Earth and Environmental Sciences

The emission of particulate matter from agricultural sources is a worldwide environmental issue due to health concerns.

The main factors influencing PM₁₀ emission from crop production are the origin of particles, the physical and chemical properties of soils, meteorological conditions, and the mechanical impacts of farm operations. Several studies have been made to determine PM₁₀ emission factors for tillage operations, but these emission factors varied depending on soil properties, especially soil texture and water content, and environmental conditions (e.g. relative humidity, and variability in wind speed and direction). This is why the use of a single emission factor for a given tillage operation is inadequate.

To estimate the yearly amount of PM₁₀ emitted from agricultural soils and crop production, emissions originating from different sources at different temporal division must be summarized. Because 56 % of the total territory of Hungary is cropland, relatively high PM₁₀ emission occurs from crop production and agricultural soils. If this is to be reduced, research should focus on the identification of soil and environmental properties related to PM₁₀ emission on characteristic Hungarian soils.

Climate change is expected to have a vigorous impact on soils and ecosystems due to elevated temperature and changes in precipitation (amount and frequency), thereby altering biogeochemical and hydrological cycles. Several phenomena associated with climate change and anthropogenic activity affect soils indirectly via ecosystem functioning (such as higher atmospheric CO₂ concentration and N deposition). Continuous interactions between climate and soils determine the transformation and transport processes. Long-term gradual changes in abiotic environmental factors alter naturally occurring soil forming processes by modifying the soil water regime, mineral composition evolution, and the rate of organic matter formation and degradation. The resulting physical and chemical soil properties play a fundamental role in the productivity and environmental quality of cultivated land, so it is crucial to evaluate the potential outcomes of climate change and soil interactions. This paper attempts to review the underlying long-term processes influenced by different aspects of climate change. When considering major soil forming factors (climate, parent material, living organisms, topography), especially climate, we put special attention to soil physical properties (soil structure and texture, and consequential changes in soil hydrothermal regime), soil chemical properties (e.g. cation exchange capacity, soil organic matter content as influenced by changes in environmental conditions) and soil degradation as a result of longterm soil physicochemical transformations. The temperate region, specifically the Carpathian Basin as a heterogeneous territory consisting of different climatic and soil zones from continental to mountainous, is used as an example to present potential changes and to assess the effect of climate change on soils. The altered physicochemical and biological properties of soils require accentuated scientific attention, particularly with respect to significant feedback processes to climate and soil services such as food security.

The traditional Hungarian method for determining soil phosphorus (P) status is ammonium-lactate acetic acid (AL) extraction. AL is an acidic solution (buffered at pH 3.75), which is also able to dissolve P reserves, so there is a need for extraction methods that also characterize the mobile P pool.

0.01 M CaCl₂-P is considered to directly describe available P forms, because the dilute salt solution has more or less the same ionic strength as the average salt concentration in many soil solutions.

The amount of AL-P may be two orders of magnitude greater than that of CaCl₂-P. Previous studies suggested that the relationship between AL-P and CaCl₂-P was influenced by soil parameters. Regression analysis between AL-P and CaCl₂-P showed medium or strong correlations when using soils with homogeneous soil properties, while there was a weak correlation between them for soils with heterogeneous properties.

The objective of this study was to increase the accuracy of the conversion between AL-P and CaCl₂-P, by constructing universal equations that also take soil properties into consideration.

The AL-P and CaCl₂-P contents were measured in arable soils (n=622) originating from the Hungarian Soil Information and Monitoring System (SIMS). These soils covered a wide range of soil properties.

A weak correlation was found between AL-P and CaCl₂-P in SIMS soils. The amounts and ratio of AL-P and CaCl₂-P depended on soil properties such as CaCO₃ content and texture. The ratio of AL-P to CaCl₂-P changed from 37 in noncalcareous soils to 141 on highly calcareous soils. CaCl₂-P decreased as a function of K_A (plasticity index according to Arany), which is related to the clay content, while the highest AL-P content was found on loam soils, probably due to the fact that a high proportion of them were calcareous.

