Optimization of River Water Quality Surveys by Multivariate Analysis of Physicochemical, Bacteriological and Ecotoxicological Data

This study aims to optimize the water quality monitoring of a polluted watercourse (Leça River, Portugal) through the principal component analysis (PCA) and cluster analysis (CA). These statistical methodologies were applied to physicochemical, bacteriological and ecotoxicological data (with the marine bacterium Vibrio fischeri and the green alga Chlorella vulgaris) obtained with the analysis of water samples monthly collected at seven monitoring sites and during five campaigns (February, May, June, August, and September 2006). The results of some variables were assigned to water quality classes according to national guidelines. Chemical and bacteriological quality data led to classify Leça River water quality as “bad” or “very bad”. PCA and CA identified monitoring sites with similar pollution pattern, giving to site 1 (located in the upstream stretch of the river) a distinct feature from all other sampling sites downstream. Ecotoxicity results corroborated this classification thus revealing differences in space and time. The present study includes not only physical, chemical and bacteriological but also ecotoxicological parameters, which broadens new perspectives in river water characterization. Moreover, the application of PCA and CA is very useful to optimize water quality monitoring networks, defining the minimum number of sites and their location. Thus, these tools can support appropriate management decisions.

Recycling old screen-printed electrodes with newly designed plastic antibodies on the wall of carbon nanotubes as sensory element for in situ detection of bacterial toxins in water

Using low cost portable devices that enable a single analytical step for screening environmental contaminants is today a demanding issue. This concept is here tried out by recycling screen-printed electrodes that were to be disposed of and by choosing as sensory element a low cost material offering specific response for an environmental contaminant. Microcystins (MCs) were used as target analyte, for being dangerous toxins produced by cyanobacteria released into water bodies. The sensory element was a plastic antibody designed by surface imprinting with carefully selected monomers to ensure a specific response. These were designed on the wall of carbon nanotubes, taking advantage of their exceptional electrical properties. The stereochemical ability of the sensory material to detect MCs was checked by preparing blank materials where the imprinting stage was made without the template molecule. The novel sensory material for MCs was introduced in a polymeric matrix and evaluated against potentiometric measurements. Nernstian response was observed from 7.24 × 10−10 to 1.28 × 10−9 M in buffer solution (10 mM HEPES, 150 mM NaCl, pH 6.6), with average slopes of −62 mVdecade−1 and detection capabilities below 1 nM. The blank materials were unable to provide a linear response against log(concentration), showing only a slight potential change towards more positive potentials with increasing concentrations (while that ofthe plastic antibodies moved to more negative values), with a maximum rate of +33 mVdecade−1. The sensors presented good selectivity towards sulphate, iron and ammonium ions, and also chloroform and tetrachloroethylene (TCE) and fast response (<20 s). This concept was successfully tested on the analysis of spiked environmental water samples. The sensors were further applied onto recycled chips, comprehending one site for the reference electrode and two sites for different selective membranes, in a biparametric approach for “in situ” analysis.