Search Results (747)

Accurate measurement of low-level radon concentrations in the environment is increasingly important for climate research, radon priority area delineation, and atmospheric studies. Adsorbent-based radon detectors offer high sensitivity but suffer from strong temperature dependence of radon adsorption and rapid degradation under humid conditions, limiting their applicability in long-term environmental monitoring. This work presents a universal design methodology for temperature- and moisture-compensated radon detectors based on hermetically packaged adsorbents enclosed by radon-permeable polymer foils. Analytical models describing the opposing temperature dependences of radon adsorption in adsorbents and radon permeability in polymers are combined to derive a general optimization criterion that minimizes temperature-induced response variations over a defined temperature range. The method is applicable to arbitrary combinations of adsorbent materials and polymer foils, provided their radon adsorption and permeability characteristics are known. The approach is demonstrated using activated carbon fabrics and common polymers (LDPE, HDPE, and polypropylene), for which optimal design parameters are identified. In addition, water vapor permeation through polymer foils is modeled to estimate moisture protection and permissible exposure durations under high humidity. The results demonstrate that appropriately designed compensation modules can significantly reduce temperature sensitivity while extending operational stability in humid environments, enabling next-generation high-sensitivity radon detectors suitable for environmental applications. Full article

Spectral methods form a cornerstone of linear dynamics, where evolution is resolved into harmonic modes governed by eigenvalues and spectral measures of normal operators. For nonlinear dynamical systems, however, the harmonic eigenfunction paradigm typically breaks down: Koopman operators are often non-normal, may possess a continuous spectrum, and rarely admit complete eigenbases on natural observable spaces. This work develops a resolvent-centered operator-theoretic framework for generalized spectral representations of nonlinear flows through their associated Koopman $C_0$ semigroups. Rather than relying on diagonalization, we construct resolvent-generated generalized spectral operators that yield weak integral representations of the semigroup valid in non-normal and continuous-spectrum regimes. We show that, under mild polynomial resolvent growth bounds along vertical lines, these spectral distributions become finite complex Radon measures on bounded spectral regions, thereby recovering a measure-theoretic interpretation analogous to classical spectral integrals. In the normal case, the framework reduces to the standard spectral theorem. The resulting resolvent-based perspective naturally incorporates pseudospectral amplification and transient growth, providing a unified description of both asymptotic and non-modal dynamics. Full article

The Hough transform (HT) is widely used in computer vision, tomography, and neural networks. Numerous algorithms for HT computation have been proposed, making their systematic comparison essential. However, existing comparative methodologies are either non-universal and limited to certain HT formulations or task-oriented, relying on application-specific criteria that do not fully capture algorithmic properties. This paper introduces a novel unified methodology for the systematic comparison of HT algorithms. It evaluates key characteristics, including computational complexity, accuracy, and auxiliary space complexity, while explicitly accounting for the property of self-adjointness. The methodology integrates both implementation-level and theoretical considerations related to the interpretation of HT as a discrete approximation of the Radon transform. A set of mathematically justified evaluation functions, not previously described in the literature, is proposed to support our methodology. Importantly, the methodology is universal, applicable across diverse HT paradigms, encompasses pattern-based and Fourier-based fast HT (FHT) algorithms, and offers a comprehensive alternative to existing task-specific methodologies. Its application to several state-of-the-art FHT algorithms ($FHT2DT$, $FHT2SP$, $ASD2$, $KHM$, and Fast Slant Stack) yields new experimentally confirmed theoretical insights, identifies $ASD2$ as the most balanced algorithm, and provides practical guidelines for algorithm selection. In particular, the methodology reveals that for image sizes up to 3000, the maximum normalized computational complexity increases as follows: $FHT2DT$ (1.1), $ASD2$ (15.3), and $KHM$ (30.6), while the remaining algorithms exhibit at least 1.1 times higher values. The maximum orthotropic approximation error equals 0.5 for $ASD2$, $KHM$, and Fast Slant Stack; lies between 0.5 and 1.5 for $FHT2SP$; and reaches 2.1 for $FHT2DT$. In terms of worst-case normalized auxiliary space complexity, the lowest values are achieved by $FHT2DT$ (2.0), Fast Slant Stack (4.0, lower bound), and $ASD2$ (6.8), with all other algorithms requiring at least 8.2 times more memory. Full article

