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In this paper, greywater samples are collected from the kitchens of different types of buildings (residential and commercial) located in different districts within the city of Jeddah, Saudi Arabia. The collected samples are analyzed and compared with the potable water from the same region. The parameters investigated are pH, conductivity, total solids (TS), total dissolved solids (TDS), total suspended solids (TSS), total hardness, temporary hardness, permanent hardness, alkalinity, chloride, and biochemical oxygen demand (BOD). It was found that the amount of total suspended solids is very high in the greywater samples. It shows the presence of both temporary and permanent hardness. Their alkalinity values are greater than hardness. It may be due to the number, lifestyle, age of the occupants, presence of children, and social and cultural behavior of residents. The concentration of BOD level is very low, which shows that the greywater samples have lower concentrations of organic compounds. Design details of the greywater treatment plant are suggested based on the results of the analysis. This includes a screening chamber, grit chamber, settling tank, and filtration unit. The treated greywater is recommended for reuse for gardening, landscaping, and toilet flushing purposes.

Water quality parameters like temperature, pH, total dissolved solids (TDS), total suspended solids (TSS), dissolved oxygen (DO), oil and grease, etc., are calculated from the field while parameters like biological oxygen demand (BOD) and chemical oxygen demand (COD) are interpreted through the laboratory tests. On one hand parameters like temperature, pH, DO, etc., can be accurately measured with the exceeding simplicity, whereas on the other hand calculation of BOD and COD is not only cumbersome but also inaccurate many times. A number of previous researchers have tried to use different empirical methods to predict BOD and COD but these empirical methods have their limitations due to their less versatile application. In this paper, an attempt has been made to calculate BOD and COD from simple field parameters like temperature, pH, DO, TSS, etc., using Artificial Neural Network (ANN) method. Datasets have been obtained from analysis of mine water discharge of one of the mines in Jharia coalfield, Jharkhand, India. 73 data sets were used to establish ANN architecture out of which 58 datasets were used to train the network while 15 datasets for testing the network. The results show encouraging similarity between experimental and predicted values. The RMSE values obtained for the BOD and COD are 0.114 and 0.983 %, respectively.

Cellulose, the main component of plant cell walls, is degradable in nature. However, to the best of our knowledge, this is the first report that compares the biodegradability of cellulose fibers with different structures in natural waters. River water, brackish water, and seawater were collected from the Kamo River and Osaka Bay, Japan. Biodegradation of cellulose fibers with different structures and crystallinities, ramie, mercerized ramie, and regenerated cellulose fibers in the collected natural water was investigated in the dark at 20 °C for 30 days. The primary and aerobic ultimate biodegradability were evaluated by weight loss and biochemical oxygen demand (BOD) tests, respectively. In the weight-loss test, cellulose fibers were found to be degraded by more than 50% in any natural water within 30 days. However, in the BOD test, biodegradation was diminished, with values of 40%, 20–30%, and 2–10% in river water, brackish water, and seawater, respectively. These results indicate that cellulose fibers are easily degraded into fine fragments, but it is difficult to cause their ultimate decomposition into water and carbon dioxide. Existence of such a tendency in the degree of biodegradation among the cellulose fibers remains unclear. The molecular weight of cellulose fibers in natural water was also measured during their degradation. The degradation behavior in river water and seawater was observed to be different from that in brackish water. The results thus obtained indicate that the microorganisms and enzymes that degrade cellulose fibers differ depending on the natural water, which influences the degree and mechanism of biodegradation.

