权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

ATD: Collaborative Research: Computationally Efficient Algorithms for Detecting Anomalous Atmospheric Emissions

ATD：协作研究：用于检测异常大气排放的计算高效算法

基本信息

批准号：
2026835
负责人：
Scot Miller
金额：
$ 16.2万
依托单位：
Johns Hopkins University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-15 至 2024-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2026835&HistoricalAwards=false
关键词：
ATD Collaborative Research Computationally Efficient

项目摘要

Large-scale anomalous emissions of greenhouse gases and air pollution pose threats to human health in the vicinity of the emissions, compromise state emissions targets, and threaten energy security. Two recent, high-profile natural gas blowouts underscore the need for early detection and intervention. Several new and forthcoming satellites have the specific purpose of detecting and monitoring greenhouse gas emissions, and recent studies have demonstrated the potential of detecting such events using satellite data. However, there are enormous computational challenges in quantifying these emission anomalies or super-emitters due to the massive amounts of satellite data to be processed and the fine-scale resolution at which reconstructions are needed for threat detection. This project aims to tackle these challenges by developing improved computational methods for use in detection of atmospheric emissions. The project supports one graduate per year at each of the three universities.The project aims to address fundamental issues in the development of computationally efficient solvers for inverse problems, and to push the traditional boundaries of threat detection via satellites by enabling researchers to detect and monitor anomalous atmospheric emissions quickly, accurately, and with quantifiable uncertainty. The main thrusts of this project are (i) efficient incorporation of prior information and parameter selection, (ii) improved spatio-temporal inverse modeling with multiple stochastic components and cost-cutting inexact and sampling approaches to handle expensive adjoint models, and (iii) evaluations, testing, and integration of the developed methods via case studies with synthetic satellite data. The aim of this project is to help identify potential immediate threats (e.g., oil and gas blowouts) using satellites, which have significant broader impacts not only in disaster response and recovery but also in minimizing the long-term environmental risks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

温室气体的大规模异常排放和空气污染对排放附近的人类健康构成威胁，损害国家排放目标，并威胁能源安全。最近发生的两起引人注目的天然气井喷事件突出表明，需要及早发现和干预。一些新的和即将发射的卫星具有探测和监测温室气体排放的具体目的，最近的研究表明，利用卫星数据探测这类事件具有潜力。然而，由于需要处理大量的卫星数据，以及威胁检测需要重建的精细尺度分辨率，因此在量化这些排放异常或超级排放者方面存在巨大的计算挑战。该项目旨在通过开发用于检测大气排放的改进计算方法来应对这些挑战。该项目每年资助三所大学各一名毕业生，旨在解决逆问题高效计算求解器开发中的基本问题，并通过使研究人员能够快速、准确地检测和监测异常大气排放，并以可量化的不确定性来推动通过卫星进行威胁检测的传统界限。该项目的主要目标是（一）有效地结合先验信息和参数选择，（二）改进的时空逆建模与多个随机组件和成本削减不精确和采样方法来处理昂贵的伴随模型，和（iii）评估，测试，并通过案例研究与合成卫星数据的开发方法的集成。该项目的目的是帮助识别潜在的直接威胁（例如，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。