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Journal of Environmental Management (2000) 58, 169–178 doi:10.1006/jema.2000.0324, available online at http://www.idealibrary.com on Development of a spatial decision support system for post-emergency management of radioactively contaminated land C. A. Salt * and M. Culligan Dunsmore A GIS-based spatial decision support system (SDSS) is being developed for long-term management of radioactively contaminated land resources. The system is designed to assist decision-makers in the evaluation and selection of remediation strategies for food production in agricultural and semi-natural ecosystems at a regional scale. The suitability assessment of different remediation techniques is based on the level of contamination, the land-use management and a wide range of environmental parameters. Techniques which are found suitable with respect to reducing contamination of food products are subjected to an assessment of potential environmental and agri-economic impacts. This involves multicriteria decision- making methodology to evaluate the advantages and disadvantages of each technique and to incorporate decision-maker preferences into the assessment. The final output from the SDSS is in the form of maps at 10 m resolution depicting for each grid cell either the most suitable countermeasure or the site suitability for a single countermeasure. This paper discusses the need for a spatial decision support system to optimise remediation strategies, provides an explanation of the methodology behind the system and describes how it can be implemented within the context of a GIS. 2000 Academic Press Keywords: multicriteria decision-making (MCDM), Geographic Information Systems (GIS), radioecology, countermeasures. Introduction Following the nuclear accident at Chernobyl on April 26, 1986, large areas of land in Europe were left contaminated by radioactive fallout. Agricultural products subsequently raised on this land as well as wild foods, such as mushrooms, venison and berries, contributed significantly to the radioactive dose received by the human populations in these areas (Kaul et al., 1996). Fortunately, it was possible to reduce the dose to humans by applying measures ranging from dietary advice to changes in agricultural manage- ment (Alexakhin, 1993). These so-called ‘countermeasures’ can have important socio- economic effects by preventing large quanti- ties of food from having to be destroyed, and enabling farming and traditional collection of L Corresponding author Department of Environmental Science, University of Stirling, Stirling, FK9 4LA, United Kingdom Received 21 December 1998; accepted 25 November 1999 wild foods to continue (Tveten et al., 1998). In vulnerable areas it may be necessary to apply countermeasures over a number of years. A wide range of long-term physical, chemical and management-based measures can be taken to reduce the soil-plant-animal transfer of radionuclides and in turn limit their entry into the human food chain (IAEA, 1994). Included are techniques such as deep ploughing (Vovk et al., 1993), soil application of fertilisers (Nisbet et al., 1994), use of bind- ing agents in soils (Vandenhove et al., 1998) or livestock (Voigt, 1993), changes in livestock management (Howard, 1993) or in land use (IAEA, 1994). In the event of a future nuclear accident, it is critical that not only the ‘opti- mal’ countermeasure should be selected for each agricultural situation, but also that the benefits gained in lowering the transfer of radionuclides into the human food chain are 0301–4797/00/030169C10 $35.00/0 2000 Academic Press

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Page 1: Development of a spatial decision support system for post-emergency management of radioactively contaminated land

Journal of Environmental Management (2000) 58, 169–178doi:10.1006/jema.2000.0324, available online at http://www.idealibrary.com on

Development of a spatial decisionsupport system for post-emergencymanagement of radioactivelycontaminated land

C. A. Salt* and M. Culligan Dunsmore

A GIS-based spatial decision support system (SDSS) is being developed for long-term managementof radioactively contaminated land resources. The system is designed to assist decision-makers in theevaluation and selection of remediation strategies for food production in agricultural and semi-naturalecosystems at a regional scale. The suitability assessment of different remediation techniques is basedon the level of contamination, the land-use management and a wide range of environmental parameters.Techniques which are found suitable with respect to reducing contamination of food products are subjectedto an assessment of potential environmental and agri-economic impacts. This involves multicriteria decision-making methodology to evaluate the advantages and disadvantages of each technique and to incorporatedecision-maker preferences into the assessment. The final output from the SDSS is in the form of maps at10 m resolution depicting for each grid cell either the most suitable countermeasure or the site suitability fora single countermeasure. This paper discusses the need for a spatial decision support system to optimiseremediation strategies, provides an explanation of the methodology behind the system and describes how itcan be implemented within the context of a GIS. 2000 Academic Press

Keywords: multicriteria decision-making (MCDM), Geographic Information Systems (GIS),radioecology, countermeasures.

Introduction

Following the nuclear accident at Chernobylon April 26, 1986, large areas of land inEurope were left contaminated by radioactivefallout. Agricultural products subsequentlyraised on this land as well as wild foods,such as mushrooms, venison and berries,contributed significantly to the radioactivedose received by the human populations inthese areas (Kaul et al., 1996). Fortunately,it was possible to reduce the dose to humansby applying measures ranging from dietaryadvice to changes in agricultural manage-ment (Alexakhin, 1993). These so-called‘countermeasures’ can have important socio-economic effects by preventing large quanti-ties of food from having to be destroyed, andenabling farming and traditional collection of

ŁCorresponding author

Department ofEnvironmental Science,University of Stirling,Stirling, FK9 4LA, UnitedKingdom

Received 21 December1998; accepted 25November 1999

wild foods to continue (Tveten et al., 1998).In vulnerable areas it may be necessary toapply countermeasures over a number ofyears. A wide range of long-term physical,chemical and management-based measurescan be taken to reduce the soil-plant-animaltransfer of radionuclides and in turn limittheir entry into the human food chain (IAEA,1994). Included are techniques such as deepploughing (Vovk et al., 1993), soil applicationof fertilisers (Nisbet et al., 1994), use of bind-ing agents in soils (Vandenhove et al., 1998)or livestock (Voigt, 1993), changes in livestockmanagement (Howard, 1993) or in land use(IAEA, 1994). In the event of a future nuclearaccident, it is critical that not only the ‘opti-mal’ countermeasure should be selected foreach agricultural situation, but also that thebenefits gained in lowering the transfer ofradionuclides into the human food chain are

0301–4797/00/030169C10 $35.00/0 2000 Academic Press

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170 C. A. Salt and M. Culligan Dunsmore

not outweighed by negative environmental,social or economic impacts from that counter-measure.

The potential environmental and socio-economic impacts of long-term remediationhave been investigated in the Countermea-sures: Environmental and Socio-EconomicResponses (CESER) project, a project fundedthrough the European Union’s Fourth Frame-work Programme from January 1997 toJune 1999 with partner contributions fromthe Universities of Stirling (UK), Bre-men (Germany), Salzburg (Austria), theFinnish Environment Institute and the Nord-Trøndelag College (Norway). One of the keytools developed is a GIS-based spatial deci-sion support system (SDSS) to assist inthe long-term management of radioactivelycontaminated land. This SDSS focuses onenvironmental and agri-economic impacts inagricultural and semi-natural food produc-tion systems using a series of case-studyareas in Scotland. It is the first system of it’skind to be developed, though the potentialbenefits of using decision support systems(DSS) in post-emergency management arerecognized (Morrey et al., 1996) and a genericEurope-wide DSS for off-site emergency man-agement has been in development since 1990(Ehrhardt et al., 1993).

Decision-makers faced with the task ofplanning countermeasures may need to oper-ate at different geographical scales. Whilefarm managers or agricultural advisors couldbe designing very specific strategies for asingle farm, this approach, although moreaccurate, is expensive and time-consuming. Itwill therefore be necessary to develop broaderbut more coordinated strategies at the scaleof a region or country. The feasibility andeffectiveness of a particular countermeasureare greatly influenced by spatially variablefactors such as topography, land use and soiltype. Hence GIS offers an ideal technologyfor the storage, manipulation and display ofthe information required to perform a coun-termeasure suitability assessment. For thepurposes of this project the river catchmentwas selected as the basic unit of study asadopted in similar assessments of land usechange (O’Callaghan, 1995) and agriculturalpollution (Hamlett et al., 1992).

This paper seeks to introduce and explainthe methodology behind the CESER SDSSand describe how it can be implemented

within the context of a GIS. Multicriteriadecision-making methodology (MCDM) isintegrated with GIS to evaluate the advan-tages and disadvantages of each counter-measure and to incorporate decision-makerpreferences into the assessment, as for exam-ple applied in previous studies of agriculturalland use (Jansen and Rietveld, 1990) andecological planning (Grabaum and Meyer,1998). The main output from the SDSS isin the form of individual countermeasuresuitability maps and thematic maps thatshow the most suitable countermeasures for acatchment area based on the qualitative andquantitative data input for each radionuclidedeposition scenario.

