Spatiotemporal Variations of Water Quality in the Zayandehrud River, Iran

Knowledge of the spatiotemporal variations of river contamination from surface water enhances management decisions. This study assessed water quality parameters from 2000 to 2020 in four seasons in the Zayandehrud River of Iran. Principal Component Analysis (PCA) was utilized to decrease the dimensionality of water surface pollutants. Then, general linear models (GLMs) were applied to determine the impact of sampling stations along the river, season, year, and different land uses (agriculture, pasture, urban, and water) on the selected water quality parameters. The best model was selected using the Akaike information criterion (AIC) method, and the Wald test results indicated that the station factor significantly influences PC1 and PC2. The most positive effect was noticed in areas between urban (1000-meter buffer) and PC1, agriculture land use (10000-meter buffer) and PC2. In addition, the most significant damaging consequence was detected between agriculture (100-meter buffer) and PC1, pasture (1000-meter buffer) and PC2. This paper revealed that a combination of statistical techniques for evaluating spatiotemporal variation in water resources could benefit integrated environmental management.