Comparison of self-organizing maps classification approach with cluster and principal components analysis for large environmental data sets

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.     

Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study

This case study reports different multivariate statistical techniques applied for evaluation of temporal/spatial variations and interpretation of a large complex water-quality data set obtained during monitoring of Gomti River in Northern part of India. Water quality of the Gomti River, a major tributary of the Ganga River was monitored at eight different sites selected in relatively low, moderate and high pollution regions, regularly over a period of 5 years (1994-1998) for 24 parameters. The complex data matrix (17,790 observations) was treated with different multivariate techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis (DA). Cluster analysis (CA) showed good results rendering three different groups of similarity between the sampling sites reflecting the different water-quality parameters of the river system. FA/PCA identified six factors, which are responsible for the data structure explaining 71% of the total variance of the data set and allowed to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall variation in water quality. However, significant data reduction was not achieved, as it needed 14 parameters to explain 71% of both the temporal and spatial changes in water quality. Discriminant analysis showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. Discriminant analysis showed five parameters (pH, temperature, conductivity, total alkalinity and magnesium) affording more than 88% right assignations in temporal analysis, while nine parameters (pH, temperature, alkalinity, Ca-hardness, DO, BOD, chloride, sulfate and TKN) to afford 91% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets with a view to get better information about the water quality and design of monitoring network for effective management of water resources.     

Evaluation of distribution, mobility and binding behaviour of heavy metals in surficial sediments of Louro River (Galicia, Spain) using chemometric analysis: a case study

Contents of total and extractable heavy metals, carbonates, MnO and Fe₂O₃, organic matter, and matrix components such as SiO₂, Al₂O₃, CaO, Na₂O, MgO, TiO₂, K₂O and P₂O₅ are used along with principal component analysis (PCA) for studying distribution, mobility and binding behaviour of Cd, Cr, Cu, Ni and Pb in the Louro River (Galicia, Spain). Eleven surficial sediment samples were taken along the beds of the river course. Total metal concentrations were obtained after microwave-assisted digestion whilst extractable metal contents were obtained following a three-stage sequential extraction scheme (i.e. soluble, reducible and oxidisable fractions). Loading plots of heavy metals bound to carbonates, Fe–Mn oxides, organic matter and aluminosilicates allowed determination of binding behaviour. Correlations found indicate that Pb and Cu are mainly discharged from urban wastes, whereas Cr and Ni are from electroplating and galvanizing industries. The occurrence of diffuse pollution sources along the river can account for the binding behaviour of Cd. Metal mobility decreased in the order: Cd>Pb>Cu>Ni>Cr. Despite total contents indicating moderate-to-high heavy metal pollution in this river, metals are mostly distributed in the residual fraction, hence showing a low risk of mobility.     

Quantification of stormwater runoff using a combined GIS and curve number approach: a case study for an urban watershed in the Ozark Highlands,USA

Managing stormwater runoff is crucial to preserving water quality in rapidly developing urban watersheds. The objective of this study was to develop a methodology to test existing stormwater drainage infrastructure, identify potential areas of improvement, and estimate potentially contaminated runoff by combining two widely used stormwater runoff prediction models. A watershed containing much of the University of Arkansas-Fayetteville campus was targeted for this study because stormwater from this watershed drains into a local river designated as an impaired water body due to siltation. The curve number method was used to estimate runoff for various flood-return periods and antecedent moisture conditions, while a flow-direction model integrated topography, land use, and stormwater drainage infrastructure in a GIS. The methodology developed and results generated will help stormwater planners visualise localised runoff and potentially adapt existing drainage networks to accommodate runoff, prevent flooding and erosion, and improve the stormwater quality entering nearby surfacewater bodies.