『Abstract
Various multivariate statistical methods including cluster analysis
(CA), discriminant analysis (DA), factor analysis (FA), and principal
component analysis (PCA) were used to explain the spatial and
temporal patterns of surface water pollution in Lake Dianchi.
The dataset, obtained during the period 2003-2007 from the Kunming
Environmental Monitoring Center, consisted of 12 variables surveyed
monthly at eight sites. The CA grouped the 12 months into two
groups, August-September and the remainder, and divided the lake
into two regions based on their different physicochemical properties
and pollution levels. The DA showed the best results for data
reduction and pattern recognition in both temporal and spatial
analysis. It calculated four parameters (TEMP, pH, CODMn,
and Chl-a) to 85.4% correct assignment in the temporal analysis
and three parameters (BOD, NH4+-N,
and TN) to almost 71.7% correct assignment in spatial analysis
of the two clusters. The FA/PCA applied to datasets of two special
clusters of the lake calculated four factors for each region,
capturing 72.5% and 62.5% of the total variance, respectively.
Strong loadings included DO, BOD, TN, CODCr,
CODMn, NH4+-N,
TP, and EC. In addition, box-whisker plots and GIS further facilitated
and supported the multivariate analysis results.
Keywords: Temporal variation; Spatial variation; Multivariate
analysis; Water quality; Lake Dianchi』
Introduction
Sampling and methods
Water sampling
Multivariate statistical methods
Results and discussion
Temporal similarity and period grouping
Spatial similarity and site grouping
Fractor analysis
Conclusions
Acknowledgements
References
Fig. 5 Spatial variations of discriminant variables (BOD,NH4+-N, and TN) produced by spatial DA Yang et al.(2010)による『Analysis of spatial and temporal water pollution patterns in Lake Dianchi using multivariate statistical methods』から |