Scientific registration no: 2504 Symposium no: 25 Présentation: poster ALLEN O. IMP,UNIL, Switzerland

Transcription

1 Scientific registration no: 2504 Symposium no: 25 Présentation: poster Geostatistical mapping and GIS of radionuclides in the Swiss soils Artificial and Natural Radionuclides Content Teneurs en radionucléides naturels et articifiels des sols en Suisse Méthodes d estimation et d évaluation et d évaluation spatiale ALLEN O. IMP,UNIL, Switzerland Abstract The updated data of swiss soil contamination by radionuclides for the period were investigated for finding out the most relevant spatio-temporal features: Network monitoring with indication of Morishita index and fractal dimension, Multivariate statistical analysis of the grades, spatial variography of the radionuclides. For cartography we used Geostatistics, General Regression Neural Networks (GRNN) and GIS estimation maps with display on the elevation maps, as well as Stochastic Simulation for risk mapping. Introduction The Swiss Federal Public Health Office has fundamentally reviewed and updated the databank of both natural and artificial contamination in the soils for the period These data which were not yet published allow to issue an atlas of the radioactive contents in the swiss soils, and allow to relate the fluctuations of radioactivity of artificial radionuclides (Cs37 of Chernobyl or of nuclear power plants), to soil and subsoil composition for the natural radionuclides, to location of rainfall in may 986 for the Chernobyl Cs37 to large preferential and clustered monitoring of measurement networks. All together, over 250 samples for several chemical elements were investigated, as they are in the newest database of the «Swiss Federal Public Health Office». They consist of yearly in situ gamma spectrometry measurements as recommended by ICRU Report 53 of the Cs 37 (artificial) from different sources and the K40, Th232, U238 (natural) at different locations. The data were delivered as Bq/m2 and NanoGr./hour. As additional data, 479 measurements of the rainfalls during the crossing of the radioactive cloud over Switzerland (~ ) were investigated. As an unexpected result, we mention a correlation between natural and artificial (atmospheric) radionuclides for every year; this undue relationship being caused by the locations of rainfalls in early may in Switzerland, this correlation constitute a causality.

2 Network Monitoring The Network Monitoring has been described by its Morishita index and fractal dimension, in order to characterise the network spatial resolution and its ability to be representative of spatial extrapolations. The clustering of samples is very high for some years, and only a few measurement locations are homotypic from year to year. Morishita diagram is the representation of the relationship between the Morishita index I δ and the dimension of the cell (δ) of the regular grid for which this index has been calculated. This index is estimated by the following formula: Q ( ) = = n n i i i Iδ Q N ( N ) Where Q is the total number of cells, N is the number of points in the network under study, n i is the number of sample points found in the i-th cell. This index is affected by the probability for 2 random points of the network to be found in the same cell of dimension δ and identifies the degree of network s contagiousness. So the maximum value of the Morishita index can be considered as the degree of contagiousness of the network and the abscise value of this point gives the characteristic cluster size. Figure. Morishita diagram for 988 For Switzerland, the Morishita diagram for the 988 network is presented in the figure. After it s examination we can conclude that in 988 the monitoring network has a contagious structure (randomly distributed points with cluster structures) with a characteristic cluster size of 20km. The results for different networks (years) are displayed in Table. We can see that as the number of points increase, the degree of contagiousness and the characteristic cluster size decrease. Year (sample number) Degree of Characteristic cluster size Maximal linear size (km) contagiousness (km) 988 (57) 49, (30) 56, (recalculated) 38,5 23,6 20 (256) Rain fall986 (480),7 33,2 398 Table. Morishita network analysis results Another approach used for characterizing the monitoring network analysis was the estimation of the fractal dimension of the network. Two methods were used for this 2

