Prediction models for radiological characterization of natural aggregates based on chemical composition and mineralogy

The radiological characterization of aggregates used in construction materials is essential to determine their suitability from a radiological protection perspective and to ensure their safety for health and the environment. While the activity concentrations of radionuclides present in construction...

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Autores: Caño, Andrés, Alonso López, M. del Mar, Pachón Montaño, Alicia, Marzal García, Queralt, Sousa, L., Suárez-Navarro, José Antonio, Hernáiz, Guillermo
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/391938
Acesso em linha:http://hdl.handle.net/10261/391938
Access Level:acceso abierto
Palavra-chave:ACI
ACP
Aggregates
Gamma spectrometry
XRF
NORM
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
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repository_id_str
spelling Prediction models for radiological characterization of natural aggregates based on chemical composition and mineralogyCaño, AndrésAlonso López, M. del MarPachón Montaño, AliciaMarzal García, QueraltSousa, L.Suárez-Navarro, José AntonioHernáiz, GuillermoACIACPAggregatesGamma spectrometryXRFNORMhttp://metadata.un.org/sdg/3Ensure healthy lives and promote well-being for all at all agesThe radiological characterization of aggregates used in construction materials is essential to determine their suitability from a radiological protection perspective and to ensure their safety for health and the environment. While the activity concentrations of radionuclides present in construction materials are typically determined using gamma spectrometry, an alternative approach involves the development of statistical methods and predictive models derived from the chemical composition of the material. A total of 39 aggregates used in construction of various types (siliceous, carbonatic, volcanic, and granitic) have been analyzed, correlating their chemical compositions obtained through X-ray fluorescence (XRF) with the activity concentrations of natural radionuclides measured via gamma spectrometry using principal component analysis (PCA). The results obtained allowed for the observation of an inversely proportional relationship between the chemical composition of the grouping of siliceous and carbonatic aggregates and the content of radionuclides. However, the set of granitic aggregates showed a strong correlation with the natural radioactive series of uranium, thorium, and K. Conversely, the radionuclide content of volcanic aggregates was independent of their chemical composition. The results obtained from the PCA facilitated the development of different models using multiple regression analysis. The chemical parameters obtained in the proposed models were related to the typical mineralogy in each grouping, ranging from primary minerals such as feldspars to accessory minerals such as anatase, apatite, and pyrolusite. Finally, the models were validated using independent samples from those used to determine the models, achieving RSD (%) values ≤ 30% in 50% of the activity concentrations of Ra, Th(Pb), and K, as well as the estimated ACI.This research has been conducted with the support of Project PID2020-116002RB-100/AEI/10.13039/501100011033 (HORRADIONEX). The authors would also like to thank the MICIM for the pre-doctoral contract (PRE2021-098535) of Andrés Caño Blanes.Peer reviewedMinisterio de Ciencia e Innovación (España)Agencia Estatal de Investigación (España)Caño, Andrés [0000-0003-3999-2791]Alonso, María del Mar [0000-0001-9096-752X]Pachón Montaño, Alicia [0000-0002-4688-1072]Marzal García, Queralt [0009-0000-6639-5111]Instituto de Ciencias de La Construcción Eduardo Torroja [https://ror.org/03x2a1f75]2025202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/391938reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-116002RB-I00The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI http://doi.org/10.3390/ma18061369http://doi.org/10.3390/ma18061369Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3919382026-05-22T06:33:51Z
dc.title.none.fl_str_mv Prediction models for radiological characterization of natural aggregates based on chemical composition and mineralogy
title Prediction models for radiological characterization of natural aggregates based on chemical composition and mineralogy
spellingShingle Prediction models for radiological characterization of natural aggregates based on chemical composition and mineralogy
Caño, Andrés
ACI
ACP
Aggregates
Gamma spectrometry
XRF
NORM
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
title_short Prediction models for radiological characterization of natural aggregates based on chemical composition and mineralogy
title_full Prediction models for radiological characterization of natural aggregates based on chemical composition and mineralogy
title_fullStr Prediction models for radiological characterization of natural aggregates based on chemical composition and mineralogy
title_full_unstemmed Prediction models for radiological characterization of natural aggregates based on chemical composition and mineralogy
title_sort Prediction models for radiological characterization of natural aggregates based on chemical composition and mineralogy
dc.creator.none.fl_str_mv Caño, Andrés
Alonso López, M. del Mar
Pachón Montaño, Alicia
Marzal García, Queralt
Sousa, L.
Suárez-Navarro, José Antonio
Hernáiz, Guillermo
author Caño, Andrés
author_facet Caño, Andrés
Alonso López, M. del Mar
Pachón Montaño, Alicia
Marzal García, Queralt
Sousa, L.
Suárez-Navarro, José Antonio
Hernáiz, Guillermo
author_role author
author2 Alonso López, M. del Mar
Pachón Montaño, Alicia
Marzal García, Queralt
Sousa, L.
Suárez-Navarro, José Antonio
Hernáiz, Guillermo
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
Agencia Estatal de Investigación (España)
Caño, Andrés [0000-0003-3999-2791]
Alonso, María del Mar [0000-0001-9096-752X]
Pachón Montaño, Alicia [0000-0002-4688-1072]
Marzal García, Queralt [0009-0000-6639-5111]
Instituto de Ciencias de La Construcción Eduardo Torroja [https://ror.org/03x2a1f75]
dc.subject.none.fl_str_mv ACI
ACP
Aggregates
Gamma spectrometry
XRF
NORM
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
topic ACI
ACP
Aggregates
Gamma spectrometry
XRF
NORM
http://metadata.un.org/sdg/3
Ensure healthy lives and promote well-being for all at all ages
description The radiological characterization of aggregates used in construction materials is essential to determine their suitability from a radiological protection perspective and to ensure their safety for health and the environment. While the activity concentrations of radionuclides present in construction materials are typically determined using gamma spectrometry, an alternative approach involves the development of statistical methods and predictive models derived from the chemical composition of the material. A total of 39 aggregates used in construction of various types (siliceous, carbonatic, volcanic, and granitic) have been analyzed, correlating their chemical compositions obtained through X-ray fluorescence (XRF) with the activity concentrations of natural radionuclides measured via gamma spectrometry using principal component analysis (PCA). The results obtained allowed for the observation of an inversely proportional relationship between the chemical composition of the grouping of siliceous and carbonatic aggregates and the content of radionuclides. However, the set of granitic aggregates showed a strong correlation with the natural radioactive series of uranium, thorium, and K. Conversely, the radionuclide content of volcanic aggregates was independent of their chemical composition. The results obtained from the PCA facilitated the development of different models using multiple regression analysis. The chemical parameters obtained in the proposed models were related to the typical mineralogy in each grouping, ranging from primary minerals such as feldspars to accessory minerals such as anatase, apatite, and pyrolusite. Finally, the models were validated using independent samples from those used to determine the models, achieving RSD (%) values ≤ 30% in 50% of the activity concentrations of Ra, Th(Pb), and K, as well as the estimated ACI.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/391938
url http://hdl.handle.net/10261/391938
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-116002RB-I00
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI http://doi.org/10.3390/ma18061369
http://doi.org/10.3390/ma18061369
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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