Urban regions are responsible for emitting significant amounts of fossil fuel carbon dioxide (FFCO₂), and emissions at the finer, city scales are more uncertain than those aggregated at the global scale. Carbon-observing satellites may provide independent top-down emission evaluations and compensate for the sparseness of surface CO₂ observing networks in urban areas. Although some previous studies have attempted to derive urban CO₂ signals from satellite column-averaged CO₂ data (XCO₂) using simple statistical measures, less work has been carried out to link upwind emission sources to downwind atmospheric columns using atmospheric models. In addition to Eulerian atmospheric models that have been customized for emission estimates over specific cities, the Lagrangian modeling approach – in particular, the Lagrangian particle dispersion model (LPDM) approach – has the potential to efficiently determine the sensitivity of downwind concentration changes to upwind sources. However, when applying LPDMs to interpret satellite XCO₂, several issues have yet to be addressed, including quantifying uncertainties in urban XCO₂ signals due to receptor configurations and errors in atmospheric transport and background XCO₂. In this study, we present a modified version of the Stochastic Time-Inverted Lagrangian Transport (STILT) model, “X-STILT”, for extracting urban XCO₂ signals from NASA's Orbiting Carbon Observatory 2 (OCO-2) XCO₂ data. X-STILT incorporates satellite profiles and provides comprehensive uncertainty estimates of urban XCO₂ enhancements on a per sounding basis. Several methods to initialize receptor/particle setups and determine background XCO₂ are presented and discussed via sensitivity analyses and comparisons. To illustrate X-STILT's utilities and applications, we examined five OCO-2 overpasses over Riyadh, Saudi Arabia, during a 2-year time period and performed a simple scaling factor-based inverse analysis. As a result, the model is able to reproduce most observed XCO₂ enhancements. Error estimates show that the 68 % confidence limit of XCO₂ uncertainties due to transport (horizontal wind plus vertical mixing) and emission uncertainties contribute to ∼33 % and ∼20 % of the mean latitudinally integrated urban signals, respectively, over the five overpasses, using meteorological fields from the Global Data Assimilation System (GDAS). In addition, a sizeable mean difference of −0.55 ppm in background derived from a previous study employing simple statistics (regional daily median) leads to a ∼39 % higher mean observed urban signal and a larger posterior scaling factor. Based on our signal estimates and associated error impacts, we foresee X-STILT serving as a tool for interpreting column measurements, estimating urban enhancement signals, and carrying out inverse modeling to improve quantification of urban emissions.

Urban areas are currently responsible for ∼70% of the global energy-related carbon dioxide (CO₂) emissions, and rapid ongoing global urbanization is increasing the number and size of cities. Thus, understanding city-scale CO₂ emissions and how they vary between cities with different urban densities is a critical task. While the relationship between CO₂ emissions and population density has been explored widely in prior studies, their conclusions were sensitive to inconsistent definitions of urban boundaries and the reliance upon CO₂ emission inventories that implicitly assumed population relationships. Here we provide the first independent estimates of direct per capita CO₂ emissions (E _pc) from spaceborne atmospheric CO₂ measurements from the Orbiting Carbon Observatory-2 (OCO-2) for a total 20 cities across multiple continents. The analysis accounts for the influence of meteorology on the satellite observations with an atmospheric model. The resultant upwind source region sampled by the satellite serves as an objective urban extent for aggregating emissions and population densities. Thus, we are able to detect emission ‘hotspots’ on a per capita basis from a few cities, subject to sampling restrictions from OCO-2. Our results suggest that E _pc declines as population densities increase, albeit the decrease in E _pc is partially limited by the positive correlation between E _pc and per capita gross domestic product. As additional CO₂-observing satellites are launched in the coming years, our space-based approach to understanding CO₂ emissions from cities has significant potential in tracking and evaluating the future trajectory of urban growth and informing the effects of carbon reduction plans.

Carbon dioxide (CO₂) and air pollutants such as carbon monoxide (CO) are co-emitted by many combustion sources. Previous efforts have combined satellite-based observations of multiple tracers to calculate their emission ratio (ER) for inferring combustion efficiency at the regional to city scale. Very few studies have focused on combustion efficiency at the sub-city scale or related it to emission sectors using space-based observations. Several factors are important for interpreting and deriving spatially resolved ERs from asynchronous satellite measurements, including (1) variations in meteorological conditions given the mismatch in satellite overpass times, (2) differences in vertical sensitivity of the retrievals (i.e., averaging kernel profiles), (3) interferences from the biosphere and biomass burning, and (4) the mismatch in the daytime variations of CO and CO₂ emissions. In this study, we extended an established emission estimate approach to arrive at spatially resolved ERs based on retrieved column-averaged CO₂ (XCO₂) from the Snapshot Area Mapping (SAM) mode of the Orbiting Carbon Observatory-3 (OCO-3) and column-averaged CO from the TROPOspheric Monitoring Instrument (TROPOMI). To evaluate the influences of the confounding factors listed above and further attribute intra-urban variations in ERs to certain sectors, we leveraged a Lagrangian atmospheric transport model with an urban land cover classification dataset and reported ER_CO values from the sounding level to the overpass and city level. We found that the differences in overpass times and averaging kernels between OCO and TROPOMI strongly affect the estimated spatially resolved ER_CO. Specifically, a time difference of >3 h typically led to dramatic changes in wind directions and urban plume shapes, thereby making the calculation of accurate sounding-specific ER_CO difficult. After removing such cases from consideration and applying a simple plume shift method when necessary to account for changes in wind direction and speed, we discovered significant contrasts in combustion efficiencies between (1) two megacities versus two industry-oriented cities and (2) different regions within a city, based on six nearly coincident overpasses per city. Results suggest that the ER_CO impacted by heavy industry in Los Angeles is slightly lower than the overall city-wide value (<10 ppb-CO$/$ ppm-CO₂). In contrast, the ER_CO related to heavy industry in Shanghai is much higher than Shanghai's city mean and more aligned with the city means of two selected industry-oriented cities in China (approaching 20 ppb-CO$/$ ppm-CO₂). Although investigations based on a larger number of satellite overpasses are needed, our unique approach (i.e., without using sector-specific information from emission inventories) provides new insights into assessing combustion efficiency within a city from future satellite missions, such as those that will map column CO₂ and CO concentrations simultaneously with high spatiotemporal resolutions.

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 and 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 (R_eco) over cities around the globe. Specifically, we leveraged space-based SIF, machine learning, eddy-covariance (EC) flux data, and ancillary remote-sensing-based products, and we developed algorithms to gap-fill fluxes for urban areas. Grid-level hourly mean net ecosystem exchange (NEE) fluxes are extracted from SMUrF and evaluated against (1) non-gap-filled measurements at 67 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 August 2017 to December 2018 (r=0.75), and (3) an urban biospheric model based on fine-grained land cover classification in 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 in both the magnitude and timing of CO₂ fluxes. To illustrate the application of SMUrF, we used it to interpret a few summertime satellite tracks over four cities and compared the urban–rural gradient in column CO₂ (XCO₂) anomalies due to NEE against 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 to inform the biogenic CO₂ fluxes over highly vegetated regions during the growing season.

Despite the need for researchers to understand terrestrial biospheric carbon fluxes to account for carbon cycle feedbacks and predict future CO₂ concentrations, knowledge of these fluxes at the regional scale remains poor. This is particularly true in mountainous areas, where complex meteorology and lack of observations lead to large uncertainties in carbon fluxes. Yet mountainous regions are often where significant forest cover and biomass are found – i.e., areas that have the potential to serve as carbon sinks. As CO₂ observations are carried out in mountainous areas, it is imperative that they are properly interpreted to yield information about carbon fluxes. In this paper, we present CO₂ observations at three sites in the mountains of the western US, along with atmospheric simulations that attempt to extract information about biospheric carbon fluxes from the CO₂ observations, with emphasis on the observed and simulated diurnal cycles of CO₂. We show that atmospheric models can systematically simulate the wrong diurnal cycle and significantly misinterpret the CO₂ observations, due to erroneous atmospheric flows as a result of terrain that is misrepresented in the model. This problem depends on the selected vertical level in the model and is exacerbated as the spatial resolution is degraded, and our results indicate that a fine grid spacing of ∼ 4 km or less may be needed to simulate a realistic diurnal cycle of CO₂ for sites on top of the steep mountains examined here in the American Rockies. In the absence of higher resolution models, we recommend coarse-scale models to focus on assimilating afternoon CO₂ observations on mountaintop sites over the continent to avoid misrepresentations of nocturnal transport and influence.