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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. How- ever, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to col- lect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, mon- itoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an ex- perimental electrochemical reactor that reduces carbon dioxide (CO 2 ) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Elucidating the Origins of High Capacity in Iron-Based Conversion Materials: Benefit of Complementary Advanced Characterization toward Mechanistic Understanding

Lithium-ion batteries are recognized as an important electrochemical energy storage technology due to their superior volumetric and gravimetric energy densities. Graphite is widely used as the negative electrode, and its adoption enabled much of the modern portable electronics technology landscape. However, developing markets, such as electric vehicles and grid-scale storage, have increased demands, including higher energy content and a diverse materials supply chain. Alternatives that provide the opportunity to increase capacity and address supply chain concerns are of interest. Understanding the fundamental mechanisms that govern battery function is crucial to driving further improvements in the field. Advanced characterization techniques, such as those enabled by synchrotron light sources and high-resolution electron microscopes, that can uncover these mechanisms have become a necessity for elucidating structural evolution upon electrochemical conversion at the nano- to mesoscales. Performing these experiments with relevant electrochemistry using in situ and operando experiments imparts the ability to identify critical reaction pathways and capture intermediate (dis)charge products not discernible by traditional experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Dynamic Life-Cycle Analysis of Fast Pyrolysis Biorefineries: Impacts of Feedstock Moisture Content and Particle Size

The commercialization of biorefineries has been met with significant obstacles due to the technical difficulties of handling solid biomass feedstocks, which are highly variable in physical properties and chemical compositions. An understanding of the principles of unit operations along the supply chain is key to the success of the biofuel industry. This study applies a dynamic life-cycle analysis (DLCA) methodology by developing quantitative relationships between the inputs and outputs for each unit operation based on scientific understanding of the causal effects. Using the DLCA, we assessed system sustainability of drop-in fuel production from fast pyrolysis of pine residues followed by hydro-processing. Life-cycle greenhouse gas (GHG) emissions were calculated for 4641 runs involving two key feedstock parameters: moisture content after field drying and particle size of the feed to pyrolyzer. Moisture contents varied between 25% and 35%, while particle sizes varied between 0.5 and 5 mm in these runs. The life-cycle GHG emissions of these cases vary from 22 to 40 g/MJ. Close examination of each unit operation's contribution to the total GHG emissions reveals that low moisture content after field drying produces savings in energy consumption during feedstock transportation and on-site drying. However, although small particle size leads to overall higher fuel yield, it also requires a significant amount of energy for feedstock size reduction and fuel production. The energy penalty outweighs the benefit of increased fuel yields, especially when fine particles (<1 mm) are used for pyrolysis; thus, small particle size leads to increased GHG emissions overall. The results highlight the trade-offs between the energy demand for preprocessing and the conversion yields, which can be addressed with DLCA.

09 BIOMASS FUELS↗
DOE OSTI2023

Durable Module Materials (DuraMAT) Consortium (Final Technical Report)

The DuraMAT Consortium brings together DOE national lab and university research capabilities with the photovoltaic (PV) and supply-chain industries to accelerate a sustainable, just, and equitable transition to zero carbon electricity generation by 2035 through our five core objectives: development of a central data resource for PV modules, multi-scale and multi-physics modeling, disruptive acceleration science, forensic tools for fielded modules, and materials solutions for more durable, reliable, and resilient modules.

14 SOLAR ENERGY↗
DOE OSTI2024

The Screening Tool for Industrial Resilience: Risk-Informed Decision Making to Support Resilience Planning

The Screening Tool for Industrial Resilience (STIR) helps small and medium-sized manufacturing plants manage risk to their production. Developed by the Pacific Northwest National Laboratory under direction and funding from the Department of Energy’s Office of Manufacturing and Energy Supply Chains, the STIR helps manufacturers enhance their resilience to a variety of disruptive events, both natural and human-caused, that could interrupt normal operations. This report provides an overview of the STIR’s risk-informed, high-level approach to resilience planning, which identifies potential solutions that could enhance site resilience based on calculated major risk drivers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Biofuel Air Emissions Analysis

