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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Analysis of Respirator Cartridge Performance Testing on the 702-AZ Primary Exhauster for the Hanford AY/AZ Tank Farms during a Waste-Disturbing Event

Washington River Protection Solutions (WRPS) conducted tests using two types of chemical cartridges for use in air-purifying respirators (APR) to determine the period of time that the cartridges would provide adequate performance1 for APRs used to protect workers when exposed to a mixture of Chemicals of Potential Concern (COPC) from vapors exiting the 702-AZ Primary Exhauster for the Hanford AY-AZ tank farms. Unlike prior cartridge testing on the 702-AZ Primary Exhauster, the recent tests were performed during a waste-disturbing event. The Occupational Safety and Health Administration (OSHA) identifies cartridge testing as a valid approach for establishing a cartridge change schedules. Testing is commonly applied in situations where mixtures of COPCs exist, and where other approaches, such as manufacturer recommendations and modeling, are less reliable. The tests were designed and conducted to assure measurement and/or control of the key variables OSHA identified as important to estimate the cartridge service life, including temperature, humidity, COPC concentration, breathing rate, and cartridge adsorption capacity. Testing was conducted from February 10–11, 2017, on a slipstream from the 702-AZ exhauster fed to a respirator cartridge test stand developed by WRPS in collaboration with HiLine Engineering (Richland, Washington). Multipurpose respirator cartridges, SCOTT 7422-SD1 and SCOTT 7422-SC1 (SCOTT Safety, Monroe, North Carolina), were assessed on separate days. Sample media (sorbent tubes) and canisters (e.g., Summa) sampling were used to collect samples of the vapor stream entering and exiting the respirator cartridge and were subsequently analyzed for COPC concentrations. Pacific Northwest National Laboratory was tasked with conducting an independent analysis of the analytical results and making recommendations based on the results for respiratory cartridge performance and service life.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Influence of water flow on heterotrophic respiration of natural soils

New evidence highlights the importance of hydrology to microbial decomposition of organic matter in soils. The objective of this study was to build a reproducible and controlled capability for measuring soil respiration in the laboratory and to test the hypothesis: Soil respiration rate in a flowing system will stay higher compared to in static water. We tested replicates for flowing versus non-flowing soil water for two different soils. The water content was maintained at a consistent saturation in the flowing system but significantly desaturated in the no-flow soils over the course of a 10-day and 28-day experiment. The measured respiration had a significantly higher rate of change in the first day for one soil but after the first day and for the entire reaction for the second soil there was no measurable difference in respiration.

58 GEOSCIENCES↗
DOE OSTI2020

Analysis of Respirator Cartridge Performance Testing on a Hanford AW Tank Farm Exhauster Slipstream

Washington River Protection Solutions (WRPS) conducted tests on two types of chemical cartridges for use in air-purifying respirators (APR) to determine the period of time that the cartridges would provide adequate performance for APRs used to protect workers when exposed to a mixture of Chemicals of Potential Concern (COPCs) from vapors exiting the exhauster for the Hanford AW tank farm. The Occupational Safety and Health Administration (OSHA) identifies cartridge testing as a valid approach for establishing a cartridge change schedules. Testing is commonly applied in situations where mixtures of COPCs exist, and where other approaches, such as manufacturer recommendations and modeling, are less reliable. The tests were designed and conducted to assure measurement and/or control of the key variables OSHA identified as important to estimate cartridge service-life, including temperature, humidity, COPC concentration, breathing rate, and cartridge adsorption capacity. Testing was conducted from September 23-25, 2016, on a slipstream from the AW exhauster, under static conditions fed to a respirator cartridge test stand developed by WRPS in collaboration with HiLine Engineering (Richland, Washington). Multipurpose respirator cartridges, SCOTT 7422-SD1 and SCOTT 7422-SC1 (SCOTT Safety, Monroe, North Carolina), were assessed on separate days. Sample media (sorbent tubes) were used to collect samples of the vapor stream entering and exiting the respirator cartridge, and were subsequently analyzed for COPC concentrations. Pacific Northwest National Laboratory was tasked with conducting an independent analysis of the analytical results and making recommendations based on the results for respiratory cartridge performance and service life.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

NIST: Soil Respiration, Moisture, Temperature, Chemistry; and Fine Root Measurements from a Transect Through a Forest Edge, Gaithersburg, Maryland, 2017-2021

