Limits...
Assessment of water quality in a subtropical alpine lake using multivariate statistical techniques and geostatistical mapping: a case study.

Liu WC, Yu HL, Chung CE - Int J Environ Res Public Health (2011)

Bottom Line: In order to understand the underlying physical and chemical processes as well as their associated spatial distribution in YYL, this study analyzes fourteen physico-chemical water quality parameters recorded at the eight sampling stations during 2008-2010 by using multivariate statistical techniques and a geostatistical method.Results show that four principal components i.e., nitrogen nutrients, meteorological factor, turbidity and nitrate factors, account for 65.52% of the total variance among the water quality parameters.The spatial distribution of principal components further confirms that nitrogen sources constitute an important pollutant contribution in the YYL.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil Disaster Prevention Engineering, National United University, Miao-Li 36003, Taiwan. w933821@hotmail.com

ABSTRACT
Concerns about the water quality in Yuan-Yang Lake (YYL), a shallow, subtropical alpine lake located in north-central Taiwan, has been rapidly increasing recently due to the natural and anthropogenic pollution. In order to understand the underlying physical and chemical processes as well as their associated spatial distribution in YYL, this study analyzes fourteen physico-chemical water quality parameters recorded at the eight sampling stations during 2008-2010 by using multivariate statistical techniques and a geostatistical method. Hierarchical clustering analysis (CA) is first applied to distinguish the three general water quality patterns among the stations, followed by the use of principle component analysis (PCA) and factor analysis (FA) to extract and recognize the major underlying factors contributing to the variations among the water quality measures. The spatial distribution of the identified major contributing factors is obtained by using a kriging method. Results show that four principal components i.e., nitrogen nutrients, meteorological factor, turbidity and nitrate factors, account for 65.52% of the total variance among the water quality parameters. The spatial distribution of principal components further confirms that nitrogen sources constitute an important pollutant contribution in the YYL.

Show MeSH
Variograms in time for PC1 and FA1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC3118881&req=5

f5-ijerph-08-01126: Variograms in time for PC1 and FA1.

Mentions: Geostatisitcal techniques were used for the mapping of principle components and factor scores over the study area. Due to the long period between each observation campaign, the temporal correlation among the observations is assumed to be ignorable in this analysis. Table 4 shows that the spatial dependence structure varies across the identified contributing factors by the common multivariate analysis. It implies the variation of spatial patterns of impacts to water quality from the contributing factors. Among them, the impact of nitrogen nutrients changes more significantly over space than other contributing factors. The experimental and modeled variograms of PC1 and FA1 are shown in Figure 4. The variogram figure in time for PC1 and FA1 is also presented in Figure 5. It is clear that the variogram value approximates to sill in cases of the temporal lags in month among observations larger than 0. It implies the low correlation between the observations collected in different months. The contaminants from nitrogen nutrient are more localized as shown in Figure 6. On the other hand, the effects from the sunlight, organic matter, and nitrate nutrition present much smoother variations across the study area. This implies the sources of these contributors are more homogeneously distributed over the lake. It is noticeable that the range of the semivariogram model of second principle component is excessively larger than those of the models of other factors. It implies that the meteorological effects derived from PCA contribute a relatively large scale variation of water quality in space with respect to the scale of the study area.


Assessment of water quality in a subtropical alpine lake using multivariate statistical techniques and geostatistical mapping: a case study.

Liu WC, Yu HL, Chung CE - Int J Environ Res Public Health (2011)

Variograms in time for PC1 and FA1.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3118881&req=5

f5-ijerph-08-01126: Variograms in time for PC1 and FA1.
Mentions: Geostatisitcal techniques were used for the mapping of principle components and factor scores over the study area. Due to the long period between each observation campaign, the temporal correlation among the observations is assumed to be ignorable in this analysis. Table 4 shows that the spatial dependence structure varies across the identified contributing factors by the common multivariate analysis. It implies the variation of spatial patterns of impacts to water quality from the contributing factors. Among them, the impact of nitrogen nutrients changes more significantly over space than other contributing factors. The experimental and modeled variograms of PC1 and FA1 are shown in Figure 4. The variogram figure in time for PC1 and FA1 is also presented in Figure 5. It is clear that the variogram value approximates to sill in cases of the temporal lags in month among observations larger than 0. It implies the low correlation between the observations collected in different months. The contaminants from nitrogen nutrient are more localized as shown in Figure 6. On the other hand, the effects from the sunlight, organic matter, and nitrate nutrition present much smoother variations across the study area. This implies the sources of these contributors are more homogeneously distributed over the lake. It is noticeable that the range of the semivariogram model of second principle component is excessively larger than those of the models of other factors. It implies that the meteorological effects derived from PCA contribute a relatively large scale variation of water quality in space with respect to the scale of the study area.

Bottom Line: In order to understand the underlying physical and chemical processes as well as their associated spatial distribution in YYL, this study analyzes fourteen physico-chemical water quality parameters recorded at the eight sampling stations during 2008-2010 by using multivariate statistical techniques and a geostatistical method.Results show that four principal components i.e., nitrogen nutrients, meteorological factor, turbidity and nitrate factors, account for 65.52% of the total variance among the water quality parameters.The spatial distribution of principal components further confirms that nitrogen sources constitute an important pollutant contribution in the YYL.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil Disaster Prevention Engineering, National United University, Miao-Li 36003, Taiwan. w933821@hotmail.com

ABSTRACT
Concerns about the water quality in Yuan-Yang Lake (YYL), a shallow, subtropical alpine lake located in north-central Taiwan, has been rapidly increasing recently due to the natural and anthropogenic pollution. In order to understand the underlying physical and chemical processes as well as their associated spatial distribution in YYL, this study analyzes fourteen physico-chemical water quality parameters recorded at the eight sampling stations during 2008-2010 by using multivariate statistical techniques and a geostatistical method. Hierarchical clustering analysis (CA) is first applied to distinguish the three general water quality patterns among the stations, followed by the use of principle component analysis (PCA) and factor analysis (FA) to extract and recognize the major underlying factors contributing to the variations among the water quality measures. The spatial distribution of the identified major contributing factors is obtained by using a kriging method. Results show that four principal components i.e., nitrogen nutrients, meteorological factor, turbidity and nitrate factors, account for 65.52% of the total variance among the water quality parameters. The spatial distribution of principal components further confirms that nitrogen sources constitute an important pollutant contribution in the YYL.

Show MeSH