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DOE OSTI2025

Treatment of antibiotic-manufacturing wastewater enriches for Aeromonas veronii, a zoonotic antibiotic-resistant emerging pathogen

Antibiotic-manufacturing wastewater treatment plants primarily target chemical pollutants, but their processes may select for antibiotic-resistant pathogens and antibiotic resistance genes. Leveraging the combined strengths of deep metagenomic sequencing, 16S rRNA gene sequencing, quantitative polymerase chain reaction, and bacterial culturing, we investigated bacterial communities and antibiotic resistomes across eleven treatment units in a full-scale antibiotic-manufacturing wastewater treatment plant processing wastewater from a β-lactam manufacturing facility. Both bacterial communities and antibiotic resistance gene compositions varied across the treatment units, but were associated. Certain antibiotic resistance gene persisted through treatment, either carried by identical bacterial species, or linked to mobile genetic elements in different species. Despite the satisfactory performance in chemical removal, this plant continuously enriched zoonotic antibiotic-resistant Aeromonas veronii (an emerging pathogen responsible for substantial economic losses in aquaculture and human health) from influent to effluent, probably due to prolonged β-lactam selection pressure and aquatic nature of A. veronii. This enrichment resulted in a significantly higher abundance of A. veronii than other aquatic samples worldwide. Furthermore, the closest evolutionary relative to the retrieved A. veronii was an isolate obtained from the stool of a local diarrhea patient. These findings highlighted a substantial public health risk posed by antibiotic-manufacturing wastewater treatment, underlining its potential role in enriching and disseminating zoonotic antibiotic-resistant pathogens. Beyond chemical monitoring, enhanced surveillance of antibiotic-resistant pathogens and antibiotic resistance genes is needed in effluent discharge standard for antibiotic-manufacturing wastewater treatment plants.

Wang, Xingshuo↗

DOE OSTI2025

PSA 2025 DPRA for Cyber Optimization

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Evaluating defense options should include quantitative evaluation of overall effectiveness to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation and have difficulty with time dependent scenarios. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related integrity is a requirement set by the U.S. Nuclear Regulatory Commission. But companies are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want reliability analysis while optimizing cost, which requires more than safety modeling methods. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with timing and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues such as state-base explosion found in Markov-based tools. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies.

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Efficient Low-Temperature Direct Lithium Extraction from Chloride Brines Enabled by High-Capacity Sorbent

The demand for lithium, a key component in rechargeable batteries for electric vehicles and renewable energy storage systems, has surged in recent years. Meeting this demand requires efficient extraction methods that are both environmentally friendly and economically viable. This study investigates the utilization of the high-capacity sorbent amorphous aluminum hydroxide for enabling efficient direct lithium extraction at low temperatures. Traditional extraction methods often involve acid-leaching and energy-intensive processes that are not only expensive but also environmentally taxing. In contrast, our approach leverages the exceptional sorption properties of amorphous aluminum hydroxide to facilitate lithium extraction directly from brine, achieving extraction efficiencies of 94.4% in case 1 and 96.2% in case 2 at low temperatures. Kinetic modeling using the Avrami–Erofe’ev framework reveals a nucleation-growth mechanism ( n = 0.71, k = 0.131 h –1 ), providing quantitative insights into the solid-state phase transformation process. This method significantly reduces energy consumption and minimizes the environmental footprint. Through systematic experimentation and optimization, we demonstrate the effectiveness and scalability of our approach, highlighting its potential to revolutionize lithium extraction processes. Our findings highlight the potential of high-capacity sorbents, particularly amorphous aluminum hydroxide toward sustainable lithium production, contributing to the advancement of clean-energy technologies.

anions↗

DOE OSTI2025

Processing behavior evolution of recycled polypropylene: An integrated experimental and Computer-Aided engineering simulation study

Polypropylene (PP) comprises 21% of global plastics production and 18% of plastics waste, yet less than 1% of solid-waste PP is recycled in the United States (U.S.), representing significant environmental and economic challenges. Mechanical recycling, the most prevalent recycling method, subject's materials to thermomechanical stresses, which typically degrade polymer properties, affecting the quality of polymer products. This study replicates the impact of mechanical recycling through multiple extrusion cycles to examine the effects on PP's processing behavior. Dynamic scanning calorimetry (DSC) measurements showed stable melting behavior across all processing conditions, while crystallization analysis exhibited consistent shifts in kinetic parameters. Rheological characterization demonstrated progressive viscosity reductions through successive cycles, particularly pronounced at elevated reprocessing temperatures. Here, the integration of this experimental data into injection molding simulations showed that recycled PP maintains viable processing characteristics. Our findings establish quantitative correlations between processing history and material behavior, enabling optimization of processing parameters directly rather than relying on trial-and-error approaches. While these results reflect idealized recycling conditions with minimal contamination, they provide a framework for understanding fundamental property evolution during mechanical recycling.

