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Radiocarbon (14C) is a powerful tracer of fossil emissions because fossil fuels are entirely depleted in 14C, but observations of 14CO2 and especially 14CH4 in urban regions are sparse. We present the first observations of 14C in both methane (CH4) and carbon dioxide (CO2) in an urban area (London) using a recently developed sampling system. We find that the fossil fraction of CH4 and the atmospheric concentration of fossil CO2 are consistently higher than simulated values using the atmospheric dispersion model NAME coupled with emission inventories. Observed net biospheric uptake in June–July is not well correlated with simulations using the SMURF model with NAME. The results show the partitioning of fossil and biospheric CO2 and CH4 in cities can be evaluated and improved with 14C observations when the nuclear power plants influence is negligible. Plain Language Summary: Radiocarbon (14C) is an ideal tracer of fossil emissions, as fossil fuels have lost all 14C during millions of years of burial underground. When fossil carbon is re‐introduced into the atmosphere, it exerts a strong dilution of the radiocarbon to total carbon ratio. By measuring this ratio in the atmosphere, we can quantify fossil methane and carbon dioxide emissions. This is the first combined study of 14C in both atmospheric methane and carbon dioxide at regional scale. Key Points: Atmospheric radiocarbon measurements in central London reveal higher fossil CH4 and CO2 present, compared to simulationsRadiocarbon measurements show biospheric uptake of CO2 in July that is stronger than simulationsNuclear power plants interfere with radiocarbon measurements in London when air is coming from Europe [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic resulted in a widespread lockdown during the spring of 2020. Measurements collected on a light rail system in the Salt Lake Valley (SLV), combined with observations from the Utah Urban Carbon Dioxide Network observed a notable decrease in urban CO2 concentrations during the spring of 2020 relative to previous years. These decreases coincided with a ∼30% reduction in average traffic volume. CO2 measurements across the SLV were used within a Bayesian inverse model to spatially allocate anthropogenic emission reductions for the first COVID‐19 lockdown. The inverse model was first used to constrain anthropogenic emissions for the previous year (2019) to provide the best possible estimate of emissions for 2020, before accounting for emission reductions observed during the COVID‐19 lockdown. The posterior emissions for 2019 were then used as the prior emission estimate for the 2020 COVID‐19 lockdown analysis. Results from the inverse analysis suggest that the SLV observed a 20% decrease in afternoon CO2 emissions from March to April 2020 (−90.5 tC hr−1). The largest reductions in CO2 emissions were centered over the northern part of the valley (downtown Salt Lake City), near major roadways, and potentially at industrial point sources. These results demonstrate that CO2 monitoring networks can track reductions in CO2 emissions even in medium‐sized cities like Salt Lake City. Plain Language Summary: High‐density measurements of CO2 were combined with a statistical model to estimate emission reductions across Salt Lake City during the COVID‐19 lockdown. Reduced traffic throughout the COVID‐19 lockdown was likely the primary driver behind lower CO2 emissions in Salt Lake City. There was also evidence that industrial‐based emission sources may of had an observable decrease in CO2 emissions during the lockdown. Finally, this analysis suggests that high‐density CO2 monitoring networks could be used to track progress toward decarbonization in the future. Key Points: High‐density CO2 measurements provided constraints on emission reductions during the COVID‐19 lockdownCO2 emissions across the Salt Lake Valley were reduced by ∼20% relative to 2019 during the first COVID‐19 lockdownThe largest reductions in CO2 were likely driven by reduced traffic, especially in downtown Salt Lake City [ABSTRACT FROM AUTHOR]

We assess the theoretical capability of the upcoming French-UK MicroCarb satellite, which has a city-scan observing mode, to determine integrated urban emissions of carbon dioxide (CO2). To achieve this we report results from a series of closed-loop numerical experiments that use an atmospheric transport model with anthropogenic and biogenic fluxes to determine the corresponding changes in atmospheric CO2 column, accounting for changes in measurement coverage due to clouds loading. We use a Maximum A Posteriori inverse method to infer the CO2 fluxes based on the measurements and the a priori information. Using an urban CO2 inversion system, we explore the relative performance of alternative two-sweep and three-sweep city observing strategies to quantify CO2 emissions over the cities of Paris and London in different months when biospheric fluxes vary in magnitude. We find that both the two-sweep and three-sweep observing modes are able to reduce a priori flux errors by 20–40 % over Paris and London. The three-sweep observing strategy, which generally outperforms the two-sweep mode, can retrieve the total emissions of the truth within 7 % over Paris and 21 % over London, by virtue of its wider scan area that typically yields more cloud-free scenes. The performance of the limited-domain city-mode observing strategies is sensitive to cloud coverage and particularly sensitive to the prevailing wind direction. We also find that seasonal photosynthetic uptake of CO2 by the urban biosphere weakens atmospheric CO2 gradients across both cities thereby reducing the sensitivity of urban CO2 enhancements and subsequently compromising the ability of MicroCarb to estimate urban CO2 emissions. This suggests that additional trace gases co-emitted with anthropogenic CO2 emissions, but unaffected by the land biosphere, are needed to quantify sub-city scale CO2 emissions during months when the urban biosphere is particularly active. [ABSTRACT FROM AUTHOR]

A growing constellation of satellites is providing near‐global coverage of column‐averaged CO2 observations. Launched in 2019, NASA's OCO‐3 instrument is set to provide XCO2 observations at a high spatial and temporal resolution for regional domains (100 × 100 km). The atmospheric column version of the Stochastic Time‐Inverted Lagrangian Transport (X‐STILT) model is an established method of determining the influence of upwind sources on column measurements of the atmosphere, providing a means of analysis for current OCO‐3 observations and future space‐based column‐observing missions. However, OCO‐3 is expected to provide hundreds of soundings per targeted observation, straining this already computationally intensive technique. This work proposes a novel scheme to be used with the X‐STILT model to generate upwind influence footprints with less computational expense. The method uses X‐STILT generated influence footprints from a key subset of OCO‐3 soundings. A nonlinear weighted averaging is applied to these footprints to construct additional footprints for the remaining soundings. The effects of subset selection, meteorological data, and topography are investigated for two test sites: Los Angeles, California, and Salt Lake City, Utah. The computational time required to model the source sensitivities for OCO‐3 interpretation was reduced by 62% and 78% with errors smaller than other previously acknowledged uncertainties in the modeling system (OCO‐3 retrieval error, atmospheric transport error, prior emissions error, etc.). Limitations and future applications for future CO2 missions are also discussed. Plain Language Summary: Several satellites are providing near‐global observations of Earth's atmospheric carbon dioxide (CO2). One example is NASA's new OCO‐3 instrument which is set to provide spatially dense CO2 measurements over targeted areas. Measurements may contain signals of emissions from cities and power plants. One method of finding the source(s) of observed CO2 is using a Lagrangian particle dispersion model such as X‐STILT. This model takes OCO‐3 measurements and runs atmospheric transport backwards in time to trace out the sources affecting these measurements. However, OCO‐3 and future satellite missions will yield many measurements, significantly increasing the computational cost for X‐STILT and other similar models. This paper presents an algorithm that will reduce the computational effort for X‐STILT by tracing the sources of only a subset of OCO‐3 measurements and then infers (interpolates) the rest. The following two questions are addressed: (1) How many OCO‐3 measurements does X‐STILT need for the interpolations to be accurate? (2) How do meteorology and topography affect the accuracy of the interpolations? Applying the algorithm on simulated OCO‐3 data at two test cities—Los Angeles and Salt Lake City—the time required to elucidate the CO2 sources was reduced by 62% and 78%, respectively. Key Points: Determining sources of spatially dense XCO2 observations with LPDM techniques can become time intensive and strain computational resourcesPresented in this work is an interpolation scheme that eases the computational burden of spatially dense XCO2 source determination studiesEvaluating the efficiency of this interpolation scheme revealed reductions of >50% in computational time at two testing locations [ABSTRACT FROM AUTHOR]

When estimating fossil fuel carbon dioxide (FFCO2) emissions from observed CO2 concentrations, the accuracy can be hampered by biogenic carbon exchanges during the growing season even for urban areas where strong fossil fuel emissions are found. While biogenic carbon fluxes have been studied extensively across natural vegetation types, biogenic carbon fluxes within an urban area have been challenging to quantify due to limited observations and differences between urban versus rural regions. Here we developed a simple model representation, i.e., Solar-Induced Fluorescence (SIF) for Modeling Urban biogenic Fluxes ("SMUrF"), that estimates the gross primary production (GPP) and ecosystem respiration (Reco) over cities around the globe. Specifically, we leveraged space-based SIF, machine learning, eddy-covariance flux data, and additional remote sensing-based products, and developed algorithms to gap fill fluxes for urban areas. Grid-level hourly mean net ecosystem exchange (NEE) are extracted from SMUrF and evaluated against 1) non-gapfilled measurements at 67 eddy-covariance (EC) sites from FLUXNET during 2010-2014 (r > 0.7 for most data-rich biomes), 2) independent observations at two urban vegetation and two crop EC sites over Indianapolis from Aug 2017 to Dec 2018 (r = 0.75), and 3) an urban biospheric model based on fine-grained land cover classification within Los Angeles (r = 0.83). Moreover, we compared SMUrF-based NEE with inventory-based FFCO2 emissions over 40 cities and addressed the urban-rural contrast regarding both the magnitude and timing of CO2 fluxes. By examining a few summertime satellite tracks over four cities, we found that the urban-rural gradient in column CO2 (XCO2) anomalies due to NEE can sometimes reach ~ 0.5 ppm and be close to XCO2 enhancements due to FFCO2 emissions. With rapid advances in space-based measurements and increased sampling of SIF and CO2 measurements over urban areas, SMUrF can be useful for informing the biogenic CO2 fluxes over highly vegetated regions during the growing season. [ABSTRACT FROM AUTHOR]

Satellite observations of the total column dry‐air CO2 (XCO2) are expected to support the quantification and monitoring of fossil fuel CO2 (ffCO2) emissions from urban areas. We evaluate the utility of the Orbiting Carbon Observatory 2 (OCO‐2) XCO2 retrievals to optimize whole‐city emissions, using a Bayesian inversion system and high‐resolution transport modeling. The uncertainties of constrained emissions related to transport model, satellite measurements, and local biospheric fluxes are quantified. For the first two uncertainty sources, we examine cities of different landscapes: "plume city" located in relatively flat terrain, represented by Riyadh and Cairo; and "basin city" located in basin terrain, represented by Los Angeles (LA). The retrieved scaling factors of emissions and their uncertainties show prominent variabilities from track to track, due to the varying meteorological conditions and relative locations of the tracks transecting plumes. To explore the performance of multiple tracks in retrieving emissions, pseudo data experiments are carried out. The estimated least numbers of tracks required to constrain the total emissions for Riyadh (<10% uncertainty), Cairo (<10%), and LA (<5%) are 8, 5, and 7, respectively. Additionally, to evaluate the impact of biospheric fluxes on derivation of the ffXCO2 enhancements, we conduct simulations for Pearl River Delta metropolitan area. Significant fractions of local XCO2 enhancements associated with local biospheric XCO2 variations are shown, which potentially lead to biased estimates of ffCO2 emissions. We demonstrate that satellite measurements can be used to improve urban ffCO2 emissions with a sufficient amount of measurements and appropriate representations of the uncertainty components. Key Points: Inversion method is utilized to constrain whole‐city fossil fuel emissions with measurement and transport model errors consideredPotential of incorporating multiple tracks to obtain regular emission estimates is evaluated by pseudo data experimentsSignificant contribution of the biospheric fluxes variability to local XCO2 variation is demonstrated [ABSTRACT FROM AUTHOR]