From radon hazard to risk prediction-based on geological maps,soil gas and indoor measurements in Germany

Mapped geological units can be regarded as proxies standing for a complex series of subsoil geochemical and physical properties including the assigned radon activity concentration in soil gas, which is taken as best estimator of the regional geogenic radon potential. Areal distribution of measuring sites for soil gas in Germany is adapted to spatial variation of geology. A grid-based and distance-weighted interpolation procedure is applied, following geologically defined neighbourhood relations of measuring sites and accounting for isolated outcrops of known geology but without measurements. To investigate the statistical relationship between indoor radon, house type and building ground specifications, measurements of the indoor radon concentration have been carried out in more than 10,000 dwellings in different regions of Germany. Multiple regression analyses of variance reveal that besides region-specific geological properties and building characteristics, various house type and living style variables significantly contribute to the explained variance for ground floor radon concentrations. These parameters are also dominant in controlling the radon transfer relation from soil gas to indoor air. Risk prediction maps for radon in houses indicating the probability to exceed certain indoor threshold values can be useful especially for regions with no or only a few measurements of indoor radon.     

Assessment of soil radon potential in Hong Kong, China, using a 10-point evaluation system

Radon and its progenies have been ranked second of being responsible for lung cancer in humans. Hong Kong has four major groups of uranium-rich plutonic and volcanic rocks and is suffering from radon emanated therefrom. However, there is a lack of radon potential maps in Hong Kong to resolve the spatial distribution of radon-prone areas. A ten-point radon potential system was developed in Germany (2005) to predict radon potential using both the in situ geogenic and geographic parameters under hierarchical ranking. Primarily, the ten-point system requires the desk study of the geological environment of sampling sites, which has an advantage of saving resources and manpower in extensive radon potential mapping over the traditional soil radon concentration sampling method. This paper presents a trial of radon potential mapping in Hong Kong to further verify the system. Despite some slight departures, the system demonstrates an acceptable correlation with soil radon concentrations (R ² = 0.62–0.66) from 768 samples of mainly intermediate radon potential. Hong Kong has a mean soil radon concentrations of 58.9 kBqm^?3, while the radon potential from the ten-point system achieves an average of 4.93 out of 10 over the territory. The vicinity of fault zone showed high soil radon concentrations and potentials, which were conducive to uranium enrichment and rapid soil-gas diffusion near faults. High uranium-238 content in soil was found to cause high soil radon concentration with a large R ², 0.84. The Jurassic granite and volcanic crystal tuff cover more than 85 % of the whole Hong Kong area, and they show relatively high radon concentrations (Geometric mean 83 and 49 kBqm^?3, respectively) which are associated with their high uranium contents (Geometric mean 234 and 197 Bqkg^?1, respectively). While indoor radon concentration is an important factor for radon risk assessment, this study has not considered the correlation between indoor radon concentration and radon potential. The reason is that almost all buildings in Hong Kong are high-rise buildings where indoor radon concentrations are governed only by the radium content in the building materials and the ventilation conditions.     

Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation

Soil erosion is one of most widespread process of degradation. The erodibility of a soil is a measure of its susceptibility to erosion and depends on many soil properties. Soil erodibility factor varies greatly over space and is commonly estimated using the revised universal soil loss equation. Neglecting information about estimation uncertainty may lead to improper decision-making. One geostatistical approach to spatial analysis is sequential Gaussian simulation, which draws alternative, equally probable, joint realizations of a regionalised variable. Differences between the realizations provide a measure of spatial uncertainty and allow us to carry out an error analysis. The objective of this paper was to assess the model output error of soil erodibility resulting from the uncertainties in the input attributes (texture and organic matter). The study area covers about 30 km² (Calabria, southern Italy). Topsoil samples were collected at 175 locations within the study area in 2006 and the main chemical and physical soil properties were determined. As soil textural size fractions are compositional data, the additive-logratio (alr) transformation was used to remove the non-negativity and constant-sum constraints on compositional variables. A Monte Carlo analysis was performed, which consisted of drawing a large number (500) of identically distributed input attributes from the multivariable joint probability distribution function. We incorporated spatial cross-correlation information through joint sequential Gaussian simulation, because model inputs were spatially correlated. The erodibility model was then estimated for each set of the 500 joint realisations of the input variables and the ensemble of the model outputs was used to infer the erodibility probability distribution function. This approach has also allowed for delineating the areas characterised by greater uncertainty and then to suggest efficient supplementary sampling strategies for further improving the precision of K value predictions.     

Assessing the risk of soil vulnerability to wind erosion through conditional simulation of soil water content in Sistan plain, Iran

Surface soil water content (SWC) is one of the key factors controlling wind erosion in Sistan plain, southeast of Iran. Knowledge of the spatial variability of surface SWC is then important to identify high-risk areas over the region. Sequential Gaussian simulation (SGSIM) is used to produce a series of equiprobable models of SWC spatial distribution across the study area. The simulated realizations are used to model the uncertainty attached to the surface SWC estimates through producing a probability map of not exceeding a specified critical threshold when soil becomes vulnerable to wind erosion. The results show that SGSIM is a suitable approach for modelling SWC uncertainty, generating realistic representations of the spatial distribution of SWC that honour the sample data and reproduce the sample semivariogram model. The uncertainty model obtained using SGSIM is compared with the model achieved through sequential indicator simulation (SISIM). According to accuracy plots, goodness statistics and probability interval width plots, SGSIM performs better for modelling local uncertainty than SISIM. Sequential simulation provided a probabilistic approach to assess the risk that SWC does not exceed a critical threshold that might cause soil vulnerability to wind erosion. The resulted risk map can be used in decision-making to delineate “vulnerable” areas where a treatment is needed.     

Determining the spatial scale of variation in soil radon concentration

Increasing concern about possible links between emissions of radon and certain types of malignant disease has led to local and regional surveys to measure radon concentrations in the soil and in dwellings. The spatial scale at which radon varies is largely unknown, and so efficient sampling schemes and methods of mapping cannot be selected. To determine the spatial scale of radon variation in the English Midlands we measured radon in the soil using solid-state nuclear track detection in three areas of different geological complexity. In two of them we used an unbalanced multistage sampling scheme with seven stages of nesting: the Hereford survey covered distances from 10 m to 7.5 km, and the Buxton survey distances from 1 m to 3.75 km. The results from the nested surveys suggested that geology exerts a strong control on the variation. Finally radon was measured every 20 m along a 2-km long transect which crossed several lithologies close to Nottingham. The soil radon values changed in an erratic way along the transect. The sample variogram of radon has a substantial nugget variance, suggesting that much of the variation occurred for distances less than the sampling interval. The structure at the longer scale seems to be controlled by the underlying geology. These results have implications for designing further surveys and for selecting a method of mapping. Stratification based on lithology might be the only feasible solution to sampling, estimating and mapping radon concentrations over large areas. Where the locally erratic component of variation is large, estimation by kriging, for example, would confer little additional benefit compared with that by classification.     

Radon soil–gas as a geological mapping tool: case study from basement complex of Nigeria

In an effort to quantify the geogenic radon soil–gas potential and appraise the use of radon technique as a geological mapping tool in a crystalline basement rock terrain of Ile–Ife Nigeria, radon measurement concentration were made using a radon detector instrument (EDA RD-200) that measures radon isotopes by a scintillator cell coupled to a photomultiplier tube. The data were collected from soils derived from three different lithologic rock units. The observed values were then correlated with the geology of the area. Significant differences in the radon soil–gas concentrations among the three geologic units were observed. Granite gneiss has the highest concentration, followed by grey gneiss and mica schist in that order. The geometric mean (GM) concentration of radon-222 measured in soils directly overlying the three different rock types were 301.4 pCi/l for granite gneiss, 202.8 pCi/l for the grey gneiss, and 199.4 pCi/l for mica schist. Conversely, the average values for radon-220 averaged 1510.0, 815.4, and 733.0 pCi/l for granite gneiss, grey gneiss, and mica schist rocks, respectively. Statistical t test (α=0.05) results indicated that there was no significant difference in the geometric mean of radon soil–gas measured between low and medium potential zones. However, significant differences were found between the low and high radon potential zones, and between the medium and high zones. The low concentrations of radon soil–gas emission observed in this study is explained in terms of the seasonal variation due to thermal convection fluid movement, while the radon concentrations were found to be controlled by the lithology and geochemistry of the underlying bedrock.     

Conditional simulation of USLE/RUSLE soil erodibility factor by geostatistics in a Mediterranean Catchment,Turkey

Soil erosion is a major environmental problem that threatens the sustainability and productivity of agricultural areas. Assessment and mapping of soil erosion are extremely important in the management and conservation of natural resources. The universal soil loss equation (USLE/RUSLE) is an erosion model that predicts soil loss as a function of soil erodibility (K-factor), as well as topographic, rainfall, cover, and management factors. The traditional approach assumes that one soil erodibility value represents the entire area of each soil series. Therefore, that approach does not account for spatial variability of soil series. This study was carried out to evaluate the use of the sequential Gaussian simulation (SGS) for mapping soil erodibility factor of the USLE/RUSLE methodology. Five hundred and forty-four surface soil samples (0–20 cm) were collected from the study area to determine the soil erodibility. A simulation procedure was carried out on 300 realizations, and histogram and semivariogram of the simulation were compared to the observed values. The results showed that the summary statistics, histogram, and semivariogram of the simulation results were close to the observed values. In contrary to the traditional approach and kriging, 95% confidence interval of the simulated realizations was formed in order to determine uncertainty standard deviation map, and the uncertainty was explained numerically. The SGS produced a more reliable soil erodibility map and it can be more successfully used for monitoring and improving effective strategies to prevent erosion hazards especially to improve site specific management plans.     

Spatial modelling and uncertainty assessment of pyroclastic cover thickness in the Sorrento Peninsula

Assessing spatial variability of soil thickness is a critical issue for understanding and predicting slope processes. The present work was aimed at estimating the spatial scales at which the variation of pyroclastic cover thickness occurs in a sample area in the Sorrento Peninsula (Italy). Stochastic simulation was used to understand the spatial variability of pyroclastic cover thickness on Mount Pendolo and to assess its spatial uncertainty. In the study area, covering about 0.7 km², thickness measurements were collected using electrical resistivity tomography profiles, continuous core drillings and steel rod penetrometric tests. Variographic analysis revealed the occurrence of an anisotropic behaviour along the N50 and N140 directions. In the latter anisotropic direction, a nested variogram was fitted including (1) a long-range component which could be related to large-scale factors, like the curvature of the slope and contributing area and (2) a shorter scale variation which is probably associated with the occurrence of denudation processes or to the articulate cover/bedrock interface. To assess the spatial variability and uncertainty of pyroclastic cover thickness, a stochastic simulation algorithm was used and 500 equally probable images of cover thickness were yielded. The results showed that a better thickness distribution map can be drawn by simulating the data collected on the slope and at the footslope separately. The approach also allowed delineating the areas characterized by greater uncertainty, suggesting supplementary measurements to further improve the cover thickness distribution model, thus reducing the uncertainty.     

Study of radon in soil gas,trace elements and climatic parameters around South Kordofan state,Sudan

This study was conducted primarily to measure and map radon activity concentration in soil gas and to understand the effect of geology and lithology and meteorology on radon concentration. Portable radon meter has been used for the measurement of soil gas radon at 30 different locations around Uro and Korn area in eastern Nuba Mountain South Kordofan State. The results indicate that the activity concentrations of ²²²Rn in soil gas fall within the range of 20–1,359 Bq/m³ with geometric mean of 102.80 Bq/cm³. The obtained data show that samples around Uro have anomaly of ²²²Rn concentrations than the sample around Korn. The reason could be attributed to differences in the geological structure, lithology and climate parameters. GIS predicative map has shown that the elevated levels of radon concentration were measured in North study area. Upon comparing the results with global data, it was found that the obtained values are far below the reported range of India, Slovenia, Portugal and Syria. However, the range of ²²²Rn concentrations in the soil observed in this study is significantly high relative to similar data reported from Libya. The regression analysis has shown that no correlation was noted between radon concentrations, climatic parameters and trace element.     