42 ENGINEERING↗

DOE OSTI2024

Analyzing Potential Failures and Effects in a Pilot-Scale Biomass Preprocessing Facility for Improved Reliability

This study demonstrates a failure identification methodology applied to a preprocessing facility generating conversion-ready feedstocks from biomass meeting conversion process critical quality attribute (CQA) specifications. Failure Modes and Effects Analysis (FMEA) was used as an industrially relevant risk analysis approach to evaluate a logging residue preprocessing system to prepare feedstock for pyrolysis conversion. Risk evaluations considered both system-level and operation unit-level assessments considering process efficiency, product quality, cost, sustainability, and safety. Key outputs included estimations of semi-quantitative risk scores for each failure, identification of the failure impacts, identification of failure causes associated with material attributes and process parameters, ranking success rates of failure detection methods, and speculation of potential mitigation strategies for decreasing failure risk scores. Results showed that deviations from moisture specifications had cascading consequences for other CQAs along with process safety implications. Failures linked to fixed carbon specifications carried the highest risk scores for product quality and process efficiency impacts. As increased throughput can be inversely related to meeting product quality specifications; achieving throughput and other material-based CQAs simultaneously will likely require system optimization or prioritization based on system economics. Ultimately, this work successfully demonstrates FMEA as a risk analysis approach for other bioenergy process systems.

09 BIOMASS FUELS↗

NASA NTRS2011

Performance Analysis of Cloud Computing Architectures Using Discrete Event Simulation

Cloud computing offers the economic benefit of on-demand resource allocation to meet changing enterprise computing needs. However, the flexibility of cloud computing is disadvantaged when compared to traditional hosting in providing predictable application and service performance. Cloud computing relies on resource scheduling in a virtualized network-centric server environment, which makes static performance analysis infeasible. We developed a discrete event simulation model to evaluate the overall effectiveness of organizations in executing their workflow in traditional and cloud computing architectures. The two part model framework characterizes both the demand using a probability distribution for each type of service request as well as enterprise computing resource constraints. Our simulations provide quantitative analysis to design and provision computing architectures that maximize overall mission effectiveness. We share our analysis of key resource constraints in cloud computing architectures and findings on the appropriateness of cloud computing in various applications.

Stocker, John C.↗

DOE OSTI2026

Performing Numerical Analysis of Cybersecurity Options Using Dynamic Risk Analysis Tool EMRALD

Cyberattacks can have many different attack paths, durations, and goals. There are also many different mitigation options involving hardware, software, and/or humans. Considering a cyber threat should involve defense-in-depth methods and a quantitative or numerical evaluation of overall effectiveness against dynamic, time-dependent attacks to make cost and risk-informed decisions. Typical cyberattack modeling methods only provide a qualitative evaluation. The main areas of cybersecurity are confidentiality, integrity, and availability. For companies with cyber-physical systems such as advanced nuclear reactors, cyber-related safety is a requirement set by North American Electric Reliability and the U.S. Nuclear Regulatory Commission. They are also concerned about availability or reliability as a business case. As cyber threats are evolving to a business-for-hire structure, more attacks may focus on disrupting business success and reliability, causing financial and economic stability risk. Companies want to know business reliability and recovery from those threats, and that requires modeling physical behavior of the targets. Dynamic-state-based and Markov-based modeling provides a method for better cyber scenario modeling with different tools having issues such as state-base explosion. Dynamic modeling enables time and conditional features not found in other numerical evaluation methods. EMRALD (Event Modeling Risk Assessment using Lined Diagrams) is a dynamic risk analysis modeling and simulation tool and has features that reduce modeling issues. It has been used to model different time-dependent events including plant behavior and operator procedures. As a general modeling tool, EMRALD can also be used to model cyberattack scenarios with varying mitigation options and quantify effectiveness, producing numerical data for risk-informed decisions. This paper uses EMRALD to demonstrate that dynamic numerical risk analysis can be used for cyber threat modeling to provide insights for design decision-making and optimize defense strategies. Keywords: cyber modeling; cyber-physical systems; numerical cyber modeling

