Passive sampling devices accumulate chemicals continuously from water and can provide time weighted average (TWA) concentrations of pollutants over the exposure period. Hence, they offer a number of advantages over other conventional monitoring techniques such as spot or grab sampling. The diffusive gradient in thin film (DGT) and the Chemcatcher passive samplers can be used to provide TWA concentrations of labile metals, but the approaches to their calibration differ. DGT uses diffusion coefficients of metals in the hydrogel layer, whereas Chemcatcher uses metal specific uptake rates, with both sets of values obtained under controlled laboratory conditions with constant aqueous metal concentrations. However, little is known of how such samplers respond to fluctuating concentrations. We evaluated the responsiveness of these two passive sampling devices to rapidly changing concentrations of Cd, Cu, Ni, Pb and Zn in natural freshwater, over a relatively short deployment time. Maximum metal concentrations in water were varied between 70 and 140 microg L(-1). Experiments were carried out in a tank with a rotating carousel system and filled with Meuse river water, allowing a degree of control over experimental conditions while using natural river water. Fluctuating concentrations were obtained by stepwise addition of standard solutions of the metals. The reliability and accuracy of the TWA concentrations measured by the samplers were assessed by comparison with concentrations of the metals in spot samples of water taken regularly over the deployment period. The spot samples of water were either unfiltered (total), filtered (0.45 microm) or ultrafiltered (5 kDa). Predictive speciation modelling using the visual MINTEQ programme was also undertaken. There was reasonable agreement between the TWA concentrations of Cd and Ni obtained with Chemcatcher and DGT and the total Cd and Ni concentrations measured in repeated unfiltered spot samples. For elements (i.e. Cu, Pb, Zn) that associate to a significant degree with suspended solids, colloids or dissolved organic carbon, or form complexes with large organic ligands, optimum agreement was with the filtered or ultrafiltered fractions and with the predicted inorganic and inorganic-fulvic acid associated fractions. While Chemcatcher-based TWA concentration ranges for Cu and Zn were in best agreement with the total filtered fraction, there was lack of agreement for Pb. The combined use of DGT devices with open pore (OP) and restricted pore (RP) gels allowed the labile fraction of metal associated with large organic ligands or DOC to be differentiated and quantified, since this is available to DGT OP but unable to diffuse into the DGT RP. This evaluation of the two sampling devices clearly demonstrated their ability to react reliably to transient peaks in concentration of metal pollutants in water and indicated where future efforts are needed to improve calibration data. Such samplers may prove valuable in responding to the monitoring requirements of the European Union's Water F...

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This work aimed to evaluate whether the performance of passive sampling devices in measuring time-weighted average (TWA) concentrations supports their application in regulatory monitoring of trace metals in surface waters, such as for the European Union's Water Framework Directive (WFD). The ability of the Chemcatcher and the diffusive gradient in thin film (DGT) device sampler to provide comparable TWA concentrations of Cd, Cu, Ni, Pb and Zn was tested through consecutive and overlapping deployments (7-28 days) in the River Meuse (The Netherlands). In order to evaluate the consistency of these TWA labile metal concentrations, these were assessed against total and filtered concentrations measured at relatively high frequencies by two teams using standard monitoring procedures, and metal species predicted by equilibrium speciation modeling using Visual MINTEQ. For Cd and Zn, the concentrations obtained with filtered water samples and the passive sampling devices were generally similar. The samplers consistently underestimated filtered concentrations of Cu and Ni, in agreement with their respective predicted speciation. For Pb, a small labile fraction was mainly responsible for low sampler accumulation and hence high measurement uncertainty. While only the high frequency of spot sampling procedures enabled the observation of higher Cd concentrations during the first 14 days, consecutive DGT deployments were able to detect it and provide a reasonable estimate of ambient concentrations. The range of concentrations measured by spot and passive sampling, for exposures up to 28 days, demonstrated that both modes of monitoring were equally reliable. Passive sampling provides information that cannot be obtained by a realistic spot sampling frequency and this may impact on the ability to detect trends and assess monitoring data against environmental quality standards when concentrations fluctuate.

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Biological early warning systems represent a set of tools that may be able to respond to certain chemical monitoring requirements of recent European legislation, the Water Framework Directive (WFD2000/60/EC), that aims to improve and protect water quality across Europe. In situ biomonitoring was performed along the rivers Meuse (NL), Aller (GER) and Rhine (F) within the frame of the European Union-funded Project SWIFT-WFD. Gammarus pulex was used as a test organism during the evaluation of the Multispecies Freshwater Biomonitor(R) (MFB), an online biomonitor to quantitatively record different behaviour patterns of animals. At the river Meuse G. pulex reacted to pulse exposure of either a mixture of trace metals or of several organic xenobiotics, by showing up to 20% decreased locomotory activity (already at the 1st pulse) and increased mortality (at 2nd or 3rd pulse only). G. pulex deployed within the MFB system were observed to survive well at the monitoring station on the Aller (100%) and monitoring did not result in the measurement of chemical irregularities. In contrast, deployment at the monitoring station on the Rhine river demonstrated that the test organism was able to detect chemical irregularities by up to 20% decreased locomotory activity in the animals. The MFB proved to be an alert system for water quality monitoring at sensitive sites and sites with accidental pollution.

emphasizes the necessity of future collaborations between research groups, referenceaccredited laboratories, and technology developers, to maintain comparability of the concentrations reported for the various nutrient parameters measured by in situ sensors.

The objective of this paper was to demonstrate how multivariate statistics combined with the analysis of variance could support decision-making during the process of redesigning a water quality monitoring network with highly heterogeneous datasets in terms of time and space. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) were selected to optimise the selection of water quality parameters to be monitored as well as the number and location of monitoring stations. Sampling frequency was specifically investigated through the analysis of variance. The data used were obtained between 2007 and 2010 at the Long-term Environmental Research Monitoring and Testing System (OPE) located in the north-eastern part of France in relation with a geological disposal of radioactive waste project. PCA results showed that no substantial reduction among the parameters was possible as strong correlation only exists between electrical conductivity, calcium or bicarbonates. HCA results were geospatially represented for each field campaign and compared to one another in terms of similarities and differences allowing us to group the monitoring stations into 12 categories. This approach enabled us to take into account not only the spatial variability of water quality but also its temporal variability. Finally, the analysis of variances showed that three very different behaviours occurred: parameters with high temporal variability and low spatial variability (e.g. suspended matter), parameters with high spatial variability and average temporal variability (e.g. calcium) and finally parameters with both high temporal and spatial variability (e.g. nitrate).

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