radon in groundwater - Mark- och vattenteknik - KTH

Kirlna Skeppström TRITA LWR.LIC 2032ii

Radon in groundwater -Influencing factors and prediction methodology for a Swedish environmentACKNOWLEDGEMENTSWithout funding, there is no prospect of doing any research. I therefore wish to firstly acknowledgethe Swedish Geological Survey (SGU) and Lars Erik Lundbergs Stipendiestiftelse for providingfinancial support for this project. I am also thankful to the various municipalities andcounty council in Stockholm that provided data for the project.My heartfelt gratitude to my main supervisor, Bo Olofsson for giving me the opportunity to workwith such an interesting project. Thank you for all feedback, support and lively discussions. Iwould also like to seize the opportunity to acknowledge Gert Knutsson who, together with BoOlofsson, initiated the work on radon in groundwater. Furthermore, I would like to acknowledgePer Erik Jansson and Jon Peter Gustafsson, my co-supervisors for critiquing part of the work andproposing interesting suggestions. Thanks must go to Per Erik for fitting me in his busy timeschedule- I really appreciate it. I acknowledge my reference group. Special thanks to GustavÅkerblom, working at Swedish Radiation Protection Agency (SSI) for all your help. Thank youfor driving to Ljusterö on a Sunday to teach me and other students how to proceed with radiometricmeasurements. Thank you also for reading the papers and providing useful suggestions.I wish to acknowledge all my colleagues (especially those in my corridor) at the department formaking the working environment feel like a second home. Bijan- your lively discussions in thefield of research and others help to develop a critical mind. You are a real driving force. I amalso thankful to Aira, Britt and Hans for all the help and advice. Muluneh and Tomo- thank youguys for all your support. I always get inspired (and sometimes stress- positive of course!!!!!) towork even harder after a talk to you. Muluneh and Joanne-thank you for helping out with formattingat the last minute. Thank you Urska for your contribution in our collaborative study andthanks also for all your support. I am thankful to Jerzy for help with software and my computer.I acknowledge the help provided by Ann Fylkner and Monika Lowen in the laboratory. I amthankful to all my friends who drove me to Ljusterö and made it pleasant to go on site duringweekends. Thanks to Anders and Linda for performing the geological study required for theproject.How can I thank the ones who have been a constant source of encouragement and support sincethe very beginning of this work? My thoughts go to my family in Mauritius. Special thanks tomum and dad for constant motivation. Finally, to the most important person in my life who hashelped me in numerous ways during the course of this research, provided me with moral supportand who has believed in me all the time, I say ‘ Tack så mycket Svante- Du är den bästa av allt ’Stockholm, November 2005Kirlna Skeppströmiii

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Radon in groundwater -Influencing factors and prediction methodology for a Swedish environmentTABLE OF CONTENTSACKNOWLEDGEMENTS.................................................................................................. IIILIST OF PAPERS ................................................................................................................ VIIABSTRACT............................................................................................................................... 1INTRODUCTION .................................................................................................................. 1Hypothesis .............................................................................................................................. 3Objectives ............................................................................................................................... 3BACKGROUND....................................................................................................................... 3Principal Component Analysis (PCA)...................................................................................... 4Risk Variable Methodology...................................................................................................... 4Visual Data Mining.................................................................................................................. 5DESCRIPTION OF STUDY AREA ...................................................................................... 6DATA AND METHODS......................................................................................................... 6Data used ................................................................................................................................7Methods .................................................................................................................................. 7RESULTS AND DISCUSSIONS.......................................................................................... 11Visual data mining ................................................................................................................. 11Statistical analyses of data - Interpretations............................................................................ 13Radon prediction using Risk Variable Modelling.................................................................... 15A prediction map in GIS ....................................................................................................... 15Detailed study on Ljusterö..................................................................................................... 18CONCLUSIONS .................................................................................................................... 19FUTURE WORK.................................................................................................................... 19REFERENCES ...................................................................................................................... 20v

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Radon in groundwater -Influencing factors and prediction methodology for a Swedish environmentLIST OF PAPERSI. Skeppström, K. and Olofsson, B. 2005. A prediction method for radon in groundwater usingGIS and multivariate statistics (Submitted to Science of the Total Environment).II. Demšar, U. and Skeppström, K. 2005. Use of GIS and 3D visualisation to investigate radonproblem in groundwater. In: H. Hauska and H. Tveite (editors), 10 th Scandinavian ResearchConference on Geographical Information Science, Scangis, Stockholm, Sweden,June13-15, pp. 39-51.III. Skeppström, K. and Olofsson, B. 2005. Uranium and radon in groundwater- An overview ofthe problem, presented at the 6 th EWRA international conference on water resources(under peer review).vii

