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All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

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 1x1 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 1x1 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 3030 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.

EIs↗
NASA NTRS2020

Role of emission controls in reducing the 2050 climate change penalty for PM2.5 in China

Previous studies demonstrated that global warming can lead to deteriorated air quality even when anthropogenic emissions were kept constant, which has been called a climate change penalty on air quality. It is expected that anthropogenic emissions will decrease significantly in the future considering the aggressive emission control actions in China. However, the dependence of climate change penalty on the choice of emission scenario is still uncertain. To fill this gap, we conducted multiple independent model simulations to investigate the response of PM2.5 to future (2050) climate warming (RCP8.5) in China but with different emission scenarios, including the constant 2015 emissions, the 2050 CLE emissions (based on Current Legislation), and the 2050 MTFR emissions (based on Maximum Technically Feasible Reduction). For each set of emissions, we estimate climate change penalty as the difference in PM2.5 between a pair of simulations with either 2015 or 2050 meteorology. Under 2015 emissions, we find a PM2.5 climate change penalty of 1.43 μg m−3 in Eastern China, leading to an additional 35,000 PM2.5-related premature deaths [95% confidence interval (CI), 21,000-40,000] by 2050. However, the PM2.5 climate change penalty weakens to 0.24 μg m−3 with strict anthropogenic emission controls under the 2050 MTFR emissions, which decreases the associated PM2.5-related deaths to 17,000. The smaller MTFR climate change penalty contributes 14% of the total PM2.5 decrease when both emissions and meteorology are changed from 2015 to 2050, and 24% of total health benefits associated with this PM2.5 decrease in Eastern China. This finding suggests that controlling anthropogenic emissions can effectively reduce the climate change penalty on PM2.5 and its associated premature deaths, even though a climate change penalty still occurs even under MTFR. Strengthened controls on anthropogenic emissions are key to attaining air quality targets and protecting human health in the context of future global climate change.

Emission controls↗
DOE OSTI2024

Large reductions in Permian Basin methane intensity shown in multi-year comparison of aerially-visible methane emissions

Spanning the US states of Texas and New Mexico, the Permian Basin has been a hotspot of methane emissions from oil and natural gas activity 1–4, although studies disagree over the magnitude of these emissions. The most comprehensive measurement campaigns published were conducted in 2019 1–5 .There have been large changes in the energy industry since then, including in the prices of oil and gas, both state and federal regulatory environments, investor and activist pressure over methane emissions, and the adoption of new technologies and policies by energy operators. Understanding how any or all of these might influence methane emissions is important for policy makers, oil and gas operators, and other stakeholders. We characterize the time evolution of Permian Basin methane emissions using a series of comprehensive aerial surveys conducted every year from 2020-2023 and compare them to the 2019 results cited above. To maintain comparability, all the data sets are from surveys using Insight M point source methane sensing technology. The scope of these surveys expanded over time: from 33-46% of wells, oil production, and gas production in 2020 to 60-65% in 2021, to 84% of wells and over 90% of both oil and gas production in 2023. These surveys by Insight M also include hundreds of gas processing plants and compressor stations as well as 1000s of km of gathering and transmission pipelines. Considering only the aerially detected portion of emissions (typically the majority of the total in such surveys 4), we find reductions of more than 70% in methane emissions intensity compared to the 2019 New Mexico-only Insight M survey, with variation depending on the year 3,4. Notably, although sources below 100 kg/hr contributed less than 10% of aerially measured emissions the 2019 New Mexico survey 3, these smaller sources constitute a larger proportion of total aerially measured emissions (although not the majority) in 2020-2023. Production facilities and gathering pipelines are responsible for the larges shares of total emissions, followed by compressor stations and gas processing plants. Permian methane emissions were also measured in a comprehensive 2019 Permian-wide survey by the Carbon Mapper team 2. That analysis led to a lower total emissions estimate at the time 4. These new Insight M-based emission rates are still roughly 30-70% lower than the aerially measured portion of the 2019 Carbon Mapper-based estimates 4. Further work is needed to harmonize these surveys in space and time to create the most intercomparable numbers possible 5. Additional analysis is needed to compare our findings to the more spatially constrained 2020, 2021, and 2023 Carbon Mapper surveys in the Permian 4,6. The evidence is strong from these two survey teams that emissions intensity has declined significantly since 2019. Reasons for this trend are currently unclear but point to possible success of emissions control programs. Future work investigating frequency, source, and operator-specific intensities could provide insights into the causes of this promising trend.

