Use of Nested Flow Models and Interpolation Techniques for Science‐Based Management of the Sheyenne National Grassland,North Dakota,USA

Noxious weeds threaten the Sheyenne National Grassland (SNG) ecosystem and therefore herbicides have been used for control. To protect groundwater quality, the herbicide application is restricted to areas where the water table is less than 10 feet (3.05 m) below the ground surface in highly permeable soils, or less than 6 feet (1.83 m) below the ground surface in low permeable soils. A local MODFLOW model was extracted from a regional GFLOW analytic element model and used to develop depth‐to‐groundwater maps in the SNG that are representative for the particular time frame of herbicide applications. These maps are based on a modeled groundwater table and a digital elevation model (DEM). The accuracy of these depth‐to‐groundwater maps is enhanced by an artificial neural networks (ANNs) interpolation scheme that reduces residuals at 48 monitoring wells. The combination of groundwater modeling and ANN improved depth‐to‐groundwater maps, which in turn provided more informed decisions about where herbicides can or cannot be safely applied.     

Current Status of Textile Industry Wastewater Management and Research Progress in Malaysia: A Review

Textile industry is one of the fastest growing industries and significantly contributes to the economic growth in Malaysia. However, this industry also has high water consumption and subsequently produces high discharge rate of wastewater with high load of contaminants. The release of dyes into the environment during textile fiber dyeing and finishing processes is a main source of water pollution. Individual wastewater treatment through physical, biological, or chemical method is often very costly and results in large amount of sludge. Thus, there is a need to look for alternative treatment processes that covers from pre to post wastewater treatment stage. This paper reviews the current scenario with respect to textile industry effluent in Malaysia and technologies available for the treatment of the effluent. Prospects, challenges, and recommendations for future direction as well as on‐going research works dedicated to the treatment of textile wastewater are also reviewed in detail.     

A system dynamics approach for urban water reuse planning: a case study from the Great Lakes region

Water reclamation and reuse practices are recently receiving growing attention due to increasing water scarcity, concerns about the effect of wastewater discharges on receiving water, and availability of high-performing and cost-effective water reuse technologies. However, incorporation of water reuse schemes into water/wastewater infrastructure systems is a complex decision making process, involving various economical, technological, and environmental criteria. System dynamics (SD) allows modeling of complex systems and provides information about the temporal and feedback behavior of the system. In this sense, a SD model of the existing water/wastewater system in Kalamazoo-Michigan, an urban area in the Great Lakes region, was created with the hypothetical incorporation of water reuse. The model simulates and optimizes the overall water system cost (including water, wastewater and water reuse components), accounting for future scenarios of population, economic growth and climate change. Results indicate significant levels of water reuse after an infrastructure build delay. The model also indicates that a decision to implement water reuse yields remarkably lower water withdrawals and lower water treatment costs even in a location with a relatively abundant water supply like Kalamazoo. This study emphasizes the fact that a true understanding of the practice of water reuse cannot be achieved without taking regional and climatic parameters into account.     

The Potential Use of Bali Wastewater for Crop Production Based on Moscow Region Experience

The article offers a new approach to the selection of a treatment plant, based on the optimization of biogeochemical matter flows. The existing treatment facilities of Bali are analyzed. The authors propose several process schemes for domestic wastewater treatment, depending on the technology of utilization of biogenic elements from the wastewater for agricultural production. These are based on water treatment technologies that have been in use in Moscow for more than 100 years.     

Biodegradability Assessment and Treatability of High Strength Complex Industrial Park Wastewater

Industrial park wastewater (IPWW) includes complex non‐biodegradable organic compounds. A wastewater treatability study using biodegradability assessment approach was carried out for an industrial park housing mainly textile and machinery sectors. Biodegradable and inert chemical oxygen demand (COD) fractions with soluble and particulate counterparts were experimentally estimated by implementing respirometric methods. Aerobic batch test methods and oxygen uptake rate profiles were used to map kinetic parameters and COD fractions. Denitrification potential was also determined by application of anoxic batch tests. Relevant kinetic constants, stoichiometric, and design parameters were determined for biological processes. Tests were carried out for raw and physico‐chemically pre‐treated IPWW. Hence, it was aimed to assess the effects and necessity of pre‐treatment on biological processes. Biological mineralization, nitrification, and denitrification processes were also designed and tested using a bench scale continuous treatment model for pre‐treated IPWW. The sum of initial inert soluble COD fraction and production of soluble microbial products decreased about 25% for the case of pre‐treated IPWW as compared to raw IPWW. Eighty‐three percent total COD and 89% total Kjeldahl nitrogen removal efficiencies were attained for the modeling experiments of pre‐treated IPWW. Results showed the significance of pre‐treatment if complex strength industrial wastewaters are to be biologically treated.     

