Evaluation of the surface water quality using statistical multi-variate techniques, case study: Aras watershed

It seems essential to understand temporal and spatial changes in water quality of rivers as one of the most dynamic ecosystems of the world. In this research, we have analyzed temporal and spatial changes in the parameters of water quality of Aras River in the period 1999-2011 at the gauge stations of Khodaafarin, Khazangah, and Jolfa, using statistical multi-variate, factor analysis, and Principal Component Analysis. The first component with the highest explanation of variance has greatest correlation with parameters of Mg2+, Ca2+, HCO-3, EC, and TDS. The component shows the ions and suspending particles at Khodaafarin station. Among the parameters of the first component, EC has the highest factor loading (0.98) as the main parameter of the component. At the Khazangah station, the first three components explain 53.6, 17.5, and 12.9 percent of variance, respectively. The first component has the highest correlation coefficient with the parameters of Mg2+, Ca2+, SO42-, Cl-, HCO-3, EC, and TDS. In Jolfa station, the first four components with the highest eigenvalues explain 50.7, 15.8, 13.2, and 5.8 percent of the variance.