Search Results (224)

Ecological security evaluation serves as the cornerstone for ecological management decision-making and spatial optimization. This study focuses on the Dongping Lake Basin. Based on the Pressure–State–Response (PSR) model framework, it integrates ecological risk, ecosystem health, and ecosystem service indicators. Utilizing methods including Local Indicators of Spatial Association (LISA), Transition Matrix, and GeoDetector, it analyzes the spatio-temporal evolution characteristics and driving mechanisms of watershed ecological security from 2000 to 2020. The findings reveal that the Watershed Ecological Security Index (WESI) exhibited a trend of “fluctuating upward followed by periodic decline”. In 2000, the status was “relatively unsafe”. It peaked in 2015 (index 0.332, moderately safe) and experienced a slight decline by 2020. Spatially, a significantly clustered pattern of “higher in the north and lower in the south, higher in the east and lower in the west” was observed. In 2020, “High-High” clusters of ecological security aligned closely with Shandong Province’s ecological conservation red line, concentrating in core protected areas such as the foothills of the Taihang Mountains and Dongping Lake Wetland. Level transitions were characterized by “predominant continuous improvement in low levels alongside localized reverse fluctuations in middle and high levels,” with the “relatively unsafe” and “moderately safe” levels experiencing the largest transfer areas. Geographical detector analysis indicates that the Human Interference Index (HI), Ecosystem Service Value (ESV), and Annual Afforestation Area (AAA) were key drivers of watershed ecological security change, influenced by dynamic interactive effects among multiple factors. This study advances watershed-scale ecological security assessment methodologies. The revealed spatio-temporal patterns and driving mechanisms provide valuable insights for protecting the ecological barrier in the lower Yellow River and informing ecological security strategies within the Dongping Lake Watershed. Full article

Aquatic vegetation plays a crucial role in freshwater ecosystems by providing habitats, regulating water quality, and supporting biodiversity. This study aims to monitor and analyze the dynamics of aquatic vegetation in Chimaditida Shallow Lake, Greece, using Sentinel-2 satellite imagery, with validation from field measurements. Data processing was performed using Google Earth Engine and QGIS. The study focuses on discriminating and mapping two classes of aquatic surface conditions: areas covered with Floating and Emergent Aquatic Vegetation and open water, covering all seasons from 1 March 2024, to 28 February 2025. Spectral bands such as B04 (red), B08 (near infrared), B03 (green), and B11 (shortwave infrared) were used, along with indices like the Modified Normalized Difference Water Index and Normalized Difference Vegetation Index. The classification was enhanced using Otsu’s thresholding technique to distinguish accurately between Floating and Emergent Aquatic Vegetation and open water. Seasonal fluctuations were observed, with significant peaks in vegetation growth during the summer and autumn months, including a peak coverage of 2.08 km² on 9 September 2024 and a low of 0.00068 km² on 28 December 2024. These variations correspond to the seasonal growth patterns of Floating and Emergent Aquatic Vegetation, driven by temperature and nutrient availability. The study achieved a high overall classification accuracy of 89.31%, with producer accuracy for Floating and Emergent Aquatic Vegetation at 97.42% and user accuracy at 95.38%. Validation with Unmanned Aerial Vehicle-based aerial surveys showed a strong correlation (R² = 0.88) between satellite-derived and field data, underscoring the reliability of Sentinel-2 for aquatic vegetation monitoring. Findings highlight the potential of satellite-based remote sensing to monitor vegetation health and dynamics, offering valuable insights for the management and conservation of freshwater ecosystems. The results are particularly useful for governmental authorities and natural park administrations, enabling near-real-time monitoring to mitigate the impacts of overgrowth on water quality, biodiversity, and ecosystem services. This methodology provides a cost-effective alternative for long-term environmental monitoring, especially in regions where traditional methods are impractical or costly. Full article

