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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.

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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Forest reorganisation effects on fuel moisture content can exceed changes due to climate warming in wet temperate forests

Abstract The distributions of vegetation and fire activity are changing rapidly in response to climate warming. In many regions, climate effects on dead fuel moisture content (FMC) are expected to increase future wildfire activity. However, forest FMC is largely driven by microclimate conditions, which are moderated from open weather by vegetation canopies. As shifts in vegetation increase under climate warming, the extent to which future fire activity will be driven by climate directly or associated vegetation shifts remains unresolved. Here, we present a study aimed at quantifying the relative magnitudes of (i) direct climate warming, and (ii) vegetation change, on FMC. Field sites to evaluate these effects were established in a natural laboratory of altered forest states to mature wet temperate forest in south‐eastern Australia. FMC was estimated using a process‐based model and 48 years of reconstructed climate data. Canopy effects on microclimate were captured by transferring inputs from climate to microclimate using models parameterised with field observations. To evaluate the relative magnitude of climate and vegetation effects, we calculated the maximum difference in mean annual FMC across annual climate replicates and compared this to FMC differences across reorganising forest sites. Our results show vegetation effects on FMC can exceed those related to expected climate change. Changes to forest structure and composition increased (+15.7%) and decreased (−12.3%) mean annual FMC, with a larger negative effect when forest cover was completely removed (−18.5%). In contrast, the largest climate effect on FMC was −6.6% across 48‐years of data. Our study demonstrates that the magnitude of vegetation effects on FMC can exceed expected climate change effects. Models of future fire activity that do not account for changing vegetation effects on microclimate are omitting a key biophysical control on FMC and therefore may not be accurately predicting future fire activity.

Brown, Tegan P.↗
DOE OSTI2025

Machining of Thin-Walled Structures From Stiffness-Driven Additively Manufactured Preform Geometry

Additive manufacturing provides the means to build component preforms with reduced excess material to create functional parts. In the case of aero-structural and aero-engine components, additive manufacturing technologies offer the possibility to substantially reduce the volume of material to be removed by machining operations. To achieve this objective, the preform must be built with the minimum material necessary to contain the final geometry and simultaneously provide enough stiffness to withstand the magnitude of the machining forces. This work describes a computationally efficient method to calculate the geometry required from the preform to reliably manufacture typical thin-walled structures via finish machining processes. This is achieved by defining the preform with near constant static stiffness across the width of the preform, in combination with a prescribed magnitude of stiffness at the top edge of the preform. The prescribed static stiffness is the function of the machining force magnitude, a direct consequence of the preselected cutting conditions. In conclusion, this article illustrates the application of the method to a straight single boundary thin-walled structure as an introduction case and for ease of description.

Additive manufacturing↗
DOE OSTI2020

O · , H · , and · OH radical etching probability of polystyrene obtained for a radio frequency driven atmospheric pressure plasma jet

Atmospheric pressure plasma jets have great potential for the surface modification of polymers. In this work, the authors report on polystyrene etching by a radio frequency driven atmospheric pressure plasma jet with a focus on the role of H · , O · , and · OH radicals in this process. The absolute flux of H · , O · , and · OH radicals reaching the surface of the polymer was determined by a COMSOL MULTIPHYSICS reacting fluid dynamics model incorporating detailed transport phenomena in the boundary layer near the substrate. The simulated results of H · and · OH densities in the jet effluent were experimentally verified by two-photon absorption laser induced fluorescence and laser induced fluorescence, respectively. The carbon atom removal flux from the polystyrene surface was taken from previously reported measurements using the same plasma source. The authors show that the boundary layer effects in the interfacial region above the substrate can have a significant impact on the calculated etching probabilities. The reaction probability (β) has a significant uncertainty although a variation of 2 orders of magnitude in β leads to uncertainties of approximately 1 order of magnitude variation in the determined etching probability. The etching probability of polystyrene by · OH radicals was confirmed to be at least an order of magnitude larger than the polystyrene etching probability by O · radicals. The authors also confirmed the weak polystyrene etching probability by H · radicals. The model suggests that the presence of a 30 ppm O 2 impurity can lead to the production of · OH radicals in the far effluent of the Ar + 1% H 2 plasma jet close to the substrate at sufficient densities to enable effective etching.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Resolving spatial and energetic distributions of trap states in metal halide perovskite solar cells

