Assessment of Environmental Radioactivity Levels and Their Health Implications: Systematic Review
Bibliographic record
Abstract
Environmental radioactivity contributes to population radiation exposure and originates from both natural and man-made sources. Understanding these levels and their health implications is critical for public health, environmental protection, and radiation safety policies. This work systematically reviews published evidence on environmental radioactivity levels across various environmental media and to evaluate associated health implications, including estimated radiation doses and reported health outcomes. PRISMA guidelines were followed in this review. Databases including PubMed, Scopus, Web of Science, Embase, and Google Scholar were screened for studies published between 2000 and 2025 quantifying environmental radioactivity in air, soil, water, food, or building materials and/or estimating human health risks or radiation doses were eligible studies. Study characteristics, measurement methods, radionuclides, dose estimates, and health outcomes were extracted. Used adapted Newcastle–Ottawa and exposure-assessment appraisal tools to assess bias. Due to heterogeneity, findings were narratively synthesised. Studies consistently found 238U, 232Th, 40K, 226Ra, 222Rn, and 137Cs in environmental media. Mining and granite-rich regions had elevated concentrations, but most regions were within global averages. In most studies, dose estimates were below the 1 mSv/yr public exposure limit, except in high natural background radiation areas and radon-prone homes. Long-term stochastic effects from ingestion pathways and lung cancer risk from radon exposure were the main health concerns. Regional radioactivity levels vary but are generally within international safety limits, with localised hotspots. Public exposure and health risk are most caused by radon. Radon mitigation, monitoring, and education are advised.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".