination coefficients greater than 0.998. Intraday repeatability, expressed as the coefficient of variation, did not exceed 4.88% for the plasma and 3.32% for the mouse brain homogenate samples across all tested concentration levels. The proposed chromatographic method was evaluated in terms of greenness, whiteness, and blueness using three ecological metrics (the Analytical Greenness software, White Analytical Chemistry model, and Blue Applicability Grade Index). The optimized procedure was proven to be suitable for implementation in the routine analytical practice. Full article

Monitoring species richness patterns across large spatial scales is essential for addressing the global biodiversity crisis. Dynamic Habitat Indices (DHIs), derived from satellite-based productivity data, have proven valuable for predicting species distributions. The original DHI framework comprises three complementary sub-indices, each corresponding to a key ecological hypothesis linking productivity and biodiversity: annual cumulative productivity (DHI Cum; available energy hypothesis), annual minimum productivity (DHI Min; environmental stress hypothesis), and the coefficient of variation in productivity (DHI CV; environmental stability hypothesis). However, current DHI formulations primarily focus on intra-annual vegetation productivity dynamics, thereby overlooking the ecological significance of inter-annual productivity variability. To address this limitation, we propose an extended DHI suite that integrates both seasonal (intra-annual) and long-term (inter-annual) productivity metrics. Using a random forest regression approach, we demonstrate that incorporating this extended DHI suite significantly improves predictions of global vertebrate species richness (cross-validated R² = 0.89, RMSE = 68.20) compared to using seasonal metrics alone (R² = 0.86). Notably, inter-annual productivity variation emerged as the most influential predictor, strongly supporting the environmental stability hypothesis. This was followed by importance in seasonal minimum productivity (environmental stress) and cumulative productivity (available energy). Our findings reveal the critical, complementary roles of seasonal and inter-annual productivity dynamics in shaping global faunal species richness patterns. This enhanced framework provides a robust scalable tool for assessing species richness distributions and informing conservation strategies amid accelerating climate shifts and anthropogenic pressures. Full article

River morphological changes significantly influence water purification functions; however, systematic research on the evolution of natural river morphology and its underlying mechanisms remains insufficient. This study investigates the Shiwuli River, a typical tributary of Chaohu Lake, by quantitatively analyzing its morphological evolution characteristics based on high-resolution satellite imagery from 2014 to 2024. Combined with field monitoring data from all four seasons of 2024, the study explores the influence mechanisms of river sinuosity, cascade flow, and wetlands on water purification. The results indicate significant morphological changes in the Shiwuli River: the total length decreased by 3.95 km, sinuosity decreased by 0.22, and the average width increased by 27.85 m. The comprehensive attenuation coefficient of pollutants in the monitored sections was consistently greater than zero, demonstrating the self-purification capacity of the natural meandering river, with the highest purification capacity observed in summer and the weakest in winter. Dissolved oxygen (DO) content was generally higher in concave banks than in convex banks, and the rate of increase in DO per unit length rose with increasing sinuosity. The cascade flow formed by rolling dams significantly enhanced DO concentration (by 19.23–26.25%), with average pollutant reduction rates ranging from 12.64% to 33.76%. The wetland sections exhibited average reduction rates of 79.07% for total phosphorus (TP), 39.33% for total nitrogen (TN), 47.43% for ammonia nitrogen (NH₃-N), and 45.67% for chemical oxygen demand (COD), demonstrating significantly better purification effects compared to the main river channel. This study reveals that the synergistic interaction among river sinuosity, cascade flow, and wetland systems enhances the water body’s self-purification capacity, providing a scientific basis for river ecological restoration and sustainable utilization of water resources. Full article