Kirlna Skeppström TRITA LWR.LIC 2032water containing 1000 Bq/l. Groundwater isextracted from different sources andgroundwater from bedrock aquifers (drilledwells) has a greater potential of containingan enhanced concentration of 222 Rn thangroundwater extracted from soil aquifers indug wells (Åkerblom and Lindgren, 1997).In bedrock aquifers, groundwater occurs andflows in fractures. Radium-rich mineralspresent along the surfaces of fractures decayand release 222 Rn atoms into groundwater.Since the recoil distance as well as the diffusionlength of 222 Rn is small (Tanner, 1980),it is widely accepted that only those mineralslying on the surfaces of fractures, in directcontact with the groundwater, contribute toincreased 222 Rn levels in the water. Changesin groundwater chemistry can lead to differentmineral fluid interactions (Siegel andBryan, 2004). 238 U is affected by redox conditions.When an oxidising environmentprevails, 238 U enters solution and is transportedalong with the water and is eventuallyprecipitated in a reducing environment(Schumann and Gundersen, 1996). 226 Raoccurrence and distribution in groundwateris guided by its production from its immediateparent isotope thorium ( 230 Th) and itsremoval from solution is governed by theadsorption or cation exchange properties(Herczeg et al., 1988). The re-deposition of238 U and 226 Ra along the walls of a fracture isknown as secondary mineral enrichment andcontributes to significantly increasing theconcentration of222 Rn in groundwater(Åkerblom and Lindgren, 1997).The above-mentioned microscopic processescombined with the heterogeneity offractures in a rock matrix often make itdifficult to quantify transport of radioactiveelements in a bedrock aquifer. Not all fracturesare hydraulically significant - only a feware hydraulic conductors and facilitate fluidflow (NRC, 1996; Olofsson et al., 2001).However, despite the complexity of thebedrock aquifer system, there is still a needto predict radon potential in groundwater. InSweden and other countries, a large numberof people depend on groundwater extractedfrom bedrock aquifers for their daily waterneeds, implying long-term exposure to radioactive222 Rn in some cases. At municipallevel, prediction of radon potential ingroundwater is important for the planningof new housing areas, as well as the considerationof remediation measures for existinghouses. In order to make realistic predictionof 222 Rn levels in groundwater, influencingfactors should be studied in a holistic way.Many studies conducted on radon in thenatural environment are principally surveysand involve sampling and measurements ofradon in groundwater. Such studies areperformed for different reasons, includingsearching for uranium deposits, searchingfor thrust and faults, searching for seismicrelatedchanges and also for better knowledgeof the spatial distribution of radon(Monnin and Seidel, 1992; Choubey et al.2001; Porsani et al., 2005; Schubert et al.,2005; Ishikawa et al., 2005). Such surveys arenecessary steps in the reconnaissance of anarea prior to more detailed analyses regardinginfluencing factors. Other studies focusmainly on geological aspects, such as bedrockcomposition and uranium occurrenceand distribution in bedrock. Åkerblom andLindgren (1997) investigated bedrock andsoil data in Sweden with the aim of mappingthe groundwater radon potential. Choubeyet al. (2000) investigated hydrogeology andfound that the groundwater flow characteristicsinfluence the concentration of radon insprings. Other studies related to bedrockand distribution of uranium include thework of Lidén et al. (1995), Ståhl (1997),Choubey and Ramola (1997) and Salih et al.(2002). Studies on temporal variations inradon in groundwater are few. Knutsson(1977) studied seasonal fluctuations for dugwells and found large variations dependingon type of soil aquifer. Nilssen (2001) observedlarge fluctuations in 222 Rn concentrationin drilled wells from granite rocks.Regarding radon and groundwater chemistry,only a few correlation studies have beenperformed (Lewin Pihblad, 1998; Lewin andSimeonidis, 1998; Salih et al., 2004). Most ofthe research work related to groundwaterchemistry focuses principally on the parentelements of 238 U and 226 Ra (Waite et al.,1994; Schumann and Gundersen, 1996;2

