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INSPIRE: A Data-Driven Approach toward Exploring Natural and Anthropogenic Methane Emissions in Regions of Shale Gas Development

INSPIRE: A Data-Driven Approach toward Exploring Natural and Anthropogenic Methane Emissions in Regions of Shale Gas Development
INSPIRE:探索页岩气开发地区自然和人为甲烷排放的数据驱动方法
批准号:
1639150
负责人:
Susan Brantley
金额:
$100.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-08-01 至 2022-07-31

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中文摘要
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英文摘要
This INSPIRE project addresses the issue of high volume hydraulic fracturing, also called fracking, and its effects on ground water resources. Fracking allows drillers to extract natural gas from shale deep within the earth. Methane gas sometimes escapes from shale gas wells and can contaminate water resources or leak into the atmosphere where it contributes to greenhouse gas emissions. Monitoring for these potential leaks is difficult because methane is also released into aquifers naturally, and because monitoring is time- and resource-intensive. Such subsurface leakage may also be relatively rare. This project seeks to improve overall understanding of the impacts of natural gas drilling using both advances in computer science and geoscience, and to teach the public about such impacts. The project will elucidate both the effects of human activities such as shale gas development as well as natural processes which release methane into natural waters. Results of the proposed research will lead to a better understanding of water quality in areas of shale-gas development and will highlight problems and potentially problematic management practices. The research will advance both the fields of geoscience and computer science, will train interdisciplinary graduate students, and involve citizen scientists in collecting data and understanding environmental data analysis. The project combines new hydro-geochemical strategies and data mining approaches to study the release of methane into streams and ground waters. For example, researchers will explore how to analyze the heterogeneous spatial data that describe distributions of methane concentrations in natural waters. The objectives of this project are to i) transform the ability to measure methane in streams; ii) train citizen scientists to work with project scientists to sample streams in an area of shale-gas development and publish large-volume datasets of methane in natural waters and aquifers; iii) innovate data mining and machine learning methods for environmental data to identify anomalous spots with potential leakage; iv) run field campaigns to measure methane concentrations and isotopic signatures of water samples in these spots; v) foster dialogue among nonscientists, consultants, university scientists, members of the gas industry, government agencies, and nonprofit organizations in and beyond the target region. Toward this end, the team will host workshops aimed to build dialogue among stakeholders and will release data analytic software for environmental measurements to benefit a broader research community.
期刊论文(20)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1021/acs.est.8b01035
发表时间: 2018
期刊: Environmental Science & Technology
影响因子: 11.4
作者: [Niu, Xianzeng, Wen, Tao, Li, Zhenhui, Brantley, Susan L.]
通讯作者: Brantley, Susan L.
DOI: 10.1021/acs.est.9b06761
发表时间: 2020-07-21
期刊: ENVIRONMENTAL SCIENCE & TECHNOLOGY
影响因子: 11.4
作者: [Agarwal, Amal, Wen, Tao, Brantley, Susan L.]
通讯作者: Brantley, Susan L.
DOI: 10.1007/s10040-020-02116-y
发表时间: 2020-02-26
期刊: HYDROGEOLOGY JOURNAL
影响因子: 2.8
作者: [Hammond, Patrick A., Wen, Tao, Engelder, Terry]
通讯作者: Engelder, Terry
Using a neural network – Physics-based hybrid model to predict soil reaction fronts
使用神经网络 – 基于物理的混合模型来预测土壤反应前沿
DOI: 10.1016/j.cageo.2022.105200
发表时间: 2022
期刊: Computers & Geosciences
影响因子: 4.4
作者: [Wen, Tao, Chen, Chacha, Zheng, Guanjie, Bandstra, Joel, Brantley, Susan L.]
通讯作者: Brantley, Susan L.
16
    Workshop Proposal: Mapping a Future for Management of Low-Temperature Geochemical Data: Atlanta, GA or Charlotte, NC - February 2020
    EAGER SitS: Emergent Properties during Soil Formation
    Collaborative research: Quantifying weathering rind formation rates using U-series isotopes along steep gradients of precipitation, bedrock ages, and topography in Guadeloupe
    Using the Susquehanna - Shale Hills CZO to Project from the Geological Past to the Anthropocene Future
    国内基金
    海外基金
    Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
    Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
    Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
    • 批准号:
      --
    • 项目类别:
      --
    • 资助金额:
      40万元
    • 批准年份:
      2020
    • 负责人:
      Vikrant Gupta
    • 依托单位:
    基于Linked Open Data的Web服务语义互操作关键技术
    • 批准号:
      61373035
    • 项目类别:
      面上项目
    • 资助金额:
      77.0万元
    • 批准年份:
      2013
    • 负责人:
      冯志勇
    • 依托单位: