Search Results (891)

Permeable dams are an important means for river management and ecology protection. Reasonable dam spacing will help regulate sediment transport and reduce sediment load in lakes. Flume experiments were carried out to investigate the effects of hydrological sediment conditions and dam spacing on sediment retention performance and permeability of the cascade permeable dams. The experimental results show that the permeability coefficient of the 1# dam decreased by about 30–40% with a large rate during the initial experiment stage. The decrease amplitude in the permeability coefficient and rising rate of the water level in front of the 1# dam for a large dam spacing (D/L) are positively correlated with the flow rate. At D/L = 5, the water level difference of 1# dam at the end of the experiment was significantly higher than that of other spacing. The sediment mass retained by 1# dam accounts for about 41–65% of the total sediment mass retained, which is about twice that of 2# dam, and plays a major role in cascade permeable dams. A mathematical model for predicting the spatial-temporal sediment concentration inside 1# dam is proposed based on the seepage theory of porous media. The research results are of great guiding significance for the design of the dam parameters. Full article

Resolving the status of soil carbon with land cover is critical for addressing the impacts of climate change arising from land cover conversion in boreal regions. However, many conventional inferential approaches inadequately gauge statistical significance for this issue, due to limited sample sizes or skewness of soil properties. This study aimed to address this drawback by adopting inferential approaches suitable for smaller samples sizes, where normal distributions of soil properties were not assumed. A two-step inference process was proposed. The Kruskal–Wallis (KW) test was first employed to evaluate disparities amongst soil properties. Generalized estimating equations (GEEs) were then wielded for a more thorough analysis. The proposed method was applied to soil samples (n = 431) extracted within the southern transition zone of the boreal forest (49°–50° N, 80°40′–84° W) in northern Ontario, Canada. Sites representative of eight land cover types and seven dominant tree species were sampled, investigating the total carbon (C), carbon-to-nitrogen ratio (C:N), clay percentage, and bulk density (BD). The KW test analysis corroborated significance (p-values < 0.05) for median differences between soil properties across the cover types. GEEs supported refined robust statistical evidence of mean differences in soil C between specific tree species groupings and land covers, particularly for black spruce (Picea mariana) and wetlands. In addition to the proposed method, the results of this study provided application for the selection of appropriate predictors for C with digital soil mapping. Full article

A group of Romanesque sculptures today at the Gosudarstvennyj Istoričeskij Muzej in Moscow, coming from the restoration of the Cathedral of Saint Sophia in Kijv, can be related to the commission of Vladimir II Monomak, Grand Prince of Kijv, cultural heir of both his great-grandfather, the grand prince Vladimir I (who had founded the church between 1011 and 1037), and of his grandfather, the Eastern Roman Emperor Constantine IX: It is argued here that, alongside the Byzantine mosaicists certainly present, the sculptures are the work of a group of artists from the Lombardy lakes (also known as Comacine masters), attested in central and eastern Europe through Bavaria, Bohemia, Poland and then arriving in Sweden, active in Kijv between 1113 and 1125. It is probable that their specific origin is from Valchiavenna. Full article