Radon is the most important of all sources of natural radiation, and it belongs to the main air pollutants in closed space. It is necessary to develop awareness of its harmful effects in buildings in order to take appropriate measures to reduce the risk of exposure to it. This study assesses public awareness of radon-related risks in Serbia by analyzing four areas: general public, legislative framework, professional practices, and student knowledge. Data were collected from media sources, legal documents, conferences and scientific publications, and surveys among students of University of Belgrade. Student answers have shown that they are not aware of the danger of radon in buildings: there is a gap between knowledge about radon and about its effects in the interior space. The results also show low presence of this topic in the media and in professional circles in Serbia. This paper is a contribution to the overall efforts to spread awareness in Serbia about the problem of the presence of radon in closed spaces and the health problems it can cause. This is also important in the context of the search for energy-efficient building solutions, where the passive house is emerging as the most sustainable form. It is a relatively new concept in Serbia, so information about the harmful effects of radon in indoor spaces and about the implementation of certain strategies in passive construction for protection against radon is necessary in order to protect the health of the environment and the population. Full article

Building materials have become a predominant source of indoor radon in mid- to high-rise buildings, making in situ measurement of radon exhalation rates from building surfaces essential for identifying radon sources and assessing associated risks. Based on practical survey requirements—addressing sealing leakage at chamber edges and ensuring device portability—this study developed an improved in situ measurement method integrated with leakage compensation through theoretical analysis and experimental validation. The method employs an acrylic accumulation chamber and a portable passive radon detector, adopts a 24 h continuous measurement duration, and processes radon concentration data using an exponential fitting approach. Comparative experiments with the activated carbon method demonstrated good consistency between the two methods. Furthermore, small-scale in situ measurements were conducted in the Beijing area, covering diverse building materials (concrete, brick), surface treatments (cement plaster, coating, wallpaper), and structural components (walls, floors). The results, which varied widely from 0.13 ± 0.11 to 28.00 ± 4.87 Bq/m²·h, confirm the reliability and applicability of the method for in situ determination of radon exhalation rates from interior building surfaces. Full article

Ionizing radiation has been an important tool in medical diagnosis and treatment. While the use of radiation for diagnostic purposes has been successful, clinicians are wary of the possible negative effects radiation may have on the patient. According to the linear no-threshold model, all levels of radiation are considered harmful and there is no safe threshold. However, some studies suggest there may instead be a hormetic response at lower doses typically defined as exposure below 100 mGy, and that low doses may be beneficial as a possible immunomodulatory therapeutic. Therefore, it is increasingly important to understand the effects of exposure to low doses of radiation. The lung is frequently exposed to radiation from both environmental and medical sources. The effects of low doses of radon, the most heavily studied public radiation exposure source, are still contested, as well as the potential risk from medical X-ray imaging and computed tomography exposures during diagnostic procedures. In order to appropriately evaluate the potential risks and benefits of a low-dose exposure, it is necessary to understand the mechanism(s) of action, particularly the role of DNA damage, reactive oxygen species, inflammation and immune response. Here, we review the mechanistic evidence of low-dose radiation exposure effects on the lung in the current literature and discuss the implications of these results on the validity of the LNT model as well as potential hormetic or adaptive responses. Full article

