Search Results (58)

Measuring indoor radon concentrations is essential for ensuring good air quality in buildings and protecting public health, but significant regulatory and methodological fragmentation still exists at the international level. This study analysed scientific articles published in the last five years, aiming to critically map the technical choices adopted in measuring radon in different indoor environments. The results show that regulatory fragmentation continues to generate inconsistent practices with regard to measurement protocols, sampling durations, devices used, and normative references used to interpret the results. In many cases, the protocols cannot be readily classified according to major technical standards as specific interpretation criteria are required, such as the sampling frequency and the overall duration of the strategy. These results highlight the importance of standardising measurement methods in order to improve the accuracy of exposure assessments and enable comparisons between studies. Full article

In recent years, China has experienced a notable increase in indoor radon concentrations. However, our understanding of residential radon exposure in Central China remains limited and primarily depends on the data collected from residential buildings in Wuhan before 2003. Given this context, the current radon exposure levels in Central China must be assessed immediately, and the factors influencing them be investigated. To address this gap, our study focused on five representative areas in Central China. We monitored indoor radon concentrations in residential areas using random cluster sampling while considering various building structures. The radon levels were measured through the alpha track method, and RSKS standard detectors were deployed in two separate batches to participating households. A total of 1300 detectors were distributed across 579 households, with a recovery rate of 97.15% (1263 detectors were retrieved). The annual average indoor radon concentration in Central China ranged widely from 6.25 Bq/m³ to 310.44 Bq/m³, with an arithmetic mean of 50.20 Bq/m³, which resulted in an average annual effective dose of 2.08 mSv. Referring to World Health Organization standards, the radon concentration in approximately 8.24% of the monitored rooms exceeded the recommended action level. Our analysis indicated that radon concentration is primarily influenced by factors, such as the time of measurement, geographical location, building structure, floor materials, household fuel, and ventilation practices. Multiple regression analysis revealed that these factors collectively account for 10.80% of the variation in radon concentration. Notably, geographical location, building structure, and ventilation mode emerged as important factors. Based on these findings, our study suggests several practical measures to effectively reduce indoor radon levels, including improving ventilation, switching to cleaner fuels, and using environmentally friendly building and decoration materials. Full article

Background: Radon, a naturally occurring radioactive gas, is increasingly recognized as a major risk factor for lung cancer (LC), especially among non-smokers. The objective of this study was to identify serum biomarkers for the early detection of LC in individuals at high risk due to prolonged residential radon exposure in Chiang Mai, Thailand, and to assess whether the use of single or combined biomarkers improves the sensitivity and specificity of detection. Methods: A total of 15 LC patients and 30 healthy controls (HC) were enrolled. The HC group was further stratified into two subgroups: low radon (LR, n = 15) and high radon (HR, n = 15) exposure. All participants were non-smokers or former smokers. Serum levels of cytokeratin 19 fragment (CYFRA 21-1), carcinoembryonic antigen (CEA), interleukin-6 (IL-6), interleukin-8 (IL-8), transforming growth factor-alpha (TGF-alpha), and indoleamine 2,3-dioxygenase-1 (IDO-1) were measured using the Milliplex^® Kit on a Luminex^® Multiplexing Instrument (MAGPIX^® System). Results: Serum CEA, IL-6 and IL-8 levels were significantly higher in LC patients compared to the HC group (p < 0.05). Among analyzed biomarkers, only IL-8 was significantly elevated in LC patients compared to the HR group (p = 0.04). Notably, CYFRA 21-1 was the only biomarker that significantly differed between LR and HR groups (p = 0.004). The diagnostic potential of these biomarkers was evaluated using receiver operating characteristic (ROC) analysis. Individually, IL-6 showed the highest discriminative ability for differentiating LC patients from both HC and HR groups, with high specificity but moderate sensitivity. Combining IL-6 and IL-8 improved specificity and increased the area under the ROC curve (AUC), though it did not enhance sensitivity for distinguishing LC from HC. For distinguishing LC from HR individuals, IL-6 and CYFRA 21-1 exhibited strong diagnostic performance. Their combination significantly improved diagnostic accuracy, yielding the highest AUC, sensitivity, and specificity. In contrast, CEA, IL-8, TGF-alpha, and IDO-1 demonstrated limited diagnostic utility. Conclusions: Based on the available literature, this is the first study to evaluate the combined use of IL-6 and CYFRA 21-1 as potential biomarkers for LC screening in individuals with high residential radon exposure. Our findings highlight their utility, particularly in combination, for improving diagnostic accuracy in this high-risk population. Full article

Monitoring soil moisture (SM) using permanently installed gamma radiation (GR) detectors is a promising non-invasive method based on the inverse relationship between SM and soil-emitted GR. In a previous study, we successfully estimated SM from environmental gamma radiation (EGR) measured by a low-cost counter-tube detector. Since this detector type provides a bulk GR response across a wide energy range, EGR signals are influenced by several confounding factors, e.g., soil radon emanation, biomass. To what extent these confounding factors deteriorate the accuracy of SM estimates obtained from EGR is not fully understood. Therefore, the aim of this study was to compare the accuracy of SM estimates from EGR with those from reference ⁴⁰K GR (1460 keV) measurements which are much less influenced by these factors. For this, a Geiger–Mueller counter (G–M), which is commonly used for EGR monitoring, and a gamma spectrometer were installed side by side in an agricultural field equipped with in situ sensors to measure reference SM and a meteorological station. The EGR_G–M and spectrometry-based ⁴⁰K measurements were related to reference SM using a functional relationship derived from theory. We found that daily SM can be predicted with an RMSE of 3.39 vol. % from ⁴⁰K using the theoretical value of α = 1.11 obtained from the effective ratio of GR mass attenuation coefficients for the water and solid phase. A lower accuracy was achieved for the EGR_G–M measurements (RMSE = 6.90 vol. %). Wavelet coherence analysis revealed that the EGR_G–M measurements were influenced by radon-induced noise in winter. Additionally, biomass shielding had a stronger impact on EGR_G–M than on ⁴⁰K GR estimates of SM during summer. In summary, our study provides a better understanding on the lower prediction accuracy of EGR_G–M and suggests that correcting for biomass can improve SM estimation from the bulk EGR data of operational radioactivity monitoring networks. Full article

This study presents a comprehensive methodology for the detection and characterization of fractures in geological samples using X-ray computed tomography (CT). By combining convolution-based image processing techniques with advanced neural network-based segmentation, the proposed approach achieves high precision in identifying complex fracture networks. The method was applied to a marly limestone sample from the Maiolica Formation, part of the Umbria–Marche stratigraphic succession (Northern Apennines, Italy), a geological context where fractures often vary in size and contrast and are frequently filled with minerals such as calcite or clays, making their detection challenging. A critical part of the work involved addressing multiple sources of uncertainty that can impact fracture identification and measurement. These included the inherent spatial resolution limit of the CT system (voxel size of 70.69 μm), low contrast between fractures and the surrounding matrix, artifacts introduced by the tomographic reconstruction process (specifically the Radon transform), and noise from both the imaging system and environmental factors. To mitigate these challenges, we employed a series of preprocessing steps such as Gaussian and median filtering to enhance image quality and reduce noise, scanning from multiple angles to improve data redundancy, and intensity normalization to compensate for shading artifacts. The neural network segmentation demonstrated superior capability in distinguishing fractures filled with various materials from the host rock, overcoming the limitations observed in traditional convolution-based methods. Overall, this integrated workflow significantly improves the reliability and accuracy of fracture quantification in CT data, providing a robust and reproducible framework for the analysis of discontinuities in heterogeneous and complex geological materials. Full article

High-density component-induced metal artifacts in industrial computed tomography (CT) severely impair image quality and make further analysis more difficult. To suppress artifacts and improve image quality, this research suggests a practical approach that combines lightweight attention-enhanced super-resolution networks with Radon-domain artifact elimination. First, the original CT slices are subjected to bicubic interpolation, which enhances resolution and reduces sampling errors during transformation. The Radon transform, which detects and suppresses metal artifacts in the Radon domain, is then used to convert the interpolated pictures into sinograms. The artifact-suppressed sinograms are then reconstructed at better resolution using a lightweight Enhanced Deep Super-Resolution (EDSR) network with a channel attention mechanism, which consists of only one residual block. The inverse Radon transform is used to recreate the final CT images. An average peak signal-to-noise ratio (PSNR) of 40.39 dB and an average signal-to-noise ratio (SNR) of 29.75 dB, with an SNR improvement of 15.48 dB over the original artifact-laden images, show the success of the suggested strategy in experiments. This method offers a workable and effective way to improve image quality in industrial CT applications that involve intricate structures that incorporate metal. Full article

The drilling core sampling and chemical analysis method for the quantitative determination of solid mineral deposits has several drawbacks, including a low core drilling efficiency, a high core sampling cost, and a long chemical analysis cycle. In current uranium quantification practices, advanced techniques have been developed to preliminarily determine the formation of uranium content based on the interpretation results of natural $\gamma$-ray total logging. However, such methods still require supplementary core chemical analysis to derive the uranium–radium–radon balance coefficient, which is then used for equilibrium correction to obtain the true uranium content within the uranium-bearing layer. Furthermore, conventional prompt neutron time spectrum logging is constrained by low count rates, resulting in slow logging speeds that fail to meet the demands of practical engineering applications. To address this, this study proposes a uranium quantification method that corrects the natural $\gamma$-ray total logging using prompt neutron time spectrum logging. Additionally, a calibration parameter determination method necessary for quantitative interpretation is constructed. Experimental results from standardized model wells indicate that, in sandstone-type uranium deposits, the absolute error of uranium content is within $\pm 0.002\%\mathrm{eU}$, and the relative error is within $\pm 2.5\%$. These findings validate the feasibility of deriving the uranium–radium–radon balance coefficient without relying on core chemical analysis. Compared with the prompt neutron time spectrum logging method, the proposed approach significantly improves the logging speed while producing results that are essentially consistent with those of natural $\gamma$-ray total logging. It provides an efficient and accurate solution for uranium quantitative interpretation. Full article

Radon is an inert gas produced by the radioactive decay of uranium-238, commonly found in the environment. Radon and its decay products are the main sources of human exposure to radiation from natural sources. When inhaled, radon’s alpha particles impact lung tissue, potentially causing lung cancer by damaging DNA and altering oxidative processes. This review article addresses the need for a deeper understanding of the genetic and molecular changes associated with radon-induced lung cancer, aiming to clarify key genetic mutations and protein markers linked to carcinogenesis. Particular attention in recent studies has been given to mutations in tumor suppressor genes (RASSF1, TP53), oncogenes (KRAS, EGFR), and changes in the expression levels of protein biomarkers associated with inflammation, stress, and apoptosis. Identifying these markers is critical for developing effective screening methods for radon-induced lung cancer, enabling timely identification of high-risk patients and supporting effective preventive strategies. Summarizing current genetic and protein biomarkers, this review highlights the importance of a comprehensive approach to studying radon-induced carcinogenesis. Understanding these molecular mechanisms could ultimately improve early diagnostic methods and enhance therapy for cancers associated with radon exposure. Full article

Among the Central Asian countries, Kazakhstan is experiencing significant growth in uranium production and plays a key role in the mining industry. The aim of this study was to assess environmental gamma radiation levels and indoor radon concentrations in the settlements of Aqsu, Saumalkol, and Arykbalyk—situated in regions with a history of uranium mining activities—to evaluate potential radiation exposure risks to the local population. Measurements of ambient gamma radiation dose rates indicated that Saumalkol exhibited the highest variability, with recorded values reaching up to 0.56 ± 0.19 µSv/h, suggesting potential influence from abandoned mining areas. The equivalent equilibrium volume activity of radon revealed severe contamination in Aqsu (mean: 303 ± 57 Bq/m³, max: 4974 Bq/m³) and Saumalkol (mean: 658 ± 114 Bq/m³, max: 2470 Bq/m³). These findings underscore the need for immediate intervention measures such as improved ventilation and radon mitigation strategies to reduce exposure risks and protect residents from radiation-induced health hazards. This study presents a screening method to identify areas with potential radon risks. However, radon dose assessment requires long-term measurements for accurate evaluation of exposure levels and health risks, with extended monitoring needed for comprehensive assessment. Full article

Ship wake detection plays a crucial role in compensating for target detection failures caused by defocusing or displacement in SAR images due to vessel motion. This study addresses the challenge of enhancing wake features in high-resolution spaceborne SAR by exploiting the distinct linear characteristics of wake echoes and the random motion of ocean background clutter. We propose a novel method based on sub-aperture image sequences, which integrates equivalent multi-channel technology to fuse wake and wave information. This approach significantly improves the quality of raw wake images by enhancing linear features and suppressing background noise. The Radon transform is then applied to evaluate the enhanced wake images. Through a combination of principle analysis, enhancement processing, and both subjective and objective evaluations, we conducted experiments using real data from the AS01 SAR satellite and compared our method with traditional wake enhancement techniques. The results demonstrate that our method achieves significant wake enhancement and improves the recognition of detail wake features. Full article

Subsurface contamination can migrate upward into overlying buildings, exposing the buildings’ inhabitants to contaminants that can cause detrimental health effects. This phenomenon is known as vapor intrusion (VI). When evaluating a building for VI, one must understand that seasonal and short-term variability are significant factors in determining the reasonable maximum exposure (RME) to the occupants. RME is a semi-quantitative term that refers to the lower portion of the high end of the exposure distribution—conceptually, above the 90th percentile exposure but less than the 98th percentile exposure. Samples were collected between December 2020 and April 2022 at six non-residential commercial buildings in Fairbanks, Alaska. The types of samples collected included indoor air (IA); outdoor air; subslab soil gas; soil gas; indoor radon; differential pressure; indoor and outdoor temperature; heating, ventilation, and air conditioning (HVAC) parameters; and other environmental factors. The buildings in close proximity to the volatile organic compound (VOC) source/release points presented less variability in indoor air concentrations of trichloroethylene (TCE) and tetrachloroethylene (PCE) compared to the buildings farther down gradient in the contaminated groundwater plume. The VOC data pattern for the source area buildings shows an outdoor air temperature-dominated behavior for indoor air concentrations in the summer season. HVAC system operations had less influence on long-term indoor air concentration trends than environmental factors, which is supported by similar indoor air concentration patterns independent of location within the plume. The use of soil temperature and indoor/outdoor temperatures as indicators and tracers (I&Ts) across the plume as predictors of the sampling period could produce a good estimation of the RME for the building occupants. These results, which show the use of soil temperature and indoor/outdoor temperatures as I&Ts, will help advance investigative methods for evaluation of VI in similar settings and thereby improve the protection of human health in indoor environments. Full article

A method was developed to estimate the average annual indoor radon activity concentration from three-week short-term measurements using active radon-222 measuring devices, taking into account the relevant influencing parameters (season, temperature difference, temporal air pressure gradient, etc.) during the short-term measurements. A total of 24 long-term measurements (6 months) and 50 short-term measurements (3 weeks) were carried out in 24 indoor spaces in private houses in four Austrian federal states between October 2022 and July 2023. At the same time as the short-term measurements, ambient parameters (outside and inside temperature, air pressure inside, outside, air humidity inside, outside, wind speed, wind direction, amount of precipitation) were also recorded to investigate their influence on the measured radon-222 activity concentrations. Building and usage data of the indoor spaces examined were also collected. Based on the evaluation of the radon-222 measurements carried out, a first guideline was developed for estimating the annual mean value of the radon-222 activity concentration from short-term measurements lasting around three weeks. The result shows that by applying the developed method, the approximation to the long-term average value can be significantly improved, at least by a factor of 2. This criterion is only valid for the 24 indoor spaces examined in this study. Generalisation requires a test and validation study of the method presented. It is planned to test and validate the developed method in other indoor spaces by means of further measurements and in-depth physical-statistical considerations, and to improve the functional relationships and the approximation to the long-term average value. Full article

The generalized Radon–Fourier transform (GRFT) is a well-established coherent accumulation technique for high-speed and high-mobility target detection. However, this method tends to suffer from the difficulty of identifying the main lobe from multiple blind speed sidelobes (BSSLs) and the computational complexity is generally high. To address these challenges, we propose a new method, namely the BSSL Traction Particle Swarm Optimization (BTPSO), to robustly and accurately extract the main lobe. In the method, the relationship between the main lobe and the BSSLs is used to attract particles to potential positions of the main lobe in the group when trapped in local optimal, and a new termination criterion in which multiple particles should converge to the same optimal value is proposed to avoid local convergence. Simulation examples show that the proposed method can improve the probability of converging to the main lobe peak while reducing cost time, and its good adaptability to low signal-to-noise ratio (SNR) cases is well verified. Full article

Micro-motion feature extraction is of great significance for target recognition. However, traditional methods mostly focus on single target and struggle to correctly separate the severely overlapping micro-motion curves of multiple targets. In this paper, a rapid micro-motion feature extraction algorithm of multiple space targets based on inverse radon transform (IRT) with a modified model is proposed. First, the high-resolution range profile (HRRP) generated from echo is subject to binarization to improve the unstable estimation caused by noise. Then, the micro-motion period in a complicated multi-target scenario is obtained by a period estimation method based on the autocorrelation coefficients of binarized HRRP. To further improve the extraction accuracy, the IRT model of the micro-range curve is modified from the sine function to second-order sine function. By searching for the remaining unknown parameters in the model in conjunction with the period, the precise micro-range curves are quickly separated. Each time the curves of a target are extracted, they are removed, and the next extraction is carried out until all the targets have been searched. Finally, simulation and experimental results indicate that the proposed algorithm can not only correctly separate the micro-motion feature curves of multiple space targets under low signal-to-noise ratio (SNR) conditions but also significantly outperforms the original IRT in terms of extraction speed. Full article

This study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over three distinct measurement periods between 2015 and 2016, with 86 households participating. Detectors were randomly placed in various rooms within each household. Normality tests (Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov) were applied to assess the fit of radon concentrations to a log-normal distribution. Additionally, analysis of variance (ANOVA) was used to evaluate the influence of environmental and structural factors on radon variability. Non-normally distributed data were normalized using a Box–Cox transformation to improve statistical assumptions, enabling subsequent geostatistical analyses. Geospatial interpolation methods, specifically Inverse Distance Weighting (IDW) and Kriging, were employed to map radon concentrations. The results revealed significant temporal variability in radon concentrations, with geometric means of 146.4 Bq·${\text{m}}^{-3}$, 162.3 Bq·${\text{m}}^{-3}$, and 150.8 Bq·${\text{m}}^{-3}$, respectively, across the three periods. Up to 9.5% of the monitored households recorded radon levels exceeding the safety threshold of 200 Bq·${\text{m}}^{-3}$. Among the interpolation methods, Kriging provided a more accurate spatial representation of radon concentration variability compared to IDW, allowing for the precise identification of high-risk areas. This study provides a framework for using advanced statistical and geospatial techniques in environmental risk assessment. Full article