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.

scholarly journals Monitoring and assessment of Deepor Beel water quality using multivariate statistical tools

2018 ◽  
Vol 13 (4) ◽  
pp. 893-908
Author(s):  
Siddhant Dash ◽  
Smitom Swapna Borah ◽  
Ajay Kalamdhad
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Quality Parameters ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Statistical Tools ◽  
Multivariate Statistical ◽  
Sampling Locations

AbstractThe aim of this study was application of multivariate statistical techniques – e.g., hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA) – to analyse significant sources affecting water quality in Deepor Beel. Laboratory analyses for 20 water quality parameters were carried out on samples collected from 23 monitoring stations. HCA was used on the raw data, categorising the 23 sampling locations into three clusters, i.e., sites of relatively high (HP), moderate (MP) and low pollution (LP), based on water quality similarities at the sampling locations. The HCA results were then used to carry out PCA, yielding different principal components (PCs) and providing information about the respective sites' pollution factors/sources. The PCA for HP sites resulted in the identification of six PCs accounting for more than 84% of the total cumulative variance. Similarly, the PCA for LP and MP sites resulted in two and five PCs, respectively, each accounting for 100% of total cumulative variance. Finally, the raw dataset was subjected to DA. Four parameters, i.e., BOD5, COD, TSS and SO42− were shown to account for large spatial variations in the wetland's water quality and exert the most influence.

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 Water Quality Assessment of Porsuk River, Turkey

2010 ◽  
Vol 7 (2) ◽  
pp. 593-599 ◽  
Author(s):  
Suheyla Yerel
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Surface Water ◽  
Surface Water Quality ◽  
Principal Component ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Porsuk River

The surface water quality of Porsuk River in Turkey was evaluated by using the multivariate statistical techniques including principal component analysis, factor analysis and cluster analysis. When principal component analysis and factor analysis as applied to the surface water quality data obtain from the eleven different observation stations, three factors were determined, which were responsible from the 66.88% of total variance of the surface water quality in Porsuk River. Cluster analysis grouped eleven observation stations into two clusters under the similarity of surface water quality parameters. Based on the locations of the observation stations and variable concentrations at these stations, it was concluded that urban, industrial and agricultural discharge strongly affected east part of the region. Finally, this study shows that the usefulness of multivariate statistical techniques for analysis and interpretation of datasets and determination pollution factors for river water quality management.

scholarly journals Spatio-Temporal Variation of Groundwater Quality and Source Apportionment Using Multivariate Statistical Techniques for the Hutuo River Alluvial-Pluvial Fan, China

2020 ◽  
Vol 17 (3) ◽  
pp. 1055 ◽  
Author(s):  
Qianqian Zhang ◽  
Long Wang ◽  
Huiwei Wang ◽  
Xi Zhu ◽  
Lijun Wang
Keyword(s):  
Water Quality ◽  
Groundwater Quality ◽  
Principal Component ◽  
Domestic Sewage ◽  
Pollution Sources ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Polluted Sites ◽  
Industrial Sewage

Groundwater quality deterioration has become an environmental problem of widespread concern. In this study, we used a water quality index (WQI) and multivariate statistical techniques to assess groundwater quality and to trace pollution sources in the Hutuo River alluvial-pluvial fan, China. Measurement data of 17 variables in 27 monitoring sites from three field surveys were obtained and pretreated. Results showed that there were 53.09% of NO3−, 18.52% of SO42− and 83.95% of total hardness (TH) in samples that exceeded the Grade III standard for groundwater quality in China (GB/T 14848-2017). Based on WQI results, sampling sites were divided into three types: high-polluted sites, medium-polluted sites and low-polluted sites. The spatial variation in groundwater quality revealed that concentrations of total dissolved solids (TDS), Cl−, TH and NO3− were the highest in high-polluted sites, followed by medium-polluted and low-polluted sites. The temporal variation in groundwater quality was controlled by the dilution of rainwater. A principal component analysis (PCA) revealed that the primary pollution sources of groundwater were domestic sewage, industrial sewage and water–rock interactions in the dry season. However, in the rainy and transition seasons, the main pollution sources shifted to domestic sewage and water–rock interactions, nonpoint pollution and industrial sewage. According to the absolute principal component scores-multivariate linear regression (APCS-MLR), most water quality parameters were primarily influenced by domestic sewage. Therefore, in order to prevent the continuous deterioration of groundwater quality, the discharge of domestic sewage in the Hutuo River alluvial-pluvial fan region should be controlled.

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 Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245525
Author(s):  
Junzhao Liu ◽  
Dong Zhang ◽  
Qiuju Tang ◽  
Hongbin Xu ◽  
Shanheng Huang ◽  
...  
Keyword(s):  
Water Quality ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Temporal Analysis ◽  
Statistical Techniques ◽  
Complex Data ◽  
Monitoring Networks ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018–2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.

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