SEARCH · Search NASA
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
What role can satellites take, as a complement to ground based measurement systems, to provide sustained observations to monitor GHG emissions (e.g., CO2, CH4, O3, N2O, CFC s, NH3, and NF3) that contribute to global warming?
In this study, we propose a concept for localizing and mitigating greenhouse gases and other emissions from areas of various sizes, using a multiscale, airborne localization fleet, supported by information from satellites. Our objective is a rapidly implementable capability for the reduction of atmospheric methane by empowering the communities in proximity to emissions to actively contribute to methane mitigation via fleet operations by local governments, first responders, and individuals . Fully autonomous operations are economically desirable and technically feasible now. However, the development of regulations for autonomous operations is in early stages. Since rapid impact is critical to reducing emissions, the concept starts with remotely controlled vehicles. As regulations for autonomous operations reach maturity, the concept can be easily transitioned from the remotely piloted to autonomous mode. We report on a preliminary system analysis of the concept. Localization of methane emissions from the Permian Basin oil and gas production site serves as the design reference mission; however, the airborne concept is applicable to a broad range of use cases, and we describe several in this analysis. We consider leveraging satellite and ground-based resources within the airborne concept of operations. The study analyses uninhabited aerial system localization operations under the most conservative assumptions on resources and available information. Even under these assumptions, the concept is shown to be viable for local community operations.
Smallholder farmers struggle to achieve food security in many countries of sub-Saharan Africa (SSA). It is urgently required to find appropriate practices for enhancing crop production while avoiding large increases in greenhouse gas (GHG) emissions in SSA. This review aims to identify common smallholder farming practices for enhancing crop production, to assess how these affect GHG emissions and to identify strategies that not only enhance crop production but also mitigate GHG emissions in SSA. To increase crop production and ensure food security, smallholder farmers usually expand agricultural land, develop water harvesting and irrigation techniques and increase cropping intensity and fertilizer use. These practices may result in changing carbon stocks and GHG emissions, potentially creating trade-offs between food security and GHG mitigation. Agricultural land expansion at the expense of forests is the most dominant source of GHG emissions in SSA. While water harvesting and irrigation can increase soil organic carbon, they can trigger GHG emissions. Increasing cropping intensity can enhance the decomposition of soil organic matter, thus releasing carbon dioxide. Increasing nitrogen fertilizer use can enhance soil organic carbon, but also leads to increasing nitrous oxide emissions. An integrated land, water and nutrient management strategy is necessary to enhance crop production and mitigate GHG emissions. Among the most relevant strategies found, agroforestry practices in degraded and marginal lands could replace expanding agricultural croplands. In addition, water management, via adequate rainwater harvesting and irrigation techniques, together with appropriate nutrient management should be considered. Therefore, a land-water-nutrient nexus (LWNN) approach will enable an integrated and sustainable solution to increasing crop production and mitigating GHG emissions. Various technical, economic and policy barriers hinder implementing the LWNN approach on the ground, but these may be overcome through developing appropriate technologies, disseminating them through farmer to farmer approaches and developing specific policies to address smallholder land tenure issues and motivate long-term investment.
As Southeast Asian economies continue to grow, so will their contribution to global greenhouse gas (GHG) emissions, driven primarily by increases in fossil fuel combustion. Left unchecked, these emissions will negatively impact air quality and climate, therefore it is imperative that emission sources are properly identified and accounted for in emission inventories. Enhancement ratios of GHGs have been used to characterize regional emissions as either dominated by fossil fuel combustion or biomass burning. In particular, airborne assessment of short-term continuous emission ratios has proven useful to quantify relative contributions of fossil fuel and biomass burning to GHG emissions. The 2024 Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign, flying over the Philippines, Korea, Thailand, and Taiwan, sampled a variety of emissions including significant biomass burning, local urban pollution, and transport events. This work will explore the impact of GHG emissions on the distinctive pollution of the sampled locations. As airborne GHG measurements over Southeast Asia are scarce if not nonexistent, this work provides a crucial link between established ground-based measurements and state-of-the-art satellite observations such as those from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS). Analysis of GHG enhancement ratios in East Asia will lead to more accurate emission inventories which can be used to implement more effective GHG control measures leading to improved air quality and minimizing the effect on climate.
