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Principal component analysis Surface water

The main goal of this chapter is to present the theoretical background of some basic chemometric methods as a tool for the assessment of surface water quality described by numerous chemical and physicochemical parameters. As a case study, long-term monitoring results from the watershed of the Struma River, Bulgaria, are used to illustrate the options offered by multivariate statistical methods such as CA, principal components analysis, principal components regression (models of source apportionment), and Kohonen s SOMs. [Pg.370]

In summary, using either principal component analysis or multivariate curve-resolution methods, the main contamination sources of semivolatile organic compounds present in the surface waters of Portugal were identified, and their geographical... [Pg.460]

Barbieri, P., Andersson, C.A., Massart, D.L., Predozani, S., Adami, G., and Reisen-hofer, E., Modeling bio-geochemical interactions in the surface waters of the Gulf of Trieste by three-way principal component analysis (PCA), Anal. Chim. Acta, 398, 227-235, 1999. [Pg.472]

Several hundreds of linear relationships between various kinds of (mostly nonspecific) biological data and n-octanol/water partition coefficients have been published e.g. [18, 182]). However, the choice of n-octanol/water as the standard system for drug partitioning must be reconsidered in the light of some recent results. Principal component analysis of partition coefficients from different solvent systems [188 —190] shows that lipophilicity depends on solute bulk, polar, and hydrogen-bonding effects [189] isotropic surface areas, i.e. areas where no water molecules bind and hydrated surface areas, were correlated with the first and the second principal components of such an analysis [190]. [Pg.29]

To selectively resolve the boundary water near a nonmetal-water interface, Hasegawa et al. [199] employed the polarized ATR at different angles of incidence in conjunction with principal component analysis (PCA). The surface of a hemicylindrical Si IRE was cleaned by use of ozone cleaner, which yielded Si—OH species at the surface. The vOH band of the interfacial water was similar to that in Fig. 3.41, spectrum 2 which implies that such nanometer-scale information can be obtained on a nonmetallic surface without using the SEIRA effect. Based on the polarization dependence, this spectrum was assigned to H-down water that forms strong symmetric double H bonds with the hydrophilic surface. The surface-perturbed water layer was found to be several monolayers thick, in agreement with the X-ray scattering data [192]. [Pg.194]

Zhou et al. [26] discussed the capacity of using principal component analysis to differentiate between the removal of surface water and bond water, and the possibility to determine precise endpoints in situations where several hydrates coexist. Note that the ability of NIR spectroscopy to determine the presence of polymorphs and pseudopolymorphs will be discussed in Chapter 5. [Pg.64]

The hydrophobic indices, i., the ratios of hydrophobic to hydrophilic surface areas, of seven monosaccharides have been determined. They were found to correlate well with the partition coefficients of the polystyrene-water system for monosaccharides. The concept of hydrophobic indices is important in the consideration of the hydrophobic interactions of flat molecules in aqueous solution. In an effort to establish quantitative structure-activity relationships for carbohydrates, an experimental data-matrix containing the values of sixteen monosaccharides in thirteen solvent systems was subjected to principal component analysis (PCA). Four PC s (t -t ) were found... [Pg.3]

The close link between lakes and their catchments was evident in a study of spatial variability in surface sediment composition in a small northern Swedish lake (Korsman et al., 1999). In this study, the information in the near-infrared spectra of surface sediment samples was used to determine how sediment composition varied over the lake bottom. The study showed that the NIR spectra per se provide information that can be used to study sediment characteristics as well as sediment focusing in a qualitative way. The variance in the NIR spectra (Fig. 7) was only to a minor extent explained by the variation in water depth or sediment organic content. More importantly, the spatial evaluation of the spectral data suggested that NIR analysis of lake sediments mainly reflects sediment properties that cannot be simply explained by water depth or amount of organic matter. Principal component modelling of NIR spectra from 165 coring sites, established along a 50m x 50m... [Pg.312]

The best inemals and the optimum values of pressure, vapor velocity, and reboil vapor ratio are those that permit production of heavy water at minimum cost. The initial cost of the plant depends on a number of factors including the total number of towers, the total amount of reboiler and condenser surface, and the total volume of tower internals. The principal operating cost is for power, which is proportional to total loss in availability of steam as it flows through the towers. A complete minimum-cost analysis requires knowledge of the unit cost of all the important cost components and is beyond the scope of this book. Design for minimum volume of tower internals or minimum loss in availability due to tower pressure drop and for minimum cost of these two important contributors to total cost can be carried out without complete unit-cost data and will be discussed. Because the same choice of reboil vapor ratio minimizes the number of towers, their volume, and the loss of availability within them, this reboil vapor ratio is close to that which leads to minimum production cost. An equation for this optimum reboil vapor ratio will now be derived, and expressions will be developed for the total volume of towers and the total loss in availability in towers designed for the optimum ratio. [Pg.728]


See other pages where Principal component analysis Surface water is mentioned: [Pg.305]    [Pg.307]    [Pg.455]    [Pg.10]    [Pg.1250]    [Pg.300]    [Pg.301]    [Pg.362]    [Pg.1896]    [Pg.80]    [Pg.1178]    [Pg.101]    [Pg.127]    [Pg.78]    [Pg.493]    [Pg.78]    [Pg.179]    [Pg.45]   
See also in sourсe #XX -- [ Pg.153 ]




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Principal Component Analysis

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