Wintertime CO2, CH4, and CO Emissions Estimation for the Washington, DC-Baltimore Metropolitan Area Using an Inverse Modeling Technique

Underestimation of Thermogenic Methane Emissions in New York City

Recent studies have shown that methane emissions are underestimated by inventories in many US urban areas. This has important implications for climate change mitigation policy at the city, state, and national levels. Uncertainty in both the spatial distribution and sectoral allocation of urban emissions can limit the ability of policy makers to develop appropriately focused emission reduction strategies. Top-down emission estimates based on atmospheric greenhouse gas measurements can help to improve inventories and inform policy decisions. This study presents a new high-resolution (0.02 × 0.02°) methane emission inventory for New York City and its surrounding area, constructed using the latest activity data, emission factors, and spatial proxies. The new high-resolution inventory estimates of methane emissions for the New York-Newark urban area are 1.3 times larger than those for the gridded Environmental Protection Agency inventory. We used aircraft mole fraction measurements from nine research flights to optimize the high-resolution inventory emissions within a Bayesian inversion. These sectorally optimized emissions show that the high-resolution inventory still significantly underestimates methane emissions within the New York-Newark urban area, primarily because it underestimates emissions from thermogenic sources (by a factor of 2.3). This suggests that there remains a gap in our process-based understanding of urban methane emissions.

Methane Emissions Show Recent Decline but Strong Seasonality in Two US Northeastern Cities

Urban methane emissions estimated using atmospheric observations have been found to exceed estimates derived by using traditional inventory methods in several northeastern US cities. In this work, we leveraged a nearly five-year record of observations from a dense tower network coupled with a newly developed high-resolution emissions map to quantify methane emission rates in Washington, DC, and Baltimore, Maryland. Annual emissions averaged over 2018-2021 were 80.1 [95% CI: 61.2, 98.9] Gg in the Washington, DC urban area and 47.4 [95% CI: 35.9, 58.5] Gg in the Baltimore urban area, with a decreasing trend of approximately 4-5% per year in both cities. We also find wintertime emissions 44% higher than summertime emissions, correlating with natural gas consumption. We further attribute a large fraction of total methane emissions to the natural gas sector using a least-squares regression on our spatially resolved estimates, supporting previous findings that natural gas systems emit the plurality of methane in both cities. This study contributes to the relatively sparse existing knowledge base of urban methane emissions sources and variability, adding to our understanding of how these emissions change in time and providing evidence to support efforts to mitigate natural gas emissions.

Government environmental control measures on CO2 emission during the 2014 Youth Olympic Games in Nanjing: Perspectives from a top-down approach

Strict air pollution control measures were conducted during the Youth Olympic Games (YOG) period at Nanjing city and surrounding areas in August 2014. This event provides a unique chance to evaluate the effect of government control measures on regional atmospheric pollution and greenhouse gas emissions. Many previous studies have observed significant reductions of atmospheric pollution species and improvement in air quality, while no study has quantified its synergism on anthropogenic CO₂ emissions, which can be co-reduced with air pollutants. To better understand to what extent these pollution control measures have reduced anthropogenic CO₂ emissions, we conducted atmospheric CO₂ measurements at the suburban site in Nanjing city from 1^st July to 30^th September 2014 and 1^st August to 31^st August 2015, obvious decrease in atmospheric CO₂ was observed between YOG and the rest period. By coupling the a priori emission inventory with atmospheric transport model, we applied the scale factor Bayesian inversion approach to derive the posteriori CO₂ emissions in YOG period and regular period. Results indicate CO₂ emissions from power industry decreased by 45%, and other categories also decreased by 16% for manufacturing combusting, and 37% for non-metallic mineral production. Monthly total anthropogenic CO₂ emissions were 9.8 (±3.6) × 10⁹ kg/month CO₂ for regular period and decreased to 6.2 (±1.9) × 10⁹ kg/month during the YOG period in Nanjing city, with a 36.7% reduction. When scaling up to whole Jiangsu Province, anthropogenic CO₂ emissions were 7.1 (±2.4) × 10¹⁰ kg/month CO₂ for regular period and decreased to 4.4 (±1.2) × 10¹⁰ kg/month CO₂ during the YOG period, yielding a 38.0% reduction.