Countermovement jump (CMJ) analysis is widely used to assess lower limb neuromuscular function, but commercial force plates often suffer from high cost, closed algorithms, and lack of bilateral independent measurement. This study developed and evaluated a dual channel strain gauge force plate system featuring open architecture and hardware-triggered video synchronization. The system consists of two physically isolated plates, each with four full bridge strain beams, a precision analog front end, and a 2000 Hz acquisition unit. A microcontroller-based hardware trigger synchronizes force data with video capture. Custom host software implements adaptive jump phase recognition and calculates peak force (PF), concentric impulse, jump height, rate of force development (RFD), and asymmetry index (ASI). Validation included static mass measurements in 14 participants, low-load static calibration (5.0–30.0 kg), free-fall impulse validation (7.00 to 31.32 N·s), 240 fps high-speed video cross validation of flight time, ecological-validity comparison with published AMTI-based force-plate data, and 48 h test–retest reliability assessment. Static mass measurement showed a mean absolute percentage error (MAPE) of 1.01% and a coefficient of determination (R²) of 0.9992, while low-load testing confirmed excellent linearity (R² > 0.996) and minimal absolute error (mean absolute error = 0.34 kg) at lighter weights. Dynamic impulse validation yielded R² > 0.997 and MAPE < 3%. Flight time agreement with high-speed video was within ±10 ms. Test–retest reliability was excellent for concentric impulse (intraclass correlation coefficient (ICC) = 0.997) and jump height (ICC = 0.987), and good for PF (ICC = 0.962) and rate of force development at 100 ms (RFD_100ms) (ICC = 0.883). The physically isolated dual-plate architecture effectively captured bilateral force differences, although the ASI demonstrated moderate reliability (ICC = 0.748), likely reflecting the inherent biological variability in bilateral coordination. The ecological-validity comparison further indicated that the macroscopic kinetic outputs of the proposed system fell within the expected physiological and biomechanical ranges reported for adult CMJ testing. Overall, these findings support the study hypothesis that the proposed dual-channel force plate system provides a valid, reliable, and cost-effective solution for synchronized bilateral CMJ kinetic assessment in sports performance monitoring and biomechanical research, while offering improved accessibility through an open-source and transparent analysis framework with a hardware cost below 500 USD. Full article

Using a balanced panel of ten major fishing and trading nations (China, Chile, Indonesia, Peru, Thailand, Vietnam, Norway, India, Denmark, and Canada) over the years 2000–2020, this study investigated the relationship between international fishery trade and the fishing footprint, a consumption-based ecological indicator measuring the bioproductive marine area required to sustain seafood consumption. Cross-sectional dependence tests, second-generation panel unit root tests (PANICCA), LM bootstrap cointegration analysis, and long-run coefficient estimation using fully modified OLS (FMOLS), dynamic OLS (DOLS), fixed effects, and method of moments quantile regression (MMQR) are all part of the sequential econometric framework used in this analysis. Findings consistently show that the domestic fishing footprint is positively correlated with imports, domestic production, real GDP, and per capita food consumption, but adversely correlated with fishery exports. Additionally, MMQR estimates show that the negative export link becomes stronger at higher quantiles of the distribution of fishing footprint, indicating that the moderating influence of exports is strongest in nations that are already under a lot of ecological strain. Although the panel data do not allow for direct dissection of these channels, these findings are interpreted considering three potential mechanisms: certification-linked catch limits, aquaculture substitution in export volumes, and distant-water fleet displacement. It is recommended that policymakers include sustainability criteria into import laws, broaden the scope of eco-certification, and make investments in aquaculture to supplement the management of wild-capture fisheries. The findings of this study contribute significantly to the monitoring of global sustainability agendas, particularly aligning with United Nations Sustainable Development Goal (SDG) 12 (Responsible Consumption and Production) and SDG 14 (Life Below Water) by providing empirical evidence on how trade dynamics influence the fishing footprint. Full article

In the context of global climate change and China’s dual-carbon strategy, this analysis examines how land-use transition is associated with land-use carbon effects in China’s resource-based cities. From the perspective of urban development stages, an analytical framework is built by linking development stage, land-use structure, and carbon source–sink structure. Using 262 resource-based cities from 2011 to 2023, we estimate land-use-related carbon emissions, carbon sequestration, and net land-use carbon effects with the carbon emission coefficient method and analyze their spatiotemporal patterns and driving factors using GeoDetector. The results show clear differences among city types. Mature cities form the largest group. Growth cities show the fastest expansion of impervious surfaces, while regenerative cities present signs of ecological recovery. This suggests that land-use transition is not simply the expansion of impervious surfaces, but a stage-dependent process of structural change. Land-use carbon effects also differ across stages. Mature cities maintain high and stable carbon-source effects. Growth cities exhibit increasing carbon-source effects, declining cities show reduced emissions but limited improvement in the carbon source–sink structure, and regenerative cities show improved carbon-sink capacity under ecological restoration. Overall, net land-use carbon effects follow a rise–decline–rebound pattern and show clear spatial heterogeneity and visually apparent clustering patterns. Population size has strong explanatory power, while interactions between socioeconomic and land-use factors further shape spatial differences. These results support stage-specific low-carbon transition strategies. Full article

