Quantitative Characterization of Hyper-Local Urban Greenhouse Gas Sources Using Tower-Based Atmospheric Sensors

Abstract:

Atmospheric greenhouse gas (GHG) emissions are often characterized using ground or tower-based sensor networks. In urban areas, these atmospheric monitors are often installed below the turbulent boundary layer and are subject to intense concentration impulses originating from hyper-local point sources. Studies using empirical data for GHG flux characterization often filter out the effects of these hyper-local point sources without direct quantification. While previous literature has shown the ability of empirical atmospheric data to directionally locate and measure hyper-local point sources, many studies are conducted in open, flat fields or do not feature directly quantified point sources.

In this study, we investigated the ability of empirical atmospheric data to locate and quantify a concurrently measured hyper-local point source in a dense urban setting. An eddy covariance tower and a low-cost sensor tower were deployed in various locations around a continuously measured restaurant exhaust vent emitting elevated levels of CO2 and CH4 acting as a hyper-local point source. A model featuring different processing methods and statistical techniques was built to examine the most effective procedures for source isolation, directional location, and quantification. Specifically, we find that the source can be directionally located and statistically quantified using bivariate polar plots of remotely collected data. Using excess concentrations above a minimum baseline, we identify the source and find a quantitative relationship between source size and receptor distance. Furthermore, we suggest that greater machine precision and fast data rate do not drastically impact our ability to characterize the source. This work may provide a basis for source identification and monitoring protocols for networks that feature sensors influenced by hyper-local point sources, subject to site-specific assumptions. This study may therefore allow for more effective characterization of GHG flux in the hyper-local vicinity of individual monitoring stations.