methane, oil and gas, data science, remote sensing↗
NASA NTRS2019

Exploiting OMI NO2 Satellite Observations to Infer Fossil-fuel CO2 Emissions from U.S. Megacities

Fossil-fuel CO2 emissions and their trends in eight U.S. megacities during 2006–2017 are inferred by combining satellite-derived NOX emissions with bottom-up city-specific NOX-to-CO2 emission ratios. A statistical model is fit to a collection NO2 plumes observed from the Ozone Monitoring Instrument (OMI), and is used to calculate top-down NOX emissions. Decreases in OMI-derived NOX emissions are observed across the eight cities from 2006 to 2017 (−17% in Miami to −58% in Los Angeles), and are generally consistent with long-term trends of bottom-up inventories (−25% in Miami to −49% in Los Angeles), but there are some interannual discrepancies. City-specific NOX-to-CO2 emission ratios, used to calculate inferred CO2, are estimated through annual bottom-up inventories of NOX and CO2 emissions disaggregated to 1 × 1 km2 resolution. Over the study period, NOX-to-CO2 emission ratios have decreased by ~40% nationwide (−24% to −51% for our studied cities), which is attributed to a faster reduction in NOX when compared to CO2 due to policy regulations and fuel type shifts. Combining top-down NOX emissions and bottom-up NOX-to-CO2 emission ratios, annual fossil-fuel CO2 emissions are derived. Inferred OMI-based top-down CO2 emissions trends vary between +7% in Dallas to −31% in Phoenix. For 2017, we report annual fossil-fuel CO2 emissions to be: Los Angeles 113 ± 49 Tg/yr; New York City 144 ± 62 Tg/yr; and Chicago 55 ± 24 Tg/yr. A study in the Los Angeles area, using independent methods, reported a 2013–2016 average CO2 emissions rate of 104 Tg/yr and 120 Tg/yr, which suggests that the CO2 emissions from our method are in good agreement with other studies' top-down estimates. We anticipate future remote sensing instruments – with better spatial and temporal resolution – will better constrain the NOX-to-CO2 ratio and reduce the uncertainty in our method.

Ozone Monitoring Instrument (OMI)↗
NASA NTRS2017

Mitigating Satellite-Based Fire Sampling Limitations in Deriving Biomass Burning Emission Rates: Application to WRF-Chem Model over the Northern sub-Saharan African Region

Largely used in several independent estimates of fire emissions, fire products based on MODIS sensors aboard the Terra and Aqua polar-orbiting satellites have a number of inherent limitations, including (a) inability to detect fires below clouds, (b) significant decrease of detection sensitivity at the edge of scan where pixel sizes are much larger than at nadir, and (c) gaps between adjacent swaths in tropical regions. To remedy these limitations, an empirical method is developed here and applied to correct fire emission estimates based on MODIS pixel level fire radiative power measurements and emission coefficients from the Fire Energetics and Emissions Research (FEER) biomass burning emission inventory. The analysis was performed for January 2010 over the northern sub-Saharan African region. Simulations from WRF-Chem model using original and adjusted emissions are compared with the aerosol optical depth (AOD) products from MODIS and AERONET as well as aerosol vertical profile from CALIOP data. The comparison confirmed an 30-50% improvement in the model simulation performance (in terms of correlation, bias, and spatial pattern of AOD with respect to observations) by the adjusted emissions that not only increases the original emission amount by a factor of two but also results in the spatially continuous estimates of instantaneous fire emissions at daily time scales. Such improvement cannot be achieved by simply scaling the original emission across the study domain. Even with this improvement, a factor of two underestimations still exists in the modeled AOD, which is within the current global fire emissions uncertainty envelope. Plain Language Summary Polar-orbiting satellites sensors, such as MODIS, have limitations in detecting fires under clouds or when viewing angles are large or in the gaps among satellites' different ground swaths. Here we developed an empirical method to mitigate the effect of these limitations in fire emission estimate. The method is applied to a fire emission inventory (FEER) based on MODIS. We show that, with our method, the adjusted emission inventory improves WRF-Chem simulation of smoke transport and distribution.

