Molecular characteristics of dissolved organic phosphorus in watershed runoff: Coupled influences of land use and precipitation.

Land use and precipitation are two major factors affecting phosphorus (P) pollution of watershed runoff. However, molecular characterization of dissolved organic phosphorus (DOP) in runoff under the joint influences of land use and precipitation remains limited. This study used Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) to study the molecular characteristics of DOP in a typical P-polluted watershed with spatially variable land use and precipitation. The results showed that low precipitation and intense human activity, including phosphate mining and associated industries, resulted in the accumulation of aliphatic DOP compounds in the upper reaches, characterized by low aromaticity and low biological stability. Higher precipitation and widespread agriculture in the middle and lower reaches resulted in highly unsaturated DOP compounds with high biological stability constituting a higher proportion, compared to in the upper reaches. While, under similar precipitation, more aliphatic DOP compounds characterized by lower aromaticity and higher saturation were enriched in the lower reaches due to more influence from urban runoff relative to the middle reaches. Photochemical and/or microbial processes did result in changes in the characteristics of DOP compounds during runoff processes due to the prevalence of low molecular weight and low O/C bioavailable aliphatic DOP molecules in the upper reaches, which were increasingly transformed into refractory compounds from the upper to middle reaches. The results of this study can increase the understanding of the joint impacts of land use and precipitation on DOP compounds in watershed runoff.

Development of a coupled model to simulate and assess arsenic contamination and impact factors in the Jinsha River Basin, China.

With the increasing severity of arsenic (As) pollution, quantifying the environmental behavior of pollutant based on numerical model has become an important approach to determine the potential impacts and finalize the precise control strategies. Taking the industrial-intensive Jinsha River Basin as typical area, a two-dimensional hydrodynamic water quality model coupled with Soil and Water Assessment Tool (SWAT) model was developed to accurately simulate the watershed-scale distribution and transport of As in the terrestrial and aquatic environment at high spatial and temporal resolution. The effects of hydro-climate change, hydropower station construction and non-point source emissions on As were quantified based on the coupled model. The result indicated that higher As concentration areas mainly centralized in urban districts and concentration slowly decreased from upstream to downstream. Due to the enhanced rainfall, the As concentration was significantly higher during the rainy season than the dry season. Hydro-climate change and the construction of hydropower station not only affected the dissolved As concentration, but also affected the adsorption and desorption of As in sediment. Furthermore, As concentration increased with the input of non-point source pollution, with the maximum increase about 30%, resulting that non-point sources contributed important pollutant impacts to waterways. The coupled model used in pollutant behavior analysis is general with high potential application to predict and mitigate water pollution.

Comparative analysis of RUSLE and SWPT for sub-watershed conservation prioritization in the Ayu watershed, Abay basin, Ethiopia.

Ethiopia is currently facing a major environmental problem caused by soil erosion. In order to tackle this problem, it is essential to implement a comprehensive watershed management approach and give priority to conservation efforts depending on the level of severity. Therefore, the objective of this research is to evaluate the mean annual soil erosion and rank the sub-watersheds for conservations in the Ayu watershed, utilizing the Revised Universal Soil Loss Equation (RUSLE) model and the Sub-Watershed Prioritization Tool (SWPT). RUSLE was utilized to predict the annual average soil erosion rate, while SWPT was applied to conduct Weighted Sum Analysis (WSA) for ranking sub-watersheds. Support Vector Machine (SVM) was employed for classifying land use and land cover. The Relative importance of morphometric and topo-hydrologic features in the SWPT was analyzed using a Random Forest model. The Bland-Altman plot and Wilcoxon Signed Rank Test were employed to assess the agreement in prioritizing watersheds between RUSLE results and the SWPT. Furthermore, field observations were conducted to validate the land use classification by collecting ground data. In addition, the study was enhanced with local viewpoints by conducting focus group discussions with agricultural experts and farmers to obtain qualitative insights and validation of resuts. The findings showed that soil loss varied from 0 to 110 t/ha/yr, with an average of 8.95 t/ha/yr, resulting in a total loss of 384365.3 tons annually. The comparison of RUSLE and SWPT showed a moderate positive relationship (r = 0.59). The results of the Bland-Altman plot indicate a consistent agreement between the two methods. However, there is inconsistency among the five sub watersheds. This study enhances the knowledge of soil erosion patterns and offers useful guidance for watershed conservation techniques. It can be also used as a beneficial framework for managing watersheds, with possible uses outside of the Ayu watershed.

