SEARCH · Search NASA
Results for “nitrous oxide emission”
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.
Policy-enabled stabilization of nitrous oxide emissions from livestock production in China over 1978–2017
Not provided.
A conceptual model explaining spatial variation in soil nitrous oxide emissions in agricultural fields
Not Available
A Review of Emerging Catalytic Strategies for Sustainable Mitigation of Nitrous Oxide Emissions [SLIDES]
Explore the source record for details and available documents.
Contribution of Microorganisms with the Clade II Nitrous Oxide Reductase to Suppression of Surface Emissions of Nitrous Oxide
The sources and sinks of nitrous oxide, as control emissions to the atmosphere, are generally poorly constrained for most environmental systems. Initial depth-resolved analysis of nitrous oxide flux from observation wells and the proximal surface within a nitrate contaminated aquifer system revealed high subsurface production but little escape from the surface. Further, to better understand the environmental controls of production and emission at this site, we used a combination of isotopic, geochemical, and molecular analyses to show that chemodenitrification and bacterial denitrification are major sources of nitrous oxide in this subsurface, where low DO, low pH, and high nitrate are correlated with significant nitrous oxide production. Depth-resolved metagenomes showed that consumption of nitrous oxide near the surface was correlated with an enrichment of Clade II nitrous oxide reducers, consistent with a growing appreciation of their importance in controlling release of nitrous oxide to the atmosphere. Our work also provides evidence for the reduction of nitrous oxide at a pH of 4, well below the generally accepted limit of pH 5.
Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA). Final report
The goal of the Stahl lab is to use a combination of isotopic, genomic, and activity-based analyses in the field, of selected field isolates, and defined synthetic communities to identify sources and sinks of different nitrogen species in the field, focusing on the FRC at the ORNL. The most general research objective is to characterize variants in the pathway for denitrification among field isolates and synthetic microbial assemblies as a foundation for predicting the biotic and abiotic controls of nitrogen and carbon transformations in the field. Specifically, we have focused on characterizing environmental controls of nitrous oxide emissions, an important greenhouse gas and ozone depleting molecule. Studies have mapped the flux of nitrous oxide and controlling genetic elements within the FRC subsurface, indicating the importance of different enzyme variants in nitrous oxide production and consumption in controlling surface emissions. We have developed methods to quantify and catalog the isotopic signatures imparted on nitrous oxide by different nitrous oxide reductase variants in isolated organisms and model synthetic communities. We have developed methods to quantify and catalog the nitrous oxide affinity of field relevant isolates and their biomass yield when respiring nitrous oxide on different carbon substrates. We continue to expand the collection of field-relevant microbial isolates within ENIGMA by focused enrichment and isolation from field material. These objectives have been facilitated by the development of new technologies, including: 1) the development of a multiplexed automated optical density measurement system for quantifying growth kinetics of microorganisms dependent on gaseous substrates in monoculture or gas exchange in synthetic communities and 2) the development of a microcalorimetric analysis format to quantify the impact of environmental stress on microbial maintenance energy. Since field relevant processes are mediated by assemblies of organisms interacting with each other and their local environment, we have also collaborated with other ENIGMA investigators on the assembly and characterization of synthetic communities. These studies have evaluated the role of organisms lacking a complete pathway for denitrification in the control of field processes, measuring process rates of assemblies of organisms that individually lack the full denitrification pathway but mediate the complete denitrification when growing together. These efforts have so far resulted in the publication of 18 peer reviewed papers ranging from ecological and physiological drivers of pathway optimization to field-scale process characterization.
Machine learning improves predictions of agricultural nitrous oxide (N 2 O) emissions from intensively managed cropping systems
Not provided.
Microbial functional genes commonly respond to elevated carbon dioxide
Atmospheric CO 2 concentration is increasing, largely due to anthropogenic activities. Previous studies of individual free-air CO 2 enrichment (FACE) experimental sites have shown significant impacts of elevated CO 2 (eCO 2 ) on soil microbial communities; however, no common microbial response patterns have yet emerged, challenging our ability to predict ecosystem functioning and sustainability in the future eCO 2 environment. Here we analyzed 66 soil microbial communities from five FACE sites, and showed common microbial response patterns to eCO 2 , especially for key functional genes involved in carbon and nitrogen fixation (e.g., pcc/acc for carbon fixation, nifH for nitrogen fixation), carbon decomposition (e.g., amyA and pulA for labile carbon decomposition, mnp and lcc for recalcitrant carbon decomposition), and greenhouse gas emissions (e.g., mcrA for methane production, norB for nitrous oxide production) across five FACE sites. Also, the relative abundance of those key genes was generally increased and directionally associated with increased biomass, soil carbon decomposition, and soil moisture. In addition, a further literature survey of more disparate FACE experimental sites indicated increased biomass, soil carbon decay, nitrogen fixation, methane and nitrous oxide emissions, plant and soil carbon and nitrogen under eCO 2 . A conceptual framework was developed to link commonly responsive functional genes with ecosystem processes, such as pcc/acc vs. soil carbon storage, amyA/pulA/mnp/lcc vs. soil carbon decomposition, and nifH vs. nitrogen availability, suggesting that such common responses of microbial functional genes may have the potential to predict ecosystem functioning and sustainability in the future eCO 2 environment.
