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Thoron ( 220 Rn) is the second most abundant radon isotope in our living environment.In some dwellings it is present in significant amount which calls for its identification and remediation. Indoor thoron originates mainly from building materials. In this work we have developed and tested an experimental technique to measure thoron generation rate in building material samples using RAD7 radon-thoron detector. The mathematical model of the measurement technique provides the thoron concentration response of RAD7 as a function of the sample thickness. For experimental validation of the technique an adobe building material sample was selected for measuring the thoron concentration at nineteen different sample thicknesses. Fitting the parameters of the model to the measurement results, both the generation rate and the diffusion length of thoron was estimated. We have also determined the optimal sample thickness for estimating the thoron generation rate from a single measurement.

Radon and thoron isotopes are responsible for approximately half of the average annual effective dose to humans. Although the half-life of thoron is short, it can potentially enter indoor air from adobe walls. Adobe was a traditional construction material in the Great Hungarian Plain. Its major raw materials are the alluvial sediments of the area. Here, seasonal radon and thoron activity concentrations were measured in 53 adobe dwellings in 7 settlements by pairs of etched track detectors. The results show that the annual average radon and thoron activity concentrations are elevated in these dwellings and that the proportions with values higher than 300 Bq m(-3) are 14-17 and 29-32% for radon and thoron, respectively. The calculated radon inhalation dose is significantly higher than the world average value, exceeding 10 mSv y(-1) in 7% of the dwellings of this study. Thoron also can be a significant contributor to the inhalation dose with about 30% in the total inhalation dose. The changes of weather conditions seem to be more relevant in the variation of measurement results than the differences in the local sedimentary geology. Still, the highest values were detected on clay. Through the year, radon follows the average temperature changes and is affected by the ventilation, whereas thoron rather seems to follow the amount of precipitation.

The spatial complexity of floodplains is a function of several processes: hydrodynamics, flow direction, sediment transportation, and land use. Sediments can bind toxic elements, and as there are several pollution sources, the risk of heavy metal accumulation on the floodplains is high. We aimed to determine whether fluvial forms have a role in metal accumulations. Topsoil samples were taken from point bars and swales in the floodplain of the Tisza River, North-East Hungary. Soil properties and metal concentrations were determined, and correlation and hypothesis testing were applied. The results showed that fluvial forms are important drivers of horizontal metal patterns: there were significant differences (p < 0.05) between point bars and swales regarding Fe, K, Mg, Mn, Cr, Cu, Ni, Pb, and Zn. Vertical distribution also differed significantly by fluvial forms: swales had higher metal concentrations in all layers. General Linear Models had different results for macro and micro elements: macro element concentrations were determined by the organic matter, while for micro elements the clay content and the forms were significant explanatory variables. These findings are important for land managers and farmers because heavy metal concentration has a direct impact on living organisms, and the risk of bioaccumulation can be high on floodplains.

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Several factors influence the performance of land change simulation models. One potentially important factor is land category aggregation, which reduces the number of categories while having the potential to reduce also the size of apparent land change in the data. Our article compares how four methods to aggregate Corine Land Cover categories influence the size of land changes in various spatial extents and consequently influence the performance of 114 Cellular Automata-Markov simulation model runs. We calculated the reference change during the calibration interval, the reference change during the validation interval and the simulation change during the validation interval, along with five metrics of simulation performance, Figure of Merit and its four components: Misses, Hits, Wrong Hits and False Alarms. The Corine Standard Level 1 category aggregation reduced change more than any of the other aggregation methods. The model runs that used the Corine Standard Level 1 aggregation method tended to return lower sizes of changing areas and lower values of Misses, Hits, Wrong Hits and False Alarms, where Hits are correctly simulated changes. The behavior-based aggregation method maintained the most change while using fewer categories compared to the other aggregation methods. We recommend an aggregation method that maintains the size of the reference change during the calibration and validation intervals while reducing the number of categories, so the model uses the largest size of change while using fewer than the original number of categories.