emissions properties↗
NASA NTRS2019

Evaluation of Matthews-WLR-CH4: A New Wetland, Lake, and Reservoir Methane Emissions Data Set

Methane (CH4) is emitted from a variety of sources, both natural and anthropogenic, and is the second most important greenhouse gas contributing to climate change. Natural wetlands are the largest single contributor to annual global CH4 emissions and other inland water sources, such as lakes and reservoirs, produce CH4 but have received less attention. These emission sources are among the most uncertain components of the global CH4 cycle. These uncertainties stem from numerous issues, including the vast variability in these ecosystems and the sensitivity of CH4 emissions to interannual climate variations. Existing CH4 emission models display large differences in wetland spatial extent and include simplified, or lack all together, wetland-type classifications. Furthermore, CH4 emissions from lakes and reservoirs are typically combined with wetlands, or ignored, on a regional and global scale. Our NASA Interdisciplinary Research in Earth Science (IDS) study is focused on developing a suite of data sets on wetlands, lakes, and reservoirs (WLR) including global distributions of types, CH4-centric classifications, and daily CH4 emission rates (hereafter Matthews-WLR-CH4). This project is unique in that it comprises source-independent emission data for WLR, the first spatially-explicit data set of lake and reservoir CH4 emissions, and wetland emissions comprising numerous different wetland-type classifications.The focus of this study is to perform the initial evaluation of the wetland and lake CH4 emissions data from Matthews-WLR-CH4. This evaluation will focus on the domain of North America and include: 1) estimates of the contribution of wetland and lake sources to total CH4 emissions and atmospheric mixing ratios, 2) comparisons of wetland fraction and CH4 emissions to a suite of existing models (e.g., Wetland and Wetland CH4 Inter-comparison of Models Project (WETCHIMP), Global Carbon Project (GCP)), and 3) an accuracy assessment with in situ airborne and tower-based atmospheric CH4 observations when the Matthews-WLR-CH4 emissions are implemented in forward and inverse CarbonTracker-Lagrange (CT-L) CH4 model simulations. Here we will present the new Matthews-WLR-CH4 wetland and lake CH4 emissions data, the initial evaluation of these products, and the path forward for our project.

Reservoir↗
DOE OSTI2026

Author Correction: US oil and gas system emissions from nearly one million aerial site measurements

Correction to: Naturehttps://doi.org/10.1038/s41586-024-07117-5 Published online 13 March 2024 In the version of the article initially published, several errors were present and have been corrected in the HTML and PDF versions of the article and Supplementary Information. The main results, conclusions, and our interpretations of the data remain unchanged. See the new Supplementary Information Section S15 for a more detailed description of the errors corrected and the resulting effects on the analysis. Data processing and methods corrections Overflight count correction: We previously used pre-computed source coverage data for some Carbon Mapper campaigns that was computed differently than was required for our analysis. We have re-computed Carbon Mapper source coverage based on flightline polygons and source coordinates. Transition point computation, well sites: The updated version now correctly compares the cumulative emissions distribution of simulated well site emissions with that of aerially detected sources (rather than plumes) when computing the transition point. Transition point computation, midstream: Additionally, the transition point calculation has been corrected to exclude aerially detected midstream emissions below the transition point, which was previously leading to double counting of these emissions. This error was not present for upstream (well site) emissions. Calculation errors Unit error: We corrected a specific unit conversion error affecting well site emissions in the Kairos Fort Worth dataset. Across all datasets, we also correct the conversion factor for converting from standard volume to mass for midstream emissions. Sorting error: We correct code that was applying incorrect sorting when computing correction factors to account for partial detection at well sites. Small typographical corrections were made in Fig. 1b and SI Section S4.1. Data processing and methods corrections Overflight count correction: We previously used pre-computed source coverage data for some Carbon Mapper campaigns that was computed differently than was required for our analysis. We have re-computed Carbon Mapper source coverage based on flightline polygons and source coordinates. Transition point computation, well sites: The updated version now correctly compares the cumulative emissions distribution of simulated well site emissions with that of aerially detected sources (rather than plumes) when computing the transition point. Transition point computation, midstream: Additionally, the transition point calculation has been corrected to exclude aerially detected midstream emissions below the transition point, which was previously leading to double counting of these emissions. This error was not present for upstream (well site) emissions. Calculation errors Unit error: We corrected a specific unit conversion error affecting well site emissions in the Kairos Fort Worth dataset. Across all datasets, we also correct the conversion factor for converting from standard volume to mass for midstream emissions. Sorting error: We correct code that was applying incorrect sorting when computing correction factors to account for partial detection at well sites. Small typographical corrections were made in Fig. 1b and SI Section S4.1. The following practices may help researchers conducting similar analyses avoid making similar errors: 1, Clear, accessible documentation explaining the interpretation of all columns in data input tables and all internal variables within the model, 2, Simple cross-check calculations computed before and after unit conversions.

