Search Results (209)

Microplastics (MPs)—synthetic polymer particles less than 5 mm in size—have emerged as ubiquitous contaminants in terrestrial and aquatic environments worldwide, raising concerns about their ecological and human health impacts. While research has predominantly focused on urban and marine settings, evidence shows that rural ecosystems are also affected, challenging assumptions of pristine conditions outside cities and coasts. This review synthesizes current knowledge on the presence, pathways, and impacts of MPs in rural environments, highlighting complex contamination dynamics driven by both local sources (agricultural plastics, domestic waste, rural wastewater, and road runoff) and regional processes (atmospheric deposition, hydrological transport, and sediment transfer). Key findings highlight that rural lakes, streams, soils, and groundwater systems are active sinks and secondary sources of diverse MPs, predominantly polyethylene (PE), polypropylene (PP), and polyethylene terephthalate (PET) in fibrous and fragmented forms. These particles vary in size, density, and color, influencing their transport, persistence, and bioavailability. Ecological effects include bioaccumulation in freshwater species, soil degradation, and potential food chain transfer, while human exposure risks stem from contaminated groundwater, air, and locally produced food. Despite these growing threats, rural systems remain underrepresented in monitoring and policy frameworks. The article calls for context-specific mitigation strategies, enhanced wastewater treatment, rural waste management reforms, and integrated microplastics surveillance across environmental compartments. Full article

National urban wetland parks serve as key platforms for ecological conservation and recreation, yet the synergistic mechanisms between plant color dynamics and public aesthetic perception remain underexplored. Understanding these mechanisms is crucial for evidence-based, climate-resilient landscape design. This study quantifies statistical associations between seasonal color and aesthetic patterns in two national wetland parks (South Dian Lake and Laoyu Lake, Kunming) using Hue–Saturation–Brightness (HSB) color metrics and Scenic Beauty Estimation (SBE) based on year-round monitoring at 24 sample sites. Regression analysis revealed that overall SBE values ranged from −1.027 to 0.756, indicating medium aesthetic quality, with South Dian Lake outperforming Laoyu Lake, particularly in aquatic plant communities. Seasonal trends showed the highest aesthetic preference in winter (orange–yellow dominant, 0.110) and the lowest in early spring (−0.167, yellow dominant), followed by relatively stable values from late spring to mid-autumn (0.007–0.020) and a secondary peak in late autumn (0.029). Higher SBE scores were associated with a dominant hue ratio of 70–75%, balancing color unity and diversity. We identify an operational plant color configuration—70–75% dominant hue, 20% evergreen foliage and 5–7 color types—that corresponds to higher SBE scores. By translating aesthetic responses into quantitative color targets, this study provides guidance for climate-adaptive planting design and seasonal management in subtropical wetland landscapes under global warming. Full article

Lakes in the Amur River Basin (ARB) are increasingly influenced by climate variability and human activities, yet long-term basin-scale patterns of colored dissolved organic matter (CDOM) remain unclear. In this study, we developed a support vector regression (SVR) model to retrieve lake CDOM from Landsat 5/7/8 imagery and generated a 40-year (1984–2023) CDOM dataset for 69 large lakes. The model provides a reliable tool for multi-decadal, large-area water quality monitoring considering its robust performance (R² = 0.88, rRMSE = 22.4%, MAE = 2.63 m⁻¹). Trend analysis revealed a significant rise in CDOM since 1999, particularly across the Mongolian Plateau and Northeast China Plain. Among the 69 lakes, 27 exhibited increasing CDOM, while 4 showed declines, highlighting pronounced regional variability. Variance partitioning indicated that human activities, especially irrigation and grazing, account for ~30% of CDOM variation, exceeding the contribution of any single climatic driver, whereas temperature represents the dominant climate driver (12.8%). Shallow systems were more sensitive to external disturbances, while deep lakes responded more strongly to thermal conditions. This study delivers the first long-term satellite-based CDOM assessment in the ARB and underscores the combined impacts of climate change and land-use pressures on lake optical dynamics. Full article

