Significance: Recent efforts to reduce greenhouse gas emissions have focused on cities due to intensive emissions, viable policy levers, and interested stakeholders. Atmospheric observations can be used to independently evaluate emissions, but suitable networks are sparse. We present a unique decadal record of atmospheric CO ₂ from five sites with contrasting urban characteristics that show divergent trends in CO ₂ emissions across a city. Comparison with population growth reveals a nonlinear relationship that may reflect how urban form affects CO ₂ emissions. Four state-of-the-art global-scale emission inventories capture the nonlinear relationship with population density but not the divergent long-term trends across the city. This demonstrates that CO ₂ monitoring networks can provide insight into urban carbon cycle processes and provide policy-relevant information to urban stakeholders.

The Stochastic Time-Inverted Lagrangian Transport (STILT) model is comprised of a compiled Fortran executable that carries out advection and dispersion calculations as well as a higher-level code layer for simulation control and user interaction, written in the open-source data analysis language R. We introduce modifications to the STILT-R code base with the aim to improve the model's applicability to fine-scale (< 1 km) trace gas measurement studies. The changes facilitate placement of spatially distributed receptors and provide high-level methods for single- and multi-node parallelism. We present a kernel density estimator to calculate influence footprints and demonstrate improvements over prior methods. Vertical dilution in the hyper near field is calculated using the Lagrangian decorrelation timescale and vertical turbulence to approximate the effective mixing depth. This framework provides a central source repository to reduce code fragmentation among STILT user groups as well as a systematic, well-documented workflow for users. We apply the modified STILT-R to light-rail measurements in Salt Lake City, Utah, United States, and discuss how results from our analyses can inform future fine-scale measurement approaches and modeling efforts.

Production of oil and natural gas in North America is at an all-time high due to the development and use of horizontal drilling and hydraulic fracturing. Methane emissions associated with this industrial activity are a concern because of the contribution to climate radiative forcing. We present new measurements from the space-based TROPOspheric Monitoring Instrument (TROPOMI) launched in 2017 that show methane enhancements over production regions in the United States. In the Uintah Basin in Utah, TROPOMI methane columns correlated within-situmeasurements, and the highest columns were observed over the deepest parts of the basin, consistent with the accumulation of emissions underneath inversions. In the Permian Basin in Texas and New Mexico, methane columns showed maxima over regions with the highest natural gas production and were correlated with nitrogen-dioxide columns at a ratio that is consistent with results fromin-situairborne measurements. The improved detail provided by TROPOMI will likely enable the timely monitoring from space of methane emissions associated with oil and natural gas production.

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.