Abstract
In 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.
Highlights
Observational records and climate projections provide abundant evidence that freshwater resources are vulnerable and have the potential to be strongly impacted by climate change, with wide-ranging consequences for human societies and ecosystems (Bates et al 2008)
A multivariate statistical technique, principal component analysis (PCA) and hierarchical cluster analysis, factor analysis (FA) have been used to assess the spatial variability of water quality parameters and discriminate the relative magnitude of anthropogenic influences on the river water quality in the Zohreh river basin located in southwest Iran in Khuzestan province
PCA of the dataset resulted in seven principal components (PCs) with eigenvalues > 1 accounting for almost 82% of total variance; the higher the eigenvalue, the more significant the corresponding component (Tables 2, 3)
Summary
Observational records and climate projections provide abundant evidence that freshwater resources are vulnerable and have the potential to be strongly impacted by climate change, with wide-ranging consequences for human societies and ecosystems (Bates et al 2008). The Zohreh river basin is one of the important arias that nowadays is affected by anthropogenic and climate change. Since the Zohreh basin is located in Fars, Kohgiluyeh and Boyer-Ahmad and Khuzestan provinces, its “Discharges from municipals and industries are considered as a point and constant polluting source, while surface runoff is a seasonal phenomenon and non-point source due to its characteristics that are highly influenced by climate and seasonal changes” (Han et al 2009; Shrestha and Kazama 2007). Temporal and spatial changes both in natural process and anthropogenic influences cause spatiotemporal variations in water quality parameters; reliable and regular monitoring programs reflecting the variations have to be set. In Iran, governmental companies have carried out water quality monitoring programs, but many of those monitoring programs contain large datasets. The datasets contain rich information about the behavior of the water resources
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