U.S. has goals to produce 3 billion gallons of sustainable aviation fuel (SAF) annually by 2030, increasing to 35 billion gallons by 2050 to decarbonize the aviation sector. Meeting these production targets will require large number of biorefineries to be set up. However, ability to comply with federal air quality standards is prerequisite to being issued a construction permit. Negotiating the permitting process for a new biorefinery can be quite onerous and cost the investors significant time and money. Although there can be numerous reasons for delayed biorefinery construction, air permitting is fraught with pitfalls because the permitting process relies on precedence, which the future SAF biorefineries lack. NREL's Biofuel Air Emissions Analysis project is unique and innovative in terms of the tools, approaches, and analyses provided. NREL is the only national laboratory that is actively working at the intersection of Federal air quality regulations, emissions and air quality analysis across the supply chain, and process design. This project is focused on providing much needed data and analyses that address biorefinery air permitting. This project develops models and quantitative analyses and measures progress towards meeting air quality regulatory requirements. These models and methods are applied to analyze air permitting related to wastewater sludge to biofuel conversion pathways using hydrothermal liquefaction (HTL), impacts of HTL pathway on local and regional air quality including an assessment of health and equity impacts. In addition to filling research gaps, this project also disseminates the findings to the relevant stakeholders at BETO, other national labs, and regulatory agencies.

air quality↗
NASA NTRS1988

Automatic Detection of Electric Power Troubles (ADEPT)

ADEPT is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system, and is designed for two modes of operation: real-time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a Laser printer. This system consists of a simulated Space Station power module using direct-current power supplies for Solar arrays on three power busses. For tests of the system's ability to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three busses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modelling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base. A load scheduler and a fault recovery system are currently under development to support both modes of operation.

Wang, Caroline↗
NASA NTRS1988

Automatic Detection of Electric Power Troubles (ADEPT)

Automatic Detection of Electric Power Troubles (A DEPT) is an expert system that integrates knowledge from three different suppliers to offer an advanced fault-detection system. It is designed for two modes of operation: real time fault isolation and simulated modeling. Real time fault isolation of components is accomplished on a power system breadboard through the Fault Isolation Expert System (FIES II) interface with a rule system developed in-house. Faults are quickly detected and displayed and the rules and chain of reasoning optionally provided on a laser printer. This system consists of a simulated space station power module using direct-current power supplies for solar arrays on three power buses. For tests of the system's ablilty to locate faults inserted via switches, loads are configured by an INTEL microcomputer and the Symbolics artificial intelligence development system. As these loads are resistive in nature, Ohm's Law is used as the basis for rules by which faults are located. The three-bus system can correct faults automatically where there is a surplus of power available on any of the three buses. Techniques developed and used can be applied readily to other control systems requiring rapid intelligent decisions. Simulated modeling, used for theoretical studies, is implemented using a modified version of Kennedy Space Center's KATE (Knowledge-Based Automatic Test Equipment), FIES II windowing, and an ADEPT knowledge base.

Wang, Caroline↗
NASA NTRS2012

A Proposed Strategy for the U.S. to Develop and Maintain a Mainstream Capability Suite ("Warehouse") for Automated/Autonomous Rendezvous and Docking in Low Earth Orbit and Beyond

The ability of space assets to rendezvous and dock/capture/berth is a fundamental enabler for numerous classes of NASA fs missions, and is therefore an essential capability for the future of NASA. Mission classes include: ISS crew rotation, crewed exploration beyond low-Earth-orbit (LEO), on-orbit assembly, ISS cargo supply, crewed satellite servicing, robotic satellite servicing / debris mitigation, robotic sample return, and robotic small body (e.g. near-Earth object, NEO) proximity operations. For a variety of reasons to be described, NASA programs requiring Automated/Autonomous Rendezvous and Docking/Capture/Berthing (AR&D) capabilities are currently spending an order-of-magnitude more than necessary and taking twice as long as necessary to achieve their AR&D capability, "reinventing the wheel" for each program, and have fallen behind all of our foreign counterparts in AR&D technology (especially autonomy) in the process. To ensure future missions' reliability and crew safety (when applicable), to achieve the noted cost and schedule savings by eliminate costs of continually "reinventing the wheel ", the NASA AR&D Community of Practice (CoP) recommends NASA develop an AR&D Warehouse, detailed herein, which does not exist today. The term "warehouse" is used herein to refer to a toolbox or capability suite that has pre-integrated selectable supply-chain hardware and reusable software components that are considered ready-to-fly, low-risk, reliable, versatile, scalable, cost-effective, architecture and destination independent, that can be confidently utilized operationally on human spaceflight and robotic vehicles over a variety of mission classes and design reference missions, especially beyond LEO. The CoP also believes that it is imperative that NASA coordinate and integrate all current and proposed technology development activities into a cohesive cross-Agency strategy to produce and utilize this AR&D warehouse. An initial estimate indicates that if NASA strategically coordinates the development of a robust AR&D capability across the Agency, the cost of implementing AR&D on a spacecraft could be reduced from roughly $70M per mission to as low as $7M per mission, and the associated development time could be reduced from 4 years to 2 years, after the warehouse is completely developed. Table 1 shows the clear long-term benefits to the Agency in term of costs and schedules for various missions. (The methods used to arrive at the Table 1 numbers is presented in Appendices A and B.)

