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DOE OSTI2022

Understanding the Physics Representation of Deep Learning Models in Environmental Applications

Deep learning (DL) models have been popular in earth and environmental modeling and analysis, which exhibit huge potential in capturing and reconstructing the non-linearity of relevant environmental processes. They are extensively used as analytical tools or emulators for multiple domains (atmosphere, land surface, ocean, and biogeochemistry). Despite their success, their internal working mechanism remains largely unknown. Such a lack of knowledge hinders the identification of physically consistent models that are fully adaptive to non-stationary climate, as well as the development of physics-informed machine learning such as physics-informed neural network (PINN). To establish preliminary knowledge and framework of such physics representation evaluation, this project focuses on an improved understanding of DL models in the environmental applications. DL models are increasingly applied to environmental modeling and prediction. However, they have been evaluated mostly from a performance perspective, and there is a gap in understanding how they represent the known physics internally. Such knowledge is especially critical when applying DL models under climate change conditions, where new inputs are likely outside the ranges of the training datasets. In this project, we reveal how the known physical processes are represented within DL models from both statistical and mechanistic perspectives. Leveraging the traditional model evaluations that focus more on the accuracies of predictions, we establish a framework that examines both the accuracy and physics representation of DL models. This analysis framework can identify DL models that make the correct predictions based on correct physics, thus enhancing the existing explainable artificial intelligence (explainable-AI) portfolio. It lays a foundation for developing novel metrics to evaluate the emerging DL models in environmental applications. This knowledge also informs the development of physics-informed DL models by revealing the direct connections between the known physical processes and specific model components or structures.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Machine Learning in Environmental Chemistry: Application to Surface Complexation Modeling

Environmental chemistry – or biogeochemistry – is the scientific discipline typically invoked when examining and quantifying groundwater or surface water contamination, and nutrient cycling in the environment. Over the last three decades, there have been significant advances in mechanistic model development to describe and predict these complex biogeochemical processes. In particular, surface complexation models (SCMs) have been developed to describe the rock/soil surface reactions of metals and radionuclides, and their partitioning between various mobile species in the aqueous phase or immobile species sorbed on solid surfaces. Often represented by a simplified linear isotherm constant – Kd – in reactive transport models, these reactions play a critical role in many environmental science applications; particularly in contamination risk assessments and nuclear waste disposal performance assessments. In the past several decades, efforts by various institutions across the world have focused on developing SCMs based on datasets from laboratory measurements, including the identification of key parameters such as equilibrium constants.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2016

NISAR Spacecraft Environmental Torque Modeling During Operations

Environmental torques imparted on the NISAR (NASA-ISRO (NASA Jet PropulsionLaboratory (JPL)-Indian Space Research Organization (ISRO) Synthetic Aperture Radar) spacecraft are modeled. The environmental torques comprise of the gravity gradient, atmospheric drag, and solar radiation pressure. These torques act as external disturbances on the spacecraft and cause momentum to accumulate. This momentum needs to be unloaded via attitude control hardware. In order to size the hardware, it is important to determine the environmental torques imparted on the spacecraft. The results indicate that the worst case gravity gradient, drag, and solar radiation pressure torques are 7.4 by 10 (sup -3) Nm (Newton meters), 6.01 by 10 (sup -3) Nm, and 14.03 by 10(sup -3) Nm, respectively.

Rizvi, Farheen↗

NASA NTRS1977

Pioneer Venus spacecraft charging model

Five environmental models were constructed to represent the solar wind and the upper, middle, and lower ionosphere of Venus. The spacecraft structure was modeled with over 140 passive electrical elements representing structural elements of the spacecraft. Electron, ion, secondary electron, and photocurrents to the spacecraft from the plasma were calculated, ignoring sheath effects. In all but one case, potentials of interest were less than 1 volt. Potential differences between widely separated points on the equipment shelf were less than 1 mV. The one area of concern is the solar panel potential when the orbiter is passing through the bowshock region.

Robinson, P. A., Jr.↗

NASA NTRS1994

A Knowledge-Based Representation Scheme for Environmental Science Models

One of the primary methods available for studying environmental phenomena is the construction and analysis of computational models. We have been studying how artificial intelligence techniques can be applied to assist in the development and use of environmental science models within the context of NASA-sponsored activities. We have identified several high-utility areas as potential targets for research and development: model development; data visualization, analysis, and interpretation; model publishing and reuse, training and education; and framing, posing, and answering questions. Central to progress on any of the above areas is a representation for environmental models that contains a great deal more information than is present in a traditional software implementation. In particular, a traditional software implementation is devoid of any semantic information that connects the code with the environmental context that forms the background for the modeling activity. Before we can build AI systems to assist in model development and usage, we must develop a representation for environmental models that adequately describes a model's semantics and explicitly represents the relationship between the code and the modeling task at hand. We have developed one such representation in conjunction with our work on the SIGMA (Scientists' Intelligent Graphical Modeling Assistant) environment. The key feature of the representation is that it provides a semantic grounding for the symbols in a set of modeling equations by linking those symbols to an explicit representation of the underlying environmental scenario.