Sherwin, Evan D↗
NASA NTRS2016

A Global Catalogue of Large SO2 Sources and Emissions Derived from the Ozone Monitoring Instrument

Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor processed with the new principal component analysis (PCA) algorithm were used to detect large point emission sources or clusters of sources. The total of 491 continuously emitting point sources releasing from about 30 kt yr(exp -1) to more than 4000 kt yr(exp -1) of SO2 per year have been identified and grouped by country and by primary source origin: volcanoes (76 sources); power plants (297); smelters (53); and sources related to the oil and gas industry (65). The sources were identified using different methods, including through OMI measurements themselves applied to a new emission detection algorithm, and their evolution during the 2005- 2014 period was traced by estimating annual emissions from each source. For volcanic sources, the study focused on continuous degassing, and emissions from explosive eruptions were excluded. Emissions from degassing volcanic sources were measured, many for the first time, and collectively they account for about 30% of total SO2 emissions estimated from OMI measurements, but that fraction has increased in recent years given that cumulative global emissions from power plants and smelters are declining while emissions from oil and gas industry remained nearly constant. Anthropogenic emissions from the USA declined by 80% over the 2005-2014 period as did emissions from western and central Europe, whereas emissions from India nearly doubled, and emissions from other large SO2-emitting regions (South Africa, Russia, Mexico, and the Middle East) remained fairly constant. In total, OMI-based estimates account for about a half of total reported anthropogenic SO2 emissions; the remaining half is likely related to sources emitting less than 30 kt yr(exp -1) and not detected by OMI.

Emissions↗
NASA NTRS2003

Emissions of Trace Gases and Particles from Two Ships in the Southern Atlantic Ocean

Measurements were made of the emissions of particles and gases from two diesel-powered ships in the southern Atlantic Ocean off the coast of Namibia. The measurements are used to derive emission factors from ships of three species not reported previously, namely, black carbon, accumulation-mode particles, and cloud condensation nuclei (CCN), as well as for carbon dioxide, carbon monoxide (CO), methane (CH4), non-methane hydrocarbons, sulfur dioxide (SO2), nitrogen oxides (NOx), and condensation nuclei. The effects of fuel grade and engine power on ship emissions are discussed. The emission factors are combined with fuel usage data to obtain estimates of global annual emissions of various particles and gases from ocean-going ships. Global emissions of black carbon, accumulation- mode particles, and CCN from ocean-going ships are estimated to be 19-26 Gg yr(sup -1), (4.4-6.1) x 10(exp 26) particles yr(sup -1), and (1.0-1.5) x l0(exp 26) particles yr(sup -1), respectively. Black carbon emissions from ocean-going ships are approximately 0.2% of total anthropogenic emissions. Emissions of NOx and SO2 from ocean-going ships are approximately 10-14% and approximately 3-4%, respectively, of the total emissions of these species from the burning of fossil fuels, and approximately 40% and approximately 70%, respectively, of the total emissions of these species from the burning of biomass. Global annual emissions of CO and CH4 from ocean-going ships are approximately 2% and approximately 2-5%, respectively, of natural oceanic emissions of these species.

