Water最新文献

{"title":"Monitoring and Assessment of the Quality of Chlorinated Water in Schools","authors":"Vanessa Stein, A. Galvão, A. T. Paulino, Alex Molina Manfredi, Edmar Martendal, Tainara Vieira, W. S. Robazza","doi":"10.3390/w16101415","DOIUrl":"https://doi.org/10.3390/w16101415","url":null,"abstract":"All water destined for human consumption must be subjected to disinfection processes via chlorination with the aim of eliminating the risk of the transmission of waterborne diseases. However, the treatment of water using this method leads to the formation of trihalomethanes (THMs), which are toxic compounds that may be ingested, inhaled, or absorbed by the skin, increasing the risk of carcinogenic and mutagenic processes. High trihalomethane concentrations in water may be directly related to physicochemical properties, such as temperature, pH, hardness, and potentially toxic metal concentrations. In this work, physicochemical characterization was performed and water quality was assessed with regard to the presence of trihalomethanes in 17 samples collected from points before and after storage in reservoirs. Atomic absorption spectroscopy to determine potentially toxic metals and the measurement of physicochemical properties demonstrated that all results were in accordance with the standards established by Brazilian legislation. Gas chromatography–mass spectrometry was used to determine levels of trihalomethanes in the water samples (trichloromethane, bromodichloromethane, chlorodibromomethane, and bromoform), which were also in accordance with the limits established by Brazilian legislation. Hierarchical clustering on principal components confirmed changes in the water quality depending on location. This work constitutes a paradigm for future studies on the monitoring of toxic organic compounds in water to avoid health problems in humans and animals.","PeriodicalId":509004,"journal":{"name":"Water","volume":"14 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140969015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal Variation, Meteorological Driving Factors, and Statistical Models Study of Lake Surface Area in the Yellow River Basin","authors":"Li Tang, Xiaohui Sun","doi":"10.3390/w16101424","DOIUrl":"https://doi.org/10.3390/w16101424","url":null,"abstract":"The surface area changes of 151 natural lakes over 37 months in the Yellow River Basin, based on remote sensing data and 21 meteorological indicators, employing spatial distribution feature analysis, principal component analysis (PCA), correlation analysis, and multiple regression analysis, identify key meteorological factors influencing these variations and their interrelationships. During the study period, lake area averages were from 0.009 km2 to 506.497 km2, with standard deviations ranging from 0.003 km2 to 184.372 km2. The coefficient of variation spans from 3.043 to 217.436, indicating considerable variability in lake area stability. Six primary meteorological factors were determined to have a significant impact on lake surface area fluctuations: 24 h precipitation, maximum daily precipitation, hours of sunshine, maximum wind speed, minimum relative humidity, and lakes in the source region of the Yellow River generally showed a significant positive correlation. For maximum wind speed (m/s), 28 lakes showed significant correlations, with five positive and twenty-three negative correlations, correlation coefficients ranging from −0.34 to −0.63, average −0.47, indicating an overall negative correlation between lake surface area and maximum wind speed. For maximum daily precipitation (mm), 36 lakes had 21 showing a positive correlation, indicating a positive correlation between lake surface area and daily precipitation in larger lakes. Furthermore, of the 117 lakes with sufficient data to model, the predictive capabilities of various models for lake surface area changes showcased distinct advantages, with the random forest model outperforming others in a dataset of 65 lakes, Ridge regression is best for 28 lakes, Lasso regression performs best for 20 lakes, Linear model is only best for 4 cases. The random forest model provides the best fit due to its ability to handle a large number of feature variables and consider their interactions, thereby offering the best fitting effect. These insights are crucial for understanding the influence of meteorological factors on lake surface area changes within the Yellow River Basin and are instrumental in developing predictive models based on meteorological data.","PeriodicalId":509004,"journal":{"name":"Water","volume":"16 22","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140966768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Slope