在整个邻近美国的地下水中绘制PFOA和PFOS的统计地图
Bumjun Park1,2, Hyunseung Kang2, Christopher Zahasky3
1*Department of Biostatistics, University of Washington, Seattle, Washington 98195, United States.
Environmental science & technology
|October 23, 2024
概括
这项研究使用新型统计模型绘制了美国地下水中的和多基物质 (PFAS) 地图. 该方法处理稀疏的数据,以揭示污染模式并指导未来的采样工作.
科学领域:
- 环境科学 环境科学
- 环境化学环境化学
- 统计建模 统计建模
背景情况:
- 和多基物质 (PFAS) 是全球地下水资源中广泛发现的合成污染物.
- 人类接触PFAS与不良健康结果有关,需要准确的分布评估.
- 由于各种测试和报告法规,现有的PFAS地下水数据稀疏且空间偏差.
研究的目的:
- 开发一种可靠的统计方法,以持续地绘制PFAS地下水污染在连续的美国的地图.
- 在当前的PFAS分布评估中解决数据稀疏性和空间偏差挑战.
- 为优先考虑未来地下水采样和缓解工作提供一个工具.
主要方法:
- 从生态统计数据中调整的不均的Poisson过程 (IPP) 模型被用于连续的空间绘图.
- 该IPP模型是使用8,910个PFAS地下水测量的综合数据集进行训练的,该数据集结合了两个关键分析物的度.
- 模型的性能和稳定性通过与随机森林建模技术的比较来验证.
主要成果:
- 不均的Poisson过程 (IPP) 模型成功地产生了PFAS地下水污染的连续空间预测.
- 该研究证明了IPP方法在处理稀疏和空间偏差的环境数据集方面的有效性.
- 对比分析证实了IPP建模策略与替代机器学习方法的稳定性.
结论:
- 开发的IPP建模方法在绘制地下水中PFAS污染的地图方面取得了重大进展.
- 这种方法为识别高风险区域和优化未来监测策略提供了有价值的框架.
- 这项研究有助于更好地了解PFAS的分布,有助于公共卫生保护工作.
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