Artificial neural network simulation for prediction of suspended sediment concentration in the River Ramganga,Ganges Basin,India

The relation between the water discharge (Q) and suspended sediment concentration (SSC) of the River Ramganga at Bareilly, Uttar Pradesh, in the Himalayas, has been modeled using Artificial Neural Networks (ANNs). The current study validates the practical capability and usefulness of this tool for simulating complex nonlinear, real world, river system processes in the Himalayan scenario. The modeling approach is based on the time series data collected from January to December (2008-2010) for Q and SSC. Three ANNs (T1-T3) with different network configurations have been developed and trained using the Levenberg Marquardt Back Propagation Algorithm in the Matlab routines. Networks were optimized using the enumeration technique, and, finally, the best network is used to predict the SSC values for the year 2011. The values thus obtained through the ANN model are compared with the observed values of SSC. The coefficient of determination (R2), for the optimal network was found to be 0.99. The study not only provides insight into ANN modeling in the Himalayan river scenario, but it also focuses on the importance of understanding a river basin and the factors that affect the SSC, before attempting to model it. Despite the temporal variations in the study area, it is possible to model and successfully predict the SSC values with very simplistic ANN models.     

Are steroids really the cause for fish feminization? A mini-review of in vitro and in vivo guided TIEs

Feminization of fish has been reported throughout the world in freshwater and marine systems. While the population impacts are conflictive, enough negative effects warrant additional research into causation. In order to ascertain the identities of specific feminizing agents, variants of toxicity identification evaluations (TIEs) have been employed. The majority of these evaluations have utilized in vitro estrogen receptor-based cell-lines to identify chromatographic fractions that possess biological activity from predominately wastewater derived from municipal treatment facilities and have concluded that synthetic and natural estrogens are the primary cause for feminization of fish. This paper will focus on three aquatic systems impacted by wastewater originating from purely domestic, and industrial/domestic secondary treatment systems. Wastewater and sediment extracts were evaluated by in vitro and in vivo biological responses in a TIE fractionation design. While in vitro responses tended to mirror in vivo responses in purely domestic wastewater systems, in vitro responses tended to severely underestimate in vivo estrogenic activity when normalized to estradiol equivalents in more complex systems. TIE fractionation schemes using in vivo biological responses failed to indicate any relationship to steroids in either wastewater or sediment extractions. These data consistently support the view that mechanisms other than direct ER binding and activation by toxicants may be important in the feminization of fish particularly residing in habitats that receive complex wastewater or agricultural effluents.     

Are steroids really the cause for fish feminization? A mini-review of in vitro and in vivo guided TIEs

Feminization of fish has been reported throughout the world in freshwater and marine systems. While the population impacts are conflictive, enough negative effects warrant additional research into causation. In order to ascertain the identities of specific feminizing agents, variants of toxicity identification evaluations (TIEs) have been employed. The majority of these evaluations have utilized in vitro estrogen receptor-based cell-lines to identify chromatographic fractions that possess biological activity from predominately wastewater derived from municipal treatment facilities and have concluded that synthetic and natural estrogens are the primary cause for feminization of fish. This paper will focus on three aquatic systems impacted by wastewater originating from purely domestic, and industrial/domestic secondary treatment systems. Wastewater and sediment extracts were evaluated by in vitro and in vivo biological responses in a TIE fractionation design. While in vitro responses tended to mirror in vivo responses in purely domestic wastewater systems, in vitro responses tended to severely underestimate in vivo estrogenic activity when normalized to estradiol equivalents in more complex systems. TIE fractionation schemes using in vivo biological responses failed to indicate any relationship to steroids in either wastewater or sediment extractions. These data consistently support the view that mechanisms other than direct ER binding and activation by toxicants may be important in the feminization of fish particularly residing in habitats that receive complex wastewater or agricultural effluents.     