We report the profiling of spatial and energetic distributions of trap states in metal halide perovskite single-crystalline and polycrystalline solar cells. The trap densities in single crystals varied by five orders of magnitude, with a lowest value of 2 × 10 11 per cubic centimeter and most of the deep traps located at crystal surfaces. The charge trap densities of all depths of the interfaces of the polycrystalline films were one to two orders of magnitude greater than that of the film interior, and the trap density at the film interior was still two to three orders of magnitude greater than that in high-quality single crystals. Suprisingly, after surface passivation, most deep traps were detected near the interface of perovskites and hole transport layers, where a large density of nanocrystals were embedded, limiting the efficiency of solar cells.

14 SOLAR ENERGY↗
DOE OSTI2022

Probing for the Trace Estimation of a Permuted Matrix Inverse Corresponding to a Lattice Displacement

We report thatpProbing is a general technique that is used to reduce the variance of the Hutchinson stochastic estimator for the trace of the inverse of a large, sparse matrix A. The variance of the estimator is the sum of the squares of the off-diagonal elements of A -1 . Therefore, this technique computes probing vectors that when used in the estimator annihilate the largest off-diagonal elements. For matrices that display decay of the magnitude of |A$^{-1}_{ij}$| with the graph distance between nodes i and j, this is achieved through graph coloring of increasing powers A k . Equivalently, when a matrix stems from a lattice discretization, it is computationally beneficial to find a distance-k coloring of the lattice. Previously, a hierarchical coloring was proposed so that k can be increased at runtime as needed without discarding previous work. In this work, we study probing for the more general problem of computing the trace of a permutation of A -1 , say PA -1 . The motivation comes from lattice quantum chromodynamics (QCD), where we need to construct “disconnected diagrams” to extract flavor-separated generalized parton functions. In lattice QCD, where the matrix has a four-dimensional toroidal lattice structure, these nonlocal operators correspond to a PA -1 , where P is the permutation relating to some displacement $\vec{p}$ in one or more dimensions. We focus on a single dimension displacement (p), but our methods are general. We show that probing on A k or (PA) k does not annihilate the largest magnitude elements. To resolve this issue, our displacement-based probing works on PA k using a new coloring scheme that works directly on appropriately displaced neighborhoods on the lattice. We prove lower bounds on the number of colors needed and study the effect of this scheme on variance reduction, both theoretically and experimentally on a real-world lattice QCD calculation. We achieve orders of magnitude speedup over the unprobed or the naively probed methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Transport of Ligand Coordinated Iron and Chromium through Cation-Exchange Membranes

Fluxes of negatively charged ligand-coordinated iron, Fe(CN) 6 3/4- , and chromium, CrPDTA 1/2− , through two promising commercial cation-exchange membranes, Aquivion E87–05S and Fumasep E-620(K), were measured as functions of current density. The magnitude of the partial current density reached a maximum of − 43 μ A cm −2 at the maximum applied current density magnitude of − 43 mA cm −2 for Fe(CN) 6 3/4− transport through Aquivion, or 0.1% of the total current density. Fumasep E-620(K) blocks practically all crossover of both compounds. Both membranes sorb more Fe(CN) 6 3/4− and CrPDTA 1/2− than predicted by Donnan equilibrium, and low crossover rates can be attributed mainly to slow diffusion, not charge-based rejection of co-ions. The magnitude of the diffusion coefficient appears to correlate with hydraulic permeability. Although Aquivion E87–05S and Fumasep E-620(K) have significant and observable differences in membrane crossover rates, cells built with the DI-soaked membranes offer similarly high coulombic efficiency, indicating the relatively small contribution that crossover makes to inefficiency over a single cycle.

25 ENERGY STORAGE↗
DOE OSTI2025

Lidar-Based Evaluation of HRRR Performance in California’s Diablo Range

The performance of the NOAA High-Resolution Rapid Refresh (HRRR) model for capturing low-level winds near a wind energy production site during summer 2019 is evaluated. This study catalogs the ability of HRRR to predict boundary layer dynamics relevant to wind energy interests over complex terrain, which has presented challenges for weather and energy forecasting. Performance is evaluated by comparing HRRR output to wind-profiling Doppler lidars at Lawrence Livermore National Laboratory Site 300. HRRR captured the diurnal profile of horizontal winds in the observed 150-m layer, despite strong underpredictions (∼4 m s −1 ) during evening and nighttime hours. These underpredictions may be a result of local speedup flows observed by the lidars, which were unresolved in HRRR due to their small spatial extent. HRRR bias magnitude relative to observations was found to be minimal during days with synoptic-scale troughs and strong 850-hPa geopotential gradients, while bias magnitude was maximal during days with synoptic ridging and weak 850-hPa geopotential gradients. To translate wind speed predictions to energy forecasting, generic turbine models were used to estimate power generation for turbines characteristic of the nearby Altamont Pass Wind Resource Area. Results show that HRRR-based energy estimates predicted daytime power generation adequately relative to lidar-based estimates with an 18-h lead time (bias magnitude < 0.4 MW from 0900 to 1400 LT) but overpredicted power during the rest of the diurnal cycle (bias > 1 MW). These results demonstrate conditions under which HRRR performs well for wind energy applications in complex terrain, while highlighting biases that require further investigation to support usage of a high-resolution model for wind energy forecasts.

Boundary layer↗
DOE OSTI2021

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

An investigation into the potential for wind turbines to cause barotrauma in bats

The high rates of bat mortality caused by operating wind turbines is a concern for wind energy and wildlife stakeholders. One theory that explains the mortality is that bats are not only killed by impact trauma, but also by barotrauma that results from exposure to the pressure variations caused by rotating turbine blades. To date, no published research has calculated the pressure changes that bats may be exposed to when flying near wind turbines and then used these data to estimate the likelihood that turbines cause barotrauma in bats. To address this shortcoming, we performed computational fluid dynamics simulations of a wind turbine and analytical calculations of blade-tip vortices to estimate the characteristics of the sudden pressure changes bats may experience when flying near a utility-scale wind turbine. Because there are no data available that characterize the pressure changes that cause barotrauma in bats, we compared our results to changes in pressure levels that cause barotrauma and mortality in other mammals of similar size. This comparison shows that the magnitude of the low-pressures bats experience when flying near wind turbines is approximately 8 times smaller than the pressure that causes mortality in rats, the smallest mammal for which data are available. The magnitude of the high-pressures that bats may experience are approximately 80 times smaller than the exposure level that causes 50% mortality in mice, which have a body mass similar to several bat species that are killed by wind turbines. Further, our results show that for a bat to experience the largest possible magnitude of low- and high-pressures, they must take very specific and improbable flight paths that skim the surface of the blades. Even a small change in the flight path results in the bat being hit by the blade or experiencing a much smaller pressure change. Accordingly, if bats have a physiological response to rapid low- and high-pressure exposure that is similar to other mammals, we conclude that it is unlikely that barotrauma is responsible for a significant number of turbine-related bat fatalities, and that impact trauma is the likely cause of the majority of wind-turbine-related bat fatalities.

17 WIND ENERGY↗
DOE OSTI2025

Exploring Uncertainty in Moment Estimation for Small Earthquakes in Southern Nevada Using the Coda Envelope Method

Compiling source parameter estimates for small earthquakes is important both for our understanding of earthquake physics and for accurately assessing earthquake hazard. Reliable source parameter estimates are difficult to achieve for small earthquakes, in part due to our inability to accurately model the relevant physical processes at high frequencies. The coda envelope methodology developed by Mayeda and Walter (1996) and Mayeda et al. (2003) can mitigate this concern and estimate the moment of small earthquakes by determining the parameters that control the shape of the S-wave coda envelope while eliminating path effects by minimizing the scatter between seismic stations. Here, we use an open-source implementation of this technique called the Coda Calibration Tool (CCT; Barno, 2017) to calculate CCT-based moment magnitude estimates of small earthquakes (M L 0–3) in the Rock Valley, Nevada, region within the Nevada National Security Site. The Rock Valley data set is of particular interest because it allows us to explore the changes in uncertainties of the coda calibration method with earthquake size and depth. We found that a consistent linear relationship exists between the local magnitude M L and our coda-derived M w estimates for earthquakes as small as M L 0–3, but that current CCT workflows do not accurately characterize very shallow events. We also demonstrate that the epistemic uncertainty in the apparent stress value assumed by the CCT algorithm can influence magnitude estimates of small earthquakes. In conclusion, these results provide valuable insight into the seismicity of this region, and inform future analysis and modeling efforts for nuclear monitoring and seismic hazard.

58 GEOSCIENCES↗
DOE OSTI2025

Factors That Influence Variability in Stress-Drop Measurements Using Spectral Decomposition and Spectral-Ratio Methods for the 2019 Ridgecrest Earthquake Sequence

Stress drop is a fundamental parameter related to earthquake source physics, but is hard to measure accurately. To better understand how different factors influence stress-drop measurements, we compare two different methods using the Ridgecrest stress-drop validation data set: spectral decomposition (SD) and spectral ratio (SR), each with different processing options. Here, we also examine the influence of spectral complexity on source parameter measurement. Applying the SD method, we find that frequency bandwidth and time-window length could influence spectral magnitude calibration, while depth-dependent attenuation is important to correctly map stress-drop variations. For the SR method, we find that the selected source model has limited influence on the measurements; however, the Boatwright model tends to produce smaller standard deviation and larger magnitude dependence than the Brune model. Variance reduction threshold, frequency bandwidth, and time-window length, if chosen within an appropriate parameter range, have limited influence on source parameter measurement. For both methods, wave type, attenuation correction, and spectral complexity strongly influence the result. The scale factor that quantifies the magnitude dependence of stress drop show large variations with different processing options, and earthquakes with complex source spectra deviating from the Brune-type source models tend to have larger scale factor than earthquakes without complexity. Based on these detailed comparisons, we make a few specific suggestions for data processing workflows that could help future studies of source parameters and interpretations.

58 GEOSCIENCES↗
DOE OSTI2021

Toward Robust and Routine Determination of Mw for Small Earthquakes: Application to the 2020 Mw 5.7 Magna, Utah, Seismic Sequence

To better characterize seismic hazard, particularly, for induced seismicity, there is an increasing interest in methods to estimate moment magnitude (M w ) for small earthquakes. M w is generally preferred over other magnitude types, but, it is difficult to estimate M w for earthquakes with local magnitude (M L ) <3-3.5, using conventional moment tensor (MT) inversion. The 2020 M ww 5.7 Magna, Utah, seismic sequence provides an opportunity to illustrate and evaluate the value of spectral methods for this purpose. Starting with a high-quality seismic catalog of 2103 earthquakes (M L <5.6), we estimate M w using two independent spectral methods—one based on direct waves, yielding M w,direct , and the other based on coda waves, yielding M w,coda . For the direct-wave method, we present a non-parametric (NP) inversion scheme that solves for apparent geometrical spreading, G(R), and site effects (S), similar to other NP procedures that have been used to calibrate regional M L scales. The NP inversion is constrained using M ws derived from MTs for nine events in the Magna sequence. We recover statistically robust and physically reasonable G(R) and S and compute M w,direct for 635 Magna earthquakes down to M L 0.7. For the coda-wave method, we consider two separate calibration schemes involving previous MT solutions and compute M w,coda for 311 earthquakes down to M L 1.0. For 280 of the events that were processed with both methods—M w,direct and M w,coda —are strongly correlated (r = 0.98), with a mean difference of only 0.05. We compare M w,direct and M w,coda with M L and find reasonably good agreement for M L <3.6 with the theoretically predicted relationship of M w =(2/3)M L +C, in which C is a regional constant. Our results imply that seismic network operators can use spectral-based M w estimates to replace M L estimates for events with M L ≥1.0, and possibly smaller. The main requirement is the existence of a small number of MT solutions for calibration purposes.