The Mongolian Plateau grassland (MPG) is critical for ecological conservation and sustainability of regional pastoral economies. Aboveground net primary productivity (ANPP) is a key indicator of grassland health and function, which is highly sensitive to variabilities in large-scale atmospheric circulations, commonly referred to as teleconnections (TCs). In this study, we analyzed the spatial and temporal variations of ANPP and their response to local meteorological and large-scale climatic variabilities across the MPG from 1982 to 2015. Our analysis indicated the following: (1) Throughout the entire study period, ANPP displayed an overall upward trend across nine ecoregions. In the Sayan montane steppe and Sayan alpine meadow ecoregions, ANPP displayed a distinct inflection point in the mid-1990s. In the Ordos Plateau arid steppe ecoregion, ANPP continuously increased without any inflection points. In the six other ecoregions, trends in ANPP exhibited two inflection points, one in the mid-1990s and one in the late-2000s. (2) Precipitation was the principal determinant of ANPP across the entire MPG. Temperature was a secondary yet important factor influencing ANPP variations in the Ordos Plateau arid steppe. Cloud cover affected ANPP in Sukhbaatar and central Dornod, Mongolia. (3) The Atlantic Multidecadal Oscillation affected ANPP by regulating temperature in the Ordos Plateau arid steppe ecoregion, whereas precipitation occurred in the other ecoregions. The Pacific/North America, North Atlantic Oscillation, East Atlantic/Western Russia, and Pacific Decadal Oscillation predominantly affected precipitation patterns in various ecoregions, indicating regional heterogeneities of the effects of TCs on ANPP fluctuations. When considering seasonal variances, winter TCs dominated ANPP variations in the Selenge–Orkhon forest steppe, Daurian forest steppe, and Khangai Mountains alpine meadow ecoregions. Autumn TCs, particularly the Pacific/North America and North Atlantic Oscillation, had a greater impact in arid regions like the Gobi Desert steppe and the Great Lakes Basin desert steppe ecoregions. This study’s findings will enhance the theoretical framework for examining the effects of TCs on grassland ecosystems. Full article

Frequent algal blooms in lakes pose a serious threat to aquatic ecosystems. It is of great significance to quickly and accurately monitor the distribution of algae in lakes for the regulation of algal blooms. While remote sensing techniques and machine learning methods can be used in combination to identify algae and analyze their spatial and temporal distribution, these methods still face challenges in practical applications due to uncertainties in lake boundaries and imbalances between algae and non-algae. In order to overcome these difficulties, we studied the dynamic open water range of Ulansuhai Lake and used a non-equilibrium data processing method to identify its algae. We also performed a spatiotemporal analysis of the algal range over a long time series. The results show that (1) the spectral characteristics of Landsat 8 images are very suitable for algal identification based on remote sensing, especially in the random forest method, where the fourth band plays an important role. (2) Among various machine learning methods, the accuracy of the random forest method on the training set and validation set is more than 90%. This indicates that the random forest method is suitable for the long-term monitoring of algal blooms. This study provides scientific and technical support for the management of Ulansuhai Lake, which will be helpful in guiding future management and control work. Full article

The encroachment of buildings into the waters of rivers and lakes can lead to increased safety hazards, but current semantic segmentation algorithms have difficulty accurately segmenting buildings in such environments. The specular reflection of the water and boats with similar features to the buildings in the environment can greatly affect the performance of the algorithm. Effectively eliminating their influence on the model and further improving the segmentation accuracy of buildings near water will be of great help to the management of river and lake waters. To address the above issues, the present study proposes the design of a U-shaped segmentation network of buildings called RDAU-Net that works through extraction and fuses a convolutional neural network and a transformer to segment buildings. First, we designed a residual dynamic short-cut down-sampling (RDSC) module to minimize the interference of complex building shapes and building scale differences on the segmentation results; second, we reduced the semantic and resolution gaps between multi-scale features using a multi-channel cross fusion transformer module (MCCT); finally, a double-feature channel-wise fusion attention (DCF) was designed to improve the model’s ability to depict building edge details and to reduce the influence of similar features on the model. Additionally, an HRI Building dataset was constructed, comprising water-edge buildings situated in a riverine and lacustrine regulatory context. This dataset encompasses a plethora of water-edge building sample scenarios, offering a comprehensive representation of the subject matter. The experimental results indicated that the statistical metrics achieved by RDAU-Net using the HRI and WHU Building datasets are better than those of others, and that it can effectively solve the building segmentation problems in the management of river and lake waters. Full article

