scholarly journals Multivariate statistical methods for characterization of waste water quality

2021 ◽  
Vol 9 (3) ◽  
Author(s):  
Darja Kavšek ◽  
Darinka Brodnjak Vončina
Keyword(s):  
Water Quality ◽  
Statistical Methods ◽  
Oxygen Demand ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Waste Waters ◽  
Prevention Measures ◽  
Mutual Correlation ◽  
Seasonal Behavior ◽  
Linear Discriminant

The aim of this work is focused on water quality classification of the waste waters and evaluation of pollution by the monitoring measurements during period 2006-2008. Environmental monitoring was performed in the region of Trbovlje, Slovenia, with two sampling sites and 15 chemical and physicochemical water quality parameters (pH, temperature, suspended solids, settling matter, chemical oxygen demand, biochemical oxygen demand, AOX (adsorbable organic halogens), total phosphorus, ammonium, nitrite, sulphate, chloride, fluoride, sulphide and mineral oil content) monitored in monthly periods (total of 60 objects x 15 variables). For handling the results different chemometric methods were employed, such as basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, the principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA). Monitoring of general pollution of waste waters and following measuring parameters which are above permitted concentration level can be used for searching of pollution source and for planning prevention measures from pollution, as well. The study allows drawing new information from the data sets such as patterns of similarity between sampling locations, sources of pollution in the environment, seasonal behavior of chemical contents and time trends.

scholarly journals Chemometrics in analytical chemistry

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Darinka Brodnjak Vončina
Keyword(s):  
Analytical Chemistry ◽  
Environmental Samples ◽  
Edible Oils ◽  
Principal Component ◽  
Chemometric Methods ◽  
Prevention Measures ◽  
Mutual Correlation ◽  
Seasonal Behavior ◽  
Linear Discriminant

Chemometrics is a scientific discipline closely connected with statistics and mathematics. It has an important role in analytical chemistry. Modern analytical methods provide opportunity to collect large amounts of data for various samples. For handling analytical results different chemometric methods are employed, such as basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, the principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA). The objectives of chemometrics in analytical chemistry are focused on characterization and chemometrical classification of different samples. The quality of environmental samples such as water, sediment, soil, air samples etc. can be determined according to measured physical and chemical parameters, which represent the individual samples. Chemometric methods give information regarding measured parameters about similarity between sampling locations, sources of pollution, seasonal behavior and time trends. Monitoring of general pollution of environmental samples and following measuring parameters which are above permitted level given by legislation can be used for searching of pollution source and for planning prevention measures from pollution. Food samples can also be characterized by chemometrical methods. Chemometrics can be used for fast and efficient determination of food sample categories, such as edible oils, wines, fruits and fruit juices etc. Classification can also be performed according to the origin, source or season. From all these facts it is evident that the aim of chemometrics in analytical chemistry is high and extensive.

scholarly journals Chemometric characterization of textile waste waters from different processes

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Tina Jerič ◽  
Darinka Brodnjak Vončina ◽  
Alenka Majcen Le Marechal ◽  
Darja Kavšek
Keyword(s):  
Waste Water ◽  
Water Quality ◽  
Suspended Solids ◽  
Pollution Level ◽  
Waste Waters ◽  
Mutual Correlation ◽  
Linear Discriminant ◽  
Textile Waste ◽  
Textile Waste Water

The aim of this work is focused on water quality classification of the textile waste water streams and evaluation of pollution. Data from the chemical characterization of the effluents were elaborated to identify a useful separation in potentially treatment for reuse. This was done with the aim of realizing a full scale characterization of effluents. In the two textile companies analyzed, machineries are used to carry out different production processes such as sizing and desizing, weaving, scouring, bleaching, mercerizing, carbonizing, fulling, dying and finishing. Different process effluents from the same machinery were found to be very diverse in pollution level. 25 and 49 samples of textile waste waters from two different textile companies were analysed and physical chemical measurements were performed. The following physicochemical and chemical water quality parameters were controlled: absorbance measured at three different wavelengths, pH, conductivity, turbidity, total suspended solids, volatile suspended solids, chemical oxygen demand, metals content (Ba, Ca, Cu, Mn, K, Sr, Fe, Al, Na) and total nitrogen content. For handling the results, basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, were performed. Different chemometric methods, namely, principal component analysis (PCA), cluster analysis (CA), and linear discriminant analysis (LDA) were used to find hidden information about textile waste water quality.

