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

点击翻译按钮获取中文摘要

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.