Turbidity Research Articles

A moving bed biofilm reactor coupled with an upgraded nanocomposite polyvinylidene fluoride membrane to treat an industrial estate wastewater

Industrial wastewaters threaten the environment potentially, however by implementing effective treatment techniques, industrial wastewater could be treated aiming to water reuse for various applications. Thus, in the present study a moving bed biofilm reactor (MBBR) followed by an efficient nanocomposite polyvinilyden fluoride (PVDF) membrane was operated to obtain high-quality effluent from an industrial estate wastewater. Hydraulic retention time (HRT) of 5–15 h, air flow rate (AFR) of 1–4 l/min, and filling ratio (FR) of 30–70% have been chosen as three independent variables to analyze, model, and optimize biological treatment process. Soluble COD (sCOD) and total nitrogen (TN) removal efficiencies were found to be 93.37 and 27.61 %, respectively, at the optimum condition with HRT of 10 h, AFR of 2 l/min, and FR of 70%. To generate high-quality treated wastewater, MBBR effluent was passed through an optimal amended nanocomposite PVDF membrane. To tailor PVDF membrane, tannic acid decorated by caffeine (caffeine-TA) was incorporated in the matrix of PVDF membrane in various loadings (0.1, 0.5, and 1 wt%). 0.5 wt% of the additive was found to be the optimal loading owing to an upsurge in the pristine membrane’s pure water flux (PWF) from 32.64 to 48.98 kg/m2.h, and a drop in the pristine’s water contact angle from 68.42° to 51.73° and also a 20% rise in the pristine’s flux recovery ratio (FRR). Also, microbial test was negative and turbidity was below 1 Nephelometric Turbidity unit (NTU) after filtering the MBBR effluent. As a conclusion, the combination of the operated MBBR and the optimally ameliorated PVDF membrane could be considered as a promising suggestion to produce high-quality effluent from industrial wastewaters.

Dissolved Oxygen Prediction Model for the Yangtze River Estuary Basin Using IPSO-LSSVM

Water ecology has always been key to environmental protection, and the combination of human activities and natural factors has caused eutrophication in the Yangtze estuary and adjacent waters. Among them, dissolved oxygen (DO) concentration is the key indicator to judge the quality of water. Firstly, using principal component analysis (PCA) to determine the number of parameters affecting dissolved oxygen concentration, the least squares support vector machine (LSSVM) prediction model with improved particle swarm optimization (IPSO) is proposed to be applied to the dissolved oxygen prediction in Shanghai’s Yangtze River basin through the data-driven modeling approach and the regression prediction capability of the neural network. Eight parameters of water temperature (WT), pH, potassium permanganate (KMnO4), ammonia nitrogen (NH4+-N), total phosphorus (TP), total nitrogen (TN), conductivity (Cond), and nephelometric turbidity unit (NTU) are selected as model inputs in the published public data, and the output is the dissolved oxygen concentration. The optimal combination of model parameters is found according to the IPSO algorithm, which effectively overcomes the parameter selection problem of regular support vector machines (SVM). The mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation coefficients of the evaluation indexes of this model (R2) are 0.1702, 0.2221, 0.0267, and 0.9751, respectively. Compared with other similar data driven models, this model has improved model accuracy and stability in predicting DO concentrations in the estuary, and thus it provides technical support for assessing and monitoring offshore water quality.

Detailed evaluation of physicochemical properties and microbial activities of Hanna Lake and Spin Karez

Abstract Access to clean drinking water is a major issue in many regions of the world, particularly in areas where groundwater is scarce. This study aims at determining the water quality of two lakes. Some parameters in this investigation were found to be above the WHO limit, while most were within the limit. For instance, the average value of electrical conductivity in Hanna Lake was 537.4 μS/cm, while it was 758.9 μS/cm in Spin Karez, which was above the WHO limits (>500 μS/cm). Turbidity in Hanna Lake was 4.17 nephelometric turbidity units (NTU), within the WHO limits (<5 NTU), while in Spin Karez, it was 9.5 NTU above the WHO limits, and dissolved oxygen concentration average values were 9.06 mg/l in Hanna Lake and 8.86 mg/l in Spin Karez, above the permitted limits provided by the WHO (6.5–8 mg/l). The study also found that both lakes had high concentrations of microbial colonies, with 65 CFU/100 ml in Hanna Lake and 56 CFU/100 ml in Spin Karez. Based on these findings, an efficient water treatment technique should be adopted to remove these highly concentrated parameters in both lakes for purified water and future water demands.

