Analysis of water quality using multivariate statistical methods in Duliujian River, China
- 10.2991/ifeesm-17.2018.263How to use a DOI?
- water quality; principle component analysis; cluster analysis.
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.
- © 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/).
Cite this article
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 -