Multivariate statistical analysis and water quality index to assessment water quality in lakes

Diaa, Seif and Medhat, Moustafa and Walid, Elbarqi (2018) Multivariate statistical analysis and water quality index to assessment water quality in lakes. In: Seventh International Conference on Advances in Bio-Informatics, Bio-Technology and Environmental Engineering - ABBE 2018, 27-28 October, 2018, Rome, Italy.

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This study concerns using Multivariate statistical analysis to handle the large complex datasets in lakes and use water quality index factor to estimate the water quality in lakes.This study investigated the seasonal and spatial variations of water quality parameters in Burullus Lake in Egypt as a case study. Significant seasonal changes (p < 0.05) were observed in temperature,pH and phosphate (PO4-P), whereas significant spatial differences (p < 0.05) were detected in pH, salinity, dissolved oxygen (DO), ammonium-nitrogen (NH4-N), nitrite-nitrogen (NO2-N), nitrate-nitrogen (NO3-N), PO4-P, silicate (SiO4) and Chlorophyll-a (Chl-a). The water quality index (WQI) estimated using the inputs of: salinity, DO, NH4-N, NO3-N, PO4-P, and Chl-a. The (WQI) of the lake was rated as Bad to Very bad. Based on principal component analysis (PCA), the first principal component (PC1: 38.78%) represented high loadings on NH4-N: 0.46, NO3-N: 0.45, and PO4-P: 0.45, indicating that the lake was mainly influenced by nutrients concentrations coming from agricultural lands. The results from cluster analysis and a dendrogram indicated that Burullus Lake was mainly influenced by spatial variations rather than seasonal changes.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Dendrogram; Principal component analysis; Spatiotemporal variability; Water quality index.
Depositing User: Mr. John Steve
Date Deposited: 06 Mar 2019 11:38
Last Modified: 06 Mar 2019 11:38

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