58 GEOSCIENCES↗
DOE OSTI2026

Variability in Performance of a Machine Learning Seismicity Catalog: Central Italy, 2016–2017

Machine learning (ML) catalogs contain many more earthquakes than routine catalogs, but their performance in phase picking and earthquake detection has not been fully evaluated. We develop station‐level detection probabilities using logistic regression and combine them across a seismic network to compute spatial magnitude‐of‐completeness fields. We apply this approach to two catalogs from the 2016–2017 Central Italy sequence that were constructed from the same seismic network, one routine and one ML‐based. At the station level, the ML picker increases detection sensitivity by identifying smaller magnitude events and detecting earthquakes at greater distances. Spatially, the magnitude of completeness decreases substantially, with median values shifting from 1.6 to 0.5 for P waves and from 1.7 to 0.5 for S waves. However, the ML catalog also shows greater variability in station‐level performance than the routine catalog. These results demonstrate that ML‐based improvements in detectability are widespread but spatially nonuniform, highlighting their benefits, their limitations, and the potential for further improvements.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

ORION: Operational FoRecastIng Of INduced Seismicity

The Operational Forecasting of Induced Seismicity toolkit “ORION” (ORION) is an open-source, observation-based ensemble forecasting toolkit which is geared towards helping operators understand the seismic hazard (i.e., probabilistic assessment of the magnitude and frequency of induced seismic events) at a site. ORION analyzes how the seismic hazard evolves during injection and suggests possible mitigation strategies to employ if an earthquake that exceeds certain threshold is observed. Through its ensemble modeling approach, ORION leverages the benefits of statistical-, physics-, and machine learning-based forecasting methodologies, while reducing the impact of each model’s respective limitations. The ORION toolkit consists of an easy-to-use GUI interface that affords a user as much or as little interaction as desired. Advanced capabilities allow the user to upload local, high-precision earthquake catalogs, projected injection profiles and/or spatiotemporal estimates of pressure/stress, and to tune various model parameters. ORION will then provide a spatial and temporal ensemble forecast of seismicity defined as the probability of exceedance of a given earthquake magnitude over a forecast period. Additionally, ORION will provide probability distribution of the statistically derived maximum possible earthquake magnitude that may be expected. Finally, ORION will provide suggested operational management strategies (e.g. reduce injection volumes at specific wells) based on the level of hazard.

NRAP,NRAP Tools,ORION,Operational Forcasting of In↗
DOE OSTI2020

Deterministic and Statistical Modeling of a New Thermal Breakout Technology for Measuring the Maximum Horizontal In-Situ Stress

The current state-of-the-art technology for in-situ stress measurements involves an integrated approach that combines borehole breakout observations, drilling-induced tensile fractures, and hydraulic fracturing tests (i.e., “mini-fracs”). This methodology has achieved wide application in the oil and gas industry but has several limitations that often prevent successful in-situ stress measurements. One major limitation is that breakouts do not appear in all boreholes and are generally only a natural occurrence that cannot easily be controlled. Because borehole breakouts are used to directly measure the maximum horizontal in-situ stress magnitude, the absence of borehole breakouts presents a major data gap for in-situ stress measurements. In response to this data gap, a new US Department of Energy (US DOE)-sponsored thermal breakout technology that will provide a method for thermally inducing borehole breakouts and allow the consistent measurement of the maximum horizontal stress magnitude is currently in development. This thermal breakout technology involves heating the borehole and increasing the thermoelastic compressive stress in the rock until a breakout develops, which can be directly correlated to the maximum horizontal stress magnitude. The first step in this project was an analytical modeling study of the thermal breakout process. Based on the Kirsch solution (Kirsch 1898), a deterministic and statistical analysis was performed on the pertinent parameters that influence the maximum horizontal stress calculation. As a result of the analysis, the findings indicate that the thermal breakout technology is feasible and provides improved accuracy and/or an enhanced ability to measure the maximum horizontal stress. Future work as part of this US DOE-sponsored project includes additional validation through more detailed numerical modeling, laboratory testing, and field testing of the thermal breakout technology.