Geostatistical Seismic Inversion with Direct Sequential Simulation and Co-simulation with Multi-local Distribution Functions

Stochastic sequential simulation is a common modelling technique used in Earth sciences and an integral part of iterative geostatistical seismic inversion methodologies. Traditional stochastic sequential simulation techniques based on bi-point statistics assume, for the entire study area, stationarity of the spatial continuity pattern and a single probability distribution function, as revealed by a single variogram model and inferred from the available experimental data, respectively. In this paper, the traditional direct sequential simulation algorithm is extended to handle non-stationary natural phenomena. The proposed stochastic sequential simulation algorithm can take into consideration multiple regionalized spatial continuity patterns and probability distribution functions, depending on the spatial location of the grid node to be simulated. This work shows the application and discusses the benefits of the proposed stochastic sequential simulation as part of an iterative geostatistical seismic inversion methodology in two distinct geological environments in which non-stationarity behaviour can be assessed by the simultaneous interpretation of the available well-log and seismic reflection data. The results show that the elastic models generated by the proposed stochastic sequential simulation are able to reproduce simultaneously the regional and global variogram models and target distribution functions relative to the average volume of each sub-region. When used as part of a geostatistical seismic inversion procedure, the retrieved inverse models are more geologically realistic, since they incorporate the knowledge of the subsurface geology as provided, for example, by seismic and well-log data interpretation.     

Modeling spatial uncertainty of heavy metal content in soil by conditional Latin hypercube sampling and geostatistical simulation

This study proposes the method of simulating spatial patterns and quantifying the uncertainty in multivariate distribution of heavy metals (Cr, Cu, Ni, and Zn) by sequential indicator simulation (SIS) combined with conditional Latin hypercube sampling (cLHS) in Changhua County, Taiwan. The cLHS is used for a sampling then for SIS mapping and assessing uncertainties of heavy metal concentrations. The indicator variogram results indicate that the 700 cLHS samples replicate statistical multivariate distribution and spatial structure of the 1,082 samples. Moreover, the SIS realizations based on 700 cLHS samples are more conservative and reliable than those based on 1,082 samples for delineating soil contamination by all heavy metals with the exception of Zn. Given adequate sampling, soil contamination simulation provides sufficient information for delineating contaminated areas and planning environmental management.     

Some measurements of radon in south west England

Summary A survey of radon in soil gas emissions was carried out in the Carnmenellis area of Cornwall to establish the location and extent of any major radon gas concentrations. Measurements of soil radon gas levels showed considerable variation. Radon emission variation with respect to atmospheric pressure was monitored but results showed no correlation between the variables. A survey of schools in the region with high indoor radon concentrations was carried out for comparison with the results of the main survey. A good correlation was found between the two surveys, suggesting that broad-based radon hazard mapping on a local scale may be feasible.     

广东中山地区土壤氡浓度填图研究

为了研究区域土壤氡填图的方法,利用RAD7电子测氡仪和RADON-JOK土壤气体渗透率仪在广东省中山市进行了面积为1 800 km2的测量工作,有效测点数为67个。中山市的平均土壤氡浓度为(100.41±154.64)kBq/m3;最大值和最小值分别为0.74 kBq/m3和1 199.24 kBq/m3。土壤气体渗透率测量结果表明,在风化花岗岩地区,高渗透率和中渗透率占优势;而第四纪沉积物地区大部分土壤气体渗透率较低。基于此,结合研究区土壤氡浓度和土壤气体渗透率,对研究区土壤氡风险进行了分级评价。为土壤氡风险分级的方法研究提供了有参考价值的研究实例。     

Reconnaissance soil-gas radon survey over the faulted crystalline area of Ile-Ife, Nigeria

Tropical rain forest areas are well known for problems with geological mapping because of the dense vegetation and thick overburden. The application of soil-gas radon measurements was carried out over known and suspected fault zones in the basement terrains of Ile-Ife, southwestern Nigeria. Radon concentration in the area ranged from 25.6 to 6,227.8 pCi/l, with distinct contrasts and overlap in concentrations among soil groups derived from various rock types. Radon concentrations in the soil exhibit a strong dependence on the local geology and reflect the bedrock geochemistry. Relatively high radon soil-gas concentrations of between 4,000 and 6,227.8 pCi/l were found directly on the axis of the known and suspected strike slip faults. The use of the radon-222 technique confirms its application as a useful tool in geological and structural mapping in basement terrains largely covered by relatively thick overburdens.     

