Geogenic radon potential mapping using geospatial analysis of multiple radon-related variables: a case study from Southeastern Ireland

2020 ◽  
Author(s):  
Mirsina Mousavi ◽  
Quentin Crowley
Keyword(s):  
Regression Model ◽  
Radon Concentration ◽  
Indoor Radon ◽  
Spatial Regression ◽  
Geostatistical Analysis ◽  
Soil Gas ◽  
Natural Radiation ◽  
Spatial Regression Model ◽  
Soil Gas Radon ◽  
Radon Potential

<p>A detailed investigation of geogenic radon potential (GRP) was carried out using geostatistical analysis on multiple radon-related variables to evaluate natural radiation in an area of Southeast Ireland. The geological setting of the study area includes basal Devonian sandstones and conglomerates overlying an offshoot of the Caledonian Leinster Granite, which intrudes Ordovician sediments. The Ordovician sediments contain traces of autunite (Ca(UO<sub>2</sub>)2(PO<sub>4</sub>)<sub>2</sub>·10–12H<sub>2</sub>O), which is a uranium-bearing mineral and a source of radon. To model radon release potential at different locations, a spatial regression model was developed in which soil gas radon concentration measured in-situ using a Radon RM-2 detector was considered as a response value. Proxy variables such as local geology, soil types, terrestrial gamma dose rates, radionuclide concentrations from airborne radiometric surveys, soil gas permeability, distance from major faults and a Digital Terrain Model were used as the main predictors. Furthermore, the distribution of indoor radon concentration was simulated using a soil-indoor transfer factor. Finally, the workability of the proposed GRP model was tested by evaluating the correlation between previously measured indoor radon concentrations and the estimated values by the GRP model at the same measurement locations. This model can also be used to estimate the GRPs of other areas where radon-related proxy values are available.        </p><p><strong>Keywords:</strong> Natural radiation, geogenic radon potential, geostatistical analysis, spatial regression model, indoor radon simulation</p>

scholarly journals Application of airborne radiometric surveys for large-scale geogenic radon potential classification

2020 ◽  
Author(s):  
Javier Elío ◽  
Quentin Crowley ◽  
Ray Scanlon ◽  
Jim Hodgson ◽  
Stephanie Long ◽  
...  
Keyword(s):  
High Risk ◽  
Radon Concentration ◽  
Indoor Radon ◽  
Low Risk ◽  
Soil Gas ◽  
Soil Gas Radon ◽  
Risk Areas ◽  
Radon Potential ◽  
Radon Risk

Background: Indoor radon represents an important health issue to the general population. Therefore, accurate radon risk maps help public authorities to prioritise areas where mitigation actions should be implemented. As the main source of indoor radon is the soil where the building is constructed, maps derived from geogenic factors ([e.g. geogenic radon potential [GRP]) are viewed as valuable tools for radon mapping. Objectives: A novel indirect method for estimating the GRP at national/regional level is presented and evaluated in this article. Design: We calculate the radon risk solely based on the radon concentration in the soil and on the subsoil permeability. The soil gas radon concentration was estimated using airborne gamma-ray spectrometry (i.e. equivalent uranium [eU]), assuming a secular equilibrium between eU and radium (226Ra). The subsoil permeability was estimated based on groundwater subsoil permeability and superficial geology (i.e. quaternary geology) by assigning a permeability category to each soil type (i.e. low, moderate or high). Soil gas predictions were compared with in situ radon measurements for representative areas, and the resulting GRP map was validated with independent indoor radon data. Results: There was good agreement between soil gas radon predictions and in situ measurements, and the resultant GRP map identifies potential radon risk areas. Our model shows that the probability of having an indoor radon concentration higher than the Irish reference level (200 Bq m-3) increases from c. 6% (5.2% – 7.1%) for an area classified as Low risk, to c. 9.7% (9.1% – 10.5%) for Moderate-Low risk areas, c. 14% (13.4% – 15.3%) for Moderate-High risk areas and c. 26% (24.5% – 28.6%) for High risk areas. Conclusions: The method proposed here is a potential alternative approach for radon mapping when airborne radiometric data (i.e. eU) are available.

The Empirical Bayesian Regression Kriging (EBRK) to map the Geogenic Radon Potential (GRP). A case of study from the Euganean Hills (Italy).

