es9b06619_si_001.pdf (18.11 MB)
Wintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC–Baltimore Metropolitan Area Using an Inverse Modeling Technique
journal contribution
posted on 2020-02-21, 19:04 authored by Israel Lopez-Coto, Xinrong Ren, Olivia E. Salmon, Anna Karion, Paul B. Shepson, Russell R. Dickerson, Ariel Stein, Kuldeep Prasad, James R. WhetstoneSince
greenhouse gas mitigation efforts are mostly being implemented
in cities, the ability to quantify emission trends for urban environments
is of paramount importance. However, previous aircraft work has indicated
large daily variability in the results. Here we use measurements of
CO2, CH4, and CO from aircraft over 5 days within
an inverse model to estimate emissions from the DC–Baltimore
region. Results show good agreement with previous estimates in the
area for all three gases. However, aliasing caused by irregular spatiotemporal
sampling of emissions is shown to significantly impact both the emissions
estimates and their variability. Extensive sensitivity tests allow
us to quantify the contributions of different sources of variability
and indicate that daily variability in posterior emissions estimates
is larger than the uncertainty attributed to the method itself (i.e.,
17% for CO2, 24% for CH4, and 13% for CO). Analysis
of hourly reported emissions from power plants and traffic counts
shows that 97% of the daily variability in posterior emissions estimates
is explained by accounting for the sampling in time and space of sources
that have large hourly variability and, thus, caution must be taken
in properly interpreting variability that is caused by irregular spatiotemporal
sampling conditions.