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Performance of Seven Water Quality Indices (WQIs) in a Mediterranean River.

Ioanna ZotouVassilios A TsihrintzisGeorgios D Gikas
Published in: Environmental monitoring and assessment (2019)
The comparative performance of seven widely used Water Quality Indices (WQIs) is evaluated in a fluvial water body. The computation of the individual WQIs was based on bi-weekly measurements of eleven water quality parameters in Vosvozis River situated in Rhodope Prefecture, North Greece. The sampling campaign lasted 16 months (from August 2005 to November 2006), and over this period, the individual parameters were measured at three gauging stations along the river. The indices which were applied were the Prati's Index of Pollution, Bhargava's Index, Oregon WQI, National Sanitation Foundation (NSF) WQI, Canadian Council of Ministers of Environment (CCME) WQI, Dinius' Second Index, and the Weighted Arithmetic WQI. Apart from the computation of the above WQIs and the subsequent qualitative classification of the water body, the most important quality parameters per methodology were determined through the calculation of the correlation coefficient between each water quality parameter and the final WQI value. The results showed that Bhargava's Index along with NSF WQI tend to classify water bodies into superior quality classes, Prati's Index into slightly lower categories, whereas Oregon and CCME WQIs were found to be relatively "stricter," giving results ranging between the lowest classes of the qualitative ranking. These findings confirm those emerged by our previous studies while they are also partially in line with similar studies comparing a smaller number of indices. CCME WQI is suggested as the most appropriate, among the examined indices, since it is both "conservative" and adequately "sensitive" to reflect changes in water quality.
Keyphrases
  • water quality
  • quality improvement
  • systematic review
  • machine learning
  • deep learning
  • risk assessment
  • heavy metals
  • contrast enhanced
  • diffusion weighted imaging
  • network analysis