97 - MATHEMATICS AND COMPUTING↗

NASA NTRS1992

The costs of introducing new technologies into space systems

A review is conducted of cost-research studies intended to provide guidelines for cost estimates of integrating new technologies into existing satellite systems. Quantitative methods are described for determining the technological state-of-the-art so that proposed programs can be evaluated accurately in terms of their contribution to technological development. The R&D costs associated with the proposed programs are then assessed with attention given to the technological advances. Also incorporated quantifiably are any reductions in the costs of production, operations, and support afforded by the advanced technologies. The proposed model is employed in relation to a satellite sizing and cost study in which a tradeoff between increased R&D costs and reduced production costs is examined. The technology/cost model provides a consistent yardstick for assessing the true relative economic impact of introducing novel techniques and technologies.

Dodson, E. N.↗

NASA NTRS2011

Space Weather, Geomagnetic Disturbances and Impact on the High-Voltage Transmission Systems

Geomagnetically induced currents (GIC) affecting the performance of high-voltage power transmission systems are one of the most significant hazards space weather poses on the operability of critical US infrastructure. The severity of the threat was emphasized, for example, in two recent reports: the National Research Council (NRC) report "Severe Space Weather Events--Understanding Societal and Economic Impacts: A Workshop Report" and the North American Electric Reliability Corporation (NERC) report "HighImpact, Low-Frequency Event Risk to the North American Bulk Power System." The NRC and NERC reports demonstrated the important national security dimension of space weather and GIC and called for comprehensive actions to forecast and mitigate the hazard. In this paper we will give a brief overview of space weather storms and accompanying geomagnetic storm events that lead to GIC. We will also review the fundamental principles of how GIC can impact the power transmission systems. Space weather has been a subject of great scientific advances that have changed the wonder of the past to a quantitative field of physics with true predictive power of today. NASA's Solar Shield system aimed at forecasting of GIC in the North American high-voltage power transmission system can be considered as one of the ultimate fruits of those advances. We will review the fundamental principles of the Solar Shield system and provide our view of the way forward in the science of GIC.

Pullkkinen, A.↗

DOE OSTI2025

Sustainable Triacetic Acid Lactone Production from Sugarcane by Fermentation and Crystallization

Triacetic acid lactone (TAL) has the potential to serve as a bioderived platform chemical for commercial products including sorbic acid and recyclable polydiketoenamine plastics. In this study, we leveraged BioSTEAM to design, simulate, and evaluate (via techno-economic analysis, TEA, and life cycle assessment, LCA) TAL production from sugarcane. We experimentally characterized TAL solubility, calibrated solubility models, and designed a process to separate TAL from fermentation broths by crystallization. The biorefinery could produce TAL at a minimum product selling price (MPSP) of $\$3.73$–5.86·kg –1 (5th–95th percentiles; baseline at $\$4.60$·kg –1 ) and a carbon intensity (CI) of 5.31 [2.60–8.71] kg CO 2 -eq·kg –1 , which could enable financially viable, low-CI production of sorbic acid and polydiketoenamines. To drive down costs and CI, we explored the theoretical fermentation space (titer, yield, productivity combinations), operation scheduling and capacity expansion strategies (e.g., integrated sorghum processing), and potential separation improvements (mitigating TAL loss through pH control). Advancements in key design and technological parameters could further reduce MPSP by 51% to $\$2.26$·kg –1 [$\$1.97$–2.80·kg –1 ] and CI by 43% to 3.05 [1.91–4.15] kg CO 2 -eq·kg –1 . This research highlights the ability of agile TEA-LCA to screen promising designs, navigate sustainability trade-offs, prioritize research needs, and chart quantitative roadmaps to advance bioproducts and biofuels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2005

Spatial Predictive Modeling and Remote Sensing of Land Use Change in the Chesapeake Bay Watershed

