2004
DOI: 10.1016/j.ecolmodel.2004.03.007
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Principal component analysis: an appropriate tool for water quality evaluation and management—application to a tropical lake system

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Cited by 236 publications
(84 citation statements)
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“…Instead of being effectively eliminated, ammonia may accumulate in water columns especially in eutrophic lakes. For example, its concentration exceeded 1 mg l −1 in a eutrophic lake in the center of Ivory Coast (Parinet et al 2004) and ranged from 1.5 to 4 mg l −1 in polluted, hypereutrophic Onondaga Lake (Effler et al 1996). The average ammonia concentrations in 33 Chinese lakes were 0.358-1.295 mg l −1 (Wu et al 2006).…”
Section: Introductionmentioning
confidence: 99%
“…Instead of being effectively eliminated, ammonia may accumulate in water columns especially in eutrophic lakes. For example, its concentration exceeded 1 mg l −1 in a eutrophic lake in the center of Ivory Coast (Parinet et al 2004) and ranged from 1.5 to 4 mg l −1 in polluted, hypereutrophic Onondaga Lake (Effler et al 1996). The average ammonia concentrations in 33 Chinese lakes were 0.358-1.295 mg l −1 (Wu et al 2006).…”
Section: Introductionmentioning
confidence: 99%
“…The overall consistency of the data was measured by the Kayser-Mayer-Olkim method (KMO) [16]. The KMO method uses a criterion to identify whether a factorial analysis model that is being used is properly adjusted to data, testing the overall consistency of the data.…”
Section: Statistical Analysesmentioning
confidence: 99%
“…Moreover, some redundant indicators may not be easily identified and discarded. The principal components analysis (PCA) is a multivariate data analysis technique that can be used to determine the underlying structure of multivariate data, and offers a possibility to scale the contribution of each factor using coefficients of linear correlation (Parinet et al 2004). Li et al (2006) developed an environment evaluation model based on spatial principal components analysis (SPCA) that involves five steps: (1) standardization of primary data; (2) establishment of a covariance matrix R for each variable; (3) computing an eigenvalue 1 i of matrix R and its corresponding eigenvectors α i ; (4) grouping α i by linear combination and putting out m principal components; (5) computing environment quality index (EQI) on the basis of selected components (equation 1).…”
Section: Evaluation Modelmentioning
confidence: 99%