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Category: Technology
  • VI Preferential Pathways: Rule or Exception

    Abstract: Trichloroethylene (TCE) releases from leaks and spills next to a large government building occurred over several decades with the most recent event occurring 20 years ago. In response to a perceived conventional vapor intrusion (VI) issue a sub-slab depressurization system (SSDS) was installed 6 years ago. The SSDS is operating within design limits and has achieved building TCE vapor concentration reductions. However, subsequent periodic TCE vapor spikes based on daily HAPSITE™ measurements indicate additional source(s). Two rounds of smoke tests conduct-ed in 2017 and 2018 involved introduction of smoke into a sanitary sewer and storm drain manholes located on effluent lines coming from the building until smoke was observed exiting system vents on the roof. Smoke testing revealed many leaks in both the storm sewer and sanitary sewer systems within the building. Sleuthing of the VI source term using a portable HAPSITE™ indicate elevated vapor TCE levels correspond with observed smoke emanation from utility lines. In some instances, smoke odors were perceived but no leak or suspect pipe was identified suggesting the odor originates from an unidentified pipe located behind or enclosed in a wall. Sleuthing activities also found building roof materials explain some of the elevated TCE levels on the 2nd floor. A relationship was found between TCE concentrations in the roof truss area, plenum space above 2nd floor offices, and breathing zone of 2nd floor offices. Installation of an external blower in the roof truss space has greatly reduced TCE levels in the plenum and office spaces. Preferential VI pathways and unexpected source terms may be overlooked mechanisms as compared to conventional VI.
  • Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals with High-Throughput Cell-Based Androgen Receptor Bioassay Data

    Abstract: Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.
  • Influence of High Volumes of Silica Fume on the Rheological Behavior of Oil Well Cement Pastes

    Abstract: Specialized classes of concrete, such as ultra-high-performance concrete, use volumes of silica fume in concrete that are higher than those in conventional concrete, resulting in increased water demand and mixing difficulty. This study considered the effects of eight different silica fumes in three dosages (10%, 20%, 30%) with three w/b (0.20, 0.30, 0.45) on rheological behavior as characterized by the Herschel-Bulkley model. Results indicated that the specific source of silica fume used, in addition to dosage and w/b, had a significant effect on the rheological behavior. As such, all silica fumes cannot be treated as equivalent or be directly substituted one for another without modification of the mixture proportion. The rheology of cement pastes is significantly affected by the physical properties of silica fume more so than any chemical effects.
  • 2017 Hurricane Season: Recommendations for a Resilient Path Forward for the Marine Transportation System

    Abstract: In October 2017, the Coordinating Board of the US Committee on the Marine Transportation System (MTS) tasked the MTS Resilience Integrated Action Team (RIAT) to identify the impacts, best practices, and lessons learned by federal agencies during the 2017 hurricane season. The RIAT studied the resiliency of the MTS by targeting its ability to prepare, respond, recover, and adapt to and from disruptions by turning to the collective knowledge of its members. Utilizing interagency data calls and a targeted workshop, the RIAT gauged the disruptive effect of the 2017 hurricane season and how Hurricanes Harvey, Irma, and Maria affected the operating status of at least 45 US ports across three major regions. This report identifies recommendations to better understand how the MTS can prepare for future storms and identifies activities by federal agencies that are contributing towards resilience. Such actions include hosting early pre-storm preparedness meetings, prioritizing communication between agencies and information distribution, and maintaining or updating existing response plans. Recommendations also target challenges experienced such as telecommunication and prioritization assistance to ports and critical infrastructure. Finally, the report offers opportunities to minimize the impacts experienced from storms and other disruptions to enhance the resilience of the MTS and supporting infrastructure.
  • An Epigenetic Modeling Approach for Adaptive Prognostics of Engineered Systems

