A Gradient-Descent Optimization of CO2-CO-NOx Emissions over the Paris Megacity─The Case of the First SARS-CoV-2 Lockdown.

Urban greenhouse gas emissions monitoring is essential to assessing the impact of climate mitigation actions. Using atmospheric continuous measurements of air quality and carbon dioxide (CO2), we developed a gradient-descent optimization system to estimate emissions of the city of Paris. We evaluated our joint CO2-CO-NOx optimization over the first SARS-CoV-2 related lockdown period, resulting in a decrease in emissions by 40% for NOx and 30% for CO2, in agreement with preliminary estimates using bottom-up activity data yet lower than the decrease estimates from Bayesian atmospheric inversions (50%). Before evaluating the model, we first provide an in-depth analysis of three emission data sets. A general agreement in the totals is observed over the region surrounding Paris (known as Île-de-France) since all the data sets are constrained by the reported national and regional totals. However, the data sets show disagreements in their sector distributions as well as in the interspecies ratios. The seasonality also shows disagreements among emission products related to nonindustrial stationary combustion (residential and tertiary combustion). The results presented in this paper show that a multispecies approach has the potential to provide sectoral information to monitor CO2 emissions over urban areas enabled by the deployment of collocated atmospheric greenhouse gases and air quality monitoring stations.

High-resolution assessment of coal mining methane emissions by satellite in Shanxi, China.

Accurate assessment of coal mine methane (CMM) emissions is a prerequisite for defining baselines and assessing the effectiveness of mitigation measures. Such an endeavor is jeopardized, however, by large uncertainties in current CMM estimates. Here, we assimilated atmospheric methane column concentrations observed by the TROPOMI space borne instrument in a high-resolution regional inversion to estimate CMM emissions in Shanxi, a province representing 15% of the global coal production. The emissions are estimated to be 8.5 ± 0.6 and 8.6 ± 0.6 Tg CH4 yr-1 in 2019 and 2020, respectively, close to upper bound of current bottom-up estimates. Data from more than a thousand of individual mines indicate that our estimated emission factors increase significantly with coal mining depth at prefecture level, suggesting that ongoing deeper mining will increase CMM emission intensity. Our results show robustness of estimating CMM emissions utilizing TROPOMI images and highlight potential of monitoring methane leakages and emissions from satellites.

From political pledges to quantitative mapping of climate mitigation plans: Comparison of two European cities.

BACKGROUND: Urban agglomerates play a crucial role in reaching global climate objectives. Many cities have committed to reducing their greenhouse gas emissions, but current emission trends remain unverifiable. Atmospheric monitoring of greenhouse gases offers an independent and transparent strategy to measure urban emissions. However, careful design of the monitoring network is crucial to be able to monitor the most important sectors as well as adjust to rapidly changing urban landscapes. RESULTS: Our study of Paris and Munich demonstrates how climate action plans, carbon emission inventories, and urban development plans can help design optimal atmospheric monitoring networks. We show that these two European cities display widely different trajectories in space and time, reflecting different emission reduction strategies and constraints due to administrative boundaries. The projected carbon emissions rely on future actions, hence uncertain, and we demonstrate how emission reductions vary significantly at the sub-city level. CONCLUSIONS: We conclude that quantified individual cities' climate actions are essential to construct more robust emissions trajectories at the city scale. Also, harmonization and compatibility of plans from various cities are necessary to make inter-comparisons of city climate targets possible. Furthermore, dense atmospheric networks extending beyond the city limits are needed to track emission trends over the coming decades.

Airborne Lidar Measurements of XCO2 in Synoptically Active Environment and Associated Comparisons With Numerical Simulations.

Frontal boundaries have been shown to cause large changes in CO2 mole-fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO2 dry air mole-fraction (XCO2) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO2 frontal contrasts (ΔXCO2, i.e., warm minus cold sector average of XCO2). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO2 during four seasonal field campaigns of the Atmospheric Carbon and Transport-America (ACT-America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting--Chemistry (WRF-Chem) showed that (a) all products had a similar sign of ΔXCO2 though with different levels of agreement in ΔXCO2 magnitudes among seasons; (b) ΔXCO2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO2 frontal contrasts. A linear regression analyses between ΔXCO2 for MFLL versus GMAO, and MFLL versus WRF-Chem for summer-2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO2 variability among four seasons provide guidance to the spatial structures of XCO2 transport errors in models and satellite measurements of XCO2 in synoptically-active weather systems.

