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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Data to Accompany: Expanding the access of wearable silicone wristbands in community-engaged research through best practices in data analysis and integration

Wearable silicone wristbands are a rapidly growing exposure assessment technology that offer researchers the ability to study previously inaccessible cohorts and have the potential to provide a more comprehensive picture of chemical exposure within diverse communities. However, there are no established best practices for analyzing the data within a study or across multiple studies, thereby limiting impact and access of these data for larger meta-analyses. We utilize data from three studies, from over 600 wristbands worn by participants in New York City and Eugene, Oregon, to present a first-of-its-kind manuscript detailing wristband data properties. We further discuss and provide concrete examples of key areas and considerations in common statistical modeling methods where best practices must be established to enable meta-analyses and integration of data from multiple studies. Finally, we detail important and challenging aspects of machine learning, meta-analysis, and data integration that researchers will face in order to extend beyond the limited scope of individual studies focused on specific populations.

Bramer, Lisa M↗
DOE OSTI2023

Data to Accompany: PM2.5 is insufficient to explain personal PAH exposure

Fine particulate matter (PM2.5) air quality index (AQI) data from outdoor stationary monitors and Hazard Mapping System (HMS) smoke density data from satellites are often used as proxies for personal chemical exposure. Silicone wristbands can quantify more individualized exposure data than stationary air monitors or smoke satellites. However, it is not understood how these proxy measurements compare to chemical data measured from wristbands. We hypothesized that predictive models for personal chemical exposure would be significantly improved by expanding beyond stationary PM2.5 AQI data or satellite HMS data to also include environmental and behavioral information. In Eugene, Oregon, participants wore daily wristbands, carried a phone that recorded locations, and answered daily questionnaires for a seven-day period in multiple seasons. We gathered publicly available daily PM2.5 AQI data and HMS data. We analyzed wristbands for 94 organic chemicals, including 53 polycyclic aromatic hydrocarbons (PAHs). Wristband chemical detections and concentrations, behavioral variables (e.g., time spent indoors), and environmental conditions (e.g., PM2.5 AQI) significantly differed between seasons. Machine learning models were fit to predict personal chemical exposure using PM2.5 AQI only, HMS only, and a multivariate feature set including PM2.5 AQI, HMS, and other environmental and behavioral information. On average, the multivariate models increased predictive accuracy by approximately 70% compared to either the AQI model or the HMS model for all chemicals modeled. This study provides evidence that PM2.5 AQI data alone or HMS data alone is insufficient to explain personal chemical exposures. Our results identify additional key predictors of personal chemical exposure.

Bramer, Lisa M↗
DOE OSTI2026

23_POPLAR_EXUDATES

This data is derived from sampling poplar trees at the GWAS common garden in Clatskanie, Oregon. This is intermediate level data from LCMS experiments that were done at ORNL from root tissue and soluble root exudates collected from 24 trees in the common garden in October of 2022.

CBI↗
DOE OSTI2026

Populus_trichocarpa_Breeding_Population_SNPs

These data are from the manuscript “Application of Genomic Prediction in a Populus trichocarpa Breeding Program”, by Brian J. Stanton, David Macaya-Sanz, Chanaka Roshan Abeyratne, David Kainer, Kathy Haiby, Austin Himes, Carlos Gantz, Gerald A. Tuskan, and Stephen P. DiFazio. The data are based on genome resequencing to approximately 10X depth on two collections of Populus trichocarpa trees from Oregon, Washington, California, and British Columbia. The first collection consists of 293 genets collected by Poplar Innovations LLC for a breeding program. The second collection consists of 961 trees collected for the purpose of genome-wide association studies. These genets were sequenced using short, paired-end Illumina sequence reads (Chhetri et al. 2019). Reads were aligned to the P. trichocarpa ′Stettler-14′ reference (Hofmeister et al. 2020), with minor modifications to correct mis-assemblies (Zhou et al. 2020), and variants were called as per methods described in (Abeyratne et al. 2023). Identified variants were filtered using GATK’s VariantFiltration tool (DePristo et al. 2011), with filter expression flag set to “AF < 0.01 || AF > 0.99 || QD < 10.0 || ExcessHet > 20.0 || FS > 10.0 || MQ < 58.0”. SNPs with severe departures from Hardy−Weinberg expectations (exact-test p< 0.01) were also removed using vcftools --hwe flag (Danecek et al. 2011), resulting in 15,627,211 bi-allelic SNPs. The data included here consist of 141,903 high quality bi-allelic genome-wide SNPs obtained by further filtering the original SNP dataset using vcftools with flags --maf 0.05, --max-maf 0.95, --max-missing 0.95, --min-meanDP 10.75, --max-meanDP 43.00, --thin 2000. Collectively, these filtering parameters removed SNPs with 1) a minor allele frequency ≤ 0.05; 2) proportion of missing data for individual loci exceeding 5%; 3) sequencing depth more than 2X mean-depth or less than 0.5X mean-depth; or 4) a distance of

