scholarly journals Interpretation of Water Quality Data in uMngeni Basin (South Africa) Using Multivariate Techniques

2021 ◽  
Author(s):  
Innocent Rangeti ◽  
Bloodless Dzwairo
Keyword(s):  
South Africa ◽  
Water Quality ◽  
Cluster Analysis ◽  
Quality Data ◽  
Multivariate Techniques ◽  
Data Sets ◽  
Water Sampling ◽  
Water Quality Data ◽  
Cluster A ◽  
Spatio Temporal

The major challenge with regular water quality monitoring programmes is making sense of the large and complex physico-chemical data-sets that are generated in a comparatively short period of time. Consequentially, this presents difficulties for water management practitioners who are expected to make informed decisions based on information extracted from the large data-sets. In addition, the nonlinear nature of water quality data-sets often makes it difficult to interpret the spatio-temporal variations. These reasons necessitated the need for effective methods of interpreting water quality results and drawing meaningful conclusions. Hence, this study applied multivariate techniques, namely Cluster Analysis and Principal Component Analysis, to interpret eight-year (2005–2012) water quality data that was generated from a monitoring exercise at six stations in uMngeni Basin, South Africa. The principal components extracted with eigenvalues of greater than 1 were interpreted while considering the pollution issues in the basin. These extracted components explain 67–76% of the water quality variation among the stations. The derived significant parameters suggest that uMngeni Basin was mainly affected by the catchment’s geological processes, surface runoff, domestic sewage effluent, seasonal variation and agricultural waste. Cluster Analysis grouped the sampling six stations into two clusters namely heavy (B) or low (A), based on the degree of pollution. Cluster A mainly consists of water sampling stations that were located in the outflow of the dam (NDO, IDO, MDO and NDI) and its water can be described as of fairly good quality due to dam retention and attenuation effects. Cluster B mainly consist of dam inflow water sampling stations (MDI and IDI), which can be described as polluted if compared to cluster A. The poor quality water observed at Cluster B sampling stations could be attributed to natural and anthropogenic activities through point source and runoff. The findings could assist in determining an appropriate set of water quality parameters that would indicate variation of water quality in the basin, with minimum loss of information. It is, therefore, recommended that this approach be used to assist decision-makers regarding strategies for minimising catchment pollution.

scholarly journals Water Quality Database Offers New Tools to Study Aquatic Systems

Eos ◽  
2017 ◽  
Author(s):  
Lily Strelich
Keyword(s):  
Water Quality ◽  
Aquatic Systems ◽  
Quality Data ◽  
Data Sets ◽  
Web Portal ◽  
Water Quality Data

Researchers assess the federal Water Quality Portal, a Web portal that unites disparate water quality data sets and resources.

scholarly journals Characterizing water quality monitoring visualization with Hadoop and Google Maps

2017 ◽  
Vol 12 (4) ◽  
pp. 882-893 ◽  
Author(s):  
Weijian Huang ◽  
Xinfei Zhao ◽  
Yuanbin Han ◽  
Wei Du ◽  
Yao Cheng
Keyword(s):  
Water Quality ◽  
Water Environment ◽  
Large Data ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Quality Data ◽  
Data Sets ◽  
Google Maps ◽  
Water Quality Data ◽  
Computer Clusters

Abstract In water quality monitoring, the complexity and abstraction of water environment data make it difficult for staff to monitor the data efficiently and intuitively. Visualization of water quality data is an important part of the monitoring and analysis of water quality. Because water quality data have geographic features, their visualization can be realized using maps, which not only provide intuitive visualization, but also reflect the relationship between water quality and geographical position. For this study, the heat map provided by Google Maps was used for water quality data visualization. However, as the amount of data increases, the computational efficiency of traditional development models cannot meet the computing task needs quickly. Effective storage, extraction and analysis of large water data sets becomes a problem that needs urgent solution. Hadoop is an open source software framework running on computer clusters that can store and process large data sets efficiently, and it was used in this study to store and process water quality data. Through reasonable analysis and experiment, an efficient and convenient information platform can be provided for water quality monitoring.

scholarly journals Application of multivariate statistical techniques for investigating climate change and anthropogenic effects on surface water quality assessment: case study of Zohreh river, Hendijan, Iran

2021 ◽  
Vol 11 (6) ◽  
Author(s):  
Jalal Valiallahi ◽  
Saideh Khaffaf Roudy
Keyword(s):  
Water Quality ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Monitoring Program ◽  
T Test ◽  
Total Variance ◽  
Quality Data ◽  
Water Quality Data ◽  
Significant Difference ◽  
The Mean