Antibiotic resistance genes (ARGs) in suburban freshwater ecosystems pose a growing public health concern by potentially reducing the effectiveness of medical treatments. This study investigated how rainfall influences ARG dynamics in Lake Katherine, a 62-hectare suburban lake in Columbia, South Carolina, over one year. Surface water was collected under both dry and post-rain conditions from three locations, and ARGs were identified using metagenomic sequencing. Statistical models revealed that six of nine ARG classes with sufficient data showed significant responses to rainfall. Three classes, Bacitracin, Aminoglycoside, and Unclassified, were more abundant after rainfall, while Tetracycline, Multidrug, and Peptide resistance genes declined. Taxonomic analysis showed that members of the Pseudomonadota phylum, especially Betaproteobacteria, were prevalent among ARG-carrying microbes. These findings suggest that rainfall can alter the distribution of ARGs in suburban lakes, highlighting the importance of routine monitoring and water management strategies to limit the environmental spread of antibiotic resistance. Full article

This study investigates the potential of spaceborne imaging spectroscopy to support the analysis of the status of two major Estonian lakes, i.e., Lake Peipsi and Lake Võrtsjärv, using data from the PRISMA and EnMAP missions. The study encompasses nine specific applications across 12 satellite scenes, including the validation of remote sensing reflectance (Rrs), optical water type classification, estimation of phycocyanin concentration, detection of macrophytes, and characterization of reflectance for lake ice/snow coverage. Rrs validation, which was performed using in situ measurements and Sentinel-2 and Sentinel-3 as references, showed a level of agreement with Spectral Angle < 16°. Hyperspectral imagery successfully captured fine-scale spatial and spectral features not detectable by multispectral sensors, in particular it was possible to identify cyanobacterial pigments and optical variations driven by seasonal and meteorological dynamics. Through the combined use of in situ observations, the study can serve as a starting point for the use of hyperspectral data in northern freshwater systems, offering new insights into ecological processes. Given the increasing global concern over freshwater ecosystem health, this work provides a transferable framework for leveraging new-generation hyperspectral missions to enhance water quality monitoring on a global scale. Full article

Turbidity is a crucial parameter for assessing the ecological health of aquatic ecosystems, particularly in shallow tropical lakes that are subject to climatic variability and anthropogenic pressures. Lake Chapala, the largest freshwater body in Mexico, has experienced persistent turbidity and sediment influx since the 1970s, primarily due to upstream erosion and reduced water inflow. In this study, we utilized Landsat satellite imagery in conjunction with near-synchronous in situ reflectance measurements to monitor spatial and seasonal turbidity patterns between 2023 and 2025. The surface reflectance was radiometrically corrected and validated using spectroradiometer data collected across eight sampling sites in the eastern sector of the lake, the area where the highest rates of horizontal change in turbidity occur. Based on the relationship between near-infrared reflectance and field turbidity, second-order polynomial models were developed for spring, fall, and the composite annual model. The annual model demonstrated acceptable performance (R² = 0.72), effectively capturing the spatial variability and temporal dynamics of the average annual turbidity for the whole lake. Historical turbidity data (2000–2018) and a particular case study in 2016 were used as a reference for statistical validation, confirming the model’s applicability under varying hydrological conditions. Our findings underscore the utility of empirical remote-sensing models, supported by field validation, for cost-effective and scalable turbidity monitoring in dynamic tropical lakes with limited monitoring infrastructure. Full article