Deforestation in the tropics contributes approximately one-fifth of the annual global greenhouse gas (GHG) emissions. In an effort to reduce GHG emissions, SERVIR - a joint USAID and NASA initiative - is currently implementing the SERVIR CArbon Pilot (S-CAP) activity. By developing a comprehensive CO2 tracking system in Google Earth Engine based on both remotely sensed data and in situ observations, S-CAP aims to build capacity within the SERVIR regional hubs to improve decision making and local monitoring efforts surrounding GHG emissions. The S-CAP estimations are being completed throughout 11 pilot countries within the SERVIR regions of Asia, Africa, and the Americas to integrate partner contributions and local input into the development process. These emission estimate calculations use country-level REDD+ FREL reported values, global remotely sensed data (e.g. Global Forest Watch) and regional datasets and are completed using the IPCC Guidelines for National Greenhouse Gas Inventories. From the S-CAP activity thus far, country CO2 estimations had varied differences, ranging from 56% to 197% between their lowest and highest estimates. The results from this study show that CO2 emission estimates can differ greatly depending on the land cover data sets used as well as the biomass values, and how a comprehensive database is essential in understanding the full range of impacts that land cover change has in these regions. Using this ensemble approach, SERVIR hubs can better select the most appropriate datasets available for estimating emissions.
Inland waters are important emitters of the greenhouse gasses (GHGs) carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) to the atmosphere. In the framework of the 2 nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2) initiative, we synthesize existing estimates of GHG emissions from streams, rivers, lakes and reservoirs, and homogenize them with regard to underlying global maps of water surface area distribution and the effects of seasonal ice cover. We then produce regionalized estimates of GHG emissions over 10 extensive land regions. According to our synthesis, inland water GHG emissions have a global warming potential of an equivalent emission of 13.5 (9.9-20.1) and 8.3 (5.7-12.7) Pg CO 2 -eq. yr -1 at a 20 and 100 year horizon (GWP 20 and GWP 100 ), respectively. Contributions of CO 2 dominate GWP100, with rivers being the largest emitter. For GWP 20 , lakes and rivers are equally important emitters, and the warming potential of CH4 is more important than that of CO2. Contributions from N 2 O are about two orders of magnitude lower. Normalized to the area of RECCAP-2 regions, S-America and SE-Asia show the highest emission rates, dominated by riverine CO 2 emissions.
New estimates of greenhouse gas (GHG) emissions from the food system were developed at the country level, for the period 1990–2018, integrating data from crop and livestock production, on-farm energy use, land use and land use change, domestic food transport and food waste disposal. With these new country-level components in place, and by adding global and regional estimates of energy use in food supply chains, we estimate that total GHG emissions from the food system were about 16 CO2eq yr−1 in 2018, or one-third of the global anthropogenic total. Three quarters of these emissions, 13 Gt CO2eq yr−1, were generated either within the farm gate or in pre- and post-production activities, such as manufacturing, transport, processing, and waste disposal. The remainder was generated through land use change at the conversion boundaries of natural ecosystems to agricultural land. Results further indicate that pre- and post-production emissions were proportionally more important in developed than in developing countries, and that during 1990–2018, land use change emissions decreased while pre- and post-production emissions increased. We also report results on a per capita basis, showing world total food systems per capita emissions decreasing during 1990–2018 from 2.9 to 2.2 t CO2eq cap−1, with per capita emissions in developed countries about twice those in developing countries in 2018. Our findings also highlight that conventional IPCC categories, used by countries to report emissions in the National GHG inventory, systematically underestimate the contribution of the food system to total anthropogenic emissions. We provide a comparative mapping of food system categories and activities in order to better quantify food-related emissions in national reporting and identify mitigation opportunities across the entire food system.