Classification and Prediction of Goldfish Population and Water Quality Using a Potentiometric E-Tongue

2017 ◽  
Vol 60 (4) ◽  
pp. 1037-1044
Author(s):  
Zhenbo Wei ◽  
Yu Zhao ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Pattern Recognition ◽  
Comprehensive Evaluation ◽  
Back Propagation ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Pattern Recognition Methods

Abstract. In this study, a potentiometric E-tongue was employed for comprehensive evaluation of water quality and goldfish population with the help of pattern recognition methods. Four water quality parameters, i.e., pH and concentrations of dissolved oxygen (DO), nitrite (NO2-N), and ammonium (NH3-N), were tested by conventional analysis methods. The differences in water quality parameters between samples were revealed by two-way analysis of variance (ANOVA). The cultivation days and goldfish population were classified well by principal component analysis (PCA) and canonical discriminant analysis (CDA), and the distribution of each sample was clearer in CDA score plots than in PCA score plots. The cultivation days, goldfish population, and water parameters were predicted by a T-S fuzzy neural network (TSFNN) and back-propagation artificial neural network (BPANN). BPANN performed better than TSFNN in the prediction, and all fitting correlation coefficients were >0.90. The results indicated that the potentiometric E-tongue coupled with pattern recognition methods could be applied as a rapid method for the determination and evaluation of water quality and goldfish population. Keywords: Classify, E-tongue, Goldfish water, Prediction.

scholarly journals A Multivariate and Spatiotemporal Analysis of Water Quality in Code River, Indonesia

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Mochamad A. Pratama ◽  
Yan D. Immanuel ◽  
Dwinanti R. Marthanty
Keyword(s):  
Water Quality ◽  
Land Cover ◽  
Agricultural Land ◽  
Water Quality Management ◽  
Temporal Trends ◽  
Oxygen Demand ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Data ◽  
Water Quality Data

The efficacy of a water quality management strategy highly depends on the analysis of water quality data, which must be intensively analyzed from both spatial and temporal perspectives. This study aims to analyze spatial and temporal trends in water quality in Code River in Indonesia and correlate these with land use and land cover changes over a particular period. Water quality data consisting of 15 parameters and Landsat image data taken from 2011 to 2017 were collected and analyzed. We found that the concentrations of total dissolved solid, nitrite, nitrate, and zinc had increasing trends from upstream to downstream over time, whereas concentrations of parameter biological oxygen demand, cuprum, and fecal coliform consistently undermined water quality standards. This study also found that the proportion of natural vegetation land cover had a positive correlation with the quality of Code River’s water, whereas agricultural land and built-up areas were the most sensitive to water pollution in the river. Moreover, the principal component analysis of water quality data suggested that organic matter, metals, and domestic wastewater were the most important factors for explaining the total variability of water quality in Code River. This study demonstrates the application of a GIS-based multivariate analysis to the interpretation of water quality monitoring data, which could aid watershed stakeholders in developing data-driven intervention strategies for improving the water quality in rivers and streams.

scholarly journals Assessment of Anwai river water quality using the weighted arithmetic water quality index (WQI) in Delta State, Nigeria

2021 ◽  
Vol 13 (3) ◽  
pp. 913-922
Author(s):  
Kate Isioma Iloba ◽  
Nelson Owese Akawo ◽  
Perry Irouoghene Godwin
Keyword(s):  
Water Quality ◽  
Water Quality Index ◽  
Quality Index ◽  
Oxygen Demand ◽  
Principal Component ◽  
Aquatic Organisms ◽  
Total Coliform ◽  
Total Hardness ◽  
Index Method ◽  
Weighted Arithmetic

The weighted arithmetic water quality index method was used to assess the water quality of anthropogenically-laden Anwai-river within the Asaba-capital territory of Delta State, Nigeria, in Stations 1(Otulu), 2(Isele- asagba) and 3(Anwai-Asaba) using pH, electrical conductivity (EC), total dissolved solids (TDS), biochemical oxygen demand (BOD), dissolved oxygen (DO), turbidity, phosphates, nitrates, total hardness (TH) and coliforms, to determine its water quality status and its suitability for humans and aquatic biota. Aside from TDS, turbidity, and TH, other parameters such as pH (5.3-8.2), DO (2.0-2.8 mg/L), BOD (1.02-2.4 mg/L), EC (110-113 µS/cm), turbidity(2.3-5.2 NTU), TDS (8.0-16.0 mg/L), TH (30-62 mg/L), phosphates (0.13-0.28 mg/L), nitrates (0.05-0.27 mg/L) and Coliform (25.75-45.5 cfu/ml) indicated non-significant variableness (p>0.05) between Stations. Water depth, TDS, turbidity, TH, phosphate, nitrate and total coliform were significant contributors to the Anwai-river's water quality by Principal component analysis (PCA). The first principal component (PC1) exhibited 94.1% variance and a 0.1860 loading factor, while the second showed 5.9% variance and 0.0117 loading factor implying depth, flooding, excessive human activities and sewage disposal as important contaminants. Although the individual physiochemical-based water qualities were below the WHO recommended drinking water values translated into poor water quality by the weighted arithmetic water quality index at the three Stations; 86.83, 75.02 and 81.27 in Station's 1, 2 and 3 respectively, correspondingly poor to very poor based on Water quality index. The water of Anwai-river is a serious health threat to humans and aquatic organisms and thus, it should not be utilized untreated.