Neural network-based pH and coagulation adjustment system in water treatment

This document presents a machine learning model development as a tool to improve chemical dosing procedure in ariari regional aqueduct (ARA). The supervised learning model has been addressed starting from the knowledge of data color, turbidity and pH at the water inlet to the aqueduct and the dosing results of type A aluminum sulfate and calcium oxide (lime) obtained through jar tests. The construction of the automatic learning model had a comprehensive implementation and improvement field through continuous system training, which allowed an optimal dosage of Aluminum Sulfate and Lime to generate an outlet pH less than 7.5 and outlet turbidity less than 8 nephelometric turbidity unit (NTU). Those outlet water parameters meet the ministry of social protection criteria in Colombia. Also, a virtual jar test was created to reduce the time required to obtain chemical dosing values to less than a minute. In contrast, a laboratory test takes approximately a half-hour to displays results.

Treatment surface water using a novel pumpkin seed-based natural bio-coagulant: optimization by CCD and toxicity evaluation

<p>This goal of this study was to evaluate the effectiveness of a new coagulant extract from pumpkin seeds (PSC) for removing turbidity and organic compounds from different natural surface waters. Two design experiment methods, namely central composite design (CCD) and response surface methodology (RSM) were used to optimize PSC for removal of turbidity and organic matter via the chemical oxygen demand (COD) of surface water. The CCD results indicated that more than 74.7% of COD and 94.4% of nephelometric turbidity unit (NTUs) were removed under optimized conditions (pH = 7.4; mPSC = 1.5 g/L; time: 88 min). They allowed for final effluent with significantly higher quality in terms of organic content and safe to Vibrio fischeri to be obtained</p>

Influence of Cellulose on the Anoxic Treatment of Domestic Wastewater in Septic Tanks: Statistical Analysis of the Chemical and Physico-Chemical Parameters

Cellulose is a very common polymer in domestic wastewater (WW), representing a not negligible part of the organic substance contained in sewage. To date, many studies have highlighted the feasibility of reusing this compound in several ways (e.g., building sector, wastewater treatment, energy production, etc.) after its separation from domestic WW. However, studies about the impact of the absence of cellulose on the chemical and physico-chemical parameters of a biological process are still lacking. In this work, two pilot-scale plants were used to simulate an anoxic treatment of WW in septic tanks, with and without cellulose (CWW and NCWW, respectively), for three months. The results of the monitoring highlighted that T, pH, and electrical conductivity (EC) remained almost constants, in both cases. The Spearman correlation coefficients (SCC) for turbidity (TUR), total suspended solids (TSS), and color (COL) indicated a higher removal in the case of CWW (65%, 66%, and 56%, respectively). Organic substance and nitrogen forms showed a similar behavior with and without cellulose, but in the case of CWW, N-NH3 was highly negatively correlated with TUR (SCC: −0.54), TSS (−0.49), and COL (−0.39). A biological denitrification process was highlighted in both cases. Despite these differences, when statistically analyzing the trends of the chemical and physico-chemical parameters for CWW and NCWW, a significant difference due to the absence of cellulose was excluded. These results will be useful to the scientific community, as they exclude that the operational parameters of anoxic treatments and the effectiveness on pollutants removal can be affected in the case of preliminary cellulose separation from domestic WW for recovery/reuse purposes.

