Determining spatial and temporal changes of surface water quality using principal component analysis

Abstract

Study regionShahr Chai River, Lake Urmia basin, Iran. Study focusThe present study investigated the ability of the Principal Component Analysis (PCA) technique in pointing the environmental effects of discharges from different activities. Major indicator parameters were extracted for water quality analysis of the Shahr Chai River located in Lake Urmia basin, Iran. The water quality parameters were measured monthly in six stream reaches and were affected by discharges from intensive recreational centers and rural and agricultural activities. New hydrological insightsThe results showed that the NSFWQI and the WQImin-p could not distinguish between highly impacted stream reaches, while the calculated WQImin-c with two parameters including turbidity and fecal coliforms could meaningfully classify the sampling stations. These two parameters were selected based on results from correlation matrix. This study showed that calculation of the WQImin-c was an effective and easily applicable assessment method for different effluents’ impacts on stream water quality. The PCA technique could justifiably show different landscape effects on river water quality whereby the river downstream was found to experience decreased water quality.