Resuspension, Redistribution, and Deposition of Oil-Residues to Offshore Depocenters After the Deepwater Horizon Oil Spill

The focus of this study was to determine the long-term fate of oil-residues from the 2010 Deepwater Horizon (DwH) oil spill due to remobilization, transport, and re-distribution of oil residue contaminated sediments to down-slope depocenters following initial deposition on the seafloor. We characterized hydrocarbon residues, bulk sediment organic matter, ease of resuspension, sedimentology, and accumulation rates to define distribution patterns in a 14,300 km2 area southeast of the DwH wellhead (1,500 to 2,600 m water depth). Oil-residues from the DwH were detected at low concentrations in 62% of the studied sites at specific sediment layers, denoting episodic deposition of oil-residues during 2010–2014 and 2015–2018 periods. DwH oil residues exhibited a spatial distribution pattern that did not correspond with the distribution of the surface oil slick, subsurface plume or original seafloor spatial expression. Three different regions were apparent in the overall study area and distinguished by the episodic nature of sediment accumulation, the ease of sediment resuspension, the timing of oil-residue deposition, carbon content and isotopic composition and foram fracturing extent. These data indicate that resuspension and down-slope redistribution of oil-residues occurred in the years following the DwH event and must be considered in determining the fate of the spilled oil deposited on the seafloor

Panoptic segmentation forecasting

Our goal is to forecast the near future given a set of recent observations. We think this ability to forecast, i.e., to anticipate, is integral for the success of autonomous agents which need not only passively analyze an observation but also must react to it in real-time. Importantly, accurate forecasting hinges upon the chosen scene decomposition. We think that superior forecasting can be achieved by decomposing a dynamic scene into individual 'things' and background 'stuff'. Background 'stuff' largely moves because of camera motion, while foreground 'things' move because of both camera and individual object motion. Following this decomposition, we introduce panoptic segmentation forecasting. Panoptic segmentation forecasting opens up a middle-ground between existing extremes, which either forecast instance trajectories or predict the appearance of future image frames. To address this task we develop a two-component model: one component learns the dynamics of the background stuff by anticipating odometry, the other one anticipates the dynamics of detected things. We establish a leaderboard for this novel task, and validate a state-of-the-art model that outperforms available baselines

Molecular Markers of Biogenic and Oil-Derived Hydrocarbons in Deep-Sea Sediments Following the Deepwater Horizon Spill

Following the Deepwater Horizon oil spill (DWHOS), the formation of an unexpected and extended sedimentation event of oil-associated marine snow (MOSSFA: Marine Oil Snow Sedimentation and Flocculent Accumulation) demonstrated the importance of biology on the fate of contaminants in the oceans. We used a wide range of compound-specific data (aliphatics, hopanes, steranes, triaromatic steroids, polycyclic aromatics) to chemically characterize the MOSSFA event containing abundant and multiple hydrocarbon sources (e.g., oil residues and phytoplankton). Sediment samples were collected in 2010–2011 (ERMA-NRDA programs: Environmental Response Management Application – Natural Resource Damage Assessment) and 2018 (REDIRECT project: Resuspension, Redistribution and Deposition of Deepwater Horizon recalcitrant hydrocarbons to offshore depocenter) in the northern Gulf of Mexico to assess the role of biogenic and chemical processes on the fate of oil residues in sediments. The chemical data revealed the deposition of the different hydrocarbon mixtures observed in the water column during the DWHOS (e.g., oil slicks, submerged-plumes), defining the chemical signature of MOSSFA relative to where it originated in the water column and its fate in deep-sea sediments. MOSSFA from surface waters covered 90% of the deep-sea area studied and deposited 32% of the total oil residues observed in deep-sea areas after the DWHOS while MOSSFA originated at depth from the submerged plumes covered only 9% of the deep-sea area studied and was responsible for 15% of the total deposition of oil residues. In contrast, MOSSFA originated at depth from the water column covered only 1% of the deep-sea area studied (mostly in close proximity of the DWH wellhead) but was responsible for 53% of the total deposition of oil residues observed after the spill in this area. This study describes, for the first time, a multi-chemical method for the identification of biogenic and oil-derived inputs to deep-sea sediments, critical for improving our understanding of carbon inputs and storage at depth in open ocean systems

Novice Counselors’ Conceptualizations and Experiences of Therapeutic Relationships

This qualitative study investigated three novice counselors’ experiences and characterizations of therapeutic relationships. Thematic analyses of interviews and diaries revealed six common themes: (a) the centrality of supervision and training experiences to navigating interpersonal experiences with clients; (b) anxiety about counselors’ roles in therapeutic relationships; (c) the perception of the therapeutic relationship as less directive than outside (lay) helping relationships; (d) experimentation with different interpersonal styles; (e)awareness of countertransference; and, (f) impact of therapeutic relationships on outside relationships. Findings expand upon the therapeutic relationship as a focal point for the training and supervision of novice counselors

Assessment of Turbulent Shock-Boundary Layer Interaction Computations Using the OVERFLOW Code

The performance of two popular turbulence models, the Spalart-Allmaras model and Menter s SST model, and one relatively new model, Olsen & Coakley s Lag model, are evaluated using the OVERFLOWcode. Turbulent shock-boundary layer interaction predictions are evaluated with three different experimental datasets: a series of 2D compression ramps at Mach 2.87, a series of 2D compression ramps at Mach 2.94, and an axisymmetric coneflare at Mach 11. The experimental datasets include flows with no separation, moderate separation, and significant separation, and use several different experimental measurement techniques (including laser doppler velocimetry (LDV), pitot-probe measurement, inclined hot-wire probe measurement, preston tube skin friction measurement, and surface pressure measurement). Additionally, the OVERFLOW solutions are compared to the solutions of a second CFD code, DPLR. The predictions for weak shock-boundary layer interactions are in reasonable agreement with the experimental data. For strong shock-boundary layer interactions, all of the turbulence models overpredict the separation size and fail to predict the correct skin friction recovery distribution. In most cases, surface pressure predictions show too much upstream influence, however including the tunnel side-wall boundary layers in the computation improves the separation predictions