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Characterisation Method Information
Characterisation Method Name:
Acidification and eutrophication impact on PDF
Version:
2000
Date Completed:
2000
Principal Method Name:
ECO-indicator: mathematical modelling of "nature planner"
Method Description:
Description of the problem
PDF: Potentially Disappeared Fraction


Acidification and eutrophication are caused by depositions of inorganic substances such as sulphates, nitrates and phosphates. These depositions occur mainly through air and directly into water. The primary effect is the change in nutrient level and acidity in the soil.

Airborne emissions also influence aquatic ecosystems. As we will see the model takes aquatic systems such as wetlands and swamps only into account if they can be classified as natural areas. The effect of airborne emissions on rivers, canals and lakes is not taken into account. In many parts in Europe this is not a big problem as the direct emissions into water are often much more important. However, especially in Scandinavia many lakes are reported to be heavily acidified by airborne emissions from central Europe.

In the case of ecotoxicity, it can be assumed that any increase in toxic pressure results in a damage. For changes in the nutrient and acidity levels this is not so simple. For almost all plant species there is a clearly defined optimum combination of nutrient level and acidity. Any deviation from this optimum is detrimental for that specific species. As a result, changes in nutrient levels will mainly cause shifts in
the species populations. Sometimes these shifts result in an increased number of species, sometimes there is a decrease.
The problem here is that we need to find out to what extent a shift can be considered to be a damage.

This means we have to differentiate between desired and less desired species populations. We cannot make general statements about desired and less desired species in general. We have to consider the
desired species population per type of ecosystem. In the method described below, a list of target species that has been developed for over 40 types of ecosystems is used [Bal et al 1995]. The target species
represent the natural state of a specific ecosystem. It is clear there are subjective elements in the selection of these target species. The main criterion is whether species can be considered to be typical
and representative for an ecosystem or not.
With these target species, we can now monitor the effect of depositions on these target species. We can only do this per ecosystem, which means we have to incorporate a Geographic Information System
(GIS) into the model, and we have to select the “projected” ecosystem into each gridcell. Once such a system is set up, we can model the effect of depositions on the PDF 18 for the target species in that cell.
As the substances causing eutrophication and acidification are inorganic, their fate cannot be modelled with EUSES(European Union System for the Evaluation of Substances). The model we use for damage analysis has its own fate model, based on the characteristics and background levels of each grid cell.
In our model we only take into account changes in natural areas. Changes in acidity and nutrient levels in agricultural areas as a result of depositions are not really relevant, as these depositions are small
compared by the application of nutrients and acidity regulating agents applied by farmers. A big problem at this moment is the lack of models for eutrophication and acidification of aquatic systems.
So far we only have found a Dutch model (the Natuureplanner, see below) that is able to translate changes in depositions into changes in the PDF of plants. This is of course a serious limitation. Our
temporary solution is to assume that the Dutch natural areas have the same sensitivity as the European natural areas. We expect that this simplification results in a number of biases, due to the specific
characteristics of the Dutch natural areas.
There are no rocks, let alone mountains in the Netherlands. Furthermore, the largest part of natural areas is formed by sandy dune-like landscapes near the coast, and in the centre or the north east of the country.

The “Nature planner”
For the Eco-indicator 99 we had access to the “Natuurplanner” [LATOUR ET AL 1997], or “Nature Planner” that has been developed by RIVM(National Institute of public health and the Environment (The Netherlands)) . The Nature Planner uses a 250 by 250 metre grid for the Netherlands. The Nature Planner contains several databases with information on vegetation, soil
conditions and fate models, which are combined directly with effect models. It aims at the combined effect assessment of eutrophication, acidification, desiccation, fragmentation, climate change and pollution by toxic substances on ecosystems and species (multi-stress). It is meant for a national or regional scale. At this moment the Nature Planner is operational for the effect assessment of eutrophication, acidification and desiccation on the vegetation and butterflies.

The Nature Planner consists of two parts: a soil model (SMART) and a vegetation response model

(MOVE):
• SMART can be regarded as a fate model, as it calculates the pH and the nutrient level, expressed as Nitrogen availability, and the availability of water. The desiccation part of the model is not used here. Calculation has been performed with a fixed groundwater level.

• MOVE can be considered as a damage model, as it calculates the effects of the SMART results on the PDF for each grid-cell.

