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Sewages from duck farms are often recognized as a major source of antimicrobial resistance and pathogenic bacteria discharged to natural water bodies, but few studies depicted the dynamic changes in resistome and microbial communities in the rivers under immense exposure of sewage discharge. In this study, we investigated the ecological and environmental risks of duck sewages to the rivers that geographically near to the duck farms with short-distance (<1 km) using 16S rRNA amplicon and metagenomic sequencing. The results showed that a total of 20 ARG types were identified with abundances ranged from 0.61 to 1.33 cpc. Of note, the genes modulate resistances against aminoglycoside, bacitracin and beta-lactam were the most abundant ARGs. Limnohabitans, Fluviibacter and Cyanobium were the top 3 predominant genera in the microbial community. The alpha diversity of overall microbial community decrease while the abundance of pathogen increase during the input of sewage within 200 m. Sul1 and bacA were the dominant ARGs brought from duck farm sewage. The community variations of ARGs and microbiome were primarily driven by pH and temperature. Total phosphorus was significantly correlated to alpha diversity and top 30 ARGs subtype. Stochastic processes was the dominated microbial assembly pattern and did not be altered by sewage. We also highlighted the ecological risk caused by blaGES which possibly could be mitigated by Cyanobacteria, and the natural water body can purify partial ARGs as well as microbiome from duck farms sewage. These findings expanded our knowledge regarding the ecological risks by wastes from the livestock farm, and underscoring the necessity to monitor ARGs in farm-surrounding water bodies.

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Lentic small water bodies (LSWBs) deteriorate owing to anthropogenic activities, such as untreated domestic and agricultural waste disposal. Moreover, different turnover mechanisms occur during different seasons, contributing to nutrient enrichment and consequent degradation of LSWBs. However, understanding their spatial, temporal, and vertical variations during different seasons is understudied. In addition, studies on the variation in water quality under varying rainfall and land-use conditions are limited. Therefore, in this study, three LSWBs located in Northern India were studied during the pre-monsoon and monsoon seasons (December 2022 to October 2023). Total nitrogen (TN), chlorophyll-a (Chl-a), total phosphorus (TP), temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), chemical oxygen demand (COD), secchi disk depth (SDD), and water level (WL) were measured monthly. Sentinel-2 and CHIRPS pentad data were used for land use, land cover classification, and rainfall analysis. The spatial analysis indicates that the seasonal shift affects the water quality distribution, especially near the inlets and at the edges. The overall concentrations of TN and TP decreased during the monsoon season; however, they increased significantly at the inlets of the LSWBs. On the other hand, the Chl-a concentration shifted towards the edges due to the inflow during the monsoon. Temporal analysis also suggests that the arrival of the monsoon lowers pH, DO, and TDS. However, the concentrations of TN and TP increased because of agricultural runoff. Chl-a and COD show distinct variations due to the individual LSWBs' local conditions. Vertical variability analysis demonstrated pH, temperature, and TN stratification during the pre-monsoon period. However, during the monsoon, stratification is less significant due to intermixing. Redundancy analysis (RDA) showed that land use and rainfall patterns affected the water quality of LSWB 1, 2, and 3 by 53.49%, 81.62%, and 92.64%, respectively. This shows that land use, land cover, and rainfall changes affect the water quality of LSWBs. This study highlights the negative impact of runoff from agricultural land use as the main factor responsible for increased nutrient levels in the LSWBs.