This dataset contains soil respiration, moisture, temperature, and chemistry, as well as fine root measurements from the National Institute of Standards and Technology (NIST) Forested Optical Reference for Evaluating Sensor Technology (FOREST) research facility at Gaithersburg, Maryland. Measurements were taken at an existing transect array that begins in a grassy meadow, crosses a sharp forest edge, then a small stream, and finally extends upwards in the interior of the forest at the top of a ridge. There are 6 different landscape positions replicated across three transects in the array. Soil respiration was measured during growing seasons in 2017-2019 (2017-06-02 to 2020-02-27). Pedons (1 m3) were isolated from surrounding tree roots using trenching and a fabric to inhibit root ingrowth. Flux measurements inside the pedons were thus assumed to represent heterotrophic only respiration in 2019, and these fluxes were paired with nearby fluxes assumed to represent total respiration. Deep vertical probes measured volumetric moisture content and temperature at the same points in the array every 10 cm in depth to either 90 cm or 120 cm total depth, at 15 minute intervals, from 2019-2021 (2019-07-09 to 2021-09-10). Soil core samples were collected from each of the array points for three different months in early- to mid-2019 (2019-03-19 to 2019-07-10), at three depths each. Soils were analyzed for gravimetric moisture content; pH; total carbon, nitrogen, and phosphorus; texture; microbial biomass carbon, nitrogen, and phosphorus; extractable dissolved organic carbon, nitrogen, and phosphorus; extractable nitrate and ammonia; and extracellular hydrolytic enzyme activities. The fine roots were separated from the cores and segregated by plant functional type (grass or tree species) and if they were dead or alive. Fine roots were then measured for length, surface area, diameter, and dry mass. This dataset contains four data files in comma separated (*.csv) format. These data serve to deepen our understanding of root and soil processes at forest edges and in transitional zones.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Throughfall-Reduction Drying Effects on Soil Respiration, Moisture, and Temperature in Four Distinct Lowland Panamanian Forests from 2015-2022

Objectives: Climatic drying is predicted for many tropical forests, yet effects on soil properties across moisture and soil gradients within tropical forests remain poorly characterized, hampering predictions of forest-climate feedbacks. We hypothesized that throughfall reduction (i.e. chronic drying) would suppress soil CO2 fluxes (i.e., respiration) in already-drier tropical forests by further reductions in soil moisture, but increases CO2 fluxes in wetter tropical forests by alleviating anaerobiosis and soil saturation. We conducted a field study in four distinct lowland Panamanian forests that vary in background rainfall and soil fertility. In each forest we constructed throughfall reduction structures that reduce moisture inputs to plots by ~50% (see methods). In drying and paired control plots we measured soil CO2 fluxes, soil moisture (volume/volume), soil temperature (°C), air temperature (°C), and forest floor biomass during wet-dry cycles (2015 – 2022). We also collected litterfall biomass and surveyed all tree species for diameter at breast height (dbh), and identified tree species in 2018 and 2019.Results:We found that soil moisture peaked in the wet season and declined in the dry season. Measured soil CO2 fluxes declined in the dry season and peaked in the early wet season ahead of peak soil moisture, resulting in a lower soil moisture optimum for respiration than previously modeled. Chronic throughfall exclusion also suppressed soil moisture across the four forests, and also initially suppressed soil CO2 fluxes across forests. There was sustained suppression of soil CO2 fluxes after four years in the wettest forest only (-28 ± 4% during the dry season), but elevated soil CO2 fluxes in a fertile forest after four years (+75 ± 28% during the late wet season). The unexpected negative drying effect in the wettest, most infertile forest could have resulted from reduced vertical flushing of nutrients into soils, as the drying effect increased with time. Including hydro-nutrient interactions in ecosystem models could improve predictions of tropical forest-climate feedbacks. Datasets included: Datasets included here include .csv and .xls files for soil respiration (carbon dioxide flux) collected using the LiCor (see methods), air temperature °C , soil moisture (vl/vl), and soil temperature °C. Soil moisture and soil temperature were collected at 0-10cm depths using hand-held probes (see methods). Data are on ~3 week timescales from 2015-2022 with some gaps. There is also a .kml file that includes coordinates for all 32 plots included in the study of four forests (n = 4 throughfall reduction and n = 4 control plots per site). No special software is needed to open these files.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Impact of burning on soil properties and respiration; Wood Buffalo National Park, Canada; June 2019

