Search Results (97)

South Dongting Lake is an essential aquatic ecosystem that receives substantial water inflows from the Xiangjiang and Zishui Rivers. However, it is significantly impacted by human activities, including mining, smelting, and farming. These activities have led to serious contamination of the lake’s sediments with heavy metals (HMs). This study investigated the distribution, mobility, and influencing factors of HMs at the sediment–water interface. To this end, sediment samples were analyzed from three key regions (Xiangjiang River estuary, Zishui River estuary, and northeastern South Dongting Lake) using traditional sampling methods and Diffusive Gradients in Thin Films (DGT) technology. Analysis of fifteen HMs (Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, V, Cr, Cu, Tl, Co, and Fe) revealed significant spatial heterogeneity. The results showed that Cr, Cu, Pb, Bi, Ni, As, Se, Cd, Sb, Mn, Zn, and Fe exhibited high variability (CV > 0.20), whereas V, Tl, and Co demonstrated stable concentrations (CV < 0.20). Concentrations were found to exceed background values of the upper continental crust of eastern China (UCC), Yangtze River sediments (YZ), and Dongting Lake sediments (DT), particularly at the Xiangjiang estuary (XE) and in the northeastern regions. Speciation analysis revealed that V, Cr, Cu, Ni, and As were predominantly found in the residual fraction (F4), while Pb and Co were concentrated in the oxidizable fraction (F3), Mn and Zn appeared primarily in the exchangeable fractions (F1 and F2), and Cd was notably dominant in the exchangeable fraction (F1), suggesting a high potential for mobility. Additionally, DGT results confirmed a significant potential for the release of Pb, Zn, and Cd. Contamination assessment using the Pollution Load Index (PLI) and Geoaccumulation Index (Igeo) identified Pb, Bi, Ni, As, Se, Cd, and Sb as major pollutants. Among these, Bi and Cd were found to pose the highest risks. Furthermore, the Risk Assessment Code (RAC) and the Potential Ecological Risk Index (PERI) highlighted Cd as the primary ecological risk contributor, especially in the XE. The study identified sediment grain size, pH, electrical conductivity, and nutrient levels as the primary influencing factors. The PMF modeling revealed HM sources as mixed smelting/natural inputs, agricultural activities, natural weathering, and mining/smelting operations, suggesting that remediation should prioritize Cd control in the XE with emphasis on external inputs. Full article

Rodents are the main wildlife species in the Dongting Lake area and can even cause significant biological disasters. In order to investigate the relationship between rodent communities and vegetation, the relationship between rodent population characteristics and vegetation cover between 2000 and 2020 was analyzed. The results showed that the rodent community in Dongting Lake has obvious spatial and temporal variability, and that Microtus fortis and Apodemus agrarius are currently the main rodent species in the area, with a higher population density in eastern Dongting Lake. Meanwhile, rodent fatness varied significantly between seasons, with the highest rodent fatness in winter at most survey sites. There was also significant spatial and temporal variability in vegetation cover, and the overall trend was increasing. There was a significant relationship between vegetation cover and rodent population density and fatness, and the relationship varied by rodent species. Microtus fortis population density was significantly correlated with medium to high levels of vegetation cover, but fatness was significantly correlated with low to medium levels of vegetation cover. The relationship between population density, fatness, and vegetation cover of A. agrarius is opposite to that of M. fortis. The study confirms that M. fortis is still the main pest rodent species in the Dongting Lake area, but the monitoring of A. agrarius and vegetation cover should be strengthened. Full article

Wetlands are critical components of the global carbon cycle, yet their carbon sink dynamics under hydrological fluctuations remain insufficiently understood. This study employed the Carnegie-Ames-Stanford Approach (CASA) model to estimate the net ecosystem productivity (NEP) of the Dongting Lake wetland and explored the spatiotemporal dynamics and driving mechanisms of carbon sinks from 2000 to 2022, utilizing the Theil-Sen median trend, Mann-Kendall test, and attribution based on the differentiating equation (ADE). Results showed that (1) the annual mean spatial NEP was 50.24 g C/m²/a, which first increased and then decreased, with an overall trend of −1.5 g C/m²/a. The carbon sink was strongest in spring, declined in summer, and shifted to a carbon source in autumn and winter. (2) Climate variability and human activities contributed +2.17 and −3.73 g C/m²/a to NEP, respectively. Human activities were the primary driver of carbon sink degradation (74.30%), whereas climate change mainly promoted carbon sequestration (25.70%). However, from 2000–2011 to 2011–2022, climate change shifted from enhancing to limiting carbon sequestration, mainly due to the transition from water storage and lake reclamation to ecological restoration policies and intensified climate anomalies. (3) NEP was negatively correlated with precipitation and water level. Land use adjustments, such as forest expansion and conversion of cropland and reed to sedge, alongside maintaining growing season water levels between 24.06~26.44 m, are recommended to sustain and enhance wetland carbon sinks. Despite inherent uncertainties in model parameterization and the lack of sufficient in situ flux validation, these findings could provide valuable scientific insights for wetland carbon management and policy-making. Full article

