From EU-SoilHydroGrids to HU-SoilHydroGrids: A leap forward in soil hydraulic mapping.

Spatially explicit, quantitative information on soil hydraulic properties is required in various modelling schemes. At European scale, EU-SoilHydroGrids proved its applicability in a number of studies, in ecological predictions, geological and hydrological hazard assessment, agri-environmental models, among others. Inspired by its continental antecedent, an analogous, but larger scale, national, 3D soil hydraulic database was elaborated for the territory of Hungary (HU-SoilHydroGrids) supported by various improvements (i-iv) in the computation process. Pedotransfer functions (PTFs) were built in the form of i) advanced machine learning methods and ensemble models, and trained on the ii) national soil hydrophysical dataset. The set of predictors used in PTFs was supplemented by iii) additional environmental auxiliary variables. Spatial layers of the soil hydraulic parameters were generated using iv) 100 m resolution information on primary soil properties, namely DOSoReMI.hu. HU-SoilHydroGrids provides information on the most frequently required soil hydraulic properties (water content at saturation, field capacity and wilting point, saturated hydraulic conductivity and van Genuchten parameters for the description of the moisture retention curve) with national coverage at 100 m spatial resolution down to 2 m depth for six GSM standard depth layers. The HU-SoilHydroGrids has significantly lower squared error in the case of describing the moisture retention curve and hydraulic conductivity than the EU-SoilHydroGrids. The derived 3D soil hydraulic database (ver1.0) is presently available in National Laboratory for Water Science and Water Safety for project partners in order to test its functional performance in describing hydrological and ecological processes.

Geospatial data on the sediments of Lake Balaton.

Freshwater lakes in most inhabited areas of the world are threatened by water quality issues. Standard water conservation measures have shown efficiency in the past; however, polluted lakes have only partially recovered from eutrophication. Our knowledge is still incomplete about the sensitivity of these lakes to different anthropogenic sources and to the changes in their internal processes due to global warming. In this article, we present a database comprising sediment data from Lake Balaton (Hungary), which can facilitate further analysis helping to better understand the internal processes and changes occurring in the lake. The published dataset includes the following parameters measured in 4211 lake bed sediment samples: pHKCl, calcium carbonate (CaCO3), organic carbon (Corg), total nitrogen (Ntotal), soluble phosphorus (AL-P2O5) and soluble potassium (AL-K2O), magnesium (Mg2+), zinc (Zn2+), copper (Cu2+) and manganese (Mn2+). We are confident that our database serves as a strong basis for further research relating to freshwater lakes influenced by human activities.

Applicability of Point- and Polygon-Based Vegetation Monitoring Data to Identify Soil, Hydrological and Climatic Driving Forces of Biological Invasions-A Case Study of Ailanthus altissima, Elaeagnus angustifolia and Robinia pseudoacacia.

Invasive tree species are a significant threat to native flora. They modify the environment with their allelopathic substances and inhibit the growth of native species by shading, thus reducing diversity. The most effective way to control invasive plants is to prevent their spread which requires identifying the environmental parameters promoting it. Since there are several types of invasive plant databases available, determining which database type is the most relevant for investigating the occurrence of alien plants is of great importance. In this study, we compared the efficiency and reliability of point-based (EUROSTAT Land Use and Coverage Area Frame Survey (LUCAS)) and polygon-based (National Forestry Database (NFD)) databases using geostatistical methods in ArcGIS software. We also investigated the occurrence of three invasive tree species (Ailanthus altissima, Elaeagnus angustifolia, and Robinia pseudoacacia) and their relationships with soil, hydrological, and climatic parameters such as soil organic matter content, pH, calcium carbonate content, rooting depth, water-holding capacity, distance from the nearest surface water, groundwater depth, mean annual temperature, and mean annual precipitation with generalized linear models in R-studio software. Our results show that the invasion levels of the tree species under study are generally over-represented in the LUCAS point-based vegetation maps, and the point-based database requires a dataset with a larger number of samples to be reliable. Regarding the polygon-based database, we found that the occurrence of the invasive species is generally related to the investigated soil and hydrological and climatic factors.

Spatial distribution of microplastics in the fluvial sediments of a transboundary river - A case study of the Tisza River in Central Europe.

