Predicting Monthly Community-Level Domestic Radon Concentrations in the Greater Boston Area with an Ensemble Learning Model
Predicting Monthly Community-Level Domestic Radon Concentrations in the Greater Boston Area with an Ensemble Learning Model
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DOI：
10.1021/acs.est.0c08792
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发表时间：
2021-05-03
影响因子：
11.4
通讯作者：
Koutrakis, Petros
中科院分区：
文献类型：
--
作者：
Li, Longxiang;Blomberg, Annelise J.;Koutrakis, Petros
吸入氡及其子体与不良健康后果有关。然而，以前的研究住宅暴露于氡在美国的健康影响通常是基于一个县一级的时间不变的氡模型，该模型是使用在20世纪80年代中后期收集的测量。我们开发了一个机器学习模型来预测大波士顿地区每个邮政编码制表区（ZCTA）的月氡浓度，该模型基于Spruce Environmental Technologies，Inc.的363，783个短期测量值，在2005-2018年期间。一个两阶段的合奏为基础的模型来预测氡浓度的所有ZCTA和月份。第一阶段包括12个基础统计模型，根据每个ZCTA的地质、建筑、社会经济和气象因素独立预测ZCTA水平的氡浓度。第二阶段使用集成学习方法聚合这12个基础模型的预测。10倍交叉验证结果表明，第二阶段模型具有较好的预测精度，加权R-2为0.63，均方根误差为22.6Bq/m3。社区水平的时变预测，从我们的模型具有良好的预测精度和准确性，并可用于未来的前瞻性流行病学研究在大波士顿地区。
Inhaling radon and its progeny is associated with adverse health outcomes. However, previous studies of the health effects of residential exposure to radon in the United States were commonly based on a county-level temporally invariant radon model that was developed using measurements collected in the mid- to late 1980s. We developed a machine learning model to predict monthly radon concentrations for each ZIP Code Tabulation Area (ZCTA) in the Greater Boston area based on 363,783 short-term measurements by Spruce Environmental Technologies, Inc., during the period 2005-2018. A two-stage ensemble-based model was developed to predict radon concentrations for all ZCTAs and months. Stage one included 12 base statistical models that independently predicted ZCTA-level radon concentrations based on geological, architectural, socioeconomic, and meteorological factors for each ZCTA. Stage two aggregated the predictions of these 12 base models using an ensemble learning method. The results of a 10-fold cross-validation showed that the stage-two model has a good prediction accuracy with a weighted R-2 of 0.63 and root mean square error of 22.6 Bq/m(3). The community-level time-varying predictions from our model have good predictive precision and accuracy and can be used in future prospective epidemiological studies in the Greater Boston area.