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Background: Childhood cancer has few established risk factors and environmental influences are underexplored. This ecologic study investigated the association between domestic radon exposure and childhood cancer risk in a large sample of United States (U.S.) counties., Methods: Monthly ZIP code-level basement radon estimates from a geographic machine learning model were aggregated annually to counties, analyzed as continuous and dichotomized (cut point: 74 Bq/cubic meter (Bq/m 3 ) or 2.0 picocuries/L (pCi/L)) versions, and lagged by one year. Annual county-level counts of sex- and site-specific (all, leukemia, brain and central nervous system [CNS], and other sites) incident cancer diagnoses among those 0-19 years from 2001 to 2018 were obtained from the National Cancer Institute's Surveillance, Epidemiology, and End Results Program database. Sex- and site-specific counts were modeled as zero-inflated Poisson distributions in a Bayesian spatiotemporal framework and sequentially adjusted for random and fixed confounder effects., Results: In 727 counties across 14 states, the average population aged 0-19 years was 41,599 people at baseline. Results from fully adjusted spatiotemporal statistical models indicated 1.05 (95% credible interval, CrI: 1.00, 1.09) times higher relative risks (RRs) of leukemia among both sexes and a RR of 1.06 (95%CrI: 1.00, 1.12) in males from a 50 Bq/m 3 (1.35 pCi/L) increase in radon concentration the year prior. For radon exposures ≥74 Bq/m 3 (2.00 pCi/L) the year prior, RRs were 1.08 (95%CrI: 1.02, 1.15) for both sexes and 1.12 (95%CrI: 1.04, 1.22) for females. No associations were found with other cancer sites or sexes from prior year radon exposures., Conclusions: County-level childhood leukemia risk in both sexes were associated with average radon levels below U.S. Environmental Protection Agency guidelines recommending mitigation (148 Bq/m 3 or 4.00 pCi/L). These findings warrant further investigation using population-based and individual-level study designs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Radon decay products include α-radiation emitting radionuclides that attach to airborne particles that have potential to promote oxidative tissue damage after inhalation. To assess associations between α-particle radioactivity (α-PR) with urinary biomarkers of oxidative tissue damage, 140 patients with chronic obstructive pulmonary disease (COPD) had up to four 1-week seasonal assessments (N = 413) of indoor (home) and ambient (central site) PM 2.5 and black carbon (BC). Following environmental sampling, urine samples were analyzed for total and free malondialdehyde (MDA), biomarkers of lipid oxidation, and 8-hydroxyl-2'-deoxyguanosine (8-OHdG), a biomarker of DNA oxidative damage. Particle radioactivity was measured as α-activity on PM 2.5 filter samples. Linear mixed-effects regression models adjusted for urinary creatinine and other personal characteristics were used to assess associations. Indoor α-PR was associated with an increase in 8-OhdG (8.53%; 95% CI: 3.12, 14.23); total MDA (5.59%; 95% CI: 0.20, 11.71); and free MDA (2.17%; 95% CI: 2.75, 7.35) per interquartile range (IQR) of α-PR [median 1.25 mBq/m 3 ; IQR 0.64], similar adjusting for PM 2.5 or BC. The ratio of indoor/ambient α-PR was positively associated with each biomarker and associations with ambient α-PR were positive but weaker than with indoor concentrations. These findings are consistent with a contribution of radon decay products as measured by α-PR to oxidative stress in patients with COPD, with a greater contribution of indoor radon decay products., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

In 1987, the United States Environmental Protection Agency recommended installing a mitigation system when the indoor concentration of radon, a well-known carcinogenic radioactive gas, is at or above 148 Bq/m 3 . In response, tens of millions of short-term radon measurements have been conducted in residential buildings over the past three decades either for disclosure or to initially evaluate the need for mitigation. These measurements, however, are currently underutilized to assess population radon exposure in epidemiological studies. Based on two relatively small radon surveys, Lawrence Berkeley National Laboratory developed a state-of-the-art national radon model. However, this model only provides coarse and invariant radon estimations, which limits the ability of epidemiological studies to accurately investigate the health effects of radon, particularly the effects of acute exposure. This study involved obtaining over 2.8 million historical short-term radon measurements from independent laboratories. With the use of these measurements, an innovative spatial random forest (SRF) model was developed based on geological, architectural, socioeconomical, and meteorological predictors. The model was used to estimate monthly community-level radon concentrations for ZIP Code Tabulation Areas (ZCTAs) in the northeastern and midwestern regions of the United States from 2001 to 2020. Via cross-validation, we found that our ZCTA-level predictions were highly correlated with observations. The prediction errors declined quickly as the number of radon measurements in a ZCTA increased. When ≥15 measurements existed, the mean absolute error was 24.6 Bq/m 3 , or 26.5% of the observed concentrations ( R = 0.70). Our study demonstrates the potential of the large amount of historical short-term radon measurements that have been obtained to accurately estimate longitudinal ZCTA-level radon exposures at unprecedented levels of resolutions and accuracy.2 = 0.70). Our study demonstrates the potential of the large amount of historical short-term radon measurements that have been obtained to accurately estimate longitudinal ZCTA-level radon exposures at unprecedented levels of resolutions and accuracy.