Lake eutrophication exhibits pronounced spatial heterogeneity at the watershed scale, yet a systematic and quantitative understanding of how landscape characteristics drive these variations remains limited. In this study, a long-term and internally consistent trophic state dataset for the Liangzi Lake Basin was constructed by integrating Landsat imagery from 1990 to 2022 with a semi-analytical water color inversion method. A multi-scale landscape feature system incorporating both land use composition and landscape pattern metrics was developed at the sub-basin level to elucidate the mechanisms by which landscape characteristics influence eutrophication dynamics. The XGBoost model was employed to characterize the nonlinear relationships between landscape attributes and trophic conditions, while the SHAP interpretability approach was applied to quantify the relative contribution of individual landscape components and their interaction pathways. The analytical framework demonstrates that landscape pattern attributes—such as fragmentation, diversity, and connectivity—play essential roles in shaping the spatial variability of eutrophication by modulating hydrological processes, nutrient transport, and ecological buffering capacity. By integrating remote sensing observations with interpretable machine learning, the study reveals the complexity and scale dependence of landscape–water interactions, providing a methodological foundation for advancing the understanding of eutrophication drivers. The findings offer theoretical guidance and practical references for optimizing watershed landscape planning, controlling non-point source pollution, and supporting ecological restoration efforts in lake basins. Full article

The widespread dispersion of microplastics (MPs) has been recognized as a pervasive and persistent environmental contaminant in worldwide freshwater ecosystems, and although relative studies have skyrocketed, there are still significant knowledge gaps in areas like southern Europe. This study assesses the microplastic pollution in seven Greek inland aquatic ecosystems which vary in morphology, trophic status, and anthropogenic pressure. Surface and vertical samples were taken with 200 μm plankton nets. MPs were present in all samples, with fibers being the dominant form, having an abundance range between 0.47 and 149.4 items/m³ with fragments between 0.08 and 9.17 items/m³. Fibers and fragments had greater abundance in the vertical than in the surface samples. There were no significant abundance differences between lakes and lagoons, and among the sampling sites in each ecosystem. Blue and transparent were the colors that prevailed, and most of the fibers and fragments were smaller than 1 mm. Four types of MPs were recorded, with PET (polyethylene terephthalate) being the most frequent. The use of the novel Relative Anthropogenic Pressure (RAP) index resulted in positive correlations between certain sociological parameters and the microplastics’ abundance, efficiently reflecting the impingement of human populations on the inland aquatic ecosystems. Full article

Satellite remote sensing is an important approach for monitoring lake water environments. However, in regions with frequent cloud and rainfall, optical remote sensing imagery often suffers from extensive data gaps caused by cloud cover, rainfall, and sun glint, which severely limit its continuity and reliability for long-term monitoring. To address this issue, this study uses Lake Taihu—a typical eutrophic lake located in a cloudy and rainy region—as a case study and systematically compares four representative gap-filling methods: Kriging Interpolation, Savitzky–Golay (SG) Filtering, Data Interpolating Empirical Orthogonal Functions (DINEOF), and the Data Interpolating Convolutional Auto Encoder (DINCAE). The results show that traditional methods retain some accuracy under low missing-data conditions (for Kriging: R = 0.84, RMSE = 7.85 μg/L; for SG Filtering: R = 0.88, RMSE = 6.67 μg/L), but tend to produce over-smoothing or distorted estimations in cases of extensive gaps or highly dynamic environments. In contrast, both DINEOF and DINCAE capture the spatiotemporal variability of chlorophyll-a more effectively, maintaining relatively high accuracy and robustness even when the missing rate exceeds 60% (for DINEOF: R = 0.84, RMSE = 6.91 μg/L; for DINCAE: R = 0.79, RMSE = 8 μg/L). Based on the optimal algorithm, a seamless long-term dataset of chlorophyll-a concentration covering Lake Taihu can be constructed, providing a solid data foundation for eutrophication trend analysis and algal bloom early warning. This study demonstrates the effectiveness of integrating statistical and deep learning approaches for lake water color remote sensing data reconstruction, offering important implications for enhancing continuous monitoring of lake water environments and supporting ecological management decisions. Full article

