scholarly journals Using multivariate statistical techniques to assess water quality of Nhu Y river in Thua Thien Hue province

2014 ◽  
Vol 17 (2) ◽  
pp. 50-60
Author(s):  
Ky Minh Nguyen ◽  
Lam Hoang Nguyen
Keyword(s):  
Water Quality ◽  
Correlation Analysis ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

The aims of this research are to assess water quality by organic and nutrient matters and identifying the environmental pressures, examine the impact of the loads to Nhu Y River, Thua Thien-Hue Province. Five stations were sampled at Nhu Y River, the research had monitoring of water quality parameters such as Temperature (Temp), Dissolved Oxygen (DO), Biological Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Nitrate (NO3-) and Phosphate (PO43-). The research used multivariate statistical techniques such as correlation analysis, principal component analysis (PCA) and cluster analysis (CA) to assess water quality. The correlation analysis shown a strong positive correlation exists between water quality parameters such as TempDO and BOD5COD (p<0.01). The PCA technique was applied to water quality data sets, which was obtained from Nhu Y River and the results show that the indices which has changed water quality. The results of the PCA using a varimax rotation technique were illustrated with two principal components (PC) and accounts for 62.207% of the overall total variance. The first PC accounted for 40.873% of the total variance, which was loaded with Temp, DO, BOD5 and COD. The second PC consists of NO3- and PO43- which accounts for 21.334% of the total variance, it can be due to the discharge of agricultural activities. Similarly, the CA has identified two major clusters involving: BOD5, COD, Temp, DO (the first cluster) and NO3-, PO43- (the second cluster).

scholarly journals Surface water quality assessment of Amingaon (Assam, India) using multivariate statistical techniques

2017 ◽  
Vol 12 (4) ◽  
pp. 997-1008
Author(s):  
Kunwar Raghvendra Singh ◽  
Nidhi Bharti ◽  
Ajay S. Kalamdhad ◽  
Bimlesh Kumar
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Trophic State ◽  
Oxygen Demand ◽  
Surface Water Quality ◽  
Quality Parameters ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Sources Of Pollution

Abstract The pollution of surface water has become a global environmental issue. Monitoring of surface water is essential to know the current status of water quality and maintain it at certain desirable level. In this study surface water quality of Amingaon has been analysed. Amingaon is a locality in North Guwahati (Assam, India). In last few decades’ the locality has undergone rapid and uncontrolled development activities which have a detrimental impact on its ecology and environment. Samples were collected from 12 lakes and analysed for 24 parameters namely temperature (T), pH, electrical conductivity (EC), turbidity (Tur), total suspended solids (TSS) and total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), chloride ion (Cl−), fluoride (F−), sulphate (SO42−), sodium (Na+), potassium (K+), calcium (Ca2+), dissolved oxygen (DO) biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonium nitrogen (NH3-N), total Kjeldahl nitrogen (TKN), nitrate (NO3−) total phosphorus (TP) and available phosphorus (AP). Multivariate statistical techniques were used for the assessment of water quality. Cluster analysis (CA) was used for classification of water quality parameters and principal component analysis (PCA) was used to identify the sources of pollution. CA grouped all the water quality parameters in three cluster. PCA resulted in six useful components which explained 90.54% of the total variance. Based on overall study it was concluded that the sources of pollution of lakes were the use of fertilizers, storm water runoff, land development and domestic waste water discharge. Trophic state of lakes was also evaluated using Carlson's Trophic State Index (TSI).

scholarly journals Evaluation of significantly modified water bodies in Vojvodina by using multivariate statistical techniques

2013 ◽  
Vol 67 (5) ◽  
pp. 823-833
Author(s):  
Svetlana Vujovic ◽  
Srdjan Kolakovic ◽  
Milena Becelic-Tomin
Keyword(s):  
Water Quality ◽  
Water Bodies ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Data Sets ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Cluster Properties

This paper illustrates the utility of multivariate statistical techniques for analysis and interpretation of water quality data sets and identification of pollution sources/factors with a view to get better information about the water quality and design of monitoring network for effective management of water resources. Multivariate statistical techniques, such as factor analysis (FA)/principal component analysis (PCA) and cluster analysis (CA), were applied for the evaluation of variations and for the interpretation of a water quality data set of the natural water bodies obtained during 2010 year of monitoring of 13 parameters at 33 different sites. FA/PCA attempts to explain the correlations between the observations in terms of the underlying factors, which are not directly observable. Factor analysis is applied to physico-chemical parameters of natural water bodies with the aim classification and data summation as well as segmentation of heterogeneous data sets into smaller homogeneous subsets. Factor loadings were categorized as strong and moderate corresponding to the absolute loading values of >0.75, 0.75-0.50, respectively. Four principal factors were obtained with Eigenvalues >1 summing more than 78 % of the total variance in the water data sets, which is adequate to give good prior information regarding data structure. Each factor that is significantly related to specific variables represents a different dimension of water quality. The first factor F1 accounting for 28 % of the total variance and represents the hydrochemical dimension of water quality. The second factor F2 accounting for 18% of the total variance and may be taken factor of water eutrophication. The third factor F3 accounting 17 % of the total variance and represents the influence of point sources of pollution on water quality. The fourth factor F4 accounting 13 % of the total variance and may be taken as an ecological dimension of water quality. Cluster analysis (CA) is an objective technique to identify natural groupings in the set of data. CA divides a large number of objects into smaller number of homogenous groups on the basis of their correlation structure. CA combines the data objects together to form the natural groups involving objects with similar cluster properties and separates the objects with different cluster properties. CA showed similarities and dissimilarities among the sampling sites and explain the observed clustering in terms of affected conditions. Using FA/PCA and CA have been identified water bodies that are under the highest pressure. With regard to the factors identified water bodies are: for factor F1 (Plazovic, Bosut, Studva, Zlatica, Stari Begej, Krivaja), for factor F2 (Krivaja, Keres), for factor F3 (Studva, Zlatica, Tamis, Krivaja i Keres) and for factor F4 (Studva, Zlatica, Krivaja, Keres).

