Identifying Predictors of Spatiotemporal Variations in Residential Radon Concentrations across North Carolina Using Machine Learning Analytics

IF 7.6 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Environmental Pollution Pub Date : 2025-01-04 DOI:10.1016/j.envpol.2024.125592

Zhenchun Yang, Lauren Prox, Clare Meernik, Yadurshini Raveendran, David Press, Phillip Gibson, Amie Koch, Olufemi Ajumobi, Jeffrey Clarke, Ruoxue Chen, Junfeng (Jim) Zhang, Tomi Akinyemiju

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Abstract

Radon is a naturally occurring radioactive gas derived from the decay of uranium in the Earth’s crust. Radon exposure is the leading cause of lung cancer among non-smokers in the US. Radon infiltrates homes through soil and building foundations. This study advances methodologies for assessing residential radon exposure by leveraging a comprehensive dataset of 126,382 short-term (2-7 days) radon test results collected across North Carolina from 2010 to 2020. Employing a combination of linear regression and advanced machine learning techniques, including random forest models.Analysis through linear regression, linear mixed-effects models (LME), and generalized additive models (GAM) using the first-time tested radon levels reveals that elevation, proximity to geological faults, and soil moisture are pivotal in determining radon concentration. Specifically, elevation consistently shows a positive relationship with radon levels across models (linear regression: β=0.12, p<0.001; LME: β=0.17, p<0.001; GAM: β=0.11, p<0.001). Conversely, the distance to geological faults negatively correlates with radon concentration (linear regression: β=-0.11, p<0.001; LME: β=-0.06, p<0.001; GAM: β=-0.07, p<0.001), indicating lower radon levels further from faults.Using the random forest model, our study identifies the most influential environmental predictors of first-time tested radon levels. Elevation is the most influential variable, followed by median instantaneous surface pressure and soil moisture in the upper 10 cm layer, illustrating the significant role of geological and immediate surface conditions. Additional important factors include precipitation, mean temperature, and deeper soil moisture levels (40-200 cm), which underscores the influence of climate on radon variability. Root zone soil moisture and the Normalized Difference Vegetation Index (NDVI) also contribute to predicting radon levels, reflecting the importance of soil and vegetation dynamics in radon emanation. By integrating multiple statistical models, this research provides a nuanced understanding of the predictors of radon concentration, enhancing predictive accuracy and reliability.

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使用机器学习分析识别北卡罗来纳州住宅氡浓度时空变化的预测因子

氡是一种自然产生的放射性气体，由地壳中的铀衰变产生。在美国，氡暴露是导致非吸烟者患肺癌的主要原因。氡通过土壤和建筑地基渗入房屋。本研究通过利用2010年至2020年在北卡罗来纳州收集的126,382个短期（2-7天）氡测试结果的综合数据集，推进了评估住宅氡暴露的方法。结合线性回归和先进的机器学习技术，包括随机森林模型。利用首次测试的氡水平，通过线性回归、线性混合效应模型（LME）和广义加性模型（GAM）进行分析表明，海拔高度、与地质断层的接近程度和土壤湿度是决定氡浓度的关键因素。具体而言，海拔高度与氡水平在各模型中一致显示出正相关关系(线性回归：β=0.12, p<0.001；LME: β=0.17, p < 0.01；GAM: β=0.11, p<0.001)。相反，与地质断层的距离与氡浓度呈负相关(线性回归：β=-0.11, p<0.001；LME: β=-0.06, p < 0.01；GAM: β=-0.07, p<0.001)，表明离断层越远，氡水平越低。使用随机森林模型，我们的研究确定了首次测试氡水平的最具影响力的环境预测因子。高程是影响最大的变量，其次是中间瞬时地表压力和上层10cm层的土壤湿度，说明地质和直接地表条件的重要作用。其他重要因素包括降水、平均温度和更深的土壤湿度水平（40-200厘米），这强调了气候对氡变异的影响。根区土壤湿度和归一化植被指数（NDVI）也有助于预测氡水平，反映了土壤和植被动态对氡辐射的重要性。通过整合多个统计模型，本研究提供了对氡浓度预测因子的细致理解，提高了预测的准确性和可靠性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Environmental Pollution 环境科学-环境科学

CiteScore

16.00

自引率

6.70%

发文量

2082

审稿时长

2.9 months

期刊介绍： Environmental Pollution is an international peer-reviewed journal that publishes high-quality research papers and review articles covering all aspects of environmental pollution and its impacts on ecosystems and human health. Subject areas include, but are not limited to: • Sources and occurrences of pollutants that are clearly defined and measured in environmental compartments, food and food-related items, and human bodies; • Interlinks between contaminant exposure and biological, ecological, and human health effects, including those of climate change; • Contaminants of emerging concerns (including but not limited to antibiotic resistant microorganisms or genes, microplastics/nanoplastics, electronic wastes, light, and noise) and/or their biological, ecological, or human health effects; • Laboratory and field studies on the remediation/mitigation of environmental pollution via new techniques and with clear links to biological, ecological, or human health effects; • Modeling of pollution processes, patterns, or trends that is of clear environmental and/or human health interest; • New techniques that measure and examine environmental occurrences, transport, behavior, and effects of pollutants within the environment or the laboratory, provided that they can be clearly used to address problems within regional or global environmental compartments.