美国本土私人井中砷的机器学习模型作为人类健康研究中暴露评估的工具,Environmental Science & Technology

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美国本土私人井中砷的机器学习模型作为人类健康研究中暴露评估的工具
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2021-03-17 , DOI: 10.1021/acs.est.0c05239
Melissa A Lombard ₁ , Molly Scannell Bryan ₂ , Daniel K Jones ₃ , Catherine Bulka ₄ , Paul M Bradley ₅ , Lorraine C Backer ₆ , Michael J Focazio ₇ , Debra T Silverman ₈ , Patricia Toccalino ₉ , Maria Argos ₁₀ , Matthew O Gribble ₁₁ , Joseph D Ayotte ₁

Affiliation

New England Water Science Center, U.S. Geological Survey, 331 Commerce Way, Pembroke, New Hampshire 03275, United States.
Institute for Minority Health Research, University of Illinois at Chicago, 1819 W. Polk, Chicago, Illinois 60612, United States.
Utah Water Science Center, U.S. Geological Survey, 2329 West Orton Circle, West Valley City, Utah 84119, United States.
University of North Carolina, 135 Dauer Drive, Chapel Hill, North Carolina 27599, United States.
South Atlantic Water Science Center, U.S. Geological Survey, Columbia, South Carolina 29210, United States.
Centers for Disease Control and Prevention, National Center for Environmental Health, 4770 Buford Highway NE, Chamblee, Georgia 30341, United States.
Toxic Substances Hydrology Program, U.S. Geological Survey, 12201 Sunrise Valley Drive, Reston, Virginia 20192 United States.
Occupational and Environmental Epidemiology Branch, National Cancer Institute, 9606 Medical Center Drive, Rockville, Maryland 20850, United States.
Northwest-Pacific Islands Region, U.S. Geological Survey, 911 NE 11th Avenue, Portland, Oregon 97232, United States.
School of Public Health, University of Illinois at Chicago, 1603 West Taylor Street, Chicago, Illinois 60612, United States.
Gangarosa Department of Environmental Health, Emory University, 1518 Clifton Road NE, Atlanta, Georgia 30322, United States.

地质来源的砷在美国 (US) 的地下水中广泛存在。在一些地区，地下水中的砷浓度超过了美国环境保护署规定的每升 10 微克 (μg/L) 的最高污染水平。但是，该标准仅适用于公共供应的饮用水，而不适用于不受联邦监管且很少受到监控的私人供应。因此，私人井中的砷暴露是一个潜在的重大但很大程度上隐藏的公共卫生问题。开发了使用增强回归树 (BRT) 和随机森林分类 (RFC) 技术的机器学习模型，以估计整个美国本土私人井中砷的概率和浓度范围三个 BRT 模型分别拟合以估计私人井砷浓度超过 1、5 或 10 μg/L 的概率，而 RFC 模型估计最可能的类别（≤5、>5 到 ≤10 或 >10 μg/L ）。总体而言，这些模型在识别私人井中砷浓度低的区域方面表现最佳。用于测试数据的 BRT 10 μg/L 模型估计的总体准确度为 91.2%，敏感性为 33.9%，特异性为 98.2%。在所有模型中确定的影响变量包括年平均降水量和土壤地球化学。模型是与公共卫生专家合作开发的，以支持美国的研究，重点是砷暴露对健康的影响。这些模型在识别私人井中砷浓度低的区域方面表现最佳。用于测试数据的 BRT 10 μg/L 模型估计的总体准确度为 91.2%，敏感性为 33.9%，特异性为 98.2%。在所有模型中确定的影响变量包括年平均降水量和土壤地球化学。模型是与公共卫生专家合作开发的，以支持美国的研究，重点是砷暴露对健康的影响。这些模型在识别私人井中砷浓度低的区域方面表现最佳。用于测试数据的 BRT 10 μg/L 模型估计的总体准确度为 91.2%，敏感性为 33.9%，特异性为 98.2%。在所有模型中确定的影响变量包括年平均降水量和土壤地球化学。模型是与公共卫生专家合作开发的，以支持美国的研究，重点是砷暴露对健康的影响。

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Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies

Arsenic from geologic sources is widespread in groundwater within the United States (U.S.). In several areas, groundwater arsenic concentrations exceed the U.S. Environmental Protection Agency maximum contaminant level of 10 μg per liter (μg/L). However, this standard applies only to public-supply drinking water and not to private-supply, which is not federally regulated and is rarely monitored. As a result, arsenic exposure from private wells is a potentially substantial, but largely hidden, public health concern. Machine learning models using boosted regression trees (BRT) and random forest classification (RFC) techniques were developed to estimate probabilities and concentration ranges of arsenic in private wells throughout the conterminous U.S. Three BRT models were fit separately to estimate the probability of private well arsenic concentrations exceeding 1, 5, or 10 μg/L whereas the RFC model estimates the most probable category (≤5, >5 to ≤10, or >10 μg/L). Overall, the models perform best at identifying areas with low concentrations of arsenic in private wells. The BRT 10 μg/L model estimates for testing data have an overall accuracy of 91.2%, sensitivity of 33.9%, and specificity of 98.2%. Influential variables identified across all models included average annual precipitation and soil geochemistry. Models were developed in collaboration with public health experts to support U.S.-based studies focused on health effects from arsenic exposure.

更新日期：2021-04-20

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