Sinha, Parikhit↗
NASA NTRS2010

ARCADE 2 Observations of Galactic Radio Emission

We use absolutely calibrated data from the Absolute Radiometer for Cosmology, Astrophysics, and Diffuse Emission (ARCADE 2) flight in July 2006 to model Galactic emission at frequencies 3, 8, and 10 GHz. The spatial structure in the data is consistent with a superposition of free-free and synchrotron emission. Emission with spatial morphology traced by the Haslam 408 MHz survey has spectral index beta_synch = -2.5 +/- 0.1, with free-free emission contributing 0.10 +/- 0.01 of the total Galactic plane emission in the lowest ARCADE 2 band at 3.15 GHz. We estimate the total Galactic emission toward the polar caps using either a simple plane-parallel model with csc|b| dependence or a model of high-latitude radio emission traced by the COBE/FIRAS map of CII emission. Both methods are consistent with a single power-law over the frequency range 22 MHz to 10 GHz, with total Galactic emission towards the north polar cap T_Gal = 0.498 +/- 0.028 K and spectral index beta = -2.55 +/- 0.03 at reference frequency 0.31 GHz. The well calibrated ARCADE 2 maps provide a new test for spinning dust emission, based on the integrated intensity of emission from the Galactic plane instead of cross-correlations with the thermal dust spatial morphology. The Galactic plane intensity measured by ARCADE 2 is fainter than predicted by models without spinning dust, and is consistent with spinning dust contributing 0.4 +/- 0.1 of the Galactic plane emission at 23 GHz.

Kogut, A.↗
NASA NTRS2017

Satellites May Underestimate Rice Residue and Associated Burning Emissions in Vietnam

In this study, we estimate rice residue, associated burning emissions, and compare results with existing emissions inventories employing a bottom-up approach. We first estimated field-level post-harvest rice residues, including separate fuel-loading factors for rice straw and rice stubble. Results suggested fuel-loading factors of 0.27 kg/sq m (+/-0.033), 0.61 kg/sq m (+/-0.076), and 0.88 kg/sq m (+/-0.083) for rice straw, stubble, and total post-harvest biomass, respectively. Using these factors, we quantified potential emissions from rice residue burning and compared our estimates with other studies. Our results suggest total rice residue burning emissions as 2.24 Gg PM2.5, 36.54 Gg CO and 567.79 Gg CO2 for Hanoi Province, which are significantly higher than earlier studies. We attribute our higher emission estimates to improved fuel-loading factors; moreover, we infer that some earlier studies relying on residue-to-product ratios could be underestimating rice residue emissions by more than a factor of 2.3 for Hanoi, Vietnam. Using the rice planted area data from the Vietnamese government, and combining our fuel-loading factors, we also estimated rice residue PM2.5 emissions for the entirety of Vietnam and compared these estimates with an existing all-sources emissions inventory, and the Global Fire Emissions Database (GFED). Results suggest 75.98 Gg of PM2.5 released from rice residue burning accounting for 12.8% of total emissions for Vietnam. The GFED database suggests 42.56 Gg PM2.5 from biomass burning with 5.62 Gg attributed to agricultural waste burning indicating satellite-based methods may be significantly underestimating emissions. Our results not only provide improved residue and emission estimates, but also highlight the need for emissions mitigation from rice residue burning.

Air pollution↗
NASA NTRS2019

Historical (1700–2012) Global Multi-Model Estimates of the Fire Emissions from the Fire Modeling Intercomparison Project (FireMIP)

Fire emissions are critical for carbon and nutrient cycles, climate, and air quality. Dynamic Global Vegetation Models (DGVMs) with interactive fire modeling provide important estimates for long-term and large-scale changes of fire emissions. Here we present the first multi-model estimates of global gridded historical fire emissions for 1700-2012, including carbon and 33 species of trace gases and aerosols. The dataset is based on simulations of nine DGVMs with different state-of-the-art global fire models that participated in the Fire Modeling Intercomparison Project (FireMIP), using the same and standardized protocols and forcing data, and the most up-to-date fire emission factor table from field and laboratory studies over various land cover types. We evaluate the simulations of present-day fire emissions by comparing them with satellite-based products. Evaluation results show that most DGVMs simulate present-day global fire emission totals within the range of satellite-based products, and can capture the high emissions over the tropical savannas, low emissions over the arid and sparsely vegetated regions, and the main features of seasonality. However, most of the models fail to simulate the interannual variability, partly due to a lack of modeling peat fires and tropical deforestation fires. Historically, all models show only a weak trend in global fire emissions before ~1850s, consistent with multi-source merged historical reconstructions. The long-term trends among DGVMs are quite different for the 20th century, with some models showing an increase and others a decrease in fire emissions, mainly as a result of the discrepancy in their simulated responses to human population density change and land-use and land-cover change (LULCC). Our study provides a basic dataset for developing regional and global multi-source merged historical reconstructions and merging methods, and analyzing historical changes of fire emissions and their uncertainties as well as their role in the Earth system. It also highlights the importance of accurately modeling the responses of fire emissions to LULCC and population density change in reducing uncertainties in historical reconstructions of fire emissions and providing more reliable future projections.

