多重数据推算方法 提前风险分析和地下水中同时出现的无机化学品的可处理性
Akhlak U Mahmood1, Minhazul Islam2, Alexey V Gulyuk1
1Materials Science and Engineering, North Carolina State University, Raleigh, North Carolina 27695, United States.
Environmental science & technology
|November 7, 2024
概括
精确地进行地下水风险评估是具有挑战性的,因为数据稀少. 像AMELIA这样的先进的归算方法可以填补数据空白,揭示更多来自无机污染物的潜在健康风险.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 数据科学数据科学数据科学
背景情况:
- 地下水质量数据往往稀少,阻碍了对无机污染物的准确风险评估和管理.
- 抽样和分析的预算有限,造成了大量数据缺口,影响了暴露评估和治疗效果.
研究的目的:
- 为了比较AMELIA和MICE多重数据归算技术在填补稀疏地下水质量数据中的空白方面的有效性.
- 评估数据归算对评估地下水污染物风险和识别潜在健康危害的影响.
主要方法:
- 利用并比较了AMELIA和MICE算法,用于对稀疏地下水质量数据集进行多重数据归算.
- 分析现场数据和计算数据,以评估超过最大污染物水平 (MCL) 的无机污染物的流行率.
主要成果:
- 在处理缺失值方面,AMELIA表现优于MICE,由于价值高估,MICE产生了更多的异常值.
- 与现场数据相比,假设数据显示,采样地点可能超过基于健康的限制的数量大幅增加 (2-5倍).
- 假定数据确定了更多的样本,其中多种同时存在的化学物质超过了基于健康的水平.
结论:
- 数据归算,特别是使用AMELIA,通过解决数据稀疏性,大大提高了对地下水污染物风险的理解.
- 该框架有助于国家机构优先考虑采样地点和监测化学品,优化地下水保护的资源配置.
- 该研究提供了一种战略方法,以减少地下水数据收集的不确定性,最大限度地提高有限资金的影响.
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