Cytotoxicity reduction of wastewater treated by advanced oxidation process

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Water Quality ◽  
Advanced Oxidation Process ◽  
Oxidation Process ◽  
Advanced Oxidation ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Treated Wastewater ◽  
Water Quality Parameter ◽  
Textile Printing ◽  
Before And After

The degradation of printing dyes from textile printing industry effluents was carried out by Advanced Oxidation Process (AOP) in which heterogeneous photocatalytic treatment of textile printing wastewater using UV/H2O2/TiO2 system was studied. For the treatment of textile effluents different concentration of titanium dioxide (TiO2) and effect of application time of UV radiation was investigated. The degradation of treated wastewater was estimated spectrophotometrically. To check the extent of mineralization and decolorization after treatment water quality parameter such as percentage degradation, COD, BOD, TOC, pH, DO and toxicity were studied. Before treatment the values of water quality parameters were as; COD (1950 mg/L), BOD (963 mg/L), TOC (3410 mg/L), pH (9.6) and DO (1.77 mg/L). After application of UV/H2O2/TiO2 degradation was observed to be 72% and reduction in COD, BOD, TOC were 58%, 57%, 48%, and increase in DO level was up to 49% respectively. For the evaluation of the toxicity of photocatalyticaly treated wastewater, Allium cepa and brine shrimp test were also carried out before and after treatment of printing wastewater.

ZnO/UV/H2O2 Based Advanced Oxidation of Disperse Red Dye

2020 ◽  
Vol 234 (1) ◽  
pp. 129-143 ◽  
Author(s):  
Aneela Jamil ◽  
Tanveer Hussain Bokhari ◽  
Munawar Iqbal ◽  
Muhammad Zuber ◽  
Iftikhar Hussain Bukhari
Keyword(s):  
Water Quality ◽  
Advanced Oxidation Process ◽  
Oxidation Process ◽  
Irradiation Time ◽  
Advanced Oxidation ◽  
Quality Parameters ◽  
Process Variables ◽  
Optimum Conditions ◽  
Initial Concentration ◽  
Red Dye

AbstractIn view of promising efficiency of advanced oxidation process, ZnO/UV/H2O2 based advanced oxidation process (AOP) was employed for the degradation of Disperse Red-60 (DR-60) in aqueous medium. The process variables such as concentration of catalysts, reaction time, pH, dye initial concentration and H2O2 dose were evaluated for maximum degradation of dye. The maximum degradation of 97% was achieved at optimum conditions of H2O2 (0.9 mL/L), ZnO (0.6 g/L) at pH 9.0 in 60 min irradiation time. The analysis of treated dye solution revealed the complete degradation under the effect of ZnO/UV/H2O2 treatment. The water quality parameters were also studied of treated and un-treated dye solution and up to 79% COD and 60% BOD reductions were achieved when dye was treated with at optimum conditions. The dissolved oxygen increased up to 85.6% after UV/H2O2/ZnO treatment. The toxicity was also monitored using hemolytic and Ames tests and results revealed that toxicity (cytotoxicity and mutagenicity) was also reduced significantly. In view of promising efficiency of UV/H2O2/ZnO system, it could possibly be used for the treatment of wastewater containing toxic dyes.

scholarly journals Panjang Karapas dan Laju Pertumbuhan Spesifik Kepiting Bakau (Scylla serrata) yang diberi Jenis Pakan Berbeda di Area Ekowisata Kampung Kepiting, Bali

2017 ◽  
Vol 4 (1) ◽  
pp. 38
Author(s):  
Ni Desak Putu Ida Suryani ◽  
Pande Gde Sasmita Julyantoro ◽  
Ayu Putu Wiweka Krisna Dewi
Keyword(s):  
Water Quality ◽  
Mangrove Ecosystem ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Scylla Serrata ◽  
Coastal Vegetation ◽  
Water Quality Parameter ◽  
Mud Crab ◽  
Different Types ◽  
Critical Components

Mangrove forest is tropical coastal vegetation that grow on muddy and sandy soils which affected by sea tides. One of important commercial species that live in mangrove ecosystem is the mud crab (Scylla serrata). Feed and water quality have been considered as critical components for supporting the growth both of weight and carapace length of this species. This study was conducted from January to February 2017 in the area of ??Ecotourism Kampung Kepiting, Bali. The influence of different natural feed such as Jerbung shrimp (Penaeus merguiensis), Mollusca, lemuru fish (Sardinella lemuru) and sea worms (Nereis sp.) on the growth performance of the mud crab were investigated. Water quality parameter data such as pH, DO, temperature, salinity and ammonium were also collected. The obtained data were analyzed by using variance analysis of Statistical Product and Service Solutions (SPSS) version 21. The result showed that the use of different types of feed have no effect on  the length of carapace, but it has significantly influence on  the specific growth rate of mud crab. Finally, different types of the given feeding were still resulted in the save range of water quality parameters for mud crab culture.