Carbon Monoxide Emissions from the Washington, DC, and Baltimore Metropolitan Area: Recent Trend and COVID-19 Anomaly

We analyze airborne measurements of atmospheric CO concentration from 70 flights conducted over six years (2015-2020) using an inverse model to quantify the CO emissions from the Washington, DC, and Baltimore metropolitan areas. We found that CO emissions have been declining in the area at a rate of ≈-4.5 % a^-1 since 2015 or ≈-3.1 % a^-1 since 2016. In addition, we found that CO emissions show a "Sunday" effect, with emissions being lower, on average, than for the rest of the week and that the seasonal cycle is no larger than 16 %. Our results also show that the trend derived from the NEI agrees well with the observed trend, but that NEI daytime-adjusted emissions are ≈50 % larger than our estimated emissions. In 2020, measurements collected during the shutdown in activity related to the COVID-19 pandemic indicate a significant drop in CO emissions of 16 % relative to the expected emissions trend from the previous years, or 23 % relative to the mean of 2016 to February 2020. Our results also indicate a larger reduction in April than in May. Last, we show that this reduction in CO emissions was driven mainly by a reduction in traffic.

Accounting for Transport Error in Inversions: An Urban Synthetic Data Experiment

We present and discuss the use of a high-dimensional computational method for atmospheric inversions that incorporates the space-time structure of transport and dispersion errors. In urban environments, transport and dispersion errors are largely the result of our inability to capture the true underlying transport of greenhouse gas (GHG) emissions to observational sites. Motivated by the impact of transport model error on estimates of fluxes of GHGs using in situ tower-based mole-fraction observations, we specifically address the need to characterize transport error structures in high-resolution large-scale inversion models. We do this using parametric covariance functions combined with shrinkage-based regularization methods within an Ensemble Transform Kalman Filter inversion setup. We devise a synthetic data experiment to compare the impact of transport and dispersion error component of the model-data mismatch covariance choices on flux retrievals and study the robustness of the method with respect to fewer observational constraints. We demonstrate the analysis in the context of inferring CO2 fluxes starting with a hypothesized prior in the Washington D.C. /Baltimore area constrained by a synthetic set of tower-based CO2 measurements within an observing system simulation experiment framework. This study demonstrates the ability of these simple covariance structures to substantially improve the estimation of fluxes over standard covariance models in flux estimation from urban regions.

Sub-Daily Natural CO2 Flux Simulation Based on Satellite Data: Diurnal and Seasonal Pattern Comparisons to Anthropogenic CO2 Emissions in the Greater Tokyo Area

During the last decade, advances in the remote sensing of greenhouse gas (GHG) concentrations by the Greenhouse Gases Observing SATellite-1 (GOSAT-1), GOSAT-2, and Orbiting Carbon Observatory-2 (OCO-2) have produced finer-resolution atmospheric carbon dioxide (CO2) datasets. These data are applicable for a top-down approach towards the verification of anthropogenic CO2 emissions from megacities and updating of the inventory. However, great uncertainties regarding natural CO2 flux estimates remain when back-casting CO2 emissions from concentration data, making accurate disaggregation of urban CO2 sources difficult. For this study, we used Moderate Resolution Imaging Spectroradiometer (MODIS) land products, meso-scale meteorological data, SoilGrids250 m soil profile data, and sub-daily soil moisture datasets to calculate hourly photosynthetic CO2 uptake and biogenic CO2 emissions with 500 m resolution for the Kantō Plain, Japan, at the center of which is the Tokyo metropolis. Our hourly integrated modeling results obtained for the period 2010–2018 suggest that, collectively, the vegetated land within the Greater Tokyo Area served as a daytime carbon sink year-round, where the hourly integrated net atmospheric CO2 removal was up to 14.15 ± 4.24% of hourly integrated anthropogenic emissions in winter and up to 55.42 ± 10.39% in summer. At night, plants and soil in the Greater Tokyo Area were natural carbon sources, with hourly integrated biogenic CO2 emissions equivalent to 2.27 ± 0.11%–4.97 ± 1.17% of the anthropogenic emissions in winter and 13.71 ± 2.44%–23.62 ± 3.13% in summer. Between January and July, the hourly integrated biogenic CO2 emissions of the Greater Tokyo Area increased sixfold, whereas the amplitude of the midday hourly integrated photosynthetic CO2 uptake was enhanced by nearly five times and could offset up to 79.04 ± 12.31% of the hourly integrated anthropogenic CO2 emissions in summer. The gridded hourly photosynthetic CO2 uptake and biogenic respiration estimates not only provide reference data for the estimation of total natural CO2 removal in our study area, but also supply prior input values for the disaggregation of anthropogenic CO2 emissions and biogenic CO2 fluxes when applying top-down approaches to update the megacity’s CO2 emissions inventory. The latter contribution allows unprecedented amounts of GOSAT and ground measurement data regarding CO2 concentration to be analyzed in inverse modeling of anthropogenic CO2 emissions from Tokyo and the Kantō Plain.