Deposition scenarios andstudy areas

Countermeasures vary greatly in their radi-ological effectiveness, i.e. their ability toreduce contamination levels in food productsbelow set thresholds, such as the CommunityFood Intervention Limits of the EU (CEC,1989). Hence, the choice of countermeasuredepends greatly on the severity of the con-tamination. In the SDSS the user may chooseone of four radionuclide deposition scenarios,designed to represent fallout ranging fromclose to a reactor burn to far away fromthe accident (see Table 1). Scenarios 1, 3and 4 represent typical activity concentra-tions of caesium-137, strontium-90 and alphaplutonium encountered after the Chernobylaccident, while Scenario 2 represents a com-paratively higher release of Sr that couldoccur as a result of accidents in spent nuclearfuel or waste processing plants. The scenarioshave been used to identify the most appro-priate countermeasures for the agriculturalproduction systems within the SDSS. In theevent that the user does not know the level ofdeposition, an option ‘Consider All Scenarios’is also available.

The Scottish catchments selected as casestudy areas for the SDSS are summarised inTable 2. These represent contrasting areasin terms of the natural environment andof agricultural production systems. Spatialdata as well as non-spatial data was collectedfor each of the areas for use in the impactquantification and in the SDSS (see Table 3).

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SDSS for countermeasure selection 171

Table 1. Deposition scenarios

137Cs 90Sr Alpha-Pu SituationkBq m�2 kBq m�2 kBq m�2

Scenario 1 100 2 0Ð2 Far-field Chernobyl-like falloutScenario 2 100 100 0Ð02 Far-field with a higher fraction of SrScenario 3 1000 200 0Ð2 Chernobyl-like fallout close to site

of releaseScenario 4 5000 500 1 Chernboyl-like fallout very close to site

of release

Table 2. Description of the case-study areas

Catchment UK grid Annaul Area Median slope Arable Improved Roughreference rainfall km2 degrees (%)c grassland grazing

mma (%) (%)

Glenstang Burn NS 245 622 1256 9 2 3Ð5 90 0Burn O’Need NS 255 629 1256 23 3 0 51 41Eden Waterb NT 366 636 690 22 2 88 7Ð5 0Lugate Water NT 340 645 858 33 9 1 31 66Water of Tarf NO 347 784 1286 49 9 0 3 97River Ythanb NJ 376 837 797 14 3 91 8 0Lusragan Burn NM 191 732 1978 7 4 0 10 85River Noe NN 208 733 1978 18 20 0 0 100River Erradale NG 174 870 1839 14 3 0 2 97

a10-year average 1986–1995 from closest met station.bA section of these catchments was assessed.cIncludes rotational grass using for cutting or grazing.

Selecting countermeasuresand quantifying their impacts

The countermeasures most likely to beemployed for long-term remediation follow-ing a nuclear accident were identified for thedominant crop and animal production sys-tems in each of the study areas (Salt et al.,1999). The selection process was based onthe radiological effectiveness of the coun-termeasures for reducing the transfer ofradiocaesium and radiostrontium to food-stuffs, their practicability and their imme-diate costs (Nisbet, 1995). Within the SDSSthe selected countermeasures were allo-cated to each deposition scenario and farmtype based on: (1) predicted contaminationlevels in food products; (2) external dose ofthe person executing a particular counter-measure; (3) the decontamination factor ofthe technique; and, (4) compatibility withtypical farming practices.

The soil treatments selected were: (1) shal-low and deep ploughing techniques designedto dilute or bury the contaminated surfacelayer (Vovk et al., 1993; Roed et al., 1996);

and, (2) application of lime and potassium fer-tilisers which compete with radiostrontiumand radiocaesium respectively in the soilsolution (Nisbet et al., 1994). In animal pro-duction systems a wide range of measures aresuitable. Binding agents, such as ammonium-ferric-hexacyano-ferrate (AFCF) or calciumcan be administered to reduce the gut absorp-tion of radiocaesium and radiostrontiumrespectively (Voigt, 1993). The feeding andfattening regime can be altered to avoid theuse of locally produced contaminated grass orsilage (Prister et al., 1993). Similarly, the useof well fertilised high quality grassland canbe increased. At high contamination levels(Scenarios 3 and 4), it is possible to convert tocultivation of industrial crops such as oilseedrape, to establish forestry or to leave the landfallow (Alexakhin et al., 1993).