The Amazon faces significant challenges related to mercury contamination, including naturally elevated concentrations and gold mining activities. Due to mercury’s toxicity and the importance of fish as a protein source for local populations, assessing mercury levels in regional fish is crucial. However, there are gaps in knowledge regarding mercury concentrations in many areas of the Amazon basin. This study aims to synthesize the existing literature on mercury concentrations in fish and the exposure of urban and traditional social groups through fish consumption. A systematic review (1990–2022) was conducted for six fish genera (Cichla spp., Hoplias spp. and Plagioscion spp., Leporinus spp., Semaprochilodus spp., and Schizodon spp.) in the Web of Science (Clarivate Analytics) and Scopus (Elsevier) databases. The database consisted of a total of 46 studies and 455 reports. The distribution of studies in the region was not homogeneous. The most studied regions were the Madeira River sub-basin, while the Paru–Jari basin had no studies. Risk deterministic and probabilistic assessments based on Joint FAO/WHO Expert Committee on Food Additives (JECFA, 2007) guidelines showed high risk exposure, especially for traditional communities. Carnivorous fish from lakes and hydroelectric reservoirs, as well as fish from black-water ecosystems, exhibited higher mercury concentrations. In the Amazon region, even if mercury levels in fish muscle do not exceed regulatory limits, the high fish consumption can still elevate health risks for local populations. Monitoring mercury levels across a broader range of fish species, including both carnivorous and non-carnivorous species, especially in communities heavily reliant on fish for their diet, will enable a more accurate risk assessment and provide an opportunity to recommend fish species with lower mercury exposure risk for human consumption. The present study emphasizes the need to protect regions that already exhibit higher levels of mercury—such as lakes, hydroelectric reservoirs, and black-water ecosystems—to ensure food safety and safeguard public health. Full article

Cyanobacterial blooms and aquatic vegetation dynamics are critical indicators of freshwater ecosystem health, increasingly shaped by climate change, nutrient enrichment, and ecological restoration efforts. Here, we present an automated monitoring system optimized for small- and medium-sized lakes. This system integrates phenology-based algorithms with Sentinel-2 MSI imagery, leveraging the AI Earth (AIE) platform developed by Alibaba DAMO Academy. Applied to monitor 12 ecologically sensitive lakes and reservoirs in Jiangsu Province, China, the system enables multi-year tracking of spatiotemporal changes from 2019 to 2024. A clear north-south gradient in cyanobacterial bloom intensity was observed, with southern lakes exhibiting higher bloom levels. Although bloom intensity decreased in lakes such as Changdang, Yangcheng, and Dianshan, Ge Lake displayed fluctuating patterns. In contrast, ecological restoration efforts in Cheng and Yuandang Lakes led to substantial increases in bloom intensity in 2024, with affected areas reaching 33.16% and 33.11%, respectively. Although bloom intensity remained low in northern lakes, increases were recorded in Hongze, Gaoyou, and Luoma Lakes after 2023, particularly in Hongze Lake, where bloom coverage surged to 3.29% in 2024. Aquatic vegetation dynamics displayed contrasting trends. In southern lakes—particularly Cheng, Dianshan, Yuandang, and Changdang Lakes—vegetation coverage significantly increased, with Changdang Lake reaching 44.56% in 2024. In contrast, northern lakes, including Gaoyou, Luoma, and Hongze, experienced a long-term decline in vegetation coverage. By 2024, compared to 2019, coverage in Gaoyou, Luoma, and Hongze Lakes decreased by 11.28%, 16.02%, and 47.32%, respectively. These declines are likely linked to increased grazing pressure following fishing bans, which may have disrupted vegetation dynamics and reduced their ability to suppress cyanobacterial blooms. These findings provide quantitative evidence supporting adaptive lake restoration strategies and underscore the effectiveness of satellite-based phenological monitoring in assessing freshwater ecosystem health. Full article

