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Wyszukujesz frazę "Wu, S.L." wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Demonstration of a new indicator for studying upwelling in the Northern South China Sea
Autorzy:
Lin, L.
Wang, Y.-S.
Sun, C.-C.
Li, N.
Wang, H.
Mitchell, B.G.
Wu, M.-L.
Song, H.
Wu, J.-F.
Powiązania:
https://bibliotekanauki.pl/articles/48889.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Instytut Oceanologii PAN
Tematy:
China Sea
cluster analysis
multivariate statistical analysis
principal component analysis
remote sensing
satellite monitoring
sea surface temperature
silicate
spatial distribution
upwelling
Opis:
In order to demonstrate that silicate (SiO3-Si) can be used as an indicator to study upwelling in the northern South China Sea, hierarchical cluster analysis (CA) and principle component analysis (PCA) were applied to analyse the metrics of the data consisting of 14 physical-chemical-biological parameters at 32 stations. CA categorized the 32 stations into two groups (low and high nutrient groups). PCA was applied to identify five Principal Components (PCs) explaining 78.65% of the total variance of the original data. PCA found important factors that can describe nutrient sources in estuarine, upwelling, and non-upwelling areas. PC4, representing the upwelling source, is strongly correlated to SiO3-Si. The spatial distribution of silicate from the surface to 200 m depth clearly showed the upwelling regions, which is also supported by satellite observations of sea surface temperature.
Źródło:
Oceanologia; 2011, 53, 2
0078-3234
Pojawia się w:
Oceanologia
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Using chemometrics to identify water quality in Daya Bay, China
Autorzy:
Wu, M.-L.
Wang, Y.-S.
Sun, C.-C.
Wang, H.
Lou, Z.-P.
Dong, J.-D.
Powiązania:
https://bibliotekanauki.pl/articles/49096.pdf
Data publikacji:
2009
Wydawca:
Polska Akademia Nauk. Instytut Oceanologii PAN
Tematy:
robust principal component analysis
water quality
chemometrics
China
cluster analysis
Daya Bay
Opis:
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.
Źródło:
Oceanologia; 2009, 51, 2; 217-232
0078-3234
Pojawia się w:
Oceanologia
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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