2021 ◽  
Author(s):  
Chiara Coletti ◽  
Giancarlo Ciotoli ◽  
Eleonora Benà ◽  
Erika Brattich ◽  
Giorgia Cinelli ◽  
...  
Keyword(s):  
Natural Radioactivity ◽  
Indoor Radon ◽  
Living Environment ◽  
Bayesian Regression ◽  
Soil Gas ◽  
Regression Kriging ◽  
Empirical Bayesian ◽  
Soil Gas Radon ◽  
Radon Potential ◽  
Radon Concentrations

<p>In the volcanic area of the Euganean Hills district (100 km<sup>2</sup>), the indoor radon often exceeds the threshold level of 300 Bq/m<sup>3 </sup>stipulated by the Council Directive 2013/59/Euratom, thus suggesting the need to investigate the possible link between observed radon concentrations and the local geology (Trotti et al., 1998,1999; Strati et al., 2014). More recently, statistical and geostatistical analysis on rock samples identified high U, Th and K concentrations associated with areas characterised by trachyte and rhyolite lithologies (Tositti et al., 2017). With this contribution, we completed our investigation on the natural radioactivity in the Euganean Hills district extending the rocks dataset, performing on-site soil gas survey, and considering other important factors which can locally increase the radon occurrence, such as hydrothermal alterations, types of soils (e.g., geochemistry or presence of organic matters), and faults. Furthermore, we elaborated a Geogenic Radon Potential map to assess the local spatial relationships between the measured soil gas radon concentrations and seven proxy-variables: fault density (FD), total gamma radiation dose (TGDR), <sup>220</sup>Rn (Tn), digital terrain mode (SLOPE), moisture index (MI), heat load index (HLI) and soil permeability (PERM). Empirical Bayesian Regression Kriging (EBRK) was used to develop the most accurate hazard map of the considered area, thus, providing the local administration an up-to-date decisional tool for the land use planning. For the high radon emission measured, the high density of dwelling, and its geomorphological features, the Euganean Hills district represented a very meaningful case of study.  </p><p> </p><p>Trotti, F., Tanferi, A., Lanciai, M., Mozzo, P., Panepinto, V., Poli, S., Predicatori, F., Righetti, F., Tacconi, A., Zorzine, R., 1998. Mapping of areas with elevated indoor radon levels in Veneto. Radiat. Prot. Dosim. 78 (1), 11–14.</p><p>Trotti, F., Tanferi, A., Bissolo, F., Fustegato, R., Lanciai, M., Mozzo, P., Predicatori, F., Querini, P., Righetti, F., Tacconi, A., 1999. A Survey to Map Areas with Elevated Indoor Radon Levels in Veneto, Radon in the Living Environment, 19-23 April 1999, Athens, Greece, 859–868.</p><p>Strati V., Baldoncini M., Bezzon G.P, Broggini C., Buso G.P., Caciolli A., Callegari I., Carmignani L, Colonna T, Fiorentini G., Guastaldi E., Kaçeli Xhixhaf M., Mantovani F, Menegazzo R., Moub L., Rossi Alvarez C., Xhixha G., Zanon A., 2014. Total natural radioactivity, Veneto (Italy). Journal of Maps, Vol. 11, Issue 4, 545–551. http://doi.org/10.1080/17445647.2014.923348.</p><p>Tositti L., Cinelli G., Brattich E., Galgaro A., Mostacci D., Mazzoli C., Massironi M., Sassi R., 2017. Assessment of lithogenic radioactivity in the Euganean Hills magmatic district (NE Italy). J. Environ. Radioact. 166, 259–269. https://doi.org/10.1016/j.jenvrad.2016.07.011</p>

scholarly journals Radiological Assessment of Indoor Radon and Thoron Concentrations and Indoor Radon Map of Dwellings in Mashhad, Iran

2020 ◽  
Vol 18 (1) ◽  
pp. 141
Author(s):  
Mohammademad Adelikhah ◽  
Amin Shahrokhi ◽  
Morteza Imani ◽  
Stanislaw Chalupnik ◽  
Tibor Kovács
Keyword(s):  
Limit Of Detection ◽  
Indoor Radon ◽  
Absolute Error ◽  
Soil Gas ◽  
Indoor Radon Concentration ◽  
Soil Gas Radon ◽  
Distance Weighting ◽  
The City ◽  
Monitoring Devices ◽  
Radon Concentrations

A comprehensive study was carried out to measure indoor radon/thoron concentrations in 78 dwellings and soil-gas radon in the city of Mashhad, Iran during two seasons, using two common radon monitoring devices (NRPB and RADUET). In the winter, indoor radon concentrations measured between 75 ± 11 to 376 ± 24 Bq·m−3 (mean: 150 ± 19 Bq m−3), whereas indoor thoron concentrations ranged from below the Lower Limit of Detection (LLD) to 166 ± 10 Bq·m−3 (mean: 66 ± 8 Bq m−3), while radon and thoron concentrations in summer fell between 50 ± 11 and 305 ± 24 Bq·m−3 (mean 115 ± 18 Bq m−3) and from below the LLD to 122 ± 10 Bq m−3 (mean 48 ± 6 Bq·m−3), respectively. The annual average effective dose was estimated to be 3.7 ± 0.5 mSv yr−1. The soil-gas radon concentrations fell within the range from 1.07 ± 0.28 to 8.02 ± 0.65 kBq·m−3 (mean 3.07 ± 1.09 kBq·m−3). Finally, indoor radon maps were generated by ArcGIS software over a grid of 1 × 1 km2 using three different interpolation techniques. In grid cells where no data was observed, the arithmetic mean was used to predict a mean indoor radon concentration. Accordingly, inverse distance weighting (IDW) was proven to be more suitable for predicting mean indoor radon concentrations due to the lower mean absolute error (MAE) and root mean square error (RMSE). Meanwhile, the radiation health risk due to the residential exposure to radon and indoor gamma radiation exposure was also assessed.