scholarly journals Assessment of Tigris River Water Quality Using Multivariate Statistical Techniques

2019 ◽  
Vol 26 (4) ◽  
pp. 26-31
Author(s):  
Muntasir Shareef
Keyword(s):  
Water Quality ◽  
Factor Analysis ◽  
Total Variance ◽  
Quality Data ◽  
Statistical Techniques ◽  
Complex Nature ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Tigris River

The present study uses the multivariate statistical techniques by applying the Factor Analysis (Principle component method) to explain the observed water quality data of Tigris river within Baghdad city. The water quality was analyzed at eleven different sites, along the river, over a period of one year (2017) using 20 water quality parameters. Five factors were identified by factor analysis which was responsible from the 72.291% of the total variance of the water quality in the Tigris river. The first factor called the pollution factor explained 34.387% of the total variance and the second factor called the surface runoff and erosion factor explained 11.875% of the total variance. While, the third, fourth, and fifth factors explained 10.213%, 8.861% and 6.956% of the total variance and called pH, Silica and nutrient factors, respectively. Multivariate statistical techniques can be effective methods to aid water resources managers understand complex nature of water quality issues and determine the priorities to sustain water quality.

scholarly journals Multivariate Statistical Analysis of Water Quality and Trophic State in an Artificial Dam Reservoir

Water ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 186
Author(s):  
Md Mamun ◽  
Ji Yoon Kim ◽  
Kwang-Guk An
Keyword(s):  
Water Quality ◽  
Trophic State ◽  
Suspended Solids ◽  
Oxygen Demand ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Trophic State Index ◽  
Multivariate Statistical ◽  
Chl A ◽  
State Index

Paldang Reservoir, located in the Han River basin in South Korea, is used for drinking water, fishing, irrigation, recreation, and hydroelectric power. Therefore, the water quality of the reservoir is of great importance. The main objectives of this study were to evaluate spatial and seasonal variations of surface water quality in the reservoir using multivariate statistical techniques (MSTs) along with the Trophic State Index (TSI) and Trophic State Index deviation (TSID). The empirical relationships among nutrients (total phosphorus, TP; total nitrogen, TN), chlorophyll-a (CHL-a), and annual variations of water quality parameters were also determined. To this end, 12 water quality parameters were monitored monthly at five sites along the reservoir from 1996 to 2019. Most of the parameters (all except pH, dissolved oxygen (DO), and total coliform bacteria (TCB)) showed significant spatial variations, indicating an influence of anthropogenic activities. Principal component analysis combined with factor analysis (PCA/FA) suggested that the parameters responsible for water quality variations were primarily correlated with nutrients and organic matter (anthropogenic), suspended solids (both natural and anthropogenic), and ionic concentrations (both natural and anthropogenic). Stepwise spatial discriminant analysis (DA) identified water temperature (WT), DO, electrical conductivity (EC), chemical oxygen demand (COD), the ratio of biological oxygen demand (BOD) to COD (BOD/COD), TN, TN:TP, and TCB as the parameters responsible for variations among sites, and seasonal stepwise DA identified WT, BOD, and total suspended solids (TSS) as the parameters responsible for variations among seasons. COD has increased (R2 = 0.63, p < 0.01) in the reservoir since 1996, suggesting that nonbiodegradable organic loading to the water body is rising. The empirical regression models of CHL-a-TP (R2 = 0.45) and CHL-a-TN (R2 = 0.27) indicated that TP better explained algal growth than TN. The mean TSI values for TP, CHL-a, and Secchi depth (SD) indicated a eutrophic state of the reservoir for all seasons and sites. Analysis of TSID suggested that blue-green algae dominated the algal community in the reservoir. The present results show that a significant increase in algal chlorophyll occurs during spring in the reservoir. Our findings may facilitate the management of Paldang Reservoir.

scholarly journals Multivariate statistical approaches to benthic macroinvertebrates and water quality for farming impact assessment in Selangor River, Malaysia