This study focuses on the Dongting Lake region in China and evaluates ecological vulnerability using the Sensitivity–Resilience–Pressure (SRP) framework, integrated with Spatial Principal Component Analysis (SPCA) to calculate the Ecological Vulnerability Index (EVI). The EVI values were classified into five levels using the Natural Breaks (Jenks) method, and spatial autocorrelation analysis was applied to reveal spatial differentiation patterns. The Geodetector model was used to analyze the driving mechanisms of natural and socioeconomic factors on EVI, identifying key influencing variables. Furthermore, the LightGBM algorithm was used for feature optimization, followed by the construction of six machine learning models—Multilayer Perceptron (MLP), Extremely Randomized Trees (ET), Decision Tree (DT), Random Forest (RF), LightGBM, and K-Nearest Neighbors (KNN)—to conduct multi-class classification of ecological vulnerability. Model performance was assessed using ROC–AUC, accuracy, recall, confusion matrix, and Kappa coefficient, and the best-performing model was interpreted using SHAP (SHapley Additive exPlanations). The results indicate that: ① ecological vulnerability increased progressively from the core wetlands and riparian corridors to the transitional zones in the surrounding hills and mountains; ② a significant spatial clustering of ecological vulnerability was observed, with a Moran’s I index of 0.78; ③ Geodetector analysis identified the interaction between NPP (q = 0.329) and precipitation (PRE, q = 0.268) as the dominant factor (q = 0.50) influencing spatial variation of EVI; ④ the Random Forest model achieved the best classification performance (AUC = 0.954, F1 score = 0.78), and SHAP analysis showed that NPP and PRE made the most significant contributions to model predictions. This study proposes a multi-method integrated decision support framework for assessing ecological vulnerability in lake wetland ecosystems. Full article

The Middle Yangtze River Urban Agglomeration, a critical ecological barrier in China, faces escalating pressures from rapid urbanization and climate change, leading to fragmented landscapes and degraded ecosystem services. To address the synergistic challenges of ecological protection and risk management, this paper takes the urban agglomeration in the middle reaches of the Yangtze River as the study area, and obtains the source patches through morphological spatial pattern analysis. Based on the spatial distribution of risky source areas, ecological blind zones are cut down by optimizing buffer zones and merging fragmented patches. Finally, a composite ecological network is constructed through circuit theory superimposed on the dual network method. The results showed that (1) there are 16 ecological source patches and 16 risk source patches in the study area. Six complementary ecological sources and four new ecological sources were obtained through the blind zone reduction strategy. The percentage of ecological blind zones reduced from 58.4% to 39.5%. (2) The integrated nodes with 11,366 connecting edges were identified. The integrated nodes are distributed around the central Jiuling-Mafushan Mountains, mainly in the western and southern areas of the Dongting Lake Plain. (3) Primary integration nodes are critical for network stability, with a 75% node failure threshold triggering systemic collapse. The proposed strategy of “mountain protection–plain control–railway monitoring” is consistent with China’s territorial and spatial planning. By incorporating the risk network into the conservation framework, this study provides feasible insights for balancing development and sustainability in ecologically fragile areas. Full article