09 BIOMASS FUELS↗
DOE OSTI2021

In-Home Mobility Frequency and Stability in Older Adults Living Alone With or Without MCI: Introduction of New Metrics

Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status. Materials and Methods: Community-dwelling older adults living alone ( n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed). Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88–0.94]; 0.59[0.48–0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (β = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (β = −0.04, p = 0.03). Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.

Wu, Chao-Yi↗
DOE OSTI2020

Large Trees Dominate Carbon Storage in Forests East of the Cascade Crest in the United States Pacific Northwest

Large-diameter trees store disproportionally massive amounts of carbon and are a major driver of carbon cycle dynamics in forests worldwide. In the temperate forests of the western United States, proposed changes to Forest Plans would significantly weaken protections for a large portion of trees greater than 53 cm (21 inches) in diameter (herein referred to as “large-diameter trees”) across 11.5 million acres (~4.7 million ha) of National Forest lands. This study is among the first to report how carbon storage in large trees and forest ecosystems would be affected by a proposed policy. We examined the proportion of large-diameter trees on National Forest lands east of the Cascade Mountains crest in Oregon and Washington, their contribution to overall aboveground carbon (AGC) storage, and the potential reduction in carbon stocks resulting from widespread harvest. We analyzed forest inventory data collected on 3,335 plots and found that large trees play a major role in the accumulated carbon stock of these forests. Tree AGC (kg) increases sharply with tree diameter at breast height (DBH; cm) among five dominant tree species. Large trees accounted for 2.0 to 3.7% of all stems (DBH ≥ 1” or 2.54 cm) among five tree species; but held 33 to 46% of the total AGC stored by each species. Pooled across the five dominant species, large trees accounted for 3% of the 636,520 trees occurring on the inventory plots but stored 42% of the total AGC. A recently proposed large-scale vegetation management project that involved widespread harvest of large trees, mostly grand fir, would have removed ~44% of the AGC stored in these large-diameter trees, and released a large amount of carbon dioxide to the atmosphere. Given the urgency of keeping additional carbon out of the atmosphere and continuing carbon accumulation from the atmosphere to protect the climate system, it would be prudent to continue protecting ecosystems with large trees for their carbon stores, and also for their co-benefits of habitat for biodiversity, resilience to drought and fire, and microclimate buffering under future climate extremes.

21-inch rule↗
DOE OSTI2023

Range wide genetic differentiation in the bull kelp Nereocystis luetkeana with a seascape genetic focus on the Salish Sea

Introduction: In temperate regions, one of the most critical determinants of present range-wide genetic diversity was the Pleistocene climate oscillations, the most recent one created by the last glacial maximum (LGM). This study aimed to describe N. luetkeana genetic structure across its entire range (Alaska to California) and test different models of population connectivity within the Salish Sea. This region was colonized after the LGM and has been under increased disturbance in recent decades. Methods: We utilized microsatellite markers to study N. luetkeana genetic diversity at 53 sites across its range. Using higher sampling density in the Salish Sea, we employed a seascape genetics approach and tested isolation by hydrodynamic transport and environment models. Results: At the species distribution scale, we found four main groups of genetic co-ancestry, Alaska; Washington with Vancouver Island’s outer coast and Juan de Fuca Strait; Washington’s inner Salish Sea; and Oregon with California. The highest allelic richness (AR) levels were found in California, near the trailing range edge, although AR was also high in Alaska. The inner Salish Sea region had the poorest diversity across the species distribution. Nevertheless, a pattern of isolation by hydrodynamic transport and environment was supported in this region. Discussion: The levels of allelic, private allele richness and genetic differentiation suggest that during the LGM, bull kelp had both northern and southern glacial refugia in the Prince of Wales Island-Haida Gwaii region and Central California, respectively. Genetic diversity in Northern California sites seems resilient to recent disturbances, whereas the low levels of genetic diversity in the inner Salish Sea are concerning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Nitrogen addition alters soil fungal communities, but root fungal communities are resistant to change