Machine learning (ML) models can complement traditional measurement-based approaches by supporting large-scale screening, spatial analysis, and prioritization of buildings for testing of indoor radon, a leading cause of lung cancer among non-smokers. Originating from uranium decay in soil and rock, radon enters homes via foundation cracks and accumulates indoors, influenced by building characteristics, ventilation, urbanization, and geogenic factors. As part of the Zagreb pilot within the “Evidence Driven Indoor Air Quality Improvement” (EDIAQI) project, this is the first ML application for indoor radon analysis in Croatia. This research evaluates residential indoor radon concentrations in Zagreb using ML applied to a dataset of 80 households. Several linear regression and tree-based ensemble methods were tested. The best-performing model (GBR) achieved an R² of 0.99 on the training set and 0.57 on the test set, with an RMSE of 33 Bq/m³ and MAE of 26 Bq/m³. Although predictive performance was moderate and generalization limited, key building characteristics such as construction year, dwelling type, occupancy details, and floor level were identified as relevant variables. The results suggest that machine learning may support radon risk prioritization in urban environments, but cannot replace direct measurements for regulatory purposes. Full article

Monitoring the hydrochemistry of groundwater and the H-O isotopes in the Jingpo Lake volcanic area, China, is fundamental to studying the mechanisms of volcanic and seismic events, as well as the associated hazards. To study the hydrogeochemistry of fluids in the Jingpo Lake volcanic area, water samples from seven sites were tested for hydrogeochemistry, H-O isotopes, and radon (Rn) content. The genesis and evolution of the groundwater system were elucidated through an integrated approach employing Gibbs diagrams, ionic ratio analyses, reservoir temperature estimation (silica–enthalpy method), and inverse geochemical modeling with PHREEQC. The results showed that the dominant water chemistry type was HCO₃⁻, primarily influenced by volcanic rock weathering and deep hydrothermal activity. Spring and well water were influenced by cation exchange, adsorption, and rock weathering dissolution. The H-O isotope composition and radon content indicate that atmospheric precipitation is the main source of supply, while well water is influenced by deep fluids. According to the Na-K-Mg triangle diagram, most of the groundwater was shallow and immature, whereas the well water was partially balanced. The temperature of the geothermal water was controlled by the geothermal gradient, depending on its occurrence and circulation depth. Additionally, the equilibrium temperature of the thermal reservoir was calculated using the silica–enthalpy equation method, with the concentrations of dissolved components in the water taken into account. The temperature of the thermal reservoir of the well water and the depth of groundwater circulation were estimated. The original reservoir temperature in the study area was calculated to range from 108 °C to 156 °C, with a geothermal water-to-shallow groundwater mixing ratio of between 71% and 85%. The estimated shallow temperature ranged from 64.9 °C to 74.9 °C. These hydrogeochemical signatures reflect active water–rock interactions and the contribution of deep-seated geothermal fluids, providing robust evidence for ongoing geothermal activity in the Jingpo Lake volcanic system. The findings enhance our understanding of the recent geological evolution and present-day hydrothermal processes of this potentially active volcanic field, which establishes a crucial hydrogeochemical baseline for future monitoring and hazard assessment studies. Full article

This study compares a set of 43 AlphaSensor units produced by RadonTec GmbH, Wittislingen, Germany against the AlphaGUARD 1000PF (Bertin Technologies, Montigny-le-Bretonneux, France), which is used as a reference monitor. During this study, around 16 k integrated measurements were conducted. The concentration range varied between 10 and 20 k Bq/m³. Multiple key performance indicators, such as sensitivity, uncertainty, background, linearity, and temporal response, were evaluated using a variety of statistical approaches. The results confirm the manufacturer’s claim of 10% or lower uncertainty in comparison with AlphaGUARD. We tentatively suggest individual calibration factors and methodologies. Our conclusion is that the AlphaSensor and commercial devices based on it, such as AlphaTracer, are affordable and applicable for home use. With modest additional hardware, AlphaSensors are also a good option for scientific studies involving the deployment of large monitoring networks. Full article