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High-mountain water bodies represent critical components of their ecosystems, serving as vital freshwater reservoirs, environmental regulators, and sentinels of climate change. To understand the environmental dynamics of these regions, comprehensive analyses of lakes across spatial and temporal scales are necessary. While remote sensing offers a powerful tool for lake monitoring, applications in high-mountain terrain present unique challenges. The Ancash and Cuzco regions of the Peruvian Andes exemplify these challenges. These regions harbor numerous high-mountain lakes, which are crucial for fresh water supply and environmental regulation. This paper presents an exploratory examination of remote sensing techniques for lake monitoring in the Ancash and Cuzco regions of the Peruvian Andes. The study compares three deep learning models for lake segmentation: the well-established DeepWaterMapV2 and WatNet models and the adapted WaterSegDiff model, which is based on a combination of diffusion and transformation mechanisms specifically conditioned for lake segmentation. In addition, the Normalized Difference Water Index (NDWI) with Otsu thresholding is used for comparison purposes. To capture lakes across these regions, a new dataset was created with Landsat-8 multispectral imagery (bands 2-7) from 2013 to 2023. Quantitative and qualitative analyses were performed using metrics such as Mean Intersection over Union (MIoU), Pixel Accuracy (PA), and F1 Score. The results achieved indicate equivalent performance of DeepWaterMapV2 and WatNet encoder-decoder architectures, achieving adequate lake segmentation despite the challenging geographical and atmospheric conditions inherent in high-mountain environments. In the qualitative analysis, the behavior of the WaterSegDiff model was considered promising for the proposed application. Considering that WatNet is less computationally complex, with 3.4 million parameters, this architecture becomes the most pertinent to implement. Additionally, a detailed temporal analysis of Lake Singrenacocha in the Vilcanota Mountains was conducted, pointing out the more significant behavior of the WatNet model.

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Continuous monitoring of reservoirs and dams is essential for efficient water management. Synthetic Aperture Radar (SAR) imagery offers the potential for continuous monitoring of surface water through all-weather ground observation. The objective of this study is to enhance the accuracy of water body detection and water quantity estimation by applying 64 combinations of speckle filtering and object detection techniques to Sentinel-1 imagery. For speckle filtering, the Median, Gaussian, Lee, and Frost techniques were used with various window sizes (3, 5, 7, and 9). For water body detection, the Otsu, Kittler-Illingworth (KI), Chan-Vese (CV), and K-means methods were employed. The study area included three reservoirs and two dams in Korea, encompassing a variety of water surface sizes and types of land cover. To validate the accuracy of each water body detection combination, manual delineation-based water mask images from Sentinel-2 were employed. Furthermore, a regression equation (y=axb) between water surface area and storage was used to estimate water storage based on SAR imagery, followed by time-series validation using in-situ data. The research results indicate that the optimal detection technique varies significantly depending on the type of surrounding land cover and the size of the water body. The highest performance was observed for the CV technique combination for waterfront pixels, and for the KI technique combination for other land cover pixels. In speckle filtering techniques using a large window size, the false detection rate caused by vegetation and buildings was low; however, the boundaries of water bodies were blurred. Consequently, using smaller window sizes in SAR imagery and leveraging optimal water body detection combinations specific to land cover types, along with post-processing using masking data, would enhance the performance of water surface area and storage estimation.

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Employing deep learning techniques for the semantic segmentation of remote sensing images has emerged as a prevalent approach for acquiring information about water bodies. Yet, current models frequently fall short in accurately extracting water bodies from high-resolution remote sensing images, as these images often present intricate details of terrestrial objects and complex backgrounds. Vegetation, shadows, and other objects close to water boundaries have increased similarity to water bodies. Moreover, water bodies in high-resolution images have different boundary complexities, shapes, and sizes. This situation makes it somewhat challenging to accurately distinguish water bodies in high-resolution images. To overcome these difficulties, this paper presents a novel network model named EU-Net, specifically designed to extract water bodies from high-resolution remote sensing images. The proposed EU-Net model, with U-net as the backbone network, incorporates improved residual connections and attention mechanisms, and designs multi-scale dilated convolution and multi-scale feature fusion modules to enhance water body extraction performance in complex scenarios. Specifically, in the proposed model, improved residual connections are introduced to enable the learning of more complex features; the attention mechanism is employed to improve the model's discriminative ability by focusing on important channels and spatial areas. The implemented multi-scale dilated convolution technique enhances the model's receptive field while maintaining the same number of parameters. The designed multi-scale feature fusion module is capable of processing both small-scale details and large-scale structures in images, while simultaneously modeling the spatial context relationships of features at different scales. Experimental results validate the superior performance of EU-Net in accurately identifying water bodies from high-resolution remote sensing images, outperforming current models in terms of water extraction accuracy.

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As one of the important lakes in the "One Lake and Two Seas" of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake.

