EAGER: Exploring Applications of Graph Theory for Improved Understanding and Predictability of Atmospheric Chemistry
EAGER:探索图论的应用以提高对大气化学的理解和可预测性
基本信息
- 批准号:2228923
- 负责人:
- 金额:$ 26.55万
- 依托单位:
- 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 2024-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This EAGER project focuses on the development of tools for simplifying the representation of complex atmospheric chemical reactions in air quality and climate models. Several mathematical techniques will be used to develop and assess reduced-complexity representations of atmospheric chemical mechanisms to enhance the research community’s ability to study atmospheric chemistry and the role it plays in causing air pollution and climate change. This effort is expected to provide new tools and approaches to characterizing, utilizing, and understanding complex chemical systems in the atmosphere.The two science objectives for this proposed work are: (1) to quantify the structural and dynamical properties of atmospheric chemical reaction mechanisms using novel graph theoretical techniques to enable new scientific insights; and (2) to apply these graph theoretical techniques to support the development and assessment of reduced-form models of atmospheric chemistry, leveraging both traditional graph clustering methods and modern graph machine learning. The first objective will use techniques including motif analysis, path and cycle analysis, and network robustness metrics, while the second objective will use Louvain clustering and graph machine learning techniques.This proposal meets the EAGER criteria because the application of methods derived from graph theory to atmospheric chemical mechanisms development and evaluation is largely untested and has the potential to transform our ability to understand complex and reduced-form chemical mechanisms. The current process of developing and evaluating atmospheric chemical mechanisms is complex, time consuming, and contains substantial subjective decision making. By taking established methods from graph theory, this work could provide new tools and new approaches to characterizing, utilizing, and understanding complex chemical systems. This project is a high-risk, high-reward project as the methods proposed have only been applied to chemical mechanisms in preliminary work and so the success of the proposed work is difficult to predict. This effort has the potential to provide transformative tools that could further our understanding of existing and to-be-developed atmospheric chemical mechanisms.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.
EAGER项目的重点是开发工具,简化空气质量和气候模型中复杂大气化学反应的表示。 将使用几种数学技术来开发和评估大气化学机制的简化表示,以提高研究界研究大气化学及其在造成空气污染和气候变化方面所起作用的能力。 这一工作的两个科学目标是:(1)利用新的图论技术量化大气化学反应机制的结构和动力学性质,以获得新的科学见解;以及(2)利用传统的图聚类方法和现代图机器学习,应用这些图论技术来支持大气化学简化形式模型的开发和评估。第一个目标将使用包括基序分析、路径和循环分析以及网络鲁棒性度量在内的技术,而第二个目标将使用Louvain聚类和图形机器学习技术。这个建议符合EAGER标准,因为从图论中导出的方法在大气化学机制开发和评估中的应用在很大程度上未经测试,并且有可能改变我们理解复杂和还原型化学机制。目前的发展和评估大气化学机制的过程是复杂的,耗时的,并包含大量的主观决策。通过从图论中获得已建立的方法,这项工作可以为表征,利用和理解复杂化学系统提供新的工具和新的方法。该项目是一个高风险、高回报的项目,因为所提出的方法仅在初步工作中应用于化学机理,因此所提出的工作的成功很难预测。这一努力有可能提供变革性的工具,可以进一步加深我们对现有和有待开发的大气化学机制的理解。该奖项反映了NSF的法定使命,并被认为值得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来支持。
项目成果
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