Department of Environmental Health, Harvard T.H. Chan School of Public Health, 401 Park Drive, Boston, Massachusetts 02114, United States.
Pulmonary, Allergy, Sleep, and Critical Care Medicine Section, VA Boston Healthcare System, 1400 VFW Parkway, West Roxbury, Boston, Massachusetts 02132, United States.
Environ Sci Technol. 2023 Nov 21;57(46):18001-18012. doi: 10.1021/acs.est.2c08840. Epub 2023 Oct 15.
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. 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, or 26.5% of the observed concentrations ( = 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.
1987 年，美国环境保护署建议在室内氡浓度达到或高于 148 Bq/m 时安装缓解系统，氡是一种已知的致癌放射性气体。作为回应，在过去的三十年中，数以千万计的短期氡测量已经在住宅建筑中进行，要么是为了披露，要么是为了初步评估缓解的必要性。然而，这些测量目前在流行病学研究中评估人群氡暴露方面未得到充分利用。劳伦斯伯克利国家实验室基于两项相对较小的氡调查，开发了一种最先进的国家氡模型。然而，该模型仅提供粗略且不变的氡估计值，这限制了流行病学研究准确调查氡对健康影响的能力，特别是急性暴露的影响。本研究从独立实验室获取了超过 280 万份历史短期氡测量值。利用这些测量值，我们基于地质、建筑、社会经济和气象预测因素，开发了一种创新的空间随机森林 (SRF) 模型。该模型用于估计 2001 年至 2020 年美国东北部和中西部地区的邮政编码区 (ZCTA) 的每月社区水平氡浓度。通过交叉验证，我们发现我们的 ZCTA 级预测与观测值高度相关。随着 ZCTA 中氡测量值的增加，预测误差迅速下降。当 ZCTA 中存在≥15 个测量值时，平均绝对误差为 24.6 Bq/m，或观测浓度的 26.5%（ = 0.70）。我们的研究表明，大量已获得的历史短期氡测量值具有巨大潜力，可以以空前的分辨率和准确性准确估计纵向 ZCTA 水平的氡暴露。