Global Tracking and Quantification of Oil and Gas Methane Emissions from Recurrent Sentinel-2 Imagery.

Methane (CH4) emission estimates from top-down studies over oil and gas basins have revealed systematic underestimation of CH4 emissions in current national inventories. Sparse but extremely large amounts of CH4 from oil and gas production activities have been detected across the globe, resulting in a significant increase of the overall oil and gas contribution. However, attribution to specific facilities remains a major challenge unless high-spatial-resolution images provide sufficient granularity within the oil and gas basin. In this paper, we monitor known oil and gas infrastructures across the globe using recurrent Sentinel-2 imagery to detect and quantify more than 1200 CH4 emissions. In combination with emission estimates from airborne and Sentinel-5P measurements, we demonstrate the robustness of the fit to a power law from 0.1 tCH4/h to 600 tCH4/h. We conclude here that the prevalence of ultraemitters (>25tCH4/h) detected globally by Sentinel-5P directly relates to emission occurrences below its detection threshold in the range >2tCH4/h, which correspond to large emitters covered by Sentinel-2. We also verified that this relation is also valid at a more local scale for two specific countries, namely, Algeria and Turkmenistan, and the Permian basin in the United States.

Assessing the Effectiveness of an Urban CO2 Monitoring Network over the Paris Region through the COVID-19 Lockdown Natural Experiment.

The Paris metropolitan area, the largest urban region in the European Union, has experienced two national COVID-19 confinements in 2020 with different levels of restrictions on mobility and economic activity, which caused reductions in CO2 emissions. To quantify the timing and magnitude of daily emission reductions during the two lockdowns, we used continuous atmospheric CO2 monitoring, a new high-resolution near-real-time emission inventory, and an atmospheric Bayesian inverse model. The atmospheric inversion estimated the changes in fossil fuel CO2 emissions over the Greater Paris region during the two lockdowns, in comparison with the same periods in 2018 and 2019. It shows decreases by 42-53% during the first lockdown with stringent measures and by only 20% during the second lockdown when traffic reduction was weaker. Both lockdown emission reductions are mainly due to decreases in traffic. These results are consistent with independent estimates based on activity data made by the city environmental agency. We also show that unusual persistent anticyclonic weather patterns with north-easterly winds that prevailed at the start of the first lockdown period contributed a substantial drop in measured CO2 concentration enhancements over Paris, superimposed on the reduction of urban CO2 emissions. We conclude that atmospheric CO2 monitoring makes it possible to identify significant emission changes (>20%) at subannual time scales over an urban region.

Under-reporting of greenhouse gas emissions in U.S. cities.

Cities dominate greenhouse gas emissions. Many have generated self-reported emission inventories, but their value to emissions mitigation depends on their accuracy, which remains untested. Here, we compare self-reported inventories from 48 US cities to independent estimates from the Vulcan carbon dioxide emissions data product, which is consistent with atmospheric measurements. We found that cities under-report their own greenhouse gas emissions, on average, by 18.3% (range: -145.5% to +63.5%) - a difference which if extrapolated to all U.S. cities, exceeds California's total emissions by 23.5%. Differences arise because city inventories omit particular fuels and source types and estimate transportation emissions differently. These results raise concerns about self-reported inventories in planning or assessing emissions, and warrant consideration of the new urban greenhouse gas information system recently developed by the scientific community.

The influence of near-field fluxes on seasonal carbon dioxide enhancements: results from the Indianapolis Flux Experiment (INFLUX).