Leveraging smart image processing techniques for early detection of foot ulcers using a deep learning network.

Purpose: To detect foot ulcers in diabetic patients by analysing thermal images of the foot using a deep learning model and estimate the effectiveness of the proposed model by comparing it with some existing studies. Material and methods: Open-source thermal images were used for the study. The dataset consists of two types of images of the feet of diabetic patients: normal and abnormal foot images. The dataset contains 1055 total images; among these, 543 are normal foot images, and the others are images of abnormal feet of the patient. The study's dataset was converted into a new and pre-processed dataset by applying canny edge detection and watershed segmentation. This pre-processed dataset was then balanced and enlarged using data augmentation, and after that, for prediction, a deep learning model was applied for the diagnosis of an ulcer in the foot. After applying canny edge detection and segmentation, the pre-processed dataset can enhance the model's performance for correct predictions and reduce the computational cost. Results: Our proposed model, utilizing ResNet50 and EfficientNetB0, was tested on both the original dataset and the pre-processed dataset after applying edge detection and segmentation. The results were highly promising, with ResNet50 achieving 89% and 89.1% accuracy for the two datasets, respectively, and EfficientNetB0 surpassing this with 96.1% and 99.4% accuracy for the two datasets, respectively. Conclusions: Our study offers a practical solution for foot ulcer detection, particularly in situations where expert analysis is not readily available. The efficacy of our models was tested using real images, and they outperformed other available models, demonstrating their potential for real-world application.

Enhancing flood mapping through ensemble machine learning in the Gamasyab watershed, Western Iran.

Floods are among the natural hazards that have seen a rapid increase in frequency in recent decades. The damage caused by floods, including human and financial losses, poses a serious threat to human life. This study evaluates two machine learning (ML) techniques for flood susceptibility mapping (FSM) in the Gamasyab watershed in Iran. We utilized random forest (RF), support vector machine (SVM), ensemble models, and a geographic information system (GIS) to predict FSM. The application of these models involved 10 effective factors in flooding, as well as 82 flood locations integrated into the GIS. The SVM and RF models were trained and tested, followed by the implementation of resampling techniques (RT) using bootstrap and subsampling methods in three repetitions. The results highlighted the importance of elevation, slope, and precipitation as primary factors influencing flood occurrence. Additionally, the ensemble model outperformed both the RF and SVM models, achieving an area under the curve (AUC) of 0.9, a correlation coefficient (COR) of 0.79, a true skill statistic (TSS) of 0.83, and a standard deviation (SD) of 0.71 in the test phase. The tested models were adapted to available input data to map the FSM across the study watershed. These findings underscore the potential of integrating an ensemble model with GIS as an effective tool for flood susceptibility mapping.

One-pot fabrication of task specific magnetic adsorbent for the efficient isolation and capture of liquid-crystal monomers pollutants in waters prior to chromatographic quantification.