Keller, Richard M.↗

DOE OSTI2025

Toward equitable environmental exposure modeling through convergence of data, open, and citizen sciences: an example of air pollution exposure modeling amidst increasing wildfire smoke

Exposure modeling is critical in environmental epidemiology and human health but may face challenges (e.g., skewed data, unequal error, context-insensitive validation, and computational demands). Modeling decisions reflect the intended use of the models and the values that modelers prioritize. We aimed to provide a conceptual framework and machine learning (ML) modeling protocols that address these issues. With 500m-gridded hourly PM 2.5 and O 3 levels in Illinois before, during, and after the 2023 Canadian wildfire season as a motivating example, we conducted modeling experiments to evaluate modeling methods, guided by three domains we propose based on theories of science: 1) Data Diversity, leveraging open and citizen science data to enhance inclusivity, parsimony, and representativeness; 2) Equitable Accuracy, ensuring fairly distributed uncertainties across subpopulations; and 3) Sustainable Modeling, balancing accuracy with reducing computational demands to promote accessibility for under-resourced researchers. Here, we found that ML with publicly available data can achieve high accuracy. Depending on methods, performance may vary substantially, even with identical input data. Large but skewed data may reduce performance. Misuse of cross-validation protocols can underestimate prediction error; although we observed R 2 s of ∼98 %, the modeled estimates varied significantly, indicating the need for careful model validation. By using new modeling protocols including representativeness-considered training and validation data and a new loss function, we achieved high agreement between estimates and ground-based measurements (e.g., R 2 = ∼90 % for PM 2.5 ; ∼80 % for O 3 ), equally distributed errors across sociodemographic strata and urban–rural divides, and reduction in computation time—from several weeks or months to a few days.

Exposure assessment↗

NASA NTRS1976

International Conference on Environmental Sensing and Assessment, Las Vegas, Nev., September 14-19, 1975, Proceedings. Volumes 1 & 2

The papers deal with the detection of hazardous environmental pollutants, the development of emission control plans, and the design of compliance monitoring systems. Topics include remote sensing techniques in environmental pollution monitoring, monitoring of atmospheric particulate matter, air pollution due to sulfur dioxide and other inorganic compounds, marine pollution, atmospheric aerosols, industrial pollution, and legal aspects of pollution monitoring. Other papers examine the toxic effects of heavy metals and halogenated hydrocarbons, pollution associated with waste-disposal processes, pesticide residues in soil and groundwater, evaluations of groundwater quality, and monitoring of nuclear wastes. The interaction of climate and pollution is also discussed along with global pollutant transport, environmental modeling, ambient environmental air quality, aircraft and ground-vehicle emissions, and pollution associated with energy extraction and utilization processes. Individual items are announced in this issue.

Source record↗

DOE OSTI2022

Produced Water-Economic, Socio, Environmental Simulation Model (PW-ESEim) Model: Proof-of-Concept for Southeastern New Mexico

A proof-of-concept tool, the Produced Water-Economic, Socio, Environmental Simulation model (PW-ESESim), was developed to support ease of analysis. The tool was designed to facilitate head-to-head comparison of alternative produced water source, treatment, and reuse water management strategies. A graphical user interface (GUI) guides the user through the selection and design of alternative produced water treatment and reuse strategies and the associated health and safety risk and economic benefits. At the highest conceptual level, alternative water strategies include the selection of a source water (locally or regionally available produced water), treatment strategy (pre-treatment, physical, chemical, biological, desalination, and post-treatment processes) and product water purpose (e.g., irrigation, industrial processing, environmental). After selection of these details, the PW-ESESim output a number of key economic, societal, environmental, public/ecological health and safety metrics to support user decision-making; specific examples include, cost of treatment, improvements in freshwater availability, human and ecologic health impacts and growth in local jobs and the economy. Through the simulation of different produced water treatment and management strategies, tradeoffs are identified and used to inform fit-for-purpose produced water treatment and reuse management decisions. While the tool was initially designed using Southeastern New Mexico (Permian Basin) as a case study, the general design of the PW-ESESim model can be extended to support other oil and gas regions of the U.S.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2006

A Dynamic/Anisotropic Low Earth Orbit (LEO) Ionizing Radiation Model

The International Space Station (ISS) provides the proving ground for future long duration human activities in space. Ionizing radiation measurements in ISS form the ideal tool for the experimental validation of ionizing radiation environmental models, nuclear transport code algorithms, and nuclear reaction cross sections. Indeed, prior measurements on the Space Transportation System (STS; Shuttle) have provided vital information impacting both the environmental models and the nuclear transport code development by requiring dynamic models of the Low Earth Orbit (LEO) environment. Previous studies using Computer Aided Design (CAD) models of the evolving ISS configurations with Thermo Luminescent Detector (TLD) area monitors, demonstrated that computational dosimetry requires environmental models with accurate non-isotropic as well as dynamic behavior, detailed information on rack loading, and an accurate 6 degree of freedom (DOF) description of ISS trajectory and orientation.