BACKGROUND: Networks of tower-based CO2 mole fraction sensors have been deployed by various groups in and around cities across the world to quantify anthropogenic CO2 emissions from metropolitan areas. A critical aspect in these approaches is the separation of atmospheric signatures from distant sources and sinks (i.e., the background) from local emissions and biogenic fluxes. We examined CO2 enhancements compared to forested and agricultural background towers in Indianapolis, Indiana, USA, as a function of season and compared them to modeled results, as a part of the Indianapolis Flux (INFLUX) project. RESULTS: At the INFLUX urban tower sites, daytime growing season enhancement on a monthly timescale was up to 4.3-6.5 ppm, 2.6 times as large as those in the dormant season, on average. The enhancement differed significantly depending on choice of background and time of year, being 2.8 ppm higher in June and 1.8 ppm lower in August using a forested background tower compared to an agricultural background tower. A prediction based on land cover and observed CO2 fluxes showed that differences in phenology and drawdown intensities drove measured differences in enhancements. Forward modelled CO2 enhancements using fossil fuel and biogenic fluxes indicated growing season model-data mismatch of 1.1 ± 1.7 ppm for the agricultural background and 2.1 ± 0.5 ppm for the forested background, corresponding to 25-29% of the modelled CO2 enhancements. The model-data total CO2 mismatch during the dormant season was low, - 0.1 ± 0.5 ppm. CONCLUSIONS: Because growing season biogenic fluxes at the background towers are large, the urban enhancements must be disentangled from the biogenic signal, and growing season increases in CO2 enhancement could be misinterpreted as increased anthropogenic fluxes if the background ecosystem CO2 drawdown is not considered. The magnitude and timing of enhancements depend on the land cover type and net fluxes surrounding each background tower, so a simple box model is not appropriate for interpretation of these data. Quantification of the seasonality and magnitude of the biological fluxes in the study region using high-resolution and detailed biogenic models is necessary for the interpretation of tower-based urban CO2 networks for cities with significant vegetation.

Policy-Relevant Assessment of Urban CO2 Emissions.

Global fossil fuel carbon dioxide (FFCO2) emissions will be dictated to a great degree by the trajectory of emissions from urban areas. Conventional methods to quantify urban FFCO2 emissions typically rely on self-reported economic/energy activity data transformed into emissions via standard emission factors. However, uncertainties in these traditional methods pose a roadblock to implementation of effective mitigation strategies, independently monitor long-term trends, and assess policy outcomes. Here, we demonstrate the applicability of the integration of a dense network of greenhouse gas sensors with a science-driven building and street-scale FFCO2 emissions estimation through the atmospheric CO2 inversion process. Whole-city FFCO2 emissions agree within 3% annually. Current self-reported inventory emissions for the city of Indianapolis are 35% lower than our optimal estimate, with significant differences across activity sectors. Differences remain, however, regarding the spatial distribution of sectoral FFCO2 emissions, underconstrained despite the inclusion of coemitted species information.

Using Space-Based Observations and Lagrangian Modeling to Evaluate Urban Carbon Dioxide Emissions in the Middle East.

Improved observational understanding of urban CO2 emissions, a large and dynamic global source of fossil CO2, can provide essential insights for both carbon cycle science and mitigation decision making. Here we compare three distinct global CO2 emissions inventory representations of urban CO2 emissions for five Middle Eastern cities (Riyadh, Mecca, Tabuk, Jeddah, and Baghdad) and use independent satellite observations from the Orbiting Carbon Observatory-2 (OCO-2) satellite to evaluate the inventory representations of afternoon emissions. We use the column version of the Stochastic Time-Inverted Lagrangian Transport (X-STILT) model to account for atmospheric transport and link emissions to observations. We compare XCO2 simulations with observations to determine optimum inventory scaling factors. Applying these factors, we find that the average summed emissions for all five cities are 100 MtC year-1 (50-151, 90% CI), which is 2.0 (1.0, 3.0) times the average prior inventory magnitudes. The total adjustment of the emissions of these cities comes out to ~7% (0%, 14%) of total Middle Eastern emissions (~700 MtC year-1). We find our results to be insensitive to the prior spatial distributions in inventories of the cities' emissions, facilitating robust quantitative assessments of urban emission magnitudes without accurate high-resolution gridded inventories.

Intercomparison of atmospheric trace gas dispersion models: Barnett Shale case study.

