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

Recent trends in the phytoremediation of radionuclide contamination of soil by cesium and strontium: Sources, mechanisms and methods: A comprehensive review

This comprehensive review examines recent trends in phytoremediation strategies to address soil radionuclide contamination by cesium (Cs) and strontium (Sr). Radionuclide contamination, resulting from natural processes and nuclear-related activities such as accidents and the operation of nuclear facilities, poses significant risks to the environment and human health. Cs and Sr, prominent radionuclides involved in nuclear accidents, exhibit chemical properties that contribute to their toxicity, including easy uptake, high solubility, and long half-lives. Phytoremediation is emerging as a promising and environmentally friendly approach to mitigate radionuclide contamination by exploiting the ability of plants to extract toxic elements from soil and water. This review focuses specifically on the removal of 90Sr and 137Cs, addressing their health risks and environmental implications. Understanding the mechanisms governing plant uptake of radionuclides is critical and is influenced by factors such as plant species, soil texture, and physicochemical properties. Phytoremediation not only addresses immediate contamination challenges but also provides long-term benefits for ecosystem restoration and sustainable development. By improving soil health, biodiversity, and ecosystem resilience, phytoremediation is in line with global sustainability goals and environmental protection initiatives. This review aims to provide insights into effective strategies for mitigating environmental hazards associated with radionuclide contamination and to highlight the importance of phytoremediation in environmental remediation efforts.

Application of GIS and spatiotemporal analyses in viral infection modelling using multiple datasets - A case study on the SARS-CoV-2 epidemic

INTRODUCTION/OBJECTIVE

Viral and infectious diseases such as COVID-19 continue to pose a significant public health threat. In order to create an early warning system for new pandemics or emerging versions of the virus, it is imperative to study its epidemiology. In this study, we created a geospatial model to predict the weekly contagion and lethality rates of COVID-19 in Ireland.

METHODS

More than forty parameters including atmospheric pollutants, metrological variables, sociodemographic factors, and lockdown phases were introduced as input variables to the model. The significant parameters in predicting the number of new cases and the death toll were identified. QGIS software was employed to process input data, and a principal component regression (PCR) model was developed using the statistical add-on XLSTAT.

RESULTS AND CONCLUSIONS

The developed models were able to predict more than half of the variations in contagion and lethality rates. This indicates that the proposed model can serve to help prediction systems for the identification of future high-risk conditions. Nevertheless, there are additional parameters that could be included in future models, such as the number of deaths in care homes, the percentage of contagion and mortality among health workers, and the degree of compliance with social distancing.

Assessment of natural radioactivity, radon gas and soil characteristics along the Volta Lake in the Kpando municipality of Volta region, Ghana

Assessment of radionuclides, indoor radon (222RnI), radon exhalation (222Rnex), and soil characteristics in the coastal part of Kpando has been studied using HPGe, CR-39 and sieving techniques. Statistical analysis between radionuclides, radon levels and soil characteristics was done using Pearson's correlation. The mean radionuclide concentration, radon levels and soil characteristics were obtained as 226Ra (23.1 ± 1.4 Bq per kg), 232Th (34.6 ± 2.9 Bq per kg), 40K (187.1 ± 13.7 Bq per kg), 222RnI (64.70 ± 2.7 Bq per m3), 222Rnex (7.9 ± 0.5 μBq per m2h), sandy (45.9 ± 3.9%), silt (40.7 ± 3.1%), clay (13.5 ± 0.8%), porosity (0.6 ± 0.1) and moisture (7.6 ± 0.8%). Radiological effects estimated were within recommended limits. The maximum positive and negative coefficients exist between 222Ra/222Rnex (1.0) and 222Rnex/MC (-0.9), respectively. Radon exhalation correlates better with soil characteristics. The statistical analysis indicated that soil characteristics have significant effects on radionuclides and radon levels in soils and dwellings.

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.

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.

Distributions and Risk Assessment of the Natural Radionuclides in the Soil of Shoubra El Kheima, South Nile Delta, Egypt

Due to heightening concern about radiation hazards protection, activity concentrations of 226Ra, 232Th, 40K in forty soil samples collected from Shoubra El Kheima in the South Nile Delta were measured using gamma-ray spectrometry. The mean activity concentrations of 226Ra and 40K were higher in 20% of the considered samples than the world average values. A comprehensive comparison with up-to-date data was carried out. Spatial distribution maps of the measured radionuclides and radiological parameters were generated. The distributions of natural radionuclides were influenced by the soil organic matter, clay content, and scavenger metals oxides, as well as differences in the physical and chemical attributes and solubility of these radionuclides. The results revealed that industrial activity and agricultural practices in the study area caused an incremental increase in 226Ra and 40K activity concentrations. It can be deduced that although there are intensive industrial activities in this area, the natural radiation that comes from the soil is normal and does not pose a significant radiological hazard to the public. The natural radioactivity of soil in this area needs to be monitored periodically to prevent unnecessary radiation exposure to inhabitants.