Kirlna Skeppström TRITA LWR.LIC 2032gations of various influencing factors affecting222 Rn in groundwater can be based onmultivariate statistical analyses. In the followingsection, the underlying principles ofprincipal component analysis, visual datamining (3D visualisation) and the risk variablemethod (the RV method) are outlined.Principal Component Analysis (PCA)Principal Component Analysis (PCA) is amultivariate statistical technique that involvesthe computation and analysis of thevariance-covariance structure of a set ofvariables through a few linear combinationsof these variables (Davis, 2002; Johnson andWichern, 2002). The method often highlightsinformation that is not easily decipheredusing univariate statistics. PCA hasoften been conducted for the followingpurposes (Johnson and Wichern, 2002):• Data reduction and structural simplificationto make interpretation easier• Sorting and grouping whereby groupsof similar variables are created• Investigation of the dependenceamong variables• To predict relationships among variables• For hypothesis construction and testingFew attempts were made in previous studiesto investigate radon problems using multivariatetechniques. In a recent study, a multivariateanalysis technique based on partialleast squares was used by Salih (2003) andSalih et al. (2004) to evaluate the impact offluoride and other water components onradon concentration. In order to makeproper interpretations, there is a need tounderstand the underlying principles of themethod and also understand its strengthsand weaknesses.In algebraic terms, principal components arelinear combinations of p random variablesX 1 , X 2 ,…….X p . Translated into geometricterms, these linear combinations representthe selection of a new coordinate systemobtained by rotating the original system withX 1 , X 2 , ……..X p as the coordinate axes. Thenew axes represent the orientations withmaximum variability and provide a simplerdescription of the covariance structure(Johnson and Wichern, 2002).As stated above, PCA involves the formationof linear combinations, which are referredto as principal components, and muchof the variability in the original data can beaccounted for by a first few principal components(Thalib et al., 1999; Johnson andWichern, 2002). Since PCA is applied to asample, linear combinations explain maximumsample variance.If S = {S ik } is a p x p sample covariancematrix with eigenvalue-eigenvector pairs (λ 1 ,e 1 ), (λ 2 , e 2 ),………………..(λp, e p ), k is thenumber of principal components and the i thsample principal component is given by:Y i = e i x = e i1 x 1 + e i2 x 2 +…..+ e ip x p [1]where x = any observation on the variablesX 1 , X 2 ,…..X pi = 1, 2,………pAdditional details on PCA principles can befound in Davis (2002) and Johnson andWichern (2002). Only quantitative variablescan be analysed using PCA. Prior to analysis,standardisation of the various data accordingto equation [2] is necessary to ensure thateach variable has the same influence in theanalysis.Z( X − µ )i i= [2]iσiiwhere Z i = standardised variable,X i = original variableµ i = meanσ ii = varianceRisk Variable MethodologyThe risk variable methodology (RV method)is a structurised variable-based method foridentifying independent variables and theirimportance for a dependent variable. It haspreviously been used for analysis of variablesaffecting the salinity of groundwater incoastal areas (Lindberg et al, 1996; Lindbergand Olofsson, 1997), vulnerability assessmentfor drawdown of groundwater due to4

Radon in groundwater - Influencing factors and prediction methodology for a Swedish environmenttunnelling at Hallandsås, SW Sweden(Olofsson, 2000) and vulnerability assessmentfor contamination of groundwateralong roads (Gontier and Olofsson, 2003). Itcomprises statistical analysis of various variablesusing e.g. PCA and ANOVA (analysisof variance) for a suitable selection of influencingvariables, sub-grouping of the variablesbased on univariate statistics and finallyformulation of a variable-based model.The latter is usually based on the statisticalmethod of regression analysis. The methodpredicts values of one or more response(dependent) variables from a collection ofpredictor (independent) variable values. Aclassical linear regression model takes thefollowing form (Johnson and Wichern,2002):Y = β 0 + β 1 Z 1 + β 2 Z 2 +…+ β r Z r + ε [3][Response] = [mean (depending on Z 1 , Z 2 ,…Z r )] + [Error]where Z 1 , Z 2 ,… Z r are predictor variablesY = response variableε = random errorSimilarly, the RV method, which ends as asimplified regression model, generates anumerical index that is derived from ratingsand weights assigned to each significantmodel parameters. The RV index takes theform:n∑i=1V iR i= FRV (Final risk value) [4]where V i = a risk value for a specific variableclass (-2 to +2)R i = the weight of the variable (1 to 3)The RV method also permits calculation ofan uncertainty index, which can be computedas:n∑i=1U iR i= FUV (Final uncertainty value) [5]where U i = the uncertainty (negative valuesindicate high uncertainty)R i = the weighting of the variable.The RV method generates risk values, whichshould not be confused with the risks definedin traditional risk analysis studies,meaning that the RV method does not includethe consequences of the increasedradon concentrations.All significant factors (qualitative as well asquantitative) are subdivided into classes, andthese are rated from (–2) to (+2), based onhow they influence radon concentration inwater. Negative ratings (or risk values) implyincreased probability of groundwater containinghigh radon concentrations, whilepositive values mean that this probability isdecreased. Each variable is then evaluatedwith respect to the others in order to determinethe relative importance of each variableand is assigned a relative weight, rangingfrom 1 to 3. The most significant variablesare given a weight of 3, while the least significantones receive a weight of 1. Theweights can be set either from the operator’sexpert knowledge and experience or, if thereare enough data available, on a statisticalbasis.Visual Data MiningData mining is the process of identifying ordiscovering useful and as yet undiscoveredstructure in the data (Demšar, 2004). Largedatasets are usually examined in data miningand since the project targets 4439 wells, datamining is considered to be an appropriatetool. The underlying principle of visual datamining is the presentation of data in somevisual form, allowing the user (human) to getinsight into the data, draw conclusions anddirectly interact with the data (Fayyad et al.,2002). Visual data mining in the form of 3Dvisualisation helps the viewer to easily gainknowledge of the relative layout and distancesbetween objects (Fayyad et al., 2002;Burrough and McDonnell, 2000; Gahegan etal., 2002). A general approach to produce a3D surface from geographical information isto map the two basic geographical dimensions,longitude and latitude, to the x and y-axis respectively and to show the variable ofinterest on the z-axis. The process of visualdata mining is often promoted as a hypothesisgenerating process; the user generates ahypothesis about the relationships and patternsin a dataset after first gaining insightinto the data (Demšar, 2004). When the5