In this study, we build an empirical model to estimate pigments in the South American Lake Villarrica. We use data from Dirección General de Aguas de Chile during the period of 1989–2024 to analyze the behavior of limnological parameters and trophic condition in the lake. Four seasonal linear regression models were developed by us, using a set of water quality variables that explain the values of phycocyanin pigment in Lake Villarrica. In the first case, we related chlorophyll-a (Chl-a) to phycocyanin, expecting to find a direct relationship between both variables, but this was not fulfilled for all seasons of the year. In the second case, in addition to Chl-a, we included water temperature, since this parameter has a great influence on the algal photosynthesis process, and we obtained better results. We discovered a typical seasonal variability given by temperature fluctuations in Lake Villarrica, where in the spring, summer, and autumn seasons, conditions are favorable for algal blooms, while in winter, the natural seasonal conditions do not allow increases in algal productivity. For a third case, we included the turbidity variable along with the variables mentioned above and the statistical performance metrics of the models improved significantly, obtaining R² values of up to 0.90 in the case of the model for the fall season and a mean squared error (MSE) of 0.04 µg/L. In the last case used, we added the variable dissolved organic matter (MOD), and the models showed a slight improvement in their performance. These models may be applicable to other lakes with harmful algal blooms in order to alert the community to the potential toxicity of these events. Full article

Macrophytes play a crucial role in maintaining the health of lake ecosystems. A thorough understanding of their long-term evolutionary processes and patterns is of great theoretical and practical significance for ecosystem restoration and mitigation of lake eutrophication. The succession process and driving factors of macrophytes in the Lake Erhai aquatic ecosystem were systematically analyzed using the investigation of macrophytes, literature research, and classification. A survey conducted in July 2022 showed that the macrophyte community in Lake Erhai is seriously degraded, with species numbers notably lower than historical levels from a decade ago (2011). The distribution area declined by over 70% compared to its peak in the 1980s. Over the past 60 years, the macrophyte community of Lake Erhai has undergone successive processes, including expansion, peak, decline, and stabilization. The dominant populations gradually transitioned from being indicative of clean water to pollution-tolerant species. The driving factors of the macrophytes succession of Lake Erhai were the development of cascade hydropower projects on the Xi’er River and the increased outflow capacity of Lake Erhai; these have resulted in substantial fluctuations in water levels, the eutrophication of the lake, pollutant discharge exceeding Lake Erhai’s environmental capacity, and substantial climate change in the Lake Erhai basin. Our research provides important theoretical references for ecological restoration and management of early eutrophic lakes in China. Full article

Hair straighteners are among the most technically complex products to be safely and effectively developed, and this challenge has increased even more with the higher incidence of resistant hair among consumers. This underscores the importance of studying new active ingredients, combinations and carrier formulations to improve performance without compromising safety. In this research, we compared eight hair-straightening formulations with different active ingredients and/or concentrations to develop new, safer and more effective texture modifiers. Eight formulations were developed and compared with each other and to controls (virgin and bleached hair) regarding mechanical and thermal resistance, cuticle morphology, hair shine and fiber diameter. Results showed that all formulations were safe and effective at straightening hair. Specifically, 13.3% and 9.4% ammonium thioglycolate (G03 and G04) were more suitable for wavy and curly hair, 12.5% and 7.9% amino methyl propanol thioglycolate (G05 and G06) for finer or chemically processed hair, 5% and 4% sodium cysteamine (G07 and G08) for curly and tight curly hair to control volume, and 2% and 1% of a combination of ammonium thioglycolate with sodium thioglycolate (G09 and G10) for more resistant wavy and curly hair. Full article

