De Jonge, H. and Møller, M., Sorbisense A/S, Denmark(free)
This presentation will present a newly developed passive sampling device allowing more effective management of water resources. The implementation of the new EU water framework directive has recently increased focus on the monitoring of water resources throughout Europe. The directive both dictates a thorough inventory of the current status of water quality, as well as the implementation of follow up measures and evaluation of these policy measures. Monitoring and temporal trend identification is crucial: is the quality of the water body improving or not? Are the employed policy measures effective or not? The monitoring task is even more challenging because in many cases pollution sources are poorly defined, and diffusively spread over the landscape. Hence, potential sampling points may be remote and therefore expensive to include in the monitoring program. Another important challenge is the ability to establish effective early warning systems that may enhance the ability of regulating authorities to detect potential threats in an early stage, allowing them to take action before the problems are no longer manageable. The detection of temporal trends - e.g. in groundwater, surface water or wastewater - is usually done with repeated “grab sampling” of water samples. The samples provide information at fixed points in time and interpolation between sampling events is necessary to estimate average loads. Clearly the sampling frequency and costs are closely correlated, and highly dynamic environments call for many samples to obtain statistically reliable data. Often reliable data retrieval is compromised by budget restraints. The main advantage of the passive sampling device presented here – we call it SorbiCell - is to allow continuous sampling in time without the use of pumps or other power consuming functions. With the device it is possible to obtain reliable data covering a given sampling period with reduced sampling frequency and costs. Also temporal trend identification is more effectively done with continuous sampling rather than grab sampling (Figure 1).
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