BACKGROUND: Liquid crystal monomers (LCMs) have been classified as emerging organic pollutants. Efficient isolation and extraction is a critical step in the determination, and then knowing the occurrence and distribution of LCMs in environmental waters. However, the reported sample preparation techniques still suffer some dilemmas such as using large amount of organic solvent, low extraction capacity, tedious operation procedure and employment of expensive extraction column. To circumvent the disadvantages, new extraction format and adsorbent with quickness, less consumption of organic solvent, superior extraction performance and low cost should be developed for the analysis of LCMs. RESULTS: Using 1H,1H,2H,2H-heptadecafluorodecyl acrylate and 9-vinylanthracene as mixed functional monomers, a task specific magnetic adsorbent (TSMA) was prepared by one-pot hydrothermal technique for the highly efficient capture of LCMs under magnetic solid phase extraction (MSPE) format. Due to the abundant functional groups, the developed TSMA/MSPE presented satisfactory capture performance towards LCMs. Satisfactory enrichment factors (132-212) and high adsorption capacity (18 mg/g) were obtained. Additionally, the relevant adsorption mechanism was studied by the combination of density functional theory calculation and experiments about adsorption kinetics and adsorption isotherm. Under the beneficial conditions, a sensitive and reliable method for the monitoring of studied LCMs in environmental waters was established by the combination of TSMA/MSPE with HPLC equipped with diode array detector (DAD). The achieved limits of detection and spiked recoveries were 0.0025-0.0061 µg/L and 81.0-112 %, respectively. Finally, the developed method was employed to monitor LCMs levels in the North Creek watershed of Jiulong River. SIGNIFICANCE AND NOVELTY: The current study provided a new adsorbent for quick and efficient capture of LCMs at trace levels. In addition, a sensitive, reliable and anti-intereference method for the monitoring of trace LCMs in actual waters was established. Moreover, for the first, the contents, occurrence and distribution of LCMs in North Creek watershed was investigated based on the developed method.

Unraveling the role of anthropogenic and hydrologic drivers with respect to the optical and molecular properties of dissolved organic matter and organic phosphorus in a P-contaminated river.

Anthropogenic and hydrological drivers are key factors influencing the fate of dissolved organic matter (DOM) and dissolved organic phosphorus (DOP) in river runoff. However, how anthropogenic disturbances and hydrological conditions jointly affect the composition and characteristics of DOM and DOP in river runoff remains unclear. This study used fluorescence spectroscopy, Fourier transform ion cyclotron resonance mass spectrometry, and the stable water isotopes to interpret the chemical composition and properties of DOM and DOP as well as their linkages to anthropogenic disturbances and hydrological conditions in a typical P-contaminated tributary to the central Yangtze River. The results show in the wet season, the average abundance of humic-like components in DOM exceeded 60 %, while the average abundance of tryptophan-like components in DOM exceeded 50 % in the dry season. During the dry season, hydrological conditions had a greater impact on highly unsaturated DOM compounds compared to anthropogenic disturbances because a decrease in precipitation reduced the transport of terrestrial DOM into aquatic systems and increased water retention time in the river, promoting the production of unsaturated compounds from photochemistry. The effects of the two factors were similar in the wet season because active agricultural activities and intense precipitation jointly facilitated the entry of exogenous humics into the runoff, leading to the similar relative abundance of highly unsaturated DOM compounds associated with both factors. Anthropogenic disturbances had a greater impact on aliphatic DOM and DOP than hydrological conditions, which was associated with intense human activities in the watershed, such as phosphate mining, agricultural cultivation, and domestic sewage discharge. This study provides new knowledge about the composition, properties and underlying mechanisms of DOM and DOP in the P-contaminated watershed runoff.

Spatio-temporal characteristics and multi-scale risk identification of pollution load based on sensitivity analysis in small watersheds located in Tuojiang River Basin, China.

High-quality development of water resources supports high-quality socio-economic development. High-quality development connects high-quality life, and clarifying the key management contents of small watersheds plays an important role in building ecologically clean small watersheds and promoting regional production and life. Previous research on pollution loads has focused on examining the impact of various external drivers on pollution loads but still lacks research on the impact of changes in pollution sources themselves on pollution loads. In this study, sensitivity analysis was used to determine the impact of changes from different sources on the total pollution loads, which can recognize the critical pollution sources. We first employed the pollutant discharge coefficient method to quantify non-point source pollution loads in the small watershed in the upstream Tuojiang River basin from 2010 to 2021. Then, combination sensitivity analysis with Getis-Ord Gi* was used to identify the critical sources and their crucial areas at the global, districts (counties), and towns (streets) scales, respectively. The results indicate: (1) The pollution loads of COD, NH3-N, TN, and TP all show a decreasing trend, reducing by 18.3%, 16.2%, 18.6%, and 28.1% from 2010 to 2021, respectively; (2) Livestock and poultry breeding pollution source is the most critical source for majority areas across watershed; (3) High-risk areas are mainly concentrated in Jingyang district and its subordinate towns (streets). There is a trend of low-pollution risk areas transitioning to high-pollution risk areas, with high-risk areas predominantly concentrated in the southeast and exhibiting a noticeable phenomenon of pollution load spilling around. This study can promote other similar small watersheds, holding significant importance for non-point source pollution control in small watersheds.

