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

这一职业生涯中期推进(MCA)项目将利用成像技术、人工智能(AI)和机器学习(ML)技术的进步来改进采样策略和预测方法，以绘制不同景观中的生物挥发性有机化合物(BVOC)排放图。BVOCs通过影响对流层的氧化能力和相关的大气痕量气体的化学循环，在大气化学中发挥着重要作用。活性BVOCs的排放对臭氧的产生、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，以规划两个不同森林生态系统类型的BVOC野外采样地点：缅因州中部和巴西亚马逊的马瑙斯附近。由于许多目标区域无法进入，将使用无人机(UAV)对选定森林上空的BVOCs进行采样。本项目将解决以下问题：(1)如何在有限数量的采样点上对异质景观上的BVOC进行最佳采样？(2)不同森林特征之间的BVOC浓度差异有多大？(3)树冠以上BVOC浓度的变化对底层森林亚类BVOC排放量的差异有何影响？以及(4)BVOC排放量的空间差异在多大程度上显著影响区域和全球排放量估计？这项研究的最终目标是将BVOC浓度地图与标准排放模型(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中具有广泛应用并引起大气化学和更广泛环境科学界极大兴趣的工具。该项目包括支持一名暑期本科生参与研究。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，为本科生提供参与创新STEM研究的新机会。该项目由地球科学局共同资助，以支持AI/ML在地球科学领域的进步，并由已建立的促进竞争性研究的计划共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.