Investigations into the use of multi-species measurements for source apportionment of the Indianapolis fossil fuel CO2 signal

Abstract

Current bottom up estimates ofCO2emission fluxes are based on a mixture of direct and indirect flux estimates relying to varying degrees on regulatory or self-reported data. Hence, it is important to use additional, independent information to assess biases and lower the flux uncertainty. We explore the use of a self-organizing map (SOM) as a tool to use multi-species observations to partition fossil fuelCO2(CO2ff) emissions by economic source sector. We use the Indianapolis Flux experiment (INFLUX) multi-species observations to provide constraints on the types of relationships we can expect to see, and show from the observations and existing knowledge of likely sources for these species that relationships do exist but can be complex. An Observing System Simulation Experiment (OSSE) is then created to test, in a pseudodata framework, the abilities and limitations of using an SOM to accurately attribute atmospheric tracers to their source sector. These tests are conducted for a variety of emission scenarios, and make use of the corresponding high-resolution footprints for the pseudo-measurements. We show here that the attribution of sector-specific emissions to measured trace gases cannot be addressed by investigating the atmospheric trace gas measurements alone. We conclude that additional a priori information such as inventories of sector-specific trace gases are required to evaluate sector-level emissions using atmospheric methods, to overcome the challenge of the spatial overlap of nearly every predefined source sector. Our OSSE additionally allows us to demonstrate that increasing the (already high) data density cannot solve the co-localization problem.

Global atmospheric CO<sub>2</sub> inverse models converging on neutral tropical land exchange, but disagreeing on fossil fuel and atmospheric growth rate

“…With the aim of quantifying CO 2 fluxes at regional scales, it is more than ever necessary to assess systematic errors of inverse modeling results with independent in situ observations. FF emissions could be optimized with the addition of additional species (e.g., Turnbull et al, 2011;Nathan et al, 2018), such as carbon monoxide (Liu et al, 2017;Bowman et al, 2017), although it can be challenging at the most local and urban scales (Ammoura et al, 2016).…”

“…Current appraisals of the global atmospheric carbon budget are informed by surface fluxes computed by inverse transport models (e.g., Newsam and Enting, 1988;Tans et al, 1990;Rayner et al, 1999;Gurney et al, 2002Gurney et al, , 2003Gurney et al, , 2004Peylin et al, 2013). Net carbon flux to the atmosphere is derived from temporal and spatial CO 2 gradients given by atmospheric observations and prior estimates of component fluxes and their uncertainties.…”

Observing local CO<sub>2</sub> sources using low-cost, near-surface urban monitors

“…A considerable amount of emission estimation work has been invested in the development of activity-based emission inventories for selected metropolitan areas, such as Indianapolis (Gurney et al, 2012), Paris (Bréon et al, 2015), Los Angeles (Newman et al, 2016), Salt Lake City (Patarasuk et al, 2016), and Toronto (Pugliese et al, 2018), as well as other inventories constructed and maintained by individual air management agencies for internal use. These inventories, when updated regularly, offer the possibility of direct source attribution without the use of computationally intense and/or heavily parameterized atmospheric transport models; they do, however, typically rely on interpolations, generalizations, or proxies to generate the necessary input activity data.…”

Source Sector Attribution of CO2 Emissions Using an Urban CO/CO2 Bayesian Inversion System

“…CO is coemitted with CO 2 from the incomplete burning of fossil fuels, thus is directly related to CO 2 ff emissions (e.g., Meijer et al, ; Newman et al, ; Turnbull et al, ). However, in order to assimilate atmospheric trace gases for accurate sector attribution, emission rates of these gases need to be known for each economic sector and for specific geographic locations (Nathan et al, ). Indeed, Super et al () show that CO observations, in particular, can be used to estimate CO 2 ff emissions given knowledge of the emission ratios, and that information gleaned from the CO observation footprints (as created using a combination of an Eulerian Model and a Lagrangian Transport model, to get appropriate resolution detail Super et al, ) can help to delineate CO 2 ff sector contributions.…”