This project was focused on modeling the processes by which increasing demand for developed land uses, brought about by changes in the regional economy and the socio-demographics of the region, are translated into a changing spatial pattern of land use. Our study focused on a portion of the Chesapeake Bay Watershed where the spatial patterns of sprawl represent a set of conditions generally prevalent in much of the U.S. Working in the region permitted us access to (i) a time-series of multi-scale and multi-temporal (including historical) satellite imagery and (ii) an established network of collaborating partners and agencies willing to share resources and to utilize developed techniques and model results. In addition, a unique parcel-level tax assessment database and linked parcel boundary maps exists for two counties in the Maryland portion of this region that made it possible to establish a historical cross-section time-series database of parcel level development decisions. Scenario analyses of future land use dynamics provided critical quantitative insight into the impact of alternative land management and policy decisions. These also have been specifically aimed at addressing growth control policies aimed at curbing exurban (sprawl) development. Our initial technical approach included three components: (i) spatial econometric modeling of the development decision, (ii) remote sensing of suburban change and residential land use density, including comparisons of past change from Landsat analyses and more traditional sources, and (iii) linkages between the two through variable initialization and supplementation of parcel level data. To these we added a fourth component, (iv) cellular automata modeling of urbanization, which proved to be a valuable addition to the project. This project has generated both remote sensing and spatially explicit socio-economic data to estimate and calibrate the parameters for two different types of land use change models and has undertaken analyses of these models. One (the CA model) is driven largely by observations on past patterns of land use change, while the other (the EC model) is driven by mechanisms of the land use change decision at the parcel level. Our project may be the first serious attempt at developing both types of models for the same area, using as much common data as possible. We have identified the strengths and weaknesses of the two approaches and plan to continue to revise each model in the light of new data and new lessons learned through continued collaboration. Questions, approaches, findings, publication and presentation lists concerning the research are also presented.

Goetz, Scott J.↗

DOE OSTI2024

Reducing the cost of home energy upgrades in the US: An industry survey

Decarbonizing the US residential building stock requires a substantial acceleration in home energy upgrades. Numerous barriers exist to accelerating adoption of efficient and electric building technologies, but foremost among these is high upfront costs. This study uses an industry survey delivered to a sample of home energy professionals to examine promising cost reduction strategies across a range of project types, including HVAC, water heating, and envelope/insulation projects. The survey included quantitative and qualitative questions to collect evidence on the estimated cost reduction potential of these strategies and their likelihood of use in the construction industry. The 167 survey respondents included contractors, energy consultants, architects, manufacturers, and others with experience in delivering energy upgrades in single-family and multifamily buildings in the US. Results show that significant cost reductions are achievable by minimizing additional infrastructure costs (such as replacing electric panels), streamlining project planning/management, and deploying innovations that simplify installation. We find that for a typical deep retrofit project, including heat pumps for space and water heating in addition to envelope upgrades, the strategies could result in a total installed cost reduction of nearly 50%, dramatically improving the customer economics of such a project. This research makes a novel contribution to the literature on strategies to reduce the costs of residential retrofits. We discuss how our study's insights on the highest-value cost reduction strategies for home energy upgrades can further accelerate their uptake in the US housing stock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2023

The Hazard Components of Representative Key Risks. The Physical Climate Perspective

The framework of Representative Key Risks (RKRs) has been adopted by the Intergovernmental Panel on Climate Change Working Group II (WGII) to categorize, assess and communicate a wide range of regional and sectoral key risks from climate change. These are risks expected to become severe due to the potentially detrimental convergence of changing climate conditions with the exposure and vulnerability of human and natural systems. Other papers in this special issue treat each of eight RKRs holistically by assessing their current status and future evolution as a result of this convergence. However, in these papers, such assessment cannot always be organized according to a systematic gradation of climatic changes. Often the big-picture evolution of risk has to be extrapolated from either qualitative effects of “low”, “medium” and “high” warming, or limited/focused analysis of the consequences of particular mitigation choices (e.g., benefits of limiting warming to 1.5 or 2C), together with consideration of the socio-economic context and possible adaptation choices. In this study we offer a representation – as systematic as possible given current literature and assessments – of the future evolution of the hazard components of RKRs. We identify the relevant hazards for each RKR, based upon the WGII authors’ assessment, and we report on their current state and expected future changes in magnitude, intensity and/or frequency, linking these changes to Global Warming Levels (GWLs) to the extent possible. We draw on the assessment of changes in climatic impact-drivers relevant to RKRs described in the 6th Assessment Report by Working Group I supplemented when needed by more recent literature. For some of these quantities - like regional trends in oceanic and atmospheric temperature and precipitation, some heat and precipitation extremes, permafrost thaw and Northern Hemisphere snow cover - a strong and quantitative relationship with increasing GWLs has been identified. For others - like frequency and intensity of tropical cyclones and extra-tropical storms, and fire weather - that link can only be described qualitatively. For some processes - like the behavior of ice sheets, or changes in circulation dynamics - large uncertainties about the effects of different GWLs remain, and for a few others - like ocean pH and air pollution - the composition of the scenario of anthropogenic emissions is most relevant, rather than the warming reached. In almost all cases, however, the basic message remains that every small increment in CO2 concentration in the atmosphere and associated warming will bring changes in climate phenomena that will contribute to increasing risk of impacts on human and natural systems, in the absence of compensating changes in these systems’ exposure and vulnerability, and in the absence of effective adaptation. Our picture of the evolution of RKR-relevant climatic impact-drivers complements and enriches the treatment of RKRs in the other papers in at least two ways: by filling in their often only cursory or limited representation of the physical climate aspects driving impacts, and by providing a fuller representation of their future potential evolution, an important component – if never the only one – of the future evolution of risk severity.