Predicting Optical Water Quality Indicators from Remote Sensing Using Machine Learning Algorithms in Tropical Highlands of Ethiopia

Water quality degradation of freshwater bodies is a concern worldwide, particularly in Africa, where data are scarce and standard water quality monitoring is expensive. This study explored the use of remote sensing imagery and machine learning (ML) algorithms as an alternative to standard field measuring for monitoring water quality in large and remote areas constrained by logistics and finance. Six machine learning (ML) algorithms integrated with Landsat 8 imagery were evaluated for their accuracy in predicting three optically active water quality indicators observed monthly in the period from August 2016 to April 2022: turbidity (TUR), total dissolved solids (TDS) and Chlorophyll a (Chl-a). The six ML algorithms studied were the artificial neural network (ANN), support vector machine regression (SVM), random forest regression (RF), XGBoost regression (XGB), AdaBoost regression (AB), and gradient boosting regression (GB) algorithms. XGB performed best at predicting Chl-a, with an R2 of 0.78, Nash–Sutcliffe efficiency (NSE) of 0.78, mean absolute relative error (MARE) of 0.082 and root mean squared error (RMSE) of 9.79 µg/L. RF performed best at predicting TDS (with an R2 of 0.79, NSE of 0.80, MARE of 0.082, and RMSE of 12.30 mg/L) and TUR (with an R2 of 0.80, NSE of 0.81, and MARE of 0.072 and RMSE of 7.82 NTU). The main challenges were data size, sampling frequency, and sampling resolution. To overcome the data limitation, we used a K-fold cross validation technique that could obtain the most out of the limited data to build a robust model. Furthermore, we also employed stratified sampling techniques to improve the ML modeling for turbidity. Thus, this study shows the possibility of monitoring water quality in large freshwater bodies with limited observed data using remote sensing integrated with ML algorithms, potentially enhancing decision making.

Modelling and optimisation of electrocoagulation/flocculation recovery of effluent from land-based aquaculture by artificial intelligence (AI) approaches.

This study examined the modelling and optimisation of the electrocoagulation-flocculation (ECF) recovery of aquaculture effluent (AQE) using aluminium electrodes. The response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) were used for the modelling, while the optimisation tools were the numerical RSM and genetic algorithm (GA). Furthermore, the kinetics of the ECF process was studied to provide insight into the mechanism governing the ECF of AQE. The experimental design was performed using the central composite design (CCD) of the RSM. The ANFIS modelling was accomplished via the Grid Partition (GP) of the data set, while the ANN used the multi-layer perceptron (MLP) based feed-forward system. Statistically, the prediction accuracy of the models followed the order: ANFIS (R2: 0.9990), ANN (R2: 0.9807), and RSM (R2: 0.9790). The process optimisation gave optimal turbidity (TD) removal efficiencies of 98.98, 97.81, and 96.01% for ANFIS-GA, ANN-GA, and RSM optimisation techniques, respectively. The ANFIS-GA gave the best optimization result at optimum conditions of pH 4, current intensity (3 A), electrolysis time (7.2min), settling time (23min), and temperature (43.8°C). In the kinetics study, the experimental data was analysed using pseudo-first-order (0.8787), pseudo-second-order (0.9395), and Elovich (R2: 0.9979) kinetic models; the Elovich model gave the best correlation with the experimental data showing that the process is governed by electrostatic interaction mechanism. This study effectively demonstrated that ECF recovery of AQE can effectively be modelled using RSM, ANN, and ANFIS and be optimised using RSM, ANN-GA, and ANFIS-GA techniques, and the order of performance is ANFIS > ANN > RSM and ANFIS-GA > ANN-GA > RSM, respectively.

