scholarly journals Assessment of Spatiotemporal Variations in the Water Quality of the Han River Basin, South Korea, Using Multivariate Statistical and APCS-MLR Modeling Techniques

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2469
Author(s):  
Yong-Chul Cho ◽  
Hyeonmi Choi ◽  
Soon-Ju Yu ◽  
Sang-Hun Kim ◽  
Jong-Kwon Im
Keyword(s):  
Water Quality ◽  
Principal Component ◽  
Domestic Sewage ◽  
Pollution Sources ◽  
Multivariate Statistical ◽  
Han River ◽  
Modeling Techniques ◽  
Han River Basin ◽  
Potential Factors ◽  
Potential Pollution

This study evaluated the spatiotemporal variability of water quality in the Han River Basin (HRB) as well as the contributions of potential pollution sources using multivariate statistical and absolute principal component score-multiple linear regression (APCS-MLR) modeling techniques. From 2011 to 2020, data on water quality parameters were collected from 14 sites in the Ministry of Environment’s water quality monitoring network. Using spatiotemporal cluster analysis, these sites were classified into two periods over the year (dry and wet seasons) and into three regions: low pollution region (LPR), moderate pollution region (MPR), and high pollution region (HPR). Through principal component analysis, we identified four potential factors accounting for 80.1% and 74.1% of the total variance in the LPR and MPR, respectively, and three that accounted for 72.7% of the total variance in the HPR. APCS-MLR results indicated domestic sewage and phytoplankton growth (25%), domestic sewage and seasonal influence (29%), and point pollution sources caused by domestic sewage and industrial wastewater discharge (31%) as potential factors for the LPR, MPR, and HPR. These results demonstrate that the multivariate statistical techniques and the APCS-MLR model can be effectively used to monitor network design, quantitatively evaluate potential pollution sources, and establish efficient water quality management policies.

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 Hydrochemical evaluation of surface water quality and pollution source apportionment in the Luan River basin, China

2011 ◽  
Vol 64 (10) ◽  
pp. 2119-2125 ◽  
Author(s):  
Huiliang Wang ◽  
Xuyong Li ◽  
Ying Xie
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
River Basin ◽  
Stream Water ◽  
Principal Component ◽  
Pollution Sources ◽  
Total Variance ◽  
Municipal Sewage ◽  
Main Stream ◽  
Potential Pollution

In the context of rapid economic growth in China, hydrochemical characteristics of stream water quality are being influenced by a variety of natural and anthropogenic inputs. We determined 10 hydrochemical parameters of the surface water at 29 monitoring sites in the Luan River basin of northern China during 2007–2009. Water quality hydrochemistry was evaluated using fuzzy comprehensive analysis based on the National Surface Water Environmental Quality Standards of China. Our results showed that 14 sites were classified as ‘meeting standard (MS)’ while the other 15 sites were classified ‘non-meeting standard (NS)’. According to principal component analysis, four potential pollution sources were identified that explained 80.6% of the total variance among these MS sites, and three potential pollution sources that explained 78.3% of the total variance among these NS sites. Furthermore, multi-linear regression of the absolute principal component scores was used to estimate contributions from identified pollution sources. Most water pollution variables were influenced primarily by municipal sewage and non-point pollution in MS sites. In NS sites, chemical industry wastewater pollution dominated. Pollution in the main stream was more serious than that in the small tributaries. Our findings provide useful information for developing better pollution control strategies for the Luan River.

scholarly journals Application of Environmetrics tools for geochemistry, water quality assessment and apportionment of pollution sources in Deepor Beel, Assam, India

2020 ◽  
Vol 15 (4) ◽  
pp. 973-992
Author(s):  
Siddhant Dash ◽  
Smitom Swapna Borah ◽  
Ajay S. Kalamdhad
Keyword(s):  
Water Quality ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Water Quality Assessment ◽  
Quality Parameters ◽  
Pollution Sources ◽  
Water Runoff ◽  
Runoff Discharge ◽  
Potential Pollution ◽  
Surface Water Runoff

Abstract The present study uses four Environmetrics tools: hierarchical cluster analysis (HCA), discriminant analysis (DA), principal component analysis (PCA), and positive matrix factorization (PMF) for the assessment of water quality and geochemistry of Deepor Beel, Assam, India. The hierarchical clustering classified the 23 sampling locations into three clusters, classifying them as sites of high, low, and moderate contamination respectively. The DA of the water quality dataset resulted in 9 parameters (EC, TDS, TSS, , Na+, Mg, Cd, Pb and OrgN), primarily responsible for the discrimination of the clusters. PCA was then employed on the normalized dataset for the identification of potential pollution sources. PCA yielded two significant principal components, describing anthropogenic and natural factors defining the water contamination. Finally, PMF was employed on the dataset matrix, with four pre-defined factors. Leaching from Boragaon landfill site, surface water runoff, discharge of effluents from the industries in the wetland and discharge from Basistha River were found to be the major contributors. The results of this study provide a comprehensive correlation between water quality parameters and their sources, which would thereby assist in better planning and management of wetland restoration.