A comparison of field and laboratory analytical methods for radon site investigation

Soil-gas radon measurements provide a valuable tool in assessing probable indoor radon levels on a regional basis. However, in Great Britain, seasonal weather changes can cause large changes in soil-gas radon concentration. Although this does not significantly constrain systematic radon potential mapping programmes, it does cause difficulties in responding to ad-hoc requests for site-specific radon investigations. The relationship between soil-gas radon and gamma spectrometry measurements made in the field with radon released from a representative sample of soil in the laboratory has been investigated as part of a program to develop a method of radon potential mapping and site investigation which can be used at any time of the year. Multiple soil and soil-gas samples were collected from sites underlain by bedrocks with widely varying radon potentials. For each geological unit, sites both free of and covered by glacial drift deposits were sampled. Soil and soil-gas samples were taken at the same depth of 60–100 cm. The effectiveness of these radon site investigation procedures has been evaluated by studying the relationship between the soil-gas radon, gamma spectrometry and radon emanation data with an independent estimate of the radon risk. The geologic radon potential (GEORP), which is the proportion of existing dwellings which exceed the UK radon Action Level (200 Bq m⁻³) for a particular combination of solid and drift geology within a defined geographic area, has been used for this study as the independent estimate of radon risk. Soil-gas radon, radon emanation and eU (equivalent uranium by field γ spectrometry) are all good geochemical indicators of radon risk (GEORP) in Derbyshire but only soil-gas radon correlates significantly with GEORP in Northamptonshire. Radon in soil gas discriminates more effectively between sites with different radon potential in Northamptonshire if soil permeability is also taken into account. In general, measurement of soil-gas radon in the field provides the most universally applicable indicator of radon potential. If soil-gas radon concentrations cannot be determined because of climatic factors, for example when the soil profile is waterlogged, measurement of radon emanation in the laboratory or measurement of eU can be used as radon potential indicators in some geological environments. This applies particularly in areas where the soil composition rather than the composition and permeability of the underlying rock or superficial deposits are the dominant controls of radon potential. It appears, therefore, that it may be necessary to use different radon site investigation methods according to the specific factors controlling radon emanation from the ground. In some cases no method will provide a reliable indicator of radon risk under unfavourable climatic conditions.     

Geogenic control on soil chemistry in urban areas: A novel method for urban geochemical mapping using parent material classified data

The purpose of the study reported here is to assess whether it may in some circumstances be useful and appropriate to use a parent material (PM) soil chemistry mapping method developed for national soil chemistry data to portray spatial variation in urban soil chemistry data in Great Britain. Analysis of variance (ANOVA) of the urban soil data suggests that spatial interpolation of soil ambient background concentrations (ABCs) using PM classified soil data may be justified for those elements with strong geogenic control. The PM soil chemistry mapping method for urban soil data is demonstrated using data from the Northampton urban area, in the English Midlands. Geometric mean (GM) and inverse distance weighting (IDW) interpolations based on the nearest four topsoil samples were evaluated. Independent validation indicated that for As, Cr, Fe and to a lesser extent K, which all exhibit relatively strong geogenic control in the Northampton urban area: (i) the PM soil chemistry mapping method is more accurate and effective than the conventional IDW grid mapping and (ii) PM soil chemistry mapping based on the average of the nearest four Ln_e element concentrations is more accurate than mapping based on IDW values calculated from the nearest four Ln_e element concentrations. The variation in effectiveness of the methods can be explained by the fact that PM exerts a significant control on As, Cr, Fe and K in the Northampton area whereas anthropogenic inputs appear to be the dominant control on the spatial variation of Pb, especially at high concentrations. The PM mapping method would be expected to work efficiently in other urban areas and for those elements where a significant proportion of the variation can be explained by PM.     

