Data-driven evaluation of background radiation safety using machine learning and statistical analysis
The entire globe is radioactive naturally, and humans are constantly exposed to background radiation from cosmic rays and the radioactive materials in their environment. The concentration and effects of background radiation can vary based on geographical location. Measuring background radiation leve...
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REA Press
2024-06-01
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| Series: | Big Data and Computing Visions |
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| Online Access: | https://www.bidacv.com/article_204149_c391c64fec7407d86ef54f2baea9cff4.pdf |
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| author | Muhammad Abid Muhammad Shahid |
| author_facet | Muhammad Abid Muhammad Shahid |
| author_sort | Muhammad Abid |
| collection | DOAJ |
| description | The entire globe is radioactive naturally, and humans are constantly exposed to background radiation from cosmic rays and the radioactive materials in their environment. The concentration and effects of background radiation can vary based on geographical location. Measuring background radiation levels is important for assessing potential health impacts. This study presents a comprehensive data analysis to investigate the levels and impact of background radiation levels in Sahiwal, Pakistan, and determine if the levels are safe according to international standards. Radiation counts were measured using a Geiger-Muller counter at several locations in Sahiwal over 40 days. The data was analyzed using normal distribution techniques to calculate the effective absorbed dose of the ionizing radiation in human tissue. The calculated dose was then compared to internationally accepted safe exposure levels. The effective absorbed dose of ionizing radiation in Sahiwal was determined as 0.27 mSv/year, significantly lower than the worldwide average background dose of 2.4 mSv/year. Based on this result and comparisons to international standards, the study concluded that Sahiwal is a safe area in terms of background radiation exposure for human living. However, more comprehensive measurements over longer periods could provide additional insights. |
| format | Article |
| id | doaj-art-e3ef0eee41fc408780eb5d8fa342e15a |
| institution | Kabale University |
| issn | 2783-4956 2821-014X |
| language | English |
| publishDate | 2024-06-01 |
| publisher | REA Press |
| record_format | Article |
| series | Big Data and Computing Visions |
| spelling | doaj-art-e3ef0eee41fc408780eb5d8fa342e15a2025-01-30T12:23:26ZengREA PressBig Data and Computing Visions2783-49562821-014X2024-06-014211013410.22105/bdcv.2024.476542.1186204149Data-driven evaluation of background radiation safety using machine learning and statistical analysisMuhammad Abid0Muhammad Shahid1Department of Mathematics, North Carolina State University, Raleigh, 27695 NC, United States.Department of Physics and Astronomy, Georgia State University, 30303 Atlanta, GA, USA.The entire globe is radioactive naturally, and humans are constantly exposed to background radiation from cosmic rays and the radioactive materials in their environment. The concentration and effects of background radiation can vary based on geographical location. Measuring background radiation levels is important for assessing potential health impacts. This study presents a comprehensive data analysis to investigate the levels and impact of background radiation levels in Sahiwal, Pakistan, and determine if the levels are safe according to international standards. Radiation counts were measured using a Geiger-Muller counter at several locations in Sahiwal over 40 days. The data was analyzed using normal distribution techniques to calculate the effective absorbed dose of the ionizing radiation in human tissue. The calculated dose was then compared to internationally accepted safe exposure levels. The effective absorbed dose of ionizing radiation in Sahiwal was determined as 0.27 mSv/year, significantly lower than the worldwide average background dose of 2.4 mSv/year. Based on this result and comparisons to international standards, the study concluded that Sahiwal is a safe area in terms of background radiation exposure for human living. However, more comprehensive measurements over longer periods could provide additional insights.https://www.bidacv.com/article_204149_c391c64fec7407d86ef54f2baea9cff4.pdfbackground radiationgeiger-muller counterradiation dosimetryenvironmental safetystatistical analysis |
| spellingShingle | Muhammad Abid Muhammad Shahid Data-driven evaluation of background radiation safety using machine learning and statistical analysis Big Data and Computing Visions background radiation geiger-muller counter radiation dosimetry environmental safety statistical analysis |
| title | Data-driven evaluation of background radiation safety using machine learning and statistical analysis |
| title_full | Data-driven evaluation of background radiation safety using machine learning and statistical analysis |
| title_fullStr | Data-driven evaluation of background radiation safety using machine learning and statistical analysis |
| title_full_unstemmed | Data-driven evaluation of background radiation safety using machine learning and statistical analysis |
| title_short | Data-driven evaluation of background radiation safety using machine learning and statistical analysis |
| title_sort | data driven evaluation of background radiation safety using machine learning and statistical analysis |
| topic | background radiation geiger-muller counter radiation dosimetry environmental safety statistical analysis |
| url | https://www.bidacv.com/article_204149_c391c64fec7407d86ef54f2baea9cff4.pdf |
| work_keys_str_mv | AT muhammadabid datadrivenevaluationofbackgroundradiationsafetyusingmachinelearningandstatisticalanalysis AT muhammadshahid datadrivenevaluationofbackgroundradiationsafetyusingmachinelearningandstatisticalanalysis |