Geographically weighted regression and geostatistical techniques to construct the geogenic radon potential map of the Lazio region: A methodological proposal for the European Atlas of Natural Radiation

Influence of rock type and geophysical properties on radon flux density

The most significant source of human exposure to ionizing radiation is the radioactive gas radon (basically 222Rn) and its daughter decay products, creating more than half of the effective dose from all natural sources. Radon enters buildings mainly from dense rocks, which are below building foundations at depths of 1 m and more. In this paper long-term measurements of radon flux density are analyzed, with radon exhalation from the surface of the most common rocks-loams, sandy loams, clays, clay shales, several types of sandy-gravel-pebble deposits, clay and rocky limestone. The influence of geophysical properties of rocks on radon flux density due to exhalation from surfaces of those rocks was studied. Based on the results obtained, a method of local assessment of the hazard from radon and its progeny in buildings is proposed, which is based on the geophysical properties of rocks below the foundations of those buildings.

A new perspective in radon risk assessment: Mapping the geological hazard as a first step to define the collective radon risk exposure

Radon is a radioactive gas and a major source of ionizing radiation exposure for humans. Consequently, it can pose serious health threats when it accumulates in confined environments. In Europe, recent legislation has been adopted to address radon exposure in dwellings; this law establishes national reference levels and guidelines for defining Radon Priority Areas (RPAs). This study focuses on mapping the Geogenic Radon Potential (GRP) as a foundation for identifying RPAs and, consequently, assessing radon risk in indoor environments. Here, GRP is proposed as a hazard indicator, indicating the potential for radon to enter buildings from geological sources. Various approaches, including multivariate geospatial analysis and the application of artificial intelligence algorithms, have been utilised to generate continuous spatial maps of GRP based on point measurements. In this study, we employed a robust multivariate machine learning algorithm (Random Forest) to create the GRP map of the central sector of the Pusteria Valley, incorporating other variables from census tracts such as land use as a vulnerability factor, and population as an exposure factor to create the risk map. The Pusteria Valley in northern Italy was chosen as the pilot site due to its well-known geological, structural, and geochemical features. The results indicate that high Rn risk areas are associated with high GRP values, as well as residential areas and high population density. Starting with the GRP map (e.g., Rn hazard), a new geological-based definition of the RPAs is proposed as fundamental tool for mapping Collective Radon Risk Areas in line with the main objective of European regulations, which is to differentiate them from Individual Risk Areas.

Application of airborne geophysical survey data in a logistic regression model to improve the predictive power of geogenic radon maps. A case study in Castleisland, County Kerry, Ireland

In this study, a novel methodology was investigated to improve the spatial resolution and predictive power of geogenic radon maps. The data inputs comprise indoor radon measurements and seven geogenic factors including geological data (i.e. bedrock and Quaternary geology, aquifer type and soil permeability) and airborne geophysical parameters (i.e. magnetic field strength, gamma-ray radiation and electromagnetic resistivity). The methodology was tested in Castleisland southwest Ireland, a radon-prone area identified based on the results of previous indoor radon surveys. The developed model was capable of justifying almost 75 % of the variation in geogenic radon potential. It was found that the attributes with the greatest statistical significance were equivalent uranium content (EqU) and soil permeability. A new radon potential map was produced at a higher spatial resolution compared with the original map, which did not include geophysical parameter data. In the final step, the activity of radon in soil gas was measured at 87 sites, and the correlation between the observed soil gas radon and geophysical properties was evaluated. The results indicate that any model using only geophysical data cannot accurately predict soil radon activity and that geological information should be integrated to achieve a successful prediction model. Furthermore, we found that EqU is a better indicator for predicting indoor radon potential than the measured soil radon concentrations.

Multivariate Modeling for Spatio-Temporal Radon Flux Predictions

Nowadays, various fields in environmental sciences require the availability of appropriate techniques to exploit the information given by multivariate spatial or spatio-temporal observations. In particular, radon flux data which are of high interest to monitor greenhouse gas emissions and to assess human exposure to indoor radon are determined by the deposit of uranium and radio (precursor elements). Furthermore, they are also affected by various atmospheric variables, such as humidity, temperature, precipitation and evapotranspiration. To this aim, a significant role can be recognized to the tools of multivariate geostatistics which supports the modeling and prediction of variables under study. In this paper, the spatio-temporal distribution of radon flux densities over the Veneto Region (Italy) and its estimation at unsampled points in space and time are discussed. In particular, the spatio-temporal linear coregionalization model is identified on the basis of the joint diagonalization of the empirical covariance matrices evaluated at different spatio-temporal lags and is used to produce predicted radon flux maps for different months. Probability maps, that the radon flux density in the upcoming months is greater than three historical statistics, are then built. This might be of interest especially in summer months when the risk of radon exhalation is higher. Moreover, a comparison with respect to alternative models in the univariate and multivariate context is provided.

