Three classification techniques (loading and score projections based on principal components analysis (PCA), cluster analysis (CA) and self-organizing maps (SOM)) were applied to a large ...environmental data set of chemical indicators of river water quality. The study was carried out by using long-term water quality monitoring data. The advantages of SOM algorithm and its classification and visualization ability for large environmental data sets are stressed. The results obtained allowed detecting natural clusters of monitoring locations with similar water quality type and identifying important discriminant variables responsible for the clustering. SOM clustering allows simultaneous observation of both spatial and temporal changes in water quality. The chemometric approach revealed different patterns of monitoring sites conditionally named “tributary”, “urban”, “rural” or “background”. This objective separation could lead to an optimization of river monitoring nets and to a better tracing natural and anthropogenic changes along the river stream.
The application of different multivariate statistical approaches for the interpretation of a large and complex data matrix obtained during a monitoring program of surface waters in Northern Greece is ...presented in this study. The dataset consists of analytical results from a 3-yr survey conducted in the major river systems (Aliakmon, Axios, Gallikos, Loudias and Strymon) as well as streams, tributaries and ditches. Twenty-seven parameters have been monitored on 25 key sampling sites on monthly basis (total of 22,350 observations). The dataset was treated using cluster analysis (CA), principal component analysis and multiple regression analysis on principal components. CA showed four different groups of similarity between the sampling sites reflecting the different physicochemical characteristics and pollution levels of the studied water systems. Six latent factors were identified as responsible for the data structure explaining 90% of the total variance of the dataset and are conditionally named organic, nutrient, physicochemical, weathering, soil-leaching and toxic-anthropogenic factors. A multivariate receptor model was also applied for source apportionment estimating the contribution of identified sources to the concentration of the physicochemical parameters. This study presents the necessity and usefulness of multivariate statistical assessment of large and complex databases in order to get better information about the quality of surface water, the design of sampling and analytical protocols and the effective pollution control/management of the surface waters.
A new Raman lidar for unattended, round-the-clock measurement of vertical water vapor profiles for operational use by the MeteoSwiss has been developed during the past years by the Swiss Federal ...Institute of Technology, Lausanne. The lidar uses narrow field-of-view, narrowband configuration, a UV laser, and four 30 cm in diameter mirrors, fiber-coupled to a grating polychromator. The optical design allows water vapor retrieval from the incomplete overlap region without instrument-specific range-dependent corrections. The daytime vertical range covers the mid-troposphere, whereas the nighttime range extends to the tropopause. The near range coverage is extended down to 100 m AGL by the use of an additional fiber in one of the telescopes. This paper describes the system layout and technical realization. Day- and nighttime lidar profiles compared to Vaisala RS92 and Snow White® profiles and a six-day continuous observation are presented as an illustration of the lidar measurement capability.
The present study deals with the assessment of surface water quality from an industrial–urban region located in northern Poland near to the city of Gdansk. Concentrations of thirteen chemicals ...including total polycyclic aromatic hydrocarbons (PAHs), halogenated volatile organic compounds (HVOCs) and major ions in the samples collected at five sampling points during six campaigns were used as variables throughout the study. The originality in the monitoring data treatment and interpretation was the combination of a traditional classification approach (self-organizing maps of Kohonen) with PAH diagnostic ratios expertise to achieve a reliable pollution source identification. Thus, sampling points affected by pollution from traffic (petroleum combustion products), from crude oil processing (petroleum release related compounds), and from phosphogypsum disposal site were properly discriminated. Additionally, it is shown that this original assessment approach can be useful in finding specific pollution source tracers.
As a result of increased healthcare requirements and the introduction of genetically modified foods, the problem of allergies is becoming a growing health problem. The concept of allergies has ...prompted the use of new methods such as genomics and proteomics to uncover the nature of allergies. In the present study, a selection of 1400 food proteins was analysed by PLS-DA (Partial Least Square-based Discriminant Analysis) after suitable transformation of structural parameters into uniform vectors. Then, the resulting strings of different length were converted into vectors with equal length by Auto and Cross-Covariance (ACC) analysis. Hierarchical and non-hierarchical (K-means) Cluster Analysis (CA) was also performed in order to reach a certain level of separation within a small training set of plant proteins (16 allergenic and 16 non-allergenic) using a new three-dimensional descriptor based on surface protein properties in combination with amino acid hydrophobicity scales. The novelty of the approach in protein differentiation into allergenic and non-allergenic classes is described in the article.
The general goal of the present study was to show the effectiveness of a traditional chemometric method for classification (PLS–DA) and the options of Cluster Analysis (CA) to separate by multivariate statistical methods allergenic from non-allergenic proteins.
A validation of the developed new orientation method of solid samples as suspension in nematic liquid crystal (NLC), applied in linear-dichroic infrared (IR-LD) spectroscopy has been carried out ...using a model system
dl-isoleucine (
dl-isoleu). Accuracy, precision and the influence of the liquid crystal medium on peak positions and integral absorbances of guest molecules have been presented. Optimization of experimental conditions has been performed as well. An experimental design for quantitative evaluation of the impact of four input factors: the number of scans, the rubbing-out of KBr-pellets, the amount of studied compounds included in the liquid crystal medium and the ratios of Lorentzian to Gaussian peak functions in the curve fitting procedure on the spectroscopic signal at five different frequencies, indicating important specifities of the system has been studied.
The present study deals with the application of self-organizing maps (SOM) and multiway principal-components analysis to classify, model, and interpret a large monitoring data set for surface water ...quality. The chemometric methods applied made it possible to reveal specific quality patterns of the chemical and biological parameters used to monitor the water quality (relation between water temperature, turbidity, hardness, colibacteria), seasonal impacts during the long period of observation and the relative independence on the spatial location of the sampling sites (water supply sources for the City of Trieste). graphic removed
This environmetric study deals with modeling and interpretation of river water monitoring data from the basin of the Saale river and its tributaries the Ilm and the Unstrut. For a period of one year ...of observation between September 1993 and August 1994 a data set from twelve campaigns at twenty-nine sampling sites from the Saale river and six campaigns from the river Ilm at seven sampling sites and from river Unstrut at ten sampling sites was collected. Twenty-seven chemical and physicochemical properties were measured to estimate the water quality. The application of cluster analysis, principal components analysis, and apportioning modeling on absolute principal components scores revealed important information about the ecological status of the region of interest:identification of two separate patterns of pollution (upper and lower stream of the rivers);identification of six latent factors responsible for the data structure with different content for the two identified pollution patterns; anddetermination of the contribution of each latent factor (source of emission) to the formation of the total concentration of the chemical burden of the river water. As a result more objective ecological policy and decision making is possible.
In this study, the attention is focused on modeling a four-way environmental data set that comes from monitoring of air quality in two industrial regions in Austria, by means of PARAFAC and Tucker ...models. It appeared that the constructed Tucker model is the most appropriate for the studied data. It gave a possibility to uncover important information about the influence of chemical composition, particle size and seasonality on the air quality at the industrial regions of interest. The Tucker model analysis made it also possible to distinguish the two industrial regions as two different air quality locations.
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