A hybrid artificial neural network-numerical model for ground water problems

Numerical models constitute the most advanced physical-based methods for modeling complex ground water systems. Spatial and/or temporal variability of aquifer parameters, boundary conditions, and initial conditions (for transient simulations) can be assigned across the numerical model domain. While this constitutes a powerful modeling advantage, it also presents the formidable challenge of overcoming parameter uncertainty, which, to date, has not been satisfactorily resolved, inevitably producing model prediction errors. In previous research, artificial neural networks (ANNs), developed with more accessible field data, have achieved excellent predictive accuracy over discrete stress periods at site-specific field locations in complex ground water systems. In an effort to combine the relative advantages of numerical models and ANNs, a new modeling paradigm is presented. The ANN models generate accurate predictions for a limited number of field locations. Appending them to a numerical model produces an overdetermined system of equations, which can be solved using a variety of mathematical techniques, potentially yielding more accurate numerical predictions. Mathematical theory and a simple two-dimensional example are presented to overview relevant mathematical and modeling issues. Two of the three methods for solving the overdetermined system achieved an overall improvement in numerical model accuracy for various levels of synthetic ANN errors using relatively few constrained head values (i.e., cells), which, while demonstrating promise, requires further research. This hybrid approach is not limited to ANN technology; it can be used with other approaches for improving numerical model predictions, such as regression or support vector machines (SVMs).     

Integrated stochastic modeling of pharmaceuticals in sewage networks

Pharmaceuticals and particularly antibiotics can harm sensitive aquatic species. Their occurrence in urban wastewater systems is the consequence of five successive processes: (i) ingestion of the substance, (ii) accumulation in the urine, (iii) excretion, (iv) degradation in the sewer system and (v) transport to the wastewater treatment plant (WTP). These processes were included in an integrated model that can be used to assess the dynamics of pharmaceuticals at a WTP inlet. First, information on sales data, posology, pharmacokinetics and toilet flushing frequency were combined to create a source model of pharmaceuticals entering a sewer system. This production function was then coupled with a transport/degradation model to simulate concentrations of pharmaceuticals at a WTP inlet. In an example application, the full model was applied to simulate the concentration of the antibiotic ciprofloxacin on an hourly time scale. In this application, the model was calibrated and validated for a case study at a WTP in Lausanne, Switzerland. Validation of the integrated approach was successful despite the high variability evident in the model results. This modeling approach has potential use in pollution management and epidemiology related to wastewater.     

Characterization of industrial wastewaters using gel-permeation chromatography with multicomponent detection

The gel-permeation chromatography system with multicomponent detection of organic carbon, organic nitrogen, organic halogen, and UV/vis absorption measurement is shown to be a useful tool for characterization of industrial wastewaters and wastewater treatment processes. The proposed system was used to investigate biologically treated wastewater from chemical industry, whereby one branch stream was identified to be the main source of persistent halogenated organics. Various treatment processes of pulp mill wastewater were also evaluated revealing that precipitation with aluminium is very effective for the removal of the high molecular fraction whereas the biological treatment is generally less selective. In the biological treatment of tannery wastewaters, it has been shown that the aerobic treatment, in contrast to anaerobic step, causes evident alterations of the composition of the polar DOC fraction. The nitrogen containing compounds are also better decomposed in the aerobic step compared to the anaerobic one.     

Estimation of the Amount of Suspended Sediment in the Tigris River using Artificial Neural Networks

The amount of sediment should be taken into consideration in the planning of water structures for efficient use of limited water resources. It is important to estimate the amount of sediment for the successful operation of these structures in their future performances. Such estimations can be achieved by Artificial Neural Network (ANNs) with low error percentages as seen in many other disciplines. These networks also enable the modeling of nonlinear relationships between the parameters affecting the event. The purpose of this research is to establish models for sediment amounts in the Tigris River at the Diyarbakir measurement station in Turkey. Rainfall, temperature and discharge are taken as independent variables in the models, whereas sediment is taken as the dependent variable. Fourteen different models are generated using ANNs and Regression Analysis (RA). The results are compared with each other and with the observed data. The relative error and determination coefficient are used as comparison criteria. It is concluded that due to their nonlinear modeling capability, ANNs give better results than RA.     

