Ecological Water Quality Index associated with factor analysis to classify surface waters

The object of this study was the development of a new water quality index called the Ecological Water Quality Index (EWQI) which is flexible enough to represent the ecological status of surface waters by assessing chemical quality based on supranational legislation. Eleven variables including nutrients, salts and total parameters were selected as index components. Threshold values were assigned to seven water-quality classes as defined by the legislation and were used to develop mathematical equations to convert observed values to index scores. Depending on the ecological importance of the parameters, weights were assigned to each variable and then a weighted sum method was performed to aggregate sub-indices. The applicability of the method was demonstrated in two basins located in Turkey. Factor analysis was applied to optimize the index component selection process. Several alternatives were tested to comprise at least one variable from each defined factor class (e.g. salinity content) to calculate an index score representing ecological status. Results showed that there were slight differences between index scores of the various tested alternatives. These differences did not cause misclassification of surface waters. The study showed that the EWQI method combined with factor analysis could be a practical and efficient way to represent the ecological status of surface waters.

Grazing Potential—A Functional Plankton Food Web Metric for Ecological Water Quality Assessment in Mediterranean Lakes

Grazing potential (GP, in % day−1) was estimated for the plankton communities of 13 Greek lakes covering the trophic spectrum, in order to examine its sensitiveness in discriminating different classes of ecological water quality. Lakes with high GP values exhibited high zooplankton biomass dominated by large cladocerans or/and calanoids while lakes with low GP values had increased phytoplankton biomass and/or domination of small-bodied zooplankton indicating intensive fish predation. GP successfully distinguished among ecological water quality classes (estimated using the phytoplankton water quality index PhyCoI) indicating its potential use as a metric for ecological water quality assessment. As a next step, PhyCoI index was modified to include GP as a metric in order to enhance the phytoplankton-based ecological status classification of lakes incorporating zooplankton as a supporting factor. The PhyCoIGP successfully assessed the ecological water quality in accordance with PhyCoI classification whereas it was significantly correlated with the eutrophication proxy TSISD based on Secchi Depth. Thus, we propose to use the modified phytoplankton index PhyCoIGP for monitoring the ecological water quality of lakes.