Microplastics (MPs) have emerged as a critical environmental challenge in China’s aquatic ecosystems, driven by rapid industrialization and population growth. This review synthesizes recent findings on the abundance, morphology, and polymer types of MPs in China’s freshwater systems (rivers, lakes, reservoirs) and coastal marine environments. Spatial analysis reveals significant variability in MP abundance, ranging from 0.1 items/L in Tibet’s Lalu Wetland to 30.8 items/L in Beijing’s Qinghe River, with polypropylene (PP) and polyethylene (PE) dominating polymer profiles. Coastal regions exhibit distinct contamination patterns, with the Yellow Sea (5.3 ± 2.0 items/L) and the South China Sea (180 ± 80 items/m³) showing the highest MP loads, primarily as fibers and fragments. Fluvial transport, atmospheric deposition, and coastal anthropogenic activities (e.g., fisheries, tourism) are identified as major pathways for marine MP influx. Secondary MPs from degraded plastics and primary MPs from industrial/domestic effluents pose synergistic risks through the adsorption of heavy metals and organic pollutants. Human exposure routes—ingestion, inhalation, and dermal contact—are linked to inflammatory, metabolic, and carcinogenic health outcomes. Policy interventions, including bans on microbeads and non-degradable plastics, demonstrate progress in pollution mitigation. This work underscores the urgency of integrated source control, advanced wastewater treatment, and transboundary monitoring to address MP contamination in aquatic ecosystems. Full article

The reducing streamflow is a major concern in the Yilong Lake Basin (YLB), which supplies water for agriculture and the growing population in the basin and to maintain the health of the regional ecosystem. The YLB has experienced remarkable land use/land cover change (LUCC) and climate change (CC) in recent years. To understand the drivers of the streamflow change in this basin, the effects of the land use change and climate variation on the temporal flow variability were studied using the Soil and Water Assessment Tool (SWAT). The calibration and validation results indicated that the SWAT simulated the streamflow well. Then the streamflow responses to the land use change between 2010 and 2020 and climate change with future climate projections (SSP245, SSP370, and SSP585) were evaluated. Results showed that the LUCC in the YLB caused a marginal decline in the annual streamflow at the whole basin scale but significantly altered rainfall–runoff relationships and intra-annual discharge patterns; e.g., monthly streamflows decreased by up to 3% in the dry season under the surface modification, with subbasins of the YLB exhibiting divergent responses attributed to spatial heterogeneity in land surface transitions. Under future climate scenarios, streamflow projections revealed general declining trends with significant uncertainties, particularly under high-emission pathways, e.g., SSP370 and SSP585, in which the streamflow could be projected to reduce by up to 5.9% in the mid-future (2031–2045). In addition, droughts were expected to intensify, exacerbating seasonal water stress in the future. It suggests that integrated water governance should synergize climate-resilient land use policies with adaptive infrastructure to address regional water resources challenges. Full article

The Qinghai–Tibet Plateau is a key region for biodiversity conservation, where alpine grasslands are ecologically important. While previous studies have mainly addressed vegetation, ecosystem processes, and soil microbes, phyllosphere microorganisms are essential for nutrient cycling, plant health, and stress tolerance. However, their communities remain poorly understood compared to those in soil. The relative influence of host identity and environmental conditions on shaping phyllosphere microbial diversity and community assembly remains uncertain. In this study, we characterized phyllosphere bacterial and fungal communities of the phyllosphere at two alpine steppe sites with similar vegetation but climatic conditions: the Qilian Mountains (QLM) and the Qinghai Lake region (LQS). At both sites, Cyanobacteriota and Ascomycota were the predominant bacterial and fungal taxa, respectively. Microbial α-diversity did not differ significantly between the two regions, implying that host-associated mechanisms may stabilize within-site diversity. In contrast, β-diversity exhibited clear spatial differentiation. In QLM, bacterial β-diversity was significantly correlated with mean annual precipitation, while fungal α- and β-diversity were associated with soil nutrient levels (including nitrate, ammonium, available potassium, and phosphorus) and vegetation coverage. At LQS, the β-diversity of both bacterial and fungal communities was strongly influenced by soil electrical conductivity, and fungal communities were further shaped by vegetation cover. Community assembly processes were predominantly stochastic at both sites, although deterministic patterns were more pronounced in QLM. Variability in moisture availability contributed to random bacterial assembly at LQS, while increased environmental heterogeneity promoted deterministic assembly in fungal communities. The elevated diversity of microbes and plants in QLM also reinforced deterministic processes. Overall, our findings support a host–environment interaction hypothesis, indicating that host factors primarily govern α-diversity, while climatic and soil-related variables have stronger effects on β-diversity and microbial assembly dynamics. These insights advance our understanding of how phyllosphere microbial communities may respond to environmental change in alpine ecosystems. Full article