Kirlna Skeppström TRITA LWR.LIC 2032hypothesis is already formulated, visual datamining is nevertheless considered to be anexcellent exploratory technique that canpotentially reveal other new structures orconfirm the formulated hypothesis.DESCRIPTION OF STUDY AREAThis study of radon problems in groundwaterwas conducted in east-central Sweden.There are more than 500 000 drilled wells inSweden (SOU, 1994), about half of whichare used on a permanent basis. High concentrationsof 222 Rn are a concern in many ofthese drilled wells since the predominantgeology in Sweden is Precambrian bedrock,consisting of granites, acid gneisses and acidvolcanics with a high content of uranium(Knutsson and Olofsson, 2002). More specifically,analyses of data were made for theCounty of Stockholm (Fig. 1). A total of4439 private wells located outside StockholmCity were used. The spatial coordinatesof the study region are: north min-6507719,north max-6691319, east min-1582230, eastmax-1725680. The altitude varies between 0and 70 m above sea level. The geology isvariable in the Stockholm County; old granitoidsexist along the coastal zone and northernregion, together with stretches ofmetavolcanites and a local massif of maficrocks. The central part consists mainly ofgneiss-granites and gneiss, while the southernpart consists of Precambrian metasedimentaryrocks. Field measurements wereconducted on Ljusterö, an island in theStockholm archipelago on which 1700 peopleare permanent residents and where radonconcentrations show extreme variations,with very high peaks and low values in almostthe same vicinity.DATA AND METHODSThe methodology principally involved thedevelopment of a prediction approach forradon levels in groundwater on a generalscale (an area of approximately 185 x 145km 2 ). For that purpose, large amounts ofdata were collected and processed, literaturereviews were performed and data were statisticallyand visually analysed. The literaturereviews had three main objectives: First, tounderstand the processes guiding the formationand migration of 222 Rn and to determinehow 222 Rn is coupled to its parent elementsIsland ofLjusteröFig. 1: Location o f the wells investigated in Stockholm County.6

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

Radon in groundwater - Influencing factors and prediction methodology for a Swedish environmentDetailed Study on LjusteröFor analyses on a detailed scale (Paper III),groundwater samples from 38 private wellswere collected during summer 2004 on theisland of Ljusterö, Stockholm archipelago(Fig. 1). Several areas on the island exhibitwide variations in radon concentrations(very low concentrations and very highconcentrations in the same neighbourhood)and these areas were delineated in a priorstep using GIS. Whereas it was easily decidedwhich areas would be sampled, it wasdifficult to determine which specific wellswould be the subject of investigation becausethe consent of the private well ownerswas needed prior to any sampling. It wasensured that sampling was carried out only ifwater had been in circulation for a satisfactoryperiod of time, hence the water wasdirectly extracted from the bedrock. Sampleswere sent to an accredited laboratory for themeasurement of 222 Rn, 226 Ra and 238 U insolution. 222 Rn was measured using a Lucascell (ZnS scintillation cell). It should, however,be mentioned that only preliminaryresults of chemical analyses are presented inthis thesis. Geochemical modelling to understandthe influence of chemical processes isto be conducted in future work. In thisdetailed study, in addition to analysis ofairborne uranium measurements collectedfrom the Geological Survey of Sweden (witha scale of 1:50 000), measurements ofequivalent uranium (via gamma radiationmeasurements) were performed on outcrops(Emell and Moen, 2005). The instrumentsused were a scintillation counter, a gammaspectrometer counter and a gamma spectrometerwith a Bismuth Germanate crystal,GR-130BGO.9