Integration of Microalgae-Based Wastewater Bioremediation–Biorefinery Process to Promote Circular Bioeconomy and Sustainability: A Review

Bioremediation of wastewater using microalgae is inexpensive, energy efficient, and effective in pollutant reduction as compared to conventional wastewater treatment technologies. Wastewater is a huge resource of minerals, nutrients, bioenergy, and valuable organic compounds and can be used for cultivation of microalgae. The microalgal biomass can be further used as biorefinery feedstock to produce biofuels and commercially important high-value products. The potential of microalgae toward bioremediation and biorefinery applications presents the avenues for integrating the two processes to support circular bioeconomy and sustainability. This review presents a holistic view of integration of bioremediation and biorefinery processes using microalgae for deriving multiple benefits like pollutant removal, resource recovery, biofuel production, and generation of high-value commercial products. The current status of high-throughput microalgal screening technologies is also discussed since the selection of suitable microalgal strains is crucial for the application. The review further summarizes various processes involved in bioremediation and biorefinery systems such as cultivation, bioremediation, harvesting, and downstream processing. Recent trends in microalgal strain improvement for bioremediation and biorefinery applications through genetic engineering, bioinformatics, omics technologies, and genome editing tools are highlighted, while addressing the risks, biosafety issues, and regulatory affairs associated with genetically modified algae.     

The Impact of Human Activities on Freshwater Aquatic Systems

The roles of oxygen and its activated species (superoxide radicals, hydrogen peroxide, hydroxyl radicals), as well as that of sulfur compounds, are considered in relation to biological quality and the self-cleaning capacity of freshwater aquatic systems. The effects on the aquatic redox-processes are discussed in terms of atmospheric fallout of sulfur compounds, bottom sediment composition, and input of wastewaters containing reducing substances. It is shown that the totality of anthropogenic influences, and/or unfavourable natural geochemical conditions, as well as climatic effects in a region can increase the significance of one-electron transfer processes in bio-geochemical cycles of oxygen, sulfur and manganese, compared with the significance of two-electron transfer processes. The resulting, reactive intermediate products of one-electron transfer processes are very important with respect to the composition and properties of aquatic systems. Examples are given of practical applications of wastewater treatment, using hydrogen peroxide and UV-irradiation, and of regulation of consumers’ activities which affect natural waters.     

The Utilization and Modeling of Photo-Fenton Process as a Single Unit in Textile Wastewater Treatment

Studies on the direct application of the photo-Fenton process (PFOP) to disinfect and decontaminate textile wastewater are rare. The output of the artificial neural network (ANN) models applied to the wastewater of a textile factory producing woven fabrics, which is used to assess the efficiency of the PFOP process, are investigated and compared with each other in this study. The highest PFOP efficiency is obtained at a pH of 3. Chemical oxygen demand (COD), suspended solids (SS) and color removal rates are 94%, 90%, and 96%, respectively. The data are modeled with ANNs and nonlinear external input autoregressive ANNs (NARX-ANN) using the MATLAB R2020a software program. Both Levenberg–Marquardt (trainlm) and scaled conjugate gradient (trainscg) algorithms are employed in the ANN and NARX-ANN models, whereas hyperbolic tangent sigmoid (Tansig) and logistic sigmoid (Logsig) functions are superimposed on the hidden layer in the ANN model, and Tansig functions are superimposed on the NARX-ANN model. It is determined that the developed ANN models are more effective in estimating the PFOP efficiency. The mean squared error is 0.000 953, and the coefficient of determination (R²) is 0.96 661.     