scholarly journals Pemberian Perasan Buah Mengkudu (Morinda citrifolia) Untuk Pengendalian Argulus Pada Ikan Mas Komet (Carassius auratus auratus) [Giving Morinda Fruit Distilation (Morinda citrifolia) For Argulus Handling On Carrasius auratus auratus]

2019 ◽  
Vol 4 (1) ◽  
pp. 45
Author(s):  
Iqbal Ghazali, Kismiyati, Gunanti Mahasri
Keyword(s):  
Water Quality ◽  
Carassius Auratus ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Morinda Citrifolia ◽  
Treatment Result ◽  
Water Quality Parameter ◽  
Randomized Design ◽  
Temperature Ranges ◽  
Completely Randomized Design

Abstract This study aims to determine the effect of giving Morinda fruit distilation for handling Argulus on Carrasius auratus auratus. The research method that used was experimentally with Completely Randomized Design (CRD) with five treatments and four replications. The used treatment are : medium with Morinda distilation mixed 0% (A), medium with Morinda distilation mixed 2,5% (B), medium with Morinda distilation mixed 3% (C), medium with Morinda distilation mixed 3,5% (D), medium with Morinda distilation mixed 4% (E). The results showed that giving Morinda fruit distillation on Carrasius auratus auratus which have Argulus infest significantly different (p <0.05) with the best treatment in D with six releasing Argulus and that fish can survive within 15 minutes dipping. The lowest treatment result in A (control) with nothing releasing Argulus. Water quality parameters are supporting this research. Supporting parameters measured during the study is the water temperature ranges 27° C, pH 7,5-8,5, DO 8 mg/L to 5 mg/L, and salinity from 0 to 3 ppt. Water quality parameter are still within tolerance limit for Carrasius auratus auratus

scholarly journals Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1782
Author(s):  
Elias Eze ◽  
Sarah Halse ◽  
Tahmina Ajmal
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Prediction Model ◽  
Mean Square Error ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
The Novel ◽  
Mean Square ◽  
Hybrid Prediction

Providing an accurate prediction of water quality parameters for improved water quality management is a topical issue in the aquaculture industry. Conventional prediction methods have shown different challenges like a poor generalization, poor prediction accuracy, and high time complexity. Aiming at these challenges, a novel hybrid prediction model with ensemble empirical mode decomposition (EEMD) and deep learning (DL) long-short term memory (LSTM) neural network is proposed in this paper. In this innovative hybrid EEMD-DL-LSTM model, firstly, the integrity of the datasets is enhanced by applying moving average filtering and linear interpolation techniques of water quality parameter datasets pre-treatment. Secondly, the measured real sensor water quality parameters dataset is decomposed with the aid of the EEMD algorithm into disparate IMFs and a corresponding residual item. Thirdly, a multi-feature selection process is applied to make a careful selection of a strongly correlated group of IMFs with the measured real water quality parameter datasets and integrate them as inputs to the DL-LSTM neural network. The presented model is built on water quality sensor data collected from an Abalone farm in South Africa. The performance of the novel hybrid prediction model is validated by comparing the results against the real datasets. To measure the overall accuracy of the novel hybrid prediction model, different statistical indices, namely the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), are used.

scholarly journals The rSPA Processes of River Water-quality Analysis System for Critical Contaminate Detection, Classification Multiple-water-quality-parameter Values and Real-time Notification

2016 ◽  
Vol 4 (1) ◽  
Author(s):  
Chalisa VEESOMMAI ◽  
Yasushi KIYOKI
Keyword(s):  
Water Quality ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Quality Analysis ◽  
Water Quality Parameter ◽  
Water Quality Analysis ◽  
High Effect ◽  
Analysis System ◽  
Total Dissolved Solid ◽  
Very High

The water quality analysis is one of the most important aspects of designing environmental systems. It is necessary to realize detection and classification processes and systems for water quality analysis. The important direction is to lead to uncomplicated understanding for public utilization. This paper presents the river Sensing Processing Actuation processes (rSPA) for determination and classification of multiple-water- parameters in Chaophraya river. According to rSPA processes of multiple-water-quality-parameters, we find the pollutants of conductivity, salinity and total dissolved solid (TDS), which are accumulated from upstream to downstream. In several spots of the river, we have analyzed water quality in a maximum value of pollutants in term of oxidation-reduction potential (ORP). The first range effect of parameter is to express high to very high effects in term of dissolved oxygen, second is to express intermediate to very high effect in term of conductivity, third is to express low to very high effect in term of total dissolved solid, fourth is to express completely safe to very high effect in term of turbidity and the final is to express completely safe for effect in term of salinity.

