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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
DOE OSTI2023

Influence on Structural Loading of a Wave Energy Converter by Controlling Variable-Geometry Components and the Power Take-Off

Oceans are harsh environments and can impose significant loads on deployed structures. A wave energy converter (WEC) should be designed to maximize the energy absorbed while ensuring the operating wave condition does not exceed the failure limits of the device itself. Therefore, the loads endured by the support structure are a design constraint for the system. Furthermore, the WEC should be adaptable to different sea states. Herethis work uses a WEC-Sim model of a variable-geometry oscillating wave energy converter (VGOSWEC) mounted on a support structure simulated under different wave scenarios. A VGOSWEC resembles a paddle pitching about a fixed hinge perpendicular to the incoming wave fronts. The geometry of the VGOSWEC is varied by opening a series of controllable flaps on the pitching paddle when the structure experiences threshold loads. It is hypothesized that opening the flaps should result in load shedding at the base of the support structure by reducing the moments about the hinge axis. This work compares the hydrodynamic coefficients, natural periods, and response amplitude operators from completely closed to completely open configurations of the controllable flaps. This work shows that the completely open configuration can reduce the pitch and surge loads on the base of the support structure by as much as 80%. Increased loads at the structure’s natural period can be mitigated by an axial power take-off damping acting as an additional design parameter to control the loads at the WEC’s support structure.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

Optical Classification of Lower Amazon Waters Based on In Situ Data and Sentinel-3 Ocean and Land Color Instrument Imagery

Optical water types (OWTs) were identified from an in situ dataset of concomitant biogeochemical and optical parameters acquired in the Amazon River and its tributaries, in the Lower Amazon region, at different hydrological conditions from 2014 to 2017. A seasonal bio-optical characterization was performed. The k-means classification was applied to the in situ normalized reflectance spectra (rn(λ)), allowing the identification of four OWTs. An optical index method was also applied to the rn(λ) defining the thresholds of the OWTs. Next, level-3 Sentinel-3 Ocean and Land Color Instrument images representative of the seasonal discharge conditions were classified using the identified in situ OWTs as reference. The differences between Amazon River and clearwater tributary OWTs were dependent on the hydrological dynamics of the Amazon River, also showing a strong seasonal variability. Each OWT was associated with a specific bio-optical and biogeochemical environment assessed from the corresponding absorption coefficient values of colored dissolved organic matter (aCDOM) and particulate matter (ap), chlorophyll-a and suspended particulate matter (SPM) concentrations, and aCDOM/ap ratio. The rising water season presented a unique OWT with high SPM concentration and high relative contribution of ap to total absorption compared to the other OWTs. This bio-optical characterization of Lower Amazon River waters represents a first step for developing remote sensing inversion models adjusted to the optical complexity of this region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Scaler Rates from the Pierre Auger Observatory: A New Proxy of Solar Activity

The modulation of low-energy galactic cosmic rays reflects interplanetary magnetic field variations and can provide useful information on solar activity. An array of ground-surface detectors can reveal the secondary particles, which originate from the interaction of cosmic rays with the atmosphere. In this work, we present an investigation of the low-threshold rate (scaler) time series recorded in 16 yr of operation by the Pierre Auger Observatory surface detectors in Malargüe, Argentina. Through an advanced spectral analysis, we detected highly statistically significant variations in the time series with periods ranging from the decadal to the daily scale. We investigate their origin, revealing a direct connection with solar variability. Thanks to their intrinsic very low noise level, the Auger scalers allow a thorough and detailed investigation of the galactic cosmic-ray flux variations in the heliosphere at different timescales and can, therefore, be considered a new proxy of solar variability.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The value of fusion energy to a decarbonized United States electric grid

Fusion could be a part of future decarbonized electricity systems, but it will need to compete with other technologies. In particular, pulsed tokamak plants have a unique operational mode, and evaluating which characteristics make them economically competitive can help select between design pathways. Using a capacity expansion and operations model, here we determined cost thresholds for pulsed tokamaks to reach a range of penetration levels in a future decarbonized US Eastern Interconnection. The required capital cost to reach a fusion capacity of 100 GW varied from $2,700 to $7,500 kW -1 , and the equilibrium penetration increases rapidly with decreasing cost. The value per unit power capacity depends on the variable operational cost and on the cost of its competition, particularly fission, much more than on the pulse cycle parameters. These findings can therefore provide initial cost targets for fusion more generally in the United States.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Data and scripts associated with “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments”

This data package is associated with the publication “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments” published in Scientific Reports (Garayburu-Caruso et al., 2026). The package contains processed data products and scripts used to quantify how drying and re-inundation of riverbed sediments influence dissolved organic matter (DOM) thermodynamic properties and their relationship with sediment oxygen (O₂) consumption across 33 stream sites in the contiguous United States. The data package contains DOM thermodynamic metrics (e.g., Gibbs free energy of carbon oxidation and thermodynamic efficiency), and O₂ consumption along with watershed-scale climate and land-cover metrics used as explanatory variables in the analyses. Underlying unprocessed and processed ultrahigh-resolution mass spectrometry data, oxygen consumption rates from laboratory moisture-manipulation experiments, within-sample environmental properties, sediment moisture content and contextual field measurements are archived separately at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2428003 (Laan et al., 2024) and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689 (Forbes et al.,2023). A preliminary version of this data package was published in February 2026 at the time of manuscript submission. It was updated in June 2026, at the time of manuscript acceptance, to include the finalized data and additional metadata (readme, data dictionary, and file level metadata). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. At the top level, the data package is organized into five main folders: (1) Data, (2)Figures, (3) Map, (4) GAM_Reulsts, and (5) src. The Data folder contains analysis-ready tabular files with oxygen consumption rates, DOM thermodynamic properties by site and treatment, site-level environmental variables, watershed-scale metrics, and other derived variables referenced in the manuscript. The Figures folder contains static image files associated with the main text and supplemental figures, while the Map folder includes spatial data and map-layer files used to create the sampling-location map. The GAM results folder contains the results for each of the general additive model (GAM).The src folder contains R scripts used to perform data processing, statistical analyses (including clustering, generalized additive models, and threshold analysis), and figure generation. This data package is associated with a GitHub repository found at https://github.com/WHONDRS-Hub/ECA_DOM_Thermodynamics.

Dissolved organic matter↗
DOE OSTI2026

High Statistics Measurements of $ν_μ$ Charged-Current Single $π^{+}$ Production with Zero Pion Kinetic Energy Threshold in MINERvA

This Letter presents measurements of single-differential cross sections of $ν_μ$-induced charged-current 1 $π^{+}$ production on scintillator using the MINERvA detector at Fermilab. These measurements use traditional track-based pion reconstruction as well as pions identified solely via Michel electron decays, allowing measurement of kinetic energies from 0 to 350 MeV. In total, 91,843 events were selected with $W_{exp}$$<$ 1.4 GeV/c. Differential cross sections as a function of pion and muon kinematic variables are presented and compared with the predictions of several neutrino event generators. Overall, modern pion production models tend to agree with data at the ends of the kinematic regions probed, but are discrepant with the main regions of the phase space probed by up to 15% in muon observables and up to 20% in pion observables. No model describes any of the variables well, and this result highlights model areas that require improvement for the next generation of neutrino oscillation experiments.

Granados, E. [Guanajuato U.]↗
DOE OSTI2026

Harnessing the Second-Order Metal−Insulator Transition for Neuromorphic Computing

Vanadium oxides are widely studied phase change materials for brain-inspired computing architectures. Systems like VO 2 and V 2 O 3 exhibit first-order metal−insulator transitions (MITs) with hysteresis and percolative switching, increasing stochasticity and device variability. Here, we focus on the less explored Magnéli phase V 4 O 7 , which undergoes a continuous, non-hysteretic, second-order MIT. This surprisingly enables highly reproducible volatile resistive switching in spiking-neuron-type devices. We synthesize V 4 O 7 films, characterize their structural and transport properties, and demonstrate voltage and current-driven threshold switching with electrothermal feedback. In a Pearson–Anson oscillator, V 4 O 7 devices produce stable, tunable spiking across 20–200 kHz, with consistent operation among multiple devices. We introduce a numerical analog leaky-integrate-and-fire (aLIF) model that captures waveform shapes and their dependence on load resistance, temperature, and voltage. Furthermore, these findings suggest that second-order MIT materials like V 4 O 7 are promising for deterministic, scalable spiking neuron arrays for neuromorphic computing.

V4O7↗
DOE OSTI2025

Can Restoring Tidal Wetlands Reduce Estuarine Nuisance Flooding of Coasts Under Future Sea‐Level Rise?

Wetland restoration is an increasingly popular nature‐based method for flood risk mitigation in coastal communities. In this study, we present a novel method using hydrodynamic modeling and harmonic analysis to quantify wetlands' ability to reduce future nuisance flooding. The method leverages a hydrodynamic model calibrated to present day data and was run for a range of future sea‐level rise (SLR) and wetland restoration scenarios to quantify changes to tidal harmonic amplitudes and phases. The harmonic constituents are used to generate water surface elevations over a time period of interest (e.g., one year) and compared to critical exceedance thresholds such as levee elevations. Then, changes to nuisance flooding are calculated by counting the number of hours critical thresholds are exceeded under different SLR and wetland restoration scenarios. We applied the method to Coos Bay, Oregon, USA as a test case. We found restoration reduces the number of hours nuisance flooding occurs in downtown Coos Bay from 15 hr (present day conditions) to 0 hr (fully restored condition) under median SLR (82 cm by 2100). Restoration had spatially variable impacts on reducing peak flood elevations with minimal impacts near the estuary mouth and greatest impact 32 km inland. The effectiveness of restoration was heavily dependent on future SLR. Restoration was maximally effective in 2050 under all SLR scenarios, less effective in 2100 under median SLR, and not effective under high SLR. Modeling results suggest increased tidal prism and accommodation space are driving restoration‐associated reductions in tidal amplitudes.

Brand, Matthew W. [Louisiana State Univ., Baton Ro↗
DOE OSTI2023

Multiclustering needlet ILC for CMB B -mode component separation

ABSTRACT The Cosmic Microwave Background (CMB) primordial B-mode signal is predicted to be much lower than the polarized Galactic emission (foregrounds) in any region of the sky pointing to the need for sophisticated component separation methods. Among them, the blind Needlet Internal Linear Combination (NILC) has great relevance given our current poor knowledge of the B-mode foregrounds. However, the expected level of spatial variability of the foreground spectral properties complicates the NILC subtraction of the Galactic contamination. We therefore propose a novel extension of the NILC approach, the Multiclustering NILC (MC-NILC), which performs NILC variance minimization on separate regions of the sky (clusters) properly chosen to have similar spectral properties of the B-mode Galactic emission within them. Clusters are identified thresholding either the ratio of simulated foregrounds-only B modes (ideal case) or the one of cleaned templates of Galactic emission obtained from realistic simulations. In this work we present an application of MC-NILC to the future LiteBIRD satellite, which targets the observation of both reionization and recombination peaks of the primordial B-mode angular power spectrum with a total error on the tensor-to-scalar ratio δr < 0.001. We show that MC-NILC provides a CMB solution with residual foreground and noise contamination that is significantly lower than the NILC one and the primordial signal targeted by LiteBIRD at all angular scales for the ideal case and at the reionization peak for a realistic ratio. Thus, MC-NILC will represent a powerful method to mitigate B-mode foregrounds for future CMB polarization experiments.

Astronomy & Astrophysics↗
DOE OSTI2023

Anomaly Detection in Liquid Sodium Cold Trap Operation with Multisensory Data Fusion Using Long Short-Term Memory Autoencoder

Sodium-cooled fast reactors (SFR), which use high temperature fluid near ambient pressure as coolant, are one of the most promising types of GEN IV reactors. One of the unique challenges of SFR operation is purification of high temperature liquid sodium with a cold trap to prevent corrosion and obstructing small orifices. We have developed a deep learning long short-term memory (LSTM) autoencoder for continuous monitoring of a cold trap and detection of operational anomaly. Transient data were obtained from the Mechanisms Engineering Test Loop (METL) liquid sodium facility at Argonne National Laboratory. The cold trap purification at METL is monitored with 31 variables, which are sensors measuring fluid temperatures, pressures and flow rates, and controller signals. Loss-of-coolant type anomaly in the cold trap operation was generated by temporarily choking one of the blowers, which resulted in temperature and flow rate spikes. The input layer of the autoencoder consisted of all the variables involved in monitoring the cold trap. The LSTM autoencoder was trained on the data corresponding to cold trap startup and normal operation regime, with the loss function calculated as the mean absolute error (MAE). The loss during training was determined to follow log-normal density distribution. During monitoring, we investigated a performance of the LSTM autoencoder for different loss threshold values, set at a progressively increasing number of standard deviations from the mean. The anomaly signal in the data was gradually attenuated, while preserving the noise of the original time series, so that the signal-to-noise ratio (SNR) averaged across all sensors decreased below unity. Results demonstrate detection of anomalies with sensor-averaged SNR < 1.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Lower and upper threshold limit for artificial neural network based chilled and condenser water temperatures set-point control in a chilled water system

In this study, an ANN (artificial neural network) based real-time optimized control algorithm for a chilled water cooling system was developed and applied in an actual building to analyze its cooling energy saving effects through in-situ application and actual measurements. For this purpose, the cooling tower’s CndWT (condenser water temperature) and the chiller’s ChWT (chilled water temperature) were set as system control variables. To evaluate algorithm performance, the electric consumption and the COP (coefficient of performance) were compared and analyzed when ChWT and CndWTs were controlled conventionally and controlled based on the ANN. During the analysis, unexpected abnormal data was observed due to insufficient training data and limited consideration of OWBT (outdoor air wet-bulb temperature) when determining the CndWT set-point. Therefore, it is necessary to further build training data from a wider range of conditions and to set the lower limit of CndWT set-point to at least +3.6 °C above OWBT when the OWBT is higher than 23 °C, so that further energy savings can be achieved.

42 ENGINEERING↗
DOE OSTI2025

Variational AutoEncoders Reveal Intensifying GPP Extremes in Continental United States based on CESM2 Simulations

Climate extremes significantly impact terrestrial carbon cycle dynamics, necessitating robust methods for detecting and analyzing anomalous behavior in plant productivity. This study presents a novel application of variational autoencoders (VAE) for identifying extreme events in gross primary productivity (GPP) from Community Earth System Model version 2 simulations across four AR6 regions in the Continental United States. We compare VAE-based anomaly detection with traditional singular spectral analysis (SSA) methods across three time periods: 1850-80, 1950-80, and 2050-80 under SSP5-8.5 scenario. The VAE architecture employs three dense layers and a latent space with input sequence length of 12 months, training on normalized GPP time series to reconstruct the GPP and identify anomalies based on reconstruction errors. Extreme events are defined using 5th percentile thresholds applied to both VAE and SSA anomalies. Results demonstrate strong regional agreement between VAE and SSA methods in spatial patterns of extreme event frequencies, despite VAE consistently producing higher threshold values (179-756 GgC for VAE vs. 100-784 GgC for SSA across regions and periods). Both methods reveal increasing magnitudes and frequencies of negative carbon cycle extremes toward 2050-80, particularly in Western and Central North America. The VAE approach shows comparable performance to established SSA techniques while offering computational advantages and enhanced capability for capturing non-linear temporal dependencies in carbon cycle variability. This research demonstrates the potential of deep learning approaches for extremes detection and provides a foundation for improved understanding of future carbon cycle risks under future conditions.

Sharma, Bharat [ORNL] (ORCID:0000000266982487)↗
DOE OSTI2022

OperonSEQer: A set of machine-learning algorithms with threshold voting for detection of operon pairs using short-read RNA-sequencing data

Operon prediction in prokaryotes is critical not only for understanding the regulation of endogenous gene expression, but also for exogenous targeting of genes using newly developed tools such as CRISPR-based gene modulation. A number of methods have used transcriptomics data to predict operons, based on the premise that contiguous genes in an operon will be expressed at similar levels. While promising results have been observed using these methods, most of them do not address uncertainty caused by technical variability between experiments, which is especially relevant when the amount of data available is small. In addition, many existing methods do not provide the flexibility to determine the stringency with which genes should be evaluated for being in an operon pair. We present OperonSEQer, a set of machine learning algorithms that uses the statistic and p-value from a non-parametric analysis of variance test (Kruskal-Wallis) to determine the likelihood that two adjacent genes are expressed from the same RNA molecule. We implement a voting system to allow users to choose the stringency of operon calls depending on whether your priority is high recall or high specificity. In addition, we provide the code so that users can retrain the algorithm and re-establish hyperparameters based on any data they choose, allowing for this method to be expanded as additional data is generated. We show that our approach detects operon pairs that are missed by current methods by comparing our predictions to publicly available long-read sequencing data. OperonSEQer therefore improves on existing methods in terms of accuracy, flexibility, and adaptability.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Optimal Control of Biomass Feedstock Processing System Under Uncertainty in Biomass Quality

Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed. Note to Practitioners—This paper is motivated by the challenges faced in the bioenergy industry. The focus of this paper is on plants which use the biochemical conversion process to generate liquid fuels. It has been observed that variations in biomass characteristics, such as moisture content, cause variations in feeding of the system which lead to under-utilization of equipment. A requirement of biochemical conversion process is to maintain the carbohydrate content of biomass processed by the reactor, larger than a threshold. We propose a model that identifies the inventory levels and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor. The goal is to improve equipment utilization while satisfying the requirements of the conversion process. The model is tested using real-life data. We found out that by sequencing bales based on moisture level and carbohydrate content, a plant can reduce variability in the system leading to improved system reliability, higher processing rates of the reactor, and higher throughput.

09 BIOMASS FUELS↗
DOE OSTI2024

Exploring the Influence of Linker Substitution and Ratios on Cooperative Framework Flexibility through the Mixed-Linker Approach

Cooperatively flexible metal–organic frameworks that exhibit step-shaped, or Type V-like, adsorption–desorption profiles can lower requisite pressure–temperature swings and thus energy input, necessary for an array of gas storage, delivery, and separations applications. However, such benefits are lost if the pressure threshold of the adsorption and desorption steps at a given temperature does not match the conditions dictated by the application, such as H 2 storage and delivery or olefin–paraffin separations. Unfortunately, the discovery of cooperatively flexible frameworks remains wholly serendipitous and de novo design remains impossible. Accordingly, there is a great need to further our understanding of flexibility such that we can intuitively derivatize known frameworks and, ultimately, design entirely new ones to meet the requisite conditions of energy-consumptive processes. In this work, we demonstrate that the mixed-linker, or multivariate, approach is a powerful tool for the derivation of a known flexible framework, with variances in linker substitution and ratio giving rise to a family wherein significant changes to the step-shaped adsorption–desorption profiles for multiple adsorbates are observed. Specifically, we report 12 isostructural mixed-linker derivatives of CdIF-13 (sod–Cd(benzimidazolate) 2 ) with six point-modified benzimidazole linkers spontaneously synthesized through prototypical solvothermal conditions. Furthermore, each is shown by PXRD to exhibit similar reversible flexibility to CdIF-13 and by TGA and DSC to be similarly thermally stable. Isothermal gas adsorption measurements with N 2 at 77 K, CO 2 at 195 K, and propane at 298 K demonstrate the wide-ranging, adsorbate-dependent effects of linker substitution and ratio, including dramatic reductions in the adsorption threshold pressure, evolution of multiple steps, and complete absence of observable adsorption. Aided by prior crystallographic characterization of CdIF-13, the observed trends were analyzed in an attempt to resolve the influence of linker substitution and ratios on structural behavior. In total, this work illustrates how the mixed-linker approach enables the synthesis and study of a large catalog of functionally modified frameworks, which can help identify the variables that influence flexibility and sorption behavior.

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

Response of Atmospheric River Width and Intensity to Aquaplanet Warming: A Detection Algorithm‐ and Background Moisture‐Independent Approach

The width of an atmospheric river (AR) is an important parameter when evaluating its impact. Although previous research suggests ARs will widen with global warming, a precise response has been muddled by the large sensitivity of width to a diverse set of AR detection techniques (ARDTs). Here, we propose a methodology that removes the influence of the ARDT by modeling AR‐integrated vapor transport (IVT) profiles as idealized exponential curves with free parameters given by background IVT, intensity above background IVT, and profile width. Notably, our definition for AR profile width does not include any explicit numerical thresholds, relative or absolute, for IVT. We apply our approach to a series of idealized aquaplanet experiments, first with a baseline sea surface temperature (SST) distribution, and then with +2K, +4K, and +6K uniform warming, so as to determine the contributions of each free parameter to AR width. We also apply our approach to high‐resolution atmosphere‐only models forced with SSTs modified to preserve historical variability but following projected warming over 2016–2050. Our results show that contributions to impacts‐relevant AR widening comes primarily from enhancements in background IVT and AR intensity, as opposed to from dynamic width changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Influence on Structural Loading of a Wave Energy Converter by Controlling Variable-Geometry Components and the Power Take-Off

Oceans are harsh environments and can impose significant loads on deployed structures. The deployment of wave energy converters (WECs) faces a design challenge with apparently contradictory goals. A WEC should be designed to maximize the energy absorbed while ensuring the operating wave condition does not exceed the failure limits of the device itself. Therefore, the loads endured by the support structure are a design constraint for the system. Adaptability to different sea states is, therefore, highly desirable. This work uses a WEC-Sim model of a variable-geometry oscillating wave energy converter (VGOSWEC) mounted on a support structure simulated under different wave scenarios. A VGOSWEC resembles a paddle pitching about a fixed hinge perpendicular to the incoming wave fronts. Therefore, the hinge experiences loads perpendicular to its axis as it maintains its position. The geometry of the VGOSWEC is varied by opening a series of controllable flaps on the pitching paddle when the structure experiences threshold loads. Because opening the flaps lets the waves transmit through the paddle, it is hypothesized that opening the flaps should result in load shedding at the base of the support structure. The load shedding is achieved by reducing the moments about the hinge axis. This work compares the hydrodynamic coefficients, natural periods, and response amplitude operators from completely closed to completely open configurations of the controllable flaps. The comparisons quantify the effects of letting the waves transmit through the VGOSWEC. This work shows that the completely open configuration can reduce the pitch and surge loads on the base of the support structure by as much as 80%. It was observed that at the paddle's resonance frequency, the loads on the structure increased substantially. This increase in loads can be mitigated by a rotational power take-off damping about the hinge axis. Changing the rotational power take-off damping was identified as an additional design parameter that can be used to control the loads experienced by the WEC's support structure.

fixed and floating structures↗
DOE OSTI2021

Efficacy of Hair Total Mercury Content as a Biomarker of Methylmercury Exposure to Communities in the Area of Artisanal and Small-Scale Gold Mining in Madre de Dios, Peru

Total mercury content (THg) in hair is an accepted biomarker for chronic dietary methylmercury (MeHg) exposure. In artisanal and small-scale gold mining (ASGM) communities, the validity of this biomarker is questioned because of the potential for contamination from inorganic mercury. As mining communities may have both inorganic and organic mercury exposures, the efficacy of the hair-THg biomarker needs to be evaluated, particularly as nations begin population exposure assessments under their commitments to the Minamata Convention. We sought to validate the efficacy of hair THg for public health monitoring of MeHg exposures for populations living in ASGM communities. We quantified both THg and MeHg contents in hair from a representative subset of participants (N = 287) in a large, population-level mercury exposure assessment in the ASGM region in Madre de Dios (MDD), Peru. We compared population MeHg-THg correlations and %MeHg values with demographic variables including community location, sex, occupation, and nativity. We observed that hair MeHg-THg correlations were high (r > 0.7) for all communities, regardless of location or nativity. Specifically, for individuals within ASGM communities, 81% (121 of 150 total) had hair THg predominantly in the form of MeHg (i.e., >66% of THg) and reflective of dietary exposure to mercury. Furthermore, for individuals with hair THg exceeding the U.S. EPA threshold (1.0 μg/g), 88 out of 106 (83%) had MeHg as the predominant form. As a result, had urine THg solely been used for mercury exposure monitoring, approximately 59% of the ASGM population would have been misclassified as having low mercury exposure. Our results support the use of hair THg for monitoring of MeHg exposure of populations in ASGM settings where alternative biomarkers of MeHg exposure are not feasible.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