Greenhouse gas emissions mitigation requires understanding the dominant processes controlling fluxes of these trace gases at increasingly finer spatial and temporal scales. Trace gas fluxes can be estimated using a variety of approaches that translate observed atmospheric species mole fractions into fluxes or emission rates, often identifying the spatial and temporal characteristics of the emission sources as well. Meteorological models are commonly combined with tracer dispersion models to estimate fluxes using an inverse approach that optimizes emissions to best fit the trace gas mole fraction observations. One way to evaluate the accuracy of atmospheric flux estimation methods is to compare results from independent methods, including approaches in which different meteorological and tracer dispersion models are used. In this work, we use a rich data set of atmospheric methane observations collected during an intensive airborne campaign to compare different methane emissions estimates from the Barnett Shale oil and natural gas production basin in Texas, USA. We estimate emissions based on a variety of different meteorological and dispersion models. Previous estimates of methane emissions from this region relied on a simple model (a mass balance analysis) as well as on ground-based measurements and statistical data analysis (an inventory). We find that in addition to meteorological model choice, the choice of tracer dispersion model also has a significant impact on the predicted down-wind methane concentrations given the same emissions field. The dispersion models tested often underpredicted the observed methane enhancements with significant variability (up to a factor of 3) between different models and between different days. We examine possible causes for this result and find that the models differ in their simulation of vertical dispersion, indicating that additional work is needed to evaluate and improve vertical mixing in the tracer dispersion models commonly used in regional trace gas flux inversions.

Synthesis of Urban CO2 Emission Estimates from Multiple Methods from the Indianapolis Flux Project (INFLUX).

Urban areas contribute approximately three-quarters of fossil fuel derived CO2 emissions, and many cities have enacted emissions mitigation plans. Evaluation of the effectiveness of mitigation efforts will require measurement of both the emission rate and its change over space and time. The relative performance of different emission estimation methods is a critical requirement to support mitigation efforts. Here we compare results of CO2 emissions estimation methods including an inventory-based method and two different top-down atmospheric measurement approaches implemented for the Indianapolis, Indiana, U.S.A. urban area in winter. By accounting for differences in spatial and temporal coverage, as well as trace gas species measured, we find agreement among the wintertime whole-city fossil fuel CO2 emission rate estimates to within 7%. This finding represents a major improvement over previous comparisons of urban-scale emissions, making urban CO2 flux estimates from this study consistent with local and global emission mitigation strategy needs. The complementary application of multiple scientifically driven emissions quantification methods enables and establishes this high level of confidence and demonstrates the strength of the joint implementation of rigorous inventory and atmospheric emissions monitoring approaches.

Assessment of methane emissions from the U.S. oil and gas supply chain.

Methane emissions from the U.S. oil and natural gas supply chain were estimated by using ground-based, facility-scale measurements and validated with aircraft observations in areas accounting for ~30% of U.S. gas production. When scaled up nationally, our facility-based estimate of 2015 supply chain emissions is 13 ± 2 teragrams per year, equivalent to 2.3% of gross U.S. gas production. This value is ~60% higher than the U.S. Environmental Protection Agency inventory estimate, likely because existing inventory methods miss emissions released during abnormal operating conditions. Methane emissions of this magnitude, per unit of natural gas consumed, produce radiative forcing over a 20-year time horizon comparable to the CO2 from natural gas combustion. Substantial emission reductions are feasible through rapid detection of the root causes of high emissions and deployment of less failure-prone systems.

The Indianapolis Flux Experiment (INFLUX): A test-bed for developing urban greenhouse gas emission measurements.