Assessment of Threshold Dose of Thoron Inhalation and Its Biological Effects by Mimicking the Radiation Doses in Monazite Placer Deposits Corresponding to the Normal, Medium and Very High Natural Background Radiation Areas

The dose contributed from thoron (²²⁰Rn) and its progeny has been neglected in the dose assessment because of its short half-life (t_1/2 = 55.6 s) and generally low concentrations. Recently, concentrations of ²²⁰Rn gas and its progeny were found to be pronounced in the traditional residential dwellings in China, on beaches of India and in other countries. Accordingly, we investigated the biological effects of thoron (²²⁰Rn) decay products in various mouse organs, succeeding inhalation of thoron gas in BALB/c mouse. We investigated the biological effects upon thoron inhalation on mouse organs with a focus on oxidative stress. These mice were divided into (4 random groups): sham inhalation, thoron inhalation for 1, 4 and 10 days. Various tissues (lung, liver and kidney) were then collected after the time points and subjected to various biochemical analyses. Immediately after inhalation, mouse tissues were excised for gamma spectrometry and 72 h post inhalation for biochemical assays. The gamma spectrometry counts and its subsequent calculation of the equivalent dose showed varied distribution in the lung, liver and kidney. Our results suggest that acute thoron inhalation showed a differential effect on the antioxidant function and exerted pathophysiological alterations via oxidative stress in organs at a higher dose. These findings suggested that thoron inhalation could alter the redox state in organs; however, its characteristics were dependent on the total redox system of the organs as well as the thoron concentration and inhalation time.

Machine learning in environmental radon science

Temporal dynamic as well as spatial variability of environmental radon are controlled by factors such as meteorology, lithology, soil properties, hydrogeology, tectonics, and seismicity. In addition, indoor radon concentration is subject to anthropogenic factors, such as physical characteristics of a building and usage pattern. New tools for spatial and time series analysis and prediction belong to what is commonly called machine learning (ML). The ML algorithms presented here build models based on sample and predictor data to extract information and to make predictions. We give a short overview on ML methods and discuss their respective merits, their application, and ways of validating results. We show examples of 1) geogenic radon mapping in Germany involving a number of predictors, and of 2) time series analysis of a long-term experiment being carried out in Chiba, Japan, involving indoor radon concentrations and meteorological predictors. Finally, we identified the main weakness of the techniques, and we suggest actions to overcome their limitations.

Detailed Geogenic Radon Potential Mapping Using Geospatial Analysis of Multiple Geo-Variables-A Case Study from a High-Risk Area in SE Ireland

A detailed investigation of geogenic radon potential (GRP) was carried out near Graiguenamanagh town (County Kilkenny, Ireland) by performing a spatial regression analysis on radon-related variables to evaluate the exposure of people to natural radiation (i.e., radon, thoron and gamma radiation). The study area includes an offshoot of the Caledonian Leinster Granite, which is locally intruded into Ordovician metasediments. To model radon release potential at different points, an ordinary least squared (OLS) regression model was developed in which soil gas radon (SGR) concentrations were considered as the response value. Proxy variables such as radionuclide concentrations obtained from airborne radiometric surveys, soil gas permeability, distance from major faults and a digital terrain model were used as the input predictors. ArcGIS and QGIS software together with XLSTAT statistical software were used to visualise, analyse and validate the data and models. The proposed GRP models were validated through diagnostic tests. Empirical Bayesian kriging (EBK) was used to produce the map of the spatial distribution of predicted GRP values and to estimate the prediction uncertainty. The methodology described here can be extended for larger areas and the models could be utilised to estimate the GRPs of other areas where radon-related proxy values are available.

Using geogenic radon potential to assess radon priority area designation, a case study around Castleisland, Co. Kerry, Ireland

Globally, indoor radon exposure is the leading cause of lung cancer in non-smokers and second most common cause after tobacco smoking. Soil-gas radon is the main contributor to indoor radon, but its spatial distribution is highly variable, which poses certain challenges for mapping and predicting radon anomalies. Measurement of indoor radon typically takes place over long periods of time (e.g. 3 months) and is seasonally adjusted to an annual average concentration. In this article we investigate the suitability of using soil-gas radon and soil-permeability measurements for rapid radon risk assessments at local scale. The area of Castleisland, Co. Kerry was chosen as a case study due to availability of indoor radon data and the presence of significant radon anomalies. In total, 135 soil-gas and permeability measurements were collected and complemented with 180 indoor radon measurements for an identical 6 km² area. Both soil-gas and indoor radon concentrations ranged from very low (<10 kBqm^-3, 0.1 Bqm^-3) to anomalously high (>1433 kBqm^-3, 65,000 Bqm^-3) values. Our method classifies almost 50% of the area as a high radon potential area, and allows assessment of geogenic controls on radon distribution by including other geological variables. Cumulatively, the percentage of indoor radon variance explained by soil-gas radon concentration, bedrock geology, subsoil permeability and Quaternary geology is 34% (16%, 10%, 4% and 4% respectively). Soil-gas and indoor radon anomalies are associated with black shales, whereas the presence of karst and geological faults are other contributing factors. Sampling of radon soil-gas and soil permeability, used in conjunction with other geogenic data, can therefore facilitate rapid designation of radon priority areas. Such an approach demonstrates the usefulness of high-resolution geogenic maps in predicting indoor radon risk categories when compared to the application of indoor radon measurements alone. This method is particularly useful to assess radon potential in areas where indoor radon measurements are sparse or lacking, with particular application to rural areas, land rezoned for residential use, or for sites prior to building construction.

RADON-PRONE AREAS IN SLOVAKIA PREDICTED BY RESCALED RADON POTENTIAL MAPS

Several scientific studies have shown that high content of radon in the soil environment can be a precursor of increased indoor radon levels. Inhabited areas where elevated indoor radon concentration appears for natural (geogenic) reasons are commonly referred to as radon-prone areas. In this study, radon-prone areas in the Slovak Republic were predicted on the basis of radon potential maps after its specific rescaling. In total, 99 municipalities have been identified in Slovakia where the annual average indoor radon concentration is expected to exceed the reference level of 300 Bq m-3; five of those are even expected to exceed 1000 Bq m-3. In these municipalities it is then required to conduct a survey of indoor radon measurements. Compared with a nationwide survey, the proposed approach of searching for houses with potentially high radon exposure is more efficient.

Gini Method Application: Indoor Radon Survey in Kpong, Ghana

In this study, the indoor radon concentrations map, starting from a sparse measurements survey, was realized with the Gini index method. This method was applied on a real dataset coming from indoor radon measurements carried out in Kpong, Ghana. The Gini coefficient variogram is shown to be a good estimator of the inhomogeneity degree of radon concentration because it allows for better constraining of the critical distance below which the radon geological source can be considered as uniform. The indoor radon measurements were performed in 96 dwellings in Kpong, Ghana. The data showed that 84% of the residences monitored had radon levels below 100 Bqm−3, versus 16% having levels above the World Health Organization’s (WHO) suggested reference range (100 Bqm−3). The survey indicated that the average indoor radon concentration (IRC) was 55 ± 36 Bqm−3. The concentrations range from 4–176 Bqm−3. The mean value 55 Bqm−3 is 38% higher than the world’s average IRC of 40 Bqm−3 (UNSCEAR, 1993).

The assessment of local geological factors for the construction of a Geogenic Radon Potential map using regression kriging. A case study from the Euganean Hills volcanic district (Italy)

The assessment of potential radon-hazardous environments is nowadays a critical issue in planning, monitoring, and developing appropriate mitigation strategies. Although some geological structures (e.g., fault systems) and other geological factors (e.g., radionuclide content, soil organic or rock weathering) can locally affect the radon occurrence, at the basis of a good implementation of radon-safe systems, optimized modelling at territorial scale is required. The use of spatial regression models, adequately combining different types of predictors, represents an invaluable tool to identify the relationships between radon and its controlling factors as well as to construct Geogenic Radon Potential (GRP) maps of an area. In this work, two GRP maps were developed based on field measurements of soil gas radon and thoron concentrations and gamma spectrometry of soil and rock samples of the Euganean Hills (northern Italy) district. A predictive model of radon concentration in soil gas was reconstructed taking into account the relationships among the soil gas radon and seven predictors: terrestrial gamma dose radiation (TGDR), thoron (²²⁰Rn), fault density (FD), soil permeability (PERM), digital terrain model (SLOPE), moisture index (TMI), heat load index (HLI). These predictors allowed to elaborate local spatial models by using the Empirical Bayesian Regression Kriging (EBRK) in order to find the best combination and define the GRP of the Euganean Hills area. A second GRP map based on the Neznal approach (GRP_NEZ) has been modelled using the TGDR and ²²⁰Rn, as predictors of radon concentration, and FD as predictor of soil permeability. Then, the two GRP maps have been compared. Results highlight that the radon potential is mainly driven by the bedrock type but the presence of fault systems and topographic features play a key role in radon migration in the subsoil and its exhalation at the soil/atmosphere boundary.

Radon Hazard in Central Italy: Comparison among Areas with Different Geogenic Radon Potential

Radon (222Rn) is a natural radioactive gas formed in rocks and soil by the decay of its parent nuclide (238-Uranium). The rate at which radon migrates to the surface, be it along faults or directly emanated from shallow soil, represents the Geogenic Radon Potential (GRP) of an area. Considering that the GRP is often linked to indoor radon risk levels, we have conducted multi-disciplinary research to: (i) define local GRPs and investigate their relationship with associated indoor Rn levels; (ii) evaluate inhaled radiation dosages and the associated risk to the inhabitants; and (iii) define radon priority areas (RPAs) as required by the Directive 2013/59/Euratom. In the framework of the EU-funded LIFE-Respire project, a large amount of data (radionuclide content, soil gas samples, terrestrial gamma, indoor radon) was collected from three municipalities located in different volcanic districts of the Lazio region (central Italy) that are characterised by low to high GRP. Results highlight the positive correlation between the radionuclide content of the outcropping rocks, the soil Rn concentrations and the presence of high indoor Rn values in areas with medium to high GRP. Data confirm that the Cimini-Vicani area has inhalation dosages that are higher than the reference value of 10 mSv/y.

Radon potential mapping in Jangsu-gun, South Korea using probabilistic and deep learning algorithms

The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe₂O₃), and lead (Pb) concentrations, were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high, moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was classified as very high or high risk. Finally, the radon potential maps were validated using the area under the receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets, respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon, and could inform the development of new residential areas. Radon screening is important to reduce public exposure to high levels of naturally occurring radiation.

Sorrentina Peninsula: Geographical Distribution of the Indoor Radon Concentrations in Dwellings—Gini Index Application

The radon isotope (222Rn, half-life 3.8 days) is a radioactive byproduct of the 238U decay chain. Because radon is the second biggest cause of lung cancer after smoking, dense maps of indoor radon concentration are required to implement effective locally based risk reduction strategies. In this regard, we present an innovative method for the construction of interpolated maps (kriging) based on the Gini index computation to characterize the distribution of Rn concentration. The Gini coefficient variogram has been shown to be an effective predictor of radon concentration inhomogeneity. It allows for a better constraint of the critical distance below which the radon geological source can be considered uniform, at least for the investigated length scales of variability; it also better distinguishes fluctuations due to environmental predisposing factors from those due to random spatially uncorrelated noise. This method has been shown to be effective in finding larger-scale geographical connections that can subsequently be connected to geological characteristics. It was tested using real dataset derived from indoor radon measurements conducted in the Sorrentina Peninsula in Campania, Italy. The measurement was carried out in different residences using passive detectors (CR-39) for two consecutive semesters, beginning in September–November 2019 and ending in September–November 2020, to estimate the yearly mean radon concentration. The measurements and analysis were conducted in accordance with the quality control plan. Radon concentrations ranged from 25 to 722 Bq/m3 before being normalized to ground level, and from 23 to 933 Bq/m3 after being normalized, with a geometric mean of 120 Bq/m3 and a geometric standard deviation of 1.35 before data normalization, and 139 Bq/m3 and a geometric standard deviation of 1.36 after data normalization. Approximately 13% of the tests conducted exceeded the 300 Bq/m3 reference level set by Italian Legislative Decree 101/2020. The data show that the municipalities under investigation had no influence on indoor radon levels. The geology of the monitored location is interesting, and because soil is the primary source of Rn, risk assessment and mitigation for radon exposure cannot be undertaken without first analyzing the local geology. This research examines the spatial link among radon readings using the mapping based on the Gini method (kriging).

Mapping indoor radon hazard in Germany: The geogenic component

Indoor radon is considered as an indoor air pollutant due to its carcinogenic effect. Since the main source of indoor radon is the ground beneath the house, we utilize the geogenic radon potential (GRP) and a geogenic radon hazard index (GRHI) for predicting the geogenic component of the indoor Rn hazard in Germany. For this purpose, we link indoor radon data (n = 44,629) to maps of GRP and GRHI and fit logistic regression models to calculate the probabilities that indoor Rn exceeds thresholds of 100 Bq/m³ and 300 Bq/m³. The estimated probability was averaged for every municipality by considering only the estimates within the built-up area. Finally, the mean exceedance probability per municipality was coupled with the respective residential building stock for estimating the number of buildings with indoor Rn above 100 Bq/m³ and 300 Bq/m³ for each municipality. We found that (1) GRHI is a better predictor than GRP for indoor radon hazard in Germany, (2) the estimated number of buildings above 100 Bq/m³ and 300 Bq/m³ in Germany is ~2 million (11.6% of all residential buildings) and ~ 350,000 (1.9%), respectively, (3) areas where 300 Bq/m³ exceedance is greater than 10% comprise only 0.8% of the German building stock but 6.3% of buildings with indoor Rn exceeding 300 Bq/m³, and (4) most urban areas and, hence, most buildings (77%) are located in low hazard regions. The implications for Rn protection are twofold: (1) the Rn priority area concept is cost-efficient in a sense that it allows to find the most buildings that exceed a threshold concentration with a given amount of resources, and (2) for an optimal reduction of lung cancer risk areas outside of Rn priority areas must be addressed since most hazardous indoor Rn concentrations occur in low to medium hazard areas.

Soil gas radon and soil permeability assessment: Mapping radon risk areas in Perak State, Malaysia

In this study geogenic radon potential (GRP) mapping was carried out on the bases of field radon in soil gas concentration and soil gas permeability measurements by considering the corresponding geological formations. The spatial pattern of soil gas radon concentration, soil permeability, and GRP and the relationship between geological formations and these parameters was studied by performing detailed spatial analysis. The radon activity concentration in soil gas ranged from 0.11 to 434.5 kBq m⁻³ with a mean of 18.96 kBq m⁻³, and a standard deviation was 55.38 kBq m⁻³. The soil gas permeability ranged from 5.2×10⁻¹⁴ to 5.2×10⁻¹² m², with a mean of 5.65×10⁻¹³ m². The GRP values were computed from the ²²²Rn activity concentration and soil gas permeability data. The range of GRP values was from 0.04 to 154.08. Locations on igneous granite rock geology were characterized by higher soil radon gas activity and higher GRP, making them radon-prone areas according to international standards. The other study locations fall between the low to medium risk, except for areas with high soil permeability, which are not internationally classified as radon prone. A GRP map was created displaying radon-prone areas for the study location using Kriging/Cokriging, based on in situ and predicted measured values. The GRP map assists in human health risk assessment and risk reduction since it indicates the potential of the source of radon and can serve as a vital tool for radon combat planning.

Radon risk mapping: A new geostatistical method based on Lorenz Curve and Gini index

In confined spaces such as living environments and workplaces, the concentration levels of radon (Rn²²²) can be very high as compared to the external environment. Since Rn has been classified as the second leading cause of lung cancer after cigarette smoking, to apply efficient locally based risk reduction actions, dense maps of indoor radon concentration are needed. These maps would provide information about the areas prone to high radon concentrations and therefore more dangerous to human health. The soil is the primary source of the Rn, hence the risk assessment and reduction for the radon exposure cannot disregard the identification of the local geology. In this regard, we propose an innovative method, based on the Gini index computation, for the realization of interpolated maps (kriging) to describe the distribution of concentration of Rn. To validate the method, a tool that simulates sets of radon concentrations is used, whose variability is, to the first order, controlled by a priori imposed different lithologies. A systematic comparison is made between the results achieved by means of a classically used geostatistical method and the proposed Gini-based tool. We show how, by using this latter tool, the kriging solutions appear to be more robust to resolve the different geogenic radon sources independently from the number of the available measurements.

