AI Summary & Key Takeaways
AI InsightWhile the study revealed that the river is exposed to agricultural pollution alongside with the water quality character generated by the climatic conditions, it also suggested that multivariate statistical methods are useful tools in evaluating complex data sets such as water quality data, and monitoring the quality of water resources.
📌 3 Key Takeaways
Abstract
Monitoring water quality is one of the high priorities for the protection of water resources. Many different approaches are used to analyse and interpret the variables that determine the variance of water quality observed in various sources. Statistical methods, especially multivariate statistical techniques, constitute an important part of these approaches. In this study, ten water quality parameters, which were measured for twelve months from seven stations determined on Filyos River, were evaluated by carrying out principal component analysis (PCA) and cluster analysis (CA) from multivariate statistical methods. In addition, dominant quality parameters designating the quality of the water source were determined. According to PCA results, 4 principal components contained the key variables and accounted for 69.49% of total variance of surface water quality from Filyos River. Dominant water quality parameters were observed to be temperature, EC, DO and pH. While the study revealed that the river is exposed to agricultural pollution alongside with the water quality character generated by the climatic conditions, it also suggested that multivariate statistical methods are useful tools in evaluating complex data sets such as water quality data, and monitoring the quality of water resources.