Interactions Evaluation between the Jouamaa Hakama Groundwater and Ouljat Echatt River in the North of Morocco, Using Hydrochemical Modeling, Multivariate Statistics and GIS

The processed discharges from Tangier Automotive City’s (TAC) Chrafate Wastewater Treatment Plant (WWTP) contaminate the Jouamaa Hakama groundwater and the Ouljat Echatt river. We aimed to study the unknown interactions between surface water (SW) and groundwater (GW). A total of nine Jouamaa Hakama GW samples and eleven Ouljat Echatt SW samples were taken and analyzed in 2021 and 2022 to determine 16 physical and chemical parameters (pH, temperature (T), electrical conductivity (EC), dissolved oxygen (DO), total hardness (TH), turbidity (TURB), and total dissolved solids (TDS), cations: Na+, K+, Mg2+ and Ca2+, anions: Cl−, CO32−, HCO3−, NO3−, and SO42−). For exploitation of the data, we used a methodology based on hydrochemical modeling (HM), principal component analysis (PCA), Water Quality Index (WQI), Irrigation Water Quality Index (IWQI), inverse distance weighted interpolation (IDW) using Geographic Information Systems (GIS), and regression analysis (RA). We studied the interaction of the surface water of the river (contaminated by discharges from the WWTP) with the shallow groundwater on a strip of 100 m on either side of the river to understand the transverse and longitudinal dispersion of this pollution The investigations indicated that the major ions found in GW and SW were characterized in a different order in the anion list order Cl− > CO32− > NO3− > HCO3− > SO42− and Cl− > SO42− > CO32− > NO3− > HCO3−, respectively, while the concentrations of cations showed the same order for both: Na+ > Ca2+ > Mg2+ > K+. As a result, GW showed in the Piper diagram the type of sodium chloride to magnesium carbonate, while SW belongs to the sodium chloride to magnesium sulfate type. The WQI showed that the river waters are all unsuitable for use (WQI > 100), while the GW is of poor quality (WQI > 76). Moreover, the results of the GW–SW interaction along the river revealed a significant relationship (R2 = 0.85), which means that strong circulation and the infiltration of contaminated SW into shallow GW occur in this area. The approaches followed have been proven effective in evaluating water quality for human and animal uses. These results can help decision-makers in the region take suitable management measures to mitigate this environmental problem.

Evaluation of water quality of Angereb reservoir: a chemometrics approach

Deterioration of water quality of lakes and reservoirs has become major global concerns that impose serious environmental impacts for both aquatic and terrestrial environments. In the current study, many parameters like temperature (Temp), electric conductivity (EC), dissolved oxygen (DO), turbidity (TU), pH, biological oxygen demand (BOD), chemical oxygen demand (COD), total alkalinity (TA), total dissolved solids (TDS), total organic carbon (TOC), nitrate(NO3−), phosphate (PO43−) and chlorophyll a (chl-a) were determined. The study covered the Angereb reservoir and its tributaries on a monthly basis from January to March 2019 at five sampling stations in accordance with APHA 2017 guide lines for physicochemical analysis. The values of all the investigated parameters, except DO (at AU, AD, KU and KD), COD and TU, were below the maximum permissible limits set by WHO. Thus, the findings for DO, TU and COD demonstrated that remedial actions should be taken to improve the quality of the water in the reservoir and its tributaries. Multivariate statistical methods (PCA and CA) were applied to detect spatial and temporal variations of water quality parameter. The first three principal components were enough to develop the PCA score plot which explained about 71.32% of the total variance in the dataset. The PCA and CA have provided similar information; grouped the 24 samples into 3 significant clusters showing spatial variations but minimal temporal variations were observed within the samples collected in the period of January in the reservoir site. The water quality parameters, TU and BOD, were moderately positively loaded on the space of the first principal component and were found to be associated with each other, whereas the EC and TDS have shown moderate negative loading and positively associated with each other. This study suggested PCA and CA methods found to be useful tools for monitoring and controlling water quality parameters for selected sampling stations of surface water.