Comparison of linear and nonlinear dimension reduction techniques for automated process monitoring of a decentralized wastewater treatment facility

Multivariate statistical methods for online process monitoring have been widely applied to chemical, biological, and engineered systems. While methods based on principal component analysis (PCA) are popular, more recently kernel PCA (KPCA) and locally linear embedding (LLE) have been utilized to better model nonlinear process data. Additionally, various forms of dynamic and adaptive monitoring schemes have been proposed to address time-varying features in these processes. In this analysis, we extend a common simulation study in order to account for autocorrelation and nonstationarity in process data and comprehensively compare the monitoring performances of static, dynamic, adaptive, and adaptive–dynamic versions of PCA, KPCA, and LLE. Furthermore, we evaluate a nonparametric method to set thresholds for monitoring statistics and compare results with the standard parametric approaches. We then apply these methods to real-world data collected from a decentralized wastewater treatment system during normal and abnormal operations. From the simulation study, adaptive–dynamic versions of all three methods generally improve results when the process is autocorrelated and nonstationary. In the case study, adaptive–dynamic versions of PCA, KPCA, and LLE all flag a strong system fault, but nonparametric thresholds considerably reduce the number of false alarms for all three methods under normal operating conditions.     

人工湿地废水处理系统的生物学基础研究进展

主要阐述了人工湿地废水处理系统中生物学基础的研究进展,至今,对作为湿地生产者的水生植物研究较多,水生植物除直接吸收利用废水中的营养物质,吸附和富集重金属外,还起输送氧气至根区,形成根区多种微环境和维护湿地水力传输的作用;而对作为湿地分解者的微生物的研究较少,湿地微生物的代谢活动是废水中有机物质降解的基础机制。     

Enhancement of water use efficiency at plants processing hydrocarbon materials

A method is proposed for multicriteria performance assessment of water supply and disposal systems. Approaches are proposed for improving the utilization efficiency of water resources in plants processing hydrocarbon materials based on the potential of main-production technological processes and in-plant systems of heat and water supply, as well as industrial waste utilization. Engineering solutions are given for involving regenerated wastewater in the production cycle and reducing water consumption from external natural sources, and their system efficiency is evaluated.     

Algal Cultivation for Treating Wastewater in African Developing Countries: A Review

The tremendous increase in human population and rapid decline in freshwater resources have necessitated the development of innovative and sustainable wastewater treatment methods. Africa as a developing continent is currently backing on sustainable solutions to tackle impending water resource crisis brought forward by wastewater‐induced environmental pollution and climate change. Microalgae‐based wastewater treatment systems represent an emerging technology that is capable of meeting the new demand for improved wastewater treatment and climate change mitigation strategies in an environmentally friendly manner. This review critically looks at the opportunities of Africa in harnessing and exploiting the potential of microalgae for the treatment of various wastewaters based on their capacity to recycle nutrients and for concurrent production of valuable biomass and several useful metabolites. Wastewaters, if improperly/completely untreated and discharged, simultaneously pollute freshwater sources and present significant health and environmental risks. Nutrients in wastewater can be utilized and recovered in the form of marketable biomass and products when integrated with the cultivation of microalgae. Several valuable bioproducts can be generated from wastewater‐grown microalgal biomass including biofuels, biofertilizers, animal feed, and various bioactive compounds. This biorefinery approach would most certainly improve wastewater treatment process economics, enhancing the technical feasibility of algae‐based wastewater remediation in African countries.     

Magnetically Separable Water Treatment Technologies and their Role in Future Advanced Water Treatment: A Patent Review

Magnetic separation has been recognized as an important property for the simple deployment of micro and sub‐microparticles into solution in the field of water treatment. Many materials with desirable properties for water decontamination are hindered due to the difficulty inherent in removing them from solution post‐treatment. By securing these materials to magnetic compounds, this important issue can be solved as removing active materials from wastewater requires only the application of a magnetic field. This review article presents and discusses many recent technologies, in the form of patents, which exploit the property of magnetic separation for advanced water treatment, including methods of adsorbing pollutants from wastewater and magnetically separating them, as well as methods of deploying active materials for the degradation of contaminants, then magnetically retrieving these catalysts. The requirement for advanced wastewater treatment methods becomes more essential as new, persistent contaminants arise as a result of pharmaceuticals, pesticides and industrial processes which cannot be addressed by traditional water treatment procedures. Magnetic separation promises to be a critical factor in these advanced methods, allowing the safe deployment of active materials which would otherwise be unusable, opening the gate to more efficient, economic and environmentally friendly water purification.