Kirlna Skeppström TRITA LWR.LIC 2032GENERAL SCALESpatial data(Original dataset)Radon data(Original dataset)Cleaned data on radon(Removal of outliers)Visual datamining inGIS- New hidden patterns?- Detection of relationships?Preprocessed such as:- Interpolation,- Rasterisation,- ReclassificationPrincipalComponentAnalysisStatisticalanalysesAnalysis ofvarianceInterpretations of principalcomponents from PCARisk variable modelingLinear regression(calibration and validation)DETAILED SCALEImplementation of results in GISSelection of site in GISSampling and analysis of groundwaterin 38 private wellsData on 222 Rn, 226 Raand U in solutionData collectionStatistical analysisof data and interpretationsDetailed field data ongamma measurements inoutcropsFig. 2: Flowchart of methodology.10

Radon in groundwater - Influencing factors and prediction methodology for a Swedish environmentRESULTS AND DISCUSSIONSThe 4439 private drilled wells analysed inthis study were found to be irregularly distributedin Stockholm County, with relativelyfew wells in the middle and southernparts of the county as shown in Fig. 1. Ananalysis of the data revealed that for themajority of the wells (73%), 222 Rn concentrationsvaried between 0 and 500 Bq/l and11% of all wells exceeded 1000 Bq/l, theSwedish regulatory limit for radon concentrationin drinking water. The maximumconcentration recorded was 63 560 Bq/lwhile the minimum was 4 Bq/l. Two otherwells also had very high radon concentrationsin the order of 14 000 Bq/l and 15 000Bq/l. The geometrical mean was found to be230 Bq/l and a standard deviation of 1227Bq/l was computed. The distribution ofradon values, viewed on a histogram, waspositively skewed and removal of outlierswas necessary (except for visual data mining)to avoid bias in statistical analyses. Outlierswere case-specifically defined as radon concentrationsexceeding 5000 Bq/l and removed.The traditional definition of outliersin the one-dimensional case that a point isan outlier if its distance from the mean isgreater than some factor times the standarddeviation (usually ± 2 standard deviations)was not considered, since its application didnot improve the skewedness of the dataset.Radon concentration values exceeding 5000Bq/l were observed in 21 wells, constitutingless than 0.5% of total available wells. Highradon concentrations can be encountered inwells drilled from granite rocks with anenriched content of uranium deposited onthe inner surfaces of fractures.Visual data miningThe visual analysis of 3D images was appropriatefor the problem since all of the variablesinvestigated could either be convertedto surface maps or visualised in GIS. It waspossible to detect visually whether a relationshipexisted between a thematic map andthe radon concentration in groundwater. Asmentioned in the methodology section, aradon surface needed to be built from pointdata prior to visualisation. In the project,radon measurements were not sufficient tobuild a representative surface over the wholearea. Interpolation of all point data to produceone surface was found to produce lotsof ‘U’ effects (sharp edges) around extremevalues and this was inevitable because visualdata mining involves analysis of all data,without the removal of outliers. Therefore,only a small sample of the study with anacceptable distribution of wells was subjectedto investigation.In the various visualisations, the followingwere observed:1) High radon values occurred predominantlyon low elevations and vice versa2) Areas overlain with till or clay had highradon concentrations in the bedrockgroundwater3) Peaks of high radon concentrations werenot always located in regions where highuranium content in bedrock had been recordedin airborne measurements.4) Radon concentrations from steep terrainwere generally not high but no clear-cutconclusions could be drawn from the thematicmap of relative altitude5) Granitic rocks were associated with highradon values6) The shorter the distance between a fracturezone and the bedrock well, the higherthe concentration of radon in groundwater.A multi-dimensional visualisation (Fig. 3)involving five different variables was aninteresting example of how the effects ofpeaks and depressions in a surface combinedwith colour effects allowed the observer toget insight into the data, draw conclusionsand directly interact with the data. In thatvisualisation, the two-dimensional geographicalextent and the radon concentrationsconstitute three spatial dimensions.The bedrock surface draped over the radonsurface is the fourth variable and finally thefracture lines constitute a fifth variable andare draped over the last surface produced.11