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The water and sediment samples were collected from the Yu River and Taowanbei River during periods of summer and winter. The NCPI, EWQI, Igeoand PERI were used to evaluate the pollution degree and cumulative ecological risk of HMs in the water and sediments. The PMF model was used to analyze the sources of HMs in river sediments. The pollution degree of Cd, Hg and Zn in the water reached the severe pollution level, in the rank of Hg > Zn > Cd. Cd and Zn in sediments are heavily polluted, Cu is lightly polluted, Pb and As are within the warning range, and the pollution rank is Cd > Zn > Cu > Pb > As. The cumulative ecological risk of HMs in sediments reached extremely strong level, mainly Cd and Hg. The main sources of HMs in sediments are mining sources, mixed agricultural and transport sources, and natural sources, which contributed 42.1 %, 34.1 % and 23.8 %, respectively.

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Understanding the spatial and temporal distribution of small water bodies is essential for managing water resources, crafting conservation policies, and preserving watershed ecosystems and biodiversity. However, existing studies often rely on a single remote sensing data source (optical or microwave), focusing on large-scale, flat areas and lacking comprehensive monitoring of small water bodies in complex terrain. Therefore, considering the complementary advantages of multisource remote sensing (multispectral and SAR), this paper proposes a multispectral and SAR fusion algorithm, named Multispectral and SAR Fusion algorithm (MASF), to better capture the remote sensing characteristics of small water bodies in complex areas. Based on this, a dataset containing spectral, texture, and geometric features is constructed, and multi-scale segmentation and random forest algorithms are applied for identification of small water bodies in complex terrain. The results demonstrate that the proposed fusion algorithm MASF exhibits minimal spectral distortion (SAM < 3.5, ERGAS <21, RMSE <0.01) and robust spatial feature enhancement (PSNR >40, SSIM >0.999, CC > 0.99). The Overall Accuracy (OA) and Kappa coefficients for both experimental areas surpassed 0.9. For rivers and reservoirs, both Producer's Accuracy (PA) and User's Accuracy (UA) exceeded 0.9. The UA for agricultural ponds exceeded 0.8. Comparative analysis with three other types of water-related data products shows that the freshwater identification results in this study have certain advantages in local small water bodies. Our research holds significant implications for the utilization of water resources in mountainous areas, prevention and control of floods and floods, as well as the development of aquaculture industry.

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Aquaculture systems are expected to act as potential hotspots for nitrous oxide (N2O) emissions, largely attributed to substantial nutrient loading from aquafeed applications. However, the specific patterns and contributions of N2O fluxes from these systems to the global emissions inventory are not well characterized due to limited data. This study investigates the patterns of N2O flux across 127 freshwater systems in China to elucidate the role of aquaculture ponds and lakes/reservoirs in landscape N2O emission. Our findings show that the average N2O flux from aquaculture ponds was 3.63 times higher (28.73 µg N2O m-2 h-1) than that from non-aquaculture ponds. Additionally, the average N2O flux from aquaculture lakes/reservoirs (15.65 µg N2O m-2 h-1) increased 3.05 times compared to non-aquaculture lakes/reservoirs. The transition from non-aquaculture to aquaculture practices has resulted in a net annual increase of 7589 ± 2409 Mg N2O emissions in China's freshwater systems from 2003 to 2022, equivalent to 20% of total N2O emissions from China's inland water. Particularly, the robust negative regression relationship between N2O emission intensity and water area suggests that small ponds are hotspots of N2O emissions, a result of both elevated nutrient concentrations and more vigorous biogeochemical cycles. This indicates that N2O emissions from smaller aquaculture ponds are larger per unit area compared to equivalent larger water bodies. Our findings highlight that N2O emissions from aquaculture systems can not be proxied by those from natural water bodies, especially small water bodies exhibiting significant but largely unquantified N2O emissions. In the context of the rapid global expansion of aquaculture, this underscores the critical need to integrate aquaculture into global assessments of inland water N2O emissions to advance towards a low-carbon future.