Krishnakumar, Kalmanje S.↗
DOE OSTI2016

Supply Chain Sustainability Analysis of Indirect Liquefaction of Blended Biomass to Produce High Octane Gasoline

This report describes the SCSA of the production of renewable high octane gasoline (HOG) via indirect liquefaction (IDL) of lignocellulosic biomass. This SCSA was developed for both the 2015 SOT (Hartley et al., 2015; ANL, 2016; DOE, 2016) and the 2017 design case for feedstock logistics (INL, 2014) and for both the 2015 SOT (Tan et al., 2015a) and the 2022 target case for HOG production via IDL (Tan et al., 2015b). The design includes advancements that are likely and targeted to be achieved by 2017 for the feedstock logistics and 2022 for the IDL conversion process. In the SCSA, the 2015 SOT case for the conversion process, as modeled in Tan et al. (2015b), uses the 2015 SOT feedstock blend of pulpwood, wood residue, and construction and demolition waste (C&D). Moreover, the 2022 design case for the conversion process, as described in Tan et al. (2015a), uses the 2017 design case blend of pulpwood, wood residue, switchgrass, and C&D. The performance characteristics of this blend are consistent with those of a single woody feedstock (e.g., pine or poplar). We also examined the influence of using a single feedstock type on SCSA results for the design case. These single feedstock scenarios could be viewed as bounding SCSA results given that the different components of the feedstock blend have varying energy and material demands for production and logistics.

09 BIOMASS FUELS↗
DOE OSTI2020

Rechargeable alkaline zinc–manganese oxide batteries for grid storage: Mechanisms, challenges and developments

Rechargeable alkaline Zn–MnO 2 (RAM) batteries are a promising candidate for grid-scale energy storage owing to their high theoretical energy density rivaling lithium-ion systems (~400 Wh/L), relatively safe aqueous electrolyte, established supply chain, and projected costs below $100/kWh at scale. In practice, however, many fundamental chemical and physical processes at both electrodes make it difficult to achieve commercially competitive energy density and cycle life. In this paper, we present a detailed and timely analysis of the constituent materials, current commercial status, electrode processes, and performance-limiting factors of RAM batteries. We also examine recently reported strategies in RAM and related systems to address these issues through additives and modifications to the electrode materials and electrolyte, special ion-selective separators and/or coatings, and unconventional cycling protocols. We conclude with a critical summary of these developments and discussion of how future studies should be focused toward the goal of energy-dense, scalable, and cost-effective RAM systems.

25 ENERGY STORAGE↗
DOE OSTI2013

Floating Silicon Method

The Floating Silicon Method (FSM) project at Applied Materials (formerly Varian Semiconductor Equipment Associates), has been funded, in part, by the DOE under a “Photovoltaic Supply Chain and Cross Cutting Technologies” grant (number DE-EE0000595) for the past four years. The original intent of the project was to develop the FSM process from concept to a commercially viable tool. This new manufacturing equipment would support the photovoltaic industry in following ways: eliminate kerf losses and the consumable costs associated with wafer sawing, allow optimal photovoltaic efficiency by producing high-quality silicon sheets, reduce the cost of assembling photovoltaic modules by creating large-area silicon cells which are free of micro-cracks, and would be a drop-in replacement in existing high efficiency cell production process thereby allowing rapid fan-out into the industry.