Electropolymerized thionine was used as a redox-active polymer to create a two-mediated microbial biosensor for determining biochemical oxygen demand (BOD). The electrochemical characteristics of the conducting system were studied by cyclic voltammetry and electrochemical impedance spectroscopy. It has been shown that the most promising in terms of the rate of interaction with the yeast B. adeninivorans is the system based on poly(thionine), single-walled carbon nanotubes (SWCNT), and neutral red (kint = 0.071 dm3/(g·s)). The biosensor based on this system is characterized by high sensitivity (the lower limit of determined BOD concentrations is 0.4 mgO2/dm3). Sample analysis by means of the developed analytical system showed that the results of the standard dilution method and those using the biosensor differed insignificantly. Thus, for the first time, the fundamental possibility of effectively using nanocomposite materials based on SWCNT and the redox-active polymer poly(thionine) as one of the components of two-mediator systems for electron transfer from yeast microorganisms to the electrode has been shown. It opens up prospects for creating stable and highly sensitive electrochemical systems based on eukaryotes.

One of the main indices of the quality of water is the biochemical oxygen demand (BOD). A little over 40 years have passed since the practical application of the first microbial sensor for the determination of BOD, presented by the Japanese professor Isao Karube. This time span has brought new knowledge to and practical developments in the use of a wide range of microbial cells based on BOD biosensors. At present, this field of biotechnology is becoming an independent discipline. The traditional BOD analysis (BOD5) has not changed over many years; it takes no less than 5 days to carry out. Microbial biosensors can be used as an alternative technique for assessing the BOD attract attention because they can reduce hundredfold the time required to measure it. The review examines the experience of the creation and practical application of BOD biosensors accumulated by the international community. Special attention is paid to the use of multiple cell immobilization methods, signal registration techniques, mediators and cell consortia contained in the bioreceptor. We consider the use of nanomaterials in the modification of analytical devices developed for BOD evaluation and discuss the prospects of developing new practically important biosensor models.

Improperly managed wastewater effluent poses environmental and public health risks. BOD evaluation is complicated by wastewater treatment. Using key parameters to estimate BOD in wastewater can improve wastewater management and environmental monitoring. This study proposes a BOD determination method based on the Artificial Neural Networks (ANN) model to combine Chemical Oxygen Demand (COD), Suspended Solids (SS), Total Nitrogen (T-N), Ammonia Nitrogen (NH4-N), and Total Phosphorous (T-P) concentrations in wastewater. Twelve different transfer functions are investigated, including the common Hyperbolic Tangent Sigmoid (HTS), Log-sigmoid (LS), and Linear (Li) functions. This research evaluated 576,000 ANN models while considering the variable random number generator due to the ten alternative ANN configuration parameters. This study proposes a new approach to assessing water resources and wastewater facility performance. It also demonstrates ANN’s environmental and educational applications. Based on their RMSE index over the testing datasets and their configuration parameters, twenty ANN architectures are ranked. A BOD prediction equation written in Excel makes testing and applying in real-world applications easier. The developed and proposed ANN-LM 5-8-1 model depicting almost ideal performance metrics proved to be a reliable and helpful tool for scientists, researchers, engineers, and practitioners in water system monitoring and the design phase of wastewater treatment plants.

This work proposes an approach to the formation of receptor elements for the rapid diagnosis of the state of surface waters according to two indicators: the biochemical oxygen demand (BOD) index and toxicity. Associations among microorganisms based on the bacteria and yeast , as well as associations of the yeasts and , were formed to evaluate these indicators, respectively. The use of nanocomposite electrically conductive materials based on carbon nanotubes, biocompatible natural polymers-chitosan and bovine serum albumin cross-linked with ferrocenecarboxaldehyde, neutral red, safranin, and phenosafranin-has made it possible to expand the analytical capabilities of receptor systems. Redox polymers were studied by IR spectroscopy and Raman spectroscopy, the contents of electroactive components were determined by atomic absorption spectroscopy, and electrochemical properties were studied by electrochemical impedance and cyclic voltammetry methods. Based on the proposed kinetic approach to modeling individual stages of bioelectrochemical processes, the chitosan-neutral red/CNT composite was chosen to immobilize the yeast association between (k = 370 ± 20 L/g × s) and (320 ± 30 L/g × s), and a bovine serum albumin (BSA)-neutral composite was chosen to immobilize the association between the yeast (k = 130 ± 10 L/g × s) and the bacteria red/CNT (170 ± 30 L/g × s). After optimizing the composition of the receptor systems, it was shown that the use of nanocomposite materials together with associations among microorganisms makes it possible to determine BOD with high sensitivity (with a lower limit of 0.6 mg/dm ) and detect the presence of a wide range of toxicants of both organic and inorganic origin. Both receptor elements were tested on water samples, showing a high correlation between the results of biosensor analysis of BOD and toxicity and the results of standard analytical methods. The results obtained show broad prospects for creating sensitive and portable bioelectrochemical sensors for the early warning of environmentally hazardous situations based on associations among microorganisms and nanocomposite materials.

