4.7 Article

Using Algal Indices to Assess the Ecological Condition of the Aras River, Northwestern Iran

Journal

JOURNAL OF MARINE SCIENCE AND ENGINEERING
Volume 11, Issue 10, Pages -

Publisher

MDPI
DOI: 10.3390/jmse11101867

Keywords

bioindication; water quality; phytoplankton; phytoperiphyton; Aras River; Iran

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This study comprehensively assessed the ecological state of the Aras River using biological indicators of water quality by diatoms. The results showed that the lower reaches of the river were in poor condition and were influenced by anthropological activity.
This work is the first in a series, and its purpose is the comprehensive assessment of the ecological state of the Aras River using biological indicators of water quality by diatoms based on species' ecological preferences, pollution indices, statistics, and ecological mapping. Samples of diatoms and soft algae and measurements of water quality were analyzed at sixteen sampling sites (between 2020 and 2022) along the Aras River. The impact of anthropological activity on the river was monitored concerning water quality, river health, and ecosystem function. The physical and chemical characteristics of the water were measured. The biological properties of the algal periphyton communities, including species composition, were also measured. Based on the studies conducted in this research, 280 species were identified. The most prosperous species were Diatoma vulgaris, Amphora ovalis, Cocconeis placentula, Rhoicosphenia abbre-viatae, Cymbella helvetica, Brevisira arentii, Navicula tripunctata, Nitzschia linearis, Microcystis botrys, Microcystis aeruginosa, Pseudanabaena limnetica, Scenedesmus obliquus, and Pleurosira laevis (a pollution-resistant and salinity-resistant species first found in aquatic habitats in the Aras River). As a result, the empirical data and algal indices showed the river's lower reaches to be in poor condition. Exploration of the algal assemblage and water chemistry data using computationally unconstrained ordination techniques such as principal component analysis (PCA) and canonical correspondence analysis (CCA) indicated two strong gradients in the data sets. The results support that water body classification is a function of water chemistry and biological and hydrological characteristics, as it is necessary to include pollutant effects on biota since the nature of the receiving waters influences the river's water quality.

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