MCA: Leveraging Artificial Intelligence to Improve Understanding of Biogenic Volatile Organic Compound Emissions and Chemistry over Heterogeneous Forest Landscapes

MCA：利用人工智能提高对异质森林景观中生物挥发性有机化合物排放和化学的了解

• 批准号: 2322325
• 负责人: Karena McKinney
• 金额: $ 25.95万
• 依托单位: Colby College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2322325&HistoricalAwards=false
• 关键词: MCA; Leveraging; Artificial; Intelligence; Improve

This Mid-Career Advancement (MCA) project will leverage advances in imaging technology, artificial intelligence (AI), and machine learning (ML) techniques to advance sampling strategies and prediction methods for mapping biogenic volatile organic compound (BVOC) emissions in heterogeneous landscapes. BVOCs play an important role in the chemistry of the atmosphere by influencing the oxidative capacity of the troposphere and the associated chemical cycles of atmospheric trace gases. The emissions of reactive BVOCs have implications for ozone production, air quality, health effects, and climate. This project will apply AI methods to forest imaging data to plan BVOC field sampling locations in two disparate forest ecosystem types: Central Maine and near Manaus, Amazonas, Brazil. As many of the target areas are inaccessible, unmanned aerial vehicles (UAVs) will be used to sample BVOCs above the selected forests. This project will address the following questions: (1) How can BVOCs over a heterogeneous landscape be optimally sampled with a limited number of sampling locations? (2) To what extent do BVOC concentrations vary across heterogeneous forest features? (3) What do varying above-canopy BVOC concentrations suggest about differences in BVOC emission rates from the underlying forest subtypes? and (4) At what scale and to what extent do spatial variations in BVOC emissions significantly affect regional and global emissions estimates? The final goal of the study is to compare the maps of BVOC concentrations with emissions predicted by standard emission models such as MEGAN.This project has the potential to develop tools that could have a wide range of applications in environmental sensing and be of great interest to the atmospheric chemistry and broader environmental science community. The project includes support for a summer undergraduate student to participate in the research. There are also plans for a summer institute on AI-driven sensing and environmental modeling provide new opportunities for undergraduate students to become involved in innovative STEM research. This project is co-funded by the Directorate for Geosciences to support AI/ML advancement in the geosciences and by the Established Program to Stimulate Competitive Research.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.

这个中期的进步（MCA）项目将利用成像技术，人工智能（AI）和机器学习（ML）技术的进步，以推进采样策略和预测方法，以绘制生物挥发性有机化合物（BVOC）排放的异质景观中的生物挥发性有机化合物（BVOC）。 BVOC通过影响对流层的氧化能力和大气痕量气体的相关化学循环，在大气化学中起重要作用。反应性BVOC的排放对臭氧的生产，空气质量，健康影响和气候具有影响。该项目将在森林成像数据中应用AI方法，以计划两种不同的森林生态系统类型中的BVOC现场采样位置：缅因州中部和巴西亚马逊群岛的Manaus附近。由于许多目标区域都无法访问，因此无人驾驶汽车（UAV）将用于在选定的森林上方采样BVOC。该项目将解决以下问题：（1）如何使用有限数量的采样位置对异质景观进行BVOC？ （2）BVOC浓度在多大程度上在异质森林特征之间有所不同？ （3）在基础森林亚型的BVOC排放率差异上，上面的BVOC浓度有什么变化？ （4）BVOC排放的空间变化在多大程度上和在多大程度上会显着影响区域和全球排放估计？该研究的最终目标是将BVOC浓度的地图与标准排放模型（例如Megan）所预测的排放进行比较。该项目有可能开发可能在环境传感中具有广泛应用的工具，并引起大气化学和更广泛的环境科学社区的极大兴趣。该项目包括支持夏季本科生参加研究的支持。还计划了一个夏季研究所的AI驱动感应和环境建模的计划，为本科生提供了新的机会，可以参与创新的STEM研究。该项目由地球科学局共同资助，以支持地球科学的AI/ML进步，并由既定的竞争研究的既定计划进行了支持。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。