Quantifying Spatiotemporal Greenhouse Gas Emissions Using Autonomous Surface Vehicles. Issue 1 (22nd July 2016)

• Record Type: Journal Article
• Authors: Dunbabin, Matthew; Grinham, Alistair
• Other Names: Pomerleau François guestEditor.; Kelly Jonathan guestEditor.
• Is Part Of: Journal of field robotics. Volume 34:Issue 1(2017)
• Page Start: 151
• Page End: 169
• Publication Date: 2016-07-22
• Subjects: Robots, Industrial -- Periodicals; Automatic control -- Periodicals; 629.892
• DOI: 10.1002/rob.21665
• Languages: English
• ISSNs: 1556-4959

Abstract

Abstract : Accurately quantifying total greenhouse gas emissions (e.g., methane) from natural systems such as lakes, reservoirs, and wetlands requires the spatial and temporal measurement of both diffusive and ebullitive (bubbling) emissions. Ebullitive emissions exhibit high spatial and temporal variability and as such are difficult to measure. Traditional manual measurement techniques provide only limited localized assessment of methane flux, often introducing significant errors when extrapolated to the whole‐of‐system . This is further exacerbated when whole‐of‐region estimates are developed for inclusion in global greenhouse gas inventories. In this paper, we directly address these current sampling limitations by comparing two robot boat‐based sampling systems with complementary sensing modalities to directly measure in real time the spatiotemporal release of methane to atmosphere across inland waterways. The first system consists of a single Autonomous Surface Vehicle (ASV) fitted with an Optical Methane Detector with algorithms to exploit the robot's mobility and transect repeatability for the accurate detection and quantification of methane bubbles across whole‐of‐system. The second system consists of multiple networked ASVs capable of persistent operation and scalable to whole‐of‐region monitoring. Each ASV carries a novel automated chamber‐based gas sampling system to allow simultaneous real‐time measurement of methane across the waterway. These ASV systems provide a foundation for persistent large‐scale spatiotemporal sampling allowing scientists to develop whole‐of‐region greenhouse gas estimates and greatly improve global inventory budgets. An overview of the single and multi‐robot sampling systems is presented, including their automated methane detection and sampling methodologies for the spatiotemporal quantification of greenhouse gas release to atmosphere. Experimental results are shown demonstrating each system's ability to autonomously navigate, detect, and quantify methane release to atmosphere across an entire inland reservoir.