scholarly journals Multivariate analysis of the dominant and sub-dominant epipelic diatoms and water quality data from South African rivers

Water SA ◽  
2019 ◽  
Vol 33 (5) ◽  
Author(s):  
F. García-Rodríguez ◽  
G.C. Bate ◽  
P. Smailes ◽  
J.B. Adams ◽  
D. Metzeltin
Keyword(s):  
Water Quality ◽  
Multivariate Analysis ◽  
South African ◽  
Quality Data ◽  
Water Quality Data ◽  
Epipelic Diatoms

scholarly journals Development of a Universal Water Quality Index (UWQI) for South African River Catchments

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1534 ◽  
Author(s):  
Talent Banda ◽  
Muthukrishnavellaisamy Kumarasamy
Keyword(s):  
Water Quality ◽  
Water Resources ◽  
South African ◽  
Water Quality Index ◽  
Quality Index ◽  
Quality Data ◽  
Index Number ◽  
Sudden Increase ◽  
Water Quality Data ◽  
The Ideal

The assessment of water quality has turned to be an ultimate goal for most water resource and environmental stakeholders, with ever-increasing global consideration. Against this backdrop, various tools and water quality guidelines have been adopted worldwide to govern water quality deterioration and institute the sustainable use of water resources. Water quality impairment is mainly associated with a sudden increase in population and related proceedings, which include urbanization, industrialization and agricultural production, among others. Such socio-economic activities accelerate water contamination and cause pollution stress to the aquatic environment. Scientifically based water quality index (WQI) models are then essentially important to measure the degree of contamination and advise whether specific water resources require restoration and to what extent. Such comprehensive evaluations reflect the integrated impact of adverse parameter concentrations and assist in the prioritization of remedial actions. WQI is a simple, yet intelligible and systematically structured, indexing scale beneficial for communicating water quality data to non-technical individuals, policymakers and, more importantly, water scientists. The index number is normally presented as a relative scale ranging from zero (worst quality) to one hundred (best quality). WQIs simplify and streamline what would otherwise be impractical assignments, thus justifying the efforts of developing water quality indices (WQIs). Generally, WQIs are not designed for broad applications; they are customarily developed for specific watersheds and/or regions, unless different basins share similar attributes and test a comparable range of water quality parameters. Their design and formation are governed by their intended use together with the degree of accuracy required, and such technicalities ultimately define the application boundaries of WQIs. This is perhaps the most demanding scientific need—that is, to establish a universal water quality index (UWQI) that can function in most, if not all, the catchments in South Africa. In cognizance of such a need, this study attempts to provide an index that is not limited to certain application boundaries, with a contribution that is significant not only to the authors, but also to the nation at large. The proposed WQI is based on the weighted arithmetic sum method, with parameters, weight coefficients and sub-index rating curves established through expert opinion in the form of the participation-based Rand Corporation’s Delphi Technique and extracts from the literature. UWQI functions with thirteen explanatory variables, which are NH3, Ca, Cl, Chl-a, EC, F, CaCO3, Mg, Mn, NO3, pH, SO4 and turbidity (NTU). Based on the model validation analysis, UWQI is considered robust and technically stable, with negligible variation from the ideal values. Moreover, the prediction pattern corresponds to the ideal graph with comparable index scores and identical classification grades, which signifies the readiness of the model to appraise water quality status across South African watersheds. The research article intends to substantiate the methods used and document the results achieved.

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 Indicators of the deteriorate water quality status of reservoir, Sagar city, MP, India by multivariate analysis

2012 ◽  
Vol 23 (2) ◽  
pp. 155-162 ◽  
Author(s):  
Hemant Pathak
Keyword(s):  
Water Quality ◽  
Multivariate Analysis ◽  
Water Resources ◽  
Water Samples ◽  
Quality Data ◽  
Complex Nature ◽  
Large Set ◽  
Reservoir Water ◽  
Water Quality Data ◽  
Systematic Calculation

AbstractThis study was carried out in the Rajghat reservoir has been used as a drinking water resources from last decades. The aim was to investigate the temporal and spatial variability of water quality. Data investigation was processed using multivariate analysis. Samples were taken in 05 stations spreading out over the water body during three seasons were analyzed for their physicochemical characteristics in order to explore the contamination of reservoir water samples, using Correlation analysis, multiregression analysis and modeling. On comparing the results against BIS water quality standards, it is found that some of the water samples are polluted. A systematic calculation of correlation coefficient between water quality parameters has been done with the objective of minimizing the complexity and dimensionality of large set of data. An attempt has been made to find the seasonal quality of water in reservoir, in order to adopt a statistical model for examine water quality. The results of this study are believed to be valuable to help water resources managers understand complex nature of water quality issues and determine the priorities to improve water quality.

Water-quality data collected at Lake Anne, Reston, Virginia, 1997-1999

2000 ◽  
Author(s):  
Kathryn M. Conko ◽  
Margaret M. Kennedy ◽  
Karen C. Rice
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Water Quality Data
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