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.

该中期职业发展（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，推进采样策略和预测方法，以绘制异质景观中生物挥发性有机化合物（BVOC）排放。 BVOCs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，在大气化学中发挥重要作用。活性BVOCs的排放对臭氧生成、空气质量、健康影响和气候都有影响。该项目将人工智能方法应用于森林成像数据，以规划两种不同森林生态系统类型中的BVOC实地采样位置：缅因州中部和巴西亚马逊州马瑙斯附近。由于许多目标区域无法进入，将使用无人驾驶飞行器在选定的森林上空对BVOCs进行采样。本项目将解决以下问题：（1）如何在有限的采样点上对异质景观中的BVOCs进行最佳采样？(2)在不同的森林特征中，BVOC的浓度有多大的差异？(3)不同的冠层以上BVOC浓度暗示了底层森林亚型BVOC排放率的差异吗？以及（4）BVOC排放的空间变化在多大尺度和程度上显著影响区域和全球排放估计？该研究的最终目标是将BVOC浓度图与标准排放模型（如MEGAN）预测的排放量进行比较，该项目有可能开发出在环境传感方面具有广泛应用的工具，并对大气化学和更广泛的环境科学界产生极大的兴趣。该项目包括支持一名暑期本科生参与研究。此外，还计划举办一个关于人工智能驱动的传感和环境建模的暑期研究所，为本科生参与创新的STEM研究提供新的机会。该项目由地球科学理事会共同资助，以支持AI/ML在地球科学领域的进步，并由既定计划刺激竞争性研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。