Upscaling ground-based backpack gamma-ray spectrometry to spatial resolution of UAV-based gamma-ray spectrometry for system validation.

Advances in the development of gamma-ray spectrometers have resulted in devices that are ideal for use in conjunction with the increasingly reliable systems of autonomously flying uncrewed aerial vehicles (UAVs) that have recently become available on the market. Airborne gamma-ray spectrometry (GRS) measurements have many different applications. Here, the technique is applied to a former uranium mining and processing site, which is characterized by relatively low specific activities and, hence, low count rates, requiring relatively large detectors and correspondingly big size UAVs. The future acceptance of the use of such UAV-based GRS systems for radionuclide mapping depends on their ability to measure absolute specific activities of natural radionuclides such as U-238 in near-surface soil that are consistent with the results of established and proven ground-based systems. To determine absolute specific activities on the ground, the gamma radiation data from airborne detectors must be corrected for attenuation caused by the flight altitude above ground. In recent years, mathematical procedures for altitude correction have been developed, that are specifically tailored to the working range of several tens of meters typical for UAVs. However, very limited experimental validation of these theoretical approaches is available. A very large dataset consisting of about 3000 UAV-based and 19,000 backpack-based measurements was collected at a low-grade uranium ore dump in Yangiabad, Uzbekistan. We applied different geostatistical interpolation methods to compare the data from both survey techniques by upscaling backpack data to airborne data. Compared to backpack systems, UAV-based systems have lower spatial resolution, so measurements average over larger areal units (or in geostatistical terminology: "spatial support"). Taking into account the change in spatial support, we illustrate that (1) the UAV-based measurements show good agreement with the upscaled backpack measurements and that (2) UAV surveys provide good delineation of contrasts of the relatively smooth U-238 specific activity distribution typical for former uranium mining and processing sites. We are able to show that the resolution of UAV-based systems is sufficient to map extended uranium waste facilities.

Approximation of van Genuchten Parameter Ranges from Hydraulic Conductivity Data.

The use of retention function and relative conductivity function is essential for the calculation of flow in a variably saturated media using the Richards equation. A widely used mathematical model for this is the Mualem-van Genuchten model which requires the shape parameters α $$ \alpha $$ and n $$ n $$ . These, however, are difficult to obtain. When data is scarce, α $$ \alpha $$ and n $$ n $$ are often taken from literature and may deviate largely from actual values. The current study presents a novel mathematical model for the approximation of α $$ \alpha $$ and n $$ n $$ and for the further estimation of realistic value ranges, which may be used as parameter space, for example, for the calibration of a numerical model. The model was developed for cases where data is scarce and only values of saturated hydraulic conductivity are available. It is based on a large data set from literature and it is demonstrated that the model estimates mean values from that data set with a good accuracy. In order to show the applicability of the model, a second data set has been compiled anew (provided as Supporting Information). The model is incorporated into the current version of the freeware computer program HYPAGS, which enables easy usage.

Capability and limitations of first-order and diffusion approaches to describe long-term sorption of chlortoluron in soil.

This paper compares the capability of a first-order and a spherical diffusion model to describe and predict long-term sorption and desorption processes of chlortoluron in two soils. Chlortoluron sorption was investigated at different time scales utilizing one rate experiment (120 days) and two sorption/desorption experiments. Experimental periods for sorption and desorption were set to 1 day (five desorption steps) and 30 days (three desorption steps), respectively. Upon fitting, the two models satisfactorily described the whole set of data. The spherical diffusion model performed better than the first-order model. We then tested the predictive capability of the models by predicting 30-day sorption/desorption data using kinetic parameters fitted on 1-day sorption/desorption data only. While the spherical diffusion model was able to predict the 30-day data set, the first-order model failed completely. Fitting both models to subsets of the data corresponding to different experimental time scales revealed that the rate parameter as well as the Freundlich coefficient of the first-order model are strongly time-dependent--a property that is not shared by parameters of the spherical diffusion model. The apparent stability of the spherical diffusion model with regard to time dependency of its parameters indicates that sorptive uptake may be diffusion-controlled. This also explains the models greater predictive power across different time scales compared to the first-order model. Finally, we investigate the suitability of solute class specific log-linear relationships between the first-order rate parameter and the Freundlich coefficient presented by earlier researchers in the light of the time dependency observed for the parameters of the first-order model.