In this study, we used burn simulations on boreal forest soil under high and low moisture conditions to investigate the effect of burning on soil temperature, soil properties such as pH, total C and total C, and soil respiration. Location and site characteristic data of 19 sampling locations within Wood Buffalo National Park, Canada are included in "sampling-site-characteristics.csv". File "Temp-all-burns.csv" contains temperature data collected 2 to 9 cm below the soil surface during 2 min. heat treatment (i.e. burn simulation) and the following 5-24 hrs. Following the burn simulations, total respiration from burned and unburned soil was collected via KOH base traps for up to 6 months ("raw-conductivity-readings.csv"). The metadata file ("all-sample-metadata.csv") contains measurements of soil pH, total C, and total N of O horizon and underlying mineral soil as well as model fits for respiration data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Models, data, and scripts associated with “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning”

This data package is associated with the publication “Prediction of Distributed River Sediment Respiration Rates using Community-Generated Data and Machine Learning’’ submitted to the Journal of Geophysical Research: Machine Learning and Computation (Scheibe et al. 2024). River sediment respiration observations are expensive and labor intensive to obtain and there is no physical model for predicting this quantity. The Worldwide Hydrobiogeochemisty Observation Network for Dynamic River Systems (WHONDRS) observational data set (Goldman et al.; 2020) is used to train machine learning (ML) models to predict respiration rates at unsampled sites. This repository archives training data, ML models, predictions, and model evaluation results for the purposes of reproducibility of the results in the associated manuscript and community reuse of the ML models trained in this project. One of the key challenges in this work was to find an optimum configuration for machine learning models to work with this feature-rich (i.e. 100+ possible input variables) data set. Here, we used a two-tiered approach to managing the analysis of this complex data set: 1) a stacked ensemble of ML models that can automatically optimize hyperparameters to accelerate the process of model selection and tuning and 2) feature permutation importance to iteratively select the most important features (i.e. inputs) to the ML models. The major elements of this ML workflow are modular, portable, open, and cloud-based, thus making this implementation a potential template for other applications. This data package is associated with the GitHub repository found at Please see the file level metadata (flmd; “sl-archive-whondrs_flmd.csv”) for a list of all files contained in this data package and descriptions for each. Please see the data dictionary (dd; “sl-archive-whondrs_dd.csv”) for a list of all column headers contained within comma separated value (csv) files in this data package and descriptions for each. The GitHub repository is organized into five top-level directories: (1) “input_data” holds the training data for the ML models; (2) “ml_models” holds machine learning models trained on the data in “input_data”; (3) “scripts” contains data preprocessing and postprocessing scripts and intermediate results specific to this data set that bookend the ML workflow; (4) “examples” contains the visualization of the results in this repository including plotting scripts for the manuscript (e.g., model evaluation, FPI results) and scripts for running predictions with the ML models (i.e., reusing the trained ML models); (5) “output_data” holds the overall results of the ML model on that branch. Each trained ML model resides on its own branch in the repository; this means that inputs and outputs can be different branch-to-branch. Furthermore, depending on the number of features used to train the ML models, the preprocessing and postprocessing scripts, and their intermediate results, can also be different branch-to-branch. The “main-*” branches are meant to be starting points (i.e. trunks) for each model branch (i.e. sprouts). Please see the Branch Navigation section in the top-level README.md in the GitHub repository for more details. There is also one hidden directory “.github/workflows”. This hidden directory contains information for how to run the ML workflow as an end-to-end automated GitHub Action but it is not needed for reusing the ML models archived here. Please the top-level README.md in the GitHub repository for more details on the automation.

13C↗
DOE OSTI2024

Remote Sensing and GIS data at 1km-grid over Chesapeake Bay used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”

The package contains the data layers used in “He et al. 2024, Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem”. The study aims to use multi-source remote sensing and GIS datasets to investigate the spatial heterogeneity and identify spatial zones with similar environmental characteristics and understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. We employed unsupervised hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, then determined the main driving factors using Random Forest regression and SHapley Additive exPlanations (SHAP). Spatial data layers include soil respiration, kernel Normalized Difference Vegetation Index (kNDVI) computed from Harmonized Landsat 8 and Sentinel-2 time series, climate variables from the Daymet dataset, land cover, biodiversity, topographical metrics, soil property, and tidal elevation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Effective antiviral coatings for deactivating SARS-CoV-2 virus on N95 respirator masks or filters

