A spatially explicit inventory scaling approach to estimate urban CO2 emissions

Abstract

Appropriate techniques to quantify greenhouse gas emission reductions in cities over time are necessary to monitor the progress of these efforts and effectively inform continuing mitigation. We introduce a scaling factor (SF) method that combines aircraft measurements and dispersion modeling to estimate urban emissions and apply it to 9 nongrowing season research aircraft flights around New York City (NYC) in 2018–2020. This SF approach uses a weighting function to focus on an area of interest while still accounting for upwind emissions. We estimate carbon dioxide (CO2) emissions from NYC and the Greater New York Area (GNA) and compare to nested inversion analyses of the same data. The average calculated CO2 emission rates for NYC and the GNA, representative of daytime emissions for the flights, were (49 ± 16) kmol/s and (144 ± 44) kmol/s, respectively (uncertainties reported as ±1σ variability across the 9 flights). These emissions are within ∼15% of an inversion analysis and agree well with inventory estimates. By using an ensemble, we also investigate the variability introduced by several sources and find that day-to-day variability dominates the overall variability. This work investigates and demonstrates the capability of an SF method to quantify emissions specific to particular areas of interest.