We develop a geometric approach to financial risk based on Crofton’s idea and the tools of the Radon transform. The trajectory of a financial instrument is defined with respect to a frame of reference (money, benchmark). A central role is played by simple instruments, inspired by the annual percentage rate (APR) concept, whose graphs in a fixed reference frame are line segments. Risk is interpreted transactionally as the density of exchange dilemmas that arise when the instrument’s trajectory intersects the trajectories of simple instruments. This perspective leads to a risk measure given by the trajectory length in the Crofton–Steinhaus sense. We also introduce new notions, such as geometric volatility, transactional entropy, and trajectory temperature, associated with the distribution of the number of intersections, enabling thermodynamic analogies to be incorporated into the description of risk and market complexity. Full article

As pore space serves as the primary migration pathway of radon in rock media, investigating the influences of pore structural characteristics on radon migration is essential. In this study, the rock pore structure was numerically reconstructed via the Quartet Structure Generation Set (QSGS) method, based on the characteristic parameters extracted from real rock pore models obtained from CT scanning. Quantitative comparison results indicate that the permeability and radon diffusion coefficient of the QSGS-reconstructed models are highly consistent with those of the CT-based model, which verifies the reliability and effectiveness of the QSGS method. A series of three-dimensional (3D) rock pore models with different porosities (η), distribution probabilities (Pd), and growth probabilities (G) were constructed using the QSGS method. The radon diffusion coefficient, tortuosity factor and permeability of these models under dry conditions were quantitatively determined. The relationship between the radon diffusion coefficient, water saturation and temperature was obtained using the tortuosity factor of the pore models and the unsaturated non-isothermal radon diffusion coefficient model. Furthermore, the relationship between the relative permeability of the air and water phases and water saturation was obtained by coupling the calculated permeability with the Brooks–Corey model. The results demonstrate that the η was positively correlated with both the radon diffusion coefficient and permeability, with a more pronounced positive correlation observed for permeability. Under low η conditions, Pd was positively correlated with both the radon diffusion coefficient and permeability; under medium-porosity conditions, Pd was positively correlated with the radon diffusion coefficient but negatively correlated with permeability; under high-porosity conditions, Pd exhibited no significant correlation with the radon diffusion coefficient, while it shows a negative correlation with permeability. G in the principal direction was positively correlated with the radon diffusion coefficient and permeability along the same direction, but negatively correlated with those along orthogonal directions. The radon diffusion coefficient was strongly negatively correlated with water saturation, and weakly positively correlated with temperature. With an increase in water saturation, the relative air permeability presented a nonlinear decrease characterized by a fast-then-slow trend, whereas the relative water permeability showed a nonlinear increase with a slow-then-fast pattern. Full article

Radon (Rn-222) is a major indoor air pollutant with significant health risks. This work presents RadonFAN, a low-cost IoT system deployed in two galleries at the Institute of Physical and Information Technologies (ITEFI-CSIC, Madrid), integrating distributed sensors, microcontrollers, cloud analytics, and automated fan control to maintain radon concentrations below recommended limits. Initially, ventilation relied on a reactive, rule-based mechanism triggered when thresholds were exceeded. To improve preventive control, two end-to-end deep learning models based on regression-to-classification (R2C) and direct classification (DC) are developed. A quantitative analysis of predictive performance and computational efficiency is reported. While the R2C model is hindered by the inherent behavior of the time series, the DC model achieves high classification performance (recall > 0.975) with low computational cost (<4 million parameters, 7 million FLOPs). Modifications to the DC model are studied to identify potential performance bottlenecks and the most relevant components, showing that most limitations arise from feature richness and time series behavior. When evaluated against the existing rule-based ventilation system, the DC model reduces both unsafe radon exposure events and energy consumption, demonstrating its effectiveness for preventive radon mitigation. Full article

