Health Effects and Preventive Strategies for Radon Exposure: A Systematic Review of the Literature
Bibliographic record
Abstract
Introduction: Radon is a radioactive noble gas formed from uranium decay in the Earth’s crust. The most significant isotope, 222Rn, emits alpha particles capable of damaging lung tissue and inducing cancer. Radon exposure is affected by geophysical and building characteristics and is recognized as a Group 1 carcinogen by the IARC. Despite regulatory thresholds (e.g., EURATOM standards), health risks remain. Various mitigation methods aim to reduce indoor radon exposure and its impact. Materials and Methods: This systematic review followed PRISMA guidelines. PubMed, Scopus, and Web of Science were searched up to 28 February 2025, using a defined string. Studies with original data on radon exposure and lung cancer risk or mitigation efficacy were included. Independent screening and quality assessment (Newcastle–Ottawa Scale) were conducted by multiple reviewers. Results: Of the 457 studies identified, 14 met the inclusion criteria. Eleven of these investigated the link between indoor radon and lung cancer risk, and three evaluated mitigation strategies. Radon levels were commonly measured using passive alpha track detectors. Levels varied depending on geographical location, season, building design and ventilation, these were higher in rural homes and during the colder months. Case–control studies consistently found an increased lung cancer risk with elevated radon exposure, especially among smokers. Effective mitigation methods included sub-slab depressurisation and balanced ventilation systems, which significantly reduced indoor radon concentrations. Adenocarcinoma was the most common lung cancer subtype in non-smokers, whereas squamous and small cell carcinomas were more prevalent in smokers exposed to radon. Discussion and Conclusions: This review confirms the robust association between indoor radon exposure and lung cancer. Risks persist even below regulatory limits and are amplified by smoking. While mitigation techniques are effective, their application remains uneven across regions. Stronger public education, building codes, and targeted interventions are needed, particularly in high-risk areas. To inform future prevention and policy, further research should seek to clarify radon’s molecular role in lung carcinogenesis, especially among non-smokers.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".