2021 ◽  
Author(s):  
Nadeesha Dilani Hettige ◽  
Rohasliney Binti Hashim ◽  
Zulfa Hanan Ash’aari ◽  
Ahmad Abas Kutty ◽  
Nor Rohaizah Jamil
Keyword(s):  
Water Quality ◽  
Benthic Macroinvertebrates ◽  
Oxygen Demand ◽  
Fish Farming ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Ammoniacal Nitrogen ◽  
Fish Farms ◽  
Organic Contamination ◽  
Multivariate Statistical

Abstract This study examined the influence of fish farming activities on water quality and benthic macroinvertebrates at the Rawang sub-basin of Selangor River. Multivariate statistical techniques were used to determine major influencing water quality parameters causing organic contamination and the dominant pollution-tolerant benthic macroinvertebrates. Sampling was conducted at Guntong River (SR1), Guntong River’s tributary (SR2, the control site), Kuang River (SR3 and SR6), Gong River (SR4), and Serendah River (SR5) using random sampling techniques based on accessibility and proximity to fish farms. Benthic macroinvertebrates and water samples were collected from April 2019 to March 2020. Based on the principal components analysis (PCA), electrical conductivity (EC), dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammoniacal-nitrogen, and total suspended solids (TSS) were major water quality parameters influenced by fish farming activities. The Canonical Correspondence Analysis (CCA) revealed that several taxa of benthic macroinvertebrates (Chironomidae, Naididae, Lumbriculidae, Tubificidae, unidentified Oligochaeta, Leeches (Helobdella sp.), Planorbidae, and some Odonata) were moderately or highly sensitive to TSS, BOD, COD, turbidity, ammoniacal-nitrogen, and EC. These taxa were dominant in the sampling sites, which were close to fish farms. Findings in this study showed that fish farming activities impacted the water quality and benthic macroinvertebrates in this sub-basin.

scholarly journals Correlations chart: Tool to analyse the dynamics of water quality parameters

2019 ◽  
Vol 23 (5) ◽  
pp. 383-390
Author(s):  
Cézar H. B. Rocha ◽  
Antoine P. Casquin ◽  
Renata O. Pereira
Keyword(s):  
Water Quality ◽  
Suspended Solids ◽  
Graphical Representation ◽  
Oxygen Demand ◽  
Total Suspended Solids ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Statistical Techniques ◽  
Statistical Correlations ◽  
Minas Gerais State

ABSTRACT The search for statistical techniques and forms of graphical representation that can explain the most relevant correlations among limnological variables can help interpret phenomena in a body of water. The objective of the article was to propose a graphical representation of the correlations among limnological variables applied in the contributing basin of the Dr. João Penido reservoir, in Juiz de Fora, Minas Gerais state, Brazil. Six sections were monitored monthly from May 2012 to April 2014, analysing 15 water quality parameters and their statistical correlations. The correlations were represented graphically with the program Gephi 0.8.2-beta. The influence of organic matter (of natural and anthropogenic origin resulting from pasture runoff and sewage) on water quality was verified, with an observed increase in water quality parameters especially nitrogen and phosphorous, oxygen consumed, chemical oxygen demand, turbidity and total suspended solids. It is concluded that the correlation chart assists in the understanding of the dynamics of the water quality parameters at the different sites analysed.

scholarly journals Assessment of Seasonal and Spatial Variations of Coastal Water Quality Using Multivariate Statistical Techniques

2021 ◽  
Vol 9 (11) ◽  
pp. 1292
Author(s):  
Mohamad Alkhalidi ◽  
Abdalrahman Alsulaili ◽  
Badreyah Almarshed ◽  
Majed Bouresly ◽  
Sarah Alshawish
Keyword(s):  
Water Quality ◽  
Coastal Water ◽  
Pearson Correlation ◽  
Chlorophyll Concentration ◽  
Principal Component ◽  
Quality Parameters ◽  
Statistical Techniques ◽  
Principal Component Factor Analysis ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

This study investigates the seasonal and spatial trends in Kuwait’s coastal water’s physical, chemical, and biological parameters by applying multivariate statistical techniques, including cluster analysis (CA), principal component/factor analysis (PCA/FA), and the Pearson correlation (PC) method to the average daily reading of water quality parameters from fifteen stations over one year. The investigated parameters are pH, turbidity, chlorophyll-a, conductivity, dissolved oxygen (DO), phycoerythrin, salinity, and temperature. The results show that the coastal water of Kuwait is subjected to high environmental pressure due to natural and human interferences. During 2017, the DO levels were below the threshold limit, and at the same time, the water temperature and salinity were very high, causing a series of fish death events. CA resulted in three different regions based on the turbidity, including high, moderate, and low regions, and three seasons (winter, summer, and autumn). Spring is very short and overlaps with winter and summer. PCA/FA applied on the datasets assisted in extracting and identifying parameters responsible for the variations in the seasons and regions obtained from CA. Additionally, Pearson’s correlation resulted in a strong positive relation between chlorophyll and phycoerythrin in 7 out of the 15 stations. However, at high turbidity regions (stations 1 and 2), chlorophyll concentration was low. Additionally, the negative correlation between DO and temperature was observed at stations with rare human activities.

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