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In this study, observations were carried out in the surface waters of Saraydüzü Dam Lake within Sinop provincial borders for 1 year to determine water quality. The basic 28 variables used to determine water quality were measured monthly at six stations. Taking into account the World Health Organization's drinking water standards, the water quality index (WQI) and Turkey’s Ministry of Forestry and Water Affairs Surface Water Quality Regulations (SWQR) were used in determining the water quality. In addition, irrigation water quality was examined. For this, sodium absorption rates (SAR), sodium percentage and residual sodium carbonate (RSC) values were calculated. WQI values in the lake were found to be between 17.62 and 29.88. Water quality parameters did not exceed the recommended limit values in all months and at all stations. According to these values, the Saraydüzü Dam Lake water belongs to the ‘very good’ class in terms of drinking water quality. The results obtained showed that there were no nitrogen or phosphate inputs that could harm the ecosystem in the lake and that there were no low/insufficient ambient oxygen conditions resulting from excessive oxygen consumption during the degradation process of organic matter. All water quality parametres are well below the permissible limits except some heavy metals according to SWQR. Cu, Zn and Fe were found to exceed the limit values. The water quality of irrigation water was found to be good in terms of SAR and sodium percentage, whereas RSC was observed to have varying qualities during the year and not be suitable for irrigation in some months. According to results of factor analysis (FA), pH, temperature, electrical conductivity, suspended solid matter (SSM), biological oxygen demand (BOD), total hardness (TH),total alkalinity (TA), calcium, nitrate, ammonium, mercury and dissolved oxygen are the main variables responsible for the processes in the ecosystem.

In the present study, three different artificial intelligence based non-linear models, i.e. feed forward neural network (FFNN), adaptive neuro fuzzy inference system (ANFIS), support vector machine (SVM) approaches and a classical multi-linear regression (MLR) method were applied for predicting the performance of Nicosia wastewater treatment plant (NWWTP), in terms of effluent biological oxygen demand (BOD ), chemical oxygen demand (COD ) and total nitrogen (TN ). The daily data were used to develop single and ensemble models to improve the prediction ability of the methods. The obtained results of single models proved that, ANFIS model provides effective outcomes in comparison with single models. In the ensemble modeling, simple averaging ensemble, weighted averaging ensemble and neural network ensemble techniques were proposed subsequently to improve the performance of the single models. The results showed that in prediction of BOD , the ensemble models of simple averaging ensemble (SAE), weighted averaging ensemble (WAE) and neural network ensemble (NNE), increased the performance efficiency of artificial intelligence (AI) modeling up to 14%, 20% and 24% at verification phase, respectively, and less than or equal to 5% for both COD and TN in calibration phase. This shows that NNE model is more robust and reliable ensemble method for predicting the NWWTP performance due to its non-linear averaging kernel.

Industrial effluents are one of the foremost concerns relating to the anthropogenic environmental pollution. The effluents from the tanning and textile industries in Dhaka, Bangladesh, were characterized chemically and physicochemically with multivariate statistical techniques. The concentrations of heavy metals viz. , Pb, Cd, Cr, Mn, Fe, Ni, Cu, and Zn were determined by atomic absorption spectrometer while concentrations of anions viz. , F − , Cl − , NO 2 − , NO 3 − , and SO 4 2− were measured by ion chromatograph. The physicochemical parameters viz. , temperature, pH, electrical conductivity (EC), salinity, turbidity, dissolved oxygen (DO), and biological oxygen demand (BOD) were measured by a multiparameter meter while total suspended solids (TSS) and total dissolved solids (TDS) were measured gravimetrically. This study showed that effluents from both industries demonstrated high levels of TSS, TDS, EC, and heavy metals. Tannery effluents have lower pH and DO, and higher BOD, Cl − , SO 4 2− , and Cr concentrations while textile dyeing effluents have higher pH, NO 2 − , and NO 3 − concentrations, compared to the standard limits promulgated by the Bangladesh government. Multivariate statistical techniques such as cluster analysis and principal component analysis along with the correlation matrices showed significant association among the measured parameters and identified pollution sources as well as effluent types in the study area which could be linked to the processes used in textile dying and tanning industries. This study will be useful for identifying pollutants emanating from the two industries and will guide future industrial aquatic studies where multiple industrial runoffs are concerned.

Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.

Rivers, as the most prominent component of water resources, have a key role to play in increasing the life expectancy of living creatures. The essential characteristics of water pollutants can be described by water quality indices (WQIs). Hence, a ferocious demand for obtaining an accurate prediction of WQIs is of high importance for perception of pollutant patterns in natural streams. Field studies conducted on different rivers indicated that there is no general relationship to yield water quality parameters with a permissible level of accuracy. Over the past decades, several artificial intelligence (AI) models have been employed to predict more precise estimation of WQIs rather than conventional models. In this way, through the current study, multivariate adaptive regression spline (MARS) and least square-support vector machine (LS-SVM), as machine learning methods, were used to predict indices of the five-day biochemical oxygen demand (BOD5) and chemical oxygen demand (COD). To improve the proposed approaches, 200 series of field data, collected from Karoun River southwest of Iran, pertain to the nine independent input parameters, namely electrical conductivity (EC), sodium (Na + ), calcium (Ca 2+ ), magnesium (Mg 2+ ), orthophosphate ( PO 4 3 − ), nitrite ( NO 2 − ), nitrate nitrogen ( NO 3 − ), turbidity, and pH. The performances of the LS-SVM and MARS techniques were quantified in both training and testing stages by means of several statistical parameters. Furthermore, the results of the proposed AI models were compared with those obtained using artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple regression equations. Results of the present research work indicated that the proposed artificial intelligence techniques, as machine learning classifiers, were found to be efficient in order to predict water quality parameters.

