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Monsoon-driven Dynamics of water quality by multivariate statistical methods in Daya Bay, South China Sea

100%

Wu M.-L. , Wang Y.-S. , Sun C.-C. , Sun F.-L. , Cheng H. , Wang Y.-T. , Dong J.-D. , Wu J.

Oceanological and Hydrobiological Studies

2012

tom Vol. 41, No. 4

66-76

Eleven physicochemical parameters of data collected from 12 stations in Daya Bay in 2003 were analyzed by multivariate statistical analysis. Cluster analysis (CA) grouped data from 4 seasons into two groups, the northeast and southwest monsoon periods, representing different natural processes. During the northeast monsoon period, principal component analysis (PCA) and CA group the 12 monitoring sites into Cluster DA1 (S1, S2 and S6) and Cluster DA2 (S3-S5 and S7-S12). During the southwest monsoon period, PCA and CA group the 12 monitoring sites into Cluster WB1 (S1, S2, S7, S9 and S11) and Cluster WB2 (S3-S6, S8, S10, S11 and S12). The spatial heterogeneity within the bay was defined by different hydrodynamic conditions and human activities. These results may be valuable for achieving sustainable use of the coastal ecosystems in Daya Bay.

Multivariate statistical analysis of water quality and phytoplankton characteristics in Daya Bay, China, from 1999 to 2002

88%

Wang Y.-S. , Lou Z.-P. , Sun C.-C. , Wu M.-L. , Han S.-H.

Oceanologia

2006

tom No. 48 (2)

193-211

This study analyzed seasonal physicochemical and phytoplankton data collected at 12 marine monitoring stations in Daya Bay from 1999 to 2002. Cluster analysis based on water quality and phytoplankton parameters measured at the 12 stations could be grouped into three clusters: cluster I - stations S1, S2, S7 and S11 in the southern part and the north-eastern part of Daya Bay; cluster II - stations S5, S6, S9, S10 and S12 in the central and north-eastern parts of Daya Bay; cluster III - stations S3, S4 and S8 in the cage culture areas in the south-western part of Daya Bay and in the north-western part of the Bay near Aotou harbor. Bivariate correlations between phytoplankton density and the major physical and nutrient factors were calculated for all stations. Factor analysis shows that there were high positive loadings of pH, TIN and the ratio of TIN to PO4-P in the three clusters, which indicates that all the stations in the three clusters were primarily grouped according to their respective nutrient conditions.

Using chemometrics to identify water quality in Daya Bay, China

88%

Wu M.-L. , Wang Y.-S. , Sun C.-C. , Wang H. , Lou Z.-P. , Dong J.-D.

Oceanologia

2009

tom No. 51 (2)

217-232

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.

Identification of water quality and benthos characteristics in Daya Bay, China, from 2001 to 2004

75%

Wang Y.-S. , Sun C.-C. , Lou Z.-P. , Wang H. , Mitchell B. G. , Wu M.-L. , Sun Z.-X.

Oceanological and Hydrobiological Studies

2011

tom Vol. 40, No.1

82-95

Physicochemical and benthos data were collected from 12 marine monitoring stations in Daya Bay, during 2001-2004. 12 stations in Daya Bay could be grouped into three clusters: cluster I consisted of stations in the southern part of Daya Bay (stations S1, S2 and S6); cluster II consisted of stations in the cage culture areas (stations S3, S4, S5 and S8); cluster III consisted of stations in the southwest, the middle and the northeast of the Bay (stations S7, S9, S10, S11 and S12). Calculation with bivariate correlations between benthos and major physicochemical factors showed that the density of benthos in all stations correlated positively with temperature, DO, pH, NH4-N, SiO3-Si, SiO3-Si /PO4-P and chlorophyll a and was negatively correlated with salinity, Secchi, COD, NO3-N, NO2-N, TIN, PO4-P, TIN/PO4-P and BOD5. Factor analysis showed that there were high positive loading salinity, Secchi and NH4-N of three clusters. Results revealed that temperature, DO, pH, SiO3-Si and SiO3-Si/PO4-P and chlorophyll a could also play an important role in determining the biomass of benthos in Daya Bay, especially near the Nuclear Power Plants, in the southern part and in the cage culture areas.