The Impact of COVID-19 on CO2 Emissions in the Los Angeles and Washington DC/Baltimore Metropolitan Areas.

Responses to COVID-19 have resulted in unintended reductions of city-scale carbon dioxide (CO2) emissions. Here, we detect and estimate decreases in CO2 emissions in Los Angeles and Washington DC/Baltimore during March and April 2020. We present three lines of evidence using methods that have increasing model dependency, including an inverse model to estimate relative emissions changes in 2020 compared to 2018 and 2019. The March decrease (25%) in Washington DC/Baltimore is largely supported by a drop in natural gas consumption associated with a warm spring whereas the decrease in April (33%) correlates with changes in gasoline fuel sales. In contrast, only a fraction of the March (17%) and April (34%) reduction in Los Angeles is explained by traffic declines. Methods and measurements used herein highlight the advantages of atmospheric CO2 observations for providing timely insights into rapidly changing emissions patterns that can empower cities to course-correct CO2 reduction activities efficiently.

Anthropogenic and biogenic CO2 fluxes in the Boston urban region.

With the pending withdrawal of the United States from the Paris Climate Accord, cities are now leading US actions toward reducing greenhouse gas emissions. Implementing effective mitigation strategies requires the ability to measure and track emissions over time and at various scales. We report CO2 emissions in the Boston, MA, urban region from September 2013 to December 2014 based on atmospheric observations in an inverse model framework. Continuous atmospheric measurements of CO2 from five sites in and around Boston were combined with a high-resolution bottom-up CO2 emission inventory and a Lagrangian particle dispersion model to determine regional emissions. Our model-measurement framework incorporates emissions estimates from submodels for both anthropogenic and biological CO2 fluxes, and development of a CO2 concentration curtain at the boundary of the study region based on a combination of tower measurements and modeled vertical concentration gradients. We demonstrate that an emission inventory with high spatial and temporal resolution and the inclusion of urban biological fluxes are both essential to accurately modeling annual CO2 fluxes using surface measurement networks. We calculated annual average emissions in the Boston region of 0.92 kg C·m-2·y-1 (95% confidence interval: 0.79 to 1.06), which is 14% higher than the Anthropogenic Carbon Emissions System inventory. Based on the capability of the model-measurement approach demonstrated here, our framework should be able to detect changes in CO2 emissions of greater than 18%, providing stakeholders with critical information to assess mitigation efforts in Boston and surrounding areas.

Using Lidar Technology To Assess Urban Air Pollution and Improve Estimates of Greenhouse Gas Emissions in Boston.

Simulation of the planetary boundary layer (PBL) is key for forecasting air quality and estimating greenhouse gas (GHG) emissions in cities. Here we conducted the first long-term and continuous study of PBL heights (PBLHs) in Boston, MA, using a compact lidar instrument. We developed an image recognition algorithm to estimate PBLHs from the lidar measurements and evaluated simulations of the PBL from seven numerical weather prediction (NWP) model versions, which showed different systematic errors and variability in simulating the PBLHs (discrepancies from -2.5 to 4.0 km). The NWP model with the best overall agreement for the fully developed PBL had R2 = 0.72 and a bias of only 0.128 km. However, this model predicted a notable number of anomalously high carbon dioxide concentrations at ground stations, because it occasionally significantly underestimated the PBLH. We also developed a novel method that combines lidar data with footprints from a Lagrangian particle dispersion model to identify long-range transport of air pollution in the nocturnal residual layer. Our framework was powerful in evaluating the performance of models used to estimate air pollution and GHG emissions in cities, which is critical to track progress on emission reduction targets and guide effective policies.

Continued emissions of carbon tetrachloride from the United States nearly two decades after its phaseout for dispersive uses.

National-scale emissions of carbon tetrachloride (CCl4) are derived based on inverse modeling of atmospheric observations at multiple sites across the United States from the National Oceanic and Atmospheric Administration's flask air sampling network. We estimate an annual average US emission of 4.0 (2.0-6.5) Gg CCl4 y(-1) during 2008-2012, which is almost two orders of magnitude larger than reported to the US Environmental Protection Agency (EPA) Toxics Release Inventory (TRI) (mean of 0.06 Gg y(-1)) but only 8% (3-22%) of global CCl4 emissions during these years. Emissive regions identified by the observations and consistently shown in all inversion results include the Gulf Coast states, the San Francisco Bay Area in California, and the Denver area in Colorado. Both the observation-derived emissions and the US EPA TRI identified Texas and Louisiana as the largest contributors, accounting for one- to two-thirds of the US national total CCl4 emission during 2008-2012. These results are qualitatively consistent with multiple aircraft and ship surveys conducted in earlier years, which suggested significant enhancements in atmospheric mole fractions measured near Houston and surrounding areas. Furthermore, the emission distribution derived for CCl4 throughout the United States is more consistent with the distribution of industrial activities included in the TRI than with the distribution of other potential CCl4 sources such as uncapped landfills or activities related to population density (e.g., use of chlorine-containing bleach).

Methane emissions from natural gas infrastructure and use in the urban region of Boston, Massachusetts.

Methane emissions from natural gas delivery and end use must be quantified to evaluate the environmental impacts of natural gas and to develop and assess the efficacy of emission reduction strategies. We report natural gas emission rates for 1 y in the urban region of Boston, using a comprehensive atmospheric measurement and modeling framework. Continuous methane observations from four stations are combined with a high-resolution transport model to quantify the regional average emission flux, 18.5 ± 3.7 (95% confidence interval) g CH4 ⋅ m(-2) ⋅ y(-1). Simultaneous observations of atmospheric ethane, compared with the ethane-to-methane ratio in the pipeline gas delivered to the region, demonstrate that natural gas accounted for ∼ 60-100% of methane emissions, depending on season. Using government statistics and geospatial data on natural gas use, we find the average fractional loss rate to the atmosphere from all downstream components of the natural gas system, including transmission, distribution, and end use, was 2.7 ± 0.6% in the Boston urban region, with little seasonal variability. This fraction is notably higher than the 1.1% implied by the most closely comparable emission inventory.

Anthropogenic emissions of methane in the United States.

This study quantitatively estimates the spatial distribution of anthropogenic methane sources in the United States by combining comprehensive atmospheric methane observations, extensive spatial datasets, and a high-resolution atmospheric transport model. Results show that current inventories from the US Environmental Protection Agency (EPA) and the Emissions Database for Global Atmospheric Research underestimate methane emissions nationally by a factor of ∼1.5 and ∼1.7, respectively. Our study indicates that emissions due to ruminants and manure are up to twice the magnitude of existing inventories. In addition, the discrepancy in methane source estimates is particularly pronounced in the south-central United States, where we find total emissions are ∼2.7 times greater than in most inventories and account for 24 ± 3% of national emissions. The spatial patterns of our emission fluxes and observed methane-propane correlations indicate that fossil fuel extraction and refining are major contributors (45 ± 13%) in the south-central United States. This result suggests that regional methane emissions due to fossil fuel extraction and processing could be 4.9 ± 2.6 times larger than in EDGAR, the most comprehensive global methane inventory. These results cast doubt on the US EPA's recent decision to downscale its estimate of national natural gas emissions by 25-30%. Overall, we conclude that methane emissions associated with both the animal husbandry and fossil fuel industries have larger greenhouse gas impacts than indicated by existing inventories.

Assessment of ground-based atmospheric observations for verification of greenhouse gas emissions from an urban region.

International agreements to limit greenhouse gas emissions require verification to ensure that they are effective and fair. Verification based on direct observation of atmospheric greenhouse gas concentrations will be necessary to demonstrate that estimated emission reductions have been actualized in the atmosphere. Here we assess the capability of ground-based observations and a high-resolution (1.3 km) mesoscale atmospheric transport model to determine a change in greenhouse gas emissions over time from a metropolitan region. We test the method with observations from a network of CO(2) surface monitors in Salt Lake City. Many features of the CO(2) data were simulated with excellent fidelity, although data-model mismatches occurred on hourly timescales due to inadequate simulation of shallow circulations and the precise timing of boundary-layer stratification and destratification. Using two optimization procedures, monthly regional fluxes were constrained to sufficient precision to detect an increase or decrease in emissions of approximately 15% at the 95% confidence level. We argue that integrated column measurements of the urban dome of CO(2) from the ground and/or space are less sensitive than surface point measurements to the redistribution of emitted CO(2) by small-scale processes and thus may allow for more precise trend detection of emissions from urban regions.

The Ensemble Mars Atmosphere Reanalysis System (EMARS) Version 1.0.

The Ensemble Mars Atmosphere Reanalysis System (EMARS) dataset version 1.0 contains hourly gridded atmospheric variables for the planet Mars, spanning Mars Year (MY) 24 through 33 (1999 through 2017). A reanalysis represents the best estimate of the state of the atmosphere by combining observations that are sparse in space and time with a dynamical model and weighting them by their uncertainties. EMARS uses the Local Ensemble Transform Kalman Filter (LETKF) for data assimilation with the GFDL/NASA Mars Global Climate Model (MGCM). Observations that are assimilated include the Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) temperature retrievals. The dataset includes gridded fields of temperature, wind, surface pressure, as well as dust, water ice, CO2 surface ice and other atmospheric quantities. Reanalyses are useful for both science and engineering studies, including investigations of transient eddies, the polar vortex, thermal tides and dust storms, and during spacecraft operations.

Enhanced North American carbon uptake associated with El Niño.

Long-term atmospheric CO2 mole fraction and δ13CO2 observations over North America document persistent responses to the El Niño-Southern Oscillation. We estimate these responses corresponded to 0.61 (0.45 to 0.79) PgC year-1 more North American carbon uptake during El Niño than during La Niña between 2007 and 2015, partially offsetting increases of net tropical biosphere-to-atmosphere carbon flux around El Niño. Anomalies in derived North American net ecosystem exchange (NEE) display strong but opposite correlations with surface air temperature between seasons, while their correlation with water availability was more constant throughout the year, such that water availability is the dominant control on annual NEE variability over North America. These results suggest that increased water availability and favorable temperature conditions (warmer spring and cooler summer) caused enhanced carbon uptake over North America near and during El Niño.