Statistical Analsysis to Evaluate Heavy Metal Pollution in the Air Obatained by Moss Technique in Hanoi and its Surrounding Region

2019 ◽  
Vol 29 (3SI) ◽  
pp. 411
Author(s):  
N. H. Quyet ◽  
Le Hong Khiem ◽  
V. D. Quan ◽  
T. T. T. My ◽  
M. V. Frontasieva ◽  
...  
Keyword(s):  
Heavy Metal ◽  
Statistical Analysis ◽  
Metal Pollution ◽  
Heavy Metal Pollution ◽  
Principal Component ◽  
Multivariate Statistical ◽  
Five Factors ◽  
Heavy Metal Elements ◽  
Surrounding Areas ◽  
Potential Pollution

The aim of this paper was the application of statistical analysis including principal component analysis to evaluate heavy metal pollution obtained by moss technique in the air of Ha Noi and its surrounding areas and to evaluate potential pollution sources. The concentrations of 33 heavy metal elements in 27 samples of Barbula Indica moss in the investigated region collected in December of 2016 in the investigated area have been examined using multivariate statistical analysis. Five factors explaining 80% of the total variance were identified and their potential sources have been discussed.

scholarly journals SPATIAL EVALUATION OF THE WATER QUALITY FROM THE VELHAS RIVER CHANNEL, IN THE STATE OF MINAS GERAIS

2018 ◽  
Vol 15 (30) ◽  
pp. 75-86
Author(s):  
C. C. PINTO ◽  
K. B. ALMEIDA ◽  
S. C. OLIVEIRA
Keyword(s):  
Water Quality ◽  
Principal Component ◽  
Industrial Effluents ◽  
Metropolitan Region ◽  
Multivariate Statistical ◽  
Pollution Levels ◽  
Group 3 ◽  
Group 2 ◽  
Belo Horizonte ◽  
Monitoring Stations

This study presents an evaluation of the water quality variability of 19 monitoring stations located in the channel of the Velhas river, using multivariate statistical techniques - Cluster Analysis (CA) and Principal Component Analysis/Factor Analysis (PCA/FA). Sixteen physical-chemical parameters were evaluated between January 2009 and June 2016, totalizing 27,232 valid observations. The CA grouped the nineteen monitoring stations into three groups based on the pollution levels. The PCA/FA resulted in six latent factors for group 1, four for group 2 and five for group 3, accounting for 71.44%, 65.32% and 61.69% of the total variance in the respective water quality. The factors indicated that the parameters responsible for the variations in water quality are mainly related to the release of sanitary sewage and industrial effluents and also to agriculture and livestock activities. These results reflect different water quality conditions of the Velhas River in its extension but, in fact, it is verified a greater variability of the water in the Metropolitan Region of Belo Horizonte and its downstream, justified by the different loads of pollutants received in this region, mainly the releases of domestic sewage and industrial effluents.

scholarly journals Danube water quality and assessment on ecotourism in the biosphere reserve ‘Bačko Podunavlje’ in Serbia

2020 ◽  
Vol 20 (4) ◽  
pp. 1215-1228
Author(s):  
Sanja Obradović ◽  
Milana Pantelić ◽  
Vladimir Stojanović ◽  
Aleksandra Tešin ◽  
Dragan Dolinaj
Keyword(s):  
Water Quality ◽  
Ecological Status ◽  
Biosphere Reserve ◽  
Surface Water Quality ◽  
Principal Component ◽  
Quality Parameters ◽  
Danube River ◽  
Multivariate Statistical ◽  
Limit Values ◽  
Starting Point

Abstract ‘Bačko Podunavlje’ represents one of the largest and the best-preserved wetland areas of the upper Danube. Water quality is crucial for nature in protected areas and ecotourism. The paper is based on data for the period 1992–2016. Using multivariate statistical analysis, water quality was defined. One-factor analysis of variations is the starting point for the analysis of time variables (annual and monthly analysis). The principal component analysis (PCA) of the ten quality parameters is in the three factors that determine the greatest impact on the change in water quality. Results revealed the satisfactory ecological status of the Danube River in these sections (Bezdan and Bogojevo) and there is no threat that the biodiversity of this area is endangered by poor water quality, which fully justifies the possibilities for intensive development of ecotourism in the biosphere reserve. Suspended solids are the only parameter that exceeds the allowed limit values in a larger number of measurements, especially in the summer period of the year. Other analyzed water quality parameters range within the allowed limit values for the second class of surface water quality based on the Law on Waters (Republic of Serbia) and in accordance with the Water Quality Classification Criteria of ICPDR.

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

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