Due to their immense economic and recreational value, the monitoring of Great Lakes water quality is of utmost importance to the region. Historically, this has taken place through a combination of ship-based sampling, buoy measurements, and physical models. However, these approaches have spatial and temporal deficiencies which can be improved upon through satellite remote sensing. This study details a new approach for using long time series of satellite remote sensing data to identify historical and near real-time anomalies across a range of data products. Anomalies are traditionally detected as deviations from historical climatologies, typically assuming that there are no long-term trends in the historical data. However, if present, such trends could result in misclassifying ordinary events as anomalous or missing actual anomalies. The new anomaly detection method explicitly accounts for long-term trends and seasonal variability by first decomposing a 10-plus year data record of satellite remote sensing-derived Great Lakes water quality parameters into seasonal, trend, and remainder components. Anomalies were identified as differences between the observed water quality parameter from the model-derived expected value. Normalizing the anomalies to the mean and standard deviation of the full model remainders, the relative anomaly product can be used to compare deviations across parameters and regions. This approach can also be used to forecast the model into the future, allowing for the identification of anomalies in near real time. Multiple case studies are detailed, including examples of a harmful algal bloom in Lake Erie, a sediment plume in Saginaw Bay (Lake Huron), and a phytoplankton bloom in Lake Superior. This new approach would be best suited for use in a water quality dashboard, allowing users (e.g., water quality managers, the research community, and the public) to observe historical and near real-time anomalies. Full article

Lidar technology is increasingly applied to the inversion of oceanic biological parameters and optical properties based on empirical and semi-empirical bio-optical models. However, these models cannot be directly applied to inland waters due to their complex composition, and research on the biological parameters and optical properties of inland waters remains limited. In this study, the Fernald method was employed to retrieve the vertical distribution of optical properties in Qiandao Lake for the first time using shipborne lidar data obtained in June 2019. By quantifying the depth-resolved optical contributions of biological components, the vertical distributions of chlorophyll-a concentration were mapped with greater precision. The lidar-estimated optical properties exhibited characteristic spatiotemporal distributions, which were closely related to water quality. At the inflow of Xin’an River, the attenuation and scattering coefficient showed a gradual increase with depth. At the north–south-oriented reservoir area and the outflow of Qiandao Lake, an apparently continuous subsurface layer with the maximum signal occurred at approximately 3.5 m. The vertical distributions of chlorophyll-a profiles were consistently classified as subsurface chlorophyll maxima, with the maximum value of chlorophyll-a concentration fluctuating between 4 and 12 μg/L. The subsurface phytoplankton layer was observed at water depths ranging from 1.5 to 3.5 m, with a thickness of 3 to 6 m. Furthermore, the influences of lidar ratio S_p(z) and reference value b_bp(z_m) were discussed as significant sources of inversion error in the Fernald method. These results indicate that lidar technology holds great potential for the long-term monitoring of lakes. Full article

This study investigates the enhancement of biomass slurry processability through the addition of rheological modifiers, focusing on carboxymethyl cellulose (CMC) and TEMPO-mediated oxidized cellulose nanofibrils (TCNF). Three sets of experiments were conducted to assess the effects of these additives on slurry processing and fermentation. Initial experiments evaluated the slurry extrudability, concluding that TCNF aids extrusion similarly to CMC. Subsequent experiments explored slurry viscosity reduction mechanisms, revealing that while CMC significantly reduced the viscosity, TCNF’s impact is negligible. Additionally, TCNF performed comparably to CMC in water retention tests across different conditions, which suggests that TCNF have potential as an effective additive for maintaining slurry fluidity at high solid concentrations through enhanced water retention. Lastly, both additives were investigated to ensure that they did not impact hydrolyzed biomass fermentation. The findings suggest that TCNF’s mechanisms differ from those of traditional water-soluble polymers like CMC, offering insights into novel approaches to improve the biomass processing efficiency and subsequent fermentation. Full article

Flood disasters are among the most severe natural calamities worldwide and typically occur in densely populated areas with abundant lakes and high rainfall. These disasters cause significant damage to the environment and human settlements. Therefore, accurately monitoring and understanding the occurrence and evolution of floods, as well as studying the influencing factors, is of great importance. This study employs CYGNSS satellite data from a constellation of small satellites equipped with reflective radar, which observe the Earth’s surface with high spatial and temporal resolution. Such systems effectively monitor the distribution of water bodies and hydrological processes on land surfaces. By collecting and analyzing CYGNSS data, we can map the distribution of water bodies during flood events to assess the extent and severity of the flooding. Additionally, this study examines various factors influencing flooding, including rainfall, land use, and topography. By compiling relevant meteorological, geographical, and hydrological data, we aim to develop a model that elucidates the impacts of these factors on the initiation and progression of floods. Ultimately, this research offers a comprehensive analysis based on CYGNSS data for monitoring floods and their influencing factors. The goal is to yield significant insights and explore the potential of using CYGNSS data in flood monitoring efforts. In the context of global climate change and the increasing frequency of flood disasters, these findings are expected to provide a crucial scientific basis for improving flood prevention and management strategies, thereby helping to mitigate losses and enhance our warning and disaster response capabilities. Full article

