This study looks into the impacts of Sarawak Barrage (located in Kuching City, State of Sarawak, Malaysia) flushing and flooding-in operations on upstream total suspended solids (TSS). Water samples were collected from three upstream stations on 17th, 18th and 19th of August 2000. During flushing, it was observed that TSS levels increased over a distance of 11 km upstream. Maximum TSS recorded 250 mg/L at 1.5 km, 120 mg/L at 6 km, and 85 mg/L at 11 km upstream. During flushing, TSS levels increased with depth, and upstream turbulence intensities were indirectly proportional to distance from barrage. During flooding-in operation, TSS decreased from 249 to 155 mg/L at 1.5 km near the bottom, while surface TSS decreased from 86 to 58 mg/L. Generally, during flooding-in operation, TSS increased from 90 to 116 mg/L between 6 and 11 km. During flooding-in operation, maximum bottom TSS recorded 216 mg/L at 6 km upstream. Differences between bottom and mid-depth TSS levels were relatively minimal ranging from 19 to 45 mg/L, whilst the maximum difference between mid-depth and surface TSS was as high as 78 mg/L. After 9 hours of gate closure, a well-mixed regime prevailed from 6 to 11 km upstream with TSS ranged from 35 to 47 mg/L at all the monitoring points regardless of depths.

Introduction: Water quality is one of the most important factors in healthy living and human life. In this sense, some of the water quality parameters should be controlled in maintaining the human health and welfare. In today’, s industrial world, most of the global natural water sources, including those in Iran, contain impurities such as the TDS. Numerous factors that are includes include cations such as sodium ion (Na+), potassium ion (K+), calcium ion (Ca+2), and magnesium ion (Mg+2) and anions such as chloride ion (Cl-) and bicarbonate ion (HCO3-) with sulphate ion (SO42-) affect the concentration of these parameters in natural water systems. The total dissolved solids (TDS) is one of its most important factors,Many water resources development programs will be implemented to identify these factors. Accurate prediction of water quality parameters is a basic need for water quality management, human health, public consumption and household consumption. In the last decades, artificial intelligence (AI) techniques have become viable and popular due to their advantages, and have been widely developed in solving a variety of environmental engineering and water quality engineering problems. Methods: For the estimation of water quality parameters (WQPs), Singh et al. (2011) utilized the clustering method, or support vector clustering (SVC), to optimize surface water quality monitoring in the city of Lucknow, India. The overall view of the water quality index of their study area revealed that most of the study area come under highly to very highly polluted zones. Tan et al. (2012) predicted phosphorus values in China with the least square support vector regression (LSSVR) method. They compared the efficiency of the LSSVR method with neural networks of the radial basis function (RBF) and back-propagation (BP). Experimental results showed that the small sample case with noise, LSSVM method was better than multi-layer BP and RBF neural network and is able to better meet the requirements of water quality prediction. Liu et al. (2013) addressed WQPs prediction in aquaculture employing the GP and real-value genetic algorithm-SVM (RGA-SVR). They used the GA to modify the coefficients of the SVR method. The results showed the superiority of the RGA-SVR algorithm over other methods based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). Ghavidel and Montaseri (2014) employed ANN, GEP, and ANFIS with grid partition as well as ANFIS with subtractive clustering (ANFIS-SC) to predict TDS values of the Zarinehroud basin, Iran. A comparison was made between the above AI approaches, and the results demonstrated the superiority of GEP over the other intelligent models. Abyane (2014) compared artificial neural network (ANN) with multivariate linear regression (MLR) for prediction of BOD and COD in the wastewater treatment plant. In their study, ANN could predict BOD and COD parameters with higher precision than MLR. Results: Due to complex characteristics of time series WQPs, a standalone model can hardly satisfy the estimation accuracy requirements. Therefore, the hybrid models combined with different single models will be an effective way to improve the WQPs estimation accuracy. This study proposes a new and accurate hybrid model for predicting WQP (i. e., TDS) using ions at Varand and Garmrood, two hydrometric stations of Tajan basin, Iran. The proposed WQP estimating framework was developed based on the combination of a data pre-processing algorithms (i. e., EEMD) with two AI-based models that was not addressed by the literature related to the WQPs modelling. Acceptance and reliability of proposed hybridized and standalone models (e. g., artificial neural networks (ANN), EEMD-ANN, support vector machine (SVM) and EEMD-SVM) using five performance criteria and visual diagrams were evaluated. Comparison of results between independent and hybrid models showed that EEMD data pre-processing algorithm can increase the performance of the hybrid SVM model for estimating the TDS quality parameter in both training and testing stages at both considered hydrometric stations. For example, the EEMD-SVM model with RMSE = 20. 23 for the training phase and RMSE = 27. 29 for the test phase at Varand station and RMSE = 45. 26 for the training phase and RMSE = 40. 06 for the test phase at Garmrood station has performed better than other hybrid and standalone models. In general, the proposed hybridized model of support vector machines based on EEMD data pre-processing algorithm can be proposed as a superior model to decision makers for planning and management in the field of river water quality detection and determination.

Background: The growth of population, improvement of quality of life and the development of industries have led to increase in the rate of urban and industrial waste. As the leachate of the waste has a lot of pollution, influences harmfully human health and the environment. Researches have shown that the advanced oxidation processes (AOPs) such as Fenton and Fenton-related processes can reduce chemical oxygen demand (COD) of the leachate effectively.Materials and Methods: In this paper, the rate of decrease in the total suspended solids (TSS) of leachate from a composting facility in Isfahan was studied using electrochemical, Fenton and electro- Fenton processes. These processes were conducted at reaction times 0, 20, 40, and 60 minutes, with the currents of 0.5, 1 and 1.5A and hydrogen peroxide dosages 1000, 2000, and 3000 mg/L.Results: In the three mentioned processes, the most TSS removal was observed at the first 40 minute of the reactions. Among the studied processes, the electro-Fenton removed up to 92.4% of the TSS and was recognized as the most effective method. Furthermore, electrochemical and Fenton removed 41.7% and 60.3% of the TSS, respectively.Conclusion: Therefore, decreasing the TSS of leachate could change its qualities and reduce the leachate pollution and the adverse environmental effects.

Sand filters are composed of sand-filled containers with determined granulation and specified thicknesses of pressurized sand layers that work under pressure and they are placed in the center control system after the cyclone and before the grid or disk filter. The aim of this study was to determine the relationship between water turbidity and Total suspended solids (TSS). To determine the samples turbidity, they were tested using a spectrophotometer and the percentage of light passing was obtained through each sample separately. The test of determining total suspended solids was also performed. The results of this experiment showed that there is a quadratic equation between the percentage of light passing and total suspended solids, which is presented as the main equation from figure 3, that is more correlated with the data. As can be seen, at 450 nm, there is a higher correlation between the turbidity and the light transmittance and less error.

Estimation of total suspended solids in upland watershed of reservoirs using simulation models is a vital key to manage reservoirs water quality. It is consequently essential that these models undergo calibration and uncertainty analysis before their application. In this study, Soil and Water Assessment Tool model was applied to estimate stream flow and total suspended solids for Sofichai Watershed upstream of the Alavian Reservoir located in East-Azarbayjan province. The Generalized Likelihood Uncertainty Equation (GLUE) and Sequential Uncertainty Fitting (SUFI-2) were used in this study to calibrate and analyze the uncertainty of SWAT model. The performance of the GLUE and SUFI-2 was evaluated using four objective functions namely: Nash– Sutcliffe Efficiency (NS), coefficient of determination (R2), RMSE-observations standard deviation ratio (RSR) and the adjusted R2 coefficient (bR2). Uncertainty statistics used were the P-factor and R-factor. SUFI-2 proved to be a very efficient optimization algorithm for Calibration and uncertainty analysis. The model calibrated with SUFI-2 can therefore be applied confidently for water resources management, for quantification of scenarios of climate and land use change, and for estimation of the Best Management Practices efficiencies in the watershed.

Stormwater runoff from urban areas is a major source of many pollutants to water bodies. Suspended solids are one of the main pollutants because of their association with other pollutants. The objective of this study was to evaluate the relationship between suspended solids and other pollutants in stormwater runoff in the city of Tijuana. Seven sites were sampled during seven rain events during the 2009-2010 season and the different particle size fractions were separated by sieving and filtration. The results have shown that the samples have high concentration of total suspended solids, the values of which ranged from 725 to 4,411.6 mg/L. The samples were analyzed for chemical oxygen demand, total phosphorus, total nitrogen, and turbidity. The results show that most of the particles in suspended solids are in the particle fraction between 10 and 62 mm. A high association between the concentrations of suspended solids was found for chemical oxygen demand, phosphorus, and turbidity but not for total nitrogen.

Electrocoagulation process is one of the new and progressing methods to remove suspended matter. In this study, electrocoagulation was assessed to reduce total suspended solids (TSS), turbidity and hardness of the thickener overflow of hematite in Gole-Gohar Sirjan. Therefore, in a laboratory scale, a glass container with 3 liter volume was prepared and equipped with 6 electrodes made of iron, steel and aluminum. Parameters such as electrode material, contact time and voltage were studied. The aluminum electrode showed the highest TSS removal comparing to the iron and steel electrodes. Aluminum electrode in a voltage of 6V and retention time of 5 minutes removed 88.5 percent of TSS while the steel electrode in voltage of 6 V and retention time of 15 minutes have maximum turbidity equal to 81% (NTU 21). Also in various voltages and different retention times, there was no significant change in the hardness. Based on the technical and economic studies, capital, operating and maintenance costs for electrodes of aluminum, iron and steel were calculated separately. The results illustrated that iron electrode with respect to the total costs (878 Ria) is preferred in comparison with steel (2978 Ria) and aluminum electrodes (3160 Ria). The iron electrode is selected based on the ease of operation, the settling of solid sludge, dewatering capability suitable and less sludge production volume.

This article presents the modeling of hydrothermal characteristics and suspended solids in a long and large-volume reservoir with a highly fluctuating water level. A laterally averaged two-dimensional hydrothermal and water quality model was configured for the Shihmen Reservoir in northern Taiwan. The model was validated with measured data of water surface elevation, water temperature, and concentration of suspended solids in 2006. The results show that the numerical model was able to reproduce the measured data. The validated model was then used to investigate the effects of water withdrawal schemes at different depths and to estimate the residence time in the reservoir. When water is withdrawn from a great depth, the relatively warm water from the upper layers can replace that in the deep layers, thereby facilitating heat transfer from the surface to the deeper layer. Bottom-water withdrawal results in a lower concentration of suspended solids compared with withdrawal from depths of 20 and 40 m. The simulated results show that the residence time is approximately 154 days in the Shihmen Reservoir.