Collaborative Research: Geologic Methane Emissions to the Atmosphere--Improving the Bottom-Up Estimates of Microseepage

甲烷（CH 4）是一种强大的温室气体，导致大气变暖和气候变化，并具有重大的社会影响。然而，其众多来源的相对规模仍然受到不良约束。在本研究中，实地观测收集的CH 4，从自然地质特征排放，并代表最不确定的来源之一的CH 4到大气中。然后，机器学习技术被用来提高美国各地这些排放的预测能力。本科生和研究生是一个不可分割的一部分，实地采样和数据分析的工作组成部分，和教育推广活动的目的是通过既定的计划与当地学区和罗切斯特博物馆科学中心。微渗漏是一个低强度的天然地质甲烷通量到大气中，被假定为是单一的最大的，但不受约束的地质排放量的组成部分。该研究旨在改进对大面积富含碳氢化合物的沉积盆地微渗漏的自下而上的估计，其总体假设是，现有清单中的微渗漏排放量被高估了。CH 4通量在夏季和冬季在美国八个不同的盆地，以覆盖广泛的地区不同的特点，气藏深度，结构/构造，气候/生态系统。一个现场部署的封闭动态通量室技术用于这些测量。微渗漏通量的大小和一组假设的通量增强参数之间的关系进行了研究，并用于开发一种机器学习方法放大微渗漏通量。由此产生的新的估计微渗漏甲烷排放量从毗邻的美国将提高我们的理解自然排放的这种重要的温室气体，也是一个关键的球员在大气化学和全球气候。这个奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的知识价值和更广泛的影响审查标准。

Methane (CH4) is a powerful greenhouse gas that contributes to warming of the atmosphere and climate change and has significant societal implications. Nevertheless, relative magnitudes of its numerous sources remain ill-constrained. In the present study, field observations are collected of CH4 that is emitted from natural geological features and represent one of the most uncertain sources of CH4 to the atmosphere. Machine learning techniques are then used to increase predictive power of these emissions across the United States. Undergraduate and graduate students are an integral part of the field sampling and data analysis components of the work, and educational outreach activities are intended through established programs with the local school district and Rochester Museum & Science Center.Microseepage is a low-intensity flux of natural geological CH4 to the atmosphere that is postulated to be the single largest, yet poorly constrained component of geological emissions. The research seeks to improve bottom-up estimates of microseepage from large areas of hydrocarbon-rich sedimentary basins, with the overarching hypothesis that microseepage emissions in existing inventories are overestimated. CH4 fluxes are determined during summer and winter in eight different US basins to cover a wide area of varying characteristics in gas reservoir depth, structure/tectonics, and climate/ecosystem. A field deployable closed dynamic flux chamber technique is used for these measurements. The relationship between microseepage flux magnitude and a set of hypothesized flux-enhancing parameters is then investigated and used to develop a machine-learning approach for upscaling microseepage fluxes. Resulting new estimates of microseepage CH4 emissions from the contiguous US will improve our understanding of natural emissions of this important greenhouse gas that is also a key player in atmospheric chemistry and global climate.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.