Spatially-Temporally Resolved Sampling System for Carbon Dioxide Concentration in The Atmospheric Boundary Layer: A Low-Cost UAS Approach

Abstract

Given the consensus around the importance of atmospheric carbon dioxide (CO2) to human life on earth, its monitoring has grown significantly in the past 60 years. However, there is still a knowledge gap regarding the intricacies of the relationship between atmospheric CO2, the biosphere, and the weather patterns. Such a knowledge gap can be explained by limitations in technology, the physical challenges of measuring atmospheric CO2, and the natural complexity of the atmosphere-biosphere interactions. Another component in this explanation is the different scales of the impacts of CO2, ranging from global greenhouse effects to stomatal closure on plants. The limitations of current atmospheric CO2 monitoring technologies motivated this thesis, which details the initial development process of a complementary Unmanned Aerial System (UAS) based sampling tool proposed in hopes of better understanding the behavior of CO2 in the atmospheric boundary layer. This new tool is expected to be suitable for remote instrument augmentation, initial exploratory studies, and measurements in under-surveyed areas. This thesis proposes a UAS autopilot integration for a fast response non-dispersive infrared (NDIR) CO2 sensor (Sensair K30-FR). Such integration provides a single source of timestamp and position for any data point acquired, ensuring data comparability across platforms. It also allows for deployment across rotary- and fixed-wing UAS, generating spatially and temporally resolved CO2 sampling. Accompanied by a customized sensor housing and a post-processing desktop application, the proposed UAS autopilot integration can make a standard UAS into a system capable of autonomously collecting atmospheric CO2 concentrations, and automatically generating a graphical presentation of the collected data for analysis by the end-user. This thesis presents an overview of the challenges faced, solutions adopted, field deployment practiced, and results achieved.