Kirlna Skeppström TRITA LWR.LIC 2032NFig. 3: A detailed view of the bedrock-fracture lines-radon visualisation. The red arrows indicatethe peaks with high radon values that are situated near or on fracture lines.12

Radon in groundwater - Influencing factors and prediction methodology for a Swedish environmentStatistical analyses of data - InterpretationsUnivariate analysis using the method ofKruskal-Wallis ANOVA by ranks was successfulin evaluating the relative influence ofthe subclasses of factors investigated inrelation to the radon concentration ingroundwater. Compared to visual data mining,quantitative units (median radon concentrations)could be computed for thevarious classes (Table 2), as a result of whichit was easier to interpret the factors. Classselection was based on expert judgementand the different median values were computedwith an appreciable significance level.For the bedrock variable, granitic rocks wereassociated with a much higher radon concentration(540 Bq/l) than the other rocktypes. This observation tallies very well withprevious studies on radon and its relationshipwith geological materials (Choubey andRamola, 1997; Knutsson and Olofsson,2002; Przylibski et al., 2004). Granite containson average a higher concentration ofuranium, the parent element of radon, and inSweden, granitic rocks can contain morethan 5 ppm of uranium (Åkerblom andLindgren, 1997). The content of uranium inbedrock computed by flight measurementsprovided a rough prediction for radon concentrationsin groundwater. As can be observedin Table 2, radon concentration ingroundwater increases with increasing uraniumcontent in the bedrock. However, ananomaly was encountered for the highesturanium class (> 8 ppm), since the observedradon concentration was low. One possibleexplanation could be that the uranium mineralsare not always located very near togroundwater-bearing fractures and hencecannot contribute to increasing the radonconcentration in the groundwater.There exists a relationship between thealtitude of the well and the correspondingradon concentration in groundwater. Radonconcentrations were relatively higher at lowaltitudes than at high altitudes. This observationcould be clarified by the fact that anyradon emanated at high altitude flows togetherwith the groundwater by convectionto lower terrains, provided the travellingdistance is short and the radon does notdecay (within 3.8 days). Alternatively, itcould instead be argued that parent elementsof 222 Rn ( 238 U and 226 Ra) leach into groundwaterat high altitudes, get transported bythe water and re-deposit on the surfaces ofthe fracture at lower altitudes and continueto decay from these spots to increase concentrationof radon in groundwater. It wasdifficult to interpret the factor of relativealtitude within 100 m, since varying radonconcentrations were observed at differentslopes.Another result related to geological propertiesrevealed that the type of soil overlyingthe bedrock could have some importance.One plausible explanation could be theinfluence of the permeability property of thesoils. Permeable soils allow infiltration readilyand this leads to a dilution of the radonconcentration in groundwater in the subsurface.It could therefore be observed thatsand and gravel are associated with slightlylower radon concentrations than clay andsilt. Another observation made possiblethrough univariate statistical analysis relatesthe land use factor to the radon concentrationof the wells. The original land use datawere classified into summer houses, permanenthouses and other land uses, such asforests and fields. In the latter a small waterusage was assumed and it was observed thatradon concentrations had a tendency to berelatively low when water was in continuouscirculation, such as in permanent houses. Asimilar observation was made by Knutssonand Olofsson (2002). Regarding the factorof distance from a fracture line or zone, theresults of univariate statistical analysis weredifficult to interpret. On the other hand, theinfluence of that factor could be observed invisual data mining on 3D images. Fracturesmight be filled with minerals containinguranium and therefore the nearer a well islocated to a fracture zone, the higher theprobability of the groundwater containing anincreased concentration of radon. The reasonwhy statistical analysis was less successfulat showing the relationship might beattributed to the class selections of distance.13