Groundwater is one of the most critical drinking water resources on Earth, and its safety significantly impacts the ecological environment and human health. This study focuses on the pollution characteristics, sources, and health risks of potentially toxic elements (PTEs) in the groundwater of the Dongting Lake basin in China. It highlights the concentration distribution and pollution causes of common toxic elements such as manganese (Mn), copper (Cu), zinc (Zn), arsenic (As), mercury (Hg), iron (Fe), chloride ions (Cl⁻), and fluoride ions (F⁻). The results indicate that the Mn concentration reached 28.6 times the background value, followed by Cu at 16.7 times. The groundwater pollution level in the study area reached a severe contamination level, with Zn classified as severely polluted and Mn categorized as moderately to heavily polluted. Approximately 47.05% of the study area was severely contaminated by PTEs. The study further reveals that the primary sources of pollution are anthropogenic activities, including agricultural fertilization, industrial discharges, and urbanization processes, which have significantly elevated PTE concentrations in groundwater. Additionally, natural geochemical processes contribute substantially to the high concentrations of specific elements in certain areas. Health risk assessments indicate that long-term exposure to PTEs in groundwater may pose various potential threats to human health, particularly in terms of carcinogenic and non-carcinogenic risks. Children are identified as the most vulnerable group. This research provides a systematic scientific basis for the management of groundwater pollution in the Dongting Lake basin, emphasizing that the current pollution levels pose serious threats to regional ecosystems and public health. The findings not only offer guidance for groundwater management in the Dongting Lake basin but also serve as a valuable reference for groundwater pollution management in similar regions. Full article

As sensitive human-environment systems, lake-intensive regions are critical governance areas for advancing global low-carbon development. Rapid economic growth has intensified the imbalance between economic carbon sources and ecological carbon sinks in these regions. However, methods for measuring territorial space carbon budgets tailored to “production–living–ecological” functions are underdeveloped, and the mechanisms driving carbon imbalance risks remain unclear. To address these issues, this study develops a spatial measurement model for “carbon sources-carbon sinks” in the Dongting Lake region. Using exploratory spatiotemporal data analysis, this study identifies grid-scale variation patterns in carbon budgets. Finally, using the logarithmic mean Divisia index (LMDI) decomposition model, this study examines the driving mechanisms of carbon budgets from a territorial space perspective. The results indicate the following: (1) The territorial space of the Dongting Lake region follows a pattern where “ecological spaces surround production spaces, with living spaces interspersed among water network spaces”. Between 2005 and 2020, functional transitions primarily occurred between agricultural production spaces and forest or water ecological spaces. (2) The study area’s territorial space carbon budgets increased annually, though the growth rate slowed. Construction land was the most significant carbon emission source in territorial space. Spatially, carbon budgets exhibit a radial pattern, with high values concentrated in plains near water bodies, gradually decreasing inland. Spatiotemporal differentiation followed a north–south development trend along the water system axis. High-High clusters were concentrated in municipal areas with dense water networks. In contrast, Low-Low clusters appeared in peripheral mountainous regions to the west, east, and south. (3) Land-use efficiency had the most potent inhibitory effect on carbon budgets, cumulatively reducing carbon emissions by 1.37 × 10⁸ tC. Economic development had the strongest positive effect, adding 1.31 × 10⁸ tC in carbon emissions. Therefore, the Dongting Lake region should promote intensive land use, adjust industrial structures, and develop a green ecological economy to achieve sustainable carbon source–sink management. Full article

Air pollution has become a major environmental issue, posing severe threats to human health and ecosystems. Accurately predicting future regional air quality is crucial for effective air pollution control and management strategies. This study proposes a novel deep learning-based approach. First, Kriging interpolation was applied to meteorological indicators such as temperature, humidity, and wind speed, as well as climate-altering gas indicators like CO₂, SO₂, and NO₂ recorded at monitoring stations to obtain their spatial distributions over the entire region. Subsequently, a long short-term memory neural network (SwinLSTM) incorporating Swin Transformer feature extraction was employed to learn the correlations from regional meteorological data and historical air quality records. This model overcomes the limitation of traditional CNNs by capturing long-range spatial dependencies when processing two-dimensional meteorological data through its sliding window attention mechanism. Ultimately, it outputs air quality predictions in both spatial and temporal dimensions. This study collected data from 29 stations across four cities surrounding China’s Dongting Lake for experimentation. Predictions for PM2.5 and PM10 levels over the entire lake area were made for 1, 6, and 24 h. The results demonstrate that the proposed SwinLSTM architecture significantly outperforms the current mainstream ConvLSTM architecture, with an average R-squared improvement of 5%, establishing a new state-of-the-art model for spatiotemporal air quality prediction. Full article