The application of antiviral coatings to masks and respirators is a potential mitigating step toward reducing viral transmission during the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The use of appropriate masks, social distancing, and vaccines is the immediate solution for limiting the viral spread and protecting people from this virus. N95 respirator masks are effective in filtering the virus particles, but they cannot kill or deactivate the virus. We report a possible approach to deactivating SARS-CoV-2 by applying an antimicrobial coating (Goldshield 75) to masks and respirators, rendering them suitable for repeated use. Masks coated with Goldshield 75 demonstrated continuous inactivation of the Alpha and Beta variants of the SARS-CoV-2 over a 3-day period and no loss of inactivation when stored at temperatures at 50 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Three-Dimensional Analysis of Particle Distribution on Filter Layers inside N95 Respirators by Deep Learning

The global COVID-19 pandemic has changed many aspects of daily lives. Wearing personal protective equipment, especially respirators (face masks), has become common for both the public and medical professionals, proving to be effective in preventing spread of the virus. Nevertheless, a detailed understanding of respirator filtration-layer internal structures and their physical configurations is lacking. Here, we report three-dimensional (3D) internal analysis of N95 filtration layers via X-ray tomography. Using deep learning methods, we uncover how the distribution and diameters of fibers within these layers directly affect contaminant particle filtration. The average porosity of the filter layers is found to be 89.1%. Contaminants are more efficiently captured by denser fiber regions, with fibers <1.8 μm in diameter being particularly effective, presumably because of the stronger electric field gradient on smaller diameter fibers. This study provides critical information for further development of N95-type respirators that combine high efficiency with good breathability.

42 ENGINEERING↗
DOE OSTI2023

Thermal acclimation of plant photosynthesis and autotrophic respiration in a northern peatland

Peatlands contain one-third of global soil carbon (C), but the responses of peatland ecosystems to long-term warming are not well understood. Here, we pursue an emergent understanding of warming effects on ecosystem C fluxes at peatlands by constraining a process-oriented model, the terrestrial ECOsystem model, with observational data from a long-term warming experiment at the Spruce and Peatland Responses Under Changing Environments site. Model-based assessments show that ecosystem-level photosynthesis and autotrophic respiration exhibited significant thermal acclimation, with temperature sensitivities being linearly decreased with warming. Using the thermal-acclimated parameter values, simulated gross primary production, net primary production, and plant autotrophic respiration (R a ), were all lower than those simulated with non-thermal acclimated parameter values. In contrast, ecosystem respiration simulated with thermal acclimated parameter values was higher than that simulated with non-thermal acclimated parameter values. Net ecosystem CO 2 exchange was much higher after constraining model parameters with observational data from the warming treatments, releasing C at a rate of 28.3 g C m -2 yr -1 °C -1 . Our data-model integration study suggests that peatlands are likely to release more C than previously estimated. Earth system models may overestimate C uptake by peatlands under warming if physiological thermal acclimation of plants is not incorporated. Thus, it is critical to consider the long-term physiological thermal acclimation of plants in the models to better predict global C dynamics under future climate and their feedback to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A scalable method of applying heat and humidity for decontamination of N95 respirators during the COVID-19 crisis

A lack of N95 Filtering Facepiece Respirators (FFRs) during the COVID-19 crisis has placed healthcare workers at risk. It is important for any N95 reuse strategy to determine the effects that proposed protocols would have on the physical functioning of the mask, as well as the practical aspects of implementation. Here we propose and implement a method of heating N95 respirators with moisture (85°C, 60-85% humidity). We test both mask filtration efficiency and fit to validate this process. Our tests focus on the 3M 1860, 3M 1870, and 3M 8210 Plus N95 models. After five cycles of the heating procedure, all three respirators pass both quantitative fit testing (score of >100) and show no degradation of mask filtration efficiency. We also test the Chen Heng V9501 KN95 and HKYQ N95 finding no degradation of mask filtration efficiency, however even for unheated masks these scored <50 for every fit test. The heating method presented here is scalable from individual masks to over a thousand a day with a single industrial convection oven, making this method practical for local application inside health-care facilities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

