Current bottom up estimates of CO2 emission 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 fuel CO2 (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.
All Science Journal Classification (ASJC) codes
- Environmental Engineering
- Geotechnical Engineering and Engineering Geology
- Atmospheric Science