Mapping Soil Gas Radon Concentration: A Comparative Study of Geostatistical Methods

Understanding soil gas radon spatial variations can allow the constructor of a new house to prevent radon gas flowing from the ground. Indoor radon concentration distribution depends on many parameters and it is difficult to use its spatial variation to assess radon potential. Many scientists use to measure outdoor soil gas radon concentrations to assess the radon potential. Geostatistical methods provide us a valuable tool to study spatial structure of radon concentration and mapping. To explore the structure of soil gas radon concentration within an area in south Italy and choice a kriging algorithm, we compared the prediction performances of four different kriging algorithms: ordinary kriging, lognormal kriging, ordinary multi-Gaussian kriging, and ordinary indicator cokriging. Their results were compared using an independent validation data set. The comparison of predictions was based on three measures of accuracy: (1) the mean absolute error, (2) the mean-squared error of prediction; (3) the mean relative error, and a measure of effectiveness: the goodness-of-prediction estimate. The results obtained in this case study showed that the multi-Gaussian kriging was the most accurate approach among those considered. Comparing radon anomalies with lithology and fault locations, no evidence of a strict correlation between type of outcropping terrain and radon anomalies was found, except in the western sector where there were granitic and gneissic terrain. Moreover, there was a clear correlation between radon anomalies and fault systems.     

Comparison of Point Estimation Techniques in the Spatial Analysis of Radium-226, Radium-228 and Potassium-40 in Soil

Many environmental surveys require the implementation of estimation techniques to determine the spatial distribution of the variable being investigated. Traditional methods of interpolation and estimation, for example, inverse distance squared and triangulation often ignore features of the data set such as anisotropy which may have a significant impact on the quality of the estimates produced. Geostatistical techniques may offer an improved method of estimation by modelling the spatial continuity of the variable using semi-variogram analysis. The theoretical model fitted to the semi-variogram is then used in the assignation of weighting factors to the samples surrounding the location to be estimated. This paper outlines the results of a comparison between three common estimation methods, polygonal, triangulation and inverse distance squared and a geostatistical method, in the estimation of soil radionuclide activities. The geostatistical estimation method known as kriging performed best over a range of parameters used to test the performance of the methods. Kriging exhibited the best correlation between actual and estimated values, the narrowest error distribution and the lowest overall estimation error. Polygonal estimation was best at reproducing the data set distribution. Conditional bias was evident in all the methods, low values being over-estimated and high values being under-estimated.     

Status of Aquatic Bioassessment in U.S. EPA Region IX

U.S. EPA Region IX is supporting bioassessment programs in Arizona, California, Hawaii and Nevada using biocriteria program and Regional Environmental Monitoring and Assessment Program (R-EMAP) resources. These programs are designed to improve the state, tribal and regional ability to determine the status of water quality. Biocriteria program funds were used to coordinate with Arizona, California and Hawaii which resulted in these states establishing reference conditions and in developing biological indices. U.S. EPA Region IX has initiated R-EMAP projects in California and Nevada. These U.S. EPA Region IX sponsored programs have provided an opportunity to interact with the States and provide them with technical and management support. In Arizona, several projects are being conducted to develop the State's bioassessment program. These include the development of a rotational random monitoring program; a regional reference approach for macroinvertebrate bioassessments; ecoregion approach to testing and adoption of an alternate regional classification system; and development of warm-water and cold-water indices of biological integrity. The indices are projected to be used in the Arizona Department of Environmental Quality (ADEQ) 2000 water quality assessment report. In California, an Index of Biological Integrity (IBI) has been developed for the Russian River Watershed using resources from U.S. EPA's Non-point Source (NPS) Program grants. A regional IBI is under development for certain water bodies in the San Diego Regional Water Quality Control Board. Resources from the U.S. EPA Biocriteria program are being used to support the California Aquatic Bioassessment Workgroup (CABW) in conjunction with the California Department of Fish & Game (CDFG), and to support the Hawaii Department of Health (DoH) Bioassessment Program to refine biological metrics. In Nevada, R-EMAP resources are being used to create a baseline of aquatic information for the Humboldt River watershed. U.S. EPA Region IX is presently working with the Nevada Division of Environmental Protection (NDEP) to establish a Nevada Aquatic Bioassessment Workgroup. Future R-EMAP studies will occur in the Calleguas Creek watershed in Southern California, and in the Muddy and Virgin River watersheds in southern Nevada, and the Walker River watershed in eastern California and west-central Nevada.     