Plants are colonized by numerous microorganisms serving important symbiotic functions that are vital to plant growth and success. Understanding and harnessing these interactions will be useful in both managed and natural ecosystems faced with global change, but it is still unclear how variation in environmental conditions and soils influence the trajectory of these interactions. In this study, we examine how nitrogen addition alters plant-fungal interactions within two species of Populus - Populus deltoides and P. trichocarpa. In this experiment, we manipulated plant host, starting soil (native vs. away for each tree species), and nitrogen addition in a fully factorial replicated design. After ~10 weeks of growth, we destructively harvested the plants and characterized plant growth factors and the soil and root endosphere fungal communities using targeted amplicon sequencing of the ITS2 gene region. Overall, we found nitrogen addition altered plant growth factors, e.g., plant height, chlorophyll density, and plant N content. Interestingly, nitrogen addition resulted in a lower fungal alpha diversity in soils but not plant roots. Further, there was an interactive effect of tree species, soil origin, and nitrogen addition on soil fungal community composition. Starting soils collected from Oregon and West Virginia were dominated by the ectomycorrhizal fungi Inocybe (55.8% relative abundance), but interestingly when P. deltoides was grown in its native West Virginia soil, the roots selected for a high abundance of the arbuscular mycorrhizal fungi, Rhizophagus. These results highlight the importance of soil origin and plant species on establishing plant-fungal interactions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Hybrid Energy-Powered Electrochemical Direct Ocean Capture Model

Offshore synthetic fuel production and marine carbon dioxide removal can be enabled by direct ocean capture, which extracts carbon dioxide from the ocean that then can be used as a feedstock for fuel production or sequestered underground. To maximize carbon capture, plants require a variety of low-carbon energy sources to operate, such as variable renewable energy. However, the impacts of variable power on direct ocean capture have not yet been thoroughly investigated. To facilitate future deployments, a generalizable model for electrodialysis-based direct ocean capture plants is created to evaluate plant performance and electricity costs under intermittent power availability. This open-source Python-based model captures key aspects of the electrochemistry, ocean chemistry, post-processing, and operation scenarios under various conditions. To incorporate realistic energy supply dynamics and cost estimates, the model is coupled with the National Renewable Energy Laboratory’s H2Integrate tool, which simulates hybrid energy system performance profiles and costs. This integrated framework is designed to provide system-level insights while maintaining computational efficiency and flexibility for scenario exploration. Initial evaluations show similar results to those predicted by the industry, and demonstrate how a given plant could function with variable power in different deployment locations, such as with wind energy off the coast of Texas and with wind and wave energy off the coast of Oregon. The results suggest that electrochemical systems with greater tolerances for power variability and low minimum power requirements may offer operational advantages in variable-energy contexts. However, further research is needed to quantify these benefits and evaluate their implications across different deployment scenarios.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Influence of Time and Frequency Domain Wave Forcing on the Power Estimation of a Wave Energy Converter Array

Industry-specific tools for analyzing and optimizing the design of wave energy converters (WECs) and associated power systems are essential to advancing marine renewable energy. This study aims to quantify the influence of phase information on the device power output of a virtual WEC array. We run the phase-resolving wave model FUNWAVE-TVD (Total Variation Diminishing) to generate directional waves at the PacWave South site offshore from Newport, Oregon, where future WECs are expected to be installed for testing. The two broad cases presented correspond to mean wave climates during warm months (March–August) and cold months (September–February). FUNWAVE-TVD time series of sea-surface elevation are then used in WEC-Sim, a time domain numerical model, to simulate the hydrodynamic response of each device in the array and estimate their power output. For comparison, WEC-Sim is also run with wave energy spectra calculated from the FUNWAVE-TVD simulations, which do not retain phase information, and with wave spectra computed using the phase-averaged model Simulating WAves Nearshore (SWAN). The use of spectral data in WEC-Sim requires a conversion from frequency to time domain by means of random superposition of wave components, which are not necessarily consistent because of the linear assumption implicit in this method. Thus, power response is characterized by multiple realizations of the wave climates.

