Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

Wyszukujesz frazę "Wu, M." wg kryterium: Wszystkie pola


Wyświetlanie 1-3 z 3
Tytuł:
Multivariate statistical analysis of water quality and phytoplankton characteristics in Daya Bay, China, from 1999 to 2002
Autorzy:
Wang, Y.S.
Lou, Z.P.
Sun, C.C.
Wu, M.L.
Han, S.H.
Powiązania:
https://bibliotekanauki.pl/articles/48049.pdf
Data publikacji:
2006
Wydawca:
Polska Akademia Nauk. Instytut Oceanologii PAN
Tematy:
South China Sea
water quality
China
phytoplankton
Daya Bay
multivariate statistical analysis
Opis:
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 ,S1 0 and S12 in the central and north-eastern parts of Daya Bay; cluster III – stations S3,S 4 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,T IN 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.
Źródło:
Oceanologia; 2006, 48, 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
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
    Wyświetlanie 1-3 z 3

    Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies