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

甲烷（CH4）是一种强大的温室气体，有助于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不受限制。在本研究中，收集了自然地质特征排放的CH4的野外观测资料，这些CH4是大气中最不确定的CH4来源之一。然后使用机器学习技术来提高美国各地这些排放的预测能力。本科生和研究生是野外抽样和数据分析工作的组成部分，教育推广活动是通过与当地学区和罗切斯特博物馆和科学中心建立的项目进行的。微渗漏是自然地质CH4向大气的低强度通量，被认为是地质排放中最大的单一组分，但约束较差。该研究旨在改进对大面积富油气沉积盆地微渗流的自下而上估计，并提出了现有库存微渗流排放量被高估的总体假设。在夏季和冬季对美国8个不同盆地的CH4通量进行了测定，这些盆地覆盖了气藏深度、构造/构造和气候/生态系统特征各异的广泛区域。这些测量使用了现场可展开的封闭动态通量室技术。然后研究了微渗流通量大小与一组假设的通量增强参数之间的关系，并用于开发微渗流通量升级的机器学习方法。由此产生的对美国周边地区甲烷微渗漏排放的新估计将提高我们对这种重要温室气体自然排放的理解，甲烷也是大气化学和全球气候的关键因素。该奖项反映了美国国家科学基金会的法定使命，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。

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.