Anthropogenic and climatic stresses threaten water security across the Yangtze River Basin (YZRB), which safeguards the population and economic development that is responsible for nearly half China’s GDP. Understanding trends and drivers of open surface water in the YZRB is crucial yet remains poorly investigated. This study proposes a new method to eliminate shadow impacts on water extraction, achieving 96% accuracy, and develops a long-term dataset from 1986 to 2022 using Landsat imagery on the Google Earth Engine platform. Trends in water area changes were analyzed for source region, typical river and lake dominated regions of YZRB, and partial least squares regression was used to attribute the major climatic and anthropogenic drivers of water change. The results show that water area generally increased by 39.88%, with divergent trends across regions. Source and river dominated regions both exhibited notable growths by 49.43% and 37.01%, respectively. Increases in the water area in the source region comes from both lakes and rivers, driven by increasing temperature and precipitation in permafrost regions, while increases in river dominated regions come from permanent water induced by construction of mega dams. Changes of the water body in lake dominated regions are mainly influenced by seasonal water and show varied trends. Poyang and Dongting lakes have decreasing water area, from 3354.24 to 2168.82 km² and 1504.79 to 850.47 km², respectively, which are both attributed to the impoundment of the Three Gorges Reservoir and alterations in precipitation patterns. While Tai Lake Basin experienced an increase from 1986 to 2003 due to expanded anthropogenic water bodies, it was followed by a decline after 2003 which was driven by urbanization. We therefore suggest systematically optimizing reservoir dispatching and land–water configurations to balance economic, societal, and environmental benefits. Full article

Identifying ecosystem service trade-offs/synergies and their influencing factors are prerequisites for formulating scientific ecosystem management strategies. We selected Dongting Lake Area as the study area, quantified four key ESs dynamics, including food production (FP), soil conservation (SC), habitat quality (HQ) and ecological leisure (EL), identified relationships between paired ecosystem services using Spearman correlation coefficients, and analyzed how various drivers impact trade-offs and synergies by leveraging spatial panel data models; we also implemented ecosystem management zoning by superposition analysis on ecosystem services, the relationships between these services, and key drivers. The results showed that FC and SC displayed synergy before a trade-off over the period 2000–2022. The FP–HQ and HQ–EL initially displayed synergy, then trade-off, and then synergy again. Spatially, the spatial trade-off ratios of FP–HQ, SC–HQ, and HQ–EL exceeded the synergy ratios, and trade-off areas of FP–HQ, SC–HQ, and HQ–EL existed around Dongting Lake. The direct or indirect effects of DEM, slope, precipitation, and population density have the greatest effect on ecosystem service pairs. Furthermore, the DTLA was divided into ecological unbalance, soil conservation, habitat quality synergy, and ecological leisure areas. These finding may provide critical scientific insights into how to balance ecological conservation and socio-economic progress in the Dongting Lake Area. Full article

Plant sterilants are used to control rodent populations due to their minimal environmental risk and other ethical considerations. However, their practical utilization is unsatisfactory due to high costs and processing difficulties. Broussonetia papyrifera is a plant material that has shown the potential to inhibit the reproduction of Microtus fortis, a species that causes serious damage to crops in the Dongting Lake region in China. M. fortis was treated with different doses of B. papyrifera leaf methanol extracts. The results show that the growth of sex organs was inhibited, and the males’ testosterone levels and sperm quality were reduced. Though there were some positive effects on females, the reproductive parameters of coupled voles were inferior; the most treated couple exhibited an increased reproductive time, fetal counts, and reduced weight. It was also found that M. fortis responded negatively to the extract after a single treatment or long-term repeated treatment compared to a short-term repeated treatment. B. papyrifera leaves showed a higher application potential as a sterilant for male rodents. These findings enrich the study of plant sterilants and provide insights into the utilization of B. papyrifera and the management of rodents. Owing to the effectiveness and accessibility of the leaves, the derived sterilant may be more economical for controlling rodent pests. Full article

Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications. Full article

Non-point source pollution from agricultural land use is a significant contributor to environmental pollution, causing serious threats to water, air, and soil quality. Environmental regulations have been shown to have a significant negative effect on pollutant emissions. However, empirical studies examining the impact of agricultural environmental regulations on cultivated land non-point source pollution are limited. To explore the effects of environmental regulations on cultivated land non-point source pollution, this study focuses on the Dongting Lake Plain. Using statistical data from 2010 to 2020, we analyze the temporal and spatial changes in cultivated land non-point source pollution before and after the implementation of the “Zero Growth of Fertilizer and Pesticide Use Action Plan”. A spatial econometric model is used to assess the impact of environmental regulations on pollution. The results indicate that non-point source pollution in the Dongting Lake Plain shows positive global spatial autocorrelation. Locally, in 2010 and 2015, high-high clusters were dominant, while low-low clusters were less common. By 2020, low-low clusters became the main pattern of pollution. The introduction of the Zero Growth Action Plan had a negative impact on pollution intensity within the region and positive spillover effects on neighboring areas. In terms of emission structure, the plan significantly reduced nitrogen emission intensity and pesticide loss, but had a positive impact on phosphorus emissions. The level of regional economic development had a significant negative effect on pollution intensity, while urbanization had a positive effect. Agricultural technological progress showed a negative spatial spillover effect on phosphorus emissions, and regional economic development had a negative spatial spillover effect on nitrogen and pesticide emissions. This study provides evidence of the impact of environmental regulations on cultivated land non-point source pollution and offers valuable insights for the development of future pollution control policies. Full article

In the context of global warming and intensified human activities, the loss and fragmentation of species habitats have been exacerbated. In order to clarify the trends in the current and future suitable wintering areas for hooded cranes (Grus monacha), the MaxEnt model was applied to predict the distribution patterns and trends of hooded cranes based on 94 occurrence records and 23 environmental variables during the wintering periods from 2015 to 2024. The results indicated the following. (1) The elevation (Elev, 43.7%), distance to major water (DW, 39.5%), minimum temperature of the coldest month (Bio6, 9.7%), and precipitation of the wettest month (Bio13, 2.6%) were dominant factors influencing the wintering distribution of hooded cranes. (2) Under current climate and land use scenarios, highly suitable areas for hooded cranes in China cover approximately 1.274 × 10⁵ km², primarily located in inland lakes such as Dongting Lake, Liangzi Lake, Poyang Lake, Shengjin Lake, and Caizi Lake in the middle and lower reaches of the Yangtze River, as well as in coastal wetlands such as Chongming East Beach, Shandong Peninsula, Bohai Bay, and Liaodong Peninsula. (3) Under future climate and land use scenarios, the suitable habitat areas (high and moderate suitability) for hooded cranes are projected to contract substantially in the middle and lower reaches of the Yangtze River and expand slightly in the areas of Shandong Peninsula, Bohai Bay, and Liaodong Peninsula. Under the SSP126 (low emissions), SSP245 (medium emissions), and SSP585 (high emissions) scenarios, the average area reduction percentages were 29.1%, 28.8%, and 31.6%, respectively. (4) The increases in Bio6 and water areas in northern China were the main reasons for the shift of the wintering distribution centroid for hooded cranes toward northeastern China. The minor expansion of suitable habitat in the north covers mainly cultivated land, and this singular foraging habitat could intensify both intraspecific and interspecific competition among waterbirds, thus exacerbating the survival risks for hooded cranes. To more effectively protect the wintering population of hooded cranes in China, the restoration of natural habitats and population monitoring in the middle and lower reaches of the Yangtze River should be strengthened. Additionally, nature reserves or protected areas should be established in the northern expansion regions. Full article

China’s Dongting Lake area is intertwined with rivers and lakes and possesses many water systems. As such, it is one of the most complicated areas in the Yangtze River Basin, in terms of the complexity of its flood control. Over time, siltation and reclamation in the lake area have greatly weakened the river discharge capacity of the lake area, and whether it can endure extreme floods remains an open question. As there is no effective scenario simulation model for the lake area, this study constructs a hydrological model for the Jingjiang–Dongting Lake system and verifies the model using data from 11 typical floods occurring from 1954 to 2020. The parameters derived from 2020 data reflect the latest hydrological relationship between the lake and the river, while meteorological data from 1954 and 1998 are used as inputs for various scenarios with the aim of evaluating the flood pressure of the lake area, using the water levels at the Chengglingji and Luoshan stations as indicators. The preliminary results demonstrate that the operation of the upstream Three Gorges Dam and flood storage areas cannot completely offset the flood pressure faced by the lake area. Therefore, the reinforcement and raising of embankments should be carried out, in order to cope with potential extreme flood events. The methodology and results of this study have reference value for policy formation, flood control, and assessment and dispatching in similar areas. Full article