Little is known about the nature, magni-tude and duration of environmental impactsfrom countermeasures. As a result, it is neces-sary to use a range of approaches to quantifythe impacts within real agricultural systems.Many countermeasures involve operationsthat are similar to those carried out routinelyin agriculture e.g. ploughing, application of

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172 C. A. Salt and M. Culligan Dunsmore

Table 3. Spatial and non-spatial data sets acquired for the study sites

Data set Source of data Scale Age Description

Soil maps Macaulay LandUse ResearchInstitute

1:50 000a

1:25 000Variable Polygon coverage of the

different soil series found inScotland

Landcover The Scottish Office 1:25 000 1988 Land use of Scotland,surveyed from aerialphotographs taken for thewhole of Scotland in March1988

Land contourdata; landformprofile

The OrdnanceSurvey

1:10 000 Survey datesvary

Elevation contours at 5–10 minterval and spot heights;used to create a DigitalTerrain Model with10 mð10 m pixel size

Parish boundaries Digitised fromcopyrightexpired maps

1:250 000 All maps>50 yearsold

Administrative boundaries

Soil Properties Macaulay LandUse ResearchInstitute

Sample agesvary

Soil profile descriptions withphysical and chemicalproperties for each horizon;one profile for each soiltype

Meteorological data The MET Office 10 years ofdaily data(1986–1995)

Taken from met stationswithin or very close to thestudy areas

Agricultural censusdata

The Scottish Office 1996 Areas (ha) of all crop typesand numbers of livestockaggregated at Parish level

Land managementdata

Interviews withagriculturaladvisors,farmers andlandowners;open literature

1997 Local practices of crop andanimal management e.g.tillage, fertilisation, feedingregimes

aFor three remote areas only data at this smaller scale is available.

fertilisers, changes in the diet of animals.However, they often represent extremes, e.g.deep ploughing, application of high rates ofpotassium or lime, feeding livestock a highproportion of concentrate. To quantify poten-tial impacts and make comparisons betweencountermeasures, a range of methods includ-ing mathematical simulation modelling, sim-ple calculations, laboratory experimentationand expert judgement were applied (Saltet al., 1999).

The most likely environmental impactsresulting from the application of eachcountermeasure were identified and cate-gorised into the following impact criteria:(1) soil erosion and sedimentation; (2) soilorganic matter; (3) soil nutrient transport towater; (4) soil pollutant transport to water;(5) ammonia emissions; (6) biodiversity;(7) landscape quality; (8) agricultural productquality; (9) agricultural product quantity;and (10) animal welfare.

Ploughing techniques or changes in croptype for example, may increase losses ofsoil (erosion) and nutrients (nitrogen andphosphorus) from agricultural land to sur-face and groundwater, resulting in a dete-rioration in water quality (Barlund et al.,1998). In a similar way measures whichlead to changes in animal stocking den-sity or increased manure application willchange nutrient inputs to soil, water andair. Models capable of simulating move-ment of water, particulate matter, nutrientsand other chemicals in terrestrial environ-ments were therefore employed to quan-tify these impacts. The model ICECREAM(Rekolainen and Posch, 1993), an extensionof the CREAMS/GLEAMS models (Knisel,1980; 1993), was used to model erosion andnutrient losses to surface and groundwater.Effects of chemical countermeasures on thecomposition of the soil solution were simu-lated with the PHREEQC model (Parkhurst,

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SDSS for countermeasure selection 173

Greatdecrease

Moderatedecrease

Slightdecrease

Nochange

Slightincrease

Moderateincrease

Greatincrease

+1+2/3+1/30–1/3–2/3–1

Figure 1. Impact scale.

1995). Changes in ammonia emissions fromlivestock were predicted using transfer coef-ficients for the UK (Pain et al., 1997).Any impact that could not be quantifiedthrough calculations, modelling or the empir-ical results of the project was assessed quali-tatively using scientific literature and expertknowledge from within the project group aswell as outside experts using the impact scaleshown in Figure 1.

