Proceedings

New Passive Sampling Device for Improved Monitoring of Drinking Water and Groundwater Resources

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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