Showing papers in "Water in 2020"

Role of Nanomaterials in the Treatment of Wastewater: A Review

Abstract: Water is an essential part of life and its availability is important for all living creatures. On the other side, the world is suffering from a major problem of drinking water. There are several gases, microorganisms and other toxins (chemicals and heavy metals) added into water during rain, flowing water, etc. which is responsible for water pollution. This review article describes various applications of nanomaterial in removing different types of impurities from polluted water. There are various kinds of nanomaterials, which carried huge potential to treat polluted water (containing metal toxin substance, different organic and inorganic impurities) very effectively due to their unique properties like greater surface area, able to work at low concentration, etc. The nanostructured catalytic membranes, nanosorbents and nanophotocatalyst based approaches to remove pollutants from wastewater are eco-friendly and efficient, but they require more energy, more investment in order to purify the wastewater. There are many challenges and issues of wastewater treatment. Some precautions are also required to keep away from ecological and health issues. New modern equipment for wastewater treatment should be flexible, low cost and efficient for the commercialization purpose.

Adsorption of Methylene Blue in Water onto Activated Carbon by Surfactant Modification

Abstract: In this paper, the enhanced adsorption of methylene blue (MB) dye ion on the activated carbon (AC) modified by three surfactants in aqueous solution was researched. Anionic surfactants—sodium lauryl sulfate (SLS) and sodium dodecyl sulfonate (SDS)—and cationic surfactant—hexadecyl trimethyl ammonium bromide (CTAB)—were used for the modification of AC. This work showed that the adsorption performance of cationic dye by activated carbon modified by anionic surfactants (SLS) was significantly improved, whereas the adsorption performance of cationic dye by activated carbon modified by cationic surfactant (CTAB) was reduced. In addition, the effects of initial MB concentration, AC dosage, pH, reaction time, temperature, real water samples, and additive salts on the adsorption were studied. When Na+, K+, Ca2+, NH4+, and Mg2+ were present in the MB dye solution, the effect of these cations was negligible on the adsorption (<5%). The presence of NO2- improved the adsorption performance significantly, whereas the removal rate of MB was reduced in the presence of competitive cation (Fe2+). It was found that the isotherm data had a good correlation with the Langmuir isotherm through analyzing the experimental data by various models. The dynamics of adsorption were better described by the pseudo-second-order model and the adsorption process was endothermic and spontaneous. The results showed that AC modified by anionic surfactant was effective for the adsorption of MB dye in both modeling water and real water.

Characteristics, main impacts, and stewardship of natural and artificial freshwater environments: consequences for biodiversity conservation

Abstract: In this overview (introductory article to a special issue including 14 papers), we consider all main types of natural and artificial inland freshwater habitas (fwh). For each type, we identify the main biodiversity patterns and ecological features, human impacts on the system and environmental issues, and discuss ways to use this information to improve stewardship. Examples of selected key biodiversity/ecological features (habitat type): narrow endemics, sensitive (groundwater and GDEs); crenobionts, LIHRes (springs); unidirectional flow, nutrient spiraling (streams); naturally turbid, floodplains, large-bodied species (large rivers); depth-variation in benthic communities (lakes); endemism and diversity (ancient lakes); threatened, sensitive species (oxbow lakes, SWE); diverse, reduced littoral (reservoirs); cold-adapted species (Boreal and Arctic fwh); endemism, depauperate (Antarctic fwh); flood pulse, intermittent wetlands, biggest river basins (tropical fwh); variable hydrologic regime—periods of drying, flash floods (arid-climate fwh). Selected impacts: eutrophication and other pollution, hydrologic modifications, overexploitation, habitat destruction, invasive species, salinization. Climate change is a threat multiplier, and it is important to quantify resistance, resilience, and recovery to assess the strategic role of the different types of freshwater ecosystems and their value for biodiversity conservation. Effective conservation solutions are dependent on an understanding of connectivity between different freshwater ecosystems (including related terrestrial, coastal and marine systems).

Antimicrobials and Antibiotic-Resistant Bacteria: A Risk to the Environment and to Public Health

Abstract: The release of antibiotics to the environment, and the consequences of the presence of persistent antimicrobial residues in ecosystems, have been the subject of numerous studies in all parts of the world. The overuse and misuse of antibiotics is a common global phenomenon, which substantially increases the levels of antibiotics in the environment and the rates of their spread. Today, it can be said with certainty that the mass production and use of antibiotics for purposes other than medical treatment has an impact on both the environment and human health. This review aims to track the pathways of the environmental distribution of antimicrobials and identify the biological effects of their subinhibitory concentration in different environmental compartments; it also assesses the associated public health risk and government policy interventions needed to ensure the effectiveness of existing antimicrobials. The recent surge in interest in this issue has been driven by the dramatic increase in the number of infections caused by drug-resistant bacteria worldwide. Our study is in line with the global One Health approach.

Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting

Abstract: Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water resources. In this research, daily streamflow to the Ermenek hydroelectric dam reservoir located in Turkey is simulated using deep recurrent neural network (RNN) architectures, including bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural networks (simple RNN). For this purpose, daily observational flow data are used during the period 2012–2018, and all models are coded in Python software programming language. Only delays of streamflow time series are used as the input of models. Then, based on the correlation coefficient (CC), mean absolute error (MAE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NS), results of deep-learning architectures are compared with one another and with an artificial neural network (ANN) with two hidden layers. Results indicate that the accuracy of deep-learning RNN methods are better and more accurate than ANN. Among methods used in deep learning, the LSTM method has the best accuracy, namely, the simulated streamflow to the dam reservoir with 90% accuracy in the training stage and 87% accuracy in the testing stage. However, the accuracies of ANN in training and testing stages are 86% and 85%, respectively. Considering that the Ermenek Dam is used for hydroelectric purposes and energy production, modeling inflow in the most realistic way may lead to an increase in energy production and income by optimizing water management. Hence, multi-percentage improvements can be extremely useful. According to results, deep-learning methods of RNNs can be used for estimating streamflow to the Ermenek Dam reservoir due to their accuracy.

GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment

Abstract: Flash floods are one of the most devastating natural hazards; they occur within a catchment (region) where the response time of the drainage basin is short. Identification of probable flash flood l ...

Research Trends in the Use of Remote Sensing for Inland Water Quality Science: Moving Towards Multidisciplinary Applications

Abstract: Remote sensing approaches to measuring inland water quality date back nearly 50 years to the beginning of the satellite era. Over this time span, hundreds of peer-reviewed publications have demonstrated promising remote sensing models to estimate biological, chemical, and physical properties of inland waterbodies. Until recently, most of these publications focused largely on algorithm development as opposed to implementation of those algorithms to address specific science questions. This slow evolution contrasts with terrestrial and oceanic remote sensing, where methods development in the 1970s led to publications focused on understanding spatially expansive, complex processes as early as the mid-1980s. This review explores the progression of inland water quality remote sensing from methodological development to scientific applications. We use bibliometric analysis to assess overall patterns in the field and subsequently examine 236 key papers to identify trends in research focus and scale. The results highlight an initial 30 year period where the majority of publications focused on model development and validation followed by a spike in publications, beginning in the early-2000s, applying remote sensing models to analyze spatiotemporal trends, drivers, and impacts of changing water quality on ecosystems and human populations. Recent and emerging resources, including improved data availability and enhanced processing platforms, are enabling researchers to address challenging science questions and model spatiotemporally explicit patterns in water quality. Examination of the literature shows that the past 10–15 years has brought about a focal shift within the field, where researchers are using improved computing resources, datasets, and operational remote sensing algorithms to better understand complex inland water systems. Future satellite missions promise to continue these improvements by providing observational continuity with spatial/spectral resolutions ideal for inland waters.

Heavy Metals in Wastewater and Sewage Sludge from Selected Municipal Treatment Plants in Eastern Cape Province, South Africa

Abstract: This study assessed the distribution of five heavy metals (Cd, Pb, Cu, Zn, and Fe) across the various stages of treatment in three selected sewage treatment facilities and their receiving waterbodies in the Eastern Cape Province, South Africa. Aqueous and solid (sludge) samples were collected monthly from September 2015 to February 2016. Quantitation was achieved by atomic absorption spectrometry after necessary sample preparations. Concentrations of heavy metal cations in the sludge generally varied from

Urban Water Demand Prediction for a City That Suffers from Climate Change and Population Growth: Gauteng Province Case Study

Abstract: The proper management of a municipal water system is essential to sustain cities and support the water security of societies. Urban water estimating has always been a challenging task for managers of water utilities and policymakers. This paper applies a novel methodology that includes data pre-processing and an Artificial Neural Network (ANN) optimized with the Backtracking Search Algorithm (BSA-ANN) to estimate monthly water demand in relation to previous water consumption. Historical data of monthly water consumption in the Gauteng Province, South Africa, for the period 2007–2016, were selected for the creation and evaluation of the methodology. Data pre-processing techniques played a crucial role in the enhancing of the quality of the data before creating the prediction model. The BSA-ANN model yielded the best result with a root mean square error and a coefficient of efficiency of 0.0099 mega liters and 0.979, respectively. Moreover, it proved more efficient and reliable than the Crow Search Algorithm (CSA-ANN), based on the scale of error. Overall, this paper presents a new application for the hybrid model BSA-ANN that can be successfully used to predict water demand with high accuracy, in a city that heavily suffers from the impact of climate change and population growth.

