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

• 批准号: 2341843
• 负责人: Julianne Chung
• 金额: $ 16.08万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2341843&HistoricalAwards=false
• 关键词: ATD; Collaborative; Research; Computationally; Efficient

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.

温室气体和空气污染的大规模异常排放构成了对人类健康的威胁，在排放，折衷状态排放目标和威胁能源安全的情况下对人类健康构成了威胁。 最近两次高调的天然气井喷强调了早期检测和干预的需求。几个新的和即将出现的卫星的特定目的是检测和监测温室气体的排放，最近的研究表明，使用卫星数据检测此类事件的潜力。但是，由于要处理大量的卫星数据以及需要重建以进行威胁检测，因此在量化这些排放异常或超级发射器时存在巨大的计算挑战。该项目旨在通过开发改进的计算方法来解决这些挑战，以检测大气排放。该项目在三所大学中的每所大学每年都支持一名毕业生。该项目旨在解决针对反问题的计算高效求解器开发的基本问题，并通过卫星通过卫星来推动传统的威胁界限，使研究人员能够检测和监视。异常的大气排放迅速，准确且具有可量化的不确定性。该项目的主要推力是（i）有效地纳入先前信息和参数选择，（ii）具有多个随机组件的时空逆建模改进，以及多个随机组件和成本削减的不精确和采样方法来处理昂贵的伴随模型，以及（iii）通过案例研究与合成卫星数据对开发方法的评估，测试和集成。该项目的目的是帮助使用卫星确定潜在的即时威胁（例如石油和天然气井喷），这些卫星不仅在灾难反应和恢复中产生了更大的影响，而且还可以最大程度地降低长期环境风险。这奖反映了NSF的奖项法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准来评估的值得支持的。