The objective of the Indianapolis Flux Experiment (INFLUX) is to develop, evaluate and improve methods for measuring greenhouse gas (GHG) emissions from cities. INFLUX's scientific objectives are to quantify CO2 and CH4 emission rates at 1 km resolution with a 10% or better accuracy and precision, to determine whole-city emissions with similar skill, and to achieve high (weekly or finer) temporal resolution at both spatial resolutions. The experiment employs atmospheric GHG measurements from both towers and aircraft, atmospheric transport observations and models, and activity-based inventory products to quantify urban GHG emissions. Multiple, independent methods for estimating urban emissions are a central facet of our experimental design. INFLUX was initiated in 2010 and measurements and analyses are ongoing. To date we have quantified urban atmospheric GHG enhancements using aircraft and towers with measurements collected over multiple years, and have estimated whole-city CO2 and CH4 emissions using aircraft and tower GHG measurements, and inventory methods. Significant differences exist across methods; these differences have not yet been resolved; research to reduce uncertainties and reconcile these differences is underway. Sectorally- and spatially-resolved flux estimates, and detection of changes of fluxes over time, are also active research topics. Major challenges include developing methods for distinguishing anthropogenic from biogenic CO2 fluxes, improving our ability to interpret atmospheric GHG measurements close to urban GHG sources and across a broader range of atmospheric stability conditions, and quantifying uncertainties in inventory data products. INFLUX data and tools are intended to serve as an open resource and test bed for future investigations. Well-documented, public archival of data and methods is under development in support of this objective.

On the Impact of Granularity of Space-based Urban CO2 Emissions in Urban Atmospheric Inversions: A Case Study for Indianapolis, IN.

Quantifying greenhouse gas (GHG) emissions from cities is a key challenge towards effective emissions management. An inversion analysis from the INdianapolis FLUX experiment (INFLUX) project, as the first of its kind, has achieved a top-down emission estimate for a single city using CO2 data collected by the dense tower network deployed across the city. However, city-level emission data, used as a priori emissions, are also a key component in the atmospheric inversion framework. Currently, fine-grained emission inventories (EIs) able to resolve GHG city emissions at high spatial resolution, are only available for few major cities across the globe. Following the INFLUX inversion case with a global 1×1 km ODIAC fossil fuel CO2 emission dataset, we further improved the ODIAC emission field and examined its utility as a prior for the city scale inversion. We disaggregated the 1×1 km ODIAC non-point source emissions using geospatial datasets such as the global road network data and satellite-data driven surface imperviousness data to a 30×30 m resolution. We assessed the impact of the improved emission field on the inversion result, relative to priors in previous studies (Hestia and ODIAC). The posterior total emission estimate (5.1 MtC/yr) remains statistically similar to the previous estimate with ODIAC (5.3 MtC/yr). However, the distribution of the flux corrections was very close to those of Hestia inversion and the model-observation mismatches were significantly reduced both in forward and inverse runs, even without hourly temporal changes in emissions. EIs reported by cities often do not have estimates of spatial extents. Thus, emission disaggregation is a required step when verifying those reported emissions using atmospheric models. Our approach offers gridded emission estimates for global cities that could serves as a prior for inversion, even without locally reported EIs in a systematic way to support city-level Measuring, Reporting and Verification (MRV) practice implementation.

Direct and Indirect Measurements and Modeling of Methane Emissions in Indianapolis, Indiana.

This paper describes process-based estimation of CH4 emissions from sources in Indianapolis, IN and compares these with atmospheric inferences of whole city emissions. Emissions from the natural gas distribution system were estimated from measurements at metering and regulating stations and from pipeline leaks. Tracer methods and inverse plume modeling were used to estimate emissions from the major landfill and wastewater treatment plant. These direct source measurements informed the compilation of a methane emission inventory for the city equal to 29 Gg/yr (5% to 95% confidence limits, 15 to 54 Gg/yr). Emission estimates for the whole city based on an aircraft mass balance method and from inverse modeling of CH4 tower observations were 41 ± 12 Gg/yr and 81 ± 11 Gg/yr, respectively. Footprint modeling using 11 days of ethane/methane tower data indicated that landfills, wastewater treatment, wetlands, and other biological sources contribute 48% while natural gas usage and other fossil fuel sources contribute 52% of the city total. With the biogenic CH4 emissions omitted, the top-down estimates are 3.5-6.9 times the nonbiogenic city inventory. Mobile mapping of CH4 concentrations showed low level enhancement of CH4 throughout the city reflecting diffuse natural gas leakage and downstream usage as possible sources for the missing residual in the inventory.

High-resolution atmospheric inversion of urban CO2 emissions during the dormant season of the Indianapolis Flux Experiment (INFLUX).