Background conditions for an urban greenhouse gas network in the Washington, D.C. and Baltimore metropolitan region

As city governments take steps towards establishing emissions reduction targets, the atmospheric research community is increasingly able to assist in tracking emissions reductions. Researchers have established systems for observing atmospheric greenhouse gases in urban areas with the aim of attributing greenhouse gas concentration enhancements (and thus, emissions) to the region in question. However, to attribute enhancements to a particular region, one must isolate the component of the observed concentration attributable to fluxes inside the region by removing the background, which is the component due to fluxes outside. In this study, we demonstrate methods to construct several versions of a background for our carbon dioxide and methane observing network in the Washington, DC and Baltimore, MD metropolitan region. Some of these versions rely on transport and flux models, while others are based on observations upwind of the domain. First, we evaluate the backgrounds in a synthetic data framework, then we evaluate against real observations from our urban network. We find that backgrounds based on upwind observations capture the variability better than model-based backgrounds, although care must be taken to avoid bias from biospheric carbon dioxide fluxes near background stations in summer. Model-based backgrounds also perform well when upwind fluxes can be modeled accurately. Our study evaluates different background methods and provides guidance determining background methodology that can impact the design of urban monitoring networks.

Fluxes of Atmospheric Greenhouse-Gases in Maryland (FLAGG-MD): Emissions of Carbon Dioxide in the Baltimore, MD-Washington, D.C. area

To study emissions of CO2 in the Baltimore, MD-Washington, D.C. (Balt-Wash) area, an aircraft campaign was conducted in February 2015, as part of the FLAGG-MD (Fluxes of Atmospheric Greenhouse-Gases in Maryland) project. During the campaign, elevated mole fractions of CO2 were observed downwind of the urban center and local power plants. Upwind flight data and HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model analyses help account for the impact of emissions outside the Balt-Wash area. The accuracy, precision, and sensitivity of CO2 emissions estimates based on the mass balance approach were assessed for both power plants and cities. Our estimates of CO2 emissions from two local power plants agree well with their CEMS (Continuous Emissions Monitoring Systems) records. For the 16 power plant plumes captured by the aircraft, the mean percentage difference of CO2 emissions was -0.3 %. For the Balt-Wash area as a whole, the 1σ CO2 emission rate uncertainty for any individual aircraft-based mass balance approach experiment was ±38 %. Treating the mass balance experiments, which were repeated seven times within nine days, as individual quantifications of the Balt-Wash CO2 emissions, the estimation uncertainty was ±16 % (standard error of the mean at 95% CL). Our aircraft-based estimate was compared to various bottom-up fossil fuel CO2 (FFCO2) emission inventories. Based on the FLAGG-MD aircraft observations, we estimate 1.9±0.3 MtC of FFCO2 from the Balt-Wash area during the month of February 2015. The mean estimate of FFCO2 from the four bottom-up models was 2.2±0.3 MtC.

Greenhouse gas observations from the Northeast Corridor tower network

We present the organization, structure, instrumentation, and measurements of the Northeast Corridor greenhouse gas observation network. This network of tower-based in situ carbon dioxide and methane observation stations was established in 2015 with the goal of quantifying emissions of these gases in urban areas in the northeastern United States. A specific focus of the network is the cities of Baltimore, MD, and Washington, DC, USA, with a high density of observation stations in these two urban areas. Additional observation stations are scattered throughout the northeastern US, established to complement other existing urban and regional networks and to investigate emissions throughout this complex region with a high population density and multiple metropolitan areas. Data described in this paper are archived at the National Institute of Standards and Technology and can be found at https://doi.org/10.18434/M32126 (Karion et al., 2019).

Greenhouse gas observations from the Northeast Corridor tower network

We present the organization, structure, instrumentation, and measurements of the Northeast Corridor greenhouse gas observation network. This network of tower-based in situ carbon dioxide and methane observation stations was established in 2015 with the goal of quantifying emissions of these gases in urban areas in the northeastern United States. A specific focus of the network is the cities of Baltimore, MD, and Washington, DC, USA, with a high density of observation stations in these two urban areas. Additional observation stations are scattered throughout the northeastern US, established to complement other existing urban and regional networks and to investigate emissions throughout this complex region with a high population density and multiple metropolitan areas. Data described in this paper are archived at the National Institute of Standards and Technology and can be found at https://doi.org/10.18434/M32126 (Karion et al., 2019).