In this paper, chemometric approaches based on cluster analysis, classical and
robust principal component analysis were employed to identify water quality in Daya Bay (DYB), China. The results show that these approaches divided water
quality in DYB into two groups: stations S3, S8, S10 and S11 belong to cluster A,
which lie in Dapeng Cove, Aotou Harbor and the north-eastern part of DYB, where
water quality is related mainly to anthropogenic activities. The other stations
belong to cluster B, which lie in the southern, central and eastern parts of DYB,
where the quality is related mainly to water exchange with the South China Sea.
Cluster analysis yields good results as a first exploratory method for evaluating
spatial difference, but it fails to demonstrate the relationship between variables
and environmental quality on the one hand and the untreated data on the other.
However, with the aid of suitable chemometric approaches, the relationship between
samples or variables can be investigated. Classical and robust principal component
analysis can provide a visual aid for identifying the water environment in DYB,
and then extracting specific information about relationships between variables and
spatial variation trends in water quality.
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