Spatiotemporal Dynamics of Water Quality Indicators in Koka Reservoir, Ethiopia

The science and application of the Earth observation system are receiving growing traction and wider application, and the scope is becoming wider and better owing to the availability of the higher resolution of satellite remote sensing products. A water quality monitoring model was developed using Sentinel-2 satellite remote sensing data set to investigate the spatiotemporal dynamics of water quality indicators at Koka Reservoir. L1C images were processed with an Atmospheric correction processor ACOLITE. The months from June 2021 to May 2022 and the years 2017 to 2022 were used for the temporal analyses. Algorithms were developed by using regression analysis and developing empirical models by correlating satellite reflectance data with in situ Chlorophyll-a (Chl-a), turbidity (TU), and Total suspended matter (TSS) measurements. All of the analyzed parameters have determination coefficients (R2) greater than 0.67, indicating that they can be turned into predictive models. R2 for the developed algorithms were 0.91, 0.92, and 0.67, indicating that good correlations have been found between field-based and estimated Chl-a, TU, and TSS, respectively. Accordingly, the mean monthly Chl-a, TU, and TSS levels have ranged from (59.69 to 144.25 g/L), (79.67 to 115.39 NTU), and (38.46 to 368.97 mg/L), respectively. The annual mean Chl-a, TU, and TSS vary from (52.86–96.19 µg/L), (71.04–83 NTU), and (36.58–159.26 mg/L), respectively, showing that the reservoir has been continuously polluted over the last seven years. The spatial study found that the distributions of Chl-a, TU, and TSS were heterogeneous, with Chl-a being greater in the south and southwest, and TU and TSS being higher on the western shore of the reservoir. In conclusion, these results show that there are spatial as well as temporal variations on water quality parameters. The proposed algorithms are capable of detecting optically active water quality indicators and can be applied in similar environmental situations.

Heavy metals in influent and effluent from 146 drinking water treatment plants across China: Occurrence, explanatory factors, probabilistic health risk, and removal efficiency

Heavy metals (HMs) in drinking water have drawn worldwide attention due to their risks to public health; however, a systematic assessment of the occurrence of HMs in drinking water treatment plants (DWTPs) at a large geographical scale across China and the removal efficiency, human health risks, and the correlation with environmental factors have yet to be established. Therefore, this study characterised the occurrence patterns of nine conventional dissolved HMs in the influent and effluent water samples from 146 typical DWTPs in seven major river basins across China (which consist of the Yangtze River, the Yellow River, the Songhua River, the Pearl River, the Huaihe River, the Liaohe River and the Haihe River) for the first time and removal efficiency, probabilistic health risks, and the correlation with water quality. According to the findings, a total of eight HMs (beryllium (Be), antimony (Sb), barium (Ba), molybdenum (Mo), nickel (Ni), vanadium (V), cobalt (Co) and titanium (Ti)) were detected, with detection frequencies in influent and effluent water ranging from 2.90 (Mo) to 99.30% (Ba) and 1.40 (Ti) to 97.90% (Ba), respectively. The average concentration range was 0.41 (Be)− 77.36 (Sb) μg/L. Among them, Sb (exceeding standard rate 8%), Ba (2.89%), Ni (21.43%), and V (1.33%) were exceeded the national standard (GB5749–2022). By combining Spearman’s results and redundancy analysis, our results revealed a close correlation among pH, turbidity (TURB), potassium permanganate index (CODMn), and total nitrogen (TN) along with the concentration and composition of HMs. In addition, the concentration of HMs in finished water was strongly affected by the concentration of HMs in raw water, as evidenced by the fact that HMs in surface water poses a risk to the quality of finished water. Metal concentration was the primary factor in assessing the health risk of a single metal, and the carcinogenic risk of Ba, Mo, Ni, and Sb should be paid attention to. In DWTPs, the removal efficiencies of various HMs also vary greatly, with an average removal rate ranging from 16.30% to 95.64%. In summary, our findings provide insights into the water quality and health risks caused by HMs in drinking water.

Development of a Frugal, In Situ Sensor Implementing a Ratiometric Method for Continuous Monitoring of Turbidity in Natural Waters.