13 HYDRO ENERGY↗
DOE OSTI2024

Intracore Natural Circulation Study in the High Temperature Test Facility

The development of the Modular High-Temperature Gas-Cooled Reactor is a significant milestone in advanced nuclear reactor technology. One of the concerns for the reactor’s safe operation is the effects of a loss-of-flow accident (LOFA) where the coolant circulators are tripped, and forced coolant flow through the core is lost. Depending on the steam generator placement, loop or intracore natural circulation develops to help transfer heat from the core to the reactor cavity, cooling system. This paper investigates the fundamental physical phenomena associated with intracore coolant natural circulation flow in a one-sixth Computational Fluid Dynamics (CFD) model of the Oregon State University High Temperature Test Facility (OSU HTTF) following a loss-of-flow accident transient. This study employs conjugate heat transfer and steady-state flow along with an SST k-ω turbulence model to characterize the phenomenon of core channel-to-channel natural convection. Previous studies have revealed the importance of complex flow distribution in the inlet and outlet plenums with the potential to generate hot coolant jets. For this reason, complete upper and lower plenum volumes are included in the analyzed computational domain. CFD results also include parametric studies performed for a mesh sensitivity analysis, generated using the STAR-CCM+ software. The resulting channel axial velocities and flow directions support the test facility scaling analysis and similarity group distortions calculation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Assessing Heterogeneity of Surface Water Temperature Following Stream Restoration and a High-Intensity Fire from Thermal Imagery

Thermal heterogeneity of rivers is essential to support freshwater biodiversity. Salmon behaviorally thermoregulate by moving from patches of warm water to cold water. When implementing river restoration projects, it is essential to monitor changes in temperature and thermal heterogeneity through time to assess the impacts to a river’s thermal regime. Lightweight sensors that record both thermal infrared (TIR) and multispectral data carried via unoccupied aircraft systems (UASs) present an opportunity to monitor temperature variations at high spatial (<0.5 m) and temporal resolution, facilitating the detection of the small patches of varying temperatures salmon require. Here, we present methods to classify and filter visible wetted area, including a novel procedure to measure canopy cover, and extract and correct radiant surface water temperature to evaluate changes in the variability of stream temperature pre- and post-restoration followed by a high-intensity fire in a section of the river corridor of the South Fork McKenzie River, Oregon. We used a simple linear model to correct the TIR data by imaging a water bath where the temperature increased from 9.5 to 33.4 °C. The resulting model reduced the mean absolute error from 1.62 to 0.35 °C. We applied this correction to TIR-measured temperatures of wetted cells classified using NDWI imagery acquired in the field. We found warmer conditions (+2.6 °C) after restoration (p < 0.001) and median absolute deviation for pre-restoration (0.30) to be less than both that of post-restoration (0.85) and post-fire (0.79) orthomosaics. In addition, there was statistically significant evidence to support the hypothesis of shifts in temperature distributions pre- and post-restoration (KS test 2009 vs. 2019, p < 0.001, D = 0.99; KS test 2019 vs. 2021, p < 0.001, D = 0.10). Moreover, we used a Generalized Additive Model (GAM) that included spatial and environmental predictors (i.e., canopy cover calculated from multispectral NDVI and photogrammetrically derived digital elevation model) to model TIR temperature from a transect along the main river channel. This model explained 89% of the deviance, and the predictor variables showed statistical significance. Collectively, our study underscored the potential of a multispectral/TIR sensor to assess thermal heterogeneity in large and complex river systems.

Barker, Matthew I. (ORCID:0000000252864930)↗
DOE OSTI2022

Tunable White Light for Elders (TWLITE): A Protocol Demonstrating Feasibility and Acceptability for Deployment, Remote Data Collection, and Analysis of a Home-Based Lighting Intervention in Older Adults

Sleep disturbances are common in older adults and may contribute to disease progression in certain populations (e.g., Alzheimer’s disease). Light therapy is a simple and cost-effective intervention to improve sleep. Primary barriers to light therapy are: (1) poor acceptability of the use of devices, and (2) inflexibility of current devices to deliver beyond a fixed light spectrum and throughout the entirety of the day. However, dynamic, tunable lighting integrated into the native home lighting system can potentially overcome these limitations. Herein, we describe our protocol to implement a whole-home tunable lighting system installed throughout the homes of healthy older adults already enrolled in an existing study with embedded home assessment platforms (Oregon Center for Aging & Technology—ORCATECH). Within ORCATECH, continuous data on room location, activity, sleep, and general health parameters are collected at a minute-to-minute resolution over years of participation. This single-arm longitudinal protocol collected participants’ light usage in addition to ORCATECH outcome measures over a several month period before and after light installation. The protocol was implemented with four subjects living in three ORCATECH homes. Technical/usability challenges and feasibility/acceptability outcomes were explored. The successful implementation of our protocol supports the feasibility of implementing and integrating tunable whole-home lighting systems into an automated home-based assessment platform for continuous data collection of outcome variables, including long-term sleep measures. Challenges and iterative approaches are discussed. This protocol will inform the implementation of future clinical intervention trials using light therapy in patients at risk for developing Alzheimer’s disease and related conditions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Wave resource spatial and temporal variability dependence on WEC size

