scholarly journals Evaluation of water pollution and source identification in Merbok River Kedah, Northwest Malaysia

2020 ◽  
Vol 16 (4) ◽  
pp. 458-463
Author(s):  
Ateshan Msahir Haidr ◽  
Misnan Rosmilah ◽  
Sinang Som Cit ◽  
Koki Baba Isa
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Anthropogenic Activities ◽  
Human Health Risk ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Data ◽  
Toxic Heavy Metals ◽  
Water Quality Data ◽  
Fish And Shellfish

This study investigates the temporal water quality variations and pollution sources identification in Merbok River using principal component analysis. The variables analyzed include As, Cd, Pb, Fe, Cr, Mn, Zn, Ni, Ca, Mg, Na, K, NH4, F, Cl, Br, NO2, NO3, SO4, PO4, pH, BOD, DO, COD, turbidity, and salinity. These variables were analyzed using inductively coupled plasma mass spectrometry, ion chromatography, and YSI multiprobe. Principal component analysis (PCA) was utilized to evaluate the variations of the most significant water quality parameters and identify the probable source of the pollutants. From the results of PCA, 86% of the total variations were observed in the water quality data with strong dominance of toxic heavy metals (As, Pb, and Cr), parameters associated with industrial discharge, domestic inputs, overland runoff (NH4, pH, BOD, DO, COD), agrochemicals (NO2, NO3, SO4, PO4), and weathering of basement rocks (Ca, Mg, Cl, F, K, and Na). Most of these parameters were present in concentrations exceeded the reference standards limits used in this study, indicating pollution of the river water. Together with the presence of microbial contamination, the results suggest potential human health risk due to water uses, fish and shellfish consumption. Moreover, the results revealed that anthropogenic activities and weathering were the main sources of pollutants in Merbok River. 

scholarly journals Multivariate Analysis of Water Quality Measurements on the Danube River

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3634
Author(s):  
Zoltan Horvat ◽  
Mirjana Horvat ◽  
Kristian Pastor ◽  
Vojislava Bursić ◽  
Nikola Puvača
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Cross Sections ◽  
Principal Component ◽  
Component Analysis ◽  
Danube River ◽  
Quality Data ◽  
Water Quality Data ◽  
The Danube River

This study investigates the potential of using principal component analysis and other multivariate analysis techniques to evaluate water quality data gathered from natural watercourses. With this goal in mind, a comprehensive water quality data set was used for the analysis, gathered on a reach of the Danube River in 2011. The considered measurements included physical, chemical, and biological parameters. The data were collected within seven data ranges (cross-sections) of the Danube River. Each cross-section had five verticals, each of which had five sampling points distributed over the water column. The gathered water quality data was then subjected to several multivariate analysis techniques. However, the most attention was attributed to the principal component analysis since it can provide an insight into possible grouping tendencies within verticals, cross-sections, or the entire considered reach. It has been concluded that there is no stratification in any of the analyzed water columns. However, there was an unambiguous clustering of sampling points with respect to their cross-sections. Even though one can attribute these phenomena to the unsteady flow in rivers, additional considerations suggest that the position of a cross-section can have a significant impact on the measured water quality parameters. Furthermore, the presented results indicate that these measurements, combined with several multivariate analysis methods, especially the principal component analysis, may be a promising approach for investigating the water quality tendencies of alluvial rivers.

scholarly journals Analysis of Water Quality for Marinculture in Moro, Karimun, Riau Islands with Principal Component Analysis

2020 ◽  
Vol 9 (2) ◽  
pp. 143
Author(s):  
Wiyoto Wiyoto ◽  
Irzal Effendi
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Data ◽  
Multiple Linear Regressions ◽  
Water Quality Data ◽  
Seawater Quality ◽  
Linear Regressions ◽  
Surface Water Samples