58 GEOSCIENCES↗
DOE OSTI2023

Wave vector and field vector orientation dependence of Fe K pre-edge X-ray absorption features in clinopyroxenes

Abstract Pre-edge X-ray absorption features are commonly used to derive redox states for transition metal oxides in crystals and glasses. Several calibrations for Fe2+ and Fe3+ in silicate glasses have utilized the general relationships among pre-edge peak intensity, energy, and redox state. However, absorption variations complicate those relationships in anisotropic crystals. Although absorption anisotropy at and above the energy of the rising edge adheres to the typical cos2 relationship observed in absorption spectroscopies at other energies, the anisotropy of the pre-edge is far more complicated. Prior studies focusing on pre-edge absorption anisotropy demonstrate a 1-cos4φ dependence of absorption magnitudes with rotation. Experiments presented here show that absorption magnitudes of the pre-edge vary as a function of both electric field vector orientation and wave vector direction. However, rotations around the field vector axis or wave vector axis individually result in cos2 dependence of absorption magnitudes. Rotations where both wave vector and field vector orientation are varied are not well fit by either model in the pre-edge. The resulting anisotropy complicates the process of measuring characteristic absorption in the pre-edge, making valence state determinations challenging for strongly anisotropic crystal structures such as pyroxene.

Geochemistry & Geophysics↗
DOE OSTI2021

Data Analysis of the 2020 Central Idaho Mainshock-Aftershock Sequence

In an effort to inform the Senior Seismic Hazard Analysis Committee for the Idaho National Laboratory, we provide an improved aftershock catalog related to the March 31, 2020, Mw6.5 Stanley, Idaho earthquake from picks related to a temporary network of two real-time and 15 non-telemetered seismometers within the epicentral area. From the permanent and temporary (XP) real-time network, the USGS cataloged 1,946 aftershocks between April 1, 2020 and October 31, 2020. To improve aftershock location and magnitudes, we manually picked arrival times of P and S waves from off-line stations in the XP temporary network, generated a new crustal velocity model, and independently relocated each event using the HypoDD double-difference earthquake algorithm. We created our new velocity model from existing broadband and active source seismic campaign data that were acquired near the epicentral region prior to the 2020 earthquake. We compare arrival time differences, epicentral locations and depths between aftershocks recorded with the two catalogs. We find the addition of local stations provides tighter aftershock clustering that suggests an improved aftershock locations. To detect lower magnitude events, we employed deep learning. Our method solves common problems associated with detecting many events that have a low signal-to-noise ratio. From the machine learning database, we detected more than 74,000 aftershocks. Based on the number of identified earthquakes and Gutenberg-Richter relationships derived from the USGS catalog, we estimate that we have reduced the completion magnitude for the Stanley earthquake sequence to below M1 using this machine learning approach. We located each aftershock with our new velocity model. Our new velocity model and picks suggests aftershocks occurred mostly at shallower depths than assessed in the USGS catalog. These aftershocks align along two linear trends that suggest the activation of two unnamed primary faults.

58 GEOSCIENCES↗
DOE OSTI2022

An Assessment of Current Understanding of the Greenhouse Gas Impacts from H 2 Emissions

With the anticipated growth in hydrogen generation and use as part of a broad shift in energy use away from fossil fuels, concerns have been raised regarding the impact of increased H 2 emissions on global warming. Atmospheric scientists have long recognized that H 2 emissions into the atmosphere do have an indirect impact on global warming, largely because a portion of emitted H 2 is consumed by the hydroxyl radical (OH), which is the primary reactant that removes the potent greenhouse gas methane from the atmosphere. Therefore, increases in H 2 emissions will result in decreases in the average OH concentrations in the atmosphere and an increase in the atmospheric lifetime of methane. Various assessments of the impact of H 2 emissions on global warming have been performed over the past couple of decades. These assessments have yielded significant variability and recognized uncertainty in the magnitude of the warming effect of a given quantity of emitted H 2 , and an even greater uncertainty in the magnitude of H 2 leakage and releases that can be anticipated with an expanded H 2 infrastructure. Consequently, definitive estimates of the magnitude of the warming effect of additional emitted H 2 are lacking. However, given the current understanding of the warming potential of emitted H 2 and given reasonable expectations of the emission rate of an expanded H 2 infrastructure, it is anticipated that warming effects from emitted H 2 will offset no more than 5% of the reduction in warming associated with avoided CO 2 emissions from using clean H 2 . Further, it is highly unlikely that the warming effects from emitted H 2 will offset more than 10% of the benefit from avoided CO 2 emissions, at least as considered over a typical 100-year accounting period. Because of the short atmospheric lifetimes of H 2 and methane, however, the warming effect of emitted H 2 is enhanced over the first few years following increases in H 2 emission.

54 ENVIRONMENTAL SCIENCES↗