The lognormal distribution,environmental data,and radiological monitoring

The lognormal distribution has become a common choice to represent intrinsically positive and often highly skewed environmental data in statistical analysis. However the implications of its use are often not carefully considered. With an emphasis on radiological monitoring applications, this paper reviews what assuming lognormality means in terms of data analysis and interpretation. The relationship of using normal theory methods on log transformed data to multiplicative errors and hypothesis testing in the original scale is also discussed.Work performed under the auspices of the U.S. Department of Energy, DOE Contract No. DE-AC-07-76ID01570.     

Mapping the Spatial Variability of Plant Diversity in a Tropical Forest: Comparison of Spatial Interpolation Methods

Knowledge of the spatial distribution of plant species is essential to conservation and forest managers in order to identify high priority areas such as vulnerable species and habitats, and designate areas for reserves, refuges and other protected areas. A reliable map of the diversity of plant species over the landscape is an invaluable tool for such purposes. In this study, the number of species, the exponent Shannon and the reciprocal Simpson indices, calculated from 141 quadrat sites sampled in a tropical forest were used to compare the performance of several spatial interpolation techniques used to prepare a map of plant diversity, starting from sample (point) data over the landscape. Means of mapped classes, inverse distance functions, kriging and co-kriging, both, applied over the entire studied landscape and also applied within vegetation classes, were the procedures compared. Significant differences in plant diversity indices between classes demonstrated the usefulness of boundaries between vegetation types, mapped through satellite image classification, in stratifying the variability of plant diversity over the landscape. These mapped classes, improved the accuracy of the interpolation methods when they were used as prior information for stratification of the area. Spatial interpolation by co-kriging performed among the poorest interpolators due to the poor correlation between the plant diversity variables and vegetation indices computed by remote sensing and used as covariables. This indicated that the latter are not suitable covariates of plant diversity indices. Finally, a within-class kriging interpolator yielded the most accurate estimates of plant diversity values. This interpolator not only provided the most accurate estimates by accounting for the indices' intra-class variability, but also provided additional useful interpretations of the structure of spatial variability of diversity values through the interpretation of their semi-variograms. This additional role was found very useful in aiding decisions in conservation planning.     

Evaluating Kriging as a Tool to Improve Moderate Resolution Maps of Forest Biomass

The USDA Forest Service, Forest Inventory and Analysis program (FIA) recently produced a nationwide map of forest biomass by modeling biomass collected on forest inventory plots as nonparametric functions of moderate resolution satellite data and other environmental variables using Cubist software. Efforts are underway to develop methods to enhance this initial map. We explored the possibility of modeling spatial structure to make such improvements. Spatial structure in the field biomass data as well as in residuals from the map was investigated across 18 ecological zones in the Interior Western U.S. Exploratory tools included directional graphs of summary statistics, three dimensional maps, Moran’s I correlograms, and variograms. Where spatial pattern was present, field and residual biomass were kriged, and predictions made for an independent test set were evaluated for improvement over predictions in the initial biomass map. While kriging has some potential benefit when analyzing the field data and exploring spatial structure, kriging residuals resulted in little or no improvement in the initial biomass map developed using Cubist software. Stationarity assumptions, variogram behavior, and appropriate model fitting strategies are discussed.     

Response of Winter Birds to Soil Remediation along the Columbia River at the Hanford Site

The Columbia River at the Hanford Site, located in south-central Washington State, U.S.A., is a regionally important refugium for overwintering birds. Some of the river shoreline has been designated by the U.S. Department of Energy for environmental clean-up following past production of materials for nuclear weapons. We evaluated the effects of soil remediation on winter birds at six inactive nuclear reactor areas. Remediation activities consisted of daily excavation and removal of approximately 1035 t of contaminated soil from previously herbicided and denuded areas located between 30 and 400 m and mostly in line-of-sight of the river shoreline. Remediation activities had no apparent effect on numbers of riverine or terrestrial birds using adjacent undisturbed shoreline and riparian habitat.     

