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Seasonally Resolved Excess Urban Methane Emissions from the Baltimore/Washington, DC Metropolitan Region
journal contribution
posted on 2019-09-18, 21:16 authored by Yaoxian Huang, Eric A. Kort, Sharon Gourdji, Anna Karion, Kimberly Mueller, John WareUrban areas are increasingly recognized
as an important source
of methane (CH4), but we have limited seasonally resolved
observations of these regions. In this study, we quantify seasonal
and annual urban CH4 emissions over the Baltimore, Maryland,
and Washington, DC metropolitan regions. We use CH4 atmospheric
observations from four tall tower stations and a Lagrangian particle
dispersion model to simulate CH4 concentrations at these
stations. We directly compare these simulations with observations
and use a geostatistical inversion method to determine optimal emissions
to match our observations. We use observations spanning four seasons
and employ an ensemble approach considering multiple meteorological
representations, emission inventories, and upwind CH4 values.
Forward simulations in winter, spring, and fall underestimate observed
atmospheric CH4 while in summer, simulations overestimate
observations because of excess modeled wetland emissions. With ensemble
geostatistical inversions, the optimized annual emissions in DC/Baltimore
are 39 ± 9 Gg/month (1 δ), 2.0 ± 0.4
times higher than the ensemble mean of bottom-up emission inventories.
We find a modest seasonal variability of urban CH4 emissions
not captured in current inventories, with optimized summer emissions
∼41% lower than winter, broadly consistent with expectations
if emissions are dominated by fugitive natural gas sources that correlate
with natural gas usage.
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geostatistical inversion methodgas usageupwind CH 4 valuesCH 4 concentrationsbottom-up emission inventoriesuse observationsCH 4Excess Urban Methane Emissionstower stationsemission inventoriesensemble geostatistical inversionsDCgas sourcesLagrangian particle dispersion modeluse CH 4CH 4 emissionsForward simulationsensemble approachwetland emissions
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