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Author Le, Thi Thu Huyen ♦ Zeunert, Stephanie ♦ Lorenz, Malte ♦ Meon, Günter
Source Paperity
Content type Text
Publisher Springer Berlin Heidelberg
File Format PDF ♦ HTM / HTML
Copyright Year ©2017
Subject Keyword Environment ♦ Environmental chemistry ♦ Ecotoxicology ♦ Environmental health ♦ Atmospheric protection/air quality control/air pollution ♦ Waste water technology / water pollution control / water management / aquatic pollution
Abstract A large complex water quality data set of a polluted river, the Tay Ninh River, was evaluated to identify its water quality problems, to assess spatial variation, to determine the main pollution sources, and to detect relationships between parameters. This river is highly polluted with organic substances, nutrients, and total iron. An important problem of the river is the inhibition of the nitrification. For the evaluation, different statistical techniques including cluster analysis (CA), discriminant analysis (DA), and principal component analysis (PCA) were applied. CA clustered 10 water quality stations into three groups corresponding to extreme, high, and moderate pollution. DA used only seven parameters to differentiate the defined clusters. The PCA resulted in four principal components. The first PC is related to conductivity, NH4-N, PO4-P, and TP and determines nutrient pollution. The second PC represents the organic pollution. The iron pollution is illustrated in the third PC having strong positive loadings for TSS and total Fe. The fourth PC explains the dependence of DO on the nitrate production. The nitrification inhibition was further investigated by PCA. The results showed a clear negative correlation between DO and NH4-N and a positive correlation between DO and NO3-N. The influence of pH on the NH4-N oxidation could not be detected by PCA because of the very low nitrification rate due to the constantly low pH of the river and because of the effect of wastewater discharge with very high NH4-N concentrations. The results are deepening the understanding of the governing water quality processes and hence to manage the river basins sustainably.
ISSN 09441344
Learning Resource Type Article
Publisher Date 2017-05-01
e-ISSN 16147499
Journal Environmental Science and Pollution Research
Volume Number 24
Issue Number 15