The relationships between AL-P, CaCl₂-P and soil properties in the SIMS dataset were evaluated using multiple linear regression analysis. In order to select the best model the Akaike Information Criterion (AIC) was used to compare different models. The soil factors included in the models were pHKCl, humus and CaCO₃ content to describe AL-P, and K_A, CaCO₃ content and pH_KCl to describe CaCl₂-P. AL-P was directly proportional to pH_KCl, humus and CaCO₃ content, while CaCl₂-P was inversely proportional to K_A, CaCO₃ content and pH_KCl. The explanatory power of the models increased when soil properties were included. The percentage of the explained variance in the AL-P and CaCl₂-P regression models was 56 and 51%, so the accuracy of the conversion between the two extraction methods was still not satisfactory and it does not seem to be possible to prepare a universally applicable equation. Further research is needed to obtain different regression equations for soils with different soil properties, and CaCl₂-P should also be calibrated in long-term P fertilization trials.

One option for adaptation to climate change is to grow a wider variety of plant species. Sorghum (Sorghum bicolor (L.) Moench) is known to tolerate unfavourable environmental conditions, so it may be feasible to grow it on areas with extreme conditions to replace other species such as maize. Nowadays, spatial decision supporting systems primarily support the crop production process rather than crop structure adjustment. In this study, potential sorghum production sites in the Great Hungarian Plain were selected based on soil characteristics including genetic soil type, parent material, physical soil type, clay composition, water management, pH, organic matter content, topsoil thickness and fertility, as well as climatic data, particularly precipitation. For all the parameters the aim was to find the extreme values at which sorghum, which is less sensitive than maize, may still give an acceptable yield. By combining map layers of soil characteristics, it could be concluded that although the soil is suitable for sorghum on 40.46% of the Great Hungarian Plain, maize is generally a better choice economically. On the other hand, the soil conditions on 0.65% of the land are still suitable for sorghum but unfavourable for maize. As regards the precipitation demand of sorghum, May is the critical period; on 698,968 ha the precipitation required for germination was only recorded once in the period 1991-2010, so these areas cannot be considererd for sorghum. As a consequence, in an alternative crop rotation system sorghum could be competitive with maize, but both the soil and climate conditions and the demands of the crop need to be assessed. The lack of precipitation in critical phenophases significantly decreases the area where maize can survive. Sorghum, however, may produce an acceptable yield, as it is a drought-resistant species.

Assessing the uncertainty in reservoir performance is a necessary step during the exploration phase. To examine the uncertainty in flow response, a large set of realizations must be processed. There are several stochastic geostatistical algorithms capable of simulating multiple equiprobable realizations. Although these can show us the possible realities highlighting the spatial uncertainty, their handling is time- and CPU-consuming during the later processes, such as flow simulations. Consequently, only a small number of realizations can be post-processed in industrial practice. The purpose of this work is to develop a method, which will reduce the huge number of realizations in a way that the remaining ones retain the spatial uncertainty of a reservoir’s flow behavior, as would be demonstrated by a larger set of realizations. To solve this problem, ranking methods can be applied. Traditional ranking techniques, such as probability selection, are highly dependent on the applied static properties. In this paper, an alternative selection method is parameterized for measuring the pairwise dissimilarity between geostatistical models, with a distance function based on the hydrodynamic properties of the hydrocarbon reservoirs. The effectivity of the method is highly dependent upon the selected criteria. Thus, the distance function refers to the flow responses and allows visualizing the space of uncertainty through multidimensional scaling. A kernel transformation of the MDS data set is required to obtain a feature space where the K-means algorithm can discover non-linear structures in the basic data set. The final step of the method is the selection of the Earth models closest to the cluster centers. This tool allows for the selection of a subset of representative realizations, containing similar properties to the larger set.

The present article discusses the applicability of thermoanalytical methods in the analysis of Hungarian soils formed on carbonate rocks. Up to now only limited mineralogical and soil chemical research has been done on these soils. Soils from the Bükk Mountains, the most varied limestone region in Hungary, were used for the investigations. The aim was to extend our incomplete knowledge on the mineral composition and formation processes of these soils and to demonstrate the possibilities and evaluation potential of thermoanalytical techniques. All the soils investigated were formed on limestone and had different surface soil thickness, influenced by the accumulation of silicate debris and the microterrain. The results of soil mineralogical analysis revealed an extraordinarily high proportion of quartz compared to that of other minerals (especially calcite), indicating that these soils could not have originated solely from the weathering of the limestone bedrock. The results also showed that thermoanalytical methods could complement classical chemical and instrumental (XRPD) methods in research on the genesis of soils formed on limestone.

In this study, the joint shear strength of low-strength Hungarian sandstones of different grain size and surface roughness was investigated. The direct shear tests along discontinuities were performed under constant normal load. Previously, the direct shear test basic rock mechanic parameters of the investigated intact rocks were determined, such as the UCS value. The goal of the investigation is to determine the effect of the surface properties, such as surface roughness, grain size, and surface quality, on the joint shear strength of Hungarian sandstones. The failure curves derived from the experimental results of direct shear tests under laboratory conditions, and the empirical results according to Barton and Choubey (1977) were compared.

Depth of reservoirs of Hungarian oil fields and related oil density data were collected from the database of the Hungarian Mineral Resource Inventory. The purpose of the investigation was to point out the correlation between oil density and reservoir depth in some of the Hungarian hydrocarbon productive regions. Oil density related to reservoir depth in a particular area is generally linked to the migration mechanism. Zala Basin trends show a different migration process regionally and locally; tertiary migration by overflow mechanism can be supposed for the latter case. In the case of the Szeged–Kiskunság region, locally and regionally, migration along carrier beds through semipermeable sediments is present, with faults playing a significant role. In the Nagykunság region, the migration processes are similar to those in Zala, but the presence of faults seems more important. At depths below 2,000 m, the Bihar region trends are similar to those of the Szeged–Kiskunság region. In the shallower zone, hydrodynamic effects are recognizable. In two studied regions, the Battonya–Pusztaföldvár High and the Hungarian Paleogene Basin, the density of crude oil data does not show any significant variability and trend. Biodegradation and water washing were recognizable in the depth sections shallower than 2,000 m below surface. In karstic reservoirs of the Zala Basin (Nagylengyel, Sávoly), alteration is presumed at greater depths due to the karst water flow. The presented results show several trends of oil migration in the explored areas, which can be used for future estimation of the hydrocarbon potential in the Hungarian part of the Pannonian Basin.

The aim of our research was to better understand the spectral characteristics of precipitation variability, because through infiltration, this is the most important source of groundwater recharge. To better understand the periodicity of the rainfalls, we used monthly and annual rainfall data. We examined precipitation time records over a 110-year period from two different cities in the Carpathian Basin, obtained from the Hungarian Meteorological Service. With discrete Fourier-transformation (DFT) and wavelet time series analysis, we defined local cycles and developed a forecast for the Debrecen area.

Using DFT, we calculated the time-period distributions (spectra) of monthly and annual rainfall data. Spectra from the annual rainfall data showed 16 dominant periods in Debrecen and 17 in Pécs. At the two stations, the most dominant cycles were 3.6 and 5 years, respectively; there were several other cycles locally present in the data sets. From the monthly data sets, several other periodic components were calculated locally and countrywide as well.

Using wavelet analysis, the time dependence of the cycles was determined in the 110-year data set for two Hungarian cities, Debrecen and Pécs.

This paper deals with a question: how many stochastic realizations of sequential Gaussian and indicator simulations should be generated to obtain a fairly stable description of the studied spatial process? The grids of E-type estimations and conditional variances were calculated from pooled sets of 100 realizations (the cardinality of the subsets increases by one in the consecutive steps). At each pooling step, a grid average was derived from the corresponding E-type grid, and the variance (calculated for all the simulated values of the pooling set) was decomposed into within-group variance (WGV) and between-group variance (BGV). The former was used as a measurement of numerical uncertainty at grid points, while the between-group variance was regarded as a tool to characterize the geologic heterogeneity between grid nodes. By plotting these three values (grid average, WGV, and BGV) against the number of pooling steps, three equidistant series could be defined. The ergodic fluctuations of the stochastic realizations may result in some “outliers” in these series. From a particular lag, beyond which no “outlier” occurs, the series can be regarded as being fully controlled by a background statistical process. The number of pooled realizations belonging to this step/lag can be regarded as the sufficient number of realizations to generate. In this paper, autoregressive integrated moving average processes were used to describe the statistical process control. The paper also studies how the sufficient number of realizations depends on grid resolutions. The method is illustrated on a computed tomography slice of a sandstone core sample.