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Predicting Arsenic in Drinking Water Wells of the Central Valley, California

机译：预测加利福尼亚中央山谷饮用水井中的砷

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摘要

Probabilities of arsenic in groundwater at depths used for domestic and public supply in the Central Valley of California are predicted using weak-leamer ensemble models (boosted regression trees, BRT) and more traditional linear models (logistic regression, LR). Both methods captured major processes that affect arsenic concentrations, such as the chemical evolution of groundwater, redox differences, and the influence of aquifer geochemistry. Inferred flow-path length was the most important variable but near-surface-aquifer geochemical data also were significant. A unique feature of this study was that previously predicted nitrate concentrations in three dimensions were themselves predictive of arsenic and indicated an important redox effect at ＞10μg/L, indicating low arsenic where nitrate was high. Additionally, a variable representing three-dimensional aquifer texture from the Central Valley Hydrologic Model was an important predictor, indicating high arsenic associated with fine-grained aquifer sediment. BRT outperformed LR at the/g L threshold in all five predictive performance measures and at 10 μg/L in four out of five measures. BRT yielded higher prediction sensitivity (39%) than LR (1896) at the 10 μg/L threshold-a useful outcome because a major objective of the modeling was to improve our ability to predict high arsenic areas.
机译：加州中部山谷用于家庭和公共供水深度的地下水中砷的概率是使用弱游合集成模型（增强回归树，BRT）和更传统的线性模型（逻辑回归，LR）来预测的。两种方法都捕获了影响砷浓度的主要过程，例如地下水的化学演化，氧化还原差异以及含水层地球化学的影响。推测的流径长度是最重要的变量，但近地表含水层地球化学数据也很重要。这项研究的独特之处在于，先前在三个维度上预测的硝酸盐浓度本身就可以预测砷，并表明在＞10μg/ L时有重要的氧化还原作用，表明硝酸盐含量高时砷含量低。此外，来自中央谷水文模型的代表三维含水层质地的变量是重要的预测因子，表明高砷与细粒含水层沉积物有关。在所有五项预测性能指标中，BRT在/ g L阈值方面均优于LR，在五项指标中的四项中以10μg/ L优于LR。在10μg/ L阈值时，BRT的预测灵敏度比LR（1896）高（39％），这是一个有用的结果，因为建模的主要目的是提高我们预测高砷区域的能力。

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  • 来源
    《Environmental Science & Technology》 |2016年第14期|7555-7563|共9页
  • 作者

    Joseph D. Ayotte; Bernard T. Nolan; Jo Ann Gronberg;

  • 作者单位

    U.S. Geological Survey, New England Water Science Center, New Hampshire - Vermont Office, 331 Commerce Way, Pembroke, New Hampshire 03301, United States;

    U.S. Geological Survey, National Center 413, 12201 Sunrise Valley Drive, Reston, Virginia 20192, United States;

    U.S. Geological Survey, McKelvey Bldg., 345 Middlefield Road, Memo Park, California 94025, United States;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
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  • 正文语种 eng
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