Results for “Environmental data” — Page 35

DOE OSTI2023

Los Alamos National Laboratory 2022 Annual Site Environmental Report (Rev. 2)

Los Alamos National Laboratory (Laboratory) annual site environmental reports are prepared each year by the Laboratory’s environmental organizations as required by U.S. Department of Energy Order 231.1B, Administrative Change 1, Environment, Safety, and Health Reporting, and Order 458.1, Administrative Change 4, Radiation Protection of the Public and the Environment. The chapters in this report discuss our compliance with environmental laws, regulations, and orders (Chapter 2, Compliance Summary); how we manage the Laboratory’s environmental performance and assure the quality of data from analysis of environmental samples (Chapter 3, Environmental Programs and Analytical Data Quality); how we monitor for air emissions of radioactive materials and for weather conditions (Chapter 4, Air Quality); how we monitor for effects of Laboratory operations on groundwater quality (Chapter 5, Groundwater Protection); how we monitor the levels of chemicals and radionuclides in storm water runoff and sediment (Chapter 6, Watershed Quality); how we monitor for the levels and effects of chemicals and radionuclides in plants, animals, soil, and vegetation (Chapter 7, Ecosystem Health); and finally, what radioactive dose or risk from chemical exposure that members of the public could experience as a result of Laboratory operations (Chapter 8, Public Dose and Risk Assessment).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Fair Data, Analysis Tools, User Communities, Publications, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

space biology↗

NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

open access↗

NASA NTRS1976

Provisional specification for satellite time in a geomagnetic substorm environment

Satellites in geosynchronous orbit have been experiencing operational anomalies. These anomalies are believed to be due to the environment charging the spacecraft surfaces to a point where discharges occur. In designing future satellites for long term operation at geosynchronous altitude, it is important that designers have a specification that will give the total time per year, the particle flux density, and particle energies that their satellites can be expected to encounter in these substorm environmental conditions. The limited data currently available on the environmental conditions was used to generate the provisional specification given.

Stevens, N. J.↗

NASA NTRS1977

Sea truth and environmental characterization studies of Mobile Bay, Alabama, utilizing ERTS-1, data collection platforms

The paper reports on the scientific results obtained during a feasibility study that evaluated the potential of using ERTS data collection platforms (DCPs) in the coastal environment of Mobile Bay, Alabama. The utility of instrumented buoys operated in a coastal marine environment as ERTS DCPs is demonstrated. It is shown that these platforms are capable of providing both sea-truth data for ERTS imagery studies and time-series data for event monitoring and/or environmental characterization studies.

Schroeder, W. W.↗

NASA NTRS1977

Provisional specification for satellite time in a geomagnetic environment

Satellites in geosynchronous orbit were experiencing operational anomalies. These anomalies are believed to be due to the environment charging the spacecraft surfaces to a point where discharges occur. In designing future satellites for long term operation at geosynchronous altitude, it is important that designers have a specification that will give the total time per year, the particle flux density and particle energies that their satellites can be expected to encounter in these substorm environmental conditions. The limited data currently available on the environmental conditions are used to generate the provisional specification given in this report.

Stevens, N. J.↗

NASA NTRS2013

Climate Informatics

The impacts of present and potential future climate change will be one of the most important scientific and societal challenges in the 21st century. Given observed changes in temperature, sea ice, and sea level, improving our understanding of the climate system is an international priority. This system is characterized by complex phenomena that are imperfectly observed and even more imperfectly simulated. But with an ever-growing supply of climate data from satellites and environmental sensors, the magnitude of data and climate model output is beginning to overwhelm the relatively simple tools currently used to analyze them. A computational approach will therefore be indispensable for these analysis challenges. This chapter introduces the fledgling research discipline climate informatics: collaborations between climate scientists and machine learning researchers in order to bridge this gap between data and understanding. We hope that the study of climate informatics will accelerate discovery in answering pressing questions in climate science.