基于二维地质剖面的三维地质结构多点统计学模拟方法

利用三维地质模拟技术重构地质现象的三维空间分布,是实现自然资源管理和风险评估的重要基础和前提。多点统计学方法通过探寻多点间的空间结构关系,结合随机模拟方法生成具有差异性的模拟结果,较好地再现了复杂的地质现象。然而,如何构建合适、有效的训练图像一直是基于多点统计学三维地质模拟的核心问题。本文提出了一种改进的多点统计学算法。本方法结合了序贯模拟和迭代的方法,将二维剖面扩展为三维训练图像,再结合EM-Like算法,实现了三维地质结构的优化模拟。建模实例结果表明,本方法能确保训练图像对内部模拟网格的约束,准确模拟研究区的地层层序,并很好地再现二维地质剖面所反映的地层结构关系。     

Catchment scale geostatistical simulation and uncertainty of soil erodibility using sequential Gaussian simulation

Soil erodibility (K) affects sediment delivery to streams and needs to be appropriately quantified and interpolated as a fundamental geographic variable for implementing suitable catchment management and conservation practices. The spatial distribution of K for erosion modelling at non-sampling grid locations has traditionally been estimated using interpolation algorithms such as kriging which do not adequately represent the uncertainty of estimates. These methods cause smoothing effects through overestimating the low values and underestimating the large values. In this study observed values were used to implement a sequential Gaussian simulation (SGS) procedure to evaluate the certainty of modelled data. Soil erodibility values were computed using 41 soil samples taken from the top 10 cm soil layer regularly distributed across four catchments, 367–770 ha in area, within Kangaroo River State forest, New South Wales (NSW). One hundred realisations were applied in the simulation process to provide spatial uncertainty and error estimates of soil erodibility. The results indicated that values simulated by the SGS algorithm produced similar K values for the neighbouring cells. At the pixel level, the SGS approach generated a reliable estimation of soil erodibility in most areas. Spatial variation of the K factor in this study was strongly related to soil landscape differences across the catchments; within catchments slope gradient did not have a substantial impact on the numerical values of the K factor using pixel-by-pixel comparisons of raster grid maps.     

Using robust kriging and sequential Gaussian simulation to delineate the copper- and lead-contaminated areas of a rapidly industrialized city in Yangtze River Delta,China

A total of 540 topsoil samples (0–15 cm), 188 subsoil samples (20–40 cm), and four individual soil profiles were collected in this study for mapping the Cu- and Pb-contaminated areas in soils of Zhangjiagang city, an industrialized city in the Yangtze River Delta region of China. Robust geostatistical methods were applied for identifying possible spatial outliers of Cu and Pb data, and then a sequential Gaussian simulation was employed for delineating the potential areas where Cu or Pb concentration was affected by diffuse pollution. The results showed that the spatial outliers of Cu and Pb were strongly associated with various types of factories. The anthropogenic input of Cu to soils at local hotspots was closely related to emissions of printing and dyeing, metallurgical, and chemical factories, whereas a lead oxide factory and a chemical factory resulted in a considerable increase of Pb in the topsoil of the study area. Approximately 30% of the total land area of the study was at potential risk from the Cu or Pb diffuse pollution resulting from rapid industrialization of the area over the past 20 years.     

Spatial association between residential radon concentration and bedrock types in New Hampshire

Using a large database of residential short-term radon measurements in New Hampshire, this study evaluated the ability of expert-assigned bedrock radon potential for predicting residential radon concentration. First, each bedrock type was assigned a radon potential level by a geologist familiar with the local geology. Then, using residential radon measurements, a continuous surface of radon concentration was generated through a kriging process. The mean residential radon concentration within the spatial extent of each bedrock type was then calculated based on that surface. The Spearman Rank Correlation Coefficient was calculated between the two ranks of the bedrock types, one based on the expert-assigned potential level and the other based on the mean residential concentration. A strong correlation between the rank correlation and the area of the bedrock type was found. When only the 15 largest bedrock types were used, the Spearman Correlation Coefficient reached 0.6. Geological knowledge is concluded to be useful in predicting and mapping residential radon concentration, but the prediction should be interpreted with caution, especially for areas in which the underlying bedrocks are highly localized.