Influence of shrub willow buffers strategically integrated in an Illinois corn-soybean field on soil health and microbial community composition
We report soil serves many important ecological functions and is an integral part of our existence as a society. However, concerns for soil health are growing globally, in part due to the negative impacts of agricultural management on soil resources. The production of perennial bioenergy crops on marginal land in row-crop production systems is one solution that could improve land-use efficiency and address the sustainability of cropland management. Because the relationship between crop management and the environment is complex, more research is needed to evaluate the potential benefits perennial bioenergy crop production has on soil health, as well as other ecosystem services. In this study, shrub willow buffers were strategically integrated into a corn-soybean cropping system with the main objective of reducing nitrate-N leaching from grain crop production while producing biomass for bioenergy. Two buffer systems (defined by landscape positions) were included for comparison, one on marginal land with exposure to nitrate-N leaching from upslope grain (southern plots) and one on fertile soils with less nitrate-N leaching potential (northern plots). Evaluation of soil (chemistry, bulk density, microbial community) and shrub willow vegetation properties ( fine roots, leaf litter decomposition, and nutrient uptake dynamics), showed that landscape position plays an important role in (1) the dynamics of soil chemical properties, (2) shrub willow's influence and productivity, and (3) the provision of additional ecosystem services such as reductions in nitrous oxide emissions and nitrate-N leaching. In addition, the combination of crop type and landscape position (N-grain, N-willow, S-grain, and S-willow) influenced the species composition of the soil microbial community, resulting in unique and identifiable communities. These results highlight the potential application of shrub willow buffers for ecosystem service provision and support of ecosystem processes; however, understanding the relationship between the microbial community, crop type, and landscape is important for understanding the sustainability of the design.
Nature-based climate solutions can help mitigate the radiative forcing that follows deforestation
Widespread expansion of agriculture and forestry has altered the surface of the Earth, the composition of the atmosphere, and as a result, the climate. Here we quantify the radiative forcing caused by the historical deforestation of an ecoregion in the U.S. Upper Midwest and the adoption of eight nature-based climate solutions. We combined regional forest inventory data with over three decades of remote sensing and in situ data from a replicated land use change experiment. Deforestation of the region caused net global warming (1626 ± 44 µW m -2 ), mainly from the 76 % reduction of ecosystem carbon stocks, but also from the 84 % reduction of the soil methane sink and the 115 % increase in soil nitrous oxide emissions. The associated albedo increase offset 24 % of this greenhouse gas induced warming. For the adoption of nature-based climate solutions, we found that conservation agriculture can provide -39 to -76 ± 31 µW m -2 of climate mitigation over a 100-year time period while short/medium length forestry rotations can provide more at -296 to -881 ± 44 µW m -2 and natural forest regeneration can provide the most at -1555 ± 44 µW m -2 . As the impacts of climate change on nature and society intensify, consideration should be given to the climate mitigation, habitat, and ecosystem services that nature-based climate solutions can provide.
Minimizing Fraud in the Carbon Offset Market Using Blockchain Technologies
Fraud in the Environmental Benefit Credit (EBC) markets is pervasive. To make matters worse, the cost of creating EBCs is often higher than the market price. Consequently, a method to create, validate, and verify EBCs and their relevance is needed to mitigate fraud. The EBC market has focused on geologic (fossil fuel) CO 2 sequestration projects that are often over budget and behind schedule and has failed to capture the "lowest hanging fruit" EBCs - terrestrial sequestration via the agricultural industry. This project reviews a methodology to attain possibly the least costly EBCs by tracking the reduction of inputs required to grow crops. The use of bio- stimulant products, such as humate, allows a farmer to use less nitrogen without adversely affecting crop yield. Using less nitrogen qualifies for EBCs by reducing nitrous oxide emissions and nitrate runoff from a farmer's field. A blockchain that tracks the bio-stimulant material from source to application provides a link between a tangible (bio-stimulant commodity) and the associated intangible (EBCs) assets. Covert insertion of taggants in the bio-stimulant products creates a unique barcode that allows a product to be digitally tracked from beginning to end. This process (blockchain technology) is so robust, logical, and transparent that it will enhance the value of the associated EBCs by mitigating fraud. It provides a real time method for monetizing the benefits of the material. Substantial amounts of energy are required to produce, transport, and distribute agricultural inputs including fertilizer and water. Intelligent optimization of the use of agricultural inputs can drive meaningful cost savings. Tagging and verification of product application provides a valuable understanding of the dynamics in the water/food energy nexus, a major food security and sustainability issue. As technology in agriculture evolves so to must methods to verify the Enterprise Resource Planning (ERP) potential of innovative solutions. The technology reviewed provides the ability to combine blockchain and taggants ("taggant blockchains") as the engine by which to (1) mitigate fraudulent carbon credits; (2) improve food chain security, and (3) monitor and manage sustainability. The verification of product quality and application is a requirement to validate benefits. Recent upgrades to humic and fulvic quality protocols known as ISO CD 19822 TC134 offers an analytical procedure. This work has been assisted by the Humic Products Trade Association and International Humic Substance Society. In addition, providing proof of application of these products and verification of the correct application of prescriptive humic and bio-stimulant products is required. Individual sources of humate have unique and verifiable characteristics. Additionally, methods for prescription of site- specific agricultural inputs in agricultural fields are available. (See US Patents 734867B2, US 90658633B2.) Finally, a method to assure application rate is required through the use of taggants. Sensors using organic solid to liquid phase change nanoparticles of various types and melting temperatures added to the naturally occurring materials provide a barcode. Over 100 types of nanoparticles exist ensuring numerous possible barcodes to reduce industry fraud. Taggant materials can be collected from soil samples of plant material to validate a blockchain of humic, fulvic and other soil amendment products. Other non-organic materials are also available as taggants; however, the organic tags are biodegradable and safe in the environment allowing for use during differing application timeliness.
Compact, high-power superconducting radio-frequency accelerators for environmental applications
Electron-beam irradiation has been proven to destructively reduce or eliminate a wide variety of organic chemicals, viruses and bacteria from wastewater, as well as reducing sulfur and nitrous oxides emission from coal-fired power plants. It is estimated that there are approximately 30,000 such particle accelerators in use worldwide for industrial processes including surface and bulk processing of material, medical sterilization, and environmental remediation. Maximum beam power from accelerators used in these applications is currently limited to less than 500 kW, and a higher beam power is needed to reduce treatment costs. The market availability of continuous-wave (CW) electron-beam accelerators with power of the order of ~100 kW is also very limited. Superconducting radio-frequency (SRF) linear accelerators (linacs) are commonly used at basic research laboratories throughout the world due to their exceptionally high efficiency as compared to current industrial varieties. Recent advances in cryogenics, SRF thin films and high-power magnetrons allow for the design of increasingly compact and efficient CW SRF electron linacs in the energy range 1-10 MeV, with up to 1 MW of beam power. A new class of compact, high-efficiency electron-beam accelerators may provide cost-effective solutions for a range of industrial and environmental remediation applications, particularly with respect to tackling one such class of contaminants so-called forever chemicals, including per- and polyfluoroalkyl substances (PFASs), which are ubiquitous in a wide range of products and for which there is currently no effective destruction technology. We have recently demonstrated the key technologies required for such accelerators by: 1) operating a Nb3Sn SRF accelerating cavity cooled by commercial cryocoolers up to an accelerating gradient of 12.4 MV/m and 2) demonstrating the phase-locking as well as a high-efficiency power combining scheme for industrial magnetron transmitters. In this presentation, we will introduce the principles and benefits of MW-class SRF linacs, based on the conduction-cooled SRF technology that we have demonstrated for environmental remediation. We propose the development of a 4 MeV, 20 kW prototype to be built at Jefferson Lab as a first accelerator demonstrator unit. We will also present the results from samples study on the effect of electron-beam irradiation on so-called forever chemicals such as 1,4-dioxane and PFAS, using an existing multi-purpose 10 MeV, low-power, CW SRF linac at Jefferson Lab.