A Study of Natural Radioactivity Levels and Radon/Thoron Release Potential of Bedrock and Soil in Southeastern Ireland

Radon (²²²Rn) and thoron (²²⁰Rn) account for almost two-thirds of the annual average radiation dose received by the Irish population. A detailed study of natural radioactivity levels and radon and thoron exhalation rates was carried out in a legislatively designated “high radon” area, as based on existing indoor radon measurements. Indoor radon concentrations, airborne radiometric data and stream sediment geochemistry were collated, and a set of soil samples were taken from the study area. The exhalation rates of radon (E²²²_Rn) and thoron (E²²⁰_Rn) for collected samples were determined in the laboratory. The resultant data were classified based on geological and soil type parameters. Geological boundaries were found to be robust classifiers for radon exhalation rates and radon-related variables, whilst soil type classification better differentiates thoron exhalation rates and correlated variables. Linear models were developed to predict the radon and thoron exhalation rates of the study area. Distribution maps of radon and thoron exhalation rates (range: E²²²_Rn [0.15–1.84] and E²²⁰_Rn [475–3029] Bq m⁻² h⁻¹) and annual effective dose (with a mean value of 0.84 mSv y⁻¹) are presented. For some parts of the study area, the calculated annual effective dose exceeds the recommended level of 1 mSv y⁻¹, illustrating a significant radiation risk. Airborne radiometric data were found to be a powerful and fast tool for the prediction of geogenic radon and thoron risk. This robust method can be used for other areas where airborne radiometric data are available.

Ambient gamma dose rate as an indicator of geogenic radon potential

Radon is the second cause of lung cancer after smoking, therefore is acknowledged as a major indoor air pollutant. Geogenic radon potential indicates regions where for natural reasons elevated indoor radon levels or elevated probability of their occurrence can be expected. The most common procedure for establishing geogenic radon potential includes measurements of soil permeability and soil gas radon concentrations. These measurements are time-consuming and expensive therefore a limited number of measurements is carried out and their results are extrapolated to the specific area. Our research aimed to analyse the usefulness of ambient gamma dose rate survey to assess radon concentration in the environment and therefore geogenic radon potential. The measurements were carried out on two granite massifs with higher (Karkonosze) and lower (Strzelin) radioactive elements contents. Seasonal variations of atmospheric radon concentrations and ambient gamma dose rates were registered with higher values during warmer and lower during colder seasons. The opposite seasonal variations were observed for soil gas radon concentrations. No distinctive seasonal variations were recorded in results of uranium, thorium and potassium contents in soil measured in situ by the gamma-ray spectrometer. The correlation coefficients were calculated on the base of annual average data. The correlations between ambient gamma dose rate and radon concentration in soil and in the atmosphere were 0.83 and 0.62 respectively, which may suggest that ambient gamma dose rate can be a useful parameter to indicate geogenic radon potential.

Near real-time soil erosion mapping through mobile gamma-ray spectroscopy

Soil erosion has been associated with various negative environmental impacts foremost of which is the potential pressure it could impose on global food security. The poor conditions of our agricultural soil can be attributed to years of unsustainable farming practices occurring throughout history that has placed significant pressure on the environment. Moreover, climate change scenarios indicate further intensification which is likely making prediction and assessment of erosion processes critical for long term agricultural sustainability. This study demonstrates the potential of mobile gamma-ray spectrometry with large volume NaI(Tl) detectors to identify, at high spatial resolution, changes in ¹³⁷Cs soil concentration within the ploughed layer of soil and enabling the soil erosion processes to be quantified. This technique represents a significant advantage over conventional spatially-isolated point measurements such as soil sampling as it offers real time mapping at the field scale. However, spectral signal derived from measurements in the field are highly dependent on the calibration procedure used and are particularly sensitive to source-detector changes such as the presence of a vehicle, ground curvature and soil moisture content. Conventional calibration procedures tend to not consider these potential sources of uncertainty potentially leaving the system vulnerable to systematic uncertainties, especially when 137Cs concentrations are low. This study used Monte Carlo simulations to investigate such changes utilising additional information including a high-resolution digital terrain model. The method was demonstrated on a ploughed site in Scotland, revealing a mixture of tillage and water erosion patterns supported by soil core data. Findings showed that the sites topography had relatively little effect (<10%) on calculated erosion rates, but moisture content could be the determining factor, albeit very difficult to measure reliably throughout a survey.

The path from geology to indoor radon

It is generally accepted that radon emission is strongly influenced by the geological characteristics of the bedrock. However, transport in-soil and entry paths indoors are defined by other factors such as permeability, building and architectural features, ventilation, occupation patterns, etc. The purpose of this paper is to analyze the contribution of each parameter, from natural to man-made, on the radon accumulation indoors and to assess potential patterns, based on 100 case studies in Romania. The study pointed out that the geological foundation can provide a reasonable explanation for the majority of the values recorded in both soil and indoor air. Results also showed that older houses, built with earth-based materials, are highly permeable to soil radon. Energy-efficient houses, on the other hand, have a tendency to disregard the radon potential of the geological foundation, causing a higher predisposition to radon accumulation indoors and decreasing the general indoor air quality.

