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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

The Importance of Human Reliability Analysis in Human Space Flight: Understanding the Risks

HRA is a method used to describe, qualitatively and quantitatively, the occurrence of human failures in the operation of complex systems that affect availability and reliability. Modeling human actions with their corresponding failure in a PRA (Probabilistic Risk Assessment) provides a more complete picture of the risk and risk contributions. A high quality HRA can provide valuable information on potential areas for improvement, including training, procedural, equipment design and need for automation.

Hamlin, Teri L.↗
NASA NTRS2005

Formation of Martian Gullies by the Action of Liquid Water Flowing Under Current Martian Environmental Conditions

Images from the Mars Orbiter Camera (MOC) on the Mars Global Surveyor (MGS) spacecraft show geologically young small-scale features resembling terrestrial water-carved gullies. An improved understanding of these features has the potential to reveal important information about the hydrological system on Mars, which is of general interest to the planetary science community as well as the field of astrobiology and the search for life on Mars. The young geologic age of these gullies is often thought to be a paradox because liquid water is unstable at the Martian surface. Current temperatures and pressures are generally below the triple point of water (273 K, 6.1 mbar) so that liquid water will spontaneously boil and/or freeze. We therefore examine the flow of water on Mars to determine what conditions are consistent with the observed features of the gullies.

Heldmann, J. L.↗
NASA NTRS2022

KSC Headquarters Building Area SWMU 104 Soil Interim Measure Report for LOC 2D and LOC 2E Kennedy Space Center, Florida

This Interim Measure (IM) Report for Location of Concern (LOC) 2D and LOC 2E summarizes the activities associated with implementation of an IM to address soil and concrete contamination at the Kennedy Space Center (KSC) Headquarters Building Area (KHQA) located at KSC, Florida. KHQA has been designated Solid Waste Management Unit (SWMU) 104 under KSC’s Resource Conservation and Recovery Act Corrective Action Program. The IM consisted of excavating/removing residual polychlorinated biphenyl (PCB) contamination in soil and concrete from two separate areas, LOC 2D and LOC 2E, which were associated with electrical transformer locations. IM activities were conducted in accordance with the Interim Measure Work Plan (IMWP), Revision 1, for KHQA dated June 2018, which was approved by the Florida Department of Environmental Protection in a correspondence letter dated April 2016 and the United States Environmental Protection Agency in a correspondence letter dated August 2018. The objective of the IM was to remove all remaining contaminated media with total PCB concentrations greater than the State of Florida Residential Soil Cleanup Target Level (RSCTL) (0.5 milligrams per kilogram [mg/kg]). This cleanup goal meets unrestrictive re-use conditions in Florida, and is below federal regulations for PCB Bulk Remediation Waste Cleanup of 1 mg/kg for high occupancy, unrestricted use areas, as outlined in 40 Code of Federal Regulations (CFR) 761.61(a). The timeline of the IM was coordinated with demolition of the former KSC Headquarters Building (demolished in 2021) in order to safely remove contaminated media after electrical equipment was de-energized and removed from the site. Excavation activities were conducted between January 10, 2022, and February 8, 2022, and included two waste streams for off-site disposal: (1) PCB-remediation waste greater than or equal to 50 mg/kg, and (2) PCB-remediation waste less than 50 mg/kg. A total of approximately 347 cubic yards (478 tons) of PCB-contaminated soil and concrete was initially removed from the LOC 2D and LOC 2E areas, as defined in the IMWP. An additional 4.35 tons of soil and concrete were removed based on verification sampling, which was conducted until the cleanup goal was achieved. The total area of excavation from both LOC 2D and LOC 2E was approximately 5,186 square feet (0.12 acres), and excavation depths ranged from 0.5 to 4.0 feet below land surface Excavated soils and concrete were stockpiled based on waste stream and subsequently transported and disposed of at Waste Connections of Osceola County, LLC – JED Landfill in St. Cloud, Florida (PCBs less than 50 mg/kg) or the Chemical Waste Management Landfill in Emelle, Alabama (PCBs greater than or equal to 50 mg/kg). Following excavation, clean backfill was brought to the site and provided to the Demolition Contractor to complete backfill and grading in conjunction with their final site restoration activities from building demolition. Post-IM groundwater sampling was conducted in May 2022 at three monitoring wells installed in April 2022 to confirm that PCBs did not leach into groundwater. These wells were installed where the highest PCB concentrations were detected at LOC 2D, LOC 2E, and from the Building Perimeter IM (previously completed in 2020). All PCB results in groundwater were non-detect. Volatile organic compounds were also analyzed based on potential past use of solvents in transformers with PCB-dielectric fluids and the potential release of dielectric fluids containing volatile organic compounds, and all results were non-detect. Based on completion of a successful IM at LOC 2D and LOC 2E, as well as previous remedial actions that have addressed other contamination at KHQA (SWMU 104), No Further Action without controls will be recommended for SWMU 104. NASA will submit separate Site Rehabilitation Completion Order request documentation for SWMU 104 to support an NFA without controls recommendation.