NEMS and MARKAL-MACRO Models for Energy-Environmental-Economic Analysis: A Comparison of the Electricity and Carbon Reduction Projections

The Annual Energy Outlook forecasts published by the United States Energy Information Administration (EIA) of the Department of Energy are based on results from the National Energy Modeling system (NEMS). This paper compares NEMS, which is used only in the U.S., with the U.S. version of MARKAL-MACRO (USMM) model, which is used in more than thirty-five countries. The two models predict similar results for the base 1999 US Annual Energy Outlook (AEO), but their results with carbon constraints are quite different. The differences of the models and those of their predictions are explained. USMM can be used to provide an alternative and complementary approach to projections of renewable technologies penetration and their potential in reducing carbon dioxide emissions in the USA.     

Comparison of stochastic and deterministic methods for mapping groundwater level spatial variability in sparsely monitored basins

In sparsely monitored basins, accurate mapping of the spatial variability of groundwater level requires the interpolation of scattered data. This paper presents a comparison of deterministic interpolation methods, i.e. inverse distance weight (IDW) and minimum curvature (MC), with stochastic methods, i.e. ordinary kriging (OK), universal kriging (UK) and kriging with Delaunay triangulation (DK). The study area is the Mires Basin of Mesara Valley in Crete (Greece). This sparsely sampled basin has limited groundwater resources which are vital for the island’s economy; spatial variations of the groundwater level are important for developing management and monitoring strategies. We evaluate the performance of the interpolation methods with respect to different statistical measures. The Spartan variogram family is applied for the first time to hydrological data and is shown to be optimal with respect to stochastic interpolation of this dataset. The three stochastic methods (OK, DK and UK) perform overall better than the deterministic counterparts (IDW and MC). DK, which is herein for the first time applied to hydrological data, yields the most accurate cross-validation estimate for the lowest value in the dataset. OK and UK lead to smooth isolevel contours, whilst DK and IDW generate more edges. The stochastic methods deliver estimates of prediction uncertainty which becomes highest near the southeastern border of the basin.     

Deposition and storage of solid-bound heavy metals in the floodplains of the River Geul (the Netherlands)

Because of past mining activities, the floodplains of the River Geul are polluted with heavy metals. The continuous supply of fresh sediments during floods has caused the floodplain soils to exhibit large quality variations in time. By measurements of ¹³⁷Cs deposition rates in part of the floodplain area were determined at 0.4 to 2.7 cm yr^–1. Analysis of soil metal concentrations at various depths at 65 locations, revealed that the upper 40 cm of the soil profile deposited during the past 30–45 yr, exhibit the highest metal levels. The geostatistical interpolation technique kriging was used to map actual and past pollution patterns. It was shown that, as a result of variable deposition rates, the spatial correlation structure of soil metal concentrations becomes less clear with increasing depth/age. Kriged maps of average metal concentrations in the upper 100 cm of the soil profile provided the basis for the calculation of the mass storage of heavy metals.     

Estimation of nested spatial patterns and seasonal variation in the longitudinal distribution of Sicyopterus japonicus in the Datuan Stream, Taiwan by using geostatistical methods

This study attempts to determine the scale-dependent hierarchical spatial variation and longitudinal distributions of Sicyopterus japonicus year round. The distribution of S. japonicus in the Datuan Stream in northern Taiwan was surveyed during the fall and winter 2007, as well as the spring and summer of 2008. The spatial structure of S. japonicus density was modeled using geostatistics. The longitudinal distributions of S. japonicus density were then estimated using kriging and hydrology distance with nested variogram models. Variography results indicate that nested variogram models could reflect the hierarchical structure in the spatial variation of seasonal S. japonicus density, with the small, median, and large ranges representing three nested scales. Models for the four seasons were consistent in that they shared the same shape of variogram models with various ranges and sill values. This model shape consistency implies stationary spatial correlations in the longitudinal fish distribution across the four seasons. The Kriging geostatistical method based on the multiple scales nested variogram models also provided robust estimates of S. japonicus densities at unsampled sections. We conclude that S. japonicus densities exhibit hierarchical patterns and variation in the four seasons along the study stream. Geostatistical methods with a nested variograms and hydrological distance are a highly effective means of delineating the hierarchical structure in longitudinal patterns of S. japonicus density in each season, providing estimates of the S. japonicus density for hierarchically structured spatial distributions and expanding knowledge of S. japonicus beyond the limits imposed by spatial and temporal scales.     