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO 4 ), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH 4 -N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient ( r ), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals ( d -factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d -factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d -factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.

Landfill leachates from municipal landfills are usually heavily contaminated and thus require treatments before direct discharge into natural waters. Selecting the appropriate technology for leachate treatment is still a major challenge for operations in municipal landfills. Biodegradation is effective for treating young leachates, whereas old leachates require processes such as chemical oxidation, coagulation–flocculation, chemical precipitation, ozonation, activated carbon adsorption, and reverse osmosis. Recently, the combination of biological pretreatments followed by physico-chemical processes has been shown to be very efficient. Here we review the efficiency of biological treatment in combination with reverse osmosis to clean landfill leachates. We studied in particular processes including a membrane bioreactor, activated sludge, a rotating biological contactor, and up-flow anaerobic sludge blanket treatments, followed by reverse osmosis. We found a 99–99.5% removal of the chemical oxygen demand (COD), and a 99–99.8% removal of N–NH 4 + using reverse osmosis and activated sludge. Using reverse osmosis with a rotating biological contactor, we observed 99% removal of COD, biochemical oxygen demand and N–NH 4 + . The combination of reverse osmosis, activated sludge and rotating biological contactor removed 98–99.2% of Cl − and 99–99.7% of Pb. Total suspended solids are best removed, up to 99%, by either a combination of reverse osmosis with membrane bioreactor, or reverse osmosis with activated sludge.

This investigation reports the quantitative assessment of endophyte-assisted floating treatment wetlands (FTWs) for the remediation of sewage and industrial wastewater. Typha domingensis was used to vegetate FTWs that were subsequently inoculated with a consortium of pollutant-degrading and plant growth-promoting endophytic bacteria. T. domingensis, being an aquatic species, holds excellent potential to remediate polluted water. Nonetheless, investigation conducted on Madhuana drain carrying industrial and sewage water from Faisalabad City revealed the percentage reduction in chemical oxygen demand (COD) and biochemical oxygen demand (BOD ) to be 87% and 87.5%, respectively, within 96 h on coupling the plant species with a consortium of bacterial endophytes. With the endophytes surviving in plant tissue, maximal reduction was obtained in not only the aforementioned pollution parameters but for other major environmental quality parameters including nutrients (N and P), ions (Na and K ), Cl , and SO as well, which showed percentage reductions up to 90%, 39%, 77%, 91.8%, 40%, and 60%, respectively. This significant improvement in polluted wastewater quality treated with the proposed method render it safe to be discharged freely in larger water bodies as per the National Environmental Quality Standards (NEQS) of Pakistan or to be reused safely for irrigation purposes; thus, FTWs provide a sustainable and affordable approach for in situ remediation of sewage and industrial wastewater.

Agro-industrial wastewaters are characterized by the presence of multiple organic and inorganic contaminants of environmental concern. The high pollutant load, the large volumes produced, and the seasonal variability makes the treatment of these wastewaters an environmental challenge. A wide range of wastewater treatment processes are available, however the continuous search for cost-effective treatment methods is necessary to comply with the legal limits of release in sewer systems and/or in natural waters. This review presents a state-of-the-art of the application of advanced oxidation processes (AOPs) to some worldwide generated agro-industrial wastewaters, such as olive mill, winery and pulp mill wastewaters. Studies carried out just with AOPs or combined with physico-chemical or biological treatments were included in this review. The main remarks and factors affecting the treatment efficiency such as chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total organic carbon (TOC), and total polyphenols removal are discussed. From all the studies, the combination of processes led to better treatment efficiencies, regardless the wastewater type or its physico-chemical characteristics.

An accurate investigation of bio-physical and chemical parameters as proxy of in situ water quality conditions in the Himalayan region is highly challenging owing to cumbersome, strenuous, and physically exhausting sampling exercises at high altitude locations. The upper stretches of Yamuna River in the Himachal Pradesh are typical examples of such sampling locations that have rarely been examined in the past studies. A widely accepted and recognized QUAL 2Kw model is applied for estimating the water quality parameters on the upper segment of the Yamuna River from Paonta Sahib to Cullackpur. These water quality indicators mainly included electric conductivity, pH, dissolved oxygen, temperature, carbonaceous biological oxygen demand (CBOD), inorganic suspended solids, total nitrogen, total phosphorus, and alkalinity, which were systematically investigated for predicting the spatio-temporal trends during the year 2018. A total of 12 distantly located river sites were identified for sample collection and data validation using QUAL 2Kw model. The present investigation attempts to reveal long-term degraded impact of untreated wastewater and biased agricultural practices on the water quality conditions over the upper stretches of Yamuna River. The QUAL 2Kw-derived values for selected variables were inter-compared with in situ values, and any deviation from measured values was ascertained based on meaningful statistical measures. The lower error of RMSE, MRE, and BIAS, corresponding to < 15%, ± 10%., ± 20%, and ~ 1 slope evidently indicated better matchup of values, wherein, higher slope correlation coefficient ( R 2 ) of ~ 90% indicated the robust performance of the QUAL 2Kw algorithm in accurately predicting the chosen variables. A comparative assessment of QUAL 2Kw and WASP has been performed to justify aptness of water quality model in scenarios of lean flow.