Quantifying vegetation ecological quality and ecological water requirement is essential for understanding ecosystem sustainability in arid regions. However, large-scale assessments of vegetation ecological quality and ecological water requirement thresholds remain limited in Central Asia. In this study, we developed a Vegetation Ecological Quality Index (VEQI) for Central Asia based on fractional vegetation cover (FVC) and net primary productivity (NPP) and estimated vegetation ecological water requirement quota (VEWRq) and total vegetation ecological water requirement (VEWR) using the Penman–Monteith method, the soil moisture limitation coefficient (SMLC), and GIS-based spatial analysis. We further examined the spatiotemporal variations in VEQI and VEWR during 2001–2020 and identified VEWRq thresholds corresponding to different VEQI levels. The results showed that (1) the multi-year mean VEQI in Central Asia was 28.46 and exhibited a slight increasing trend during 2001–2020; (2) the annual mean minimum, maximum, and optimal VEWRq were 147.53, 179.71, and 162.52 mm, respectively, corresponding to mean annual VEWR values of 146.98 × 10⁹ m³, 179.04 × 10⁹ m³ and 161.91 × 10⁹ m³, respectively; and (3) VEQI was positively correlated with VEWRq in 89.48% of the vegetation area. The VEWRq threshold increased with vegetation ecological quality. The five VEQI levels in Central Asia, namely very poor, poor, moderate, good, and very good, corresponded to VEWRq thresholds of 28.62–35.96, 88.33–107.81, 190.69–233.32, 362.86–432.81, and 678.59–838.31 mm, respectively. This study provides a remote sensing-based framework for evaluating vegetation ecological quality and quantifying ecological water requirement thresholds in arid regions and offers scientific support for regional ecological management and water resource allocation. Full article

Soil erosion poses a severe threat to agricultural productivity and ecological security on the Loess Plateau. However, previous studies have rarely integrated physical modeling, elasticity coefficients, and spillover effects into a unified framework at the county level. To address this gap, this study coupled the Revised Universal Soil Loss Equation (RUSLE) with the Spatial Durbin Model (SDM) to systematically investigate the spatiotemporal dynamics, factor elasticity characteristics, and spatial dependence mechanisms of soil erosion on the Loess Plateau from 1990 to 2024. Results show that the annual average erosion rate decreased by 15.5%, with a highly volatile phase before 2001 and a stabilized, low-erosion phase thereafter. The driving factors exhibited marked heterogeneity in direction and strength. The land cover and management factor (C) was the strongest erosion-reducing factor, whereas annual precipitation (PRE) was the primary natural erosion-enhancing factor. County-level erosion also displayed significant positive spatial dependence. PRE had a stable positive indirect effect, whereas C and the support practice factor (P) mainly contained erosion within local jurisdictions. These findings of a unified RUSLE–SDM framework reveal a joint driving mechanism of localized human interventions and climate-driven cross-regional spillovers, providing quantitative support for differentiated soil and water conservation strategies on the Loess Plateau. Full article

Most remote sensing extraction studies utilizing vegetation indices typically classify and extract land cover features based on the phenological characteristics of the study area or rely on a single vegetation index. When attempting to extract multiple land cover types simultaneously, classification accuracy often declines significantly because a single vegetation index is unsuitable for all features. While some recent studies employ deep learning and neural networks for classification and extraction, their complex mechanisms and “black-box effect” hinder clear explanations for accuracy outcomes. In response to the issues outlined above, this paper proposes a simpler and more intuitive method for the hierarchical extraction of typical land cover features. This approach analyzes the difficulty of separating these features based on spectral reflectance data to determine the following extraction order: first water bodies, followed by reed, then Suaeda salsa, and finally tidal flat. Furthermore, by selecting appropriate parameters and substituting vegetation indices for bands that perform better, high extraction accuracy is achieved. The classification and interpretation results were validated using a combination of field survey data and Google imagery, together with a validation sample. Accuracy assessments using overall accuracy and Kappa coefficient demonstrate the following optimal results for the hierarchical approach: NDWI for water, S2REP for reeds, and MSAVI for Suaeda salsa. Overall accuracy reached 98.5% with a Kappa coefficient of 0.9796, validating the effectiveness of this spectral-feature-based hierarchical extraction method using diverse vegetation indices. Using a hierarchical extraction approach to classify typical land cover features in the study area from 2020 to 2025, accuracy rates exceeded 98% in all cases. Based on these classification results, the INVEST model was employed to simulate carbon stock trends in the Liaohe Estuary region over the past five years. The study found that, although factors such as tides and the date of image acquisition had a certain impact on the study area compared with the problems caused by historical development, the ecological environment in the study area is gradually stabilizing at the present stage. Full article

