Radon Concentration in Biological Samples of Smokers and Non-smokers Using Lexan Detector

This study was conducted in the Najaf Governorate, Iraq, to analyze radon concentration in biological samples from smokers and non-smokers. The samples, including blood serum, urine, hair, and nails, were used as biomarkers to determine the presence or absence of radon (222Rn). Using a natural exposure method, the nuclear track detector (Lexan, Belgium) was utilized to measure these radon concentrations in the samples. Seventy-five samples of blood serum, urine, hair, and nails were collected for smokers of healthy people and fifty samples for non-smokers of healthy people in five age groups. This study was based on age and smoking to compare the results and determine their effects on radon concentrations. The results show that the average values of radon concentrations (in Bq/m3) in blood serum, urine, hair, and nails for smokers were 54.7 ± 22.1, 62.9 ± 23.1, 34.7 ± 11.2, and 41.7 ± 15.2, respectively. Meanwhile, the average values of radon concentrations (in Bq/m3) in blood serum, urine, hair, and nails for non-smokers were 24.2 ± 6.0, 30.0 ± 6.3, 18.7 ± 5.2, and 21.6 ± 6.9 respectively. The results and comparisons indicate that radon concentrations depend on the variables on which this study was based (age and smoking). Smokers and non-smokers had different levels of radon in all biological samples. The P-value was

Authors

Institutions

Publication Details

Journal
Atom Indonesia
Published
2024-08-08
DOI
https://doi.org/10.55981/aij.2024.1402
Citations
5
Primary Topic
Radioactivity and Radon Measurements
Type
article
Field-Weighted Citation Impact
2.25

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Radon Concentration in Biological Samples of Smokers and Non-smokers Using Lexan Detector

Mohanad H. Oleiwi, Abdulhussein A. Alkufi, Ali Abid Abojassim
5 citations
Atom Indonesia
Radioactivity and Radon Measurements
2.25
article

Radon Concentration in Biological Samples of Smokers and Non-smokers Using Lexan Detector

Mohanad H. Oleiwi, Abdulhussein A. Alkufi, Ali Abid Abojassim
article en
5 citations

Abstract

This study was conducted in the Najaf Governorate, Iraq, to analyze radon concentration in biological samples from smokers and non-smokers. The samples, including blood serum, urine, hair, and nails, were used as biomarkers to determine the presence or absence of radon (222Rn). Using a natural exposure method, the nuclear track detector (Lexan, Belgium) was utilized to measure these radon concentrations in the samples. Seventy-five samples of blood serum, urine, hair, and nails were collected for smokers of healthy people and fifty samples for non-smokers of healthy people in five age groups. This study was based on age and smoking to compare the results and determine their effects on radon concentrations. The results show that the average values of radon concentrations (in Bq/m3) in blood serum, urine, hair, and nails for smokers were 54.7 ± 22.1, 62.9 ± 23.1, 34.7 ± 11.2, and 41.7 ± 15.2, respectively. Meanwhile, the average values of radon concentrations (in Bq/m3) in blood serum, urine, hair, and nails for non-smokers were 24.2 ± 6.0, 30.0 ± 6.3, 18.7 ± 5.2, and 21.6 ± 6.9 respectively. The results and comparisons indicate that radon concentrations depend on the variables on which this study was based (age and smoking). Smokers and non-smokers had different levels of radon in all biological samples. The P-value was

Atom IndonesiaVol. 50(2)
University of Babylon (IQ), University of Kufa (IQ)
University of Kufa
Good health and well-being
Openalex Percentile: Top 13%
Radioactivity and Radon Measurements
2.25
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.