Kirlna Skeppström TRITA LWR.LIC 2032The assumed fracture zones are also givenby the lineament maps and the position ofthe zones and their water conducting propertiesare uncertain.Results of principal component analysis(PCA) are presented in Table 3. The methodanalyses only quantitative factors and inorder to explain 86% of the variance in theoriginal dataset, five components (PC) wereneeded. Although it was possible to see thevarious loadings coming from each factor ina component, it was often difficult to interpretthese components and the interpretationwas to some extent subjective.PC 1 was principally loaded by the factors ofaltitude and the relative altitude within100 m and 500 m. This component wasinterpreted as a property that guides thetransport of radon in water. The factor ofaltitude and its derivatives indirectly providedinformation about the flow paths andflow speed of groundwater. The secondprincipal component (PC 2) was moderatelyloaded by uranium content in the bedrock,distance to fracture zone and radon concentrationin groundwater. PC 2 was interpretedas an enrichment of uranium in the fracturezones. Uranium content in the bedrock,radon concentrations in groundwater anddifference in altitude carried the highestloading in PC 3. That component might bean indicator of radon and uranium transportin groundwater, as a result of the heightdifference. PC 4 was loaded by a singlefactor and was probably related directly tothe fracture fillings. The fifth principal component(PC 5) was retained as the emanationof radon from uranium, since the loadingscame from these parameters.Table 2: Results of Kruskal-Wallis ANOVAby ranksFactorsSoil at well locationBedrock at welllocationLand use at welllocationUranium content(ppm) in bedrockAltitude (m a.s.l)Relative altitudewithin 100 m (%)Distance fromfracture (m)Clay/SiltTillRockClassesSand/gravelMetasedimentaryFelsic gneissesMafic rocksGraniteSummer housesPermanent housesFields, forests0-22-44-66-8> 80-2020-4040-60>600-2020-4040-6060-8080-100200RadonMedianvalue (Bq/l)280252200224150240150540190163370190260495410195240240147125200240250250210220210205230Table 3: Principal component loadings for quantitative factors . Significant loadings are marked inboldPC 1 PC 2 PC 3 PC 4 PC 5Radon content in groundwater (z 1 ) -0.093 0.493 0.427 0.159 0.723Altitude of the well (z 2 ) 0.586 -0.114 0.129 -0.258 0.235Relative altitude within 100 m (z 3 ) 0.432 0.280 -0.375 0.331 -0.079Relative altitude within 500 m (z 4 ) 0.608 0.238 -0.116 -0.014 -0.007Difference in altitude (z 5 ) 0.246 -0.538 0.354 -0.325 0.146Uranium content in rock (z 6 ) 0.106 0.370 0.666 -0.045 -0.627Distance to fracture zone (z 7 ) -0.140 0.428 -0.280 -0.831 0.01114

Radon in groundwater - Influencing factors and prediction methodology for a Swedish environmentRadon prediction using Risk VariableModellingThe RV modelling, which was based onlinear regression, generated risk values associatedwith each group of radon concentrations.Calibration of the model was based onhalf of the wells comprising the database(2209 wells) and the result is presented inthe form of a boxplot (Fig. 4).Radon concentrations were classified intodifferent concentration ranges based on theregulatory limits applicable in Sweden. It wasobserved that risk values became more negativewith increasing radon concentrations,although the standard deviations of thegroups were considerable.A test (or validation) of the Risk VariableModel was conducted on the other half ofthe dataset, comprising 2209 wells and distributedin 12 subareas. Risk values associatedwith corresponding radon concentrationswere computed. The results arepresented in the form of a scatterplot(Fig. 5), in which the median radon concentrationprevailing in each area was plottedagainst the corresponding median risk valuein that area. As can be observed in Fig. 5, ahigh negative correlation (-0.87) existedbetween the two plotted quantities, implyingthat the higher the radon concentration, themore negative the risk value. A similar trendwas observed in the calibration stage.A prediction map in GISRaster maps of elevation, bedrock, soil, landuse and uranium concentration in bedrockwere reclassified and the different classeswere assigned their corresponding ratings.An overlay operation was performed to get afinal risk map. Each pixel represents a finalrisk value equivalent to the sum of the riskvalues. The factor distance to fracture zonewas not included in the map productionsince it was not a surface map. Differentwater bodies (e.g. lakes, rivers) were thenadded to the final map. Three categories ofrisk areas were defined: Low risk for all finalrisk values, FRV > 0; medium risk for all,–5 < FRV < 0; high risk for all, FRV < -5.The risk map in a sample study area isshown in Fig. 6.In Paper I, the prediction map based on theRV method was compared to the map ofinterpolated airborne uranium concentrationsin the bedrock. It was observed thatsome regions with low to moderate uraniumconcentrations in the bedrock were definedas high-risk areas in the corresponding RVmap. However, such observations need tobe compared with field measurements infuture work. It should also be mentionedthat geological materials (bedrock and uraniumdistribution) used in the various analysesand available on a scale of 1:50 000 donot always reflect the geological conditionsthat prevail at a specific site.15

Kirlna Skeppström TRITA LWR.LIC 2032Fig. 4: Boxplot showing ranges of risk values for different radon concentrations (model calibration,based on 2209 wells). SE=Standard error, SD=Standard deviation.Fig. 5: Scatterplot showing the relationship between median risk values and median radon valuesfor 12 test areas within the study region (total n = 2209).16