Water quality assessment and its pollution source analysis from spatial and temporal perspectives in small watershed of Sichuan Province, China.

Rapid socio-economic development has led to many water environmental issues in small watersheds such as non-compliance with water quality standards, complex pollution sources, and difficulties in water environment management. To achieve a quantitative evaluation of water quality, identify pollution sources, and implement refined management in small watersheds, this study collected monthly seven water quality indexes of four monitoring points from 2010 to 2023, and ten water quality indexes of 23 sampling points in the Shiting River and Mianyuan River which are tributaries of the Tuojiang River Basin. Then, water quality evaluation and pollution source analysis were conducted from both temporal and spatial perspectives using the Water Quality Index (WQI) method, the Absolute Principal Component Scores/Multiple Linear Regression (APCS-MLR) method, and the Positive Matrix Factorization (PMF) receptor modeling technique. The results indicated that except for total nitrogen (TN), the concentrations of other water quality indexes exhibited a decreasing trend, and all were divided into two obvious stages before and after 2016. Furthermore, the proportion of water quality grade of Good and above increased from 73.96 to 84.94% from 2010-2015 to 2016-2023, and the water quality grade of Good and above from upstream to downstream dropped from 100 to 23.33%. From the temporal scale, four and five pollution sources were identified in the first and second stages, respectively. The distinct TN pollutant is mainly affected by agricultural non-point sources (NPS), whose impact is enhanced from 17.76 to 78.31%. Total phosphorus (TP) was affected by the phosphorus chemical industry, whose contribution gradually weakened from 50.8 to 24.9%. From a spatial perspective, four and five pollution sources were identified in the upstream and downstream, respectively. Therefore, even though there are some limitations due to the data availability of water monitory and hydrology data, the proposed research framework of this study can be applied to the water environmental management of other similar watersheds.

Multi-machine learning methods to predict spatial variation characteristics of total nitrogen at watershed scale: Evidences from the largest watershed (Yangtze River Watershed), Asian.

Nitrogen pollution has emerged as a significant threat to the health of global river systems, garnering considerable attention. However, numerous challenges persist in understanding the characteristics and predicting the spatial changes of total nitrogen (TN) at the catchment scale. We leveraged data from 530 monitoring sections to calculate a land-use composite index and perform statistical analyses to explore the primary factors influencing nitrogen enrichment in the Yangtze River Watershed. We developed three machine learning models to forecast future TN concentrations at monitoring points. Our results showed that agricultural activities and rainfall were the primary drivers of monthly variations in TN concentrations. The upstream region of the watershed exhibited larger variations in TN concentrations (0.097 to 11.099 mg/L), significantly higher than the middle and downstream areas (0.348 to 6.844 mg/L). Microbial-mediated organic matter decomposition in sediment and changes in land-use were identified as key contributors to regional differences in nitrogen enrichment. Potential nitrogen sources include sediment release, urban sewage, and agricultural fertilization. Random Forest model achieved a prediction accuracy of 77.6 %, surpassing the BP and LSTM models. We identified 37 high-risk areas of nitrogen enrichment, concentrated in the Chengdu-Chongqing, Yunnan-Central urban cluster, and the Chaohu Lake sub-watershed. Increased urban land-use and industrial inputs primarily influenced nitrogen enrichment in the upstream area, while agricultural inputs were the main drivers in the middle and downstream regions. Our multi-machine learning models identified the relationship between TN and influencing factors, providing a reliable method for assessing nitrogen enrichment risk in the watershed.

A hybrid approach to improvement of watershed water quality modeling by coupling process-based and deep learning models.

Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process-based models (PBMs) are typically used to simulate water quality modeling. In watershed modeling utilizing PBMs, it is crucial to effectively reflect the actual watershed conditions by appropriately setting the model parameters. However, parameter calibration and validation are time-consuming processes with inherent uncertainties. Addressing these challenges, this research aims to address various challenges encountered in the calibration and validation processes of PBMs. To achieve this, the development of a hybrid model, combining uncalibrated PBMs with data-driven models (DDMs) such as deep learning algorithms is proposed. This hybrid model is intended to enhance watershed modeling by integrating the strengths of both PBMs and DDMs. The hybrid model is constructed by coupling an uncalibrated Soil and Water Assessment Tool (SWAT) with a Long Short-Term Memory (LSTM). SWAT, a representative PBM, is constructed using geographical information and 5-year observed data from the Yeongsan River Watershed. The output variables of the uncalibrated SWAT, such as streamflow, suspended solids (SS), total nitrogen (TN), and total phosphorus (TP), as well as observed precipitation for the day and previous day, are used as training data for the deep learning model to predict the TP load. For the comparison, the conventional SWAT model is calibrated and validated to predict the TP load. The results revealed that TP load simulated by the hybrid model predicted the observed TP better than that predicted by the calibrated SWAT model. Also, the hybrid model reflects seasonal variations in the TP load, including peak events. Remarkably, when applied to other sub-basins without specific training, the hybrid model consistently outperformed the calibrated SWAT model. In conclusion, application of the SWAT-LSTM hybrid model could be a useful tool for decreasing uncertainties in model calibration and improving the overall predictive performance in watershed modeling. PRACTITIONER POINTS: We aimed to enhance process-based models for watershed water-quality modeling. The Soil and Water Assessment Tool-Long Short-Term Memory hybrid model's predicted and total phosphorus (TP) matched the observed TP. It exhibited superior predictive performance when applied to other sub-basins. The hybrid model will overcome the constraints of conventional modeling. It will also enable more effective and efficient modeling.

How can the microbial community in watershed sediment maintain its resistance in the presence of shifting antibiotic residuals?

The widespread presence of antibiotics in global watershed environments poses a serious threat to public health and ecosystems. It is essential to examine the resistance of microbial communities in watershed environments in response to shifting antibiotic residues. Sediment samples were collected from seven sites across a watershed, encompassing surface sediment (0-10 cm) and bottom sediment (30-40 cm) depths. The aim was to replicate exposure scenarios to different antibiotics (oxytetracycline (OTC) and sulfadiazine (SD)) at varying concentrations (0, 10, and 100 µg/L) in sediment overlying water, within controlled laboratory settings. The study findings revealed significant variations in the microbial community structure of sediments between different treatments, with distinct differences observed in the upper stream and top sediment layers compared to the sediments located downstream and in the bottom layers. After the introduction of antibiotics, a significant decrease in microbial nodes was observed in the genus-level co-occurrence network analysis of the bottom sediment layer, particularly in the OTC treatment groups. In contrast, the downstream region displayed more robust correlations among the top 20 genera than the upstream area. There was no significant variance observed in the expression of Antibiotic resistance genes (ARGs), consisting of tetracycline resistance genes (tetC, tetG, tetM, tetW, and tetX) and sulfonamide resistance genes (sul1, sul2, and sul3), between sediments in the top and bottom layers. Nevertheless, downstream samples exhibited significantly higher levels of ARGs when compared to upstream samples. Network correlation analysis indicated notably lower correlations between ARGs and bacterial genera in sediments from upstream or surface layers compared to those in downstream or deeper layers. Moreover, correlations in the sediments from surface layers and upstream regions showed a decreasing trend with increasing SD exposure concentrations, while those in deeper layers and downstream areas remained relatively stable. The presence of antibiotics notably enhanced the correlation between sediment properties and ARGs, particularly emphasizing associations with total carbon, nitrogen, and sulfur content. However, the introduction of SD and OTC resulted in a decrease in the influence of these sediment factors on microbial community functions related to sulfur and nitrogen metabolism, as indicated by KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation. The research provided empirical evidence on how microbial resistance responds to changes in antibiotics in sediment samples taken from various depths and locations within a watershed. It emphasized the urgent need for heightened awareness of the movement and alteration of antibiotic resistance patterns in watershed ecosystems.