Microplastics and Nanoplastics in the Freshwater and Terrestrial Environment: A Review

Abstract: This review is a critical analysis of current freshwater and terrestrial research with an emphasis on transport, behaviour, fate and subsequent ecological impacts that plastic pollution poses. The current methods of extraction and evaluation of organic-rich samples are also explored for both micro- and nanoplastics. Furthermore, micro- and nanoplastics are discussed with reference to their environmental and health implications for biota. Regulations imposed on the manufacture and distribution of plastics globally are also noted. Within the review, the current literature has been presented and knowledge gaps identified. These include the characterization and quantification of micro- and nanoplastics entering and forming within the freshwater and terrestrial environment, the fate and behaviour of micro- and nanoplastics under varying conditions and the impacts of micro- and nanoplastics on freshwater and terrestrial ecosystems.

Massive Influx of Pelagic Sargassum spp. on the Coasts of the Mexican Caribbean 2014–2020: Challenges and Opportunities

Abstract: Since late 2014, the Mexican Caribbean coast has periodically received massive, atypical influxes of pelagic Sargassum spp. (sargasso). Negative impacts associated with these influxes include mortality of nearshore benthic flora and fauna, beach erosion, pollution, decreasing tourism and high management costs. To understand the dynamics of the sargasso influx, we used Landsat 8 imagery (from 2016 to mid-2020) to record the coverage of sargasso in the sea off the Mexican Caribbean coastline, with a maximum reported in September 2018. Satellite image analysis also showed local differences in the quantity of beached sargasso along the coastline. Over the years, good practice for collection on the beach and for off-shore collection of sargasso have been established through trial and error, and the Mexican Government and hotel industry have spent millions of dollars on removal and off-shore detention of sargasso. Notwithstanding, sargasso also has various properties that could be harnessed in local industries. The stimulation of local industrial growth would offer alternatives to the dependence on tourism, as a circular economy, based on sargasso, is developed.

Hybridised Artificial Neural Network Model with Slime Mould Algorithm: A Novel Methodology for Prediction of Urban Stochastic Water Demand

Abstract: Urban water demand prediction based on climate change is always challenging for water utilities because of the uncertainty that results from a sudden rise in water demand due to stochastic patterns of climatic factors. For this purpose, a novel combined methodology including, firstly, data pre-processing techniques were employed to decompose the time series of water and climatic factors by using empirical mode decomposition and identifying the best model input via tolerance to avoid multi-collinearity. Second, the artificial neural network (ANN) model was optimised by an up-to-date slime mould algorithm (SMA-ANN) to predict the medium term of the stochastic signal of monthly urban water demand. Ten climatic factors over 16 years were used to simulate the stochastic signal of water demand. The results reveal that SMA outperforms a multi-verse optimiser and backtracking search algorithm based on error scale. The performance of the hybrid model SMA-ANN is better than ANN (stand-alone) based on the range of statistical criteria. Generally, this methodology yields accurate results with a coefficient of determination of 0.9 and a mean absolute relative error of 0.001. This study can assist local water managers to efficiently manage the present water system and plan extensions to accommodate the increasing water demand.

Biochar as an Eco-Friendly and Economical Adsorbent for the Removal of Colorants (Dyes) from Aqueous Environment: A Review

Abstract: Dyes (colorants) are used in many industrial applications, and effluents of several industries contain toxic dyes. Dyes exhibit toxicity to humans, aquatic organisms, and the environment. Therefore, dyes containing wastewater must be properly treated before discharging to the surrounding water bodies. Among several water treatment technologies, adsorption is the most preferred technique to sequester dyes from water bodies. Many studies have reported the removal of dyes from wastewater using biochar produced from different biomass, e.g., algae and plant biomass, forest, and domestic residues, animal waste, sewage sludge, etc. The aim of this review is to provide an overview of the application of biochar as an eco-friendly and economical adsorbent to remove toxic colorants (dyes) from the aqueous environment. This review highlights the routes of biochar production, such as hydrothermal carbonization, pyrolysis, and hydrothermal liquefaction. Biochar as an adsorbent possesses numerous advantages, such as being eco-friendly, low-cost, and easy to use; various precursors are available in abundance to be converted into biochar, it also has recyclability potential and higher adsorption capacity than other conventional adsorbents. From the literature review, it is clear that biochar is a vital candidate for removal of dyes from wastewater with adsorption capacity of above 80%.