Finding a good location is of important aspects in mariculture. This can be done by evaluating the water quality data. The aims of the study were to evaluate the seawater quality at Moro, Karimun, Riau Islands and to analyze its compatibility for mariculture by using principal component analysis (PCA) and multiple linear regressions. Generally, seawater qualities in the study area were in the tolerance range for mariculture. Surface water samples were collected from five different sampling points around Moro Sea. PCA results demonstrated that there were eleven variation factors which explained 95.4% of the total variance. In addition, based on PCA and multiple linear regressions, four water quality predictors for environmental quality could be identified, that is nitrite (NO2), temperature, pH and dissolved oxygen. Multiple linear regressions showed that the contribution of each parameter to the water quality was significant (R2=1, P < 0.05).

scholarly journals SPATIAL AND TEMPORAL ASSESSMENT OF WATER QUALITY DATA USING MULTIVARIATE STATISTICAL TECHNIQUES

2016 ◽  
Vol 38 (2) ◽  
pp. 577
Author(s):  
Nícolas Reinaldo Finkler ◽  
Taison Anderson Bortolin ◽  
Jardel Cocconi ◽  
Ludmilson Abritta Mendes ◽  
Vania Elisabete Schneider
Keyword(s):  
Water Quality ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Quality Data ◽  
Spatial And Temporal Variation ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
And Cluster Analysis ◽  
Quality Variation ◽  
Human Density

The natural factors and anthropogenic activities that contribute to spatial and temporal variation in superficial waters in Caxias do Sul’s urban hydrographic basins were determined applying multivariate analysis of data. The techniques used in this study were Principal Component Analysis and Cluster Analysis. The monitoring was executed in 12 sampling stations, during January, 2009 to January, 2010 with monthly periodicity in total of 13 campaigns. Between chemical, biological and physical, 20 parameters were analyzed. The results state that with the use of ACP, a data variance of 70.94% was observed. Therefore, it testifies that major pollutants that contribute to a water quality variation in the county are classified as domestic and industrial pollutants, mainly from galvanic industry. Moreover, two clusters were found which differentiated regarding their location and distance from areas with a high human density, corroborating on identifying of impact due to human activities in urban rivers.

scholarly journals Principal component analysis of surface water quality data of the River Drava in eastern Croatia (24 year survey)

2012 ◽  
Vol 14 (4) ◽  
pp. 1051-1060 ◽  
Author(s):  
Vlatka Gvozdić ◽  
Josip Brana ◽  
Nela Malatesti ◽  
Danijela Roland
Keyword(s):  
Principal Component Analysis ◽  
Heterotrophic Bacteria ◽  
Surface Water Quality ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Data ◽  
Pollution Level ◽  
Complex Data ◽  
Contributing Factors ◽  
Water Quality Data

The River Drava is one of the major, inexhaustible water sources not only for Croatia, but also for the other European countries it flows through. This study is based on the observations of 15 water variables at three sampling stations in the lower River Drava over a 24 year period. Although the obtained results revealed an improvement of most of the parameters, the values of some of them (i.e. NH4-N, NO3-N, BOD5, total coliform and heterotrophic bacteria) are still above the approved limits for water Class II. The results of principal component analysis (PCA) confirmed an existence of three clearly separated zones. The first zone corresponds to a rural upstream part of River Drava, which is characterised with low level pollution. The influences of untreated domestic waters become more noticeable in the second more densely populated suburban zone (II) located upstream of the city of Osijek. According to the results of the PCA, untreated wastewaters from Osijek are becoming contributing factors to the high pollution level of the river in the third (III) suburban zone. This study shows the usefulness of the PCA method for analysis and interpretation of complex data sets as well as for determination of pollution sources.