Source identification of microplastics in highly urbanized river environments and its implications for watershed management.

The extensive use of plastic products has resulted in a significant influx of microplastics into aquatic ecosystems, particularly in highly urbanized areas and their associated river environments. However, the specific pathways and quantities through which these microplastics enter the river environment are still unclear, which poses a challenge in developing effective measures to mitigate their sources. In this paper, the spatiotemporal variations of microplastics from different sources in highly urbanized rivers within the Shenzhen Bay watershed were investigated through field sampling, experimental and statistical analysis, and the measures of microplastic reduction were discussed. The observation results exhibited a negative logarithmic correlation between the abundance of microplastics in river water and monthly rainfall (R = 0.994, MSE = 0.051, p < 0.05). When the monthly rainfall was <6 mm, the abundance of microplastics was absolutely dependent on point sources. While the rainfall exceeded 470 mm, the abundance was absolutely predominantly influenced by nonpoint source microplastics. The annual load of microplastics from the watershed was 5.39 × 1012 items, of which 61.6 % originated from point sources. Among the microplastics from point sources, 92.1 % were derived from fibers generated by textile washing. Fragmented microplastics (41.9 %) were the most common type of microplastics from nonpoint sources, primarily originating from the disintegration and weathering of disposable plastics. In the future, there is an expectation to reduce the microplastic load in the watershed to 15.9 % of the total by improving sewage treatment processes and infrastructure. This study can provide scientific guidance for environmental planning and serve as a warning regarding the impact of microplastics on ecosystems in urbanized areas.

Application of the Modified Universal Soil Loss Equation (MUSLE) for the prediction of sediment yield in Agewmariam experimental watershed, Tekeze River basin, Northern Ethiopia.

The study utilized the Modified Universal Soil Loss Equation (MUSLE) to predict sediment loss and evaluate the model's performance in the Agewmariam experimental watershed in order to support the planning, management, and appropriate use of the soil and water resources in the watershed. The natural resources conservation service (NRCS) curve number method was used to model runoff energy factor. By overlaying maps of runoff energy, soil erodibility, slope length and steepness, cover management, and support practice factors with assigned values, the cumulative effect of these parameters for the suspended sediment yield was calculated using the ArcGIS raster calculator. The runoff energy factor was the most sensitive parameter, followed by slope length and steepness factor. To improve the model's fit to the local conditions, the initial abstraction to storage ratio (λ) of the runoff energy factor was reduced to 0.023, and the MUSLE model coefficient and exponent were adjusted to 1 and 0.59, respectively. During calibration, the mean observed and estimated suspended sediment yields were 0.2 and 0.23 ton/ha, respectively, while during validation, they were 0.7 and 0.53 ton/ha, respectively. The model evaluation showed that the MUSLE model, without calibration, was not appropriate for estimating runoff and sediment yield. However, with appropriate calibration, the model showed good performance with a coefficient of determination (R2), coefficient of efficiency (E), and index of agreement (d) of 0.85, 0.85, and 0.96 respectively, during calibration and 0.84, 0.65, and 0.83 respectively, during validation. Based on these findings, this study suggests that the calibrated MUSLE model can be used to prioritize soil and water conservation interventions within the watershed or can be extrapolated to neighboring similar watersheds. Further refinement of model input parameters using more data from the watershed is recommended to increase the prediction accuracy of the model.

A methodological framework for assessing sea level rise impacts on nitrate loading in coastal agricultural watersheds using SWAT+: A case study of the Tar-Pamlico River basin, North Carolina, USA.