AbstractIn the present study, evaluation of spatial variations and interpretation of Zohrehh River water quality data were made by using multivariate analytical techniques including factor analysis and cluster analysis also the Arc GIS® software was used. The research method was formulated to achieve objectives herein, including field observation, numerical modeling, and laboratory analyses. The results showed that dataset consisted of 11,250 observations of seven-year monitoring program (measurement of 15 variables at 3 main stations from April 2010 to March 2017). Factor analysis with principal component analysis extraction of the dataset yielded seven varactors contributing to 82% of total variance and evaluated the incidence of each varactor on the total variance. The results of cluster analysis became complete with t-test and made water quality comparison between two clusters possible. Results of factor analysis were employed to facilitate t-test analysis. The t-test revealed the significant difference in a confidence interval of 95% between the mean of calculated varactors 1, 2, 6 and 7 between two clusters, but there was no significant difference in the mean of other varactors 3, 4 and 5 between two groups. The result shows the effect of agricultural fertilizers on stations located at downstream of the ASK dam.

Water quality management in the Crocodile River catchment, Eastern Transvaal, South Africa

1995 ◽  
Vol 32 (5-6) ◽  
pp. 201-208
Author(s):  
P. J. Ashton ◽  
F. C. van Zyl ◽  
R. G. Heath
Keyword(s):  
South Africa ◽  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Quality Data ◽  
Catchment Management ◽  
Land Uses ◽  
Water Quality Data ◽  
River Catchment ◽  
Management Actions

The Crocodile River catchment lies in an area which currently has one of the highest rates of sustained economic growth in South Africa and supports a diverse array of land uses. Water quality management is vital to resource management strategies for the catchment. A Geographic Information System (GIS) was used to display specific catchment characteristics and land uses, supplemented with integrative overlays depicting land-use impacts on surface water resources and the consequences of management actions on downstream water quality. The water quality requirements of each water user group were integrated to optimise the selection of rational management solutions for particular water quality problems. Time-series water quality data and cause-effect relationships were used to evaluate different water supply scenarios. The GIS facilitated the collation, processing and interpretation of the enormous quantity of spatially orientated information required for integrated catchment management.

Preliminary Trial Results for the Redesigned STRIDER Platform With Sampling Capability From Different Depths

2017 ◽  
Author(s):  
Rakesh Joshi ◽  
Nathan Bane ◽  
Justin Derickson ◽  
Mark E. Williams ◽  
Abhijit Nagchaudhuri
Keyword(s):  
Water Quality ◽  
Water Samples ◽  
Depth Profiling ◽  
The United States ◽  
Environmental Research ◽  
Quality Data ◽  
United States Department ◽  
Water Sampling ◽  
Water Quality Data ◽  
Preliminary Trial

STRIDER: Semi-Autonomous Tracking Robot with Instrumentation for Data-Acquisition and Environmental Research, a semi-autonomous aquatic vessel, was envisioned for automated water sampling, data collection, and depth profiling to document water quality variables related to agricultural run-offs. Phase-I of the STRIDER project included the capability for STRIDER to collect water samples and water quality data on the surface of water bodies. This paper discusses the Phase-II efforts of the project, in which the previous design of STRIDER was adapted to extend its capabilities to include monitoring, depth profiling, and visualization of in-situ water quality data at various depths as well as collect water samples at each depth for bacterial analysis. At present, the vessel has been utilized for navigation to specified locations using remote control for collecting water quality data and water samples from the surface, as well as 2 feet and 4 feet below the surface at multiple UMES ponds. In a series of preliminary trial runs with the supervision of UMES faculty members and collaborators from the United States Department of Agriculture (USDA), STRIDER successfully collected 48 water samples for bacterial analysis at different locations and depths of ponds on the UMES campus. Design alternatives are being explored for more efficient water sampling capabilities.

scholarly journals 2-D Numerical modelling of hydrodynamic and sediment transport in Agathis Lake

2019 ◽  
Vol 270 ◽  
pp. 04019
Author(s):  
Rian Mantasa Salve Prastica ◽  
Herr Soeryantono ◽  
Dwinanti Rika Marthanty
Keyword(s):  
Water Quality ◽  
Rainy Season ◽  
Quality Data ◽  
Sampling Location ◽  
Water Sampling ◽  
Water Quality Data ◽  
Data Prediction ◽  
The Future ◽  
Water Quality Problem ◽  
Resource Modelling