As the wave energy sector grows and looks to the Blue Economy for commercialization opportunities, there is a distinct and pressing need to clearly understand and quantify the coupled impacts of wave energy converter (WEC) size and wave resource characteristics on the annual energy production, spatial variability and temporal variability. Utilizing generic frequency domain representations of the Oscilla Power Triton WEC and spectral wave conditions at PacWave (Oregon), Los Angeles (California) and WETS (Hawaii), a series of interesting results emerge. Firstly, the ‘optimal’ WEC size, from an energy standpoint, is fundamentally dependent on the frequency distribution of the incoming wave variance density spectrum. Secondly, and from a seasonality perspective, the seasonal WEC energy generation doesn’t necessarily follow the seasonal distribution of gross wave power. Finally, from an hourly power variability perspective, a reduction in WEC size generally decreases variability. However, for each of the locations investigated, there appears to be a WEC size threshold; a threshold where further reducing WEC size results in increased power variability.

16 TIDAL AND WAVE POWER↗
DOE OSTI2020

Mapping of Benthic Habitats at Marine Renewable Energy Sites Using Multibeam Echosounder and Sediment Profile Imaging Technologies

Objectives/Scope: The goal of this work was to develop a consistent and semi-automated seafloor survey method for generating high-resolution, benthic habitat maps for environmental assessments and monitoring of marine renewable energy sites. Sediment profile and plan view imaging (SPI/PV) technology was combined with multibeam bathymetry and acoustic backscatter methods to demonstrate a rapid, cost-effective benthic mapping protocol. A key technical innovation of this project was the development of an image processing platform that automatically measures key features from the images. Methods: Multibeam echosounder (MBES) acoustic and SPI/PV surveys were conducted at three coastal areas off the U.S. west coast, including the PacWave South energy test site off of Newport, Oregon. Point data on physical and biological sediment conditions obtained from the SPI/PV imagery were used to efficiently ground-truth the high-resolution MBES bathymetry and backscatter mosaics. The SPI camera is an optical corer that obtains an undisturbed 21 by 15 cm, high-resolution, cross-sectional image of the sediment–water interface and upper sediment column. The plan view camera attached to the SPI camera frame captures a downward looking view of the seabed immediately before the SPI image is obtained. As part of this project, we developed a computer vision image processing platform (iSPI) that uses deep convolutional neural networks and other approaches to automatically identify and measure key features in the images, such as grain size and surface relief. Results: Detailed benthic habitat maps were generated using this mapping approach for three different marine settings. The sites mapped included a silt-dominated embayment; a sloping, nearshore, transitional very fine to coarse sand bottom; and a medium, sand-dominated continental shelf marine energy test site. In each case, acoustic and imaging surveys were completed in less than week, and detailed benthic habitat maps were generated within 60 days. The results were placed within the Coastal and Marine Ecological Classification Standard (CMECS) habitat mapping framework, and various combinations of the bathymetry, backscatter, and SPI and PV data were used to generate CMECS component maps. Novel Information: This project developed and demonstrated a repeatable and cost-effective approach for efficiently mapping benthic habitat conditions over broad areas of the seafloor by combining state-of-the-art acoustic and imaging techniques. The primary survey tools used in this project have been used previously in both the offshore renewable (wind) and oil and gas sectors to map and monitor benthic environments. This project’s innovations include 1) the focused use of high-resolution SPI/PV imagery to ground-truth acoustic mosaics, and 2) the development of a computer automated image analysis processing tool that both streamlines and standardizes the generation of data from the imagery and makes the data extraction process more cost-effective and repeatable.