An integrative information aqueduct to close the gaps between satellite observation of water cycle and local sustainable management of water resources

Abstract: The past decades have seen rapid advancements in space-based monitoring of essential water cycle variables, providing products related to precipitation, evapotranspiration, and soil moisture, often at tens of kilometer scales. Whilst these data effectively characterize water cycle variability at regional to global scales, they are less suitable for sustainable management of local water resources, which needs detailed information to represent the spatial heterogeneity of soil and vegetation. The following questions are critical to effectively exploit information from remotely sensed and in situ Earth observations (EOs): How to downscale the global water cycle products to the local scale using multiple sources and scales of EO data? How to explore and apply the downscaled information at the management level for a better understanding of soil-water-vegetation-energy processes? How can such fine-scale information be used to improve the management of soil and water resources? An integrative information flow (i.e., iAqueduct theoretical framework) is developed to close the gaps between satellite water cycle products and local information necessary for sustainable management of water resources. The integrated iAqueduct framework aims to address the abovementioned scientific questions by combining medium-resolution (10 m–1 km) Copernicus satellite data with high-resolution (cm) unmanned aerial system (UAS) data, in situ observations, analytical- and physical-based models, as well as big-data analytics with machine learning algorithms. This paper provides a general overview of the iAqueduct theoretical framework and introduces some preliminary results.

Comparative Analysis of Drought Indicated by the SPI and SPEI at Various Timescales in Inner Mongolia, China

Abstract: The global climate is noticeably warming, and drought occurs frequently. Therefore, choosing a suitable index for drought monitoring is particularly important. The standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) are commonly used indicators in drought monitoring. The SPEI takes temperature into account, but the SPI does not. In the context of global warming, what are their differences and applicability in regional drought monitoring? In this study, after calculating the SPI and SPEI at 1-, 3-, 6-, and 12-month timescales at 102 meteorological stations in Inner Mongolia from 1981 to 2018, we compared and analyzed the performances of the SPI and SPEI in drought monitoring from temporal and spatial variations, and the consistency and applicability of the SPI and SPEI were also discussed. The results showed that (1) with increasing timescale, the temporal variations in the SPI and SPEI were increasingly consistent, but there were still slight differences in the fluctuation value and continuity; (2) due to the difference in time series, the drought characteristics identified by the SPI and SPEI were quite different in space at various timescales, and with the increase in timescale, the spatial distributions of the drought trends in Inner Mongolia were basically consistent, except in Alxa; (3) at the shortest timescale, the difference between the SPI and SPEI was the largest, and the drought reflected by the SPI and SPEI may be consistent at long timescales; and (4) compared with typical drought events and vegetation indexes, the SPEI may be more suitable than the SPI for drought monitoring in Inner Mongolia. It should be noted that the adaptability of the SPI and SPEI may be different in different periods and regions, which remains to be analyzed in the future.

Treatment of Wastewaters by Microalgae and the Potential Applications of the Produced Biomass—A Review

Abstract: The treatment of different types of wastewater by physicochemical or biological (non-microalgal) methods could often be either inefficient or energy-intensive. Microalgae are ubiquitous microscopic organisms, which thrive in water bodies that contain the necessary nutrients. Wastewaters are typically contaminated with nitrogen, phosphorus, and other trace elements, which microalgae require for their cell growth. In addition, most of the microalgae are photosynthetic in nature, and these organisms do not require an organic source for their proliferation, although some strains could utilize organics both in the presence and absence of light. Therefore, microalgal bioremediation could be integrated with existing treatment methods or adopted as the single biological method for efficiently treating wastewater. This review paper summarized the mechanisms of pollutants removal by microalgae, microalgal bioremediation potential of different types of wastewaters, the potential application of wastewater-grown microalgal biomass, existing challenges, and the future direction of microalgal application in wastewater treatment.

Constructed Wetlands for Sustainable Wastewater Treatment in Hot and Arid Climates: Opportunities, Challenges and Case Studies in the Middle East

Abstract: Many countries and regions around the world are facing a continuously growing pressure on their limited freshwater resources, particularly those under hot and arid climates. Higher water demand than availability led to over-abstraction and deterioration of the available freshwater resources’ quality. In this context, wastewater, if properly treated, can represent a new water source added in the local water balance, particularly in regions of Colorado, California, Australia, China and in the wide region of the Middle East, which is characterized as one of most water-stressed regions in the world. This article summarizes the status of wastewater treatment and management in the Middle East and discusses the challenges, the various barriers and also the opportunities that arise by introducing the sustainable technology of Constructed Wetlands in the region. Furthermore, the aim of the article is to provide a better insight into the possibility and feasibility of a wider implementation of this green technology under the hot and arid climate of Middle East by presenting several successful case studies of operating Constructed Wetlands facilities in the region for the treatment of various wastewater sources.

Mapping of Groundwater Potential Zones in Crystalline Terrain Using Remote Sensing, GIS Techniques, and Multicriteria Data Analysis (Case of the Ighrem Region, Western Anti-Atlas, Morocco)

Abstract: This research work is intended as a contribution to the development of a multicriteria methodology, combining several factors to control the availability of groundwater resources, in order to optimize the choice of location of future drilling and increase the chances to take water from productive structures which will satisfy the ever-increasing water demand of local population (Arghen basin in the Western Anti-Atlas chain of Morocco). The geographic information system is used to develop thematic maps describing the geometry and the hydrodynamic functioning of the aquifer. In this study, 11 factors including geology, topography, and hydrology, influencing the distribution of water resources were used. Based on the Analytical Hierarchy Process (AHP) model, GIS, and remote sensing, the study mapped and classified areas according to their hydrogeological potential. The favorable potential sectors cover 17% of the total area of the basin. The medium potential sectors account for 64%, while the unfavorable areas cover 18% of the basin area. The groundwater potential map of the study area has been validated by comparing with data from 159 boreholes scattered throughout the basin.

Worldwide Regulations and Guidelines for Agricultural Water Reuse: A Critical Review

Abstract: Water reuse is gaining momentum as a beneficial practice to address the water crisis, especially in the agricultural sector as the largest water consumer worldwide. With recent advancements in wastewater treatment technologies, it is possible to produce almost any water quality. However, the main human and environmental concerns are still to determine what constituents must be removed and to what extent. The main objectives of this study were to compile, evaluate, and compare the current agricultural water reuse regulations and guidelines worldwide, and identify the gaps. In total, 70 regulations and guidelines, including Environmental Protection Agency (EPA), International Organization for Standardization (ISO), Food and Agriculture Organization of the United Nations (FAO), World Health Organization (WHO), the United States (state by state), European Commission, Canada (all provinces), Australia, Mexico, Iran, Egypt, Tunisia, Jordan, Palestine, Oman, China, Kuwait, Israel, Saudi Arabia, France, Cyprus, Spain, Greece, Portugal, and Italy were investigated in this study. These regulations and guidelines were examined to compile a comprehensive database, including all of the water quality monitoring parameters, and necessary treatment processes. In summary, results showed that the regulations and guidelines are mainly human-health centered, insufficient regarding some of the potentially dangerous pollutants such as emerging constituents, and with large discrepancies when compared with each other. In addition, some of the important water quality parameters such as some of the pathogens, heavy metals, and salinity are only included in a small group of regulations and guidelines investigated in this study. Finally, specific treatment processes have been only mentioned in some of the regulations and guidelines, and with high levels of discrepancy.

Susceptibility Mapping of Soil Water Erosion Using Machine Learning Models

Abstract: Soil erosion is a serious threat to sustainable agriculture, food production, and environmental security. The advancement of accurate models for soil erosion susceptibility and hazard assessment is of utmost importance for enhancing mitigation policies and laws. This paper proposes novel machine learning (ML) models for the susceptibility mapping of the water erosion of soil. The weighted subspace random forest (WSRF), Gaussian process with a radial basis function kernel (Gaussprradial), and naive Bayes (NB) ML methods were used in the prediction of the soil erosion susceptibility. Data included 227 samples of erosion and non-erosion locations through field surveys to advance models of the spatial distribution using predictive factors. In this study, 19 effective factors of soil erosion were considered. The critical factors were selected using simulated annealing feature selection (SAFS). The critical factors included aspect, curvature, slope length, flow accumulation, rainfall erosivity factor, distance from the stream, drainage density, fault density, normalized difference vegetation index (NDVI), hydrologic soil group, soil texture, and lithology. The dataset cells of samples (70% for training and 30% for testing) were randomly prepared to assess the robustness of the different models. The functional relevance between soil erosion and effective factors was computed using the ML models. The ML models were evaluated using different metrics, including accuracy, the kappa coefficient, and the probability of detection (POD). The accuracies of the WSRF, Gaussprradial, and NB methods were 0.91, 0.88, and 0.85, respectively, for the testing data; 0.82, 0.76, and 0.71, respectively, for the kappa coefficient; and 0.94, 0.94, and 0.94, respectively, for POD. However, the ML models, especially the WSRF, had an acceptable performance regarding producing soil erosion susceptibility maps. Maps produced with the most robust models can be a useful tool for sustainable management, watershed conservation, and the reduction of soil and water loss.