Application of intrawell testing of RCRA groundwater monitoring data when no upgradient well exists

A statistical quality control approachto detect changes in groundwater quality from aregulated waste unit is described. The approachapplies the combined Shewhart-CUSUM control chartmethodology for intrawell comparison of analyteconcentrations over time and does not require anupgradient well. A case study from the U.S.Department of Energy's Hanford Site is used forillustration purposes. This method is broadlyapplicable in groundwater monitoring programs wherethere is no clearly defined upgradient location, thegroundwater flow rate is exceptionally slow, or wherea high degree of spatial variability exists inparameter concentrations. This study also indicatesthat the use of the Data Quality Objectives (DQO)process can assist in designing an efficient andcost-effective groundwater monitoring plan to achievethe optimum goal of both low false positive and lowfalse negative rates (high power).     

Analyzing censored environmental data using survival analysis: Single sample techniques

The occurrence of censored data due to less than detectable measurements is a common problem with environmental data. The methods of survival analysis, although designed primarily for right censored data and time measured variables, can be adapted to apply censored environmental data. These methods have several theoretical and practical advantages over many existing techniques for dealing with less than detectable measurements.Work performed under the auspices of the U.S. Department of Energy, DOE Contract No. DE-AC07-76ID01570.     

Multi-scale spatial structure of heavy metals in agricultural soils in Beijing

To effectively investigate the spatial variability of heavy metals in soil, produce a higher quality spatial distribution map, and identify the potential pollution sources of heavy metals, geostatistics was employed to evaluate the effect of scale on spatial variability of heavy metals in Beijing agricultural soils. The results revealed that spatial variability of Cr, Ni, Zn, and Hg was dependent on scale. Validation of the optimality of theoretical semivariance and comparative analysis of the estimation accuracy demonstrated that the multi-scale nested model can reveal the spatial structure of heavy metals effectively and improve the estimation accuracy better than the single-scale method, thereby enabling production a higher quality spatial interpolation map. Thus, the multi-scale kriging nested model is a useful tool for revealing spatial variability of heavy metals in soils, while the spatial distribution maps allow the identification of hot spots with high concentrations of heavy metals.     

Assessment of radionuclide and metal contamination in a thorium rich area in Norway

The Fen Central Complex in southern Norway, a geologically well investigated area of magmatic carbonatite rocks, is assumed to be among the world largest natural reservoirs of thorium ((232)Th). These rocks, also rich in iron (Fe), niobium (Nb), uranium ((238)U) and rare earth elements (REE), were mined in several past centuries. Waste locations, giving rise to enhanced levels of both radionuclides and metals, are now situated in the area. Estimation of radionuclide and metal contamination of the environment and radiological risk assessment were done in this study. The average outdoor gamma dose rate measured in Fen, 2.71 μGy h(-1), was significantly higher than the world average dose rate of 0.059 μGy h(-1). The annual exposure dose from terrestrial gamma radiation, related to outdoor occupancy, was in the range 0.18-9.82 mSv. The total activity concentrations of (232)Th and (238)U in soil ranged from 69 to 6581 and from 49 to 130 Bq kg(-1), respectively. Enhanced concentrations were also identified for metals, arsenic (As), lead (Pb), chromium (Cr) and zinc (Zn), in the vicinity of former mining sites. Both radionuclide and heavy metal concentrations suggested leaching, mobilization and distribution from rocks into the soil. Correlation analysis indicated different origins for (232)Th and (238)U, but same or similar for (232)Th and metals As, Cr, Zn, nickel (Ni) and cadmium (Cd). The results from in situ size fractionation of water demonstrated radionuclides predominately present as colloids and low molecular mass (LMM) species, being potentially mobile and available for uptake in aquatic organisms of Norsj? Lake. Transfer factors, calculated for different plant species, showed the highest radionuclide accumulation in mosses and lichens. Uptake in trees was, as expected, lower. Relationship analysis of (232)Th and (238)U concentrations in moss and soil samples showed a significant positive linear correlation.     