Representative Key Risks↗

NASA NTRS2009

Discrimination of Spore-Forming Bacilli Using spoIVA

A method of discriminating between spore-forming and non-spore-forming bacteria is based on a combination of simultaneous sporulation-specific and non-sporulation-specific quantitative polymerase chain reactions (Q-PCRs). The method was invented partly in response to the observation that for the purposes of preventing or reducing biological contamination affecting many human endeavors, ultimately, only the spore-forming portions of bacterial populations are the ones that are problematic (or, at least, more problematic than are the non-spore-forming portions). In some environments, spore-forming bacteria constitute small fractions of the total bacterial populations. The use of sporulation-specific primers in Q-PCR affords the ability to assess the spore-forming fraction of a bacterial population present in an environment of interest. This assessment can provide a more thorough and accurate understanding of the bacterial contamination in the environment, thereby making it possible to focus contamination- testing, contamination-prevention, sterilization, and decontamination resources more economically and efficiently. The method includes the use of sporulation-specific primers in the form of designed, optimized deoxyribonucleic acid (DNA) oligonucleotides specific for the bacterial spoIVA gene (see table). [In "spoIVA," "IV" signifies Roman numeral four and the entire quoted name refers to gene A for the fourth stage of sporulation.] These primers are mixed into a PCR cocktail with a given sample of bacterial cells. A control PCR cocktail into which are mixed universal 16S rRNA primers is also prepared. ["16S rRNA" denotes a ribosomal ribonucleic acid (rRNA) sequence that is common to all organisms.] Following several cycles of heating and cooling according to the PCR protocol to amplify amounts of DNA molecules, the amplification products can be analyzed to determine the types of bacterial cells present within the samples. If the amplification product is strong, relative to the product of a control PCR sequence, then it is concluded that the bacterial population in the sample consists predominantly of spore-forming cells. If the amplification product is weak or nonexistent, then it is concluded that the bacterial population in the sample consists predominantly or entirely of non-spore-forming cells.

Venkateswaran, Kasthuri↗

NASA NTRS2008

Irrigation Requirement Estimation using MODIS Vegetation Indices and Inverse Biophysical Modeling; A Case Study for Oran, Algeria

Human demand for food influences the water cycle through diversion and extraction of fresh water needed to support agriculture. Future population growth and economic development alone will substantially increase water demand and much of it for agricultural uses. For many semi-arid lands, socio-economic shifts are likely to exacerbate changes in climate as a driver of future water supply and demand. For these areas in particular, where the balance between water supply and demand is fragile, variations in regional climate can have potentially predictable effect on agricultural production. Satellite data and biophysically-based models provide a powerful method to quantify the interactions between local climate, plant growth and water resource requirements. In irrigated agricultural lands, satellite observations indicate high vegetation density while the precipitation amount indicates otherwise. This inconsistency between the observed precipitation and the observed canopy leaf density triggers the possibility that the observed high leaf density is due to an alternate source of water, irrigation. We explore an inverse process approach using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS), climatological data, and the NASA's Simple Biosphere model, SiB2, to quantitatively assess water demand in a semi-arid agricultural land by constraining the carbon and water cycles modeled under both equilibrium (balance between vegetation and prevailing local climate) and nonequilibrium (water added through irrigation) conditions. We postulate that the degree to which irrigated lands vary from equilibrium conditions is related to the amount of irrigation water used. We added water using two distribution methods: The first method adds water on top of the canopy and is a proxy for the traditional spray irrigation. The second method allows water to be applied directly into the soil layer and serves as proxy for drip irrigation. Our approach indicates that over the study site, for the month of July, spray irrigation resulted in an irrigation amount of about 1.4 mm per occurrence with an average frequency of occurrence of 24.6 hours. The simulated total monthly irrigation for July was 34.85 mm. In contrast, the drip irrigation resulted in less frequent irrigation events with an average water requirement about 57% less than that simulated during the spray irrigation case. The efficiency of the drip irrigation method rests on its reduction of the canopy interception loss compared to the spray irrigation method. When compared to a country-wide average estimate of irrigation water use, our numbers are quite low. We would have to revise the reported country level estimates downward to 17% or less

Bounoua, L.↗

NASA NTRS2024

Assessing the effects of landcover and land use change on wildfire exposure and risk to communities and olive orchards in Mediterranean landscapes

A growing concern in the Mediterranean region is that recent landcover and land use change is increasing wildfire risk, or the exposure and impacts of wildfire to valued resources. However, the magnitude of these effects is not well understood given the widely diverse landscapes of communities, natural vegetation, and agricultural land. In this study, we use wildfire simulation modeling to assess how landcover and land use changes in three areas of southern Greece have affected exposure of- and fire risk to- communities and economically important permanent agriculture such as Olea europaea (European olive) orchards. We mapped agricultural and wildland fuel change from 2000 to 2020 and simulated fuel scenarios in agricultural land to assess the impacts of agricultural land practices, such as understory clearing, on fire risk. We show that wildfire exposure and risk to communities and permanent agriculture has increased in some areas due to natural fuel densification and decreased in other areas, mainly due to agricultural land expansion. These results highlight that wildfire exposure and risk are driven by local conditions including density and location of communities, spatial arrangement of natural fuels and agricultural land, and agricultural land use practices. Human settlement burn probability and modeled permanent crop loss increased with greater unmaintained agricultural area in all study areas, emphasizing the importance of agricultural land maintenance practices, such as understory clearing, to reduce fuel continuity and fire intensity. Ultimately, quantitative fire risk analyses such as this study provide a useful framework to identify areas of concern where either fuel mitigation or landowner incentives to maintain agricultural land in a less-burnable condition could be applied.

Aaron M Sparks↗

NASA NTRS2008

Estimating the Effect of Gypsy Moth Defloiation Using MODIS

The area of North American forests affected by gypsy moth defoliation continues to expand despite efforts to slow the spread. With the increased area of infestation, ecological, environmental and economic concerns about gypsy moth disturbance remain significant, necessitating coordinated, repeatable and comprehensive monitoring of the areas affected. In this study, our primary objective was to estimate the magnitude of defoliation using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery for a gypsy moth outbreak that occurred in the US central Appalachian Mountains in 2000 and 2001. We focused on determining the appropriate spectral MODIS indices and temporal compositing method to best monitor the effects of gypsy moth defoliation. We tested MODIS-based Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), and two versions of the Normalized Difference Infrared index (NDIIb6 and NDIIb7, using the channels centered on 1640 nm and 2130 nm respectively) for their capacity to map defoliation as estimated by ground observations. In addition, we evaluated three temporal resolutions: daily, 8-day and 16-day data. We validated the results through quantitative comparison to Landsat based defoliation estimates and traditional sketch maps. Our MODIS based defoliation estimates based on NDIIb6 and NDIIb7 closely matched Landsat defoliation estimates derived from field data as well as sketch maps. We conclude that daily MODIS data can be used with confidence to monitor insect defoliation on an annual time scale, at least for larger patches (greater than 0.63 km2). Eight-day and 16-day MODIS composites may be of lesser use due to the ephemeral character of disturbance by the gypsy moth.

deBeurs, K. M.↗

DOE OSTI2025

Front-end engineering design (FEED) studies: a quantitative analysis

NETL has devised a methodology for normalizing FEED study metrics of interest and allowing cautious quantitative comparison across FEED studies. This presentation introduces the novel quantitative comparison methodology and presents results from utilizing this methodology to examine data presented in recent FEED study reports. The quantitative methodology developed for the examination of FEED study performance and cost also allows comparison of real-world performance and costs against NETL TEA model predicted performance and cost. Learnings from examining NETL model predicted performance and cost versus real world reported values are highlighted. These learnings provide insight into NETL TEA model uncertainty and highlight opportunities for further model development.

FEED Studies↗