Gradient Effects on Sediment Yield of Different Land Cover and Soil Types","authors":"Yu War Nang, Shin-ichi Onodera, Kunyang Wang, Y. Shimizu, Mitsuyo Saito","doi":"10.3390/w16101419","DOIUrl":"https://doi.org/10.3390/w16101419","url":null,"abstract":"Water majorly contributes to soil erosion. Considering Japan’s humid and rainy climate, severe soil erosion challenges persist even though forests are the country’s dominant land type. Although numerous studies have emphasized the impact of factors such as land use, soil type, and slope steepness on sediment yield, the synergetic effects of slope gradient with varying land cover and soil types are underexplored. Herein, we used the Soil and Water Assessment Tool (SWAT) on a steep catchment to identify high sediment yield areas—as well as factors influencing high sediment yield—and evaluate the effect of slope gradient on the sediment yield of different land cover and soil types. The findings reveal an average annual sediment yield of 0.55 tons ha−1 yr−1 in the Takahashi catchment, with yields tripling in some western subbasins under heavy rainfall. Furthermore, the slope gradient effect is most considerable in bare land, agriculture, and rice land cover, with the average sediment yield of bare land resulting in 2.2 tons ha−1 yr−1 at slope > 45%. Meanwhile, deciduous forests on steep slopes exhibit extreme sediment yield, peaking at 7.2 tons ha−1 yr−1 at slope > 45%. The regosol soil type has one of the highest sediment yield variations in all soil types due to slope gradient.","PeriodicalId":509004,"journal":{"name":"Water","volume":"52 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140970795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Oil–Water Flow Prediction in Heterogeneous Porous Media Using Machine Learning","authors":"Gaocheng Feng, Kai Zhang, Huan Wan, Weiying Yao, Yuande Zuo, Jingqi Lin, Piyang Liu, Liming Zhang, Yongfei Yang, Jun Yao, Ang Li, Chen Liu","doi":"10.3390/w16101411","DOIUrl":"https://doi.org/10.3390/w16101411","url":null,"abstract":"The rapid and accurate forecasting of two-phase flow in porous media is a critical challenge in oil field development, exerting a substantial impact on optimization and decision-making processes. Although the Convolutional Long Short-Term Memory (ConvLSTM) network effectively captures spatiotemporal dynamics, its generalization in predicting complex engineering problems remains limited. Similarly, although the Fourier Neural Operator (FNO) demonstrates adeptness at learning operators for solving partial differential equations (PDEs), it struggles with three-dimensional, long-term prediction. In response to these limitations, we introduce an innovative hybrid model, the Convolutional Long Short-Term Memory-Fourier Neural Operator (CL-FNO), specifically designed for the long-term prediction of three-dimensional two-phase flows. This model integrates a 3D convolutional encoder–decoder structure to extract and generate hierarchical spatial features of the flow fields. It incorporates physical constraints to enhance the model’s forecasts with robustness through the infusion of prior knowledge. Additionally, a temporal function, constructed using gated memory-forgetting mechanisms, augments the model’s capacity to analyze time series data. The efficacy and practicality of the CL-FNO model are validated using a synthetic three-dimensional case study and application to an actual reservoir model.","PeriodicalId":509004,"journal":{"name":"Water","volume":"35 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140971067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Endogenous Processes in Water Supply Systems: Insights from Statistical Methods and δ18O Analysis","authors":"Nikolina Novotni-Horčička, Tamara Marković, Ivan Kovač, Igor Karlovic","doi":"10.3390/w16101425","DOIUrl":"https://doi.org/10.3390/w16101425","url":null,"abstract":"Water used for water supply undergoes numerous changes that affect its composition prior to entering the water supply system (WSS). Once it enters the WSS, it is subject to numerous influences altering its physical and chemical composition, redox potential, and microbial quality. Observations of water quality parameters at different locations within the WSS indicate that it is justified to assume that these processes take place from the source to the end user. In this study, we used the results of routine everyday analyses (EC, T, pH, ORP, chloride, nitrate, nitrite, ammonium, and bacteria) supplemented by experimental data from a one-year sampling campaign assessing the main cations and anions and stable isotopes δ2H and