Towards the Implementation of Circular Economy in the Wastewater Sector: Challenges and Opportunities

Abstract: The advancement of science has facilitated increase in the human lifespan, reflected in economic and population growth, which unfortunately leads to increased exploitation of resources. This situation entails not only depletion of resources, but also increases environmental pollution, mainly due to atmospheric emissions, wastewater effluents, and solid wastes. In this scenario, it is compulsory to adopt a paradigm change, as far as the consumption of resources by the population is concerned, to achieve a circular economy. The recovery and reuse of resources are key points, leading to a decrease in the consumption of raw materials, waste reduction, and improvement of energy efficiency. This is the reason why the concept of the circular economy can be applied in any industrial activity, including the wastewater treatment sector. With this in view, this review manuscript focuses on demonstrating the challenges and opportunities in applying a circular economy in the water sector. For example, reclamation and reuse of wastewater to increase water resources, by paying particular attention to the risks for human health, recovery of nutrients, or highly added-value products (e.g., metals and biomolecules among others), valorisation of sewage sludge, and/or recovery of energy. Being aware of this situation, in the European, Union 18 out of 27 countries are already reusing reclaimed wastewater at some level. Moreover, many wastewater treatment plants have reached energy self-sufficiency, producing up to 150% of their energy requirements. Unfortunately, many of the opportunities presented in this work are far from becoming a reality. Still, the first step is always to become aware of the problem and work on optimizing the solution to make it possible.

Review: Sources of Hydrological Model Uncertainties and Advances in Their Analysis

Abstract: Despite progresses in representing different processes, hydrological models remain uncertain. Their uncertainty stems from input and calibration data, model structure, and parameters. In characterizing these sources, their causes, interactions and different uncertainty analysis (UA) methods are reviewed. The commonly used UA methods are categorized into six broad classes: (i) Monte Carlo analysis, (ii) Bayesian statistics, (iii) multi-objective analysis, (iv) least-squares-based inverse modeling, (v) response-surface-based techniques, and (vi) multi-modeling analysis. For each source of uncertainty, the status-quo and applications of these methods are critiqued in gauged catchments where UA is common and in ungauged catchments where both UA and its review are lacking. Compared to parameter uncertainty, UA application for structural uncertainty is limited while input and calibration data uncertainties are mostly unaccounted. Further research is needed to improve the computational efficiency of UA, disentangle and propagate the different sources of uncertainty, improve UA applications to environmental changes and coupled human–natural-hydrologic systems, and ease UA’s applications for practitioners.

Water Supply and Water Scarcity

Abstract: This paper provides an overview of the Special Issue on water supply and water scarcity. The papers selected for publication include review papers on water history, on water management issues under water scarcity regimes, on rainwater harvesting, on water quality and degradation, and on climatic variability impacts on water resources. Overall, the issue underscores the need for a revised water management, especially in areas with demographic change and climate vulnerability towards sustainable and secure water supply. Moreover, general guidelines and possible solutions, such as the adoption of advanced technological solutions and practices that improve water use efficiency and the use of alternative (non-conventional) water resources are highlighted and discussed to address growing environmental and health issues and to reduce the emerging conflicts among water users.

Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach

Abstract: A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a deep learning approach was created by combining CNN and LSTM networks simulated water quality including total nitrogen, total phosphorous, and total organic carbon. Water level and water quality data in the Nakdong river basin were collected from the Water Resources Management Information System (WAMIS) and the Real-Time Water Quality Information, respectively. The rainfall radar image and operation information of estuary barrage were also collected from the Korea Meteorological Administration. In this study, CNN was used to simulate the water level and LSTM used for water quality. The entire simulation period was 1 January 2016–16 November 2017 and divided into two parts: (1) calibration (1 January 2016–1 March 2017); and (2) validation (2 March 2017–16 November 2017). This study revealed that the performances of both of the CNN and LSTM models were in the “very good” range with above the Nash–Sutcliffe efficiency value of 0.75 and that those models well represented the temporal variations of the pollutants in Nakdong river basin (NRB). It is concluded that the proposed approach in this study can be useful to accurately simulate the water level and water quality.

Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration

Abstract: Reanalysis data are being increasingly used as gridded weather data sources for assessing crop-reference evapotranspiration (ET0) in irrigation water-budget analyses at regional scales. This study assesses the performances of ET0 estimates based on weather data, respectively produced by two high-resolution reanalysis datasets: UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). The study is conducted in Campania Region (Southern Italy), with reference to the irrigation seasons (April–September) of years 2008–2018. Temperature, wind speed, vapor pressure deficit, solar radiation and ET0 derived from reanalysis datasets, were compared with the corresponding estimates obtained by spatially interpolating data observed by a network of 18 automatic weather stations (AWSs). Statistical performances of the spatial interpolations were evaluated with a cross-validation procedure, by recursively applying universal kriging or ordinary kriging to the observed weather data. ERA5-Land outperformed UMS both in weather data and ET0 estimates. Averaging over all 18 AWSs sites in the region, the normalized BIAS (nBIAS) was found less than 5% for all the databases. The normalized RMSE (nRMSE) for ET0 computed with E5L data was 17%, while it was 22% with UMS data. Both performances were not far from those obtained by kriging interpolation, which presented an average nRMSE of 14%. Overall, this study confirms that reanalysis can successfully surrogate the unavailability of observed weather data for the regional assessment of ET0.

A Novel Methodology for Prediction Urban Water Demand by Wavelet Denoising and Adaptive Neuro-Fuzzy Inference System Approach

Abstract: Accurate and reliable urban water demand prediction is imperative for providing the basis to design, operate, and manage water system, especially under the scarcity of the natural water resources. A new methodology combining discrete wavelet transform (DWT) with an adaptive neuro-fuzzy inference system (ANFIS) is proposed to predict monthly urban water demand based on several intervals of historical water consumption. This ANFIS model is evaluated against a hybrid crow search algorithm and artificial neural network (CSA-ANN), since these methods have been successfully used recently to tackle a range of engineering optimization problems. The study outcomes reveal that (1) data preprocessing is essential for denoising raw time series and choosing the model inputs to render the highest model performance; (2) both methodologies, ANFIS and CSA-ANN, are statistically equivalent and capable of accurately predicting monthly urban water demand with high accuracy based on several statistical metric measures such as coefficient of efficiency (0.974, 0.971, respectively). This study could help policymakers to manage extensions of urban water system in response to the increasing demand with low risk related to a decision.

Watershed Hydrological Response to Combined Land Use/Land Cover and Climate Change in Highland Ethiopia: Finchaa Catchment

Abstract: Land use/land cover (LULC) and climate change affect the availability of water resources by altering the magnitude of surface runoff, aquifer recharge, and river flows The evaluation helps to identify the level of water resources exposure to the changes that could help to plan for potential adaptive capacity In this research, Cellular Automata (CA)-Markov in IDRISI software was used to predict the future LULC scenarios and the ensemble mean of four regional climate models (RCMs) in the coordinated regional climate downscaling experiment (CORDEX)-Africa was used for the future climate scenarios Distribution mapping was used to bias correct the RCMs outputs, with respect to the observed precipitation and temperature Then, the Soil and Water Assessment Tool (SWAT) model was used to evaluate the watershed hydrological responses of the catchment under separate, and combined, LULC and climate change The result shows the ensemble mean of the four RCMs reported precipitation decline and increase in future temperature under both representative concentration pathways (RCP45 and RCP85) The increases in both maximum and minimum temperatures are higher for higher emission scenarios showing that RCP85 projection is warmer than RCP45 The changes in LULC brings an increase in surface runoff and water yield and a decline in groundwater, while the projected climate change shows a decrease in surface runoff, groundwater and water yield The combined study of LULC and climate change shows that the effect of the combined scenario is similar to that of climate change only scenario The overall decline of annual flow is due to the decline in the seasonal flows under combined scenarios This could bring the reduced availability of water for crop production, which will be a chronic issue of subsistence agriculture The possibility of surface water and groundwater reduction could also affect the availability of water resources in the catchment and further aggravate water stress in the downstream The highly rising demands of water, owing to socio-economic progress, population growth and high demand for irrigation water downstream, in addition to the variability temperature and evaporation demands, amplify prolonged water scarcity Consequently, strong land-use planning and climate-resilient water management policies will be indispensable to manage the risks