scholarly journals Multiplatform Earth Observation Systems for Monitoring Water Quality in Vulnerable Inland Ecosystems: Maspalomas Water Lagoon

2020 ◽  
Vol 12 (2) ◽  
pp. 284
Author(s):  
Francisco Eugenio ◽  
Javier Marcello ◽  
Javier Martín
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Concentration ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
Atmospheric Effects ◽  
Chl A ◽  
Inorganic Matter

The accurate monitoring of water quality indicators, bathymetry and distribution of benthic habitats in vulnerable ecosystems is key to assessing the effects of climate change, the quality of natural areas and to guide appropriate biodiversity, tourism or fisheries policies. Coastal and inland water ecosystems are very complex but crucial due to their richness and primary production. In this context, remote sensing can be a reliable way to monitor these areas, mainly thanks to satellite sensors’ improved spatial and spectral capabilities and airborne or drone instruments. In general, mapping bodies of water is challenging due to low signal-to-noise (SNR) at sensor level, due to the very low reflectance of water surfaces as well as atmospheric effects. Therefore, the main objective of this work is to provide a robust processing framework to estimate water quality parameters in inland shallow waters using multiplatform data. More specifically, we measured chlorophyll concentrations (Chl-a) from multispectral and hyperspectral sensors on board satellites, aircrafts and drones. The Natural Reserve of Maspalomas, Canary Island (Spain), was chosen for the study because of its complexity as well as being an inner lagoon with considerable organic and inorganic matter and chlorophyll concentration. This area can also be considered a well-known coastal-dune ecosystem attracting a large amount of tourists. The water quality parameter estimated by the remote sensing platforms has been validated using co-temporal in situ measurements collected during field campaigns, and quite satisfactory results have been achieved for this complex ecosystem. In particular, for the drone hyperspectral instrument, the root mean square error, computed to quantify the differences between the estimated and in situ chlorophyll-a concentrations, was 3.45 with a bias of 2.96.

scholarly journals Numerical solution of initial-boundary value problems with integral conditional for third-order-differential equations

2018 ◽  
Vol 22 (Suppl. 1) ◽  
pp. 211-219
Author(s):  
Mehmet Karakas
Keyword(s):  
Water Quality ◽  
Numerical Solution ◽  
Linear Connection ◽  
Quality Measurement ◽  
Real Data ◽  
Initial Boundary ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Water Quality Parameter ◽  
Third Order

Water quality differential equation based on the theoretical bases of change is a multiparameter mathematical. When we compared with water quality measurement valves, it is determined that the concentration valve rate is not balanced and the two parameters, change solution is current and unique. When change conditions only one solution will not be the determinant of Jacobi matrix linear connection. Therefore, this research will help the availability in theory and uniqueness of the solution to the problem of water quality parameters. This method provides compatibility between real data to issue water quality parameter change obtained using the equation of the estimated value of the third row and differantive. The numerical solution of start-border value problem which is integral conditioned for third-order-differential balance and the analytical property of problem is analyzed. The application phases are shown, contribution is given theorem, some remarks about the results produced and made in the light of their theorems.

scholarly journals Application of Artificial Neural Network for Forecasting Nitrate Concentration as a Water Quality Parameter: A Case Study of Feitsui Reservoir, Taiwan

2020 ◽  
Vol 15 (5) ◽  
pp. 647-652
Author(s):  
Sarmad Dashti Latif ◽  
Muhammad Shukri Bin Nor Azmi ◽  
Ali Najah Ahmed ◽  
Chow Ming Fai ◽  
Ahmed El-Shafie
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Water Quality Parameters ◽  
Water Quality Parameter ◽  
Ann Model ◽  
Artificial Neural ◽  
Feitsui Reservoir

Water resources play a vital role in various economies such as agriculture, forestry, cattle farming, hydropower generation, fisheries, industrial activity, and other creative activities, as well as the need for drinking water. Monitoring the water quality parameters in rivers is becoming increasingly relevant as freshwater is increasingly being used. In this study, the artificial neural network (ANN) model was developed and applied to predict nitrate (NO3) as a water quality parameter (WQP) in the Feitsui reservoir, Taiwan. For the input of the model, five water quality parameters were monitored and used namely, ammonium (NH3), nitrogen dioxide (NO2), dissolved oxygen (DO), nitrate (NO3) and phosphate (PO4) as input parameters. As a statistical measurement, the correlation coefficient (R) is used to evaluate the performance of the model. The result shows that ANN is an accurate model for predicting nitrate as a water quality parameter in the Feitsui reservoir. The regression value for the training, testing, validation, and overall are 0.92, 0.93, 0.99, and 0.94, respectively.

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