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Water resources are essential for the ecological system and the development of civilization. Water is imperative factor for health preservation and sustaining various human activities, including industrial production, agriculture, and daily life. Remote sensing provides a cost-effective and practical means to detect and monitor water bodies, offers valuable insights into the impact of climatic events on water structures, especially in coastal lake regions. The research primarily utilizes Landsat-9 OLI-2 satellite images to evaluate the effectiveness of various water indices (WRI, NWI, MNDWI, NDWI) in combination with global automatic thresholding methods (K-Means, Zhenzhou's, Adaptive, Intermodes, Prewitt and Mendelsohn's Minimum, Maximum Entropy, Median, Concavity, Percentile, Intermeans, Kittler and Illingworth's Minimum Error, Tsai's Moments, Otsu's, Huang's fuzzy, Triangle, Mean, IsoData, Li's). The study was carried out on Lake Nazik, Lake Iznik, and Lake Beysehir, which have unique geographical characteristics, and examined the adaptability and robustness of the selected indices and thresholding methods. MNDWI consistently stands out as a robust index for water extraction, delivering accurate results across different thresholding methods in regions all three lakes. As a result of quite extensive analysis, it is obtained that MNDWI and NDWI are reliable choices for water feature extraction in various lake environments, but the specific index should consider the thresholding method and unique lake characteristics. The Minimum thresholding method stands out as the most effective thresholding technique, demonstrating impressive results across different lakes. Specifically, it achieved an average Peak Signal-to-Noise Ratio (PSNR) of 78.97 and Structural Similarity Index (SSIM) of 99.37 for Lake Nazik, 74.08 PSNR and 98.34 SSIM for Lake Iznik, and 63.96 PSNR and 93.61 SSIM for Lake Beysehir.

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The typology and classification of rivers are highly relevant concepts in the field of limnology and freshwater ecology. Water body typology systematically categorizes water bodies based on their natural attributes, while water body classification groups them based on specific criteria or purposes for management, regulatory, or administrative reasons. Both concepts play important roles in understanding and managing water resources effectively. This scientific article focuses on the ZAT River in Morocco as a model for studying low-flow and intermittent rivers. The objective is to develop an accurate model for the typology and classification of small, low-flow rivers into homogeneous classes based on natural and anthropogenic factors. The study also investigates the impact of human activities on altering the uniformity and reference nature of the water body. The typology of water bodies is carried out according to the European methodology specified in The European Commission's Water Framework Directive (WFD) in 2000. The classification of water bodies is conducted by assessing their chemical and biological quality using the weighted index (WI), the Iberian Biological Monitoring Working Group (IBMWP) index, and multivariate statistical methods such as principal component analysis (PCA) for confirming water quality assessment. The results indicate the possibility of dividing the basin into four water bodies. Water bodies show homogeneity in terms of chemical quality when human influence is minimal or during periods of high river flow. However, increased human influence and decreased river flows lead to heterogeneity in chemical quality, indicating an unstable state. This study is the first of its kind in arid and semi-arid intermittent rivers, where such an approach could be suggested to determine their typology and classification.

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Analysis of dissolved organic matter (DOM) composition and microbial characteristics is crucial for tracing the sources of rural black and odorous water bodies (BOWB). The aim of this study was to explore the DOM and microbial diversity and identify the primary environmental factors in BOWB from various pollution sources during different periods using EEMs-PARAFAC and Illumina sequencing. It was found that the physicochemical properties vary widely across different pollution types of BOWB, with higher overall content during the high-water period compared to the normal-water period. The types of dissolved organic matter in BOWB are Tyrosine proteins, Fulvic acid, Dissolved microbial metabolites, and Humic acid. During the normal-water period, DOM originates primarily from terrestrial sources in various water bodies. However, DOM affected by livestock and poultry waste and industrial effluents is influenced by both internal and external sources during periods of high water levels. In industrial waste-type BOWB, the biological sources of water are weak. Proteobacteria, Actinobacteria, Chloroflexi, Firmicutes were the dominant bacterial phyla. According to the redundancy analysis, pH (p = 0.047), Total nitrogen (TN) (p = 0.045), Organic carbon (OC) (p = 0.044), and Nickel (Ni) (p = 0.047) are the primary environmental factors influencing the composition of bacterial communities.