Accurate monitoring of total suspended matter (TSM) concentration is essential for aquatic ecosystem protection and water quality assessment. Multispectral remote sensing provides an effective approach for large-scale TSM monitoring. However, robust retrieval models are difficult to develop due to limited in situ data. This study presents a Deep Feature Extraction–Machine Learning fusion framework that integrates a pre-trained back-propagation neural network (BPNN) with support vector regression (SVR) to enhance TSM retrieval. High-level spectral features extracted by BPNN are used as inputs to SVR (termed DFE-SVR) for regional TSM retrieval, using in situ measurements from five inland lakes in Jiangsu and Anhui Provinces, China. The generated TSM maps showed spatial patterns consistent with TSM concentration distributions visually observed in true-color imagery. Validation results demonstrated that DFE-SVR outperformed BPNN and SVR models, achieving R² of 0.85 and 0.90 and RMSE of 7.95 and 4.76 mg/L for GF-1 and Sentinel-2 imagery, respectively. Compared with SVR models using principal component analysis or band combinations, DFE-SVR reduced RMSE by over 20%. Under reduced training samples, the DFE-SVR model also maintained higher stability and accuracy. These findings showed its potential for multispectral water quality monitoring with limited in situ data. Full article

Wheat landraces are considered a valuable resource of potential phenotypic variation that could be used in germplasm improvement. Here, we examined 588 local wheat genotypes collected from farmers’ fields at 127 locations around Van Lake Basin and evaluated the morphological diversity and trait associations using Multidimensional Scaling Analysis. Spike and yield traits were measured and scored according to the UPOV and ICARDA phenotypic characterization criteria. Multidimensional Scaling Analysis divided the wheat samples into four main groups based on the number of spikelets (NOS), number of fertile spikelets (NFS), thousand-grain weight (TGW), and number of seeds per spike (NSS) and indicated a strong correlation between NOS and NFS. Furthermore, the analysis revealed that the glume and awn color of most of the genotypes was black, and they were within the locally known Karakılçık group. Only two genotypes were excluded from the Karakılçık group; No. 231 was within the Geverik local wheat group, and genotype No. 579 was found to be Tir. The Hevidik and Kirik groups had the same spike color, but the Hevidik group had spikes similar to compactum wheat, whereas the Kirik group had larger spikes. Finally, genotype No. 57 varied from all other genotypes when all the measured traits were taken into consideration. Overall, the Van Lake Basin landraces combine broad similarity with meaningful phenotypic heterogeneity shaped by local environments and traditional on-farm selection. These findings provide practical cues for conservation efforts and for the use of landraces as valuable resources in future wheat breeding programs. Full article

Natural disasters often result in significant damage to infrastructure, generating vast amounts of debris in towns and water bodies. Timely post-disaster damage assessment is critical for enabling swift cleanup and recovery efforts. This study presents a combination of methods to efficiently estimate and analyze debris on land and on water. Specifically, analyses were conducted at Claytor Lake and Damascus, Virginia where flooding occurred as a result of Hurricane Helene on 27 September 2024. We use the Phoenix U15 motor glider equipped with the GoPro Hero 9 camera to collect aerial imagery. Orthomosaic images and 3D maps are generated using OpenDroneMap (ODM) software, version 3.5.6, providing a detailed view of the affected areas. For lake debris estimation, we employ a hybrid approach integrating machine learning-based tools and traditional techniques. Lake regions are isolated using segmentation methods, and the debris area is estimated through a combination of color thresholding and edge detection. The debris is classified based on the thickness and a volume range of debris is presented based on the data provided by the Virginia Department of Environmental Quality (VDEQ). In Damascus, debris estimation is achieved by comparing pre-disaster LiDAR data (2016) with post-disaster 3D ODM data. Furthermore, we conduct flood modeling using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS) to simulate disaster impacts, estimate the flood water depth, and support urban planning efforts. The proposed methodology demonstrates the ability to deliver accurate debris estimates in a time-sensitive manner, providing valuable insights for disaster management and environmental recovery initiatives. Full article