Turbidity is a commonly used indicator of water quality in continental and marine waters and is mostly caused by suspended and colloidal particles such as organic and inorganic particles. Many methods are available for the measurement of turbidity, ranging from the Secchi disk to infrared light-based benchtop or in situ turbidimeters as well as acoustic methods. The operational methodologies of the large majority of turbidity instruments involve the physics of light scattering and absorption by suspended particles when light is passed through a sample. As such, in the case of in situ monitoring in water bodies, the measurement of turbidity is highly influenced by external light and biofouling. Our motivation for this project is to propose an open-source, low-cost in situ turbidity sensor with a suitable sensitivity and operating range to operate in low-to-medium-turbidity natural waters. This prototype device combines two angular photodetectors and two infrared light sources with different positions, resulting in two different types of light detection, namely nephelometric (i.e., scattering) and attenuation light, according to the ISO 7027 method. The mechanical design involves 3D-printed parts by stereolithography, which are compatible with commercially available waterproof enclosures, thus ensuring easy integration for future users. An effort was made to rely on mostly off-the-shelf electronic components to encourage replication of the system, with the use of a highly integrated photometric front-end commonly used in portable photoplethysmography systems. The sensor was tested in laboratory conditions against a commercial benchtop turbidimeter with Formazin standards. The monitoring results were analyzed, obtaining a linear trendline from 0 to 50 Nephelometric Turbidity Unit (NTU) and an accuracy of +/-0.4 NTU in the 0 to 10 NTU range with a response time of less than 100 ms.

Capacity Assessment of the Sewage Treatment plant at the Residency Complex / Al- Dour / salah Al-Deen

The efficiency of the waste water treatment plant at Salah Al-deen governorate was evaluated by measuring certain physical and chemical properties. The results showed that water temperature rates (19.840C), electric conductivity (544.2 Micro Siemens/cm), pH (7.76), biological oxygen demand (40.3 mg/L.) and chloride ion concentration (209.16 mg/ L.) in drained water were accordance the Iraqi standard properties determinants. Turbidity (7.9 naphthalene turbidity unit), total hardness (396.8 mg calcium carbonate /L.), and magnesium hardness (194.3 mg magncium carbonate /L.) were removed efficiently by the station, as the removal capacity of these determinants where (93.404%), (2.935%) and (15.337) respectively. The station was not capable enough to remove the calcium hardness (202.80mg calcium carbonate/L.) and total alkalinity (345.2 mg calcium carbonate/L (. The statistical analysis of the studied factors in the station have no significant differences among the studded sites and have highly significant differences among the studded months.

Prediction of stability parameters of ferric oxide nanofluids using response surface methodology based on desirability approach

Nanofluids are well known for their enhanced thermal properties. In spite of their excellent properties, there are certain hindrances to their applications on large scale. The issue of nanoparticle agglomeration in the base fluid with the consequent stability related issues is one of the main obstacles to the usage of nanofluids. Stability is crucial because the longer the nanofluids remain stable, the better their capacity to retain their thermal properties. Hence there is a need to evolve long-term stable nanofluids. Since there are a lot of factors, which are affecting the stability of the nanofluids, there is a need to optimize the process parameters. In this regard, central composite rotatable design (CCRD) was applied in this study to optimize the independent parameters of stability of ferric oxide nanofluids. For this, the performance of nanofluids was assessed by measuring the nephelometric turbidity units (NTU), based on the independent variables such as percentage of intensity of the nanoparticle, pH of the base fluid, and percentage volume concentration of the surfactant. All the parameters that are affecting individually and mutually were validated statistically using analysis of variance (ANOVA). A regression equation to evaluate the NTU was developed. The obtained results showed that the values predicted by the model and that obtained from the experiments were in good agreement with each other. It is observed that more than 99.65% of the variation could be predicted by the model developed for NTU. The response surface methodology (RSM) has revealed that the ideal process parameters for greater stability of nanofluids are 0.01% particle volume intensity, pH 3.2, and 0.6% surfactant intensity.

Extreme Hydrometeorological Conditions of Sediment Waves’ Formation and Migration in Peter the Great Bay (The Sea of Japan)

New data were obtained on the formation mechanisms of relief sediment waves (SWs) in Peter the Great Bay of the Sea of Japan. From the studies, we can assume that the sediment waves in the bay originate from two types of turbidity (TB) currents: (1) fine-dispersed TB currents caused by disintegrating internal waves and entrained by the Primorsky current along the shelf through SWs troughs; (2) fine- and coarse-grained TB currents, formed and entrained by the Primorsky current, intensified by the typhoons. This work presents the geological structure of the shelf area containing SWs. We established that the area of SWs location is on the border of change in facies conditions of sediment bedding in the bay. The transition from the inner shelf facies to the outer shelf facies occurs at a depth of 50–60 m. We analyzed the space variability of temperature and sound speed fields peculiar to the region on the results of hydrological CTD measurements and long-term measurements with moored vertical thermostrings. In the course of measurements, seven typhoons during their passage off the Primorye coast of the Russian Federation were registered. We found a significant change in the hydrological situation in the bay under the influence of extreme external factors. Under calm meteorological conditions, up to three mild thermoclines were observed in the bay, located at depths of 8–15 m, 30–35 m, and 45–60 m. During the passage of typhoons, they combined into one powerful thermocline at a depth of approximately 50–60 m, and in some cases, a high-gradient thermocline formed at the depths of 30–40 m (about 12–15 °C per 10 m of depth). The high-gradient layer significantly increases the amplitude and strength of internal gravity waves, thereby increasing the speed of currents in the water media. We should note that this period of transformation of the hydrological profile on the shelf under the influence of typhoons covers the period from August to October-November. We established that in the SWs location area, firstly, the process of internal waves disintegration intensifies in the autumn season, resulting in the formation of the waves with lengths close to the SWs horizontal scales (SWs wavelengths); secondly, under the influence of the typhoons entering the Sea of Japan, the kinetic energy of the shelf branch of the Primorsky current increases. In this case, it acts as a source of TB currents, including medium- and coarse-grained sedimentary material that forms SWs.

Study of hybrid performance of the methods applied for recycling Aq Qala-industrial park effluent

Aim: Currently, recycling of secondary effluent can be used sustainably as a new water source to minimize the water consumption in areas which are exposed to water crisis. Hence, it is necessary to appraise hybrid advanced treatment technologies performance and to determine the best alternative for reusing secondary effluent of industrial park in full-scale. Materials and Methods: The secondary effluent (biological-treated) of the centralized industrial park wastewater treatment plant (Aq Qala, Golestan province, Iran) is introduced into combined systems such as (1) sand filter (SF) and membrane bioreactor (MBR), (2) SF, MBR and granular activated carbon (GAC) (3) SF and GAC (4) SF, MBR, GAC, and reverse osmosis (RO), as the hybrid advanced scenarios. Results: The effluent of SF/MBR/GAC/RO showed the highest quality (>99% removal efficiency). In this scenario, pH, silica, manganese, iron, total suspended solids, turbidity, total coliform, and chemical oxygen demand (COD), alkalinity, hardness, total dissolved solids (TDS), chloride, and sulfate were determined 6.93 ± 0.19, 1.4 ± 0.6 mg/L, not detectable (ND), ND, <2 ± 0.2 mg/L, <1 Nephelometric Turbidity unit (NTU), ND and <2 ± 0.2 mg/L, 54.8 ± 1 2.5 mg/L, 50 ± 17 mg/L, 100 ± 14.89 mg/L, 68 ± 10.9 mg/L, and 44 ± 3.67 mg/L were observed in the range of product water standard for sensitive industries. Also, the maximum of efficiency of SF/MBR, SF/MBR/GAC, and SF/GAC systems was obtained 97.75% (as total coliforms), 62.65% (as COD), and 55.8% (as COD), respectively. Other parameters removed slight about 2% to 40%. However, hardness, alkalinity, and manganese concentrations not reduced after these systems (0% efficiency). Conclusions: The hybrid system of SF/MBR/GAC/RO was produced a clean and suitable water supply for the sensitive industries (e.g., intermediate-pressure boilers, cooling water, textile, etc.) of Aq Qala industrial park according to the environmental protection agency standards.

