From Lubeck to Milan

[drawn by Erwin Raisz].

Memoirs, maps, &c., issued by the Geological Survey of the United Kingdom and the Museum of Practical Geology.

At head of title: List F.

The spatial analysis and speciation of uranium and thorium series radionuclides in the soil of the Cronamuck Valley, County Donegal, Ireland

This thesis details the findings of a study into the spatial distribution and speciation of 238U, 226Ra and 228Ra in the soils of the Cronamuck valley, County Donegal . The region lies on the north-eastern edge of the Barnesmore granite and has been the subject of uranium prospecting efforts in the past. The results of the project provide information on the practicability of geostatistical techniques as a means of estimating the spatial distribution of natural radionuclides and provide insight into the behaviour of these nuclides and their modes of occurrence and enrichment in an upland bog environment. The results of the geostatistical survey conducted on the area indicate that the primary control over the levels of the studied nuclides in the soil of the valley is the underlying geology. Isopleth maps of nuclide levels in the valley indicate a predominance of elevated nuclide levels in the samples drawn from the granite region, statistical analysis of the data indicating that levels of the nuclides in samples drawn from the granite are greater than levels drawn from the non-granite region by up to a factor of 4.6 for 238U and 4.9 for 226Ra. Redistribution of the nuclides occurs via drainage systems within the valley, this process being responsible for transport of nuclides away from the granite region resulting in enrichment of nuclides in soils not underlain by the granite. Distribution of the nuclides within the valley is erratic, the effect of drainage f lows on the nuclides resulting in localized enriched areas within the valley. Speciation of the nuclides within one of the enriched areas encountered in the study indicates that enrichment is as a result of saturation of the soil with drainage water containing trace amounts of radionuclides. 238U is primarily held within the labile fractions (exchangeable cat ions + easily oxidisable organics + amorphous iron oxides ) of the soil , 226Ra being associated with the non- labile fractions, most probably the resistant organic material. 228Ra displays a significant occurrence in both the labile and non- labile fractions. The ability of the soil to retain uranium appears to be affected largely by the redox status of the soil, samples drawn from oxidizing environments tending to have little or no uranium in the easily oxidisable and amorphous iron oxide fractions. This loss of uranium from oxidised soil samples is responsible for the elevated 226Ra /238U disequilibrium encountered in the enriched areas of the valley. Analysis of the data indicates that samples displaying elevated 226Ra/238U ratios also exhibit elevated 228Ra/238U ratios indicating a loss of uranium from the samples as opposed to an enrichment of 226Ra.

Pittsburgh -- Atlantic

Shows also part of Delaware.

Pittsburgh -- Atlantic

Shows also part of Delaware.

Mexico

[drawn by Erwin Raisz].

England

[drawn by Erwin Raisz].

From Black Sea thru Turkey to Cyprus

[drawn by Erwin] Raisz.

Australia

Shows cities, geology and topography, agricultural products and mineral resources.

Santiago -- Buenos Aires

[drawn by Erwin Raisz].

China N-S

[drawn by Erwin Raisz].

Disaggregation of polygons of surficial geology and soil maps using spatial modelling and legacy data

Examples from the Murray-Darling basin in Australia are used to illustrate different methods of disaggregation of reconnaissance-scale maps. One approach for disaggregation revolves around the de-convolution of the soil-landscape paradigm elaborated during a soil survey. The descriptions of soil ma units and block diagrams in a soil survey report detail soil-landscape relationships or soil toposequences that can be used to disaggregate map units into component landscape elements. Toposequences can be visualised on a computer by combining soil maps with digital elevation data. Expert knowledge or statistics can be used to implement the disaggregation. Use of a restructuring element and k-means clustering are illustrated. Another approach to disaggregation uses training areas to develop rules to extrapolate detailed mapping into other, larger areas where detailed mapping is unavailable. A two-level decision tree example is presented. At one level, the decision tree method is used to capture mapping rules from the training area; at another level, it is used to define the domain over which those rules can be extrapolated. (C) 2001 Elsevier Science B.V. All rights reserved.