Fate analysis with SMART
SMART [KROS 1995] is a simple one-layer dynamic soil model, which includes a N-cycle (litter fall, mowing, litter removal, mineralization, nitrification, denitrification and uptake), geochemical processes (weathering and cation exchange) and a simple hydrological cycle including solute transport through upward seepage. This model predicts the changes in soil pH and N-availability in the root zone on a
year by year basis. An indicative validation shows generally a reasonable agreement with pH and N-data from other literature.
Normally SMART is used with actual or projected depositions per grid cell. The result is the total effect of the damage. For our model we are interested in the marginal effect of adding a certain flow
over a certain period. For our calculation we used the latest version of the deposition data (used for [RIVM 1998]), and we added a deposition of 10 mole NOx, SOx and NH3 per km 2 to each grid cell. As depositions are usually in the order of several 100 Mole’s, this increase can be considered to be a
marginal change. Of course the deposition of ten moles is not the same as the emission of ten moles. We propose to use the same basic reasoning as in the EUSES model. We assume Europe to be closed, so every mole emitted in Europe is deposited there. Next we must consider that only a part of the emissions is actually deposited on the natural soils. If we assume an even distribution, we can assume that only 60% of the emission is deposited on natural soil, since 60% of the surface area of Europe consists of natural soil [EUSES 1996].

The MOVE model was used to calculate the decrease in pH and the increase of Nitrogen availability due to the increased deposition for each substance in each grid cell. As the model also calculates the average, and the distribution.


From the calculation results we can make a number of observations:
• There is a strong relation between the deposition of NH3 and an increased nutrient availability, while the deposition of SOx results in an slight decrease of nutrient availability. This can be explained as nutrients become less available if the pH drops, and as Nutrient availability is
expressed as nitrogen availability.
• There is a weak relation between the deposition of NH3 and acidity, while the relation between acidity and NOx deposition is strong.

Damage modelling
The results from SMART form the input for the vegetation model MOVE. This model consists of the response functions of more than 900 Dutch plant species. The response functions describe the relationship between the PDF and the soil acidity, nutrient condition and the moisture condition and
their mutual interactions. The most important source for these response functions is [ELLENBERG ET AL 1992]. [ALKEMADE ET AL 1996] made a verification study to compare (and adjust) the Ellenberg data with measured data.

The model MOVE can calculate the potentially disappeared fraction for these values per grid-cell. A species is considered to meet unfavourable conditions if this probability is lower than some threshold value (set to 2,5%). These species suffer from stress caused by the combined effect of acidification and eutrophication. The number of stressed target species are counted per grid-cell and the results can be aggregated for the total natural area of the Netherlands, resulting in a percentage of threatened species caused by a specific deposition.

According to the model, the PDF for target species is three quarters of the full
range of target species. This means the damage due to acidification and eutrophication is high in Dutch natural systems. The standard deviation on this figure is 0.32.

As the PDFreference and the PDF10mol/hectare are almost equal, the difference is very small. This is still permitted as the uncertainties in the PDF calculations are synchronised. This subtraction is a way to
find the slope of the damage curve (coefficient of direction).

Literature Reference:
1. [Bal et al 1995] D. Bal, H.M. Beije, Y.R. Hoogeveen, S.R.J. Jansen, P.J van der Reest. Handboek natuurdoeltypen in Nederland. IKC, Wageningen, 1995. 2. [Latour et al 1997] Latour, J.B.; Staritsky, I.G.;Alkemade, J.R.M.; Wiertz, J.;De natuurplanner, Decision support system natuur en milieu, RIVM report 711901019; September 1997
Methodological Range:
Geographical range is Europe Data are based on hierarchist perspective
Notes:

Existing Characterisation Factors of Acidification and eutrophication impact on PDF
Characterisation Parameter Category Indicator Impact Indication Principle Aspect Substance Quantity Unit Notes
CFactor PDF ECO-indicator/1999
Type = Emission
Direction = Output
Media = Air
Geography = *
Ammonia 3.04E-03 PDF m2 yr/kg
CFactor PDF ECO-indicator/1999
Type = Emission
Direction = Output
Media = Air
Geography = *
NO 1.71E-03 PDF m2 yr/kg
CFactor PDF ECO-indicator/1999
Type = Emission
Direction = Output
Media = Air
Geography = *
NO2 1.11E-03 PDF m2 yr/kg
CFactor PDF ECO-indicator/1999
Type = Emission
Direction = Output
Media = Air
Geography = *
NOx 1.11E-03 PDF m2 yr/kg
CFactor PDF ECO-indicator/1999
Type = Emission
Direction = Output
Media = Air
Geography = *
SO2 2.03E-04 PDF m2 yr/kg
CFactor PDF ECO-indicator/1999
Type = Emission
Direction = Output
Media = Air
Geography = *
SO3 1.62E-04 PDF m2 yr/kg
CFactor PDF ECO-indicator/1999
Type = Emission
Direction = Output
Media = Air
Geography = *
SOx 2.03E-04 PDF m2 yr/kg