scholarly journals Biological Treatment of Car Wash Waste Waters

2017 ◽  
Author(s):  
Essi Malinen ◽  
Nico Id ◽  
Sanni Valtonen ◽  
Janne Hakala ◽  
Tiina Mononen ◽  
...  
Keyword(s):  
Waste Water ◽  
Water Quality ◽  
Water Treatment ◽  
Biological Treatment ◽  
Treatment Process ◽  
Waste Water Treatment ◽  
Oxygen Demand ◽  
Water Tank ◽  
Waste Waters ◽  
Car Wash

The purpose of this study was to examine how efficient a biological treatment process is in purifying car wash waste waters. Two Finnish automatic car washes having rotating bed biofilm reactors for waste water treatment were included in the study. Both of them are using 87 % of recycled water per car wash and only from 35 to 60 liters of fresh water. Samples were taken from the purified water tank every second week altogether seven times between the beginning of February and the end of May, 2012. The reduction of surfactants was at least 95 % and reduction of chemical oxygen demand (COD) between 87 and 95 % during the sampling period. Outdoor temperature seems not to have any significant effect on purification efficiency. Other water quality parameters such as conductivity, pH, oxygen concentration, total solids, and biological oxygen demand (BOD) were found to be on acceptable level based in comparison to values found in the literature. The high concentration of total nitrogen and total phosphorus in the purified water was caused by nutrients added to the bioreactor for optimal conditions for the microbes. In the studied car washes, the waste water treatment process managed to reduce input load considerably. The main challenges for the quality of purified water seems to be optimal nutrient input as well as on-line monitoring system for water quality.

Detection of Aflatoxin B1 in Pistachio Kernels Using Visible/Near-Infrared Hyperspectral Imaging

2019 ◽  
Vol 62 (5) ◽  
pp. 1065-1074
Author(s):  
Qifang Wu ◽  
Huirong Xu
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Nutrient Content ◽  
Aflatoxin Contamination ◽  
Quantitative Prediction ◽  
Spectra Analysis ◽  
Linear Discriminant ◽  
Standard Normal Variate

Abstract. Pistachios are susceptible to aflatoxin contamination because of their rich nutrient content. Hyperspectral imaging (HSI), a new method for collecting spectral and image information, has been successfully employed in contamination research to classify staple agricultural products, such as maize, that are contaminated with aflatoxins. However, only a few studies have been conducted on the nondestructive discrimination among contaminated nuts using HSI for both qualitative and quantitative purposes. Thus, the feasibility of directly detecting aflatoxin B1 (AFB1) in individual pistachio kernels using visible/near-infrared HSI (VNIR HSI) was explored in this study. A total of 300 pistachio kernels were randomly selected to prepare target samples that were artificially contaminated with 5, 10, 20, 30, or 50 ppb (parts per billion) of AFB1. Principal component analysis (PCA) showed an overall separation trend between the control and all contaminated kernels. Accuracies greater than 90.0% were obtained by linear discriminant analysis (LDA) for samples that were artificially contaminated with different concentrations of AFB1 based on spectra at 694 to 988 nm that had been preprocessed with standard normal variate (SNV) and Savitzky-Golay (SG) smoothing. The correlation coefficients of calibration and validation (rc and rv) from stepwise multiple linear regression (SMLR) models were all >0.9100. Moreover, five key wavelengths (708, 771, 892, 915, and 941 nm) closely associated with AFB1 contamination were identified using principal component spectra analysis. Generally, the results indicated that VNIR HSI could be employed for preliminary screening of pistachio kernels that were artificially contaminated with AFB1, even at the 5 ppb level. However, the quantitative prediction of the specific AFB1 concentration needed to be further improved. Keywords: Aflatoxins, Detection analysis, Hyperspectral information, Pistachios, Visible/near-infrared.