This study addresses the urgent need to understand the impacts of climate change on coastal ecosystems by demonstrating how to use the SWAT+ model to assess the effects of sea level rise (SLR) on agricultural nitrate export in a coastal watershed. Our framework for incorporating SLR in the SWAT+ model includes: (1) reclassifying current land uses to water for areas with elevations below 0.3 m based on SLR projections for mid-century; (2) creating new SLR-influenced land uses, SLR-influenced crop database, and hydrological response units for areas with elevations below 2.4 m; and (3) adjusting SWAT+ parameters for the SLR-influenced areas to simulate the effects of saltwater intrusion on processes such as plant yield and denitrification. We demonstrate this approach in the Tar-Pamlico River basin, a coastal watershed in eastern North Carolina, USA. We calibrated the model for monthly nitrate load at Washington, NC, achieving a Nash-Sutcliffe Efficiency (NSE) of 0.61. Our findings show that SLR substantially alters nitrate delivery to the estuary, with increased nitrate loads observed in all seasons. Higher load increases were noted in winter and spring due to elevated flows, while higher percentage increases occurred in summer and fall, attributed to reduced plant uptake and disrupted nitrogen cycle transformations. Overall, we observed an increase in mean annual nitrate loads from 155,000 kg NO3-N under baseline conditions to 157,000 kg NO3-N under SLR scenarios, confirmed by a statistically significant paired t-test (p = 2.16 × 10-10). This pioneering framework sets the stage for more sophisticated and accurate modeling of SLR impacts in diverse hydrological scenarios, offering a vital tool for hydrological modelers.

Assessment of the viral contamination of fecal origin over a wide geographical area using an active approach with Dreissena polymorpha.

Biomonitoring appears to be a key approach to assess chemical or microbiological contaminations. The freshwater mussel, Dreissena polymorpha (D. polymorpha), is a suitable tool already used to monitor chemical and, more recently, microbiological pollution. In the present study, we used this sentinel species to monitor viral contamination of fecal origin over a wide geographical distribution. An active approach was implemented based on caging of calibrated and pathogen-free organisms with the same exposure conditions, allowing spatio-temporal comparisons between different water bodies. In addition, different types of sites were selected to investigate the range of environmental concentrations that D. polymorpha are able to translate. Different viral genome targets were measured: norovirus genogroup I and II (NoV GI and GII) and F-specific RNA bacteriophages belonging to the genogroup -I and -II (FRNAPH-I and -II). Total infectious FRNAPH were also monitored. D. polymorpha was able to translate a wide range of concentrations for all the viral targets studied, meaning that this sentinel species can be used for both low and highly anthropised sites. Moreover, D. polymorpha caging proved effective in achieving gradients of viral contamination of fecal origin pressure and to highlight the contribution of tributaries to the main rivers. D. polymorpha provided spatial and temporal variations of the viral contamination. It allowed to highlight the prevalence of the FRNAPH-I and -II genogroups according to the caging site. FRNAPH-II was found to be dominant in urban areas and FRNAPH-I in rural areas. This strategy uses the caging of the sentinel species D. polymorpha on selected sites with standardised analysis methods has proven to be a promising tool for characterizing viral contamination at both large and very fine scales.

Spatio-temporal evaluation of metals and metalloids in the water of high Andean livestock micro-watersheds, Amazonas, Peru.

Cattle ranching is a fundamental economic activity in northern Peru, where proper management of water resources is crucial. This study, a pioneer in the region, evaluated water quality and its suitability for human consumption, vegetable irrigation, and livestock production. It is also the first study to document the presence of metals and metalloids in vulnerable areas because they are located at the headwaters of river watersheds. The spatiotemporal evaluation of physicochemical parameters, metals, and metalloids was performed in five micro-watersheds (Cabildo, Timbambo, Pomacochas, Atuen, and Ventilla) from water samples collected in the dry season (October 2017) and wet season (March 2018). The parameters were analyzed using microwave plasma atomic emission spectrometry. The results were contrasted with international and Peruvian quality standards related to dairy cow production. The highest values of pH, total dissolved solids, and electrical conductivity were reported during the dry season, and the highest turbidity during the wet season. Of the metals evaluated, arsenic (As) was omnipresent in all the micro-watersheds, followed by lead (Pb). In contrast to World Health Organization regulations, concentrations of As, cadmium (Cd), Pb, and iron represent a risk; according to Peruvian regulations, As and Pb exceed the concentrations established for use in animal drinking water and vegetable irrigation, and according to water guidelines for dairy cattle, concentrations of As, Pb, Cd, and Al exceed the permitted limits. The high concentrations of these metals in the study area are attributable to a synergy between natural factors, such as Andean geology and livestock activity. The data reported will allow for proper water resource management, pollution prevention, and the design and adoption of mitigation measures.