A Comprehensive Greenhouse Gas Assessment of Biomass-Based Carbon Dioxide Removal and Storage
Biomass-based Carbon Removal and Storage (BiCRS) is a suite of technological pathways that provide a relatively affordable and high-potential way to remove carbon dioxide from the atmosphere and simultaneously re-purpose residual biomass. BiCRS technologies are individually optimized to different biomass feedstocks, temperatures and oxygen, with the primary goal of capturing a high percentage of biomass-derived carbon dioxide and storing it in permanent below-ground storage (i.e. geologic storage), and a secondary goal of producing renewable energy. The current standard for BiCRS life cycle assessments does not account for greenhouse gas (carbon dioxide, methane and nitrous oxide) fluxes from surface soil amendment of BiCRS byproducts (e.g. char, ash) and their alternate fates. This is partly due to a paucity of empirical data due to the novelty of BiCRS conversion technologies, the range in feedstocks, and the heterogeneity of soils to which they might be amended. To fill this gap in in the life-cycle assessment and provide a realistic range of parameters for a soil amendment component of the BiCRS life cycle assessment, we conducted an incubation experiment to measure soil carbon changes, microbial respiration, methane fluxes, and nitrous oxide fluxes from two different soils amended with char- and ash- byproducts from biomass after gasification to hydrogen, fast pyrolysis to bio-oil, and torrefaction. Our results suggest that nitrous oxide and methane emission or consumption from BiCRS char amendments to soil are context dependent. Nitrous oxide emissions from amendments are higher in soils with higher pH, whereas some char amendments to low pH soil can reduce N 2 O emissions relative to control. In contrast, methane is emitted from BiCRS char amended to low pH soil, but consumed or neutral relative to control when amended to high pH soil. We present carbon and nitrogen mass balance throughout the experiment to help understand short-term durability of BiCRS chars and their counterfactuals, and preliminary suggestions for revisions to the broader BiCRS Measurement, Reporting, and Verification framework.
Toward Enhancing Wastewater Treatment with Resource Recovery in Integrated Assessment and Computable General Equilibrium Models
Sustainable water management is essential to increasing water availability and decreasing water pollution. The wastewater sector is expanding globally and beginning to incorporate technologies that recover nutrients from wastewater. Nutrient recovery increases energy consumption but may reduce the demand for nutrients from virgin sources. We estimate the increase in annual global energy consumption (1,100 million GJ) and greenhouse gas emissions (84 million t CO2e) for wastewater treatment in the year 2030 compared to today’s levels to meet sustainable development goals. To capture these trends, integrated assessment and computable general equilibrium models that address the energy-water nexus must evolve. We reviewed 16 of these models to assess how well they capture wastewater treatment plant energy consumption and GHG emissions. Only three models include biogas production from the wastewater organic content. Four explicitly represent energy demand for wastewater treatment, and eight include explicit representation of wastewater treatment plant greenhouse gas emissions. Of those eight models, six models quantify methane emissions from treatment, five include representation of emissions of nitrous oxide, and two include representation of emissions of carbon dioxide. Our review concludes with proposals to improve these models to better capture the energy-water nexus associated with the evolving wastewater treatment sector.
Post-fire soil emissions of nitric oxide (NO) and nitrous oxide (N 2 O) across global ecosystems: a review
Wildfires may increase soil emissions of trace nitrogen (N) gases like nitric oxide (NO) and nitrous oxide (N 2 O) by changing soil physicochemical conditions and altering microbial processes like nitrification and denitrification. When 34 studies were synthesized, we found a significant increase in both NO and N 2 O emissions up to 1 year post-fire across studies spanning ecosystems globally. However, when fluxes were separated by ecosystem type, we found that individual ecosystem types responded uniquely to fire. Forest soils tended to emit more N 2 O after fire, but there was no significant effect on NO. Shrubland soils showed significant increases in both NO and N 2 O emissions after fires; often with extremely large but short-lived NO pulses occurring immediately after fire. Grassland NO emissions increased after fire, but the size of this effect was small relative to shrublands. N 2 O emissions from burned grasslands were highly variable with no significant effect. To better understand the variation in responses to fire across global ecosystems, more consistent measurements of variables recognized as important controls on soil fluxes of NO and N 2 O (e.g., N cycling rates, soil water content, pH, and substrate availability) are needed across studies. We also suggest that fire-specific elements like burn severity, microbial community succession, and the presence of char be considered by future studies. Our synthesis suggests that fires can exacerbate ecosystem N loss long after they burn, increasing soil emissions of NO and N 2 O with implications for ecosystem N loss, climate, and regional air quality as wildfires increase globally.
Coupled machine learning–ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands
Nitrous oxide (N 2 O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N 2 O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high-emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N 2 O fluxes from US cropland. Trained and validated on ~12,000 N 2 O chamber measurements at 17 US Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N 2 O at both training (R 2 = 0.84, RMSE = 16.4 g N ha −1 d −1 ) and held-out testing sites (R 2 = 0.84, RMSE = 6.2 g N ha −1 d −1 ). Analyses identified six dominant N 2 O drivers: soil organic carbon (SOC), NH 4 + , NO 3 - , water-filled pore space, temperature, and aboveground biomass production. Wet, warm soils produced large N 2 O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N 2 O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.
KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments
Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.