The pollution of water bodies has deteriorated the quality of freshwater and the health of the natural ecosystem. In the present study, the water quality index (WQI) was used to evaluate the spatial and temporal contamination levels in Lake Yuriria, Guanajuato, Mexico. Water quality was monitored at 27 different locations (monitoring points) in the dry season (April) and after the rainy season (November), measuring 21 physicochemical water parameters, 2 biological parameters, and 19 metal concentrations. The data analysis revealed that Yuriria Lake is a eutrophic water body. Six monitoring points exhibited a poor WQI (25–50) in April, and seven monitoring sites were classified as having poor water quality in November. The remaining monitoring points showed a WQI categorized as fair (51–70) in both periods. The present study analyzes an extensive distribution of monitoring points over the lake’s surface in two periods, showing a significant spatial and temporal representation of water quality. In addition, the major pollution sources identified include agricultural runoff and effluents from a nearby waterway and freshwater river. Finally, the key physicochemical parameters that determined the water quality were identified. BOD₅, NH₄⁺, P, orthophosphates, DO, conductivity, TSS, and color were linked to anthropogenic pollution sources, and Li, Ni, Zn, Cd, Ba, and Pb concentrations were linked to natural contamination sources. This study demonstrates the utility and versatility of these methodologies in water quality research, and it is the first spatial and temporal WQI analysis of Yuriria Lake. Full article

Harmful algal blooms (HABs), driven by environmental pollution, pose significant threats to water quality, public health, and aquatic ecosystems. This study enhances the prediction of HABs in Lake Erie, part of the Great Lakes system, by utilizing ensemble machine learning (ML) models coupled with explainable artificial intelligence (XAI) for interpretability. Using water quality data from 2013 to 2020, various physical, chemical, and biological parameters were analyzed to predict chlorophyll-a (Chl-a) concentrations, which are a commonly used indicator of phytoplankton biomass and a proxy for algal blooms. This study employed multiple ensemble ML models, including random forest (RF), deep forest (DF), gradient boosting (GB), and XGBoost, and compared their performance against individual models, such as support vector machine (SVM), decision tree (DT), and multi-layer perceptron (MLP). The findings revealed that the ensemble models, particularly XGBoost and deep forest (DF), achieved superior predictive accuracy, with R² values of 0.8517 and 0.8544, respectively. The application of SHapley Additive exPlanations (SHAPs) provided insights into the relative importance of the input features, identifying the particulate organic nitrogen (PON), particulate organic carbon (POC), and total phosphorus (TP) as the critical factors influencing the Chl-a concentrations. This research demonstrates the effectiveness of ensemble ML models for achieving high predictive accuracy, while the integration of XAI enhances model interpretability. The results support the development of proactive water quality management strategies and highlight the potential of advanced ML techniques for environmental monitoring. Full article

Cyanobacterial harmful blooms (CyanoHABs) in lakes, estuaries, and freshwater reser-voirs represent a significant risk to water authorities worldwide due to their cyanotoxins and economic impacts. The duration, spread, and severity of CyanoHABs have markedly increased over the past decades. The article addresses CyanoHABs, cyanotoxins, and monitoring methodologies in post-Soviet and Central Asian countries. This particular region was selected for the systematic review due to its relative lack of representation in global CyanoHABs reporting, particularly in Central Asia. The main aim of this systematic review was to analyze the primary literature available from 2010–2024 to examine the current situation of CyanoHAB detection, monitoring, and management in Central Asia and post-Soviet countries. Following a detailed database search in several selected data-bases (Google Scholar, Pubmed, Web of Science (WOS), Scopus, Elibrary, ENU, and KazNU) along with additional hand searching and citation searching, 121 primary articles reporting 214 local cyanobacterial bloom cases were selected for this review. Aquatic cyanotoxins were reported in water bodies of eight countries, including high concentrations of microcystins that often exceeded reference values established by the World Health Organization (WHO). Advancing monitoring efforts in Baltic countries, Belarus, and the Russian Federation differed from only a few Central Asian reports. However, Central Asian aquatic ecosystems are especially threatened by rising anthropogenic pressures (i.e., water use, intensive agriculture, and pollution), climate change, and the lack of adequate ecological surveillance. We hypothesize that recent Caspian seal mass mortality events have been caused by a combination of infection (viral or bacterial) and exposure to algal neurotoxins resulting from harmful algal blooms of Pseudo-nitzschia. We conclude that there is an urgent need to improve the assessment of cyanobacterial blooms in Central Asia and post-Soviet countries. Full article

Microcystins (MCs), hepatotoxins produced by cyanobacteria, represent a potential threat to aquatic ecosystems and human health. Measuring various environmental and cyanobacterial parameters in water samples can be useful for monitoring water quality and assessing risk but remains a short-term approach. Beyond local risk assessments, estimating global and medium-term levels of freshwater contamination by MC-producing cyanobacteria is challenging in large lakes due to the spatio-temporal variability of their proliferation and the need to multiply sampling dates and locations. In such conditions, a sentinel organism can be valuable for monitoring MCs in situ and providing a time-integrated picture of contamination levels at various stations. We previously assessed the ability of the freshwater bivalves Anodonta anatina and Dreissena polymorpha to act as biointegrators of MCs, even under low exposure levels to cyanobacteria. In this study, through a two-season investigation in several French lakes experiencing moderate cyanobacterial blooms, we evaluated the relevance of various parameters (cyanobacterial density and biovolume, chlorophyll-a, and phycocyanin) as well as the use of bivalves as indicators of medium-term freshwater contamination by MC-producing cyanobacteria. MC concentrations in cyanobacterial biomass (intracellular MCs) and in bivalves (free MCs, being unbound, and total free and protein-bound accumulated MCs) were measured alongside the characterization of phytoplankton communities. Both mussels integrated and highlighted the presence of intracellular MCs in the environment over the period between two successive water samplings, even at low contamination levels, demonstrating their suitability for in situ biomonitoring of MC-producing cyanobacteria. The results are discussed in terms of the strengths and limitations of different parameters for assessing MC contamination levels in waters depending on the objective (managing, preventing, or global evaluation) and the monitoring strategies used. Full article

Microplastics (MPs), defined as plastic particles smaller than 5 mm, are an emerging global environmental and health concern due to their pervasive presence in aquatic ecosystems. This systematic review synthesizes data on the distribution, shapes, materials, and sizes of MPs in various water sources, including lakes, rivers, seas, tap water, and bottled water, between 2014 and 2024. Results reveal that river water constitutes the largest share of studies on MP pollution (30%), followed by lake water (24%), sea water (19%), bottled water (17%), and tap water (11%), reflecting their critical roles in MP transport and accumulation. Seasonal analysis indicates that MP concentrations peak in the wet season (38%), followed by the dry (32%) and transitional (30%) seasons. Spatially, China leads MP research globally (19%), followed by the USA (7.8%) and India (5.9%). MPs are predominantly composed of polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET), with fibers and fragments being the most common shapes. Sub-millimeter MPs (<1 mm) dominate globally, with significant variations driven by anthropogenic activities, industrial discharge, and environmental factors such as rainfall and temperature. The study highlights critical gaps in understanding the long-term ecological and health impacts of MPs, emphasizing the need for standardized methodologies, improved waste management, and innovative mitigation strategies. This review underscores the urgency of addressing microplastic pollution through global collaboration and stricter regulatory measures. Full article