Abstract. 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.

Abstract. 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.

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Numerous mountain valleys experience wintertime particulate pollution events, when persistent cold air pools (PCAPs) develop and inhibit atmospheric mixing, leading to the accumulation of pollutants. Here we examine the relationships between trace gases and criteria pollutants during winter in Utah's Salt Lake Valley, in an effort to better understand the roles of transport versus chemical processes during differing meteorological conditions as well as insights into how targeted reductions in greenhouse gases will impact local air quality in varying meteorological conditions. CO2 is a chemically inert gas that is coemitted during fossil fuel combustion with pollutants. Many of these coemitted pollutants are precursors that react chemically to form secondary particulate matter. Thus, CO2 can serve as a stable tracer and potentially help distinguish transport versus chemical influences on pollutants. During the winter of 2015–2016, we isolated enhancements in CO2 over baseline levels due to urban emissions (“CO2ex”). CO2ex was paired with similar excesses in other pollutant concentrations. These relationships were examined during different wintertime conditions and stages of pollution episodes: (a) Non‐PCAP, (b) beginning, and (c) latter stages of an episode. We found that CO2ex is a good indicator of the presence of gaseous criteria pollutants and a reasonable indicator of PM2.5. Additionally, the relationships between CO2ex and criteria pollutants differ during different phases of PCAP events which provide insight into meteorological and transport processes. Lastly, we found a slight overestimation of CO:CO2 emission ratios and a considerable overestimation of NOx:CO2 by existing inventories for the Salt Lake Valley.

Urban areas are responsible for a substantial proportion of anthropogenic carbon emissions around the world. As global populations increasingly reside in cities, the role of urban emissions in determining the future trajectory of carbon emissions is magnified. Consequently, a number of research efforts have been started in the United States and beyond, focusing on observing atmospheric carbon dioxide (CO2) and relating its variations to carbon emissions in cities. Because carbon emissions are intimately tied to socioeconomic activity through the combustion of fossil fuels, and many cities are actively adopting emission reduction plans, such urban carbon research efforts give rise to opportunities for stakeholder engagement and guidance on other environmental issues, such as air quality. This paper describes a research effort centered in the Salt Lake City, Utah, metropolitan region, which is the locus for one of the longest-running urban CO2 networks in the world. The Salt Lake City area provides a rich environment for studying anthropogenic emissions and for understanding the relationship between emissions and socioeconomic activity when the CO2 observations are enhanced with a) air quality observations, b) novel mobile observations from platforms on light-rail public transit trains and a news helicopter, c) dense meteorological observations, and d) modeling efforts that include atmospheric simulations and high-resolution emission inventories. Carbon dioxide and other atmospheric observations are presented, along with associated modeling work. Examples in which the work benefited from and contributed to the interests of multiple stakeholders (e.g., policymakers, air quality managers, municipal government, urban planners, industry, and the general public) are discussed.

Large CH4 leak rates have been observed in the Uintah Basin of eastern Utah, an area with over 10,000 active and producing natural gas and oil wells. In this paper, we model CH4 concentrations at four sites in the Uintah Basin and compare the simulated results to in situ observations at these sites during two spring time periods in 2015 and 2016. These sites include a baseline location (Fruitland), two sites near oil wells (Roosevelt and Castlepeak), and a site near natural gas wells (Horsepool). To interpret these measurements and relate observed CH4 variations to emissions, we carried out atmospheric simulations using the Stochastic Time‐Inverted Lagrangian Transport model driven by meteorological fields simulated by the Weather Research and Forecasting and High Resolution Rapid Refresh models. These simulations were combined with two different emission inventories: (1) aircraft‐derived basin‐wide emissions allocated spatially using oil and gas well locations, from the National Oceanic and Atmospheric Administration (NOAA), and (2) a bottom‐up inventory for the entire U.S., from the Environmental Protection Agency (EPA). At both Horsepool and Castlepeak, the diurnal cycle of modeled CH4 concentrations was captured using NOAA emission estimates but was underestimated using the EPA inventory. These findings corroborate emission estimates from the NOAA inventory, based on daytime mass balance estimates, and provide additional support for a suggested leak rate from the Uintah Basin that is higher than most other regions with natural gas and oil development.

Urban environments are characterized by pronounced spatiotemporal heterogeneity, which can present sampling challenges when utilizing conventional greenhouse gas (GHG) measurement systems. In Salt Lake City, Utah, a GHG instrument was deployed on a light rail train car that continuously traverses the Salt Lake Valley (SLV) through a range of urban typologies. CO2 measurements from a light rail train car were used within a Bayesian inverse modeling framework to constrain urban emissions across the SLV during the fall of 2015. The primary objectives of this study were to (1) evaluate whether ground-based mobile measurements could be used to constrain urban emissions using an inverse modeling framework and (2) quantify the information that mobile observations provided relative to conventional GHG monitoring networks. Preliminary results suggest that ingesting mobile measurements into an inverse modeling framework generated a posterior emission estimate that more closely aligned with observations, reduced posterior emission uncertainties, and extends the geographical extent of emission adjustments.

Observing air quality from sensors onboard light rail cars in Salt Lake County, Utah began as a pilot study in 2014 and has now evolved into a five-year, state-funded program. This metropolitan region suffers from both elevated ozone levels during summer and high PM 2.5 events during winter. Pollution episodes result predominantly from local anthropogenic emissions but are also impacted by regional transport of dust, chemical precursors to ozone, and wildfire smoke, as well as being exacerbated by the topographical features surrounding the city. Two electric light-rail train cars from the Utah Transit Authority light-rail Transit Express ("TRAX") system were outfitted with PM 2.5 and ozone sensors to measure air quality at high spatial and temporal resolutions in this region. Pollutant concentration data underwent quality control procedures to determine whether the train motion affected the readings and how the sensors compared against regulatory sensors. Quality assurance results from data obtained over the past year show that TRAX Observation Project sensors are reliable, which corroborates earlier preliminary validation work. Three case studies from summer 2019 are presented to illustrate the strength of the finely-resolved air quality observations: (1) an elevated ozone event, (2) elevated particulate pollution resulting from 4th of July fireworks, and (3) elevated particle pollution during a winter time inversion event. The mobile observations were able to capture spatial gradients, as well as pollutant hotspots, during both of these episodes. Sensors have been recently added to a third light rail train car, which travels on a north-south oriented rail line, where air quality was unable to be monitored previously. The TRAX Observation