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

• 批准号: 2228923
• 负责人: Sam Silva
• 金额: $ 26.55万
• 依托单位: University of Southern California
• 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: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2228923&HistoricalAwards=false
• 关键词: EAGER; Exploring; Applications; Graph; Theory

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.

这个急切的项目着重于开发工具，以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减少复杂性表示，以增强研究社区研究大气化学的能力及其在引起空气污染和气候变化方面所扮演的作用。预计这种努力将提供新的工具和方法来表征，使用和理解大气中的复杂化学系统。这项提出的工作的两个科学目标是：（1）使用新型图理论技术量化大气化学反应机制的结构和动态特性，以启用新的科学见解； （2）应用这些图理论技术来支持大气化学模型的开发和评估，利用传统的图形聚类方法和现代的图形机器学习。第一个目标将使用包括主题分析，路径和周期分析以及网络鲁棒性指标在内的技术，而第二个目标将使用Louvain群集和图形机器学习技术。该建议符合急切的标准，因为从图形论的应用程序应用到大气化学机制的发展和评估可以使我们的化学机制不大化，并且可以使我们的化学机制变得复杂，并且可以理解复杂的功能，并改善了复杂的功能。当前开发和评估大气化学机制的过程很复杂，耗时，并且包含实质性的主观决策。通过从图理论中采用既定方法，这项工作可以提供新的工具和新方法来表征，使用和理解复杂的化学系统。该项目是一个高风险，高回报的项目，因为提出的方法仅应用于初步工作中的化学机制，因此很难预测拟议的工作的成功。这项努力具有提供变革性工具的潜力，可以进一步了解我们对现有和开发的大气化学机制的理解。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响来审查标准，被认为是珍贵的支持。