Analysis of water quality using multivariate statistical methods in Duliujian River, China

DOI

10.2991/ifeesm-17.2018.263How to use a DOI?

Keywords

water quality; principle component analysis; cluster analysis.

Abstract

Water quality assessments are essential for providing information to water resource management processes. The objective of this study was to investigate the internal correlation among water parameters, to analyze main water contamination and to identify the most polluted section along a seagoing river. Five water quality parameters (WT, EC, NH4+-N, NO3 -N, NO2-N) were chosen to assess the water quality using multivariate statistical methods (Spearman's correlation, PCA, CA). PCA results showed 2 PCs can explain 78% of the total variation. The main water contamination was related to anthropogenic activities (domestic wastewater, industrial effluents and livestock operations) as well as natural condition (seawater intrusion). CA results gave 3 clusters by analyzing similarities of each section, indicating that the middle-downstream was the most polluted and the last section was mainly influenced by seawater intrusion.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Xuewei Sun
AU  - Xiaoqian Liang
AU  - Tousheng Huang
AU  - Huayong Zhang
AU  - Hai Huang
PY  - 2018/02
DA  - 2018/02
TI  - Analysis of water quality using multivariate statistical methods in Duliujian River, China
BT  - Proceedings of the 2017 3rd International Forum on Energy, Environment Science and Materials (IFEESM 2017)
PB  - Atlantis Press
SP  - 1451
EP  - 1456
SN  - 2352-5401
UR  - https://doi.org/10.2991/ifeesm-17.2018.263
DO  - 10.2991/ifeesm-17.2018.263
ID  - Sun2018/02
ER  -