Groundwater supplies 50% of drinking water worldwide and 30% in the United States. Geogenic and anthropogenic contaminants can, however, compromise water quality, thus limiting groundwater availability. Reduction/oxidation (redox) processes and redox conditions affect groundwater quality by influencing the mobility and transport of common geogenic and anthropogenic contaminants. In the glacial aquifer system, northern United States (GLAC, 1.87 million km²), groundwater with high arsenic or manganese concentration is associated with reducing conditions and high nitrate with oxidizing conditions. This study uses machine learning to identify the relative influence of drivers of redox conditions (e.g., residence time vs. reactivity) across the glacial landscape. We developed three‐dimensional boosted regression tree models to predict redox conditions using the likelihood of low dissolved oxygen or high iron as indicators of anoxic conditions. Results indicate that variation in redox condition is controlled primarily by residence time (e.g., groundwater age and relative depth) and to a lesser extent by geochemical reactivity (e.g., subsurface contact time, soil carbon). Older water and deeper wells, along with more water storage or slower water movement was associated with higher probability of anoxic conditions. Mapped model results illustrate regions where anoxic redox conditions may mobilize geogenic contaminants or oxic conditions may limit denitrification potential. Results may also provide simplified redox input for process or predictive models of, for example, arsenic, manganese, or nitrate. Machine learning modeling methods can lead to improved understanding of contaminant occurrence and what drives redox conditions, and the methods may be transferable to other settings.

中文翻译：

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美国北部大陆冰川蓄水层系统中的机器学习预测的氧化还原条件

地下水在全球范围内提供50％的饮用水，在美国则提供30％。但是，成因和人为污染物会损害水质，从而限制了地下水的可利用性。还原/氧化（氧化还原）过程和氧化还原条件通过影响常见的地质和人为污染物的迁移和迁移来影响地下水质量。在美国北部的冰川含水层系统（GLAC，187万千米²），砷或锰浓度高的地下水与还原条件有关，而硝酸盐与氧化条件有关。这项研究使用机器学习来确定整个冰川景观中氧化还原条件驱动因素（例如停留时间与反应性）的相对影响。我们开发了三维增强的回归树模型，以低溶解氧或高铁作为缺氧状况的指标来预测氧化还原状况。结果表明，氧化还原条件的变化主要由停留时间（例如，地下水年龄和相对深度）控制，而在较小程度上受地球化学反应性（例如，地下接触时间，土壤碳）控制。较老的水和更深的水井，再加上更多的储水或较慢的水流与缺氧条件的可能性更高有关。映射的模型结果说明了缺氧氧化还原条件可能动员地质污染物或有氧条件可能限制反硝化潜力的区域。结果还可为例如砷，锰或硝酸盐的过程或预测模型提供简化的氧化还原输入。机器学习建模方法可以更好地理解污染物的发生以及驱动氧化还原条件的因素，并且这些方法可以转移到其他设置。砷，锰或硝酸盐。机器学习建模方法可以更好地理解污染物的发生以及驱动氧化还原条件的因素，并且这些方法可以转移到其他设置。砷，锰或硝酸盐。机器学习建模方法可以更好地理解污染物的发生以及驱动氧化还原条件的因素，并且这些方法可以转移到其他设置。