Badavi, Francis F.↗

DOE OSTI2022

Quantifying subsurface parameter and transport uncertainty using surrogate modelling and environmental tracers

Here, we combine physics-based groundwater reactive transport modelling with machine-learning techniques to quantify hydrogeological model and solute transport predictive uncertainties. We train an artificial neural network (ANN) on a dataset of groundwater hydraulic heads and 3 H concentrations generated using a high-fidelity groundwater reactive transport model. Using the trained ANN as a surrogate model to reproduce the input–output response of the high-fidelity reactive transport model, we quantify the posterior distributions of hydrogeological parameters and hydraulic forcing conditions using Markov chain Monte Carlo calibration against field observations of groundwater hydraulic heads and 3 H concentrations. We demonstrate the methodology with a model application that predicts Chlorofluorocarbon-12 (CFC-12) solute transport at a contaminated field site in Wyoming, United States. Our results show that including 3 H observations in the calibration dataset reduced the uncertainty in the estimated permeability field and infiltration rates, compared to calibration against hydraulic heads alone. However, predictive uncertainty quantification shows that CFC-12 transport predictions conditioned to the parameter posterior distributions cannot reproduce the field measurements. We found that calibrating the model to hydraulic head and 3 H observations results in groundwater mean ages that are too large to explain the observed CFC-12 concentrations. The coupling of the physics-based reactive transport model with the machine-learning surrogate model allows us to efficiently quantify model parameter and predictive uncertainties, which is typically computationally intractable using reactive transport models alone.

58 GEOSCIENCES↗

NASA NTRS1971

Development of mathematical models of environmental physiology

Selected articles concerned with mathematical or simulation models of human thermoregulation are presented. The articles presented include: (1) development and use of simulation models in medicine, (2) model of cardio-vascular adjustments during exercise, (3) effective temperature scale based on simple model of human physiological regulatory response, (4) behavioral approach to thermoregulatory set point during exercise, and (5) importance of skin temperature in sweat regulation.

Stolwijk, J. A. J.↗

NASA NTRS2023

Technical Note: NASAaccess – A Tool for Access, Reformatting, and Visualization of Remotely Sensed Earth Observation and Climate Data

The National Aeronautics and Space Administration (NASA) has launched a new initiative, the Open-Source Science Initiative (OSSI), to enable and support science towards openness. The OSSI supports open-source software development and dissemination. In this work, we present NASAaccess, which is an open-source software package and web-based environmental modeling application for earth observation data accessing, reformatting, and presenting quantitative data products. The main objective of developing the NASAaccess platform is to facilitate exploration, modeling, and understanding of earth data for scientists, stakeholders, and concerned citizens whose objectives align with the new OSSI goals. The NASAaccess platform is available as software packages (i.e., the R and conda packages) as well as an interactive-format web-based environmental modeling application for earth observation data developed with Tethys Platform. NASAaccess has been envisioned as lowering the technical barriers and simplifying the process of accessing scalable distributed computing resources and leveraging additional software for data and computationally intensive modeling frameworks. Specifically, NASAaccess has been developed to meet the need for seamless earth observation remote-sensing and climate data ingestion into various hydrological modeling frameworks. Moreover, NASAaccess is also contributing to keeping interested parties and stakeholders engaged with environmental modeling, accessing the information available in various remote-sensing products. NASAaccess' current capabilities cover various NASA datasets and products that include the Global Precipitation Measurement (GPM) data products, the Global Land Data Assimilation System (GLDAS) land surface states and fluxes, and the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) Coupled Model Intercomparison Project Phase 5 (CMIP5) and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate change dataset products.

