Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, Peru
This study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over...
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2025-01-01
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| author | Rafael Liza Félix Díaz Patrizia Pereyra Daniel Palacios Nhell Cerna Luis Curo Max Riva |
| author_facet | Rafael Liza Félix Díaz Patrizia Pereyra Daniel Palacios Nhell Cerna Luis Curo Max Riva |
| author_sort | Rafael Liza |
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| description | This study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over three distinct measurement periods between 2015 and 2016, with 86 households participating. Detectors were randomly placed in various rooms within each household. Normality tests (Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov) were applied to assess the fit of radon concentrations to a log-normal distribution. Additionally, analysis of variance (ANOVA) was used to evaluate the influence of environmental and structural factors on radon variability. Non-normally distributed data were normalized using a Box–Cox transformation to improve statistical assumptions, enabling subsequent geostatistical analyses. Geospatial interpolation methods, specifically Inverse Distance Weighting (IDW) and Kriging, were employed to map radon concentrations. The results revealed significant temporal variability in radon concentrations, with geometric means of 146.4 Bq·<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mtext>m</mtext><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>, 162.3 Bq·<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mtext>m</mtext><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>, and 150.8 Bq·<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mtext>m</mtext><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>, respectively, across the three periods. Up to 9.5% of the monitored households recorded radon levels exceeding the safety threshold of 200 Bq·<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mtext>m</mtext><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>. Among the interpolation methods, Kriging provided a more accurate spatial representation of radon concentration variability compared to IDW, allowing for the precise identification of high-risk areas. This study provides a framework for using advanced statistical and geospatial techniques in environmental risk assessment. |
| format | Article |
| id | doaj-art-ee43fe5d9f59413ba866062b6b9cb686 |
| institution | Kabale University |
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| spelling | doaj-art-ee43fe5d9f59413ba866062b6b9cb6862025-01-24T13:31:35ZengMDPI AGEng2673-41172025-01-01611410.3390/eng6010014Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, PeruRafael Liza0Félix Díaz1Patrizia Pereyra2Daniel Palacios3Nhell Cerna4Luis Curo5Max Riva6Departamento Académico de Cursos Básicos, Universidad Científica del Sur, Lima 15067, PeruVicerrectorado de Investigación, Universidad Autónoma del Peru, Lima 15067, PeruDepartamento de Ciencias, Seccion Física, Pontificia Universidad Catolica del Peru, Lima 15088, PeruDepartamento de Ciencias, Seccion Física, Pontificia Universidad Catolica del Peru, Lima 15088, PeruFacultad de Ingeniería, Universidad Tecnológica del Perú, Lima 15046, PeruDepartamento de Física, Universidad Nacional Pedro Ruiz Gallo, Lambayeque 14001, PeruDepartamento de Física, Universidad Nacional Pedro Ruiz Gallo, Lambayeque 14001, PeruThis study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over three distinct measurement periods between 2015 and 2016, with 86 households participating. Detectors were randomly placed in various rooms within each household. Normality tests (Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov) were applied to assess the fit of radon concentrations to a log-normal distribution. Additionally, analysis of variance (ANOVA) was used to evaluate the influence of environmental and structural factors on radon variability. Non-normally distributed data were normalized using a Box–Cox transformation to improve statistical assumptions, enabling subsequent geostatistical analyses. Geospatial interpolation methods, specifically Inverse Distance Weighting (IDW) and Kriging, were employed to map radon concentrations. The results revealed significant temporal variability in radon concentrations, with geometric means of 146.4 Bq·<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mtext>m</mtext><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>, 162.3 Bq·<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mtext>m</mtext><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>, and 150.8 Bq·<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mtext>m</mtext><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>, respectively, across the three periods. Up to 9.5% of the monitored households recorded radon levels exceeding the safety threshold of 200 Bq·<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mtext>m</mtext><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>. Among the interpolation methods, Kriging provided a more accurate spatial representation of radon concentration variability compared to IDW, allowing for the precise identification of high-risk areas. This study provides a framework for using advanced statistical and geospatial techniques in environmental risk assessment.https://www.mdpi.com/2673-4117/6/1/14radonstatistical methodsenvironmental monitoringgeostatistical mappingpublic health |
| spellingShingle | Rafael Liza Félix Díaz Patrizia Pereyra Daniel Palacios Nhell Cerna Luis Curo Max Riva Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, Peru Eng radon statistical methods environmental monitoring geostatistical mapping public health |
| title | Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, Peru |
| title_full | Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, Peru |
| title_fullStr | Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, Peru |
| title_full_unstemmed | Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, Peru |
| title_short | Application of Statistical Methods for the Characterization of Radon Distribution in Indoor Environments: A Case Study in Lima, Peru |
| title_sort | application of statistical methods for the characterization of radon distribution in indoor environments a case study in lima peru |
| topic | radon statistical methods environmental monitoring geostatistical mapping public health |
| url | https://www.mdpi.com/2673-4117/6/1/14 |
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