Light pollution is becoming more widespread every year, accompanied by the active use of LED lighting. Currently, the ability of organisms to adapt to this pollution and the potential impact of LED lighting of different color temperatures and intensities on organisms remains poorly understood. In this study, we aimed to find out how long-term light pollution affects the behavior of amphipods Gammarus lacustris, and to compare their locomotor activity under different lighting conditions, taking into account the factor of shelter from light. The response of individuals was compared in group and individual experiments under daylight, without light, warm and cold LED light up to 30 lx. The individuals were from two populations: the first is not exposed to light pollution (lake No. 14), while the second is affected (the Angara River within the city of Irkutsk). The locomotor activity of amphipods was assessed in daylight, without light, warm and cold light of 2–2.5 lx and 10–11 lx in the presence and absence of shelters from light. As a result of the experiments, adaptive changes in the reaction of G. lacustris to warm light were identified in individuals from the Angara River. The importance of LED light color temperature and warm light intensity in determining amphipod response to light was also confirmed. It was found that warm and cold light have different effects on the behavior of G. lacustris, and the presence of shelters from light can reduce the negative impact of light pollution in natural conditions. Full article

Atmospheric correction (AC) over inland and coastal waters remains a key challenge in ocean color remote sensing, often limiting the effective use of satellite data for aquatic monitoring. AC algorithm performance is highly sensitive to water type and optical properties. To address this, we systematically evaluated six state-of-the-art AC algorithms—ACOLITE, C2RCC, iCOR, L2GEN, OC-SMART, and POLYMER—using Landsat-8/9 OLI data. This study leverages 440 high-quality in situ radiometric matchups spanning a wide range of aquatic environments, including inland lakes from China’s Satellite-Ground Synchronous Campaign and coastal waters from the globally distributed GLORIA dataset. These complementary datasets provide a robust benchmark for evaluating AC algorithm performance. A unified Optical Water Type (OWT) classification framework ensured consistency across environmental conditions. Results highlight significant variability in algorithm performance based on water type. In coastal waters, L2GEN demonstrated the lowest errors in visible bands, whereas OC-SMART achieved superior overall accuracy in inland waters. Notably, ACOLITE exhibited better performance than other algorithms in the blue spectral region (443 and 482 nm) for inland waters. OWT-specific analysis showed that OC-SMART maintained robust accuracy across the turbidity gradient, while ACOLITE and iCOR excelled in highly turbid waters (OWTs 5–6). In contrast, L2GEN, C2RCC, and POLYMER were more effective in clearer waters (OWTs 3–4). The study further discusses the applicability of each algorithm and offers recommendations for mitigating adjacency effects (AE) to improve AC accuracy. These findings provide valuable guidance for selecting and optimizing AC strategies for inland and coastal water monitoring. Full article

The genus Raiamas currently comprises 18 valid species, only 2 of which occur in Asia; the remaining 16 are endemic to Africa. Raiamas harmandi was originally described by Sauvage in 1880 as Bola harmandi, which is distributed in the Great Lakes, Cambodia, the Mekong River Basin. It was considered a synonym of R. guttatus by later researchers. In this study, we examined 49 Raiamas individuals from the Mekong, Irrawaddy, and Salween river basins, recording both meristic counts and morphometric measurements. Based on the morphological evidence, we revised the taxonomy of Raiamas in the Mekong River Basin, confirming R. harmandi as a valid species and providing a comprehensive redescription. Raiamas harmandi can be distinguished from R. guttatus mainly by having more predorsal scales (25–28 vs. 21–23) and a different color pattern on the lateral body. Utilizing a total of 44 aligned COI and Cyt b sequences—including eight newly sequenced individuals of Raiamas from three river basins—we reconstructed its phylogenetic relationships. The analysis strongly supported four R. harmandi individuals from the Mekong River Basin forming a distinct clade, which was the sister to the clade comprising five R. guttatus individuals from the Irrawaddy and Salween river basins. Genetic distances between R. harmandi and R. guttatus ranged from 14.0 to 14.9% for COI and 16.1 to 17.0% for Cyt b. Distributionally, R. harmandi occurs throughout the Mekong River Basin, as evidenced by combined voucher specimens and molecular sequence data. Full article