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As an inland dryland lake basin, the rivers and lakes within the Lake Bosten basin provide scarce but valuable water resources for a fragile environment and play a vital role in the development and sustainability of the local societies. Based on the Google Earth Engine (GEE) platform, combined with the geographic information system (GIS) and remote sensing (RS) technology, we used the index WI2019 to extract and analyze the water body area changes of the Bosten Lake basin from 2000 to 2021 when the threshold value is -0.25 and the slope mask is 8°. The driving factors of water body area changes were also analyzed using the partial least squares-structural equation model (PLS-SEM). The result shows that in the last 20 years, the area of water bodies in the Bosten Lake basin generally fluctuated during the dry, wet, and permanent seasons, with a decreasing trend from 2000 to 2015 and an increasing trend between 2015 and 2019 followed by a steadily decreasing trend afterward. The main driver of the change in wet season water bodies in the Bosten Lake basin is the climatic factors, with anthropogenic factors having a greater influence on the water body area of dry season and permanent season than that of wet season. Our study achieved an accurate and convenient extraction of water body area and drivers, providing up-to-date information to fully understand the spatial and temporal variation of surface water body area and its drivers in the basin, which can be used to effectively manage water resources.

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Background and Objectives: Water is crucial to human existence but may be contaminated with microorganisms, thus making it unfit for consumption. This study aimed to evaluate the physicochemical properties and bacterial loads of selected river waters in Ondo State, Nigeria. Materials and Methods: Ten major rivers were sampled between April and August, 2021. The pH, temperature, total alkalinity, colour, turbidity, electrical conductivity, dissolved oxygen, ammonium, aluminium, organic matters, nitrate levels including the microbial loads were determined according to standard procedures. Confirmation of identified isolates was achieved by API 20E and API 20NE. Results: The turbidity, colour, conductivity, ammonium, and aluminium ranged from 4.3 to 15.2 NTU, 4 to 20 NTU, 123.5 to 580.2 mgL-1, 0 to 3.5 mgL-1, and 0.05 to 1.7, respectively. Most physicochemical parameters showed no significant differences from the WHO permissible limits for drinking water (p > 0.05). The total viable bacterial count in the rivers ranged from 1.5 × 105 to 6.3 × 105 CFUmL-1, while the total coliform count ranged from 1.3 × 103 to 4.8 × 103 CFUmL-1. The predominant bacteria were Escherichia coli and Pseudomonas aeruginosa. Conclusion: This study revealed that the physiochemical properties of the waters were majorly within the WHO permissible standards but with significantly higher bacterial loads.

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Florfenicol is used worldwide for its low side effects and strong bactericidal effect. Florfenicol is physicochemically stable and can persist in natural water bodies and affect water denitrification. Indoor aquatic microcosm models were constructed and water samples were collected at different florfenicol concentrations (0.1, 1, 10, and 100 mg/L) on days 0, 7, 30, and 60 to extract the microbial genome DNA and determine the water properties. qPCR and amplicon sequencing were used to study the dynamic changes of nirS gene and nirS-type denitrifying communities structure, diversity and abundance, respectively. The results showed that higher florfenicol concentrations caused accumulation of nitrate and ammonium nitrogen in water. Florfenicol stress caused orders of magnitude changes in nirS gene abundance, showing a trend of increasing first and then decreasing. 100 mg/L florfenicol addition led to a sustained increase of nirS gene abundance in water bodies. The florfenicol addition altered denitrifying community structure and suppressed the richness and diversity index of denitrifying bacteria in water body. Over time, the richness and diversity index gradually recovered. Proteobacteria was always the dominant denitrifying phylum in water. The relative abundance of Pseudomonas and beta proteobacterium showed obvious positive correlation with nirS gene abundance and were the dominant genera under florfenicol stress. Our study provided a scientific basis for the rational use of florfenicol in aquaculture to maintain a healthy and stable microecological environment, and also provided a preliminary understanding of the response characteristics of water denitrifying microorganisms to florfenicol exposure.