Impact map creation

The results produced for each study areawere combined with the data on topogra-phy, soil and land use, to create raster-based spatial data coverages in ArcViewTM

(ESRI, 1997) with a 10 m resolution, depict-ing the magnitude of ‘impact risk’ posed byeach countermeasure. For each countermea-sure and assessment criterion combinationthere exists an individual impact map. Theimpact maps form the basis of the coun-termeasure site suitability scores calculatedduring the MCDM process. Due to a num-ber of modelling, time and data constraints,it was necessary to pre-process these mapsfor CESER. However, future systems couldbe linked dynamically to the environmentalmodels used to assess the relative impacts ofdifferent countermeasures.

The impact maps used by the SDSS resultfrom a site suitability assessment under-taken for each countermeasure/criterion com-bination. This includes an assessment of thelimiting factors of the physical environmentthat would prevent the implementation ofthe countermeasure and are used to elimi-nate or ‘mask out’ cells from within the studyarea that were unsuitable. Ploughing, forexample, is not recommended on land withslopes greater than 15 degrees, in agree-ment with general agricultural practice inScotland (Bibby et al., 1991). Therefore, allcells with a slope greater than this valuewere excluded from consideration for counter-measures that involve ploughing. Likewise,the effectiveness of potassium application

depends greatly on the soil properties (Nis-bet et al., 1994). This was taken into accountby creating a series of soil-specific thresholdvalues for cation exchange capacity (CEC)and pH based on geochemical modelling (Hor-mann and Kirchner, 1999). Thresholds wereset to ensure a 50% reduction in the Cs:Kratio in soil solution following the applica-tion of the potassium fertiliser. For example,all cells occupied by soils with a sandy orloamy texture were excluded if the CEC wasgreater than 15 meq/100 g and the pH greaterthan 6Ð2.

The values on the impact maps range frombetween �1Ð0 (greatly decreases impact oncriterion) and C1Ð0 (greatly increases impacton criterion) with a score of zero indicat-ing that no impact has been incurred (seeFigure 1). Both qualitatively and quantita-tively assessed impact maps were portrayedin this way, although, it was necessary tofirst normalise the results from the quantita-tively assessed criteria, such as the soil lossfigures generated by the erosion model, to fitthis scale. Figure 2 shows an example of animpact map for long-term soil erosion riskfollowing deep ploughing in the Ythan sub-catchment. This area is dominated by one soiltype (series) and the predicted reduction inerosion risk is due to the lower erodibility ofthe subsoil brought to the surface comparedto the original topsoil.

Multicriteria decision-making(MCDM)

MCDM is the methodology chosen to assesscountermeasure suitability within the SDSS.MCDM is a well-known branch of decision-making techniques that logically structureand evaluate problems with multiple attri-butes and objectives (e.g. Pitel, 1990; Voogd,1983; Zeleny, 1982). It has also been endorsedby the International Commission on Radio-logical Protection for use in the appraisal ofradiological protection problems (Merkhoferand Keeney, 1987). MCDM is based on the

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174 C. A. Salt and M. Culligan Dunsmore

saran

Figure 2. Soil erosion risk in the upper Ythan catchment following deep ploughing. Areas marked as ‘notmodelled’ are mainly alluvial soils for which no specific soil data exists. Only 1Ð2% of the catchment hasslopes exceeding the ploughing threshold of 15°. The contour interval is 5 m.

evaluation of a two-dimensional matrix inwhich one dimension consists of alterna-tives and the other of criteria (Voogd, 1983).Alternatives are the different possible choicesor scenarios from which the decision-makermust choose. Criteria, on the other hand,are the means by which the alternativesare assessed. The MCDM ranking techniqueused in the SDSS is compensatory in thatit allows for a poor performance by a partic-ular alternative on one or more criteria tobe ‘compensated for’ by a good performanceon other criteria (Jankowski and Richard,1995). The ability to make ‘trade-offs’ incriteria performance, within the bounds ofcertain thresholds, is viewed as a key com-ponent of the assessment methodology, as itaccurately simulates the real-world decision-making environment in which losses in theone arena can be justified by the gains madein another.