Freshwater ecosystems in southern Europe are increasingly impacted by fish invasions from central and northern regions, often facilitated by warmer climates and reduced natural-enemy pressure. The European catfish (Silurus glanis), the largest freshwater fish in Europe, is now widely established across various southern European basins, where its high fecundity, ecological plasticity, and predatory behaviour pose significant risks to native communities. Despite its rapid spread, growth dynamics across native and non-native populations remains scatteredly described in studies with different approaches. Objective: This study compares growth rates between native populations in the Czech Republic and non-native populations in Portugal, Spain, and Italy, and assesses whether growth rates are influenced by introduction timelines, reflecting differences in population age and invasion stage. Methodology: Nine populations spanning the native range (central Europe) and non-native range (southern Europe) were analysed. A total of 427 different vertebrae were used to age the fish and growth was modelled using the von Bertalanffy growth function. Generalised linear models were used to identify environmental and demographic predictors of variation in the growth coefficient (K). Moreover, mark-recapture data from the native populatations was also analysed. Results: Preliminary results indicate substantial variation in growth among populations, with higher growth rates exhibited in non-native populations (Iberian), while native populations showed consistently lower growth rates. Growth was primarily associated with population age and minimum temperature, decreasing with increasing population age and increasing under warmer thermal conditions. These patterns suggest faster growth in recently established and warmer populations. Conclusions: Growth dynamics of European catfish vary markedly across native and non-native ranges, driven mainly by thermal conditions and invasion history. Faster growth in warmer and recently established populations may enhance invasion success by accelerating size-at-age and reproductive potential. This study highlights the importance of integrating environmental and demographic factors to improve predictions of invasion dynamics and ecological impacts in freshwater ecosystems. Full article

Niche and interspecific association are important components of community ecology and are of great significance for revealing the mechanisms of community assembly and its stability. In this study, the woody plant communities of Betula platyphylla Sukaczev forests in the Niyang River Basin of southeastern Qinghai–Tibet Plateau were taken as the research object. The niche, interspecific association, and community stability of dominant tree species in B. platyphylla forests were analyzed using the Levins index (B_L), Shannon index (B_S), Pianka index (O_ik), Schoener index (C_ik), variance ratio (VR), chi-square test, association coefficient (AC), Spearman rank correlation, and M. Godron stability methods. The results showed that a total of 71 woody plant species were recorded across 48 plots, mainly belonging to Rosaceae, Ericaceae, and Caprifoliaceae. B. platyphylla, Quercus aquifolioides Rehder & E. H. Wilson, Sorbus rehderiana Koehne, and Berberis gyalaica Ahrendt had relatively large niche breadths, indicating strong resource utilization ability and a wide range of spatial adaptation. They were the main constructive species and dominant species of B. platyphylla forest communities in this basin. The overall niche overlap of woody plant communities was relatively low, indicating relatively obvious differentiation in resource utilization among different species. Interspecific association analysis showed that the dominant species in the tree layer exhibited an overall significantly positive association, whereas those in the shrub layer exhibited an overall non-significantly positive association. The associations between species pairs were mainly non-significant, and the overall interspecific association was weak. Most species showed a relatively independent distribution pattern, reflecting weak interspecific competition within the community. Community stability analysis showed that the Euclidean distance between the tree layer and the theoretical stability point (20, 80) was 20.17, whereas that of the shrub layer was 27.98, indicating that the tree layer was more stable than the shrub layer. Overall, the community may not yet have reached a fully stable state. The results provide important references for biodiversity conservation, vegetation restoration, and sustainable forest management in alpine canyon ecosystems. Future studies should incorporate environmental factors such as soil properties and hydrothermal conditions to further reveal the ecological mechanisms driving community succession and stability. Full article