scholarly journals EVALUATING SURFACE WATER QUALITY AND WATER MONITORING PARAMETERS IN THE TIEN RIVER, VIETNAMESE MEKONG DELTA

2021 ◽  
Vol 83 (3) ◽  
pp. 29-36
Author(s):  
Thanh Giao Nguyen ◽  
Vo Quang Minh
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Monitoring Program ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Monitoring ◽  
Water Quality Parameters

The study aimed to evaluate the surface water quality of the Tien River and identify water quality parameters to be monitored using the water quality monitoring data in the period of 2011 - 2019. The water samples were collected at five locations from Tan Chau to Cho Moi districts, An Giang province for three times per year (i.e., in March, June, and September). Water quality parameters included temperature (oC), pH, dissolved oxygen (DO), total suspended solids (TSS), nitrate (NO3--N), orthophosphate (PO43--P), biological oxygen demand (BOD), and coliforms. These parameter results were compared with the national technical regulation on surface water quality QCVN 08-MT: 2015/BTNMT, column A1. Principal component analysis (PCA) was used to identify the sources of pollution and the main factors affecting water quality. The results of this study showed that DO concentration was lower and TSS, BOD, PO43--P, coliforms concentrations in the Tien river exceeded QCVN 08-MT: 2015/BTNMT, column A1. pH, temperature, and NO3--N values were in accordance with the permitted regulation. The water monitoring parameters were seasonally fluctuated. DO, BOD, TSS, and coliforms concentrations were higher in the rainy season whereas NO3--N and PO43--P were higher in the dry season. The PCA results illustrated that pH, TSS, DO, BOD, PO43--P and coliforms should be included in the monitoring program. Other indicators such as temperature and NO3--N could be considered excluded from the program to save costs. 

scholarly journals Water quality of the Atibaia river in the city of Paulínia/SP: evaluation of the Electrical Conductivity and Chloride variables

2021 ◽  
Vol 43 ◽  
pp. e66
Author(s):  
Camila Pereira Montovani ◽  
Cassiana Maria Reganhan Coneglian ◽  
Elaine Cristina Catapani Poletti
Keyword(s):  
Water Quality ◽  
Electrical Conductivity ◽  
Pearson Correlation ◽  
Oxygen Demand ◽  
Principal Component ◽  
E Coli ◽  
Thermotolerant Coliforms ◽  
Linear Correlation Analysis ◽  
The City

The present study aimed to evaluate the characteristics that most influenced the water quality variability of the Atibaia river in the city of Paulínia/SP, the coordinates of the water collection point are given by 22º44'23”(S) and 47º07 ' 40 ”(W), in the dry and rainy seasons, from 2006 to 2016. The data used in this study come from the monitoring of parameters carried out in the spring by a research team, accompanied by periodic collections of surface water samples and analyzes laboratory tests. The parameters addressed included: precipitation, temperature, Hydrogenionic potential (pH), turbidity, thermotolerant coliforms (Escherichia coli (E. coli)), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (Ntotal), total phosphorus (Ptotal), total dissolved solids (TDS), electrical conductivity (EC) and chlorides (Cl-). Water quality was assessed using linear correlation analysis, using the Pearson Correlation coefficient (r), and Multivariate Analysis, using Principal Component Analysis (PCA). The presence of EC, Cl- and COD in both periods indicates quality characteristics related to the mineralization of organic compounds present in the water and the eutrophication process.

Assessment of Aquatic Ecological Health: A Case Study of Lake Rewalsar, Himachal Pradesh

2020 ◽  
Vol 7 (01) ◽  
Author(s):  
RAMA KUMARI ◽  
PARMANAND KUMAR
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Quality Index ◽  
Quality Index ◽  
Management Strategies ◽  
Oxygen Demand ◽  
Principal Component ◽  
Component Analysis ◽  
Different Seasons

The present study was conducted for two years to analyze the water quality of the sacred lake Rewalsar. Water quality of different seasons was evaluated by water quality index. Various statistical techniques, such as correlation, principal component analysis were applied. Based on Water Quality Index, water quality of the lake was in the range of 33-80 in different seasons. Cluster analysis of similarity indicates the relationship intensity between the seasons as cluster ranged 80-100% during the study period. In the principal component analysis maximum variables (Conductivity, Alkalinity, Biochemical Oxygen Demand, Nitrates, Phosphates, and Chloride) shows maximum influence during the summer and monsoon. The outcome revealed that the major driving factors of water quality deterioration are the runoff of effluent from the domestic area and offering food materials to the fishes. So, it is necessary to implement effective management strategies for the conservation of the Rewalsarlake.

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