Measurements of radon flux and soil-gas radon concentration along the Main Central Thrust, Garhwal Himalaya, using SRM and RAD7 detectors

2013 ◽  
Vol 61 (4) ◽  
pp. 950-957 ◽  
Author(s):  
Abhay Anand Bourai ◽  
Sunita Aswal ◽  
Anoop Dangwal ◽  
Mukesh Rawat ◽  
Mukesh Prasad ◽  
...  
Keyword(s):  
Radon Concentration ◽  
Soil Gas ◽  
Garhwal Himalaya ◽  
Main Central Thrust ◽  
Radon Flux ◽  
Soil Gas Radon

Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model

Soil Research ◽  
2009 ◽  
Vol 47 (7) ◽  
pp. 651 ◽  
Author(s):  
John Triantafilis ◽  
Scott Mitchell Lesch ◽  
Kevin La Lau ◽  
Sam Mostyn Buchanan
Keyword(s):  
Regression Model ◽  
Cation Exchange Capacity ◽  
Spatial Regression ◽  
Soil Management ◽  
Exchange Capacity ◽  
Ancillary Data ◽  
Trend Surface ◽  
Spatial Regression Model ◽  
Field Level ◽  
Mlr Model

At the field level the demand for spatial information of soil properties is rapidly increasing owing to its requirements in precision agriculture and soil management. One of the most important properties is the cation exchange capacity (CEC, cmol(+)/kg) because it is an index of the shrink–swell potential and hence is a measure of soil structural resilience to tillage. However, CEC is time-consuming and expensive to measure. Various ancillary datasets and statistical methods can be used to predict CEC, but there is little scientific literature which implements this approach to map CEC or addresses the issue of the amount of ancillary data required to maximise precision and minimise bias of spatial prediction at the field level. We compare a standard least-squares multiple linear regression (MLR) model which includes 2 proximally sensed (EM38 and EM31), 3 remotely sensed (Red, Green and Blue spectral brightness), and 2 trend surface (Easting and Northing) variables as ancillary data or independent variables, and a stepwise MLR model which only includes the statistically valid EM38 signal data and the Easting trend surface vector. The latter is used as the basis for developing a hierarchical spatial regression model to predict CEC. The reliability of the model is analysed by comparing prediction precision (root mean square error) and bias (mean error) using degraded EM38 transect spacing (i.e. 96, 144, 192, 240, and 288 m) and comparing these with predictions achieved with the 48-m spacing. We conclude that the EM38 data available on the 96- and 144-m spacing are suitable at a reconnaissance level (i.e. broad-scale farming) and 24- or 48-m spacing are suitable at smaller levels where detailed information is necessary for siting the location of water reservoirs. In terms of soil management, CEC predictions determine where suitable subsoil exists for the purpose of soil profile inversion to improve the structural resilience of a topsoil that is susceptible to dispersion and surface crusting.

SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

2021 ◽  
Vol 14 (1) ◽  
pp. 89-97
Author(s):  
Dewi Retno Sari Saputro ◽  
Sulistyaningsih Sulistyaningsih ◽  
Purnami Widyaningsih
Keyword(s):  
Regression Model ◽  
Ensemble Learning ◽  
Spatial Data ◽  
Lognormal Distribution ◽  
Multiplicative Noise ◽  
Spatial Regression ◽  
Spatial Error ◽  
Spatial Regression Model ◽  
Sar Model ◽  
Spatial Autoregressive

The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise which has a lognormal distribution of cases on poverty data in East Java in 2016. The results showed that the estimated value of the non-hybrid spatial ensemble spatial regression model with multiplicative noise with a lognormal distribution was obtained from the average parameter estimation of 10 Spatial Error Model (SEM) resulting from resampling. The multiplicative noise used is generated from lognormal distributions with an average of one and a standard deviation of 0.433. The Root Mean Squared Error (RMSE) value generated by the non-hybrid spatial ensemble regression model with multiplicative noise with a lognormal distribution is 22.99.

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