Climate change↗

NASA NTRS1992

The use of remotely sensed data for operational fisheries oceanography

Satellite remote sensing data are used under two contexts in fisheries: as a tool for fisheries research and as a means to provide operational support to fishing activities. Fishing operations need synoptic data provided timely; fisheries research needs that type of data and, also, good short-term climatologies. A description is given of several experiences conducted around the world which have employed or are using satellite data for operational fisheries problems. An overview is included of the Portuguese program for fisheries support using remotely sensed data provided by satellites and in situ observations conducted by fishermen. Environmental products useful for fisheries necessarily combine satellite and in situ data. The role of fishermen as a source of good, near-real-time in situ environmental data is stressed; so far, this role seems to have been largely overlooked.

Fiuza, Armando F. G.↗

DOE OSTI2022

Subsurface redox potential and water level at the Elkhorn Slough NERR

This resource contains various hydrological, and geochemical data from Elkhorn Slough National Estuarine Research Reserve from the years 2020 and 2021. These data have been used to assess how continuous measurements of environmental variables. The data can be used to understand processes at timescales over which biochemical transformations can happen. Especially, the data were used to explain the local subsurface hydrology, and its implication, in an experimental transect in a coasta estuary. Water level and water temperature were measured in-situ with Solinst pressure transducer loggers (Ontario, Canada). Redox potential was collected using in-situ, redox sensors (Paleoterra, The Netherlands) connected to CR1000X Campbell data loggers (Logan, Utah). Meteorological data was gathered from the Elkhorn Slough meteorological station.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Ontology-Enriched Specifications Enabling Findable, Accessible, Interoperable, and Reusable Marine Metagenomic Datasets in Cyberinfrastructure Systems

Marine microbial ecology requires the systematic comparison of biogeochemical and sequence data to analyze environmental influences on the distribution and variability of microbial communities. With ever-increasing quantities of metagenomic data, there is a growing need to make datasets Findable, Accessible, Interoperable, and Reusable (FAIR) across diverse ecosystems. FAIR data is essential to developing analytical frameworks that integrate microbiological, genomic, ecological, oceanographic, and computational methods. Although community standards defining the minimal metadata required to accompany sequence data exist, they haven’t been consistently used across projects, precluding interoperability. Moreover, these data are not machine-actionable or discoverable by cyberinfrastructure systems. By making ‘omic and physicochemical datasets FAIR to machine systems, we can enable sequence data discovery and reuse based on machine-readable descriptions of environments or physicochemical gradients. In this work, we developed a novel technical specification for dataset encapsulation for the FAIR reuse of marine metagenomic and physicochemical datasets within cyberinfrastructure systems. This includes using Frictionless Data Packages enriched with terminology from environmental and life-science ontologies to annotate measured variables, their units, and the measurement devices used. This approach was implemented in Planet Microbe, a cyberinfrastructure platform and marine metagenomic web-portal. Here, we discuss the data properties built into the specification to make global ocean datasets FAIR within the Planet Microbe portal. We additionally discuss the selection of, and contributions to marine-science ontologies used within the specification. Finally, we use the system to discover data by which to answer various biological questions about environments, physicochemical gradients, and microbial communities in meta-analyses. This work represents a future direction in marine metagenomic research by proposing a specification for FAIR dataset encapsulation that, if adopted within cyberinfrastructure systems, would automate the discovery, exchange, and re-use of data needed to answer broader reaching questions than originally intended.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Environmental Testing of SolarSCADA's SSI Data Acquisition System

In April of 2022, SolarSCADA, Inc., was awarded voucher funding to conduct research and analysis at the National Renewable Energy Laboratory (NREL) per the American-Made Solar Prize. SolarSCADA produces a fully integrated data acquisition system that is designed specifically for PV fields and includes a data monitoring platform. SolarSCADA is focused providing the SSI unit with all sensors/measurements per requirements in IEC-61724-1 [1] and with utility scale bankability. The objective of the SolarSCADA voucher funding was established as follows: Leverage NREL's existing calibration and environmental testing capabilities to evaluate the stability of the SSI channels and overall systeme in response to extreme environmental conditions associated with fielded PV.