13 HYDRO ENERGY↗
DOE OSTI2025

Electrifying Subsea Infrastructure Through Ocean-Powered Systems

This paper explores the potential for electrifying subsea oil and gas infrastructure using wave-powered ocean energy systems. It details how a type of system, an Autonomous Offshore Power System (AOPS), can provide a potential solution and outlines the work and findings to data of joint industry project that integrates and co-demonstrates an AOPS with an electrified subsea asset. In this Project (Project), an AOPS will provide power and data communications to an electrified subsea asset at the PacWave South test site off the U.S. Oregon coast. Here, this paper provides a description and analysis of the system configuration, subsea asset integration process, and pre-deployment testing plans, along with projected benefits for the industry.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Intensive aerosol properties of boreal and regional biomass burning aerosol at Mt. Bachelor Observatory: larger and black carbon (BC)-dominant particles transported from Siberian wildfires

We characterize the aerosol physical and optical properties of 13 transported biomass burning (BB) events. BB events included long-range influence from fires in Alaskan and Siberian boreal forests transported to Mt. Bachelor Observatory (MBO) in the free troposphere (FT) over 8–14+ d and regional wildfires in northern California and southwestern Oregon transported to MBO in the boundary layer (BL) over 10 h to 3 d. Intensive aerosol optical properties and normalized enhancement ratios for BB events were derived from measured aerosol light scattering coefficients (σ scat ), aerosol light-absorbing coefficients (σ abs ), fine particulate matter (PM 1 ), and carbon monoxide (CO) measurements made from July to September 2019, with particle size distribution collected from August to September. The observations showed that the Siberian BB events had a lower scattering Ångström exponent (SAE), a higher mass scattering efficiency (MSE; Δσ scat /ΔPM 1 ), and a bimodal aerosol size distribution with a higher geometric mean diameter (D g ). We hypothesize that the larger particles and associated scattering properties were due to the transport of fine dust alongside smoke in addition to contributions from condensation of secondary aerosol, coagulation of smaller particles, and aqueous-phase processing during transport. Alaskan and Siberian boreal forest BB plumes were transported long distances in the FT and characterized by lower absorption Ångström exponent (AAE) values indicative of black carbon (BC) dominance in the radiative budget. Significantly elevated AAE values were only observed for BB events with <1 d transport, which suggests strong production of brown carbon (BrC) in these plumes but limited radiative forcing impacts outside of the immediate region.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The influence of burn severity on dissolved organic carbon concentrations across a stream network differs based on seasonal wetness conditions

Large, high-severity wildfires in many regions across the globe have increased concerns about their impacts on carbon cycling in watersheds. Altered sources of carbon and changes in catchment hydrology after wildfire can lead to shifts in dissolved organic carbon (DOC) concentrations in streams, which can have negative impacts on aquatic ecosystem health and downstream drinking-water treatment. Despite its importance, post-fire DOC responses remain relatively unconstrained in the literature, and we lack critical knowledge of how burn severity, landscape elements, and climate interact to affect DOC concentrations. To improve our understanding of the impact of burn severity on DOC concentrations, we measured DOC at 129 sites across a stream network extending upstream, within, and downstream of a large, high-severity wildfire in Oregon, USA. We collected samples across the study sub-basin during four distinct seasonal wetness conditions. We used our high-spatial-resolution data to develop spatial stream network (SSN) models to predict DOC across the stream network and to improve our understanding of the controls on DOC concentrations. Spatially, we found no obvious wildfire signal – instead, we observed a pattern of increasing DOC concentrations from the high-elevation headwaters to the sub-basin outlet, while the mainstem maintained consistently low DOC concentrations. This suggests that effects from large wildfires may be “averaged” out at higher stream orders and larger spatial scales. When we grouped DOC concentrations by burn severity group, we observed a significant decrease in the variability of DOC concentrations in the moderate and high burn severity sub-catchments. However, our SSN models were able to predict decreases in DOC concentrations with increases in burn severity across the stream network. Decreases in DOC concentrations were also highly variable across seasonal wetness conditions, with the greatest (–1.40 to –1.64 mg L –1 ) decrease occurring in the high-severity group during the wetting season. Additionally, our models indicated that in all seasons, baseflow index was more influential in predicting DOC concentrations than burn severity was, indicating that groundwater discharge can obscure the impacts of wildfire in a stream network. Overall, our results suggested that landscape characteristics can regulate the DOC response to wildfire. Moreover, our results also indicated that the seasonal timing of sampling can influence the observed response of DOC concentrations to wildfire.

54 ENVIRONMENTAL SCIENCES↗