Comparison between detailed digital and conventional soil maps of an area with complex geology

Since different pedologists will draw different soil maps of a same area, it is important to compare the differences between mapping by specialists and mapping techniques, as for example currently intensively discussed Digital Soil Mapping. Four detailed soil maps (scale 1:10.000) of a 182-ha sugarcane farm in the county of Rafard, São Paulo State, Brazil, were compared. The area has a large variation of soil formation factors. The maps were drawn independently by four soil scientists and compared with a fifth map obtained by a digital soil mapping technique. All pedologists were given the same set of information. As many field expeditions and soil pits as required by each surveyor were provided to define the mapping units (MUs). For the Digital Soil Map (DSM), spectral data were extracted from Landsat 5 Thematic Mapper (TM) imagery as well as six terrain attributes from the topographic map of the area. These data were summarized by principal component analysis to generate the map designs of groups through Fuzzy K-means clustering. Field observations were made to identify the soils in the MUs and classify them according to the Brazilian Soil Classification System (BSCS). To compare the conventional and digital (DSM) soil maps, they were crossed pairwise to generate confusion matrices that were mapped. The categorical analysis at each classification level of the BSCS showed that the agreement between the maps decreased towards the lower levels of classification and the great influence of the surveyor on both the mapping and definition of MUs in the soil map. The average correspondence between the conventional and DSM maps was similar. Therefore, the method used to obtain the DSM yielded similar results to those obtained by the conventional technique, while providing additional information about the landscape of each soil, useful for applications in future surveys of similar areas.

Predictive analysis and mapping of indoor radon concentrations in Switzerland

It is estimated that around 230 people die each year due to radon (222Rn) exposure in Switzerland. 222Rn occurs mainly in closed environments like buildings and originates primarily from the subjacent ground. Therefore it depends strongly on geology and shows substantial regional variations. Correct identification of these regional variations would lead to substantial reduction of 222Rn exposure of the population based on appropriate construction of new and mitigation of already existing buildings. Prediction of indoor 222Rn concentrations (IRC) and identification of 222Rn prone areas is however difficult since IRC depend on a variety of different variables like building characteristics, meteorology, geology and anthropogenic factors. The present work aims at the development of predictive models and the understanding of IRC in Switzerland, taking into account a maximum of information in order to minimize the prediction uncertainty. The predictive maps will be used as a decision-support tool for 222Rn risk management. The construction of these models is based on different data-driven statistical methods, in combination with geographical information systems (GIS). In a first phase we performed univariate analysis of IRC for different variables, namely the detector type, building category, foundation, year of construction, the average outdoor temperature during measurement, altitude and lithology. All variables showed significant associations to IRC. Buildings constructed after 1900 showed significantly lower IRC compared to earlier constructions. We observed a further drop of IRC after 1970. In addition to that, we found an association of IRC with altitude. With regard to lithology, we observed the lowest IRC in sedimentary rocks (excluding carbonates) and sediments and the highest IRC in the Jura carbonates and igneous rock. The IRC data was systematically analyzed for potential bias due to spatially unbalanced sampling of measurements. In order to facilitate the modeling and the interpretation of the influence of geology on IRC, we developed an algorithm based on k-medoids clustering which permits to define coherent geological classes in terms of IRC. We performed a soil gas 222Rn concentration (SRC) measurement campaign in order to determine the predictive power of SRC with respect to IRC. We found that the use of SRC is limited for IRC prediction. The second part of the project was dedicated to predictive mapping of IRC using models which take into account the multidimensionality of the process of 222Rn entry into buildings. We used kernel regression and ensemble regression tree for this purpose. We could explain up to 33% of the variance of the log transformed IRC all over Switzerland. This is a good performance compared to former attempts of IRC modeling in Switzerland. As predictor variables we considered geographical coordinates, altitude, outdoor temperature, building type, foundation, year of construction and detector type. Ensemble regression trees like random forests allow to determine the role of each IRC predictor in a multidimensional setting. We found spatial information like geology, altitude and coordinates to have stronger influences on IRC than building related variables like foundation type, building type and year of construction. Based on kernel estimation we developed an approach to determine the local probability of IRC to exceed 300 Bq/m3. In addition to that we developed a confidence index in order to provide an estimate of uncertainty of the map. All methods allow an easy creation of tailor-made maps for different building characteristics. Our work is an essential step towards a 222Rn risk assessment which accounts at the same time for different architectural situations as well as geological and geographical conditions. For the communication of 222Rn hazard to the population we recommend to make use of the probability map based on kernel estimation. The communication of 222Rn hazard could for example be implemented via a web interface where the users specify the characteristics and coordinates of their home in order to obtain the probability to be above a given IRC with a corresponding index of confidence. Taking into account the health effects of 222Rn, our results have the potential to substantially improve the estimation of the effective dose from 222Rn delivered to the Swiss population.