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)

Combining chemical analysis and toxicological methods to access the ecological risk of complex contamination in Daye Lake

As one of the nine primary non-ferrous metal smelting bases in China, Daye Lake basin was polluted due to diverse human activities. But so far the pollution status and related ecological risks of this region have not been detailly investigated. In current study, pollutants including heavy metals, polycyclic aromatic hydrocarbons (PAHs) and organochlorine pesticides (OCPs) in eight sediment samples from Daye Lake were quantified. 18S rRNA gene sequencing was employed to profile the nematode community structure within these sediments. Model organism Caenorhabditis elegans (C. elegans) were further applied for a comprehensive ecological risk assessment of Daye Lake. Notably, Cadmium (Cd) was identified as a key driver of ecological risk, reaching an index of 1287.35. At sample point S4, OCPs particularly p,p'-DDT, displayed an extreme ecological risk with a value of 23.19. Cephalobidae and Mononchida showed strong sensitivity to pollutant levels, reinforcing their suitability as robust bioindicators. The composite pollutants in sampled sediments caused oxidative stress in C. elegans, with gene Vit-2 and Mtl-1 as sensitive biomarkers. By employing the multiple analysis methods, our data can offer valuable contributions to environmental monitoring and health risk assessment for composite polluted areas.

A large and feasible national survey representative of population exposure to outdoor gamma radiation in urban areas

Background

Although data on outdoor gamma radiation are available for many countries, they have generally been obtained with measurements performed in undisturbed environments instead of in urban areas where most of the population lives. Only one large national survey, with on-site measurements in urban areas, has been identified worldwide, probably due to high costs (e.g., personnel and instrumentation) and difficulties in selecting measuring points.

Methods

A campaign of outdoor gamma radiation measurements has been carried out in the entire Italian territory. All measurement points were selected at the infrastructures of an Italian telecommunications company as representatives of all the possible situations of outdoor exposure to gamma radiation for population in urban areas. Ten replicates of portable gamma (X) detectors carried out all the measurements.

Results

Approximately 4,000 measurements have been performed. They are distributed across 2,901 Italian municipalities, accounting for 75% of the Italian population. The national population-weighted mean of the gamma ambient dose equivalent rate (ADER) is 117 nSv h-1, and it ranges from 62 to 208 nSv h-1 and from 40 to 227 nSv h-1 for 21 regions and 107 provinces, respectively. The average variability at the municipal level, in terms of the coefficient of variation (CV) is 21%, ranging from 3 to 84%. The impact of land coverage and the distance from a building on the outdoor gamma radiation level was assessed with complementary measurements, leading to differences ranging from -40 to 50% and to 50%, respectively.

Conclusion

A representative campaign of outdoor gamma dose rate measurements has been performed in Italy, only in urban areas, to assess the exposure effect due to outdoor gamma radiation on the population. It is the largest national campaign in urban areas worldwide, with a total of 3,876 on-site measurements. The land coverage and the distance from surrounding buildings were recognized to strongly affect outdoor gamma radiation levels, leading to high variability within small areas. The collaboration with a company that owns a network of facilities on a national territory as dense as the residing population made this survey feasible and affordable. Other countries might adopt this methodology to conduct national surveys in urban environments.

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.

Unraveling the complex interplay between soil characteristics and radon surface exhalation rates through machine learning models and multivariate analysis

