Analysis of the Water Quality of the Yoshinogawa, Tokushima, Japan, by Using Multivariate Analysis

Junko KAMBEa, Tomoo AOYAMAb, Aiko YAMAUCHIc and Umpei NAGASHIMAd,e*

aFaculty of Foreign Language, Daito Bunka University
1-9-1 Takashimadaita, Itabashi, Tokyo 175-8571, Japan
bFaculty of Engineering , Miyazaki University
Gakuen Kihanadai Nishi, Miyazaki, 889-2192, Japan
cGraduate School of Pharmaceutical Sciences, University of Tokushima
1-78 Sho-machi, Tokushima 770-8505, Japan
dResearch Institute for Computational Science, National Institute of Advanced Industrial Science and Technology
1-1-1 Umezono, Tsukuba, Ibaraki 305-8568, Japan
eCore Research for Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST)
Kawaguchi Center Building,4-1-8, Honcho, Kawaguchi, Saitama 332-0012 Japan

(Received: September 11, 2006; Accepted for publication: October 3, 2006; Published on Web: December 1, 2006)

Principal component analysis, cluster analysis and neural network were applied to assess the pollution levels along the Yoshinogawa River in Tokushima, Japan, by using water quality data (Dissolved Oxygen (DO), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD),and Total Phosphorus (TP)) measured in 2002. Because there are a few defects in the data, we estimated them by linear equation and neural network to avoid information loss.
In the Yoshinogawa River, there are four branching bays (Figure 1). Though it is usually difficult to find the relationships between the water quality of the Yoshinogawa River and the distances from their estuaries, the first principal components consisting of five parameters explained the nature of the river well.
The water quality at Heiwabashi of the Shinmachigawa River was the most contaminated in the Yoshinogawa River (Figure 6, Table 12). This suggests that there is a source of water pollution in the upper region of Heiwabashi and that there is a source to improve water quality in the lower region of Heiwabashi.

Keywords: Water Pollution, Yoshinogawa River, Chemometrics, Principal Component Analysis, Cluster Analysis


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