Transgenerational plasticity (TGP) refers to the influence of ancestral environmental signals on offspring’s traits across generations. While evidence of TGP in plants is growing, its role in plant adaptation over successive generations remains unclear, particularly in floating plants facing fluctuating environments. Duckweed (Lemna minor), a common ecological remediation material, often coexists with the harmful bloom-forming cyanobacterium Microcystis aeruginosa, which releases a highly toxic exudate mixture (MaE) during its growth. In this study, we investigate the TGP of duckweed and its adaptive role under stress from MaE during the bloom-forming process. We found that exposure to MaE induces significant phenotypic plasticity in duckweed, manifested by alterations in morphological, physiological, and transcriptomic profiles. Specifically, MaE exposure significantly affected duckweed, promoting growth at low concentrations but inhibiting it at high concentrations, affecting traits like biomass, frond number, total frond area, and photosynthetic efficiency. Additionally, the activities of antioxidant enzymes, together with the levels of proline, soluble sugars, and proteins, are elevated with increasing MaE concentrations. These plastic changes are largely retained through asexual reproductive cycles, persisting for several generations even under MaE-free conditions. We identified 619 genes that maintain a ‘transcriptional memory’, some of which correlate with the TGP-linked alterations in morphological and physiological traits in response to MaE stress. Notably, progeny from MaE-exposed lineages demonstrate enhanced fitness when re-exposed to MaE. These results enhance our comprehension of the adaptive significance of TGP in plants and suggest feasible approaches for utilizing duckweed’s TGP in the bioremediation of detrimental algal blooms. Full article

This Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review synthesized effects of background levels of per- and polyfluorylalkyl substance (PFAS) levels on reproductive health outcomes in the general public: fertility, preterm birth, miscarriage, ovarian health, menstruation, menopause, sperm health, and in utero fetal growth. The inclusion criteria included original research (or primary) studies, human subjects, and investigation of outcomes of interest following non-occupational exposures. It drew from four databases (Web of Science, PubMed, Embase and Health and Environmental Research Online (HERO)) using a standardized search string for all studies published between 1 January 2017 and 13 April 2022. Risk of bias was assessed by two independent reviewers. Data were extracted and reviewed by multiple reviewers. Each study was summarized under its outcome in terms of methodology and results and placed in context, with recommendations for future research. Of 1712 records identified, 30 were eligible, with a total of 27,901 participants (33 datasets, as three studies included multiple outcomes). There was no effect of background levels of PFAS on fertility. There were weakly to moderately increased odds of preterm birth with higher perfluorooctane sulfonic acid (PFOS) levels; the same for miscarriage with perfluorooctanoic acid (PFOA) levels. There was limited yet suggestive evidence for a link between PFAS and early menopause and primary ovarian insufficiency; menstrual cycle characteristics were inconsistent. PFAS moderately increased odds of PCOS- and endometriosis-related infertility, respectively. Sperm motility and DNA health were moderately impaired by multiple PFAS. Fetal growth findings were inconsistent. This review may be used to inform forthcoming drinking water standards and policy initiatives regarding PFAS compounds and drinking water. Future reviews would benefit from more recent studies. Larger studies in these areas are warranted. Future studies should plan large cohorts and open access data availability to capture small effects and serve the public. Funding: Great Lakes Water Authority (Detroit, MI), the Erb Family Foundation through Healthy Urban Waters at Wayne State University (Detroit, MI), and Wayne State University CLEAR Superfund Research (NIH P42ES030991). Full article