Mathematical Modeling of the activated sludge process (ASP) enhances the understanding of the process and improves the quality of the effluent released. However, as the process is complex and nonlinear, mathematical modeling of the process has been a challenge. In this study, multi-layer perceptron neural networks (MLP-ANN) are investigated to predict water quality parameters for better control of wastewater treatment plants employing an activated sludge process. The study area selected was in a central district of the southern state of India. The parameters to be investigated are biochemical oxygen demand (BOD), suspended solids (SS), and pH. The model is evaluated based on statistical parameters of correlation coefficient R and mean square error (MSE). The neural network toolbox of MATLAB 2015b is used for modeling and simulation study. It has been found that effluent biochemical oxygen demand was predicted with a maximum correlation coefficient of 0.927 and minimum mean square error of 0.0022, effluent suspended solids were predicted with a maximum correlation coefficient value of 0.947 and minimum mean square value of 0.0058, effluent pH was predicted with a maximum correlation coefficient value of 0.8299 and minimum mean square value of 0.0132.

In order to protect the water environment in seriously polluted basins, the impacts of anthropogenic activities (sewage outfalls and land use) on water quality should be assessed. The Bayesian network (BN) provides a convenient way to model these complex processes. In this study, anthropogenic impacts on chemical oxygen demand (COD) and biochemical oxygen demand (BOD) were evaluated in the Huaihe River basin (HRB) considering dry and wet seasons and different spatial scales. The results showed that anthropogenic activities had the most significant impacts on COD and BOD at the catchment scale. In dry seasons, sewage outfalls played an important role in organic pollution. Farmland became the most important source in wet seasons although it had a “sink” process in dry seasons. Intensive human activities in urban made significant contributions to increased COD levels. Grassland had a negative relationship with organic pollution, especially in dry seasons. Therefore, governments should implement strategies to control organic matters transported from urban and farmland regions. Increasing the efficiency of wastewater treatments and the percentage of grassland in the riparian zone could improve water quality. These results can enhance understanding of anthropogenic impacts on water quality and contribute to efficient management for river basins.

This study examined the relationship between water quality indicators (five-day biochemical oxygen demand (BOD5) and ammonia nitrogen (NH3-N)) by modeling them at sewage treatment plants (STPs) and receiving waters. Discharge loads from 2016 to 2018 were input into the QUAL-MEV water quality model, which is used for the total maximum daily loads (TMDLs) in Korea, and the results showed that the simulated BOD5 values decreased according to the reduction in the BOD discharged pollution load, while the observed water quality increased. This was caused by an increase in the total nitrogen discharged pollution load and the NH3-N effluence ratio from the STPs. Hence, the model was modified to reflect real-world conditions. After inputting a calibration factor calculated based on real data and the measured ratio of each type of nitrogen, the simulation was repeated, achieving results within ±20% of observed values. This model confirmed that BOD5 in the lower part of the river varies according to the change in the NH3-N ratio of the STP effluent. To better manage BOD5 and to establish achievable water quality targets, a plan that reflects the linkage among water quality indicators, such as the TMDLs used in the United States, should be introduced in Korea.