Li, Fang↗
NASA NTRS2024

Aera-Mip: Emission Pathways, Remaining Budgets, and Carbon Cycle Dynamics Compatible With 1.5 and 2 °C Global Warming Stabilization

While international climate policies now focus on limiting global warming to well below 2 °C or pursuing a 1.5 °C level of global warming, the climate modelling community has not provided an experimental design in which all Earth system models (ESMs) converge and stabilize at the same prescribed global warming levels. This gap hampers accurate estimations based on comprehensive ESMs of the carbon emission pathways and budgets needed to meet such agreed warming levels and of the associated climate impacts under temperature stabilization. Here, we apply the Adaptive Emission Reduction Approach (AERA) with ESMs to provide such simulations in which all models converge at 1.5 and 2.0 °C warming levels by adjusting their emissions over time. These emission-driven simulations provide a wide range of emission pathways and resulting atmospheric CO 2 projections for a given warming level, uncovering uncertainty ranges that were previously missing in the traditional Coupled Model Intercomparison Project (CMIP) scenarios with prescribed greenhouse gas concentration pathways. Meeting the 1.5 °C warming level requires a 40 % (full model range: 7 % to 76 %) reduction in multi-model mean CO 2 -forcing-equivalent (CO 2 -fe) emissions from 2025 to 2030, a 98 % (57 % to 127 %) reduction from 2025 to 2050, and a stabilization at 1.0 (−1.7 to 2.9) PgC yr−1 from 2100 onward after the 1.5 °C global warming level is reached. Meeting the 2.0 °C warming level requires a 47 % (8 % to 92 %) reduction in multi-model mean CO 2 -fe emissions until 2050 and a stabilization at 1.7 (−1.5 to 2.7) PgC yr−1 from 2100 onward. The on-average positive emissions under stabilized global temperatures are the result of a decreasing transient climate response to cumulative CO 2 -fe emissions over time under stabilized global warming. This evolution is consistent with a slightly negative zero emissions commitment – initially assumed to be zero – and leads to an increase in the post-2025 CO 2 -fe emission budget by a factor of 2.2 (−0.8 to 6.9) by 2150 for the 1.5 °C warming level and a factor of 1.4 (0.9 to 2.4) for the 2.0 °C warming level compared to its first estimate in 2025. The median CO 2 -only carbon budget by 2150, relative to 2020, is 800 GtCO 2 for the 1.5 °C warming level and 2250 GtCO 2 for the 2.0 °C warming level. These median values exceed the median IPCC AR6 estimates by 60 % for the 1.5 °C warming level and 67 % for 2.0 °C. Some of the differences may be explained by the choice of the mitigation scenario for non-CO 2 radiative agents. Our simulations highlight shifts in carbon uptake dynamics under stabilized temperature, such as a cessation of the carbon sinks in the North Atlantic and in tropical forests. On the other hand, the Southern Ocean remains a carbon sink centuries after temperatures stabilize. Overall, this new type of warming-level-based emission-driven simulation offers a more coherent assessment across climate models and opens up a wide range of possibilities for studying both the carbon cycle and climate impacts, such as extreme events, under climate stabilization.