This research seeks to elucidate the intricate interplay between soil characteristics and the rates of radon surface exhalation rate. To achieve this aim, Light Gradient Boosting Machine (LightGBM) and eXtreme Gradient Boosting (XGBoost) machine learning (ML) algorithms are employed, supported by Multivariate Analysis (MA). An analysis was performed on a collection of soil samples, examining radon surface exhalation rates and other pertinent properties such as moisture content, particle size distributions, and the concentrations of Ra-226, Th-232, and K-40. The analysis revealed several key factors influencing radon exhalation rates, namely Ra-226 concentration, moisture content, and larger soil particles. To visualize the intricate relationships between these variables, contour plots of experimental and ML-generated data were created. These visual representations demonstrated that elevated soil moisture levels decrease radon exhalation rates. In contrast, higher concentrations of Ra-226 and a greater proportion of large soil particles led to an increase in exhalation rates. This endeavor presents these complex relationships in an accessible manner, furthering our understanding of the factors in radon surface exhalation. MA techniques, including Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA), were initially employed to investigate the complex interactions of soil attributes on radon exhalation. HCA identified three distinct clusters but faced limitations in detecting strong negative impacts. PCA successfully captured these inverse effects, indicating that the first two principal components accounted for approximately 80% of the total variance, primarily attributed to Ra-226 concentration, moisture content, and the percentage of large soil particles. However, neither technique could quantify the effects of soil attributes on radon exhalation rates. LightGBM outperformed XGBoost, but both successfully quantified the impacts of the studied soil characteristics on radon exhalation. Sensitivity analysis confirmed the robustness and accuracy of both models. This study highlights that XGBoost and LightGBM algorithms can effectively quantify radon exhalation rates based on soil characteristics, providing valuable insights for environmental policies, land use planning, and radon mitigation strategies.

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.

Evaluation of tectonically enhanced radon in fault zones by quantification of the radon activity index

This work highlights the importance of the Geogenic Radon Potential (GRP) component originated by degassing processes in fault zones. This Tectonically Enhanced Radon (TER) can increase radon concentration in soil gas and the inflow of radon in the buildings (Indoor Radon Concentrations, IRC). Although tectonically related radon enhancement is known in areas characterised by active faults, few studies have investigated radon migration processes in non-active fault zones. The Pusteria Valley (Bolzano, north-eastern Italy) represents an ideal geological setting to study the role of a non-seismic fault system in enhancing the geogenic radon. Here, most of the municipalities are characterised by high IRC. We performed soil gas surveys in three of these municipalities located along a wide section of the non-seismic Pusteria fault system characterised by a dense network of faults and fractures. Results highlight the presence of high Rn concentrations (up to 800 kBq·m^-3) with anisotropic spatial patterns oriented along the main strike of the fault system. We calculated a Radon Activity Index (RAI) along north-south profiles across the Pusteria fault system and found that TER is linked to high fault geochemical activities. This evidence confirms that TER constitutes a significant component of GRP also along non-seismic faults.

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).

Harmonization and mapping of terrestrial gamma dose rate data in Belgium

With several databases available, including two sets of in situ measurements of the ambient gamma dose rate and an airborne survey of K, Th, U in soil, Belgium is a favourable case for exploring the mapping methodology for terrestrial radiation. The first step is the harmonization of the different data sets, taking in situ measurements with an ion chamber as the reference. Corrections are necessary, based on the data themselves (a) to the measurements of permanent monitoring stations, (b) to the data calculated from airborne measurements of the soil activity, due in particular to the attenuation by the forest cover, and (c) to the other data calculated from the soil activity, due to the lower activity of the upper layer. After subtracting the cosmic contribution, a harmonized database of the terrestrial gamma dose rate (TGDR) based on 379 in situ measurements was built, together with a harmonized data set of 30134 TGDR values calculated from the concentrations of K, Th, U in soil deduced from the airborne survey. The two data sets are in good agreement with each other for all statistical characteristics that were examined like basic statistics, qq-plots, analysis of variance (ANOVA) or variograms, which validates the airborne-based data set by the link with in situ ion chamber measurements. ANOVA reveals the strong relation between TGDR and the soil class, which justifies the use of a soil map as the framework for developing the TGDR map. The variograms show the absence of residual spatial correlations within soil classes. The two harmonized TGDR data sets were mapped at the nodes of a kilometric grid by the moving average method within soil groups. There is a rather good agreement between the maps, confirming the equivalence between the two data sets and the validation of the airborne based one, which can obviously give more detail. After reducing the maps to a 10 km × 10 km grid, the two data sets were used to check the accuracy of the Belgian part of the European TGDR contained in the European Atlas of Natural Radiation.

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.