Heavy Metals Distribution, Sources, and Ecological Risk Assessment in Huixian Wetland, South China

Abstract: This research has focused on the source identification, concentration, and ecological risk assessment of eight heavy metals in the largest karst wetland (Huixian) of south China. Numerous samples from superficial soil and sediment within ten representative landuse types were collected and examined, and the results were analyzed using multiple methods. Single pollution index (Pi) results were underpinned by the Geoaccumulation index (Igeo) method, in which Cd was observed as the priority pollutant with the highest contamination degree in this area. As for the most polluted landuse type, via applying Nemerow’s synthetical contamination index (PN) and Potential ecological risk index (RI), the river and rape field posed the highest ecological risks, while moderate for the rest. To quantify the drivers of the contaminants, a principal component analysis (PCA) was carried out and weathering of the watershed’s parent carbonate rocks was found to be the main possible origin, followed by anthropogenic sources induced by agricultural fertilizer. Considering the impacts of these potentially toxic elements on public health, the results of this study are essential to take preventive actions for environmental protection and sustainable development in the region.

Comparison of Long Short Term Memory Networks and the Hydrological Model in Runoff Simulation

Abstract: Runoff modeling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data driven models. In this paper, we propose a data driven approach using the state-of-the-art Long-Short-Term-Memory (LSTM) network. The proposed model was applied in the Poyang Lake Basin (PYLB) and its performance was compared with an Artificial Neural Network (ANN) and the Soil & Water Assessment Tool (SWAT). We first tested the impacts of the number of previous time step (window size) in simulation accuracy. Results showed that a window in improper large size will dramatically deteriorate the model performance. In terms of PYLB, a window size of 15 days might be appropriate for both accuracy and computational efficiency. We then trained the model with 2 different input datasets, namely, dataset with precipitation only and dataset with all available meteorological variables. Results demonstrate that although LSTM with precipitation data as the only input can achieve desirable results (where the NSE ranged from 0.60 to 0.92 for the test period), the performance can be improved simply by feeding the model with more meteorological variables (where NSE ranged from 0.74 to 0.94 for the test period). Moreover, the comparison results with the ANN and the SWAT showed that the ANN can get comparable performance with the SWAT in most cases whereas the performance of LSTM is much better. The results of this study underline the potential of the LSTM for runoff modeling especially for areas where detailed topographical data are not available.

An Overview of Per- and Polyfluoroalkyl Substances (PFAS) in the Environment: Source, Fate, Risk and Regulations

Abstract: The current article reviews the state of art of the perfluoroalkyl and polyfluoroalkyl substances (PFASs) compounds and provides an overview of PFASs occurrence in the environment, wildlife, and humans. This study reviews the issues concerning PFASs exposure and potential risks generated with a focus on PFAS occurrence and transformation in various media, discusses their physicochemical characterization and treatment technologies, before discussing the potential human exposure routes. The various toxicological impacts to human health are also discussed. The article pays particular attention to the complexity and challenging issue of regulating PFAS compounds due to the arising uncertainty and lack of epidemiological evidence encountered. The variation in PFAS regulatory values across the globe can be easily addressed due to the influence of multiple scientific, technical, and social factors. The varied toxicology and the insufficient definition of PFAS exposure rate are among the main factors contributing to this discrepancy. The lack of proven standard approaches for examining PFAS in surface water, groundwater, wastewater, or solids adds more technical complexity. Although it is agreed that PFASs pose potential health risks in various media, the link between the extent of PFAS exposure and the significance of PFAS risk remain among the evolving research areas. There is a growing need to address the correlation between the frequency and the likelihood of human exposure to PFAS and the possible health risks encountered. Although USEPA (United States Environmental Protection Agency) recommends the 70 ng/L lifetime health advisory in drinking water for both perfluorooctane sulfonate (PFO) perfluorooctanoic acid (PFOA), which is similar to the Australian regulations, the German Ministry of Health proposed a health-based guidance of maximum of 300 ng/L for the combination of PFOA and PFOS. Moreover, there are significant discrepancies among the US states where the water guideline levels for the different states ranged from 13 to 1000 ng L−1 for PFOA and/or PFOS. The current review highlighted the significance of the future research required to fill in the knowledge gap in PFAS toxicology and to better understand this through real field data and long-term monitoring programs.