3 estimation: the sandbox-counting method (D s ) which relation can be express as N(R)~R Ds, where R is the radius and N(R) the mean number of points within this radius; the boxcounting method (D b ) which relation is express as N(L)~L Ds, where (N(L)) is the number of cells of a regular grid necessary to cover all sample points and (L) the linear size of those cells. It was shown that D s < D b. The value Ds and D b are very useful for the network analysis, because if we are in he case of an absolute regular grid, D s =2 and D b =2. So the closer the value of D s and D b are to 2 the more regular is the grid under study and the more the study area is covered. The fractal dimensions obtained by both methods are presented in Table.2. Year sandbox fracral dimension box fracral dimension (reaclculated),36,48 Rain fall,43,62 Table.2 Fractal dimension of networks All the results show that the different networks are not regular and may present gaps in their structure. One logical point is that as the number of points increase, the value of the fractal dimensions increase. The poor values obtained <.5 are due to the irregular covering of the whole area. The very low dimensions of 0.7/0.88 for 994 reflect the clustering of the samples on some locations. Hence the difficulty for mapping the whole area is increased. Multivariate Descriptive Statistics In addition to basic statistical description: histograms, regressions, automatic classification, the principal component analysis (PCA) summarise the main features and the main segregation inside the data and inside the variables, with the first factorial axis linked to the Cs37, and then the second to the natural radionuclides. On figure.2 the display of the variables factors shows the grouping of the natural radionuclides. The different variables are displayed on the factorial circle. Two groups of variables can be seen. The group on the axis is the different Cs souces and the other group is the naturals. On figure.3 the cloud of cases shows two groups, one with few radioactivity and one with high radioactivity. This analysis show a positive correlation between the naturals and the artificials (casual) as well as a good statistical segregation of the data and variables K40 AC228 RD CS-37-F-B CS37_MAI CS-37-T-B CS-37-TOT CS-34-T-B

4 Figure.2 Factorial plan for variables factors Figure.3 Factorial plan for cases factors Spatial Correlation (Variography) The spatial correlations with consideration of anisotropy are calculated with the variograms and modelled with appropriate functions. For all the years taken separately the second order stationnarity hypothesis is not verified. Therefore we had to build the variograms on the two populations separately. There are enough samples for fitting a variogram only with the background measurements, and the variograms related to the hot spots are estimated when there are enough measures. The variogram ranges (zones of influence) for the Chernobyl Cs 37 background lie between 53km and 00km depending of the year (network). Figure.4 Figure.4 Modelled Variogram for the Cs 37 (986 recalculated) background show the model for two directions, for the Cs 37 background. We can see that there is a light anisotropy. In the direction 50 a drift is present beyond 30km. The variogram for the high values was calculated only for 986 recalculated because of the number of points. For the natural radionuclides (Th232, U238, K40) a strong anisotropy was detected and modelled with a spherical model. The largest range is in the direction of the alpine arc and can be explain by the geological structures (south west-north east). All the radionuclides display a nugget effect. Two variograms were also modelled for the high and low precipitation values area respectively. Estimation Maps with Geostatistcs and GIS This unique dataset of radionuclides content in swiss soils has been mapped by using a specific GIS program MapStudio with advanced inteprolation techniques.the GIS program provides a vectorized layers system, each layer being characteristic of lakes, cities, administrative boundaries, etc. The data are related to elementary city information for Switzerland. One layer relates to a 2km raster DEM digital elevation model (not displayed here because of readability). The geoquery within this GIS allows all kind of interrogations on localized variables. The traditionnal inverse-square distance interpolation techniques are by far insufficient for elaborating the estimation maps of radionuclides, and geostatistical techniques as well as techniques of artificial intelligence have been implemented in the GIS package, which allows to measure the spatial autocorrelation and variograms of the measures, and allows to establish various estimations such as: BLUE Best Linear unbiased estimation (Ordinary Kriging system = Equation ), GRNN general regression Neural network, Cokriging. The kriging maps were established for the 3 types of variables which 4

5 * = N α z ( x) λ z( x ) α = N α = λ α α = Equation we considered: the Cs37 artifical radionuclides (figure.5), the sum of the natural radionuclides (figure.6), and the precipitation in early may 986 (figure.7). The radionuclides measures were expressed in Bq/km2 and NanoGr/hour (naturals), so to be additive, and the precipitations were expressed in mm. The BLUE is an exact interpolator, and provides the estimation variance which is minimised under constraints, thus providing an indicator of the error. The BLUE cartography requires a global variogram, which was not easy to establish for the highly polluted areas, so that the variogram for the large-scale low grade areas was used. In addition, a patchwork was elaborated, by estimating separately the highly polluted zone with an adequate variogram (high sill, that means high variance), and drawing the isolines on both areas at once. The cartographic representation chosen was an isoline map. Figure.5 Ordinary Kriging of Cs 37 Chernobyl Figure.6 Ordinary Krigging of Naturals reconstructed data for The GRNN Generalised Regression by Neural Network was used for the cartography of the precipitations (figure.8), where the support for measurements is totally different from the measures of radioactivity. The GRNN method (Equation 2, D(x, x i ) being a distance n yi exp( D( x, xi )) i= yˆ ( x) = n Equation 2 exp( D( x, x )) i= function) was considered appropriate for large-scale non-stationnary smooth phenomena. The obvious correlation seen on the maps between Cs37 and rainfall was processed further by exporting these maps into an IDRSI raster system, and performing additional statistics on the correlation for different radioactivity levels. Non-stationnary geostatistics based on generalized covariance have been elaborated also, according to the method of 5 i

