N-way modelling of sediment monitoring data from Mar Menor lagoon, Spain.

The present paper deals with the application of Tucker3 modelling to a sediment monitoring data set from the area of Mar Menor coastal lagoon (Spain). The aim of the study is to model and interpret the fractionation of heavy metals in the suspended particulate matter and sediment fractions resulting by sedimentation processes. Since the lagoon is seriously influenced by anthropogenic activities the modelling aims an assessment of the environmental hazard, too.

Ecotoxicity and chemical sediment data classification by the use of self-organising maps.

The present paper deals with the presentation in a new interpretation of sediment quality assessment. This original approach studies the relationship between ecotoxicity parameters (acute and chronic toxicity) and chemical components (polluting species like polychlorinated biphenyls (PCBs), pesticides, polycyclic aromatic hydrocarbons (PAH), heavy metals) of lake sediments samples from Turawa Lake, Poland by an application of self-organising maps (SOMs) to the monitoring dataset (59 samplesx44 parameters) in order to obtain visual images of the components distributed at each sampling site when all components are included in the classification and data projection procedure. From the SOMs obtained, it is possible to select groups of similar ecotoxicity (either acute or chronic) and to analyse within each one of them the relationship of the other chemicals to the toxicity determining parameters (EC(50) and mortality).

Multivariate statistical study of simultaneously monitored cloud water, aerosol and rainwater data from different elevation levels in an Alpine valley (Achenkirch, Tyrol, Austria).

The present paper deals with data interpretation of monitoring of various atmospheric events (cloud water, aerosol and rainwater) at three different elevation levels at Achenkirch profile in an Alpine valley, Tyrol, Austria (Christlumkopf-1758 m, Christlumalm-1280 m and Talboden-930 m a.s.l.

Assessment of the surface water quality in Northern Greece.

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).

Environmetric modeling and interpretation of river water monitoring data.

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.

Multivariate statistical interpretation of coastal sediment monitoring data.

Multivariate statistical analysis of sediment data (input matrix 122 x 15) collected from 122 sampling sites from the western coastline of the USA and analyzed for 15 analytes indicates that the data structure could be explained by four latent factors. These factors are conditionally named "anthropogenic", "organic", "natural", and "hot spots". They explain over 85% of the total variance of the data system, which is an acceptable value for the PCA model.

Trend, seasonal and multivariate modelling study of wet precipitation data from the Austrian Monitoring Network (1990-1997).

The aim of the present study was to analyse the data structure of a large data set from rainwater samples collected during a long-term interval (1990-1997) by the Austrian Precipitation Monitoring Network. Eleven sampling sites from the network were chosen as data sources (chemical concentrations of major ions only) covering various location characteristics (height above sea level, rural and urban sampling positions, Alpine rim and Alpine valley disposition, etc.).

Assessment of metal pollution based on multivariate statistical modeling of 'hot spot' sediments from the Black Sea.

The paper deals with application of different statistical methods like cluster and principal components analysis (PCA), partial least squares (PLSs) modeling. These approaches are an efficient tool in achieving better understanding about the contamination of two gulf regions in Black Sea. As objects of the study, a collection of marine sediment samples from Varna and Bourgas "hot spots" gulf areas are used.