radon in groundwater - Mark- och vattenteknik - KTH

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Radon in groundwater - Influencing factors and prediction methodology for a Swedish environment( 226 Ra and 238 U) in both geological materialsand groundwater; second, to analyse approachesthat have been used in the predictionof 222 Rn in groundwater; and third, towells were provided by Stockholm Countyand various municipalities. Table 1 providesadditional information about the data used.Elevation is an important variable since itTable 1 Data used in analyses, their sources and formatsData Units Original format Desired formatRadon concentration Bq/l ASCII xyz Shape files - PointsElevation m a.s.l Standard ASCII fileRaster file50 m x 50 m pixel sizeSoil -Shape files- polygonsScale: 1:50 000Raster file50 m x 50 m pixel sizeUraniumppmASCII fileRaster file50 m x 50 m pixel sizeLand use -Bedrock -Fracture distribution -Shape files – polygonsScale: 1:50 000Shape files- polygonsScale: 1:50 000Shape files- lines1:50 000Raster file50 m x 50 m pixel sizeRaster file50 m x 50 m pixel sizeShape files- Linesreview some important concepts in the fieldof spatial data analysis since the projectinvolved the treatment of different spatialdata. A field study involving sampling andanalysis of groundwater from 38 privatewells was performed on Ljusterö island inthe Stockholm archipelago. Whether on ageneral scale or a detailed scale, the datacollected necessitated pre-processing. In thefollowing sections, clarifications on dataused and the specific methods adopted forthe various analyses are presented. A flowchartof the methodology is illustrated inFig. 2.Data usedFor analyses on a general scale (Papers I andII, the same data were used as input. Dataobtained from the Geological Survey ofSweden (SGU) consisted of soil, bedrock,fracture distribution (derived from a lineamentmap) and airborne radiometric measurementsof uranium along flight linesspaced at 200 m and with measurementsevery 40 m. Elevation data and land use datawere obtained from the Swedish NationalLand Survey (Lantmäteriet). Radon concentrationsin groundwater for 4439 privateprovides a gross indication of the flow ofgroundwater in the subsurface. Bedrock dataprovide information on the average uraniumconcentrations that different rock types canpotentially contain, as well as the flow possibilitiesgiven by the lineament pattern, thehydraulic conductivity and the kinematicporosity. Soils overlying bedrock provide anindication of recharge potential. Fracturezones are often enriched e.g. in uraniumcontainingminerals. Proximity to such fracturescan result in high radon concentrationsin groundwater and thus distance to mappedlineaments, such as fracture zones, was alsoinvestigated as an influencing factor.MethodsData pre-processingAnalyses of the various spatial data (exceptfor the distribution of fracture zones andradon concentrations) required that the databe in raster format, with a spatial resolutionof 50 m. As observed in Table 1, not all datawere acquired in that format. A preprocessingof data was performed using theArcGIS software. Soil maps, bedrock data,topography and land use obtained as shapefiles were rasterised to produce continuous7
Kirlna Skeppström TRITA LWR.LIC 2032surface maps. For airborne uranium concentrationsin the bedrock, flight-line measurementswere transformed from ASCII formatto a series of point data. An interpolation ofthe point data using the Inverse DistanceWeighting (IDW) method (Burrough andMcDonnell, 2000) was performed to producea raster map of uranium. Two interpolationmethods, namely IDW and simplekriging, were applied to the dataset andevaluated against their ability to predict aknown measured uranium concentration.The root mean square error was computedfor each method and it was found that IDWmethod gave the best interpolated results forthe dataset. IDW preserved the main patternsof variation.Radon concentrations available as an ASCIIfile were converted to vector point data. Thetotal number of private wells available foranalysis was 4439. Additional factors werederived from original spatial data and theseincluded: predominant soil and land use with200 m, slope of the terrain and relative altitudewithin 200 m. The latter two werederived from elevation data. For the relativealtitude factor, the following formula wasused in ArcGIS:E(x)− Emin( x)RA ( x)=× 100 [6]E ( x)− E ( x)maxminwhere RA is relative altitude in %E(x) elevation of the current location x(pixel)E min (x) minimum elevation within 200 mE max (x) maximum elevation within 200 mfrom the current location.The minimum and maximum elevationswithin a certain vicinity (e.g. 200 m) werecalculated using the neighbourhood analysisprinciple.For visual data mining, processed data in theform of maps and other data layers weresufficient for analyses, while for multivariatestatistical analyses, the corresponding spatialdata for each well were compiled in the formof a database. Extraction of data from eachthematic map for each well was achievedusing the principle of zonal statistics and theoperation was done using the spatial analystfunction in ArcMap.Data analysisVisual data mining using 3D images wasperformed using the ArcScene function inthe ArcGIS software. A radon surface mapwas created as a base 3D using the IDWmethod. Various thematic maps, also processedas continuous surfaces, were in turndraped over the radon surface in order toproduce a pseudo 3D-model. Visual analyseswere then made on screen. The RV methodwas applied to the dataset. The methodinvolved statistical analyses of data usingSTATISTICA release 6 StatSoft-2001. Basicdescriptive statistics (including mean, minimumand maximum values, standard deviationsand shape of the distribution) wereevaluated to describe the radon variable. Theunivariate non-parametric test of Kruskal-Wallis ANOVA by ranks was performed.This method caters for both qualitative andquantitative variables and is independent ofthe distribution of the variables. Multivariatestatistical analyses using Principal ComponentAnalysis (PCA) were performed anddata were first standardised according toequation [2]. The expert part of the RVmethod involved the choice of variables tobe modelled, their class subdivisions andtheir weights (Paper I). Computation of riskvalues was performed using the Risk VariableModelling software (RVM). The modelwas first calibrated using half of the datafrom the 4439 wells, chosen randomly. In atest stage, risk indices were calculated for 12subregions, each of area 25 x 25 km 2 . In afinal stage, calculated risk values were integratedin GIS. Different thematic maps werereclassified according to their assignedweights and ratings and overlain to developa prediction map.8
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