This study illustrates the benefits of statistical techniques to analyze spatial and temporal variations in water quality. In this scope water quality differentiation caused by anthropogenic and natural factors in the Tahtali and Balçova reservoirs in western Turkey was investigated using discriminant analysis-DA, Mann Whitney U techniques. Effectiveness of pollution prevention measures was analyzed by Mann Kendall and Sen’s Slope estimator methods. The water quality variables were divided into three groups as physical-inorganic, organic and inorganic pollution parameters for the study. Results showed that water quality between reservoirs was differentiated for “physical-inorganic” and “organic pollution” parameters. Degree of influence of water quality by urbanization was higher in the Tahtali reservoir and in general, no trend detection at pollution indicators explained by effective management practices at both sites.
In the study, environmetric methods were successfully performed a) to explore natural and anthropogenic controls on reservoir water quality, b) to investigate spatial and temporal differences in quality, and c) to determine quality variables discriminating three reservoirs in Izmir, Turkey. Results showed that overall water quality was mainly governed by “natural factors” in the whole region. A parameter that was the most important in contributing to water quality variation for one reservoir was not important for another. Between summer and winter periods, difference in arsenic concentrations were statistically significant in the Tahtalı, Ürkmez and iron concentrations were in the Balçova reservoirs. Observation of high/low levels in two seasons was explained by different processes as for instance, dilution from runoff at times of high flow seeped through soil and entered the river along with the rainwater run-off and adsorption. Three variables “boron, arsenic and sulphate” discriminated quality among Balçova & Tahtalı, Balçova & Ürkmez and two variables “zinc and arsenic” among the Tahtalı & Ürkmez reservoirs. The results illustrated the usefulness of multivariate statistical techniques to fingerprint pollution sources and investigate temporal/spatial variations in water quality.