Gábor Szatmári

Maps of heavy metals in the soils of the European Union and proposed priority areas for detailed assessment

Soil contamination is one of the greatest concerns among the threats to soil resources in Europe and globally. Despite of its importance there was only very course scale (1/5000km(2)) data available on soil heavy metal concentrations prior to the LUCAS topsoil survey, which had a sampling density of 200km(2). Based on the results of the LUCAS sampling and auxiliary information detailed and up-to-date maps of heavy metals (As, Cd, Cr, Cu, Hg, Pb, Zn, Sb, Co and Ni) in the topsoil of the European Union were produced. Using the maps of heavy metal concentration in topsoil we made a spatial prediction of areas where local assessment is suggested to monitor and eventually control the potential threat from heavy metals. Most of the examined elements remain under the corresponding threshold values in the majority of the land of the EU. However, one or more of the elements exceed the applied threshold concentration on 1.2Mkm(2), which is 28.3% of the total surface area of the EU. While natural backgrounds might be the reason for high concentrations on large proportion of the affected soils, historical and recent industrial and mining areas show elevated concentrations (predominantly of As, Cd, Pb and Hg) too, indicating the magnitude of anthropogenic effect on soil quality in Europe.

Mapping geogenic radon potential by regression kriging.

Radon ((222)Rn) gas is produced in the radioactive decay chain of uranium ((238)U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly.

Mapping of topsoil texture in Hungary using classification trees

ABSTRACT Spatial information about physical soil properties is in great demand, being basic input data in numerous applications. Soil texture can be characterized by different approaches, such as particle size distribution, plasticity index or soil texture classification. In accordance with the increasing demands for spatial soil texture information, our aim was to compile a topsoil texture class map for Hungary with an appropriate spatial resolution, using the United States Department of Agriculture soil texture classes. The ‘Classification and Regression Trees’ method was applied because it is widely used in Digital Soil Mapping, and has numerous advantages. Primary soil data were provided by the Hungarian Soil Information and Monitoring System. A digital elevation model and its derived components, geological and land cover map, and appropriate remotely sensed products together with the soil map featuring overall physical properties provided by the Digital Kreybig Soil Information System were used as auxiliary environmental co-variables. The resulting map can be used as direct input data in meteorological and hydrological modelling as well as in spatial planning.

Variations for the Implementation of SCORPAN’s “S”

Development of DSM can be notably attributed to frequent limitations in the availability of proper soil information; consequently, it has been typically used in cases featured by limited soil data. Since SCORPAN equation includes other or previously measured properties of soil, the usage of legacy soil data supports the applicability of DSM and improves the accuracy of DSM products as well. Nevertheless, the occurrent abundance of available soil information poses new demands on and at the same time opens new possibilities in the application of DSM methods. A great amount of soil information has been collected in Hungary in the frame of subsequent surveys and assessments. The majority of these legacy soil data were integrated in various spatial soil information systems. Our paper presents three approaches for the application of Hungary’s most extended legacy soil data source in goal-oriented digital soil mapping.

Compilation of Functional Soil Maps for the Support of Spatial Planning and Land Management in Hungary

The tasks of national spatial planning (i.e., delineation of areas with natural constraints or areas with excellent productivity; support of irrigation strategies; flood, drought, and climate change impact risk assessment) increasingly demand advanced or new kinds of spatial soil information, which cannot be fully satisfied by legacy soil maps or formerly elaborated databases. Due to the lack of recent, extended, nationwide mapping, the data of previous surveys should be exploited thoroughly. Digital soil mapping integrates geographic information systems (GIS), geostatistical, and data mining tools and makes possible the elaboration of target-specific soil maps with improved and/or specific thematic, spatial, and temporal accuracy as opposed to former, more general soil maps. For the satisfaction of the recent demands, soil conditions of Hungary have been digitally mapped based on various available recent and legacy soil datasets, and spatially exhaustive, environmental, and auxiliary information. The produced digital soil property maps have been miscellaneously utilized in various regional planning activities.

Remarks to the debate on mapping heavy metals in soil and soil monitoring in the European Union

We provide an overview of the main features of the LUCAS topsoil survey of the EU in comparison to the GEMAS survey. In addition we describe the policy requirements and scientific principles of soil monitoring programs.

Multivariate Sampling Design Optimization for Digital Soil Mapping

In this study, we have extended the spatial simulated annealing (SSA) methodology to be able to simultaneously optimize a completely new sampling design for more than one pedological variable using regression kriging prediction-error variance (RKV) as optimization criterion. For this purpose, the following soil properties were chosen: soil organic matter content, rooting depth, calcium carbonate content, and plasticity index according to Arany. The number of new observations was set to 100. The methodology is illustrated with a legacy soil dataset and auxiliary information from a study site in Central Hungary. The combined structure of the regression models and the variogram of the dominant soil parameter were applied in the optimization process provided by SSA to calculate the quality measure (i.e., spatially averaged RKV). The resulted sampling design was evaluated by various statistical and point pattern analysis tools. The Kolmogorov–Smirnov test’s results and the observed empty space function showed that the optimized sampling configuration represents properly both the feature and geographic space. Furthermore, the empty space function pointed out that there is an inhibition between the sampling points, which caused a “quasi”-regular point pattern. The extended SSA methodology is suitable to optimize the sampling design for more than one soil variable.

Spatial distribution of selected soil features in Hajdú-Bihar county represented by digital soil maps

With the ongoing DOSoReMI.hu project we aimed to significantly extend the potential, how soil information requirements could be satisfied in Hungary. We started to compile digital soil maps, which fulfil optimally general as well as specific national and international demands from the aspect of thematic, spatial and temporal accuracy. In addition to relevant and available auxiliary, spatial data themes related to soil forming factors and/or to indicative environmental elements we heavily lean on the various national soil databases. The set of the applied digital soil mapping techniques is gradually broadened. In our paper we present some results in the form of brand new soil maps focusing on the territory of Hajdú-Bihar county.

Testing a sequential stochastic simulation method based on regression kriging in a catchment area in Southern Hungary

Modelling spatial variability and uncertainty is a highly challenging subject in soil- and geosciences. Regression kriging (RK) has several advantages; nevertheless it is not able to model the spatial uncertainty of the target variable. The main aim of this study is to present and test a sequential stochastic simulation approach based on regression kriging (SSSRK), which can be used to generate alternative and equally probable realizations in order to model and assess the spatial variability and uncertainty of the target variable, meanwhile the advantages of the RK technique are retained. The SSSRK method was tested in a catchment area, in Southern Hungary for the modelling of spatial variability and uncertainty of soil organic matter (SOM) content. In the first step, the auxiliary information was derived according to the soil forming factors; then the RK system was built up, which provides the base of SSSRK. 100 realizations were generated, which reproduced the model statistics and honored the input dataset. These realizations provide 100 simulated values for each grid node, which number is appropriate to calculate the cumulative distributions. Using these distributions the following maps were derived: map of expected values, the corresponding 95% confidence interval’s width, furthermore the probability of the event of {SOM < 1.5%}, since this threshold value is highly informative in soil protection and management planning. The resulted maps showed that, SSSRK is a valuable technique to model and assess the spatial variability and uncertainty of the target variable. Furthermore, the comparison of RK and SSSRK showed that, the SSSRK’s E-type estimation and the RK estimation gave almost the same results due to the fairly high R 2 value of the regression model (R 2 =0.809), which decreased the smoothing effect.

Geostatisztika a talajtérképezésben – Szemle –

Az 1980-as evek elejetől kezdődően a geostatisztikai modszerek es a regio-nalizalt valtozok elmelete egyre szelesebb korben kerult felhasznalasra a talajterke-pezesben, illetve a tagabb ertelemben vett talajtani kutatasokban. Ez annak tulajdo-nithato, hogy a talaj idealis medium a geostatisztika megkozeliteseire, mely a talaj-tanos szakemberek reszeről szamos elmeleti, illetve gyakorlati fejlesztest eredme-nyezett, mint peldaul a nem normal eloszlasok kezelhetősege, a nem stacionarius valoszinűsegi fuggvenyek, illetve a lokalis es terbeli bizonytalansag ertekelese. A talajterkepezes szemlelete es gyakorlata drasztikusan megvaltozott a geostatisz-tikanak koszonhetően, hisz szamos talajtulajdonsag terbeli valtozekonysaga mutat folytonossagot a terben (es időben egyarant), mely a geostatisztika megkozelitesei-vel sikeresen modellezhető. Napjaink digitalis talajterkepezese nagymertekben tamaszkodik a geostatisztika nyujtotta lehetősegekre, melyre szamos hazai es nem-zetkozi peldat talalunk. Dolgozatunk legfőb...