Multi-habitat distribution and coalescence of resistomes at the watershed scale based on metagenomics.

The characteristics of the resistome distribution in rivers have been extensively studied. However, the distribution patterns of resistomes in multiple habitats and contributions of upstream habitats to the resistome profile in water bodies remains unclear. The current study explored the distribution and coalescence of antibiotic resistance genes (ARGs), metal resistance genes (MRGs), and mobile genetic elements (MGEs) in four habitats (including water bodies, sediments, biofilms, and riparian soils) within the Shichuan River watershed. The results revealed significant variations in the abundances and diversity of resistomes across the four habitats and two seasons. Assembly processes of resistomes were predominated by stochastic processes in summer but deterministic processes in winter. The main source of the resistome in summer water bodies was the movement of genes from upstream water bodies. However, the main sources of resistome in downstream water bodies in winter were the movement of resistomes in upstream sediments and the input of external pollution. The physicochemical properties of winter water bodies significantly influenced the movement of the resistomes across habitats. The current study elucidated the multi-habitat distribution pattern and migration mechanism of the resistome in the river system, providing new insights for effectively monitoring and controlling bacterial resistance.

Wildfires Influence Mercury Transport, Methylation, and Bioaccumulation in Headwater Streams of the Pacific Northwest.

The increasing frequency and severity of wildfires are among the most visible impacts of climate change. However, the effects of wildfires on mercury (Hg) transformations and bioaccumulation in stream ecosystems are poorly understood. We sampled soils, water, sediment, in-stream leaf litter, periphyton, and aquatic invertebrates in 36 burned (one-year post fire) and 21 reference headwater streams across the northwestern U.S. to evaluate the effects of wildfire occurrence and severity on total Hg (THg) and methylmercury (MeHg) transport and bioaccumulation. Suspended particulate THg and MeHg concentrations were 89 and 178% greater in burned watersheds compared to unburned watersheds and increased with burn severity, likely associated with increased soil erosion. Concentrations of filter-passing THg were similar in burned and unburned watersheds, but filter-passing MeHg was 51% greater in burned watersheds, and suspended particles in burned watersheds were enriched in MeHg but not THg, suggesting higher MeHg production in burned watersheds. Among invertebrates, MeHg in grazers, filter-feeders, and collectors was 33, 48, and 251% greater in burned watersheds, respectively, but did not differ in shredders or predators. Thus, increasing wildfire frequency and severity may yield increased MeHg production, mobilization, and bioaccumulation in headwaters and increased transport of particulate THg and MeHg to downstream environments.

Agricultural watershed conservation and optimization using a participatory hydrological approach.

Maximizing the impact of agricultural wastewater conservation practices (CP) to achieve total maximum daily load (TMDL) scenarios in agricultural watersheds is a challenge for the practitioners. The complex modeling requirements of sophisticated hydrologic models make their use and interpretation difficult, preventing the inclusion of local watershed stakeholders' knowledge in the development of optimal TMDL scenarios. The present study develops a seamless modeling approach to transform the complex modeling outcomes of Hydrologic Simulation Program Fortran (HSPF) into a simplified participatory framework for developing optimized management scenarios. The study evaluates seven conservation practices in the Pomme de Terre watershed in Minnesota, USA, focusing on sediment and phosphorus pollutant load reductions incorporating farmers' opinions to guide practitioners toward implementing cost-effective CPs. Results show reduced tillage and filter strips are the most cost-effective practices for non-point source pollution reduction, followed by conservation cover perennials. The integration of SAM with HSPF is crucial for sustainable field-scale implementation of conservation practices through enhanced involvement of amateur-modeling stakeholders and farmers directly connected to fields.