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

B. J. Nathan, T. Lauvaux, J. C. Turnbull, S. J. Richardson, N. L. Miles, Kevin Gurney

Research output: Contribution to journalArticle

Abstract

We assimilate multiple trace gas species within a single high-resolution Bayesian inversion system to optimize CO2ff emissions for individual source sectors. Starting with carbon monoxide (CO), an atmospheric trace gas with fairly well-known emissions, we use emission factors of CO and CO2ff (called RCO) defined for each source sector to enable us to jointly use CO and CO2 atmospheric mole fractions to constrain CO2ff sectoral emissions. We first show that our combined CO-CO2 inversion is theoretically capable of estimating the relative magnitude of sectoral emissions for two, specially defined sectors over Indianapolis, while CO2-only inversions failed at quantifying sectoral emissions. When assimilating hourly mole fractions collected over 4 months, inverse sectoral emissions converge toward high-resolution CO2ff bottom-up emissions from Hestia. The emission ratios between the two sectors agree within 15% with Hestia across various inversion configurations. The assimilation of CO mole fractions preferentially improves flux estimates from traffic emissions, because the CO levels originating from the combustion engine sector are large relative to those from other economic sectors. In a further investigation, we find that including an additional third tracer sensitive to the other sectors only slightly improves the accuracy of the inversion compared to our current two-sector inversions with CO and CO2 mole fractions. We finally examined the impact of errors in trace gas emission factors and quantify their relative impact on sector-based inverse emissions. We conclude that multispecies inversions can constrain sectoral emissions at policy-level uncertainties if trace gas emission factors are sufficiently well known at the city level.

Original languageEnglish (US)
Pages (from-to)13,611-13,621
JournalJournal of Geophysical Research: Atmospheres
Volume123
Issue number23
DOIs
StatePublished - Dec 16 2018

Fingerprint

carbon monoxide
Carbon Monoxide
sectors
carbon dioxide
inversions
trace gas
emissions factor
Gas emissions
gas emissions
Gases
inversion
gases
atmospheric gas
traffic emission
Fluxes
Engines
engines
combustion
Economics
traffic

Keywords

  • atmospheric inversion
  • carbon cycle
  • data assimilation
  • greenhouse gas emissions
  • trace gases
  • urban

ASJC Scopus subject areas

  • Geophysics
  • Forestry
  • Oceanography
  • Aquatic Science
  • Ecology
  • Water Science and Technology
  • Soil Science
  • Geochemistry and Petrology
  • Earth-Surface Processes
  • Atmospheric Science
  • Space and Planetary Science
  • Earth and Planetary Sciences (miscellaneous)
  • Palaeontology

Cite this

Nathan, B. J., Lauvaux, T., Turnbull, J. C., Richardson, S. J., Miles, N. L., & Gurney, K. (2018). Source Sector Attribution of CO2 Emissions Using an Urban CO/CO2 Bayesian Inversion System. Journal of Geophysical Research: Atmospheres, 123(23), 13,611-13,621. https://doi.org/10.1029/2018JD029231

Source Sector Attribution of CO2 Emissions Using an Urban CO/CO2 Bayesian Inversion System. / Nathan, B. J.; Lauvaux, T.; Turnbull, J. C.; Richardson, S. J.; Miles, N. L.; Gurney, Kevin.

In: Journal of Geophysical Research: Atmospheres, Vol. 123, No. 23, 16.12.2018, p. 13,611-13,621.

Research output: Contribution to journalArticle

Nathan, BJ, Lauvaux, T, Turnbull, JC, Richardson, SJ, Miles, NL & Gurney, K 2018, 'Source Sector Attribution of CO2 Emissions Using an Urban CO/CO2 Bayesian Inversion System', Journal of Geophysical Research: Atmospheres, vol. 123, no. 23, pp. 13,611-13,621. https://doi.org/10.1029/2018JD029231
Nathan, B. J. ; Lauvaux, T. ; Turnbull, J. C. ; Richardson, S. J. ; Miles, N. L. ; Gurney, Kevin. / Source Sector Attribution of CO2 Emissions Using an Urban CO/CO2 Bayesian Inversion System. In: Journal of Geophysical Research: Atmospheres. 2018 ; Vol. 123, No. 23. pp. 13,611-13,621.
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