This work presents the results of an integrated monitoring of soil radon gas and seismic activity at Mt. Etna from August 2023 to May 2025, aimed at enhancing comprehension of magma migration and eruption dynamics. Radon data were collected using a permanent station with an alpha particle probe, aggregated hourly. The INGV-OE network monitored seismic activity at 100 Hz; volcanic tremor was analyzed using Root-Mean-Square (RMS) values from the Serra La Nave station. Earthquakes were located using the Hypoellipse algorithm and a 1D crustal velocity model. A robust correlation was found between radon and RMS anomalies, with the former preceding the latter with increasing probability over time (e.g., 30.1% within 1 day, 46.4% within 3 days). Correlations were also found between radon anomalies and Strombolian activity at the summit craters (e.g., 23.8% within 1 day for the Central Crater), suggesting a potential predictive role for radon. Conversely, correlations with paroxysmal events were weaker in the short term but increased over longer time windows. No clear correlation was found between radon anomalies and seismic strain release, likely due to differing temporal resolutions. These results support the idea that radon plays a role as a short-term precursor in volcanic unrest. Full article

This study is carried out to investigate the radiological characteristics and mineralogical controls of natural radioisotopes (²³⁸U, ²²⁶Ra, ²³²Th, and ⁴⁰K) in granitic pegmatites from Abu Zawal Area (AZA) in the Eastern Desert of Egypt. The analyzed pegmatites, containing thorite, zircon, monazite, ferrocolumbite, and fergusonite, exhibit exceptionally high radioactivity concentrations of ²³⁸U ≤ 568; ²³²Th ≤ 674; ²²⁶Ra ≤ 170 (Bq kg⁻¹), significantly exceeding the world average permissible limits (35, 30, 30, and 400 Bq kg⁻¹ for ²³⁸U, ²²⁶Ra, ²³²Th, and ⁴⁰K, respectively). Comprehensive radiological assessment reveals severely elevated radiological impact associated with Ra_eq ≤ 1243 (Bq kg⁻¹) and hazard indices (H_ex≤ 3.36; ELCR ≤ 12.2 × 10⁻³) surpassing international safety thresholds (H_ex ≤ 1; ELCR ≤ 1 × 10⁻³). The observed disequilibrium between ²³⁸U and ²²⁶Ra (with ²²⁶Ra activities approximately half those of ²³⁸U) is attributed to the geochemical mobility of radium and potential selective leaching during late-stage hydrothermal alteration, while the overall enrichment of the uranium series over the thorium series is linked to the predominance of uranium-bearing minerals like zircon and fergusonite in these pegmatites. Mineralogical analysis demonstrates distinct radiation patterns: thorite and monazite dominate Th-derived gamma radiation and radon/thoron exhalation, while zircon and fergusonite control U enrichment and decay chain disequilibrium. Notably, nominally low-activity minerals like ferrocolumbite contribute to localized radiation hotspots through U/Th co-concentrations. The calculated absorbed dose rates ranged from 182 to 978 (nGy h⁻¹) and annual effective doses show extreme spatial variability correlated with Th-rich mineral assemblages. Full article

Long-term monitoring of radon (²²²Rn) and thoron (²²⁰Rn) radioactive gases has been used in earthquake forecasting. Seismic activity before earthquakes raises the levels of these gases, causing abnormalities in the baseline values of radon and thoron time series (RTTS) data. This study reports applications of kernel density estimation (KDE) and wavelet-based density estimation (WBDE) to detect anomalies in radon, thoron, and meteorological time-series data. Anomalies appearing in the RTTS data have been assessed for their potential correlation with seismic events. Using KDE and WBDE, radon anomalies were observed on 12 March, 15 August, 17 September, in the year 2017, and 19 January 2018. Thoron anomalies were recorded on 12 March, 15 August, 17 September 2017, and 28 February 2018. Irregularities in RTTS were observed several days before seismic events. Anomalies in RTTS, detected using KDE, successfully correlated five out of nine seismic events while WBDE identified four anomalies in RTTS which were successfully correlated with the corresponding seismic events. The wavelet transform has been used to reduce noise at higher decomposition levels in radon and thoron time series. Findings of the study reveal the potential of radon and thoron time series that can be used as precursors for earthquake forecasting. Full article