[First flush effects of storm events of Baoxiang River in Lake Dianchi Watershed].

To understand riverine process of non-point source effectively, first flush effects of storm events were investigated at Baoxiang River of Lake Dianchi Watershed. Three sampling stations were selected along Baoxiang River for observing the flow rate and pollutant concentrations of the first three storm events from June 2009 to August 2009. Net discharged volume, net discharged loading, and net event mean concentration (EMC(n)) were proposed with their calculation methods. According to the analysis of three storm events at three stations, the following results colcd be extracted: (1) the larger the percent of impervious land and population density were, the higher EMC(n) of TSS, TN, TP, permanganate index and their cumulative curves [M(V)] were along the river; (2) TSS, TP loadings as well as their M (V) were positively correlated to the storm intensity, while TN and permanganate index loadings were consistent with the total rainfall of each storm event, where the percent of NO3(-) -N in total nitrogen decreased gradually when the number of storm events increased; (3) compared to tradition EMC, EMC(n) was proven to be a better indicator to accurately uncover and magnify the differences in first flush effects of storm events among different stations or storm events.

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, 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.