Monitoring river turbidity after a mine tailing dam failure using an empirical model derived from Sentinel-2 imagery.

Sudden failure of a mine tailing dam occurred in the municipality of Brumadinho, Minas Gerais, Brazil, on January 25, 2019. Approximately 12 million cubic meters of mine tailings discharged into the Paraopeba River, producing strong environmental and societal impacts, mainly due to a massive increase in turbidity (occasionally exceeding 50,000 Nephelometric Turbidity Units [NTU] (CPRM 2019). Remote sensing is a well-established tool for quantifying spatial patterns of turbidity. However, a few empirical models have been developed to map turbidity in rivers impacted by mine tailings. Thus, this study aimed to develop an empirical model capable of producing turbidity estimates based on images from the Sentinel-2 satellite, using the Paraopeba River as the study area. We found that river turbidity was most strongly correlated with the sensor's near-infrared band (NIR) (band 8). Thus, we built an empirical single-band model using an exponential function with an (R2 of 0.91) to characterize the spatial-temporal variation of turbidity based on satellite observations of NIR reflectance. Although the role of discharged tailings in the seasonal variation of turbidity is not well understood, the proposed model enabled the monitoring of turbidity variations in the Paraopeba River associated with seasonal resuspension or deposition of mine tailings. Our study shows the capability of single-band models to quantify seasonal variations in turbidity in rivers impacted by mine tailing pollution.

Tratamiento de lixiviados mezclados tipo I y tipo III mediante filtración y oxidación química

In Riobamba city from Ecuador, for more than thirty years solid urban waste was placed in an open dump, without gas and leachate control. In 2016, the landfill was technically closed and an emerging cell with the characteristics of a sanitary landfill was built nearby. The Environmental Health and Hygiene Management Department of the Riobamba Municipal Decentralized Autonomous Government monitors leachate generation, recording 19.24 m3/day of young leachate (Type I) from the emerging cell and 5.78 m3/day of mature leachate (Type III) from the old landfill. The two types of leachates are mixed in the main collection pipe that reaches a storage pool that receives 25.02 m3/day. The results of the physical-chemical analysis determine that the values of the parameters are outside the permissible limits. Turbidity is &gt;1000 NTU (Nephelometric Turbidity Unit), and the concentration of suspended solids is 1200 mg. L-1 and iron 135.85 mg. L-1. Calcium (1223 mg. L-1) and manganese (356.15 mg. L-1) were also evaluated, as these minerals are related to the 16,000 mg. L-1 of hardness. For the treatment of the mixed leachate, two phases are proposed: a) physical treatment by downward filtration in a granular bed to reduce the concentrations of suspended solids, and b) chemical treatment by oxidation using sodium hypochlorite. Laboratory results showed a 96.40% decrease in the concentration of suspended solids. Turbidity stabilized at 65 UTN. Iron, calcium, and manganese concentrations decreased by 95.60%, 96.28%, and 50.60%, respectively.

Taguchi optimization of YSZ/alumina/silica colloids for suspension plasma sprayed coating process

The stability of suspensions plays a key role in the water-based suspension plasma sprayed coating process. The effects of suspension parameters, such as the solid content and the type/content of dispersants on the suspension stability were studied in this work. In this regard, a Taguchi L9 array design of experiments was used to optimize submicron-sized YSZ/nano-sized alumina/silica additive suspensions with low viscosity, high steady-state turbidity, and inter-particle electrostatic charges. The optimal stable suspension with the viscosity of 4.2 cp, the zeta potential of 65 mV, and the Nephelometric Turbidity Units (NTU) of 3503 was used as the feedstock for suspension plasma spraying. Accordingly, a typical columnar coating morphology was obtained with the porosity of almost 19.3 vol%, which is a desirable feature for high-temperature applications. In conclusion, this study reflects the merit of the Taguchi design of experiments to optimize multicomponent thermal barrier coatings.