Surface water, especially rivers, are the important sources for drinking water, agricultural and industrial uses. These reservoirs are easily affected by pollution and various activities. The vulnerability of surface water is greater than that the groundwater. Therefore, the importance of water quality evaluation, especially for drinkable water, has increased due to the reduction in its quality and quantity in recent years. Optimal use and conservation of water resources in terms of quantity and quality are the principles of sustainable development of any country. Water quality indices are among the useful tools in water quality assessment and management. The aim of this study was to evaluate the water quality of the Beheshtabad River in Chaharmahal and Bakhtiari Province, Iran by using the Liou Pollution Index and selecting the most important parameters based on Principal Component Analysis (PCA).
In this study, 7 water quality parameters including temperature, dissolved oxygen, biological oxygen demand, ammonia nitrogen, electrical conductivity, total suspended solids, and potential hydrogen were measured by standard methods along the river in 7 selected stations for 6 months (April to September 2016). Some of these parameters were measured at the sampling site and others in the laboratory. Then, the values of the Liou Pollution Index were calculated to evaluate the water quality of the Beheshtabad River in different stations. In this study, SPSS software was used to analyze the principal component. In the next step, the appropriateness of the statistical universe was assessed using the Kaiser-Meyer-Olkin test.
The results of this study showed that the water quality was good during the study period at sampling stations\, according to the Liou Pollution Index. The value of Liou Pollution Index was in the slightly polluted class in March in station 4. Then, the average of Liou Pollution Index in the Beheshtabad River was compared to different rivers. The result showed that the average of Liou Pollution Index in the studied river is higher than rivers outside Iran. In addition, according to the statistical technique of PCA, two components were introduced as the main component. The first component expressed 57.26% of the total variance and included dissolved oxygen, ammonia nitrogen, biological oxygen demand, electrical conductivity, total suspended solids and potential hydrogen parameters. The second component, temperature, expressed 21.3% of the total variance. Furthermore, the result of comparing the measured quality parameters with the standard value of surface water showed that biological oxygen demand, electrical conductivity, and total suspended solids parameters in some stations were within the standard range and in some others were higher, which indicated a negative result. The best and worst water quality in terms of biological oxygen demand was observed in May and June, respectively. The electrical conductivity in April and May in all stations was within the standard range. However, electrical conductivity was higher than the standard level in June in stations 4 and 5, higher again in July and August in stations 4 to 7, and higher as well in September in stations 2 to 7. The fish farming workshops, industrial pollution and geological survey may be the reasons. The value of potential hydrogen in all of the stations was within the standard range of 6.5 to 9.5. The value of dissolved oxygen was high because of increasing rainfall and stream flows due to the snow melting.
The results of this study showed that the water quality in the Beheshtabad River did not change during the last 6 months (April to September 2016), and water quality was good. In addition, PCA plays an important role in prioritizing the importance of each parameter in the pollution. Therefore, PCA places more important parameters in the first component and less important parameters in the subsequent, respectively. On the other hand, the measurement of physicochemical parameters is important for the study of water quality. This research demonstrates the usefulness and efficiency of the multivariate statistical technique of PCA and the use of indicators for effective management of surface water quality. Therefore, using water resources in the future is possible, and does not endanger their management based on sustainable development
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