PCB↗
DOE OSTI2023

Collaboration and Negotiation

Collaboration and Negotiation is a critical high-level function of an Autonomous Intelligent Cyber-Defense Agent (AICA) that enables communication among agents, central cyber C2, and human operators. Maintaining the Confidentiality, Integrity, and Availability (CIA) triad while achieving mission goals requires stealthy AICA agents to exercise: 1) minimal communication as needed for avoiding detection, 2) verification of information received with possibly limited resources, and 3) active learning during operations to address dynamic conditions. Moreover, negotiations to jointly identify and execute a Course of Action (COA) solution will require building consensus under distributed and/or decentralized multiagent settings with information uncertainties. This chapter presents algorithmic approaches for enabling the collaboration and negotiation function. Strengths and limitations of potential techniques are identified, and a representative example is illustrated. Recommendations for future development are also discussed.

Chatterjee, Samrat↗
DOE OSTI2021

Synaptic Zn2+ potentiates the effects of cocaine on striatal dopamine neurotransmission and behavior

Abstract Cocaine binds to the dopamine (DA) transporter (DAT) to regulate cocaine reward and seeking behavior. Zinc (Zn 2+ ) also binds to the DAT, but the in vivo relevance of this interaction is unknown. We found that Zn 2+ concentrations in postmortem brain (caudate) tissue from humans who died of cocaine overdose were significantly lower than in control subjects. Moreover, the level of striatal Zn 2+ content in these subjects negatively correlated with plasma levels of benzoylecgonine, a cocaine metabolite indicative of recent use. In mice, repeated cocaine exposure increased synaptic Zn 2+ concentrations in the caudate putamen (CPu) and nucleus accumbens (NAc). Cocaine-induced increases in Zn 2+ were dependent on the Zn 2+ transporter 3 (ZnT3), a neuronal Zn 2+ transporter localized to synaptic vesicle membranes, as ZnT3 knockout (KO) mice were insensitive to cocaine-induced increases in striatal Zn 2+ . ZnT3 KO mice showed significantly lower electrically evoked DA release and greater DA clearance when exposed to cocaine compared to controls. ZnT3 KO mice also displayed significant reductions in cocaine locomotor sensitization, conditioned place preference (CPP), self-administration, and reinstatement compared to control mice and were insensitive to cocaine-induced increases in striatal DAT binding. Finally, dietary Zn 2+ deficiency in mice resulted in decreased striatal Zn 2+ content, cocaine locomotor sensitization, CPP, and striatal DAT binding. These results indicate that cocaine increases synaptic Zn 2+ release and turnover/metabolism in the striatum, and that synaptically released Zn 2+ potentiates the effects of cocaine on striatal DA neurotransmission and behavior and is required for cocaine-primed reinstatement. In sum, these findings reveal new insights into cocaine’s pharmacological mechanism of action and suggest that Zn 2+ may serve as an environmentally derived regulator of DA neurotransmission, cocaine pharmacodynamics, and vulnerability to cocaine use disorders.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Multiscale investigation of the mechanism of biomass deconstruction in the dimethyl isosorbide/water Co-solvent pretreatment system

In the context of promoting a circular bioeconomy, the development of green and efficient lignocellulosic biomass pretreatment technologies so as to realize high value-added biomass utilization is of intense interest. We demonstrated the potential of the bio-based green solvent dimethyl isosorbide (DMI) for the fractionation of Eucalyptus biomass with excellent performance. Here, to investigate the mechanisms involved in biomass fractionation, microimaging and microspectroscopic techniques were employed together with molecular dynamics (MD) simulation and COSMO-RS quantum chemical calculations to derive multiscale information. Both the microstructure and regional chemistry of the cell wall vary significantly with the volume ratio of DMI/H 2 O. The strongest effects were found at DMI/H 2 O = 9:1 and showed visible cell wall tearing cracks and cell wall deformation and collapse as well as the lowest values of cell wall thickness and circularity. From the MD simulations, lignin exhibits collapsed-like structure in pure H 2 O with low solvent accessibility surface area (SASA) and radius of gyration (R g ). In contrast, lignin in DMI/H 2 O shows extended structure with high SASA and solvent interactions dominated by van der Waals forces, with maximal contact in the 9:1 (v/v) system. Further, the COSMO-RS calculated sigma (σ-) potential suggests the intermolecular interactions in DMI and DMI/H 2 O co-solvent are weak, leading to stronger interaction with lignin and correspondingly higher lignin dissolution. The radial distribution functions and σ-potential all show that again DMI/H 2 O at 9:1 is an optimal volume ratio for high lignin dissolution. Finally, this study provides a solvent-ratio dependent mechanism for the action of polar aprotic solvents in the deconstruction of biomass.

09 BIOMASS FUELS↗
NASA NTRS1998

Making the Impossible Possible: Strategies for Fast POMDP Monitoring

Systems modeled as partially observable Markov decision processes (POMDPs) can be tracked quickly with three restrictions: all actions are grouped together, the out-degree of each system state is bounded by a constant, and the number of non-zero elements in the belief state is bounded by a (different) constant. With these restrictions, the tracking algorithm operates in constant time and linear space. The first restriction assumes that the action itself is unobservable. The second restriction defines a subclass of POMDPs that covers however a wide range of problems. The third restriction is an approximation technique that can lead to a potentially vexing problem: an observation may be received that has probability according to the restricted belief state. This problem of impossibility will cause the belief state to collapse. In this paper we discuss the tradeoffs between the constant bound on the belief state and the quality of the solution. We concentrate on strategies for overcoming the impossibility problem and demonstrate initial experimental results that indicate promising directions.

Washington, Richard↗
NASA NTRS2020

Prospects for Interstellar Propulsion

In recognition of the increasing prospects for Earth-like exoplanet discoveries and its significance for spurring future interstellar voyages of discovery, the United States Congress recently directed NASA to undertake an interstellar mission technology assessment report. In response to this legislative charge to action, NASA has undertaken a series of extramural interstellar workshops aimed at identifying and evaluating technology concepts for enabling an interstellar scientific probe mission, associated technical challenges, technology readiness level assessments, risks, potential near-term milestones, and funding requirements. This paper summarizes these activities and discusses the scientific and technical rationale for a long-term program consisting of incremental, staged technical developments that are extensible for interstellar travel to a nearby star system over many decades.

Litchford, Ronald J.↗
DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Interpreting Forced Oscillation Notifications from ESAMS: General Guidance for Reliability Coordinators

The Eastern Interconnection Situational Awareness Monitoring System (ESAMS) project demonstrated the feasibility of aggregating synchrophasor measurements from across an interconnection, analyzing them, and providing real-time wide-area situational awareness to system operators who may have excellent visibility within their footprint but lack an interconnection-wide view. An application within ESAMS that has garnered industry interest involves detecting forced oscillations visible across multiple areas, identifying the region where the oscillation originated from, quantifying the uncertainty in source localization results, and notifying users in real-time if the detected oscillation amplitudes cross a specified threshold. It is expected that system operators will utilize their internal SCADA/EMS/synchrophasor systems in conjunction with information provided by ESAMS to take effective mitigation actions if forced oscillation notifications are received. This report provides some general guidance on how the ESAMS information can be used for source localization and coordination among multiple reliability coordinators; and also identifies potential enhancements to ESAMS notifications for improved interpretability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1974

Mainshaft seals for small gas turbine engines

An experimental evaluation of mainshaft seals for small gas turbine engines was conducted with shaft speeds to 213 m/sec, air pressures to 215 psia, and air temperatures to 412 K. A radial face seal incorporating self-acting geometry for lift augmentation was evaluated. In addition, three conventional carbon seal types (face, circumferential segmented, and rotating ring) were run for comparison. Test results indicated that the conventional seals used in this evaluation may not be satisfactory in future advanced engines because of excessive air leakage. On the other hand, the self-acting face seal was shown to have the potential capability of limiting leakages to one-half that of the conventional face seals and one-fifth that of conventional ring seals. A 150 hour endurance test of the self-action face seal was conducted at speeds to 145 m/sec, air pressures to 180 psia, and air temperatures to 408 K. The seal wear was not measurable.