Food systems—encompassing activities in food production, land-use change, supply chains and waste management—contribute significantly to climate change. Recent estimates indicate that food systems produce over 30% of annual anthropogenic greenhouse gas (GHG) emissions (about 20% of CO 2 , 50% of CH 4 , and 75% of N 2 O), with the Intergovernmental Panel on Climate Change (IPCC) estimating a notably broad range of 23%–42% of global GHG emissions. This paper synthesizes current research on the contributions of food systems to climate change, highlights challenges in quantifying their impact and proposes a harmonized accounting framework for more effective climate action. We recommend that an expert committee aligned with the IPCC develop guidance for food systems emissions accounting in four key areas, including: (1) defining system boundaries and nomenclature; (2) developing protocols to allocate broader sectoral emissions to food systems; (3) prioritizing critical areas for research into activity data and emissions factors; and (4) developing a balanced framework for evaluating the impact of mitigation interventions in light of other food systems imperatives. The committee should be integrated into two key international policy processes—the United Nations Framework Convention on Climate Change and the United Nations Food Systems Summit—to support coordinated action towards global net-zero goals. Guidance from the committee could significantly improve the ability of governments, companies, and researchers to estimate, report, monitor and ultimately reduce the climate impacts of food systems.
Space based solar power (SBSP) entails in-space collection of solar energy, transmission of that energy to one or more stations on Earth, conversion to electricity, and delivery to the grid or to batteries for storage. Experts in both the aerospace and energy sectors are debating the benefits of SBSP as more organizations globally begin SBSP technology development programs. We conducted a study to evaluate the potential benefits, challenges, and options for NASA to engage with growing global interest in SBSP. We performed a first order lifecycle study of two representative SBSP designs for 2 GW utility-scale power generation that, for the purposes of the study, are presumed to begin in 2050. We assumed baseline capabilities to develop, assemble, operate, maintain, and dispose of the SBSP systems are a mix of capabilities that are above, below, or comparable to capabilities demonstrated to date. We then compared the lifecycle cost of electricity and lifecycle GHG emission intensity of the SBSP designs to alternative terrestrial renewable electricity production technologies. Our findings indicate the SBSP designs may produce lifecycle GHG emissions per unit of electricity that are comparable to terrestrial alternatives, pending further studies of upper atmosphere responses to launch emissions. We find the SBSP designs are more expensive than terrestrial alternatives and may have lifecycle costs per unit of electricity that are 12-80 times higher. Though, we do find cost competitiveness may be achieved if a favorable combination of cost and capability improvements related to launch and manufacturing that are beyond the advancements assumed in the baseline assessment can be attained.
Space based solar power (SBSP) entails in-space collection of solar energy, transmission of that energy to one or more stations on Earth, conversion to electricity, and delivery to the grid or to batteries for storage. Experts in both the aerospace and energy sectors are debating the benefits of SBSP as more organizations globally begin SBSP technology development programs. We conducted a study to evaluate the potential benefits, challenges, and options for NASA to engage with growing global interest in SBSP. We performed a first order lifecycle study of two representative SBSP designs for 2 GW utility-scale power generation that, for the purposes of the study, are presumed to begin in 2050. We assumed baseline capabilities to develop, assemble, operate, maintain, and dispose of the SBSP systems are a mix of capabilities that are above, below, or comparable to capabilities demonstrated to date. We then compared the lifecycle cost of electricity and lifecycle GHG emission intensity of the SBSP designs to alternative terrestrial renewable electricity production technologies. Our findings indicate the SBSP designs may produce lifecycle GHG emissions per unit of electricity that are comparable to terrestrial alternatives, pending further studies of upper atmosphere responses to launch emissions. We find the SBSP designs are more expensive than terrestrial alternatives and may have lifecycle costs per unit of electricity that are 12-80 times higher. Though, we do find cost competitiveness may be achieved if a favorable combination of cost and capability improvements related to launch and manufacturing that are beyond the advancements assumed in the baseline assessment can be attained.
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.