In the SDSS, the assessment criteriaare made up of a mixture of environmen-tal and agri-economic considerations. Nor-mally, within a two-dimensional assessmentmatrix, the alternatives would be the differ-ent countermeasures that can be applied toa contaminated area following a radiologi-cal accident. However, when working withspatial data coverages in a raster environ-ment, this approach must be altered slightly.A site suitability assessment must be carriedout for each possible countermeasure and theresults compared between countermeasuresfor every spatial unit. Therefore, an extradimension must be added to the matrix to

account for the spatial variability of the databeing evaluated. The third dimension, in the-ory, becomes the countermeasures, while theindividual raster cells are treated as theindividual alternatives. The raster cells con-tain layers of data about a particular areain space. For each raster cell within thestudy area, this information is used to assessthe cell’s suitability for the application ofeach individual countermeasure. By compar-ing suitability scores for raster cells acrosscountermeasures, the ‘optimal’ countermea-sure for each area becomes apparent. This isshown in Figure 3.

Ideal point analysis

Owing to the large amounts of data thatmust be processed when performing a landsuitability assessment for the application of

( j = n)

( j = 3) Animal welfare( j = 2) Landscape quality

I (Raster cells) by

...

J (Assessment Criteria) byK (Countermeasures)

( j = 1) Soil erosion

i=1

i=2

i=3

i...

i=n

(k = 1) Afforestation(k = 2) Deep ploughing

(k = n)...

saran

Figure 3. Model of 3D data structure used fordetermining the ‘optimal’ countermeasure for eachraster cell. Alternatives, (i); criteria, .j/ countermea-sures, .k/.

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SDSS for countermeasure selection 175

countermeasures in a whole catchment, thespecific types of MCDM assessment method-ology that can be used are significantlyrestricted. Many MCDM techniques are com-putationally impossible to apply to this situ-ation in which each raster cell is consideredto be an alternative. For this reason, IdealPoint Analysis, which is based on a singlecalculation of the weighted absolute distancebetween the ideal set of scores and the actualscores for an alternative, was chosen forthe assessment (Carver, 1991; Zeleny, 1976;1982).

Ideal Point Analysis (also called Goal Pro-gramming) measures the deviation betweenthe scores for each set of ‘alternative’ solu-tions and the ‘ideal’ set of solutions (Zeleny,1976). The alternative, which minimises thedistance between itself and the ideal, isdeemed the optimal solution (Carver, 1991).This is described mathematically in Equa-tion (1) (Zeleny, 1982). The variables hj andqji, which are the ideal-point values and alter-native scores, must be standardised to allowfor comparisons to be made across criteriascores. This can be undertaken using Equa-tion (2), which normalises the ‘distance fromthe ideal’ such that the highest distance isto equal a score of zero. The variable p sym-bolises the metric parameter, which variesaccording to the assessment’s compensatorylevel. In most cases, it will be equal to one,two or infinity. Should p be equal to infinity,then the Chebyshev metric or minimax willbe used to calculate the distance calculationand the results are considered to be that of anon-compensatory assessment (Pitel, 1990).The decision-maker must also define weightsfor each of the assessment criteria. Theseweights, which are represented as gj in Equa-tion (2), follow the ‘rating system’ in which thenumber of points allocated to each criterionis representative of that criterion’s relativeimportance within the decision-making pro-cess (Nijkamp et al., 1990).

min dD∑

jD1

gj.jhj�qjij/ .1/

qjiDmaxj

rji�rji/maxj

rji�minj

rji. .2/

where: dDdistance score to be minimised; hjDstandardised ideal point value for criteria, j;qjiDstandardised value, pji; gjDweight for

criteria, j; pDmetric parameter (usually 1, 2or1).

Spatial decision supportsystem

The GIS-based SDSS described here, isdesigned primarily to be used for regionalcountermeasure suitability assessments. Itis intended as a tool for optimising theselection of countermeasures in relation tospatially variable parameters, which greatlyaffect radiological effectiveness, technical fea-sibility and environmental and agriculturalimpacts. A key objective was to create a flex-ible and user-friendly tool, hence, the innerworkings of the spatial assessment processare shielded from the user by an interface,created using the programming languageavailable in ArcViewTM called Avenue (ESRI,1997). The output from the system is in theform of a suitability map for a particularcountermeasure or a thematic map depict-ing the ‘most suitable’ countermeasures for agiven area.

