Water quality assessment and source identification of Daliao River Basin using multivariate statistical methods.
Multivariate statistical methods, such as cluster analysis (CA), discriminant analysis (DA) and principal component analysis (PCA), were used to analyze the water quality dataset including 13 parameters at 18 sites of the Daliao River Basin from 2003-2005 (8424 observations) to obtain temporal and spatial variations and to identify potential pollution sources. Using Hierarchical CA it is classified 12 months into three periods (first, second and third period) and the 18 sampling sites into three groups (groups A, B and C). Six significant parameters (temperature, pH, DO, BOD(5), volatile phenol and E. coli) were identified by DA for distinguishing temporal or spatial groups, with close to 84.5% correct assignment for temporal variation analysis, while five parameters (DO, NH(4)(+)-N, Hg, volatile phenol and E. coli) were discovered to correctly assign about 73.61% for the spatial variation analysis. PCA is useful in identifying five latent pollution sources for group B and C (oxygen consuming organic pollution, toxic organic pollution, heavy metal pollution, fecal pollution and oil pollution). During the first period, sites received more oxygen consuming organic pollution, toxic organic pollution and heavy metal pollution than those in the other two periods. For group B, sites were mainly affected by oxygen consuming organic pollution and toxic organic pollution during the first period. The level of pollution in the second period was between the other two periods. For group C, sites were mainly affected by oil pollution during the first period and oxygen consuming organic pollution during the third period. Furthermore, source identification of each period for group B and group C provided useful information about seasonal pollution. Sites were mainly affected by fecal pollution in the third period for group B, indicating the character of non-point source pollution. In addition, all the sites were also affected by physical-chemistry pollution. In the second and third period for group B and second period for group C sites were also affected by natural pollution.
River, Estuarine and Coastal Environmental Research Center, Chinese Research Academy of Environmental Science, Beijing, 100012, People's Republic of China. zhangyuan@craes.org.cn
Yuan
Y
Zhang
Fen
F
Guo
Wei
W
Meng
Xi-Qin
XQ
Wang
D002681:China; D016000:Cluster Analysis; D016002:Discriminant Analysis; D004784:Environmental Monitoring / Q000379:methods*; D004926:Escherichia coli; D005618:Fresh Water / Q000737:chemistry / Q000382:microbiology; D006801:Humans; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D045483:Rivers; D014873:Water Pollutants / Q000032:analysis*; D014881:Water Supply / Q000032:analysis*
D014873:Water Pollutants