Despite the pervasive impact of drought on human and natural systems, the large-scale mechanisms conducive to regional drying remain poorly understood. Here we use a multivariate approach to identify two distinct externally forced fingerprints from multiple ensembles of Earth system model simulations. The leading fingerprint, F(M1)(x), is characterized by global warming, intensified wet–dry patterns and progressive large-scale continental aridification, largely driven by multidecadal increases in greenhouse gas (GHG) emissions. The second fingerprint, F(M2)(x), captures a pronounced interhemispheric temperature contrast, associated meridional shifts in the intertropical convergence zone and correlated anomalies in precipitation and aridity over California, the Sahel and India. F(M2)(x) exhibits nonlinear temporal behaviour: the intertropical convergence zone moves southwards before 1975 in response to increases in hemispherically asymmetric sulfate aerosol emissions, and it shifts northwards after 1975 due to reduced sulfur dioxide emissions and the GHG-induced warming of Northern Hemisphere landmasses. Both fingerprints are statistically identifiable in observations of joint changes in temperature, rainfall and aridity during 1950–2014. We show that the reliable simulation of these changes requires combined forcing by GHGs, direct and indirect effects of aerosols, and large volcanic eruptions. Our results suggest that GHG-induced aridification may be modulated regionally by future reductions in sulfate aerosol emissions.
NASA Dryden Flight Research Center is a world-class flight research facility located at Edwards AFB, CA. With access to a 44 sq. mile dry lakebed and 350 testable days per year, it is the ideal location for flight research. DFRC has been undertaking aircraft research for approximately six decades including the famous X-aircraft (X-1 through X-48) and many science and exploration platforms. As part of this impressive heritage, DFRC has garnered more hours of full-sized electric aircraft testing than any other facility in the US, and possibly the world. Throughout the 80 s and 90 s Dryden was the home of the Pathfinder, Pathfinder Plus, and Helios prototype solar-electric aircraft. As part of the ERAST program, these electric aircraft achieved a world record 97,000 feet altitude for propeller-driven aircraft. As a result of these programs, Dryden s staff has collected thousands of man-hours of electric aircraft research and testing. In order to better answer the needs of the US in providing aircraft technologies with lower fuel consumption, lower toxic emissions (NOx, CO, VOCs, etc.), lower greenhouse gas (GHG) emissions, and lower noise emissions, NASA has engaged in cross-discipline research under the Aeronautics Research Mission Directorate (ARMD). As a part of this overall effort, Mark Moore of LaRC has initiated a cross-NASA-center electric propulsion working group (EPWG) to focus on electric propulsion technologies as applied to aircraft. Electric propulsion technologies are ideally suited to overcome all of the obstacles mentioned above, and are at a sufficiently advanced state of development component-wise to warrant serious R&D and testing (TRL 3+). The EPWG includes participation from NASA Langley Research Center (LaRC), Glenn Research Center (GRC), Ames Research Center (ARC), and Dryden Flight Research Center (DFRC). Each of the center participants provides their own unique expertise to support the overall goal of advancing the state-of-the-art in aircraft electric propulsion technologies. DFRC will leverage its vast experience in flight test to assist in the integration and flight test phases of any electric propulsion program. DFRC s core competencies, that have particular relevance to the goals of the EPWG, include flight research planning and execution and providing aircraft test beds for researching and testing electric propulsion concepts and equipment. There are three flight regimes that the EPWG is focusing on: subsonic small GA and UAV, subsonic transport class, and supersonic. DFRC proposes two classes of test bed aircraft, to answer the early- and mid-phase testing requirements of all flight regimes the EPWG is concerned with. First, a highly efficient PIK motor glider will be used to test concepts and equipment associated with the subsonic GA and UAV aircraft regime (N+1). Second, a small fleet of subscale remotely-piloted aircraft test beds, similar to the X48B Blended Wing Body aircraft tested at Dryden, will be developed to answer the unique testing requirements of the subsonic GA and UAV, subsonic transport and possibly the supersonic class of aircraft (N+2, N+3). These aircraft can be tested in either serial stages or concurrent stages, depending on the actual test requirements and program schedules. Both classes of test bed aircraft are described below.
