Constraining Urban CO2Emissions Using Mobile Observations from a Light Rail Public Transit Platform

Derek V. Mallia, Logan E. Mitchell, Lewis Kunik, Ben Fasoli, Ryan Bares, Kevin R. Gurney, Daniel L. Mendoza, John C. Lin

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Urban environments are characterized by pronounced spatiotemporal heterogeneity, which can present sampling challenges when utilizing conventional greenhouse gas (GHG) measurement systems. In Salt Lake City, Utah, a GHG instrument was deployed on a light rail train car that continuously traverses the Salt Lake Valley (SLV) through a range of urban typologies. CO2 measurements from a light rail train car were used within a Bayesian inverse modeling framework to constrain urban emissions across the SLV during the fall of 2015. The primary objectives of this study were to (1) evaluate whether ground-based mobile measurements could be used to constrain urban emissions using an inverse modeling framework and (2) quantify the information that mobile observations provided relative to conventional GHG monitoring networks. Preliminary results suggest that ingesting mobile measurements into an inverse modeling framework generated a posterior emission estimate that more closely aligned with observations, reduced posterior emission uncertainties, and extends the geographical extent of emission adjustments.

Original languageEnglish (US)
Pages (from-to)15613-15621
Number of pages9
JournalEnvironmental Science and Technology
Issue number24
StatePublished - Dec 15 2020
Externally publishedYes

ASJC Scopus subject areas

  • General Chemistry
  • Environmental Chemistry


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