AccScience Publishing / AJWEP / Volume 13 / Issue 2 / DOI: 10.3233/AJW-160015
RESEARCH ARTICLE

Water Quality Assessment with Varied Lake Depths by  Using Multivariate Statistical Approach

Abdul Jalil1,3,4* Li Yiping1,4 Ijaz Ahmad2,3 Khalida Khan3
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1 College of Environment, Hohai University, Nanjing, 210098, P.R. China
2 College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210098, P.R. China
3 Center for Integrated Mountain Research, University of the Punjab, Lahore, Pakistan
4 Key Laboratory of Integrated Regulation and Resource Development on Shallow Lakes, Ministry of Education Hohai University, Nanjing, 210098, P.R. China
AJWEP 2016, 13(2), 39–48; https://doi.org/10.3233/AJW-160015
Submitted: 18 November 2015 | Revised: 21 March 2016 | Accepted: 21 March 2016 | Published: 18 April 2016
© 2016 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Lakes depth is a most important component to evaluate the impacts on water quality scenarios. Present  study discusses the impacts of depth on water quality by using multivariate statistical analysis. This well established  phenomenon of correlation between lake depth and water is firstly proved by using multivariate statistical  techniques. Depth of Rawal Lake was divided into three groups of surface, middle and bottom to analyze impacts  between these stages on water quality. There were sixteen parameters (physico-chemical, bacteriological and metals)  analyzed for which samples were collected and analyzed from a fresh water Rawal lake for four seasons in 2012- 2013. The statistical correlation was developed between the water quality parameters and between the layers, by  using multivariate scatterplot, cluster analysis and discriminant analysis. Results of these statistical techniques  revealed a strong correlation (positive or negative) among most of the water quality parameters. Aluminum was  found to be with medium variability and temperature at high variability in cluster analysis. Statistically significant  correlation was found between the two dimensions of canonical discriminant functions with canonical correlation  of 0.957 and 0.586. Therefore, these statistical analyses validated the high impact of depth on different water  quality parameters.

Keywords
Water quality
statistical analysis
lake depth
discriminant analysis
cluster analysis
Conflict of interest
The authors declare they have no competing interests.
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Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing