Water-quality data analysis of the upper Gunnison River watershed, Colorado, 1989-99

Objectives : 14 reservoirs in the Geum river watershed were clustered and classified using the results of factor analysis based on water quality characteristics. Also, correlation analysis between pollutants (land system, living system, livestock system) and water quality characteristics was performed to elucidate the effect of pollutants on water quality.Methods : Cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed during the last 5 years (2014-2018) were performed to derive the principal components. Then, correlation analysis between principal components and pollutants was performed to verify the feasibility of clustering.Results and Discussion : From the factor analysis (FA) using water quality data of 14 reservoirs in the Geum river watershed, three to six principal components (PCs) were extracted and extracted PCs explained approximately 74% of overall variations in water quality. As a result of clustering reservoirs based on the extracted PCs, the reservoirs clustered by nitrogen and seasonal PCs were Ganwol, Geumgang, and Sapgyo, the reservoirs clustered by organic pollution and internal production PCs were Tapjung, Dae, Seokmun, and Yongdam, the reservoirs clustered by organic pollution, internal production, and phosphorus are Bunam, Yedang, and Cheongcheon, and finally the remaining Boryeong, Daecheong, Chopyeong, and Songak were clustered as other factors. From the correlation analysis between principal components and pollutants, significant correlation between the land, living, and livestock pollutants and water quality characteristics was found in Ganwol, Topjeong, Daeho, Bunam, and Daecheong. These reservoirs are considered to require continuous and careful management of specific (land, living, livestock) pollutants. In terms of water quality and pollutant management, the Ganwol, Sapgyo, and Seokmunho are considered to implement intensive measures to improve water quality and to reduce the input of various pollutants.Conclusions : Although the water quality of the reservoir is a result of complex interactions such as influent water factors, morphological and hydrological factors, internal production factors, and various pollutants, optimized watershed and water quality management measures can be implemented through multivariate statistical analysis.

Download Full-text

Summary of Ground-Water-Quality Data in the Anacostia River Watershed, Washington, D.C., September - December 2005

Open-File Report ◽

10.3133/ofr20061392 ◽

2007 ◽

Author(s):

Cheryl A. Klohe ◽

Linda M. Debrewer

Keyword(s):

Water Quality ◽

Ground Water ◽

Quality Data ◽

Ground Water Quality ◽

Water Quality Data ◽

Anacostia River ◽

River Watershed

Download Full-text

Data analysis on sea water quality data in Jakarta Bay using Principal Components Analysis (PCA) method during transitional monsoon 2012

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/339/1/012023 ◽

2019 ◽

Vol 339 ◽

pp. 012023

Author(s):

A Martina ◽

I M Radjawane

Keyword(s):

Water Quality ◽

Data Analysis ◽

Principal Components Analysis ◽

Principal Components ◽

Sea Water ◽

Quality Data ◽

Water Quality Data ◽

Pca Method ◽

Components Analysis

Download Full-text

Impact of non-detects in water quality data on estimation of constituent mass loading

Water Science & Technology ◽

10.2166/wst.2002.0243 ◽

2002 ◽

Vol 45 (9) ◽

pp. 219-225 ◽

Cited By ~ 14

Author(s):

M. Kayhanian ◽

A. Singh ◽

S. Meyer

Keyword(s):

Water Quality ◽

Data Analysis ◽

Censored Data ◽

Quality Data ◽

Data Sets ◽

Mass Loading ◽

Water Quality Data ◽

Mean Values ◽

Constituent Mass

Often, fractions of stormwater constituents are not detected above laboratory reporting limits and are reported as non-detect (ND), or censored data. Analysts and stormwater modelers represent these NDs in stormwater data sets using a variety of methods. Application of these different methods results in different estimates of constituent mean concentrations that will, in turn, affect mass loading computations. In this paper, different methods of data analysis were introduced to determine constituent mean concentrations from water quality datasets that include ND values. Depending on the number of NDs and the method of data analysis, differences ranging from 1 to 70 percent have been observed in mean values. Differences in mean values were, as shown by simulation, found to have significant impacts on estimations of constituent mass loading.

Download Full-text