Toward quantification and source sector identification of fossil fuel CO2 emissions from an urban area: Results from the INFLUX experiment. Issue 1 (12th January 2015) - Turnbull, Jocelyn C.; Sweeney, Colm; Karion, Anna; Newberger, Timothy; Lehman, Scott J.; Tans, Pieter P.; Davis, Kenneth J.; Lauvaux, Thomas; Miles, Natasha L.; Richardson, Scott J.; Cambaliza, Maria Obiminda; Shepson, Paul B.; et al.
Abstract The Indianapolis Flux Experiment (INFLUX) aims to develop and assess methods for quantifying urban greenhouse gas emissions. Here we use CO2, 14CO2, and CO measurements from tall towers around Indianapolis, USA, to determine urban total CO2, the fossil fuel derived CO2 component (CO2ff), and CO enhancements relative to background measurements. When a local background directly upwind of the urban area is used, the wintertime total CO2 enhancement over Indianapolis can be entirely explained by urban CO2ff emissions. Conversely, when a continental background is used, CO2ff enhancements are larger and account for only half the total CO2 enhancement, effectively representing the combined CO2ff enhancement from Indianapolis and the wider region. In summer, we find that diurnal variability in both background CO2 mole fraction and covarying vertical mixing makes it difficult to use a simple upwind‐downwind difference for a reliable determination of total CO2 urban enhancement. We use characteristic CO2ff source sector CO:CO2ff emission ratios to examine the contribution of the CO2ff source sectors to total CO2ff emissions. This method is strongly sensitive to the mobile sector, which produces most CO. We show that the inventory‐based emission product ("bottom up") and atmospheric observations ("top down") can be directly compared throughout the diurnal cycle using this ratio method. For Indianapolis, the top‐down observations are consistent with the bottom‐up Hestia data product emission sector patterns for most of the diurnal cycle but disagree during the nighttime hours. Further examination of both the top‐down and bottom‐up assumptions is needed to assess the exact cause of the discrepancy.