14 SOLAR ENERGY↗

NASA NTRS1983

Environmental Measurements Session summaries

Emphasis was placed on data from payloads flown on the subject flights including results from the Induced Environment Contamination monitor (IECM). Brief summaries of the vibroacoustics, loads, electromagnetic and thermal aspects of the environment, as derived from Shuttle system measurements, were presented primarily to indicate where the environment was different than observed and, therefore, where specification changes may be forthcoming. In addition, brief summaries of two somewhat unexpected effects, the vehicle glow and interaction between the low Earth environment and Shuttle payload by materials were presented as an aid in interpreting other environmental data. Papers for each payload/experiment involved in Shuttle flights were presented essentially in flight related chronological order. A significant portion of time was allocated for presentation of IECM data since this payload was flown on STS-2, STS-3, and STS-4 and, therefore, represents the largest data base relative to the contamination environment. Summaries of papers are presented.

Lehmann, J.↗

DOE OSTI2024

Data and scripts associated with “Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds” (v2)

This data package is associated with the publication “Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds” submitted to Biogeochemistry by Ryan et al., 2024 (DOI: https://doi.org/10.1007/s10533-024-01169-5). This study aims to investigate fundamental and transferable drivers of dissolved organic matter (DOM) diversity across five nested watersheds within the contiguous United States. DOM diversity was explored using ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The samples and the unprocessed FTICR-MS data used in this study are publicly available on the Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) data repository (see DOIs below). The data for the Willamette, Gunnison, Connecticut, and Deschutes basins were collected as part of a collaboration between the Watershed Rules of Life (WROL) project and Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS). The data for the Yakima River basin (YRB) was collected by the PNNL River Corridor SFA. The raw, unprocessed FTICR-MS data with additional (meta)data can be found at doi:10.15485/1895159 for WROL samples and doi:10.15485/1898912 for YRB samples. This data package contains the processed data used in the associated manuscript. This package also contains ancillary geospatial, hydrological, and geochemical information that supports the interpretation of the FTICR-MS data within Ryan et al., 2024. This data package is associated with the GitHub repository found at https://github.com/WHONDRS-Hub/rcsfa-RC4-WROL-YRB_DOM_Diversity. This data package was originally published August 2024. It was updated January 2025 (modified files). See the change history in the readme more details. At the directory level, the data package is comprised of three folders: (1) data, (2) output, and (3) src; and five additional files including the data dictionary (file ending in "_dd.csv”) and file-level metadata (file ending in “_flmd.csv”). The “src” folder contains the scripts used to process the FTICR data, conduct the analyses, and produce the manuscript figures. The inputs for these scripts are in the “data” folder and the returned outputs in the “output” folder. Inputs include temporal and spatial metadata associated with the sampling efforts, processed FTICR data, and total and normalized putative biochemical transformations per sample. Outputs include cleaned and combined data presented as tables, descriptive statistics, and plots. The file-level metadata file lists all files contained in this data package and descriptions for each. The data dictionary describes the units and definitions for each tabular data column or row header.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

A Physics-Based Data-Driven Approach for Modeling of Environmental Degradation in Elastomers

Abstract Elastomers are now commonly used in a number of industries, including aerospace, structure, transportation, shipbuilding, and automotive, due to their excellent workability, formability, and flexibility. During their activity, elastomers are subjected to harsh environmental conditions, which decreases their resilience. False predictions made early in their lives can have major financial and environmental implications. Elastomers’ performance and properties, such as strength, durability, and density, are influenced by chemical changes in these materials, known as degradation, which occurs over time. This process can alter the morphology of a polymer matrix as well as cause chain scission and cross-linking, resulting in different behaviors than that of the unaged material. To demonstrate the effect of thermaloxidative aging on the mechanical behavior of elastomers, several experimental and theoretical models have been proposed. In view of the large volume of experimental data available on micro-structural evolution in the course of aging, we propose a physics-based data-driven approach to overcome the shortcomings of both phenomenological and micro-mechanical models. This work presents a novel thermodynamically consistent, multiagent machine-learned model for predicting the constitutive behavior of cross-linked elastomers during environmental aging, such as thermo-oxidative and hydrolytic aging for various states of deformation. Single mechanism degradation changes the polymer matrix over time where it is causing chain scission, reduction of cross-links, and morphology change. To capture the idealized Mullins effect and permanent set due to the effect of single aging mechanisms on nonlinear mechanical responses of elastomers, we propose a data-driven model for simulating inelastic elements in a polymer matrix. By using a sequential order reduction, we were able to reduce the 3D stress-strain tensor mapping problem to a small number of super-constrained 1D mapping problems. To systematically classify such mapping problems into a few categories, an assembly of multiple replicated conditional neural network learning agents (L-agents) is used based on our recent work. Each category is represented by a different type of agent. The effect of deformation history, aging time, and aging temperature is captured by this model. The model is validated using a broad collection of data, ranging from our experimental results to data from the literature. In addition, thermodynamic consistency and frame independence are investigated. The most significant achievements of this model are its precision, simplicity, and prediction of inelasticity under various states of deformation. The model’s accuracy and simplicity make it a good option for commercial and industrial applications. Conveniently, due to the model modular nature, it can be expanded in the future to include viscoelasticity and non-isotropic formation for better precision.

