台湾地下水:从20世纪中叶的危机到风险减轻策略的预测模型
Kai-Yun Li1, Joel Podgorski1, Ching-Ping Liang2
1Eawag, Swiss Federal Institute of Aquatic Science and Technology, Department Water Resources and Drinking Water, 8600 Dübendorf, Switzerland.
Environment international
|January 16, 2026
概括
一个机器学习模型识别了台湾的高风险地下水区,减少了接触这种污染物的人口. 这有助于减轻饮用水,农业和水产养殖中的健康风险.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 地质化学 地质化学
背景情况:
- 台湾经历了一场严重的黑脚病流行病,与地下水中的地质有关.
- 尽管采取了干预措施,但污染仍然存在于地下水中,影响饮用水,灌和水产养殖.
研究的目的:
- 开发一个可扩展的机器学习模型,用于识别台湾有超出世卫组织地下水 (10微克/升) 准则风险的地区.
- 分析影响动员的地化学和水文地质因素.
- 估计人口和农业对的暴露,并为公共卫生战略提供信息.
主要方法:
- 开发并验证了一种具有高性能的机器学习模型 (平均AUC为0.93,平衡精度为0.87).
- 解释预测变量以了解溶解和调动控制.
- 创建了一个危险地图,以评估人口,农业和水产养殖的暴露.
主要成果:
- 在南,平和兰阳平原确定了热点,许多社区使用未经处理的地下水.
- 据估计,到2023年,有148,000人面临风险,这比1998年的303,000人显著减少.
- 发现超过80%的水产养殖和33%的田位于高风险区域.
- 突出监管盲点与指定控制区域以外的高风险区域.
结论:
- 机器学习模型有效地预测了台湾各地的地下水风险.
- 显著的人口和农业暴露仍然存在,需要有针对性的干预措施.
- 研究结果支持基于证据的战略,以减少暴露,防止未来的健康影响.
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