Improving estimates of greenhouse gas (GHG) emissions from food production, supply, consumption, and disposal is fundamental to identifying effective policy solutions. Through broader awareness of the food-climate nexus, climate mitigation as well as resilience can be enhanced. However, work is needed to address knowledge gaps, promote better policies and improve public understanding of issues related to the food system and climate change. The intention of this paper is not only to highlight the need for better scientific understanding of the processes through which GHGs are emitted—from production to processing, from supply chains and retail to food preparation and waste (figure 1)—but also to integrate science and policy in order to scale up impact on climate change action.
Monitoring changes in greenhouse gas (GHG) emission is critical for assessing climate mitigation efforts towards the Paris Agreement goal. A crucial aspect of science-based GHG monitoring is to provide objective information for quality assurance and uncertainty assessment of the reported emissions. Emission estimates from combustion events (gas flaring and biomass burning) are often calculated based on activity data (AD) from satellite observations, such as those detected from the visible infrared imaging radiometer suite (VIIRS) onboard the Suomi-NPP and NOAA-20 satellites. These estimates are often incorporated into carbon models for calculating emissions and removals. Consequently, errors and uncertainties associated with AD propagate into these models and impact emission estimates. Deriving uncertainty of AD is therefore crucial for transparency of emission estimates but remains a challenge due to the lack of evaluation data or alternate estimates. This work proposes a new approach using machine learning (ML) for combustion detection from NASA's Black Marble product suite and explores the assessment of potential uncertainties through comparison with existing detections. We jointly characterize combustion using thermal and light emission signals, with the latter improving detection of probable weaker combustion with less distinct thermal signatures. Being methodologically independent, the differences in ML-derived estimates with existing approaches can indicate the potential uncertainties in detection. The approach was applied to detect gas flares over the Eagle Ford Shale, Texas. We analyzed the spatio-temporal variations in detections and found that approximately 79.04% and 72.14% of the light emission-based detections are missed by ML-derived detections from VIIRS thermal bands and existing datasets, respectively. This improvement in combustion detection and scope for uncertainty assessment is essential for comprehensive monitoring of resulting emissions and we discuss the steps for extending this globally.
Greenhouse gas (GHG) emissions estimation methods that use atmospheric trace gas observations, including inverse modeling techniques, perform better when carbon dioxide (CO2) fluxes are more accurately transported and dispersed in the atmosphere by a numerical model. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric CO2 from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations).
Heat is among the deadliest weather-related phenomena in the United States, and the number of heat-related deaths may increase under a changing climate, particularly in urban areas. Regional adaptation planning is unfortunately often limited by the lack of quantitative information on potential future health responses. This study presents an assessment of the future impacts of climate change on heat-related mortality in 12 cities using 16 global climate models, driven by two scenarios of greenhouse gas emissions. Although the magnitude of the projected heat effects was found to differ across time, cities, climate models and greenhouse pollution emissions scenarios, climate change was projected to result in increases in heat-related fatalities over time throughout the 21st century in all of the 12 cities included in this study. The increase was more substantial under the high emission pathway, highlighting the potential benefits to public health of reducing greenhouse gas emissions. Nearly 200,000 heat-related deaths are projected to occur in the 12 cities by the end of the century due to climate warming, over 22,000 of which could be avoided if we follow a low GHG emission pathway. The presented estimates can be of value to local decision makers and stakeholders interested in developing strategies to reduce these impacts and building climate change resilience.
Climate change presents a great challenge to the agricultural sector as changes in precipitation, temperature, humidity, and circulation patterns alter the climatic conditions upon which many agricultural systems rely. Projections of future climate conditions are inherently uncertain owing to a lack of clarity on how society will develop, policies that may be implemented to reduce greenhouse-gas (GHG) emissions, and complexities in modeling the atmosphere, ocean, land, cryosphere, and biosphere components of the climate system. Global climate models (GCMs) are based on well-established physics of each climate component that enable the models to project climate responses to changing GHG concentration scenarios (Stocker et al., 2013).The most recent iteration of the Coupled Model Intercomparison Project (CMIP5; Taylor et al., 2012) utilized representative concentration pathways (RCPs) to cover the range of plausible GHG concentrations out past the year 2100, with RCP8.5 representing an extreme scenario and RCP4.5 representing a lower concentrations scenario (Moss et al., 2010).