Optimization of sampling for the determination of mean radium-226 concentration in surface soil

There are thousands of properties in the United States on which the soil has been contaminated to some degree with uranium mill tailings. An effort is now underway by the United States Department of Energy to identify sites contaminated with tailings and to perform remedial action when ²²⁶Ra levels exceed current guidelines. Because of the large number of sites involved, it is imperative that sample collection be performed in a cost-effective manner. In this paper we describe the results of a study in which we compared the efficiencies of different methods of sample collection in order to determine an optimal method for estimating the mean ²²⁶Ra concentration in soil. The study involved a field experiment in which extensive sampling was performed on sites known to be contaminated with uranium tailings. The experiment was designed to identify the advantages and limitations of composite sampling, the relative merits of random and uniformly spaced sample collection, the use of field gamma measurements for supplementing and reducing soil sample collection, and practical levels of accuracy and precision that can be obtained. Conclusions regarding gamma measurements are unique to ²²⁶Ra contamination. On the other hand, conclusions concerning composite sampling and random versus uniformly spaced sampling may depend primarily on the way the contamination was spread by man and hence may not be unique to ²²⁶Ra.Research sponsored by the Department of Remedial Action Projects, Office of Terminal Waste Disposal, U. S. Department of Energy under contract DE-AC05-84OR21400 with the Martin Marietta Energy Systems, Inc.ORNL—Grand Junction Office, Grand Junction, Colorado.     

Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India

Two non-parametric kriging methods such as indicator kriging and probability kriging were compared and used to estimate the probability of concentrations of Cu, Fe, and Mn higher than a threshold value in groundwater. In indicator kriging, experimental semivariogram values were fitted well in spherical model for Fe and Mn. Exponential model was found to be best for all the metals in probability kriging and for Cu in indicator kriging. The probability maps of all the metals exhibited an increasing risk of pollution over the entire study area. Probability kriging estimator incorporates the information about order relations which the indicator kriging does not, has improved the accuracy of estimating the probability of metal concentrations in groundwater being higher than a threshold value. Evaluation of these two spatial interpolation methods through mean error (ME), mean square error (MSE), kriged reduced mean error (KRME), and kriged reduced mean square error (KRMSE) showed 3.52% better performance of probability kriging over indicator kriging. The combined result of these two kriging method indicated that on an average 26.34%, 65.36%, and 99.55% area for Cu, Fe, and Mn, respectively, are coming under the risk zone with probability of exceedance from a cutoff value is 0.6 or more. The groundwater quality map pictorially represents groundwater zones as ??desirable?? or ??undesirable?? for drinking. Thus the geostatistical approach is very much helpful for the planners and decision makers to devise policy guidelines for efficient management of the groundwater resources so as to enhance groundwater recharge and minimize the pollution level.     

Bio-monitoring for uranium using stream-side terrestrial plants and macrophytes

This study evaluated the abilities of various plant species to act as bio-monitors for environmental uranium (U) contamination. Vegetation and soil samples were collected from a U processing facility. The water-way fed from facility storm and processing effluents was the focal sample site as it represented a primary U transport mechanism. Soils and sediments from areas exposed to contamination possessed U concentrations that averaged 630 mg U kg(-1). Aquatic mosses proved to be exceptional accumulators of U with dry weight (dw) concentrations measuring as high as 12,500 mg U kg(-1) (approximately 1% of the dw mass was attributable to U). The macrophytes (Phragmites communis, Scripus fontinalis and Sagittaria latifolia) were also effective accumulators of U. In general, plant roots possessed higher concentrations of U than associated upper portions of plants. For terrestrial plants, the roots of Impatiens capensis had the highest observed levels of U accumulation (1030 mg kg(-1)), followed by the roots of Cyperus esculentus and Solidago speciosa. The concentration ratio (CR) characterized dry weight (dw) vegetative U levels relative to that in associated dw soil. The plant species that accumulated U at levels in excess of that found in the soil were: P. communis root (CR, 17.4), I. capensis root (CR, 3.1) and S. fontinalis whole plant (CR, 1.4). Seven of the highest ten CR values were found in the roots. Correlations with concentrations of other metals with U were performed, which revealed that U concentrations in the plant were strongly correlated with nickel (Ni) concentrations (correlation: 0.992; r-squared: 0.984). Uranium in plant tissue was also strongly correlated with strontium (Sr) (correlation: 0.948; r-squared: 0.899). Strontium is chemically and physically similar to calcium (Ca) and magnesium (Mg), which were also positively-correlated with U. The correlation with U and these plant nutrient minerals, including iron (Fe), suggests that active uptake mechanisms may influence plant U accumulation.     

An approach for land suitability evaluation using geostatistics, remote sensing, and geographic information system in arid and semiarid ecosystems

This study was undertaken to incorporate geostatistics, remote sensing, and geographic information system (GIS) technologies to improve the qualitative land suitability assessment in arid and semiarid ecosystems of Arsanjan plain, southern Iran. The primary data were obtained from 85 soil samples collected from tree depths (0–30, 30–60, and 60–90 cm); the secondary information was acquired from the remotely sensed data from the linear imaging self-scanner (LISS-III) receiver of the IRS-P6 satellite. Ordinary kriging and simple kriging with varying local means (SKVLM) methods were used to identify the spatial dependency of soil important parameters. It was observed that using the data collected from the spectral values of band 1 of the LISS-III receiver as the secondary variable applying the SKVLM method resulted in the lowest mean square error for mapping the pH and electrical conductivity (ECe) in the 0–30-cm depth. On the other hand, the ordinary kriging method resulted in a reliable accuracy for the other soil properties with moderate to strong spatial dependency in the study area for interpolation in the unstamped points. The parametric land suitability evaluation method was applied on the density points (150 × 150 m²) instead of applying on the limited representative profiles conventionally, which were obtained by the kriging or SKVLM methods. Overlaying the information layers of the data was used with the GIS for preparing the final land suitability evaluation. Therefore, changes in land characteristics could be identified in the same soil uniform mapping units over a very short distance. In general, this new method can easily present the squares and limitation factors of the different land suitability classes with considerable accuracy in arbitrary land indices.     

Assessment of spatial distribution of soil loss over the upper basin of Miyun reservoir in China based on RS and GIS techniques

Soil conservation planning often requires estimates of the spatial distribution of soil erosion at a catchment or regional scale. This paper applied the Revised Universal Soil Loss Equation (RUSLE) to investigate the spatial distribution of annual soil loss over the upper basin of Miyun reservoir in China. Among the soil erosion factors, which are rainfall erosivity (R), soil erodibility (K), slope length (L), slope steepness (S), vegetation cover (C), and support practice factor (P), the vegetative cover or C factor, which represents the effects of vegetation canopy and ground covers in reducing soil loss, has been one of the most difficult to estimate over broad geographic areas. In this paper, the C factor was estimated based on back propagation neural network and the results were compared with the values measured in the field. The correlation coefficient (r) obtained was 0.929. Then the C factor and the other factors were used as the input to RUSLE model. By integrating the six factor maps in geographical information system (GIS) through pixel-based computing, the spatial distribution of soil loss over the upper basin of Miyun reservoir was obtained. The results showed that the annual average soil loss for the upper basin of Miyun reservoir was 9.86 t ha(-1) ya(-1) in 2005, and the area of 46.61 km(2) (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.9% very low, 21.89% low, 6.18% moderate, 2.89% severe, and 1.84% very severe. Thus, by using RUSLE in a GIS environment, the spatial distribution of water erosion can be obtained and the regions which susceptible to water erosion and need immediate soil conservation planning and application over the upper watershed of Miyun reservoir in China can be identified.