Ludwig, L. P.↗
NASA NTRS1978

Satellite Power System (SPS) military applications

The potential military role, both offensive and defensive, of a Satellite Power System (SPS) is examined. A number of potential military support possibilities are described. An SPS with military capabilities may have a strong negative impact on international relations if it is not internationalized. The SPS satellite would be vulnerable to military action of an enemy with good space capability, but would experience little or no threat from saboteurs or terrorists, except via the ground controls. The paper concludes with an outline of some of the key issues involved, and a number of recommendations for future study, including some areas for long term efforts.

Ozeroff, M. J.↗
NASA NTRS1994

Goal Directed Model Inversion: Learning Within Domain Constraints

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: 1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e. would not need to learn everything from scratch as normally required at present, and 2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.

Colombano, Silvano P.↗
NASA NTRS1994

Goal Directed Model Inversion: Learning Within Domain Constraints

Goal Directed Model Inversion (GDMI) is an algorithm designed to generalize supervised learning to the case where target outputs are not available to the learning system. The output of the learning system becomes the input to some external device or transformation, and only the output of this device or transformation can be compared to a desired target. The fundamental driving mechanism of GDMI is to learn from success. Given that a wrong outcome is achieved, one notes that the action that produced that outcome "would have been right if the outcome had been the desired one." The algorithm makes use of these intermediate "successes" to achieve the final goal. A unique and potentially very important feature of this algorithm is the ability to modify the output of the learning module to force upon it a desired syntactic structure. This differs from ordinary supervised learning in the following way: in supervised learning the exact desired output pattern must be provided. In GDMI instead, it is possible to require simply that the output obey certain rules, i.e., that it "make sense" in some way determined by the knowledge domain. The exact pattern that will achieve the desired outcome is then found by the system. The ability to impose rules while allowing the system to search for its own answers in the context of neural networks is potentially a major breakthrough in two ways: (1) it may allow the construction of networks that can incorporate immediately some important knowledge, i.e., would not need to learn everything from scratch as normally required at present; and (2) learning and searching would be limited to the areas where it is necessary, thus facilitating and speeding up the process. These points are illustrated with examples from robotic path planning and parametric design.

Colombano, Silvano P.↗
NASA NTRS2010

The Importance of HRA in Human Space Flight: Understanding the Risks

Human performance is critical to crew safety during space missions. Humans interact with hardware and software during ground processing, normal flight, and in response to events. Human interactions with hardware and software can cause Loss of Crew and/or Vehicle (LOCV) through improper actions, or may prevent LOCV through recovery and control actions. Humans have the ability to deal with complex situations and system interactions beyond the capability of machines. Human Reliability Analysis (HRA) is a method used to qualitatively and quantitatively assess the occurrence of human failures that affect availability and reliability of complex systems. Modeling human actions with their corresponding failure probabilities in a Probabilistic Risk Assessment (PRA) provides a more complete picture of system risks and risk contributions. A high-quality HRA can provide valuable information on potential areas for improvement, including training, procedures, human interfaces design, and the need for automation. Modeling human error has always been a challenge in part because performance data is not always readily available. For spaceflight, the challenge is amplified not only because of the small number of participants and limited amount of performance data available, but also due to the lack of definition of the unique factors influencing human performance in space. These factors, called performance shaping factors in HRA terminology, are used in HRA techniques to modify basic human error probabilities in order to capture the context of an analyzed task. Many of the human error modeling techniques were developed within the context of nuclear power plants and therefore the methodologies do not address spaceflight factors such as the effects of microgravity and longer duration missions. This presentation will describe the types of human error risks which have shown up as risk drivers in the Shuttle PRA which may be applicable to commercial space flight. As with other large PRAs of complex machines, human error in the Shuttle PRA proved to be an important contributor (~12 percent) to LOCV. An existing HRA technique was adapted for use in the Shuttle PRA, but additional guidance and improvements are needed to make the HRA task in space-related PRAs easier and more accurate. Therefore, this presentation will also outline plans for expanding current HRA methodology to more explicitly cover spaceflight performance shaping factors.

