Using chemometrics to identify water quality in Daya Bay, China
This research was supported by the project of knowledge innovation program of the Chinese Academy of Sciences (No. KZCX2-YW-Q07-02 & No. KSCX2-SW-132), the project of knowledge innovation program of the South China Sea Institute of Oceanology (No. LYQ200701) and the National 908 project (No. 908...
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Institute of Oceanology of the Polish Academy of Sciences
2009-06-01
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| Series: | Oceanologia |
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| Online Access: | http://www.iopan.gda.pl/oceanologia/51_2.html#A5 |
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| author | Jun-De Dong Mei-Lin Wu1 You-Shao Wang1, Cui-Ci Sun Haili Wang Zhi-Ping Lou |
| author_facet | Jun-De Dong Mei-Lin Wu1 You-Shao Wang1, Cui-Ci Sun Haili Wang Zhi-Ping Lou |
| author_sort | Jun-De Dong |
| collection | DOAJ |
| description | This research was supported by the project of knowledge innovation program of the Chinese Academy of Sciences (No. KZCX2-YW-Q07-02 & No. KSCX2-SW-132), the project of knowledge innovation program of the South China Sea Institute of Oceanology (No. LYQ200701) and the National 908 project (No. 908-02-04-04).AbstractIn 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. |
| format | Article |
| id | doaj-art-c12a6bd4196e427487501c174ee7f533 |
| institution | Kabale University |
| issn | 0078-3234 |
| language | English |
| publishDate | 2009-06-01 |
| publisher | Institute of Oceanology of the Polish Academy of Sciences |
| record_format | Article |
| series | Oceanologia |
| spelling | doaj-art-c12a6bd4196e427487501c174ee7f5332025-08-20T03:49:12ZengInstitute of Oceanology of the Polish Academy of SciencesOceanologia0078-32342009-06-01512217232Using chemometrics to identify water quality in Daya Bay, ChinaJun-De DongMei-Lin Wu1You-Shao Wang1,Cui-Ci SunHaili WangZhi-Ping LouThis research was supported by the project of knowledge innovation program of the Chinese Academy of Sciences (No. KZCX2-YW-Q07-02 & No. KSCX2-SW-132), the project of knowledge innovation program of the South China Sea Institute of Oceanology (No. LYQ200701) and the National 908 project (No. 908-02-04-04).AbstractIn 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.http://www.iopan.gda.pl/oceanologia/51_2.html#A5Cluster analysisRobust principal component analysisWater qualityDaya Bay (DYB)South China Sea |
| spellingShingle | Jun-De Dong Mei-Lin Wu1 You-Shao Wang1, Cui-Ci Sun Haili Wang Zhi-Ping Lou Using chemometrics to identify water quality in Daya Bay, China Oceanologia Cluster analysis Robust principal component analysis Water quality Daya Bay (DYB) South China Sea |
| title | Using chemometrics to identify water quality in Daya Bay, China |
| title_full | Using chemometrics to identify water quality in Daya Bay, China |
| title_fullStr | Using chemometrics to identify water quality in Daya Bay, China |
| title_full_unstemmed | Using chemometrics to identify water quality in Daya Bay, China |
| title_short | Using chemometrics to identify water quality in Daya Bay, China |
| title_sort | using chemometrics to identify water quality in daya bay china |
| topic | Cluster analysis Robust principal component analysis Water quality Daya Bay (DYB) South China Sea |
| url | http://www.iopan.gda.pl/oceanologia/51_2.html#A5 |
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