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This study aimed to understand the occurrence of mercury in the water environment of typical cold and arid lakes and the regulating environmental factors. Water and surface sediment samples were collected from July to August, 2022 in the Wuliangsuhai Lake region for the analysis of total mercury (THg) and total methylmercury (TMeHg). Lake water THg and TMeHg ranged between 19.20 ~ 668.10 and 0.10 ~ 11.40 ng/L, respectively, exceeding China's environmental quality standards and contents of other lakes and reservoirs in China and other areas. Surface sediments showed lower mean THg and TMeHg of 261.85 and 0.18 µg/kg, respectively, with the former significantly exceeding the background value of Inner Mongolia and unpolluted natural lakes but lower than those of lakes affected by human factors, such as aquaculture. Sediments showed relatively low methylation and TMeHg (0.01-0.21%) concentrations. Correlation analysis identified salinity, total dissolved solids, conductivity, and redox potential as important factors affecting mercury speciation in water, whereas those in surface sediments were organic matter, pH, and total iron content. This study conducted preliminary research on the different species of Hg in Wuliangsuhai Lake water environment, which can provide scientific evidence for the specific treatment of Hg pollution in agriculture, or industry and other related fields. Our results suggest that upstream and downstream regulatory agencies should strengthen the regulation of agricultural and industrial production, moderately reduce human activities, and reduce the use of mercury-rich substances such as pesticides.

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Antibacterials represent a pharmaceutical class that is extensively used and consumed worldwide. The presence of a large number of antibacterial agents in water could result in antibiotic resistance. Thus, the development of a fast, accurate, and high-throughput method to analyze these emerging contaminants in water is necessary. Herein, a method was developed to achieve the simultaneous determination of 43 antibacterials from nine pharmaceutical categories (i.e., sulfonamides, quinolones, fluoroquinolones, tetracyclines, lincosamides, macrolides, nitroimidazoles, diterpenes, and dihydrofolate reductase inhibitors) in water using automatic sample loading-solid phase extraction (SPE)-ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Because the properties of these 43 antibacterials are quite different, the main objective of this work is to develop an extraction procedure that would enable the simultaneous analysis of a wide range of multiclass antibacterials. Given this context, the work presented in this paper optimized the SPE cartridge type, pH, and sample loading amount. Multiresidue extraction was performed as follows. The water samples were filtered through 0.45 µm filter membranes, added with Na2EDTA and NaH2PO4, and pH-adjusted to 2.34 using H3PO4. The solutions were then mixed with the internal standards. An automatic sample loading device fabricated by the authors was used for sample loading, and Oasis HLB cartridges were used for enrichment and purification. The optimized UPLC conditions were as follows: chromatographic column, Waters Acquity UPLC BEH C18 column (50 mm×2.1 mm, 1.7 µm); mobile phases, methanol-acetonitrile (2∶8, v/v) solution containing 0.1% formic acid and 0.1% formic acid aqueous solution; flow rate, 0.3 mL/min; injection volume, 10 µL. The compounds were step scanned using an electrospray ionization source in the positive and multiple-reaction monitoring (MRM) modes, and analyzed by internal and external standard methods. The results showed that the 43 compounds achieved high linearity in their respective linear ranges, with correlation coefficients (r2) greater than 0.996. The limits of detection (LODs) of the 43 antibacterial agents ranged from 0.004 ng/L to 1.000 ng/L, and their limits of quantification (LOQs) ranged from 0.012 ng/L to 3.000 ng/L. The average recoveries ranged from 53.7% to 130.4%, and the relative standard deviations (RSDs) were between 0.9% and 13.2%. The method was successfully applied to the determination of six tap water samples from different districts and six water samples obtained from the Jiangyin section of the Yangtze River and Xicheng Canal. No antibacterial compound was detected in any of the tap water samples, but a total of 20 antibacterial compounds were detected in the river and canal water samples. Among these compounds, sulfamethoxazole showed the highest mass concentrations, ranging from 8.92 to 11.03 ng/L. The types and contents of antibacterials detected in the Xicheng Canal were greater than those found in the Yangtze River, and two kinds of diterpenes, namely tiamulin and valnemulin, were found easily and commonly in water sample. The findings indicate that antibacterial agents are widespread in environmental waters. The developed method is accurate, sensitive, rapid, and suitable for the detection of the 43 antibacterial compounds in water samples.

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Mycobacterium ulcerans is an environmental bacterium responsible for Buruli ulcer. This disease has a high frequency index in humid tropical regions, with a high incidence in Sub-Saharan Africa. The ecology and mode of transmission of this disease is not well established. Based on dilution effect hypothesis, acting as lowering disease transmission due to greater biodiversity, floristic inventory was carried out in the Health Districts of Daloa and Bouaké in Côte d'Ivoire. In each district, high and low endemic sites were investigated. A total of 169 plant species were inventoried for both low and high endemicity of Buruli ulcer sites in the districts. The Indval index revealed that 13 plant species were good indicators for Buruli ulcer highly endemic areas. The plants which correlate with high endemicity area were Leersia hexandra, Panicum laxum, Mimosa pudica, Paspalum distichum, Persicaria senegalensis, Calopogonium mucunoides, Echinochloa colona, Ipomoea sagittata, and Eichhornia crassipes. For low endemic sites, a strong relationship was recorded for 37 plants. The indices revealed low similarity between high and low endemicity sites. Low endemicity sites expressed the highest plant species diversity. These results suggest the hypothesis that floristic richness is more important in sites of low endemicity than in those of high endemicity. Moreover, we observed a co-occurrence of some plant species and Buruli ulcer endemicity. This finding may lead to the fact that it is important to care about the biodiversity to prevent outbreak of Buruli ulcer cases. Supplementary Information: The online version contains supplementary material available at 10.1007/s41742-023-00520-2.

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Both gradual and abrupt changes in lake surface area in permafrost regions are crucial for understanding the water cycles in cold regions under climate change. However, seasonal changes in lake area in permafrost regions are not available, and their occurrence conditions are still unclear. Based on remotely sensed water body products at a 30 m resolution, this study provides a detailed comparison of lake area changes across seven basins characterized by clear gradients in climatic, topographic and permafrost conditions in the Arctic and Tibetan Plateau between 1987 and 2017. The results show that the maximum surface area of all lakes net increased by 13.45 %. Among them, the seasonal lake area net increased by 28.66 %, but there was also a 2.48 % loss. The permanent lake area net increased by 6.39 %, and the area loss was approximately 3.22 %. The total permanent lake area generally decreased in the Arctic but increased in the Tibetan Plateau. At lake region scale (0.1° grid), the changes in permanent area of contained lakes were divided into four types including no change, homogeneous changes (only expansion or only shrinkage), heterogeneous changes (expansion neighboring shrinkage) and abrupt changes (newforming or vanishing). The lake regions with heterogeneous changes accounted for over one-quarter of all lake regions. All types of changes in lake regions, especially the heterogeneous changes and abrupt changes (e.g., vanishing), occurred more extensively and intensely on low and flat terrain, in high-density lake regions and in warm permafrost regions. These findings indicate that, considering the increase in surface water balance in these river basins, surface water balance alone cannot fully explain changes in permanent lake area in the permafrost region, and the thawing or disappearance of permafrost plays a tipping point effect on the lake changes.

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The effect of sediment and residual fish feed on aquaculture water bodies has gained increasing attention to alleviate the eutrophication and heavy metals enrichment induced by aquaculture. Thus, this study intended to reveal the possible interactions among nutrients, heavy metals, and Chlorella vulgaris (C. vulgaris) in aquaculture water bodies containing fish feed and sediment. The analyses showed that consistent with the composition of heavy metals in fish feed, manganese (Mn) and zinc (Zn) accounted for the highest proportions (68-78%) of heavy metals in sediment. Meanwhile, sediment in the centre of aquaculture water bodies (S2) contained more heavy metals than those in the perimeter (S1), but the released concentrations and rates (Rrelease) of heavy metals from S1 were higher than those from S2. Moreover, the biomass, growth rate, specific growth rate, and nitrogen and carbon fixation rate of C. vulgaris increased with adding fish feed, whereas superoxide dismutase (SOD) and malondialdehyde (MDA) decreased. In addition, with C. vulgaris, influenced by the release process from sediment and the uptake by C. vulgaris, the concentration and Rrelease of Mn, Pb, Cu, Mn, Cr and Cd from sediments coexisting with fish feed in water first increased and then decreased in general. The C. vulgaris biomass was significantly negatively related to Mn, Pb, Cu, Ni, Cr, and Cd and PO43-P (P < 0.05), which was caused by the uptake of C. vulgaris and indicated that C. vulgaris biomass is easily affected by these factors. Accordingly, the input of residual fish feed and sediment should be controlled.

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