High seasonal variations of the radon exhalation from soil surface in the fault zones (Baikal and North Caucasus regions)

The seasonal variations of radon exhalation rate from soil surface were studied in two seismically active regions of the Russian Federation - the Baikal rift and the North Caucasus. In each region, monthly measurements of the radon exhalation have been carried out at two relatively proximal sites, one of which was located within the active fault zone and the other outside of the fault zone. The Open Charcoal Chamber Method was used. Very high radon exhalation rate values were found in the fault zones at both regions. At the Baikal rift, the radon exhalation reached 1.4 Bq m^-2 s^-1, and at the Caucasian region in some periods it even achieved 24 Bq m^-2 s^-1, which is an extremely high value. The same pattern of seasonal variations of radon levels with abnormal high radon exhalation rate values in summer and extremely low in winter were observed in both the Baikal and Caucasus regions. Clear correlation between radon exhalation and air temperature were also revealed. The obtained data and simulation results indicate that seasonal fluctuations in the radon exhalation rate are caused by the inversion of the direction of convective air flow in the fractured zones of the rock massif. In summer, the convective air flow is directed from the rock massif to the atmosphere and in winter, vice versa, from the atmosphere to the rock massif. This phenomenon is similar to the well-known "chimney effect", i. e. in winter there is a direct draft in the system of fractures, and in summer - the reverse one. Thus, the detected radon anomalies are due to near-surface convective air circulation in permeable zones of the mountain ranges and most probably are not associated with deep crustal or mantle degassing. Seasonal thermally induced radon anomalies should be taken into account both in the radon risk mapping and in the application of radon as a tracer of natural processes in various fields of geology and geophysics.

Evaluation of radon exposure risk and lung cancer incidence/mortality in South-eastern Italy

INTRODUCTION

Radon and its decay products may cause substantial health damage after long-term exposure. The aim of the study was to perform a spatial analysis of radon concentration in the Salento peninsula, province of Lecce (South-eastern Italy) in order to better characterize possible risk for human health, with specific focus on lung cancer.

METHODS

Based on previous radon monitoring campaigns carried out in 2006 on behalf of the Local Health Authority (ASL Lecce) involving 419 schools and through the application of kriging estimation method, a radon risk map was obtained for the province of Lecce, in order to determine if areas with higher radon concentrations were overlapping with those characterized by the highest pulmonary cancer incidence and mortality rates.

RESULTS

According to our data, areas at higher radon concentrations seem to overlap with those characterized by the highest pulmonary cancer mortality and incidence rates, thus indicating that human exposure to radon could possibly enhance other individual or environmental pro-carcinogenic risk factors (i.e. cigarette smoking, air pollution and other exposures).

CONCLUSIONS

The radon risk should be further assessed in the evaluation of the causes resulting in higher mortality and incidence rates for pulmonary cancer in Salento area vs Italian average national data. For these reasons, ASL Lecce in cooperation with ARPA Puglia and CNR-IFC has included the monitoring of individual indoor radon concentrations in the protocol of PROTOS case-control Study, aimed at investigating the role of different personal and environmental risk factors for lung cancer in Salento.

PRE-ANTHROPIC AND PRESENT OUTDOOR GAMMA EQUIVALENT DOSE RATE OF THE HISTORIC CENTER OF ROME (ITALY)

The outdoor gamma background of the historic center of Rome was studied by in situ measurements and average values of the outcropping geological formations. The survey resulted in two maps of dose equivalent rate, related to pre-anthropic and present conditions. Presently, the average of the dose equivalent rate from outdoor gamma-ray field is equal to 0.31 μSv h-1, corresponding to an outdoor annual effective dose equivalent of 0.548 mSv a-1 and to an outdoor excess lifetime cancer risk [International Commission on Radiological Protection (ICRP). Recommendations of the ICRP, 21, 1/3, Publication 60, 1990] of 2.56 × 10-3. The originary radioactivity was enhanced by anthropic action up to a level of health risk comparable to that one deriving by fine particulate matter. The assessment of the evolution and dispersion of the outdoor gamma background offers a new perspective to study the urban architectural evolution. Such a mapping allows us to individuate mitigation actions and neighborhoods in which the monitoring of illicit trafficking of radioactive material can be efficiently tested.

The FIRST large-scale mapping of radon concentration in soil gas and water in Romania

In the framework of the last Council Directive 2013/59 (Euratom, 2014) laying down basic safety standards for protection against the dangers arising from exposure to ionizing radiation, the problem of radon was assumed in Romania at national level by responsible authorities through the design and development of a National Radon Action Plan and an adequate legislation (HG nr. 526/2018). In order to identify radon risk areas, however, it is necessary to perform systematic radon measurements in different environmental media (soil gas, water, indoor air) and to map the results. This paper presents an atlas of up-to-date radon in soil and water levels for central and western part of Romania. The radon in soil map includes data from 2564 measurements carried out on-site, using Luk3C radon detector. The Luk-VR system was used to measure radon activity concentration from 2452 samples of drinking water. The average radon activity concentration was 29.3 kBq m^-3 for soil gas, respectively 9.8 Bq l^-1 for water dissolved air. Mapping of radon can be a useful tool to implement radon policies at both the national and local levels, defining priority areas for further study when land-use decisions must be made.