Based on a uniquely dense network of surface towers measuring continuously the atmospheric concentrations of greenhouse gases (GHGs), we developed the first comprehensive monitoring systems of CO2 emissions at high resolution over the city of Indianapolis. The urban inversion evaluated over the 2012-2013 dormant season showed a statistically significant increase of about 20% (from 4.5 to 5.7 MtC ± 0.23 MtC) compared to the Hestia CO2 emission estimate, a state-of-the-art building-level emission product. Spatial structures in prior emission errors, mostly undetermined, appeared to affect the spatial pattern in the inverse solution and the total carbon budget over the entire area by up to 15%, while the inverse solution remains fairly insensitive to the CO2 boundary inflow and to the different prior emissions (i.e., ODIAC). Preceding the surface emission optimization, we improved the atmospheric simulations using a meteorological data assimilation system also informing our Bayesian inversion system through updated observations error variances. Finally, we estimated the uncertainties associated with undetermined parameters using an ensemble of inversions. The total CO2 emissions based on the ensemble mean and quartiles (5.26-5.91 MtC) were statistically different compared to the prior total emissions (4.1 to 4.5 MtC). Considering the relatively small sensitivity to the different parameters, we conclude that atmospheric inversions are potentially able to constrain the carbon budget of the city, assuming sufficient data to measure the inflow of GHG over the city, but additional information on prior emission error structures are required to determine the spatial structures of urban emissions at high resolution.

Aircraft-Based Estimate of Total Methane Emissions from the Barnett Shale Region.

We present estimates of regional methane (CH4) emissions from oil and natural gas operations in the Barnett Shale, Texas, using airborne atmospheric measurements. Using a mass balance approach on eight different flight days in March and October 2013, the total CH4 emissions for the region are estimated to be 76 ± 13 × 10(3) kg hr(-1) (equivalent to 0.66 ± 0.11 Tg CH4 yr(-1); 95% confidence interval (CI)). We estimate that 60 ± 11 × 10(3) kg CH4 hr(-1) (95% CI) are emitted by natural gas and oil operations, including production, processing, and distribution in the urban areas of Dallas and Fort Worth. This estimate agrees with the U.S. Environmental Protection Agency (EPA) estimate for nationwide CH4 emissions from the natural gas sector when scaled by natural gas production, but it is higher than emissions reported by the EDGAR inventory or by industry to EPA's Greenhouse Gas Reporting Program. This study is the first to show consistency between mass balance results on so many different days and in two different seasons, enabling better quantification of the related uncertainty. The Barnett is one of the largest production basins in the United States, with 8% of total U.S. natural gas production, and thus, our results represent a crucial step toward determining the greenhouse gas footprint of U.S. onshore natural gas production.

Evaluating atmospheric CO2 inversions at multiple scales over a highly inventoried agricultural landscape.

An intensive regional research campaign was conducted by the North American Carbon Program (NACP) in 2007 to study the carbon cycle of the highly productive agricultural regions of the Midwestern United States. Forty-five different associated projects were conducted across five US agencies over the course of nearly a decade involving hundreds of researchers. One of the primary objectives of the intensive campaign was to investigate the ability of atmospheric inversion techniques to use highly calibrated CO2 mixing ratio data to estimate CO2 flux over the major croplands of the United States by comparing the results to an inventory of CO2 fluxes. Statistics from densely monitored crop production, consisting primarily of corn and soybeans, provided the backbone of a well studied bottom-up inventory flux estimate that was used to evaluate the atmospheric inversion results. Estimates were compared to the inventory from three different inversion systems, representing spatial scales varying from high resolution mesoscale (PSU), to continental (CSU) and global (CarbonTracker), coupled to different transport models and optimization techniques. The inversion-based mean CO2 -C sink estimates were generally slightly larger, 8-20% for PSU, 10-20% for CSU, and 21% for CarbonTracker, but statistically indistinguishable, from the inventory estimate of 135 TgC. While the comparisons show that the MCI region-wide C sink is robust across inversion system and spatial scale, only the continental and mesoscale inversions were able to reproduce the spatial patterns within the region. In general, the results demonstrate that inversions can recover CO2 fluxes at sub-regional scales with a relatively high density of CO2 observations and adequate information on atmospheric transport in the region.