Problems about lakes are inclining every year, especially for water quality problem. Policy decisions to conserve lakes could be well achieved by data prediction. Modelling by using software could describe the future conditions of lake and give policymakers to legislate the best alternative solution. This research studies Agathis lake characteristics. The lake is situated in Universitas Indonesia, Depok, West Java, Indonesia. The research employs Resource Modelling Associates (RMA) program to simulate hydraulic and water quality data. This research will determine the most representative water sampling location and type of TSS for calibration analysis. Next, this research simulates two scenarios of lake’s conditions. The simulation is run by modelling the lake with wetland scenario and with rainy season scenario. After running several iterations, the most representative water sampling location is in the upper part of the water column and the best model of TSS is mixed TSS. Two simulated scenarios produce a reasonable result and could predict the future conditions of Agathis Lake. The research recommends that the sediment trap, that is located in the inlet of the lake, should be well-treated regularly in rainy season, and Universitas Indonesia should manage and arrange the suitable plants to be applied in the future constructed wetland.

Impact of non-detects in water quality data on estimation of constituent mass loading

2002 ◽  
Vol 45 (9) ◽  
pp. 219-225 ◽  
Author(s):  
M. Kayhanian ◽  
A. Singh ◽  
S. Meyer
Keyword(s):  
Water Quality ◽  
Data Analysis ◽  
Censored Data ◽  
Quality Data ◽  
Data Sets ◽  
Mass Loading ◽  
Water Quality Data ◽  
Mean Values ◽  
Constituent Mass

Often, fractions of stormwater constituents are not detected above laboratory reporting limits and are reported as non-detect (ND), or censored data. Analysts and stormwater modelers represent these NDs in stormwater data sets using a variety of methods. Application of these different methods results in different estimates of constituent mean concentrations that will, in turn, affect mass loading computations. In this paper, different methods of data analysis were introduced to determine constituent mean concentrations from water quality datasets that include ND values. Depending on the number of NDs and the method of data analysis, differences ranging from 1 to 70 percent have been observed in mean values. Differences in mean values were, as shown by simulation, found to have significant impacts on estimations of constituent mass loading.

Identification and characterization of water quality transients using wavelet analysis. II. Application to electronic water quality data

1997 ◽  
Vol 36 (5) ◽  
pp. 337-348 ◽  
Author(s):  
Paul H. Whitfield ◽  
Kathleen Dohan
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Data Sets ◽  
Water Quality Data ◽  
Periodic Processes ◽  
Small Streams ◽  
Transient Events ◽  
Identification And Characterization

Two wavelet transform techniques for identifying water quality transients are applied to example data sets from two small streams. Temperature and conductance represent the range of properties from periodic processes to transient events. Both methods were successful in identifying the location, duration and magnitude of the transient events in these data sets. The methods may be refined to automate the detection and classification of transient events.

scholarly journals Surface Water Quality Assessment of Wular Lake, A Ramsar Site in Kashmir Himalaya, Using Discriminant Analysis and WQI

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Salim Aijaz Bhat ◽  
Ashok K. Pandit
Keyword(s):  
Water Quality ◽  
Discriminant Analysis ◽  
Quality Data ◽  
Stepwise Discriminant Analysis ◽  
Multivariate Techniques ◽  
Kashmir Himalaya ◽  
Water Quality Data ◽  
Data Set ◽  
Total N ◽  
The Mean

Multivariate techniques, discriminant analysis, and WQI were applied to analyze a water quality data set including 27 parameters at 5 sites of the Lake Wular in Kashmir Himalaya from 2011 to 2013 to investigate spatiotemporal variations and identify potential pollution sources. Spatial and temporal variations in water quality parameters were evaluated through stepwise discriminant analysis (DA). The first spatial discriminant function (DF) accounted for 76.5% of the total spatial variance, and the second DF accounted for 19.1%. The mean values of water temperature, EC, total-N, K, and silicate showed a strong contribution to discriminate the five sampling sites. The mean concentration of NO2-N, total-N, and sulphate showed a strong contribution to discriminate the four sampling seasons and accounted for most of the expected seasonal variations. The order of major cations and anions was Ca2+>Mg2+> Na+>K+ and Cl->SO42->SiO22- respectively. The results of water quality index, employing thirteen core parameters vital for drinking water purposes, showed values of 49.2, 46.5, 47.3, 40.6, and 37.1 for sites I, II, III, IV, and V, respectively. These index values reflect that the water of lake is in good condition for different purposes but increased values alarm us about future repercussions.

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