Adaptive Emission Reduction Approach↗
DOE OSTI2024

Choosing the Best Carbon Factor for the Job: Exploring Available Carbon Emissions Factors and the Impact of Factor Selection: Preprint

Over 600 local governments in the United States, including nearly half of the largest 100 cities, have enacted climate action plans that include carbon reduction goals and greenhouse gas inventories (Markolf et al. 2020). The magnitude of these goals ranges from modest reduction targets to carbon neutrality. None will be met without significant contributions from the buildings sector. Understanding how energy efficiency and building electrification impact greenhouse gas emissions requires local, time-sensitive, and forward-looking carbon emissions factors for electricity use in buildings. There are a variety of emissions factors currently available from various sources, including average emissions factors and historical short-run marginal emissions factors. Long-run marginal emissions factors and future-year short-run marginal emissions factors are also now available from the National Renewable Energy Laboratory's (NREL's) Cambium data sets. In this paper, we describe the different carbon emissions factors available, including both conventional sources and newly available options. We discuss the types of analyses each emissions factor is best suited to support. Then, using residential energy efficiency and electrification load profiles, we demonstrate how different conclusions result from different choices of carbon emissions factors. For two grid regions, we explore takeaways of using current versus future-year emissions, short-run versus long-run, and levelized versus single-year values. We include a framework for selecting the best carbon emissions factor for the job.

carbon emissions factors↗
NASA NTRS2014

Prompt Emission of GRB 121217A from Gamma-Rays to the Near-Infrared

The mechanism that causes the prompt-emission episode of gamma-ray bursts (GRBs) is still widely debated despite there being thousands of prompt detections. The favoured internal shock model relates this emission to synchrotron radiation. However, it does not always explain the spectral indices of the shape of the spectrum, which is often fit with empirical functions, such as the Band function. Multi-wavelength observations are therefore required to help investigate the possible underlying mechanisms that causes the prompt emission. We present GRB 121217A, for which we were able to observe its near-infrared (NIR) emission during a secondary prompt-emission episode with the Gamma-Ray burst Optical Near-infrared Detector (GROND) in combination with the Swift and Fermi satellites, which cover an energy range of 5 orders of magnitude (10(exp −3) keV to 100 keV). We determine a photometric redshift of z = 3.1 +/- 0.1 with a line-of-sight with little or no extinction (AV approx. 0 mag) utilising the optical/NIR SED. From the afterglow, we determine a bulk Lorentz factor of Gamma(sub 0) approx. 250 and an emission radius of R < 1018 cm. The prompt-emission broadband spectral energy distribution is well fit with a broken power law with beta1 = −0.3 +/- 0.1 and beta2 = 0.6 +/- 0.1 that has a break at E = 6.6 +/- 0.9 keV, which can be interpreted as the maximum injection frequency. Self-absorption by the electron population below energies of Ea < 6 keV suggest a magnetic field strength of B approx. 10(exp 5) G. However, all the best fit models underpredict the flux observed in the NIR wavelengths, which also only rebrightens by a factor of approx. 2 during the second prompt emission episode, in stark contrast to the X-ray emission, which rebrightens by a factor of approx. 100. This suggests an afterglow component is dominating the emission. We present GRB 121217A, one of the few GRBs that has multi-wavelength observations of the prompt-emission period and shows that it can be understood with a synchrotron radiation model. However, due to the complexity of the GRB's emission, other mechanisms that result in Band-like spectra cannot be ruled out.

prompt↗
NASA NTRS2024

High-Resolution Mapping of Nitrogen Oxide Emissions in Large US Cities From TROPOMI Retrievals of Tropospheric Nitrogen Dioxide Columns

Satellite-derived spatiotemporal patterns of nitrogen oxide (NO x ) emissions can improve accuracy of emission inventories to better support air quality and climate research and policy studies. In this study, we develop a new method by coupling the chemical transport Model-Independent SATellite-derived Emission estimation Algorithm for Mixed-sources (MISATEAM) with a divergence method to map high-resolution NO x emissions across US cities using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric nitrogen dioxide (NO 2 ) retrievals. The accuracy of the coupled method is validated through application to synthetic NO 2 observations from the NASA-Unified Weather Research and Forecasting (NU-WRF) model, with a horizontal spatial resolution of 4 km × 4 km for 33 large and mid-size US cities. Validation reveals excellent agreement between inferred and NU-WRF-provided emission magnitudes (R= 0.99, normalized mean bias, NMB = −0.01) and a consistent spatial pattern when comparing emissions for individual grid cells (R=0.88 ± 0.06). We then develop a TROPOMI-based database reporting annual emissions for 39 US cities at a horizontal spatial resolution of 0.05° × 0.05° from 2018 to 2021. This database demonstrates a strong correlation (R= 0.90) with the National Emission Inventory (NEI) but reveals some bias (NMB = −0.24). There are noticeable differences in the spatial patterns of emissions in some cities. Our analysis suggests that uncertainties in TROPOMI-based emissions and potential misallocation of emissions and/or missing sources in bottom-up emission inventories both contribute to these differences.