14 SOLAR ENERGY↗
DOE OSTI2021

Federal Oversight of Hydrogen Systems

The application of hydrogen as an energy carrier has been expanding into industrial and transportation sectors enabling sustainable energy resources and providing a zero-emission energy infrastructure. The hydrogen supply infrastructure includes processes from production and storage, to transportation and distribution, to end use. Each portion of the hydrogen supply infrastructure is regulated by international, federal, state, and local entities. Regulations are enforced by entities which provide guidance and updates as necessary. While energy sources such as natural gas are currently regulated via the Code of Federal Regulations and United States Code, there might be some ambiguity as to which regulations are applicable to hydrogen and where regulatory gaps may exist. This report contains an overview of the regulations that apply to hydrogen, and those that may indirectly cover hydrogen as an energy carrier participating in a sustainable zero emission global energy system. As part of this effort, the infrastructure of hydrogen systems and regulation enforcement entities are defined, and a visual map and reference table are developed. This regulatory map and table can be used to identify the boundaries of federal oversight for each component of the hydrogen supply value chain which includes production, storage, distribution, and use.

08 HYDROGEN↗
DOE OSTI2021

Remote methane sensor for emissions from pipelines and compressor stations using chirped-laser dispersion spectroscopy

Leak rates of methane (CH 4 ) from the natural gas supply chain result in lost profit from unsold product, public safety and property concerns due to potential explosion hazards, and a potentially large source of economic damages from legal liabilities. Yet large measurement challenges exist in identifying and quantifying CH 4 leak rates along the vast number and type of components in the natural gas supply chain. This is particularly true of the “midstream” components involved in the gathering, processing, compression, transmission, and storage of natural gas. This project developed and deployed new advances in chirped laser dispersion spectroscopy (CLaDS) to detect methane leaks from pipelines, compressor stations, and other midstream infrastructure from a remote position (standoff detection). The system was deployed from a van to measure fugitive methane leaks from a local compressor station as a proof-of-concept. The technique was validated through mobile laboratory measurements with in-situ sensors as well as controlled releases of methane. The system also mapped a plume from a controlled release of methane by tracking a small unmanned aerial system (sUAS) that carried a corner cube retroreflector which reflected the beam back to the instrument. The drone-based system quantified a leak rate to within 30% of the actual rate and localized the emission location within 5 m of the actual release location at standoff distances of 25-45 m. This sUAS-reflector tracking approach has benefits for mapping leak locations remotely using small drones flying around a facility. Benefits of a commercial sensor with these capabilities include reductions of leaks for pipeline operators (more profit), earlier detection of leaks to avoid catastrophic explosion hazards for public health and to mitigate property damage, and reduced methane emissions to the atmosphere (improving air quality).

03 NATURAL GAS↗
DOE OSTI2016

Rating Correlations Between Customs Codes and Export Control Lists: Assessing the Needs and Challenges

Correlation tables are the linchpins between the customs codes used to classify commodities in international trade and the control lists used for strategic trade control (STC) purposes. While understanding the customs classification system can help the STC community better understand strategic trade flows, better identify which trade operations require permits, and more effectively detect illegal exports, the two systems are different in scope, philosophy, content, and objectives. Many indications point to the limitations of these correlation tables, and it is important to understand the nature of the limitations and the complex underlying reasons to conceive possible improvements. As part of its Strategic Trade and Supply Chain Analytics Initiative, Argonne National Laboratory supported a study of a subset of the European Union’s TARIC correlation table. The study included development of a methodology and an approach to rating the quality and relevance of individual correlations. The study was intended as a first step to engage the STC community in deflections and initiatives to improve the conception and use of correlations, and its conclusions illustrate the scope and complex nature of the challenges to overcome. This paper presents the two classification systems, analyzes the needs for correlation tables and the complex challenges associated with them, summarizes key findings, and proposes possible ways forward.

commodity classification↗
DOE OSTI2024

Challenges and Opportunities for Floating Offshore Wind Energy in Ultradeep Waters of the Central Atlantic

