Since
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.
There is increased interest in understanding urban greenhouse gas (GHG) emissions. To accurately estimate city emissions, the influence of extraurban fluxes must first be removed from urban greenhouse gas (GHG) observations. This is especially true for regions, such as the U.S. Northeastern Corridor‐Baltimore/Washington, DC (NEC‐B/W), downwind of large fluxes. To help site background towers for the NEC‐B/W, we use a coupled Bayesian Information Criteria and geostatistical regression approach to help site four background locations that best explain CO2 variability due to extraurban fluxes modeled at 12 urban towers. The synthetic experiment uses an atmospheric transport and dispersion model coupled with two different flux inventories to create modeled observations and evaluate 15 candidate towers located along the urban domain for February and July 2013. The analysis shows that the average ratios of extraurban inflow to total modeled enhancements at urban towers are 21% to 36% in February and 31% to 43% in July. In July, the incoming air dominates the total variability of synthetic enhancements at the urban towers (R2 = 0.58). Modeled observations from the selected background towers generally capture the variability in the synthetic CO2 enhancements at urban towers (R2 = 0.75, root‐mean‐square error (RMSE) = 3.64 ppm; R2 = 0.43, RMSE = 4.96 ppm for February and July). However, errors associated with representing background air can be up to 10 ppm for any given observation even with an optimal background tower configuration. More sophisticated methods may be necessary to represent background air to accurately estimate urban GHG emissions.
Responses to public health threats presented by the global COVID-19 pandemic dramatically altered daily activities in cities around the world, including in the Los Angeles and Washington DC/Baltimore metropolitan areas. Researchers have attempted to determine the extent to which CO 2 emissions were impacted by the pandemic, linking changes in emissions to processes and sectors using different types of activity data and baselines for comparisons (Le Quéré et al., 2020;Liu et al., 2020;Zheng et al., 2020). One study shows that CO 2 emissions declined by 3.9% globally in the first 4 months in 2020, attributing half of this decline to changes in traffic and mobility (Le Quéré et al., 2020). Unlike these studies, which use only activity data to estimate declines, here we also use atmospheric CO 2 observations to detect when and how emissions were impacted, and focus on CO 2 emissions reductions at the city scale.Our analysis relies on high-accuracy atmospheric CO 2 observations from urban networks, building on a recently published study that used lower-accuracy CO 2 sensors to estimate COVID-19 related impacts for the San Francisco Bay area (Turner et al., 2020). Here, we evaluate impacts in two separate metropolitan areas: Los Angeles and Washington DC/Baltimore, allowing for an inter-comparison between two large urban regions. In Los Angeles and Washington DC/Baltimore, traffic congestion and commuting play dominant
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