The Use of Phytoplankton Biodiversity Index for Predicting Water Quality at the Flood Control Area in An Giang Province, Vietnam

Authors

  • Nguyen Thanh Giao

DOI:

https://doi.org/10.37933/nipes/4.2.2022.1

Abstract

The study was conducted to assess biodiversity and water quality
using the Shannon-Wiener biodiversity index (H') in the Bac Vam
Nao flood control area, An Giang province in 2020. The finding
shows that the composition of phytoplankton in the rainy season is
more diverse than that in the dry season. The results of analysis of
phytoplankton samples at three locations in the Bac Vam Nao flood
control area recorded 71 species belonging to 5 different algae
phyla, including Cyanophyta, Bacillariophyta, Chlorophyta,
Charophyta and Euglenophyta. In which, Euglenophyta has the
richest species composition with 25 species (accounted 35.2%) and
the lowest is the Cyanophyta with 05 species (7%). The dominant
species through two monitoring periods are those of the
Bacillariophyta (Cyclotella meneghiniana, Melosira granulata) and
Cyanophyta (Oscillatoria sp.). At the monitoring positions of the Bac
Vam Nao flood control area, one toxic algae species and two harmful
algae species were detected out of a total of 04 species of blue-green
algae. The Shannon-Wiener (H') diversity index ranges from 0.56 to
4.18, the H' index in the dry season is higher than that in the rainy
season. Surface water quality in Bac Vam Nao flood control area
according to diversity index H' is classified from very polluted to
clean. It is necessary to expand the scope of research areas to better
understand the role of phytoplankton as a water quality indicator,
serving effective water quality monitoring, reducing costs and
environmental pollution due to the use of chemicals during water
sample analysis.

Downloads

Published

2022-06-10

How to Cite

Nguyen Thanh Giao. (2022). The Use of Phytoplankton Biodiversity Index for Predicting Water Quality at the Flood Control Area in An Giang Province, Vietnam. NIPES - Journal of Science and Technology Research, 4(2). https://doi.org/10.37933/nipes/4.2.2022.1

Issue

Section

Articles