Abstract
Rivers are critical to agriculture, industry, and the needs of humans and wildlife. This study evaluates the water quality of the Beheshtabad River in Iran’s Chaharmahal and Bakhtiari Province, using water quality index and multivariate statistical methods. Nitrate, temperature, phosphate, turbidity, dissolved oxygen, biological oxygen demand, electrical conductivity, total solids, and pH were measured at five selected stations along the river over 6 months using standard methods. Water quality index results demonstrated that water quality varied in the selected stations between average and good and that pollution in this section of the Beheshtabad River increases from upstream to downstream. Clustering and principal component analysis were also utilized. Multivariate statistical methods were used to analyze water conditions for efficient management of surface water quality. Agricultural fertilizers, upstream wastewater discharge, and fish farms constitute the main elements that decrease the water quality of the Beheshtabad River. To preserve this water resource against pollution, the implementation of stringent rules and guidelines are needed to enhance health and preserve water resources for future generations.
Highlights
For human beings, rivers have always been vital, and to use water resources, cities and industrial as well as agricultural centers have been established close to them
To analyze other parameters, such as phosphate (PO4), nitrate ( NO3), total solids (TS), and biological oxygen demand (BOD), water samples were transferred to the laboratory and were analyzed using methods described in the American Public Health Association manual (APHA 1992)
The distance between each of the selected stations in the clusters obtained from cluster analysis (CA) is the result of correlation and autocorrelation between surface water qualitative parameters
Summary
Rivers have always been vital, and to use water resources, cities and industrial as well as agricultural centers have been established close to them. PCA is a multivariate statistical technique, and in the cases where large amount of data are available, it is a suitable means to decrease the data (Noori et al 2009, 2010). The Beheshtabad River is one of the most important rivers in Iran It provides water for several purposes including agricultural activities, fish farms, hydroelectric power plants, and drinking water. This is why monitoring the water quality of this river is very important. The water quality of the Beheshtabad River has been evaluated using NSFWQI, CA, and PCA in selected sampling stations
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