Analysis of spatial and temporal water pollution patterns in Lake Dianchi using multivariate statistical methods

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, COD_Mn, and Chl-a) to 85.4% correct assignment in the temporal analysis and three parameters (BOD, NH 2009/11/20Heavy metals (Cd, Cu, Fe, Mn and Zn) concentrations were determined in different tissues (muscle, kidney, liver, brain, gonads, heart and feathers) of Glaucous Gulls (Larus hyperboreus) from Bjørnøya and Jan Mayen. The age and spatial dependent variations in heavy metals were quantified and interpreted in view of the three chemometric techniques, i.e. non-parametric Mann–Whitney U test, redundancy gradient analysis and detrended correspondence analysis. The Glaucous Gulls from Bjørnøya contained significantly higher (p?p?