Integrating radon and thoron flux data with gamma radiation mapping in radon-prone areas. The case of volcanic outcrops in a highly-urbanized city (Roma, Italy)

An integration of laboratory radon and thoron exhalation data with gamma radiation mapping is applied to assess the geogenic radon and the exposure of people to natural radiation in a highly-urbanized city (Roma, Italy). The study area is a protected territory where ignimbrites from Colli Albani volcano and alluvial sediments largely crop out. A map of total gamma radiation, a gamma transect across Caffarella valley and 9 vertical gamma profiles have been carried out, showing that the main control of gamma levels is, of course, the lithological nature, without neglecting the simultaneous effect of other parameters such as slope morphology, erosion/weathering processes, occurrence of sinkholes or underground tunnels. The surveys allowed to distinguish the medians of ignimbrites (from 816 ± 16 cps to 936 ± 19 cps) from that of alluvial materials (611 ± 14) cps), but showed also that alluvial sediments with anomalously high radioactivity (769 ± 14 cps) can be locally recognized, providing valuable information on the interaction between sedimentation and erosion in fluvial valleys. Total gamma activity was converted into absorbed gamma dose rate ranging from 0.33 to 0.38 μSv/hr. Outdoor Annual Effective Dose Equivalents were also estimated between 0.58 and 0.67 mSv y^-1. Laboratory radon and thoron exhalation rates of collected material are positively correlated with gamma radiation. Volcanic and alluvial sediments are well-discriminated. The correlation between the two variables is evident, but not robust because of the variable concentration of ⁴⁰ K, which is not contributing to radon and thoron exhalation rates. Anomalous data of soil samples located at the foot of a slope can be interpreted as due to reworking and accumulation processes. Similar gamma radiation data documents analogous concentration of radon and thoron parent-nuclides, but coexisting different radon and thoron exhalation rates provides an additional information on different grain size distributions which can be considered as a proxy for soil gas permeability. The integration of gamma mapping and radon and thoron exhalation measurements is a very useful tool to assess people exposure to natural radiation, in terms of dose rates and potential indoor radon. Gamma mapping, which provides data on the radiation source (the bedrock) is fast and not expensive. It allows to obtain very detailed pictures of a study area, but it needs to be combined with laboratory determination of radon and thoron release in order to definitely and correctly interpret variations of gamma signal. Furthermore, laboratory determination of soil radon exhalation gives information on the release of radon and is a good proxy for soil gas permeability. It has the great advantage over in-situ measurements of gas flow not to be influenced by seasonal pedoclimatic parameters and is affected by lower analytical uncertainties. These data are thus reproducible and precise and can be used to estimate potential radon hazard, which is the main source of exposure and thus the most important parameter for human protection from environmental radioactivity.

An Assessment of Groundwater Contamination Risk with Radon Based on Clustering and Structural Models

There is currently some controversy in the scientific community regarding the efficiency of the water–rock interaction process in the contamination of radon in groundwater. In this study, some difficulties were found in the sampling phase. Many of the water collection points are used for human consumption. As such, some municipalities did not want to collaborate. When this natural contaminant is undetectable to the human sense and may cause pulmonary neoplasms in the long term, it is difficult to obtain collaboration from the municipalities concerned. To overcome this controversy, it is important to understand that geogenic, climatic, hydrological, and topographic features may contribute to the effective transfer of radon from rocks to groundwater. In brief, this new approach combines the radon transfer from the geological substrate to the groundwater circulation through hierarchic agglomerative clustering (HAC) and partial least squares-path modeling (PLS-PM) methods. The results show that some lithologies with higher radon production may not always contribute to noticeable groundwater contamination. In this group, the high-fracturing density confirms the recharge efficiency, and the physical-chemical properties of the hydraulic environment (electric conductivity) plays the main role of radon unavailability in the water intended for human consumption. Besides, the hydraulic turnover time of the springs can be considered an excellent radiological indicator in groundwater. In the absence of an anomalous radioactive source near the surface, it means that the high-turnover time of the springs leads to a low-radon concentration in the water. Besides linking high-risk areas with a short period required to free local flow discharges, this study exposes the virtues of a new perspective of a groundwater contamination risk modeling.

Mapping the geogenic radon potential and radon risk by using Empirical Bayesian Kriging regression: A case study from a volcanic area of central Italy

A detailed geochemical study on radon related to local geology was carried out in the municipality of Celleno, a little settlement located in the eastern border of the Quaternary Vulsini volcanic district (central Italy). This study included soil-gas and terrestrial gamma dose rate survey, laboratory analyses of natural radionuclides (²³⁸U, ²²⁶Ra, ²³²Th, ⁴⁰K) activity in rocks and soil samples, and indoor radon measurements carried out in selected private and public dwellings. Soil-gas radon and carbon dioxide concentrations range from 6 to 253 kBq/m³ and from 0.3 to11% v/v, respectively. Samples collected from outcropping volcanic and sedimentary rocks highlight: significant concentrations of ²³⁸U, ²²⁶Ra and ⁴⁰K for lavas (151, 150 and 1587 Bq/kg, respectively), low concentrations for tuffs (126, 123 and 987 Bq/kg, respectively), and relatively low for sedimentary rocks (108, 109 and 662 Bq/kg, respectively). Terrestrial gamma dose rate values range between 0.130 and 0.417 μSv/h, being in good accordance with the different bedrock types. Indoor radon activity ranges from 162 to 1044 Bq/m³; the calculated values of the annual effective dose varied from 4.08 and 26.31 mSv/y. Empirical Bayesian Kriging Regression (EBKR) was used to develop the Geogenic Radon Potential (GRP) map. EBKR provided accurate predictions of data on a local scale developing a spatial regression model in which soil-gas radon concentrations were considered as the response variable; several proxy variables, derived from geological, topographic and geochemical data, were used as predictors. Risk prediction map for indoor radon was tentatively produced using the Gaussian Geostatistical Simulation and a soil-indoor transfer factor was defined for a 'standard' dwelling (i.e., a dwelling with well-defined construction properties). This approach could be successfully used in the case of homogeneous building characteristics and territory with uniform geological characteristics.

Geolocation of premises subject to radon risk: Methodological proposal and case study in Madrid

Useful information on the potential radon risk in existing buildings can be obtained by combining data from sources such as potential risk maps, the 'Sistema de Información sobre Ocupación del Suelo de España' (SIOSE) [information system on land occupancy in Spain], cadastral data on built property and population surveys. The present study proposes a method for identifying urban land, premises and individuals potentially subject to radon risk. The procedure draws from geographic information systems (GIS) pooled at the municipal scale and data on buildings possibly affected. The method quantifies the magnitude of the problem in the form of indicators on the buildings, number of premises and gross floor area that may be affected in each risk category. The findings are classified by type of use: residential, educational or office. That information may guide health/prevention policies by targeting areas to be measured based on risk category, or protection policies geared to the construction industry by estimating the number of buildings in need of treatment or remediation. Application of the methodology to Greater Madrid showed that 47% of the municipalities have houses located in high radon risk areas. Using cadastral data to zoom in on those at highest risk yielded information on the floor area of the vulnerable (basement, ground and first storey) premises, which could then be compared to the total. In small towns, the area affected differed only scantly from the total, given the substantial proportion of low-rise buildings in such municipalities.

Influences of meteorological parameters on indoor radon concentrations (222Rn) excluding the effects of forced ventilation and radon exhalation from soil and building materials

Elevated indoor radon concentrations (²²²Rn) in dwellings pose generally a potential health risk to the inhabitants. During the last decades a considerable number of studies discussed both the different sources of indoor radon and the drivers for diurnal and multi day variations of its concentration. While the potential sources are undisputed, controversial opinions exist regarding their individual relevance and regarding the driving influences that control varying radon indoor concentrations. These drivers include (i) cyclic forced ventilation of dwellings, (ii) the temporal variance of the radon exhalation from soil and building materials due to e.g. a varying moisture content and (iii) diurnal and multi day temperature and pressure patterns. The presented study discusses the influences of last-mentioned temporal meteorological parameters by effectively excluding the influences of forced ventilation and undefined radon exhalation. The results reveal the continuous variation of the indoor/outdoor pressure gradient as key driver for a constant "breathing" of any interior space, which affects the indoor radon concentration with both diurnal and multi day patterns. The diurnally recurring variation of the pressure gradient is predominantly triggered by the day/night cycle of the indoor temperature that is associated with an expansion/contraction of the indoor air volume. Multi day patterns, on the other hand, are mainly due to periods of negative air pressure indoors that is triggered by periods of elevated wind speeds as a result of Bernoulli's principle.

Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data

Machine learning is a class of statistical techniques which has proven to be a powerful tool for modelling the behaviour of complex systems, in which response quantities depend on assumed controls or predictors in a complicated way. In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon (²²²Rn). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms "learn" from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques. By applying learning machines, we show, as our second purpose, that missing data or periods of Rn series data can be reconstructed and resampled on a regular grid reasonably, if data of appropriate physical controls are available. The techniques also identify to which degree the assumed controls contribute to imputing missing Rn values. Our third purpose, though no less important from the viewpoint of physics, is identifying to which degree physical, in this case environmental variables, are relevant as Rn predictors, or in other words, which predictors explain most of the temporal variability of Rn. We show that variables which contribute most to the Rn series reconstruction, are temperature, relative humidity and day of the year. The first two are physical predictors, while "day of the year" is a statistical proxy or surrogate for missing or unknown predictors.

Quantile regression and Bayesian cluster detection to identify radon prone areas

Albeit the dominant source of radon in indoor environments is the geology of the territory, many studies have demonstrated that indoor radon concentrations also depend on dwelling-specific characteristics. Following a stepwise analysis, in this study we propose a combined approach to delineate radon prone areas. We first investigate the impact of various building covariates on indoor radon concentrations. To achieve a more complete picture of this association, we exploit the flexible formulation of a Bayesian spatial quantile regression, which is also equipped with parameters that controls the spatial dependence across data. The quantitative knowledge of the influence of each significant building-specific factor on the measured radon levels is employed to predict the radon concentrations that would have been found if the sampled buildings had possessed standard characteristics. Those normalised radon measures should reflect the geogenic radon potential of the underlying ground, which is a quantity directly related to the geological environment. The second stage of the analysis is aimed at identifying radon prone areas, and to this end, we adopt a Bayesian model for spatial cluster detection using as reference unit the building with standard characteristics. The case study is based on a data set of more than 2000 indoor radon measures, available for the Abruzzo region (Central Italy) and collected by the Agency of Environmental Protection of Abruzzo, during several indoor radon monitoring surveys.