Spatial pattern analysis for sediments in Lake Dianchi based on SOM and multivariate statistics
Three multivariate statistical techniques, i.e. hierarchical cluster analysis (HCA), discriminant analysis (DA) and self-organizing maps (SOM), were applied for spatial pattern analysis of sediments in Lake Dianchi of southwestern China. The dataset of nine pollutants was observed and collected for 17 monitoring sites from 2008 to 2010, including 10 current and 7 additional monitoring stations. The results demonstrated that the holistic pollution level was the highest in Caohai, followed by central and southern Waihai and northern Waihai. Duanqiao site had the highest concentration of As, Hg, Pb, Cd, Cu and Zn, while Caohai Zhongxin site had the second highest concentration of these pollutants and highest concentration of Total Kjeldahl Nitrogen (TKN). Panlongjiang Ⅱ site had the highest concentration of Cr and TP. In comparison, Haigeng site is the cleanest among all the monitoring sites. However, the current monitoring stations were unable to reveal the spatial heterogeneity and homogeneity of sediment pollution level. Two suggestions were proposed to optimize the spatial location of the monitoring sites, including (a) removing four sites in the current monitoring system, i. c. Baiyukou, Guanyinshanxi, Guanylnshandong and Luojiaying, and (h) adding three monitoring stations into the monitoring network, i.e. Panlongjiang Ⅱ, Haigeng and Maliaohe.
NETL
NSTL
万方数据
维普
