污染物大规模排放的量化和不确定性:在受污染的地点应用的方法和挑战
A Bøllingtoft1, P L Bjerg1, V Rønde2
1Department of Environmental and Resource Engineering, Technical University of Denmark, Kongens Lyngby, Denmark.
Journal of contaminant hydrology
|November 14, 2024
概括
这项研究引入了贝叶斯地缘统计方法,以估计污染物大规模排放 (CMD) 和地下水中的不确定性. 整合特定地点的知识可以提高准确性,即使数据有限,有助于风险评估和补救工作.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 地质统计学 在地质统计学
背景情况:
- 污染物质量排放 (CMD) 估计对于地下水管理至关重要,但由于地质异质性和数据限制而复杂,导致显著的不确定性.
- 现有的量化和处理CMD估计不确定性的方法不足,阻碍了准确的风险评估和有效的补救计划.
研究的目的:
- 开发和测试一种使用贝叶斯地缘统计学来量化不确定性的新型CMD估计方法.
- 整合特定站点的概念理解和宏分散式运输模型,以提高CMD估计的准确性.
- 为了证明该方法在各种受污染地点和数据可用性场景中的适用性和稳定性.
主要方法:
- 采用贝叶斯地质统计学来结合多层次度和流量测量来量化CMD.
- 利用空间度分布和流动的地理统计条件模拟来生成一个CMD实现集.
- 整合了宏分散式运输模型来模拟空间集中趋势,整合了特定地点的概念信息.
主要成果:
- 贝叶斯地缘统计方法成功量化了CMD的不确定性,从生成的CMD实现中产生了累积分布函数.
- 强大的特定地点概念知识和高采样密度显著限制了CMD不确定性 (CV = 21%),使估计与概念模型保持一致.
- 即使数据和概念知识有限,该方法也提供了可行的CMD和度分布估计,尽管不确定性更高 (CV = 41%).
结论:
- 开发的贝叶斯地缘统计方法有效量化了CMD的不确定性,这对于稳健的地下水污染评估和管理至关重要.
- 整合特定站点的概念知识对于减少CMD不确定性至关重要,特别是在数据稀疏的站点.
- 该方法有助于确定整治目标,评估有效性和优化各种溶解污染物的采样策略.
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