ESS-DIVE reporting format for field measurements of soil respiration

This is a new data and metadata reporting format for soil respiration data, based on engagement with a wide range of researchers in the earth and ecological sciences as well as expert advisory panels. Reporting formats are intended to be ‘bottom-up’, community-developed documents and templates that can enable consistent, findable, accessible, interoperable, and reproducible (FAIR) data; our goal was a reporting format that would be relevant and useful for synthesis activities, optimizing data discoverability and usability while not placing an undue burden on data contributors. The format is split into two levels of information—metadata about chambers (used to measure soil respiration in the field), and the measured data themselves. The file data_reportingformat_template.xlsx contains information on how the data table should be structured, including field names, field descriptions, and units; the chammetadata_reportingformat_template.xlsx file contains similar information for the chamber-level metadata. Fundamentally, this format aims to enable FAIR data through an easy-to-use guide for data providers interested in formatting their data in a common way, and provide ‘future-proofing’ capabilities to support reanalyses of soil respiration data, a globally-important component of the carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Laboratory time series moisture manipulative experiment from sediment across the contiguous US: time series aerobic respiration and geochemistry (v2)

This dataset supports a broader study examining the effects of wetting and drying on hyporheic zone respiration across the contiguous United States (CONUS). The dataset provides data generated from a laboratory moisture manipulation experiment. The contents include time series aerobic respiration and moisture; dissolved oxygen; sediment geochemistry data; and field metadata (including qualitative information on instream and river corridor characteristics). Samples were collected as part of the WHONDRS CONUS-Scale Model-Sample Study (CM). This study was designed following ICON (integrated, coordinated, open, and networked) principles to facilitate a model-experiment (ModEx) iteration approach, leveraging crowdsourced sampling across the CONUS. The data package associated with the CM study is available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689. CM sampling began in April 2022 and ended in October 2023. This study uses subsamples from a subset of CM samples collected between June 2022 and June 2023. The original field samples were labeled as CM_###. Subsequent subsamples for this study were labeled as EC_###. The labels from the field samples and the EC subsamples can be mapped directly based on the digits following the prefix and underscore (i.e., EC_001 is a subsample from CM_001). See the critical details section below for more details on sample naming. This data package was originally published in August 2024. It was updated in February 2026 (v2; new and modified files). See the change history section in the readme for more details. For details on how to navigate this data package, see this infographic from the River Corridor SFA https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one folder of raw Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) data and one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) field protocol; and a (6) a subfolder with sediment sample data from the incubation experiment. The sample data subfolder contains (1) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC); (2) total nitrogen (TN); (3) adenosine triphosphate (ATP); (4) percent carbon and nitrogen; (5) effect size; (6) iron (II); (7) gravimetric moisture; (8) respiration rates and raw dissolved oxygen values; (9) specific conductance; (10) pH; (11) temperature; (12) a summary containing median values of each data type for each treatment (wet and dry); (13) methods codes; (14) FTICR-MS methods; and (15) a subfolder of 9.4 Tesla FTICR-MS data. This folder contains three subfolders, one containing the sediment .xml data files, one containing the sediment CoreMS output files, the other containing instructions and scripts for processing the files in CoreMS (https://github.com/EMSL-Computing/CoreMS). All files are .csv, .pdf, .R, .ref, or .xml.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Laboratory time series moisture manipulative experiment from sediment across San Antonio, Texas: time series aerobic respiration and geochemistry

This dataset supports a broader study examining the effects of wetting and drying on hyporheic zone respiration. The dataset provides data generated from a laboratory moisture manipulation experiment. The contents include time series aerobic respiration and moisture; dissolved oxygen; sediment geochemistry data; and field metadata (including qualitative information on instream and river corridor characteristics). Samples were collected as part of the WHONDRS Allison Veach collaboration (AV1). The data package associated with the AV1 study is available at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2529428. AV1 sampling occurred across 7 perennial and 7 intermittent streams in San Antonio, Texas. Each stream/site was visited both in summer during base flow (July-September 2023) and winter during peak flow (January-February 2024). This study uses subsamples from a subset of AV1 samples. The original field samples were labeled as AV1_###. Subsequent subsamples for this study were labeled as EV_###. The labels from the field samples and the EV subsamples can be mapped directly based on the digits following the prefix and underscore (i.e., EV_001 is a subsample from AV1_001). See the critical details section below for more details on sample naming. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) field protocol; and a (6) a subfolder with sediment sample data from the incubation experiment. The sample data subfolder contains (1) effect size; (2) iron (II); (3) gravimetric moisture; (4) respiration rates; (5) raw dissolved oxygen values and plots; (6) specific conductance; (7) pH; (8) temperature; (9) a summary containing mean, median, and standard deviation values of each data type for each treatment (wet and dry); and (10) methods codes. All files are .csv or.pdf.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluation of an Additively Manufactured Respirator for Personnel Protection from Particulates

Additively manufactured respirators (AMRs) made using rigid plastic can be fitted with repurposed filter media to yield a wearable mask capable of passing a quantitative fit test with criteria set for commercial, N95 health care particulate respirators. This has been verified through an evaluation study assessing AMR masks fabricated using open-source design files and a commercial fused-filament fabrication machine, using N95 filter cloth and a P100 respirator cartridge. A few examples of filter media not designed for human respiratory protection were also evaluated in an AMR mask but did not demonstrate filtering sufficient to pass the N95 test criteria. All attributes of an AMR mask system must be considered during design and when testing performance, including the mask frame, gasket, and the cord used to secure the mask to the face. AMR masks are not the same as commercially available respiratory masks. If AMR masks are used as personal protective equipment (PPE) during a crisis when there is a shortage of regular PPE it is important that wearers are fully informed of the differences, tradeoffs, and risks associated with their use. Quantitative fit testing and proper training on how to don and doff the AMR mask are important to help achieve the best possible outcome when they are used.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Data and Scripts Associated with the Manuscript “Water Column Respiration in the Yakima River Basin is Explained by Temperature, Nutrients and Suspended Solids”

This data package is associated with the publication “Water Column Respiration in the Yakima River Basin is Explained by Temperature, Nutrients and Suspended Solids” published in EGU Biogeochemistry (Laan et al. 2025). In this research, water column respiration (ERwc) data, surface water chemistry data, organic matter (OM) chemistry data, and publicly available geospatial data were used in analysis to evaluate the variability in ERwc at 47 sites across the Yakima River basin in Washington, USA. In addition to this 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. The data package includes the data inputs, and outputs, and R scripts to reproduce all the analyses performed in the manuscript and create manuscript figures. The data package is comprised of three main folders (Code, Data, and Figures). The Code folder is comprised of four scripts and three analysis-specific subfolders that contain the R scripts to perform the analyses described in the publication and create publication figures. The Data folder is comprised of two “.csv” files and four subfolders that contain data input and output files. The Published_Data folder contains a readme that directs the user to download the appropriate files and add to this folder when using scripts. The Figures folder includes figures from the manuscript in “.pdf” and “.png” formats and a folder with intermediate figure files. This data package is associated with a GitHub repository which can be found at https://github.com/river-corridors-sfa/rcsfa-RC2-SPS-ERwc. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Real-Time Partitioning of Diurnal Stem CO 2 Efflux into Local Stem Respiration and Xylem Transport Processes

The apparent respiratory quotient (ARQ) of tree stems, defined as the ratio of net stem CO 2 efflux (E S_CO2 ) to net stem O 2 influx (E S_O2 ), offers insights into the balance between local respiratory CO 2 production and CO 2 transported via the xylem. Traditional static chamber methods for measuring ARQ can introduce artifacts and obscure natural diurnal variations. Here, we employed an open flow-through stem chamber with ambient air coupled with cavity ring-down spectrometry, which uses the molecular properties of CO 2 and O 2 molecules to continuously measure E S_CO2 , E S_O2 , and ARQ, at the base of a California cherry tree (Prunus ilicifolia) during the 2024 growing season. Measurements across three stem chambers over 3–11-day periods revealed strong correlations between E S_CO2 and E S_O2 and mean ARQ values ranging from 1.3 to 2.9, far exceeding previous reports. Two distinct diurnal ARQ patterns were observed: daytime suppression with nighttime recovery, and a morning peak followed by gradual decline. Partitioning E S_CO2 into local respiration and xylem-transported CO 2 indicated that the latter can dominate when ARQ exceeds 2.0. Furthermore, transported CO 2 exhibited a higher temperature sensitivity than local respiration, with both processes showing declining temperature sensitivity above 20 °C. These findings underscore the need to differentiate stem CO 2 flux components to improve our understanding of whole-tree carbon cycling.

59 BASIC BIOLOGICAL SCIENCES↗