The geographical environment fundamentally influences the transport and deposition of sediments, including microplastics. In addition, the socioeconomic differences inherent in transboundary catchments result in various waste management strategies among the different countries influencing the input of microplastics into rivers. The catchment of the Tisza River in Central Europe is shared by five countries with different economic statuses and wastewater treatment practices. The aim of this research is to study the spatial changes in microplastic debris deposition along the Tisza and its main tributaries. The mean number of microplastic particles in the sediments of the Tisza was 3177 ± 1970 items/kg, while 3808 ± 1605 items/kg were counted in the sediments of the tributaries. Most of the particles were fibres, indicating the dominance of municipal wastewater input; this is especially the case in the upstream sub-catchments, where there are low degrees of wastewater management. The highest amount of microplastics was found in the sediments of the most-upstream section, where a low number of households are connected to wastewater treatment plants. Thus, it is hypothesized that suburban areas where high population densities and improper waste management co-exist may contribute to the direct input of microplastics into river systems and the development of local microplastic contamination hotspots. In addition, a high microplastic concentration was measured at the furthest downstream section, resulting from the decreased flow velocity related to impoundment by a dam. The efficiency of the microplastic trapping of the various depositionary forms varies along the river, suggesting various influencing factors and the complexity of the process. The higher concentration of microplastics observed in the tributaries compared to that observed in sediments of the main stream may reflect the importance of local sources and the complexity of source-to-sink relations for microplastic routes through the fluvial system; these processes do not always reflect progressive downstream increases.

Geostatistical evaluation of the design of the precipitation stable isotope monitoring network for Slovenia and Hungary.

A detailed knowledge of the stable isotope signature of precipitation is the basis of investigations in a variety of scientific fields and applications. To obtain robust and reliable results, the representativity of the currently operating (at least, as of 2018) precipitation stable isotope monitoring stations across Slovenia (n = 8) and Hungary (n = 9) was evaluated on the basis of amount-weighted annual averages with the aim of revealing any redundantly (i.e. over-) represented or un(der)represented areas. In the case of the latter, optimal locations for additional sites were suggested in Slovenia and Hungary. The networks of both countries are design-based systems that need to be fine-tuned for long-term optimized operation. The evaluation of the monitoring network was performed taking into consideration the stations operating in Slovenia and Hungary, as well as closely situated ones operating in neighboring countries. The evaluation was carried out in nine different combinations, using spatial simulated annealing, with regression kriging variance as a quality measure. The results showed that (i) there are over- and un(der)represented areas in the network, an issue requiring remedial action, (ii) the mutual information exchange of the precipitation stable isotope monitoring networks of Slovenia and Hungary increases the precision of precipitation δ18O estimation by ~0.3‰ in a 15-30 km wide zone near the borders, and (iii) by an even greater degree in the neighboring countries' stations. The current research may be termed pioneering in the matter of the detailed geostatistical assessment of spatial representativity of a precipitation stable isotope monitoring network, and as such, can serve as an example for future studies aiming for the spatial optimization of other regional precipitation stable isotope monitoring networks.

Understanding the Environmental Background of an Invasive Plant Species (Asclepias syriaca) for the Future: An Application of LUCAS Field Photographs and Machine Learning Algorithm Methods.

For developing global strategies against the dramatic spread of invasive species, we need to identify the geographical, environmental, and socioeconomic factors determining the spatial distribution of invasive species. In our study, we investigated these factors influencing the occurrences of common milkweed (Asclepias syriaca L.), an invasive plant species that is of great concern to the European Union (EU). In a Hungarian study area, we used country-scale soil and climate databases, as well as an EU-scale land cover databases (CORINE) for the analyses. For the abundance data of A. syriaca, we applied the field survey photos from the Land Use and Coverage Area Frame Survey (LUCAS) Land Cover database for the European Union. With machine learning algorithm methods, we quantified the relative weight of the environmental variables on the abundance of common milkweed. According to our findings, soil texture and soil type (sandy soils) were the most important variables determining the occurrence of this species. We could exactly identify the actual land cover types and the recent land cover changes that have a significant role in the occurrence the common milkweed in Europe. We could also show the role of climatic conditions of the study area in the occurrence of this species, and we could prepare the potential distribution map of common milkweed for the study area.

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.

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.