Suppr超能文献

区域尺度下的浅层地下水非常规油气开采污染脆弱性评估

Regional Scale Assessment of Shallow Groundwater Vulnerability to Contamination from Unconventional Hydrocarbon Extraction.

机构信息

School of the Environment, Yale University, New Haven, Connecticut 06511, United States.

School of Public Health, Yale University, New Haven, Connecticut 06510, United States.

出版信息

Environ Sci Technol. 2022 Sep 6;56(17):12126-12136. doi: 10.1021/acs.est.2c00470. Epub 2022 Aug 12.

Abstract

Concerns over unconventional oil and gas (UOG) development persist, especially in rural communities that rely on shallow groundwater for drinking and other domestic purposes. Given the continued expansion of the industry, regional (vs local scale) models are needed to characterize groundwater contamination risks faced by the increasing proportion of the population residing in areas that accommodate UOG extraction. In this paper, we evaluate groundwater vulnerability to contamination from surface spills and shallow subsurface leakage of UOG wells within a 104,000 km region in the Appalachian Basin, northeastern USA. We test a computationally efficient ensemble approach for simulating groundwater flow and contaminant transport processes to quantify vulnerability with high resolution. We also examine metamodels, or machine learning models trained to emulate physically based models, and investigate their spatial transferability. We identify predictors describing proximity to UOG, hydrology, and topography that are important for metamodels to make accurate vulnerability predictions outside their training regions. Using our approach, we estimate that 21,000-30,000 individuals in our study area are dependent on domestic water wells that are vulnerable to contamination from UOG activities. Our novel modeling framework could be used to guide groundwater monitoring, provide information for public health studies, and assess environmental justice issues.

摘要

人们对非常规油气(UOG)开发仍存在担忧,特别是在依赖浅层地下水作为饮用水和其他家庭用途的农村社区。考虑到该行业的持续扩张,需要采用区域(而非局部)模型来描述越来越多居住在容纳 UOG 开采区域的人口所面临的地下水污染风险。在本文中,我们评估了美国东北部阿巴拉契亚盆地一个 104000 平方公里区域内,地表溢出物和 UOG 井浅层地下泄漏对地下水污染的脆弱性。我们测试了一种计算效率高的集合方法,用于模拟地下水流动和污染物运移过程,以高分辨率量化脆弱性。我们还研究了元模型,或经过训练以模拟基于物理的模型的机器学习模型,并探讨了它们的空间可转移性。我们确定了描述与 UOG 距离、水文和地形的预测因子,这些预测因子对于元模型在其训练区域之外进行准确的脆弱性预测非常重要。使用我们的方法,我们估计在我们的研究区域内,有 21000-30000 人依赖于家庭水井,这些水井容易受到 UOG 活动的污染。我们的新型建模框架可用于指导地下水监测、为公共卫生研究提供信息,并评估环境公正问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/da49/9454823/2fc868308852/es2c00470_0002.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验