Ghaderi, Aref↗

NASA NTRS1992

The role of the Long Duration Exposure Facility in the development of space systems

The Long Duration Exposure Facility (LDEF) presents the international, aerospace community with an unprecedented opportunity to examine the synergistic, long term, space environmental effects on systems and materials. The analysis of the data within appropriate environmental contexts is essential to the overall process of advancing the understanding of space environmental effects needed for the continuing development of strategies to improve the reliability and durability of space systems and to effectively deal with the future challenges that new space initiatives will likely present.

Sally A. Little↗

DOE OSTI2022

Triton: Environmental Monitoring Technology Development, Collision Risk Data Collection (Final Report)

In this project, we assembled a sensor suite that combines a scientific echosounder (sonar) system with video and acoustic cameras (secondary sensors). The sensor suite generates data that is amenable to automated target detection algorithms and can provide inputs to animal encounter models. We developed software (archiving software) to collect data simultaneously from all sensors in the suite, analyze the sonar data automatically in near real-time to identify time periods when targets of interest were present, and automatically archive data from the secondary sensors for these time periods. The product of the archiving software is a data set for the secondary sensors, containing data only for the times when targets of interest were determined to be present by the sonar. We performed controlled field testing to verify the operation of the sensor suite and the archiving software.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

The value of human data annotation for machine learning based anomaly detection in environmental systems

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2018

Satellite and Reanalysis Air Quality Data and Services at NASA GES DISC for Public Health Study

Outbreaks of infectious diseases and health can be influenced by airborne and water-borne pollutants. Furthermore, air and water quality are associated with climate variability, industrialization, land use and land cover change, and water resource management. It is therefore crucial to understand environment-disease connections with existing long-term observed and modeled data, particularly for development of early warning systems for infectious disease outbreaks. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) (https://disc.gsfc.nasa.gov) archives large volumes of global environment data that are useful for research and applications regarding environmental factors and public health. Examples of air quality measurements are: Daily satellite remotely sensed data, including Aerosol Index (AI), O3, SO2, CO, and NO2 from Aura/OMI (October 2014 to present), and OMPS-NPP (January 2012 to present, currently research data products only) Hourly and monthly reanalysis modeled data, including PM2.5, O3, CO, SO2, BC, dust, AOD, and aerosol types from MERRA-2 (January 1980 to present) Examples of surface meteorology and land surface measurements are: hourly, daily, and monthly satellite precipitation from TRMM (December 1997 to March 2015) 30-minute and monthly satellite precipitation from GPM (March 2014 to present); Hourly and monthly modeled surface meteorology and land surface condition from MERRA-2 (January 1980 to present) and land surface assimilation models (January 1948 to present), including precipitation, surface temperature, relative humidity, wind, and soil moisture.This presentation will give an overview of relevant environmental data at the NASA GES DISC. Through a number of use cases, such as dust events and active fires, we will introduce data services that assist in finding the right data, enable visualization and analysis of the data online, and allow downloading of data in user-preferred format.

remote sensing data↗