δ18O. Through these data, the statistical significance of the differences between the concentrations of the basic water quality parameters among different WSS locations was determined, together with the water retention time in the system. The results indicate minor changes in water chemical composition within the observed WSS, remaining below the prescribed Maximum Contaminant Level (MCL) for human consumption. However, factors such as water retention time, CaCO3 deposition, pH fluctuations, and bacterial growth may influence its suitability, which necessitates further investigation into potential risks affecting water quality.","PeriodicalId":509004,"journal":{"name":"Water","volume":"34 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140971255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of 9 Years of Swine Wastewater Application on the Mineral and Organic Quality of Soil in Various Agricultural Crops","authors":"A. P. C. Maciel, G. Medeiros, Amanda de Souza Machado, Maria Clara Pilatti, R. R. dos Reis, S. C. Sampaio","doi":"10.3390/w16101412","DOIUrl":"https://doi.org/10.3390/w16101412","url":null,"abstract":"This study evaluates the long-term effects of swine wastewater (SWW) on relevant parameters for soil fertility, including calcium (Ca), magnesium (Mg), potassium (K) cations, cation exchange capacity (CEC), and organic matter (OM) in an agricultural area with 9 years of crop cultivation. Three types of SWW (raw, after leaving the biodigester, and after the manure plant) were analyzed with four application rates of SWW (0, 100, 200, and 300 m3.ha−1), associated or not with mineral fertilization, resulting in eight treatments. The study found that the long-term use of SWW had significant effects on soil parameters. Principal component analysis (PCA) was used to summarize the data. The soil’s calcium (Ca), magnesium (Mg), and cation exchange capacity (CEC) levels were higher in soybean compared to other crops and natural soil. Similarly, the treatment with 0 m3.ha−1 of pig manure and without mineral fertilization showed higher levels of these nutrients. In contrast, potassium (K) was found in greater quantities in oats, SWW from the biodigester, higher doses of manure, and with mineral fertilization. The crops had a higher organic matter (OM) content compared to the natural soil, with corn and raw SWW showing the most significant increase.","PeriodicalId":509004,"journal":{"name":"Water","volume":"18 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140969543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study of the Bunsen–Roscoe Reciprocity Law in Solar Water Disinfection (Optical Effect) for E. coli, E. faecalis and C. perfringens","authors":"Julia Torres, Ana María Palacios, Manuel Fuentes, M. Vivar","doi":"10.3390/w16101406","DOIUrl":"https://doi.org/10.3390/w16101406","url":null,"abstract":"Water stress and water quality represent major environmental challenges in the 21st century. In response, wastewater management and its potential reuse emerge as strategies to mitigate these problems. This research aims to verify the law of reciprocity in the solar disinfection process of real secondary wastewater effluents for different faecal microorganisms. Flat disinfection reactors, subjected only to natural and continuous UV radiation, were used. The study focused on the optical effect of UV radiation, eliminating the significant influence of the thermal effect and its synergy in solar disinfection at temperatures above 45 °C, by controlling the temperatures of the water samples to levels below 20 °C. Three experimental tests were carried out on sunny days. Each test comprised two trials, under the following conditions: (a) low solar irradiance over a prolonged time (duration approximately: 2.6 h) and (b) high solar irradiance and a shorter period of time (approximately 2 h), with each receiving the same UV dose. Inactivation kinetics was analysed for E. coli, E. faecalis, and C. perfringens (including spores). The results validated the reciprocity law for E. coli in all tests for UV doses > 20 Wh/m2, showing no significant deviations, with inactivation rates of 0.44 to 0.51 m2/Wh for initial concentrations of 106–107 CFU/100 mL. In contrast, for E. faecalis, the reciprocity was only valid at intensities < 700 W/m2, with rates of 0.04 and 0.035 m2/Wh for 105–106 CFU/100 mL; above this irradiance value, the law varied significantly and was not valid. C. perfringens did not show significant disinfection results during the experiments to verify this law, mainly due to the resistance of its spores. Additional experimentation with C. perfringens is necessary, by extending the length of the experiments and/or conducting them at higher irradiance values, in order to reach bacterial inactivation to enable the analysis of the reciprocity law. In general, the main conclusion from these results is that the reciprocity law in solar disinfection would be difficult to use for the estimation of water solar disinfection based on the irradiance and exposure times, as there are deviations from it at least in one specie (E. faecalis). Mores studies should be carried out to fully understand and determine the validity of this law and its potential application for forecasting solar water disinfection.","PeriodicalId":509004,"journal":{"name":"Water","volume":"5 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Review of Methods for Hydrological Forecasting Based on Deep Learning","authors":"Xinfeng Zhao, Hongyan Wang, Mingyu Bai, Yingjie Xu, Shengwen Dong, Hui Rao, Wuyi Ming","doi":"10.3390/w16101407","DOIUrl":"https://doi.org/10.3390/w16101407","url":null,"abstract":"Artificial intelligence has undergone rapid development in the last thirty years and has been widely used in the fields of materials, new energy, medicine, and engineering. Similarly, a growing area of research is the use of deep learning (DL) methods in connection with hydrological time series to better comprehend and expose the changing rules in these time series. Consequently, we provide a review of the latest advancements in employing DL techniques for hydrological forecasting. First, we examine the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in hydrological forecasting, along with a comparison between them. Second, a comparison is made between the basic and enhanced long short-term memory (LSTM) methods for hydrological forecasting, analyzing their improvements, prediction accuracies, and computational costs. Third, the performance of GRUs, along with other models including generative adversarial networks (GANs), residual networks (ResNets), and graph neural networks (GNNs), is estimated for hydrological forecasting. Finally, this paper discusses the benefits and challenges associated with hydrological forecasting using DL techniques, including CNN, RNN, LSTM, GAN, ResNet, and GNN models. Additionally, it outlines the key issues that need to be addressed in the future.","PeriodicalId":509004,"journal":{"name":"Water","volume":"10 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140974913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in the Management of Solid Waste and Wastewater Treatment","authors":"Carlos Costa","doi":"10.3390/w16101404","DOIUrl":"https://doi.org/10.3390/w16101404","url":null,"abstract":"The management of solid waste and wastewater treatment is an ever-pressing concern for countries around the world [...]","PeriodicalId":509004,"journal":{"name":"Water","volume":"19 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140974009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Study on Near-Wall Laser-Induced Cavitation Bubble Micro-Dimple Formation on 7050 Aluminum Alloy","authors":"Yupeng Cao, Ranran Hu, Weidong Shi, Rui Zhou","doi":"10.3390/w16101410","DOIUrl":"https://doi.org/10.3390/w16101410","url":null,"abstract":"To investigate the feasibility and formation laws of fabricating micro-dimples induced by near-wall laser-induced cavitation bubble (LICB) on 7050 aluminum alloy. A high-speed camera and a fiber-optic hydrophone system were used to capture pulsation evolution images and acoustic signals of LICB. Meanwhile, a three-dimensional profilometer was employed to examine the contour morphology of the surface micro-dimple on the specimen. The results show that at an energy level of 500 mJ, the total pulsation period for the empty bubble is 795 μs, with individual pulsation periods of 412.5 μs, 217 μs, and 165 μs for the first, second, and third cycles, respectively, with most energy of the laser and bubble being consumed during the first evolution period. Under the synergy of the plasma shock wave and collapse shock wave, a spherical dimple with a diameter of 450 μm is formed on the sample surface with copper foil as the absorption layer. A model of micro-dimple formed by LICB impact is established. As the energy increases, the depth of the surface micro-dimple peaks at an energy of 400 mJ and then decreases. The depth of the surface micro-dimple increases with the increase in the number of impacts; the optimal technology parameters for the micro-dimple formation by LICB impact are as follows: the absorption layer is copper foil, the energy is 400 mJ, and the number of impacts is three.","PeriodicalId":509004,"journal":{"name":"Water","volume":"56 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}