NOx emissions↗
DOE OSTI2024

High-resolution mapping of nitrogen oxide emissions in large US cities from TROPOMI retrievals of tropospheric nitrogen dioxide columns

Satellite-derived spatiotemporal patterns of nitrogen oxide (NOx) emissions can improve accuracy of emission inventories to better support air quality and climate research and policy studies. In this study, we develop a new method by coupling the chemical transport Model-Independent SATellite-derived Emission estimation Algorithm for Mixed-sources (MISATEAM) with a divergence method to map high-resolution NO x emissions across US cities using TROPOspheric Monitoring Instrument (TROPOMI) tropospheric nitrogen dioxide (NO 2 ) retrievals. The accuracy of the coupled method is validated through application to synthetic NO 2 observations from the NASA-Unified Weather Research and Forecasting (NU-WRF) model, with a horizontal spatial resolution of 4 km × 4 km for 33 large and mid-size US cities. Validation reveals excellent agreement between inferred and NU-WRF-provided emission magnitudes (R= 0.99, normalized mean bias, NMB = —0.01) and a consistent spatial pattern when comparing emissions for individual grid cells (R = 0.88 ± 0.06). We then develop a TROPOMI-based database reporting annual emissions for 39 US cities at a horizontal spatial resolution of 0.05° × 0.05° from 2018 to 2021. This database demonstrates a strong correlation (R= 0.90) with the National Emission Inventory (NEI) but reveals some bias (NMB = —0.24). There are noticeable differences in the spatial patterns of emissions in some cities. Our analysis suggests that uncertainties in TROPOMI-based emissions and potential misallocation of emissions and/or missing sources in bottom-up emission inventories both contribute to these differences.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Galaxy interactions and star formation: Results of a survey of global H-alpha emission in spiral galaxies in 8 clusters

Kennicutt and Kent (1983) have shown that the global H alpha emission from a spiral galaxy is an indicator of the formation rate of massive stars. Moss, Whittle and Irwin (1988) have surveyed two clusters (Abell 347 and 1367) for galaxies with H alpha emission using a high dispersion objective prism technique. The purpose of the survey is to investigate environmental effects on star formation in spiral galaxies, and in particular to ascertain whether star formation is enhanced in cluster spirals. Approximately 20 percent of CGCG galaxies were detected in emission. Two plates of excellent quality were obtained for each of the two clusters, and galaxies were only identified to have emission if this was detected on both plates of a plate pair. In this way, plate flaws and other spurious identifications of emission could be rejected, and weak emission confirmed. The results of this survey have been discussed by Moss (1987). The detected galaxies are of types SO-a and later. The frequency with which galaxies are detected in emission increases towards later morphological type as expected (cf. Kennicutt and Kent 1983). There is no evidence of any dependence of the frequency of detected emission on the absolute magnitude of the galaxy (cf. Moss and Whittle 1990), but there is a strong correlation between a disturbed morphological appearance of the galaxy and the detection of emission. Furthermore it is found that the emission is more centrally concentrated in those galaxies which show a disturbed morphology. It may be noted that the objective prism plate gives a spectrum of a 400 A region around rest wavelength H alpha, but superposed on this is the H alpha emission from the galaxy which, because the light is essentially monochromatic, results in a true two-dimensional image of the H alpha distribution. The visual appearance of the emission on the prism plates was classified according to its diffuseness on a 5 point scale (very diffuse, diffuse, intermediate, compact, and very compact). In tabular form, the relation is shown between this classification and a morphologically disturbed appearance for the galaxy.

Moss, C.↗