Multivariate analysis for spatial distribution of dissolved organic matters in a large river-type dam reservoir

Abstract

In contrast to extensive studies of dissolved organic matters (DOM) in natural lakes, the distributions and the characteristics of DOM in artificial dam reservoirs have not been well documented despite a growing demand for the construction worldwide. For this study, spatial variations in the concentrations and the characteristics of DOM in Lake Paldang, a large river-type dam reservoir, were investigated using the concentrations, the specific UV absorbance (SUVA), the synchronous fluorescence spectra and the molecular weight (MW(w)) values. In addition, environmental factors determining the DOM spatial distribution were examined based on a principal component analysis (PCA). Variations in the DOM characteristics were greater than those for the concentrations (1.1-2.4 mg C/L). In contrast to typical lakes, vertical variations with a depth were much smaller than those observed among horizontal sampling sites within the reservoir. Irrespective of the depth, four individual sampling locations were easily distinguished by comparison of some selected DOM characteristics. The protein-like fluorescence (PLF), MW(w) and SUVA values observed at the location near the dam exceeded the corresponding values for the sampling locations near major influent rivers, suggesting that, even for the river-type dam reservoir, the downstream DOM characteristics may be governed by in-lake DOM production processes such as the release from sediments and algal activities. The results of principal component analysis (PCA) revealed that approximately 61% of the variance in DOM distribution might be explained by allochthonous/autochthonous carbon sources and predominant presence of either total nitrogen or total phosphorous over the other.

Principal component analysis to assess the composition and fate of impurities in a large river-embedded reservoir: Qingcaosha Reservoir

“…The PCA of the normalized data set was conducted using SPSS 19.0 following similar processes to previous studies. 20,21 Aer PCA calculation, three key parameters are obtained: (1) eigenvectors, (2) loadings, and (3) scores. The loadings reect information as to how the variables are related to each other, while the scores reect information as to how the samples are related to each other.…”

Seasonal baseline of nutrients and stable isotopes in a saline lake of Argentina: biogeochemical processes and river runoff effects

“…Dissolved organic matter (DOM) often surpasses POM by an order of magnitude (Wetzel 1984). DOM attenuates UV radiation, modifies nutrient availability and contaminants toxicity, and is generally the largest source of carbon and nitrogen for microorganisms (Williamson et al 1999;Jiao et al 2010;Nguyen et al 2011). Subsequent consumption of bacteria by heterotrophic flagellates and microzooplankton provides a pathway to transfer DOM into food webs (Pace et al 2004).…”

Water Environment Characteristics at Taige Canal-Taihu Lake: a Comparative Study on Interaction between Chlorophyll α and Environmental Variables

“…Discriminant function was tested by , and the first two factors generated from PCA (independent variables), stepwise multiple regressions between Chl.a (dependent variable) and other variables (independent variables), as well as correlate analysis were implemented in river and lake regions, respectively. PCA was a useful method for explaining the variance of a large set of intercorrelated variables with a smaller set of new independent factors [14], being widely used in environmental science [15] and limnology [16][17][18].…”