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

Colby College, Waterville ME

Investigators

Abstract

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.