This study, funded under an interagency agreement between the U.S. Department of Energy's (DOE) National Renewable Energy Laboratory (NREL) and Bureau of Ocean Energy Management (BOEM), is intended to provide BOEM with key information to inform their decision making about current and future leasing in the Central Atlantic region of the United States. The report will also benefit state governments, developers, research institutions, and the public which are seeking technical and market-based information about the unique aspects of the offshore wind energy development along the outer continental shelf of the Central Atlantic region of the United States. The study provides a broad top-level assessment of the key challenges and opportunities that are unique to offshore wind energy development in the Central Atlantic region. It focuses on BOEM's Central-Atlantic region Call areas. The research is based on the most current technology, deployment, and stakeholder information available to NREL. The topics include assessments of the physical environment, current leasing status and major stakeholder issues, state and federal energy policy, an assessment of future leasing requirements based on state targets, status and limitations of the technology, and supply chain status. The primary intent is to inform the readers about the prospects for deploying offshore wind in the designated deep water Call areas, E and F, identified by BOEM. The report makes recommendations regarding development in these regions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

A multi-objective programming model for assessment the GHG emissions in MSW management

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application of the model in a Greek region.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Assessing Nutrient and Carbon Responses to Agricultural Conservation Practices in Two Midwest Watersheds

The United States is undertaking efforts to transition to a low-carbon economy in response to the heightened impacts of climate change, which are largely attributed to decades of carbon-intensive development. A concerted effort by the energy sector to achieve net-zero carbon emissions is aimed at strategically reducing the carbon footprint of the energy supply chain, particularly in the production of feedstocks for biofuels. The production of biofuels relies heavily on land use and management practices in biomass cultivation. Among other factors, soil organic carbon (SOC) plays a crucial role in the biofuel carbon cycle, impacting land productivity, greenhouse gas emissions, and water quality. The process of carbon drawdown during plant growth and storage helps to mitigate the release of carbon dioxide (CO 2 ) into the atmosphere. Another key parameter is the release of nitrous oxide (N 2 O) — a potent greenhouse gas with a global warming potential 273 times that of CO 2 — from soil. While prioritizing low-carbon production, sustainable bioenergy also necessitates improved water quality and ecosystem services. Agricultural conservation practices can contribute to enhanced soil health and reduce nutrient and sediment loss into streams. However, studies that explore the broader impact of these practices on soil carbon storage and N 2 O emissions at a watershed scale are limited. Specifically, our understanding of how low-carbon feedstock production and management affect nutrient cycle dynamics is incomplete. This study assesses watershed responses to land management practices in two agriculturally dominant watersheds: the Raccoon River watershed and the Southfork of Iowa River watershed. The study focuses on carbon and nutrient dynamics, characterizing spatial and temporal variations in SOC, N 2 O, and nutrient loadings to examine the relationship between nutrient and carbon responses and agricultural conservation practices. The study employs the newly developed Soil Water Analysis Tool for Carbon (SWAT-C) model, calibrated using 20 years’ of climate and water monitoring data, to simulate and evaluate two agricultural conservation practices: no-till and crop residue harvest with cover crop planting. We compared the calibrated SWAT-C model with a historical baseline model, which allowed us to analyze various aspects of the watershed, including stream flow, suspended sediments, nitrogen, phosphorus, organic carbon, SOC at different depths, and N 2 O emissions from topsoil. Our SWAT-C modeling results indicate that, compared with results from the historical baseline model, no-till practices are positively correlated with SOC accumulation, reduced N 2 O emissions, and decreased soil loss. We also observed no change or slightly increased nitrogen and phosphorus loss to water bodies in the watersheds compared with the baseline. Conversely, crop residue harvest with cover crop planting improved water quality by reducing nutrient and soil losses but also increased N 2 O emissions. This SWAT-C modeling study establishes the groundwork for further smaller-scale and/or sub-basin-level assessments of nitrogen, phosphorus, and carbon cycles. It provides valuable science-based insights to inform policy decisions — particularly in the context of transitioning to biofuel feedstock production in these watersheds — by addressing concerns about nutrient exports to local water bodies and, ultimately, the Mississippi River.

54 ENVIRONMENTAL SCIENCES↗