Radon in groundwater - Influencing factors and prediction methodology for a Swedish environmentFig. 6: Example of a radon prediction map based on the RV method.17

Kirlna Skeppström TRITA LWR.LIC 2032Detailed study on LjusteröThe results of the detailed study are presentedin Fig.7. The observed concentrationof 222 Rn in groundwater tended to increasewith increasing concentration of uranium(total U) and radium ( 226 Ra) in the solution.However, the correlations were weak, 0.16and 0.18 for 238 U and 226 Ra respectively. Thisobservation supports the concept that radonconcentration in groundwater principallycomes from its parent elements ( 238 U and226 Ra) deposited on the surfaces of fractures(Åkerblom and Lindgren, 1997). Regardingground measurements of equivalent uranium(eU) made directly on outcrops, it could beobserved that high radon concentrationscould occur in bedrock of low to moderateuranium content. It was also observed thatthe radon concentration in groundwater didnot increase with increasing content of uraniumin the bedrock. The same observationwas made for the analysis of 222 Rn ingroundwater and airborne uranium measurements.These observations could beexplained by the fact that the bedrock identifiedby visual inspection above the groundsurface might not be the same in the subsurfacewhere groundwater is extracted. Anotherplausible argument relates to the migrationof radionuclides (mainly 226 Ra and238 U) in groundwater flow. The bedrockmight originally contain low concentrationsof actinide but transportation of 238 U and226 Ra can contribute to increasing the radonconcentration in the groundwater. The localconditions are difficult to identify in reality.The activity ratio of 226 Ra/ 238 U in solutionvaried between 0 and 12, which clearly indicatesa state of disequilibrium in thegroundwater system.(a)(b)(c)Fig. 7: (a) Relationship between 222 Rn anduranium in groundwater; (b) Relationshipbetween 222 Rn and radium in groundwater;(c) Relationship between 222 Rn in groundwaterand measured uranium in bedrock outcrops.18

Radon in groundwater - Influencing factors and prediction methodology for a Swedish environmentCONCLUSIONS• Radon concentration in groundwaterwas successfully analysed throughmultivariate statistical analyses. Therisk variable method was useful toidentify areas with increased radonconcentrations in the groundwater.• Factors of significance for the predictionof radon in groundwater were:type of bedrock, type of soil, altitude,distance to a fracture zone and the distributionof uranium in the bedrock.• Visual data mining revealed correlationpatterns in a preliminary stage ofdata analysis but were not useful as anindependent method to draw conclusionsregarding radon concentrationsin groundwater. The 3D visualisationsof the dataset did not show any newpattern but instead confirmed the correlationsgiven from statistical analyses.• A weak correlation existed between222 Rn and 226 Ra or 238 U in the groundwater,indicating that the main sourceof 222 Rn in groundwater was not originatingfrom its parent elements (asions) in solution.FUTURE WORKThe present research evaluated differentspatial data for the purpose of developing a222prediction methodology for Rn ingroundwater on a general scale. However,the dynamics of subsurface processes, whichprobably have a strong influence on theoccurrence and migration of radionuclides( 222 Rn, 226 Ra and 238 U), were not studied. Thegoals for future work would thus include thefollowing issues:1. Clarification of groundwater chemistryin connection with high natural radioactivityin groundwater. Geochemicalmodelling will be carried outin order to identify and quantify geochemicalprocesses governing uraniummobilisation and fixation and to developan understanding of the longtermeffects of oxidation-reduction onactinide (uranium) behaviour. Additionalchemical data are needed forthat purpose and a sampling programmeconsisting of water samplingand chemical analyses of groundwaterneeds to be carried out.2. The variation in radionuclides ingroundwater over time and season willalso be investigated. For that purpose,about five wells will be sampledmonthly over a period of one year.3. The transport mechanisms of 222 Rnand its parent elements ( 226 Ra and238 U) are highly relevant subsurfaceprocesses that need to be studied. Existingtransport models for these radionuclideshave previously been developedin connection with investigationsregarding repository sites fornuclear waste. These models are oftenvery detailed and their applications ona general scale have not been tested.The goal is to evaluate these modelsand formulate a simplified processbasedtransport model for radon ingroundwater.4. In a final stage, an operative methodfor vulnerability assessment of radoncontent in wells based on the outcomeof the modelling and previous generalisedstudies in GIS will be formulated.Such a tool will be helpful in decisionmakingprocesses concerning planningof new wells or effective remediationmeasures.19

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