    Abstract: Prognostics and health management (PHM) frameworks are widely used in engineered systems, such as manufacturing equipment, aircraft, and vehicles, to improve reliability, maintainability, and safety. Prognostic information for impending failures and remaining useful life is essential to inform decision-making by enabling cost versus risk estimates of maintenance actions. These estimates are generally provided by physics-based or data-driven models developed on historical information. Although current models provide some predictive capabilities, the ability to represent individualized dynamic factors that affect system health is limited. To address these shortcomings, we examine the biological phenomenon of epigenetics. Epigenetics provides insight into how environmental factors affect genetic expression in an organism, providing system health information that can be useful for predictions of future state. The means by which environmental factors influence epigenetic modifications leading to observable traits can be correlated to circumstances affecting system health. In this paper, we investigate the general parallels between the biological effects of epigenetic changes on cellular DNA to the influences leading to either system degradation and compromise, or improved system health. We also review a variety of epigenetic computational models and concepts, and present a general modeling framework to support adaptive system prognostics.
  • Hydrocarbon Treatability Study of Antarctica Soil with Fenton’s Reagent

    Abstract: The study objectives were to determine the effectiveness of Fenton’s Reagent and Modified Fenton’s Reagent in reducing Total Petroleum Hydrocarbon (TPH) concentrations in petroleum-contaminated soil from McMurdo Station, Antarctica. Comparisons of the contaminated soils were made, and a treatability study was completed and documented. This material was presented at the Association for Environmental Health and Sciences Foundation (AEHS) 30th Annual International Conference on Soil, Water, Energy, and Air (Virtual) on March 25, 2021.
  • Sublimation of Terrestrial Permafrost and the Implications for Ice-loss Processes on Mars

    Abstract: Sublimation of ice is rate-controlled by vapor transport away from its outer surface and may have generated landforms on Mars. In ice-cemented ground (permafrost), the lag of soil particles remaining after ice loss decreases subsequent sublimation. Varying soil-ice ratios lead to differential lag development. Here we report 52 years of sublimation measurements from a permafrost tunnel near Fairbanks, Alaska, and constrain models of sublimation, diffusion through porous soil, and lag formation. We derive the first long-term in situ effective diffusion coefficient of ice-free loess, a Mars analog soil, of 9.05 × 10⁻⁶ m² s⁻¹, ~5× larger than past theoretical studies. Exposed ice-wedge sublimation proceeds ~4× faster than predicted from analogy to heat loss by buoyant convection, a theory frequently employed in Mars studies. Our results can be used to map near-surface ice-content differences, identify surface processes controlling landform formation and morphology, and identify target landing sites for human exploration of Mars.
  • Effects of Milling on the Metals Analysis of Soil Samples Containing Metallic Residues

    Abstract: Metallic residues are distributed heterogeneously onto small-arms range soils from projectile fragmentation upon impact with a target or berm backstop. Incremental Sampling Methodology (ISM) can address the spatially heterogeneous contamination of surface soils on small-arms ranges, but representative kilogram-sized ISM subsamples are affected by the range of metallic residue particle sizes in the sample. This study compares the precision and concentrations of metals in a small-arms range soil sample processed by a puck mill, ring and puck mill, ball mill, and mortar and pestle prior to analysis. The ball mill, puck mill, and puck and ring mill produced acceptable relative standard deviations of less than 15% for the anthropogenic metals of interest (Lead (Pb), Antimony (Sb), Copper (Cu), and Zinc (Zn)), with the ball mill exhibiting the greatest precision for Pb, Cu, and Zn. Precision by mortar and pestle, without milling, was considerably higher (40% to >100%) for anthropogenic metals. Media anthropogenic metal concentrations varied by more than 40% between milling methods, with the greatest concentrations produced by the puck mill, followed by the puck and ring mill and then the ball mill. Metal concentrations were also dependent on milling time, with concentrations stabilizing for the puck mill by 300 s but still increasing for the ball mill over 20 h. Differences in metal concentrations were not directly related to the surface area of the milled sample. Overall, the tested milling methods were successful in producing reproducible data for soils containing metallic residues. However, the effects of milling type and time on concentrations require consideration in environmental investigations.
  • Modernizing Environmental Signature Physics for Target Detection

    Abstract: The objective of this study was to determine the effect of environmental phenomonology on the ability to detect buried objects and to provide a predictive capability of when targets are best detectable with IR sensors. Jay Clausen presented this material at the ERDC RD20 Conference.
  • Machine Learning Analyses of Remote Sensing Measurements Establish Strong Relationships Between Vegetation and Snow Depth in the Boreal Forest of Interior Alaska

    Abstract: The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.