Although deep-water environments make up the world’s largest ecosystem, they are poorly characterized. Lake Baikal, the only freshwater reservoir possessing rich deep-water fauna, offers unique opportunities to examine the evolutionary processes that occurred independently and concurrently with adaptation to these environments in the ocean. Here, we focus on amphipods as one of the dominant elements of Baikal deep-water fauna. This study examines the genetic diversity across broad vertical (~1 km) and horizontal (~500 km) ranges, as well as depth-related traits in two deep-water scavengers, Ommatogammarus flavus (Dybowsky, 1874) and Ommatogammarus albinus (Dybowsky, 1874). Our results revealed low intraspecific diversity of the cytochrome c oxidase subunit I gene marker fragment across locations and depths, indicating the absence of significant barriers in the distribution of either species and a bottleneck event in their evolutionary histories. At the same time, we found depth-related stratification in carotenoid-based body coloration and eye shape in O. flavus, as well as in eye color for both species. These findings suggest partial isolation between vertically stratified populations and help to characterize the ecological differences between the two studied species. Full article

Inland aquatic ecosystems, encompassing lakes, reservoirs, and ponds, serve as vital repositories of water resources and provide essential ecological, social, and cultural services. Water color, a key indicator of water quality, reflects the complex interactions among physicochemical, biological, and environmental drivers. Heilong Pool (HP) in Southwest China, which consists of a Clear Pool (CP) and a Turbid Pool (TP), has recently exhibited an anomalous reddish-brown “pumpkin soup” phenomenon in the CP, while the TP remains unchanged. This unusual phenomenon has raised widespread public concern regarding water resource security and its potential association with geological disasters. To elucidate the ecological and geochemical mechanisms of this phenomenon, we employed a multifaceted analytical approach that included assessing nutrient elements, quantifying heavy metal concentrations, analyzing dissolved substances, characterizing algal community composition, and applying δD-δ¹⁸O isotope analytical models. Our findings illustrated that while Bacillariophyta predominate (>79.3% relative abundance) in the algal community of HP, they were not the primary determinant of water color changes. Instead, Fe(OH)₃ colloidal particles, originating from groundwater–surface water interactions and controlled by redox environment dynamics periodically, emerged as the principal factors of the reddish-brown discoloration. The genesis of the “pumpkin soup” water coloration was attributed to the precipitation-induced displacement of anoxic groundwater from confined karst conduits. Subsequent exfiltration and atmospheric exposure facilitate oxidative precipitation, forming authigenic rust-hued Fe(OH)₃ colloidal complexes. This study provides new insights into the geochemical and hydrological mechanisms underlying water color anomalies in karst-dominated catchments. Full article

A novel near-infrared (NIR) squaraine-based chemosensor, SQ-68, has been designed and synthesized for the sensitive and selective detection of Cu²⁺ and Ag⁺ ions, offering a compact solution for multi-analyte sensing. SQ-68 demonstrates high selectivity, with its performance influenced by the solvent environment: It selectively detects Cu²⁺ in acetonitrile and Ag⁺ in an ethanol–water mixture. Upon binding with either ion, SQ-68 undergoes significant absorption changes in the NIR region, accompanied by visible color changes, enabling naked-eye detection. Spectroscopic studies confirm a 1:1 binding stoichiometry with both Cu²⁺ and Ag⁺, accompanied by hypochromism. The detection limits are 0.09 μM for Cu²⁺ and 0.38 μM for Ag⁺, supporting highly sensitive quantification. The sensor’s practical applicability was validated in real water samples (sea, lake, and tap water), with recovery rates ranging from 73–95% for Cu²⁺ to 59–99% for Ag⁺. These results establish SQ-68 as a reliable and efficient chemosensor for environmental monitoring and water quality assessment. Its dual-analyte capability, solvent-tunable selectivity, and visual detection features make it a promising tool for rapid and accurate detection of heavy metal ions in diverse aqueous environments. Full article