6 IRF-k intrinsic random Function of order k, PRNN probabilistic neural network were also used for the mapping with 2 classes (highly polluted, not polluted). Figure.7 Ordinary Kriging of the Figure.8 GRNN of the precipitations (mm) precipitations (mm) May 986 May 986 Stochastic Simulations (risk mapping) The objectives of simulation are not the same as estimation. Simulations reproduce the spatial variability and can produce as many different realisations as needed, all of them being possible equiprobable realisation of the phenomena under study. There are different approaches to simulate spatially distributed continuous data. In this study we used the Turning Bands algorithm. In this context, simulated realisations should satisfy the following principles: simulated realisations reproduce representative histogram of the original data, simulated realisations reproduce spatial variability described by variograms, conditionals simulations honour the data. By respecting the measures (conditional simulations), the spatial convolution (variogram), the statistical distributions (histogram) of the measures, the simulated points acts as additional samples, and allow to rebuild the variability of the phenomena, which has been largely decreased by the smoothing effect of the previous mapping techniques. The simulations are repeated, and they can be use to answer such questions as: how many percent of the simulations deliver a local value for the raster grid at x i, y i, above a given threshold. Such answer will be identified to Risk Mapping: what is the chance for one local area to be above a threshold? The simulations were done using specific geostatistical programs, and the results were imported in the geostatistic-gis system for consistent representation with the kriging and GRNN mapping., and as DSS decision Support System. 6

7 Figure.9 Probability that the Cs 37 Figure.0 Probability that the Cs 37 Chernobyl exceed 0000 Bq/m 2 exceed Bq/m 2 We present in figure.8 and figure.9 the compilation of 20 conditional simulations for two cutoffs of the Chernobyl Cs 37 (0000 Bq/m2 and Bq/m2 respectively). This gives maps of probability indices that are displayed on high resolution GIS maps. These results show coherency with estimation maps and can be useful for decision making in a case of high radioactivity atmospheric deposition. Conclusion and PerspectivesThe uniqueness of the radionulcides database for Switzerland is worth enough so that high-level mapping methods are used to the cartography of soil pollution. The heterogeneity of measurement network, as well as its clustering, and its complex spatial auto-correlation motivated to use the geostatistics techniques. However, the difficulty in establishing global consistent variograms, induce us to use NN artificial intelligence methods for solving the problem of non-stationnarity. The local values, either as point estimations or as estimations on a raster 2*2 km have been stored, and further statistical investigations are done on these data repository. The dilemma between large-scale non-stationnary smoothing and local variability achieved by simulations puts in balance the cartography of a random continuous function, and the cartography of a probabilistic distribution with locally conditional distribution. These geostatistical investigations, and the resulting maps as well as the GIS maps are the building stone of an advanced methodology. The final goal of this is the comparison and the assessment of the different methods and the production of high quality maps. Acknowledgements The authors thank the CIVERT European project for partial funding. C. Murith have authorised the access to swiss radionuclides datasets. References MapStudio, a geostatistical GIS, Users manual, by K. Krivoruchko, ISIR International Sakharov Institute of radioecology, Minsk, Belarus, 997 Geostat Office, Prof.M. Kanevski, IBRAE, Institute for Nuclear safety, Moscow, Russia,997 Isatis, Geostatistical Package under windows NT, geovariances, Fontainebleau, 997 IAMG 97, Proceedings of 3 rd Annual Conference of the International Association for Mathematical Geology, V. Pawlowsky, ed.997 GSLIB Geostatistical Software Library, Oxford U. P., 997 7

8 Chernobyl Fallout: review of advanced spatial data analysis, M. Kanevsky and al., in geoenv., geostatistics for environmental application, Ed. A Soares, Kluwer Ac.Pr, 997 Soil Pollution : Cartography, risks, decision Support System, IBRAE/ UNIL & al, INTAS report , 997 Keywords : network monitoring, statistical analysis, estimation maps, geostatistics, GIS, risk mapping, soil contamination, Swisserland. Mots clés : réseau de surveillance, analyses statistiques, cartes d estimation des risques, géostatistiques, SIG, contamination des sols, Suisse 8