Ibrahim Nourein Mohammed↗

DOE OSTI2022

Urban working groups in the IAEA’s model testing programmes: overview from the MODARIA I and MODARIA II programmes

The IAEA’s model testing programmes have included a series of Working Groups concerned with modelling radioactive contamination in urban environments. These have included the Urban Working Group of Validation of Environmental Model Predictions (1988–1994), the Urban Remediation Working Group of Environmental Modelling for Radiation Safety (EMRAS) (2003–2007), the Urban Areas Working Group of EMRAS II (2009–2011), the Urban Environments Working Group of (Modelling and Data for Radiological Impact Assessments) MODARIA I (2013–2015), and most recently, the Urban Exposures Working Group of MODARIA II (2016–2019). The overarching objective of these Working Groups has been to test and improve the capabilities of computer models used to assess radioactive contamination in urban environments, including dispersion and deposition processes, short-term and long-term redistribution of contaminants following deposition events, and the effectiveness of various countermeasures and other protective actions, including remedial actions, in reducing contamination levels, human exposures, and doses to humans. This paper describes the exercises conducted during the MODARIA I and MODARIA II programmes. These exercises have included short-range and mid-range atmospheric dispersion exercises based on data from field tests or tracer studies, hypothetical urban dispersion exercises, and an exercise based on data collected after the Fukushima Daiichi accident. We report improvement of model capabilities will lead to improvements in assessing various contamination scenarios (real or hypothetical), and in turn, to improved decision-making and communication with the public following a nuclear or radiological emergency.

61 RADIATION PROTECTION AND DOSIMETRY↗

NASA NTRS1975

Viking 1975 orbiter development test model/Lander dynamic test model, dynamic environmental testing

A series of dynamic environmental tests on the Viking 1975 Orbiter/Lander model provided information regarding damping and linearity. The quality of mode isolation was made with orthogonality checks. A pretest criterion established for orthogonality provided good correlation with test results. Part of the post-test analysis was dedicated to a review of instrumentation selection for control and/or response limiting. Limited load values were also examined.

Milder, G.↗

NASA NTRS1990

Space-time modeling using environmental constraints in a mobile robot system

Grid-based models of a robot's local environment have been used by many researchers building mobile robot control systems. The attraction of grid-based models is their clear parallel between the internal model and the external world. However, the discrete nature of such representations does not match well with the continuous nature of actions and usually serves to limit the abilities of the robot. This work describes a spatial modeling system that extracts information from a grid-based representation to form a symbolic representation of the robot's local environment. The approach makes a separation between the representation provided by the sensing system and the representation used by the action system. Separation allows asynchronous operation between sensing and action in a mobile robot, as well as the generation of a more continuous representation upon which to base actions.

Slack, Marc G.↗

NASA NTRS2016

Characterization of SiCSiC Composites in Support of Environmental Degradation Modeling

SiCSiC (silicon carbide fiber reinforced silicon carbide) composites are candidate materials for various turbine engine applications because of their high specific strength and good creep and oxidation resistance at elevated temperatures. This study was performed to characterize the microstructure of a melt infiltrated (MI) SiCSiC, and to examine environmental degradation mechanisms occurring in precracked MI SiCSiC CMC specimens under tensile stresses of 30 ksi or less at 815C in dry air or argon. In addition, the oxidation of the BN interface was characterized at815C, and crack opening displacement as a function of stress measurements were made. This material characterization is being performed to obtain data to support NASA GRC modeling of SiCSiC environmental degradation. The comparison of experimentally-observed phenomena with model predictions can lead to improved understanding of material degradation mechanisms.

SiC/SiC composites↗

NASA NTRS2011

Modeling the Environmental Impact of Air Traffic Operations

There is increased interest to understand and mitigate the impacts of air traffic on the climate, since greenhouse gases, nitrogen oxides, and contrails generated by air traffic can have adverse impacts on the climate. The models described in this presentation are useful for quantifying these impacts and for studying alternative environmentally aware operational concepts. These models have been developed by leveraging and building upon existing simulation and optimization techniques developed for the design of efficient traffic flow management strategies. Specific enhancements to the existing simulation and optimization techniques include new models that simulate aircraft fuel flow, emissions and contrails. To ensure that these new models are beneficial to the larger climate research community, the outputs of these new models are compatible with existing global climate modeling tools like the FAA's Aviation Environmental Design Tool.

Chen, Neil↗

NASA NTRS1986

Analytical investigation of the dynamics of tethered constellations in Earth orbit, phase 2

The following topics related to the dynamics of the 4-mass tethered system are addressed: (1) the development of damping algorithms for damping the out-of-plane libration of the system and the interaction of the out-of-plane control with the other degrees of freedom; and (2) the development of environmental models to be added to the dynamics simulation computer code. The environmental models are specifically a new drag routine based on the Jacchia's 1977 model, a J(2) model and an accurate thermal model of the wire. Regarding topic (1) a survey of various out-of-plane libration control laws was carried out. Consequently a yo-yo control law with amplitude of the tether length variation proportional to the amplitude of the out-of-game libration has been selected. This control law provides good damping when applied to a (theoretical) two-dimensional system. In the actual 3-dimensional 4-mass tethered system, however, energy is transferred to the least damped degrees of freedom (the out-of-plane lateral deflections are still undamped in the present simulations) in such a way as to decrease the effectiveness of the algorithm for out-of-plane libration control. The addition of damping algorithms for the out-of-plane lateral deflections is therefore necessary.

Lorenzini, E. C.↗