Hamlin, Teri↗
NASA NTRS2012

Using Agent Based Modeling (ABM) to Develop Cultural Interaction Simulations

Today, most cultural training is based on or built around "cultural engagements" or discrete interactions between the individual learner and one or more cultural "others". Often, success in the engagement is the end or the objective. In reality, these interactions usually involve secondary and tertiary effects with potentially wide ranging consequences. The concern is that learning culture within a strict engagement context might lead to "checklist" cultural thinking that will not empower learners to understand the full consequence of their actions. We propose the use of agent based modeling (ABM) to collect, store, and, simulating the effects of social networks, promulgate engagement effects over time, distance, and consequence. The ABM development allows for rapid modification to re-create any number of population types, extending the applicability of the model to any requirement for social modeling.

Drucker, Nick↗
NASA NTRSDate not supplied

The Additive Manufacturing Moment Measure - A Parallel Computation Technique for Determining Build Variance in the Laser Powder Bed Fusion Process

Additive manufacturing (AM) has unique process attributes that facilitate the creation of optimized, complex, and unique parts for aerospace applications. However, the multi-scale and complicated building process for AM parts can cause unexpected build conditions that result in microstructural variability within the deposited material. The need for richer datasets and computational modeling capabilities to improve AM process reliability is a consequence of this microstructural variability. A computational approach, referred to as the Additive Manufacturing Moment Measure (AM3), addresses this need by leveraging the way AM processes fuse material in precise, incremental steps. During the layering sequence, each previous step contributes to the condition of the current step at the center of the heat source. The precise steps can be selected to systematically create a time-space point field with co-located machine input and in-situ sensor data. The sequential nature of the AM process, coupled with nearest neighbor calculations, allows for a fully parallel computation for part-scale build profile analysis. The AM3 concept introduced here enables part-scale assessment directly from build files and in-situ process monitoring sensors alike. The AM moment measures were calculated for build point fields and compared with co-located in-situ and ex-situ nondestructive evaluation and optical microscopy observations. These comparisons permit a better understanding of how the sequential process actions can affect the quality of a laser powder bed fusion (LPBF) build. Details of the AM3technique will be discussed and compared to measured LPBF part characteristics. The AM3 results indicate a strong potential to advance the qualification process for aerospace applications.

Additive Manufacturing↗
DOE OSTI2023

Mercury methylation potential and bioavailability in the sediments of two distinct aquatic systems

Here, this study explored mercury (Hg) methylation potential in two distinct aquatic systems. Fourmile Creek (FMC) was historically polluted with Hg effluents from groundwater as it is a typical gaining stream, where organic matter and microorganisms in streambed are continuously winnowed. The H02 constructed wetland only receives atmospheric Hg and is rich in organic matter and microorganisms. Both systems receive Hg from atmospheric deposition now. Surface sediments were collected from FMC and H02, spiked with inorganic Hg, and cultivated in an anaerobic chamber to stimulate microbial Hg methylation reactions. Total mercury (THg) and methylmercury (MeHg) concentrations were measured at each spiking stage. Mercury methylation potential (MMP, %MeHg in THg) and Hg bioavailability were assessed with the deployment of diffusive gradients in thin films (DGTs). During the methylation process and at the same incubation stage, FMC sediment showed faster increasing rates of %MeHg and higher MeHg concentrations than H02, demonstrating a stronger MMP in the FMC sediment. Similarly, higher Hg bioavailability was observed in FMC sediment compared to the H02 as indicated by DGT-Hg concentrations. In conclusion, the H02 wetland with high levels of organic matter and microorganisms presented low MMP. But the Fourmile Creek as a gaining stream and a historical site of Hg pollution showed strong MMP and high Hg bioavailability. A related study on microbial community activities characterized the microorganisms between FMC and H02, which is attributed to be the main reason for their different methylation capabilities. Our study further brought up the considerations on remediated sites from Hg contamination: Hg bioaccumulation and biomagnification can still be elevated and higher than the surrounding environment due to lagged changes in microbial community structures. This study supported the sustainable ecological modifications of legacy Hg contamination and raised the necessity of long-term monitoring actions even after executing a remediation plan.

54 ENVIRONMENTAL SCIENCES↗