Spatial-temporal variation assessment of the water quality in the Baiyangdian Lake of North China for the period of 2006 to 2016

2020 ◽  
Author(s):  
Han Quan ◽  
Sun Wenchao ◽  
Li Zhanjie
Keyword(s):  
Water Quality ◽  
Water Resource Management ◽  
North China ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Quality Data ◽  
Spatial Clusters ◽  
Water Quality Data ◽  
Baiyangdian Lake ◽  
Monitoring Stations

&lt;p&gt;Baiyangdian Lake is the largest freshwater lake in the North China Plain. In order to examine the driving mechanisms of changes on the lake&amp;#8217;s water quality, an improved Water Quality Index (WQI) method and multivariate statistical techniques were applied to analyze water quality in this study. Water quality data from six monitoring stations for the period of 2006 to 2016 were used. The calculation of the annual WQI indicated an improvement in lake&amp;#8217;s water quality over the past decade. Cluster analysis classified 12 months and the six monitoring stations into two clusters (dry-wet period and western-eastern part), respectively. Discriminant analysis provided fewer effective variable with only two parameters and six parameters to afford 96.0% and 93.8% correct assignations in the temporal and spatial clusters. Principal component analysis and factor analysis detected similar varifactors in the two temporal clusters, interpreting more variance related human activities in the water quality variation than the ones representing natural factors. The different varifactors related to pollution source were evaluated in the two spatial clusters. The result indicated water quality in the two regions are influenced by different types of anthropogenic activities. Our findings provide valuable information for decision-making related to pollution control, ecosystem restoration, and water resource management in Baiyangdian Lake, as well as other large, shallow lakes in high-intensity hu+man activities regions.&lt;/p&gt;

scholarly journals Water Quality Modelling of River Mahanadi using Principal Component Analysis (PCA) and Multiple Linear Regression (MLR)

2021 ◽  
Vol 10 (1) ◽  
pp. 83-98
Author(s):  
Chandra Sekhar Matli ◽  
Nivedita
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Quality Management ◽  
Surface Water Quality ◽  
Principal Component ◽  
Quality Data ◽  
Multivariate Statistical ◽  
Water Quality Data ◽  
Sampling Locations ◽  
Statistical Approaches

Surface water quality is one of the critical environmental concerns of the globe and water quality management is top priority worldwide. In India, River water quality has considerably deteriorated over the years and there is an urgent need for improving the surface water quality. The present study aims at use of multivariate statistical approaches for interpretation of water quality data of Mahanadi River in India. Monthly water quality data pertaining to 16 parameters collected from 12 sampling locations on the river by Central Water Commission (CWC) and Central Pollution Control Board (CPCB) is used for the study. Cluster analysis (CA), is used to group the sampling locations on the river into homogeneous clusters with similar behaviour. Principal component analysis (PCA) is quite effective in identifying the critical parameters for describing the water quality of the river in dry and monsoon seasons. PCA and Factor Analysis (FA) was effective in explaining 69 and 66% of the total cumulative variance in the water quality if dry and wet seasons respectively. Industrial and domestic wastewaters, soil erosion and weathering, soil leaching organic pollution and natural pollution were identified as critical sources contribution to pollution of river water. However, the quantitative contributions were variable based on the season. Results of multiple linear regression (MLR) are effective in explaining the factor loadings and source contributions for most water quality parameters. The study results indicate suitability of multivariate statistical approaches to design and plan sampling and sampling programs for controlling water quality management programs in river basins.

Principal Component Analysis of Hydrological Data

2010 ◽  
pp. 364-388
Author(s):  
Petr Praus
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Drinking Water ◽  
Ground Water ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Hydrological Data ◽  
First Case

In this chapter the principals and applications of principal component analysis (PCA) applied on hydrological data are presented. Four case studies showed the possibility of PCA to obtain information about wastewater treatment process, drinking water quality in a city network and to find similarities in the data sets of ground water quality results and water-related images. In the first case study, the composition of raw and cleaned wastewater was characterised and its temporal changes were displayed. In the second case study, drinking water samples were divided into clusters in consistency with their sampling localities. In the case study III, the similar samples of ground water were recognised by the calculation of cosine similarity, the Euclidean and Manhattan distances. In the case study IV, 32 water-related images were transformed into a large image matrix whose dimensionality was reduced by PCA. The images were clustered using the PCA scatter plots.

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