Environmental Engineering Reference
In-Depth Information
final WoE findings, and concluded that tabular decision matrices are the most
transparent and quantitative representations.
Ideally, ERA for aquatic, sediment and terrestrial systems should follow the same
set of conventions for scaling. In practice, however, there are slight differences for
the following reasons:
1. There is a wide range of standardization levels and terrestrial methods differ in
sensitivity, making it difficult to define one set of homogeneous 'rules' for inter-
pretation. Although initial thoughts for scaling of e.g. bioassays, biomarkers and
community-level end-points are obtained from best professional judgments , still
much experience is lacking. It is expected that these rules can be obtained step
by step from the building up of practical experience from ERAs at contaminated
sites.
2. Interpretation of test results in terms of 'effect' or 'no-effect' inevitably will
result in the loss of valuable quantitative information. Except for the situation for
ERA in surface water and sediment systems, the limited experience with use of
the Triad approach for terrestrial systems demands for exploration and efficient
use of virtual all available information in a quantitative manner.
3. In aquatic systems toxicity can be determined after a pre-concentration step,
allowing the application of relatively insensitive tools and producing fewer false
negative results. It is virtually impossible to concentrate soil samples putting
higher demands on tools and the use of results in ERA.
For evaluation and integration of the results from the three lines of evidence in
the Triad (chemistry, toxicity, ecology) a quantitative decision matrix is constructed.
To this purpose, it is necessary to use a uniform effect scale for the quantification
of each of the separate effect levels in the Triad approach, running from zero (no
effect) up to 1 (maximal ecosystem effect). Consequently, the results from each tool
(bioassay, biomarker or ecological field survey) should be projected on this effect
scale, according to best available knowledge from the literature or best professional
judgments (BPJ) from consulted experts. Useful and advanced examples of scaling
rules and the construction of such a quantitative decision matrix can be found in
Jensen and Mesman ( 2006 ), Dagnino et al. ( 2008 ) and Semenzin et al. ( 2008 ).
Different tools will obviously require different approaches. For instance, for a
growth test the percentage of inhibition can be implicitly used as the measure for
effects. For ecological field monitoring, however, the results should be scaled rel-
atively to the ecological state of a reference site (
=
0), and a (theoretical) state
indicating 100% effects. Information from field monitoring is often composed from
multiple variables putting specific demands on the scaling of multi-dimensional
information to a one-dimensional effect value (Jensen and Mesman 2006 ).
Furthermore, the method of scaling should account for limitations in working
range of an assessment tool with respect to the effect scale. This is sometimes
denoted as the biological scale of the measurements (e.g. Gaudet et al. 1995 ; Wright
and Welbourn 2002 ). The effect scale is usually defined on the level of popula-
tions of protected species, whole communities, ecosystem functions or some kind
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