Protecting native coastal wetland vegetation and controlling the invasion of Spartina alterniflora (SA) have long been key ecological and management priorities in China. The accurate and rapid mapping of vegetation distribution is critical for effective invasion control and wetland restoration. While phenological information improves remote sensing classification, most studies rely on the Normalized Difference Vegetation Index (NDVI), which has limited capability to distinguish morphologically similar species in coastal wetlands. To better exploit the unique reddening phenology of one such species, Suaeda salsa (SS), this study builds on our previously developed Red Suaeda salsa Index (RSSI) and introduces two novel phenological indicators: the Redness Contribution Coefficient (RCC) and Reddening Rate Index (RCI). Using the coastal wetlands of Jiangsu Province as the study area, we employed multi-temporal Sentinel-2 image composites (spring, summer, autumn) from 2019, 2022, 2024, and 2025 to construct a multi-dimensional feature set and implemented classification using a random forest algorithm. Results showed that the feature scheme integrating SS reddening phenological parameters achieved the highest accuracy, with an overall accuracy of 97.32% and a Kappa coefficient of 0.9625 in 2019, confirming the method’s reliability at the provincial scale. Between 2019 and 2025, SA coverage in Jiangsu decreased by 90.8%, with most cleared areas converting to non-vegetated land, indicating the remarkable effectiveness of recent control projects. This study scales up a locally validated high-precision classification approach to the provincial scale, supporting sustainable coastal wetland management in line with United Nations (UN) SDG 14 (Life Below Water) and SDG 15 (Life on Land). Full article

Tropical rainforests are characterized by high species diversity and complex canopy structure, making accurate forest stand classification important for ecosystem assessment, biodiversity monitoring, and forest carbon estimation. However, single-source remote sensing data lacks sufficient discrimination ability to address the issue of spectral similarity among classes, and conventional convolutional neural networks often struggle to extract discriminative features and integrate heterogeneous data in highly complex forests. To address these challenges, this study developed a dual-branch deep learning framework that integrates DenseNet and ConvNeXt for classification in Hainan Tropical Rainforest National Park. The framework combines sub-meter Google Earth imagery to capture spatial–textural detail with multi-temporal Sentinel-2 imagery to represent phenological variation. The results showed that multi-temporal Sentinel-2 data outperformed single-date imagery by capturing phenological patterns, and that the fusion of high-resolution spatial information and multi-temporal spectral information yielded higher accuracy than either data source alone. The dual-branch model achieved an overall accuracy of 94.47% and a Kappa coefficient of 0.94, outperforming all benchmark models. These findings indicate that branch-specific feature extraction and adaptive fusion can improve fine-scale classification in complex tropical rainforest environments. The proposed framework provides a practical approach for fine-scale forest stand mapping and may support biodiversity monitoring, ecological assessment, and sustainable forest management. Full article

While land development opportunities stemming from planning regulations demonstrably influence rural household income, quantitative evidence quantifying these effects remains limited. Measuring and decomposing these effects can empirically support territorial spatial planning policies aimed at alleviating associated regional development imbalances and advancing sustainable rural development. This study selects Wuhan’s Sino-French Eco-City (urban development zone) and Xiaosi Township (wetland conservation zone) as typical zones. Based on 573 randomly sampled rural households, we explore the effects of land development opportunities on rural household incomes and find that: (1) Land development opportunities for non-agricultural conversion in the urban development zone significantly increase rural households’ total income, wage income, though their corresponding contribution rates are limited. Endogenously accumulated endowments such as human capital and economic status dominate the formation of such income gaps. (2) Planning-induced land development opportunities yield coefficients of 1.0442 for local employment income and −0.4567 for agricultural business income, with both statistically significant at the 1% significance level. Decomposition results show their respective contribution rates of 70.68% and 86.77%, demonstrating that such opportunities primarily account for cross-regional rural household income gaps. (3) Whereas non-agricultural land development opportunities narrow disparities in households’ local employment income, they raise inequality in rural households’ migrant employment, business, property and transfer income. These growth and equality-enhancing effects on local wage income are particularly pronounced for households possessing high-quantity but low-quality human capital. This study recommends supporting protected zones via farmer vocational training, expanded rural public service expenditure, and a benefit-sharing mechanism that channels land development gains to ecological and agricultural regions to strengthen households’ endogenous development capacity. Full article