The MCDM-GIS countermeasure selectionprocess begins by asking the user to selectone of the five deposition scenario optionsand the catchment of interest. Then, based onthe farm-production types that occur withinthe boundaries of the assessment area andthe deposition scenario selected, the useris presented with a list of countermeasuresthat can be applied. From this list, theuser can opt to either undertake a sitesuitability assessment for a single selectedcountermeasure or run an assessment thatincludes several countermeasures.

The decision-maker is then asked to definethe weights and ideals for each of theassessment criteria. The ideal values usethe same scale as the impact maps (seeFigure 1). The ideal value for the criterion‘soil erosion’, for example, would most likelybe the objective ‘greatly decrease’ or the value�1, while for a criterion such as ‘animalwelfare’ the ideal objective might be for itto ‘greatly increase’ or the value C1. Theweights, on the other hand, range on a scaleof one through 10 and should be used toreflect the decision-makers own biases andobjectives in the decision-making process.For example, a user might rate water quality

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176 C. A. Salt and M. Culligan Dunsmore

highly in a catchment designated for drinkingwater abstraction.

Once the alternatives, weights and idealshave been defined, the MCDM is called tocalculate a final score for each alternative(raster cell) based on its specific distanceaway from the ideal criteria vector. Theresulting scores for the alternatives arethen stored as a raster map for furtheranalysis and display. Once this process hasbeen completed, there will be a raster mapfor each countermeasure evaluated. Eachcell within these coverages will contain avalue relating to its calculated suitability. Bycomparing the values of each of the rastercells across the countermeasure suitabilitycoverages, a map depicting the ‘most suitable’countermeasures for the study area can becreated. As the scores are normalised, thecoverage with the highest score for eachcell is deemed to be the most suitablecountermeasure for that cell.

The SDSS has been initially developed atthe catchment scale, however, ultimately itis intended for application at a regional ornational scale. This would then include useroptions to select the coordinates of a studyarea and the resolution of the map output. Anoverview of the countermeasure evaluationprocess is given in Figure 4.

Conclusions

A methodology for the creation of a SDSS toaid in the long-term management of radio-actively contaminated land resources hasbeen described. The system is designed tohelp decision-makers in the choice of coun-termeasures by taking into account the levelof contamination, the effectiveness of differ-ent techniques and their compatibility withagricultural practices. The countermeasure

Begin evaluation

Select deposition scenario

Define coordinates of study area

Define grid cell size

Select countermeasures for evaluation basedon farm types present and deposition scenario

Define ideal points

For eachcountermeasureselect do

Determine alternatives – create mask

End

Define weights

Apply mask to study area

Run Ideal Point AnalysisFinished?

Combine resulting countermeasuresuitability grids

Display map depicting only the highest scoringcountermeasures for each grid cell

Individual countermeasuresuitability map

No

Yes

Figure 4. Overview of the countermeasure evaluation process.

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SDSS for countermeasure selection 177

assessment process allows decision-makersto take account of the geographical variabilityof environmental and agricultural parame-ters that influence the response of differentecosystems to remediation. In addition, usersare given the opportunity to influence theassessment criteria according to their ownobjectives. In this way undesirable impactson the environment and farm output canbe anticipated and mitigation measures canbe planned prior to countermeasure imple-mentation. The decision support system hasbeen developed using data from several casestudy catchments across Scotland as a work-ing model from which a more comprehensiveSDSS for the whole of Scotland can be built.The output is in the form of countermeasuresuitability maps at 10 m resolution. Ulti-mately, any future system should seek toseamlessly link spatial data for a whole coun-try with the environmental models used toassess countermeasure suitability. It shouldalso incorporate economic and social impactsto a greater extent into the assessment.

Acknowledgements

The CESER project was part funded by theEuropean Union’s Fourth Framework, NuclearFission Safety Programme (DGXII) from January1997 until June 1999. The authors would liketo thank their project partners in the Universityof Bremen (Germany), the Finnish EnvironmentInstitute (Finland), the North-Trøndelag College(Norway) and University of Salzburg (Austria) fortheir contribution to the work presented here.Special thanks go to Gordon Hudson at MLURIfor his assistance with the soil data and to DavidAitchison at the University of Stirling for drawingthe diagrams.

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