Potential impacts of offshore oil spills on polar bears in the Chukchi Sea

Sea ice decline is anticipated to increase human access to the Arctic Ocean allowing for offshore oil and gas development in once inaccessible areas. Given the potential negative consequences of an oil spill on marine wildlife populations in the Arctic, it is important to understand the magnitude of impact a large spill could have on wildlife to inform response planning efforts. In this study we simulated oil spills that released 25,000 barrels of oil for 30 days in autumn originating from two sites in the Chukchi Sea (one in Russia and one in the U.S.) and tracked the distribution of oil for 76 days. We then determined the potential impact such a spill might have on polar bears (Ursus maritimus) and their habitat by overlapping spills with maps of polar bear habitat and movement trajectories. Only a small proportion (1 -10%) of high-value polar bear sea ice habitat was directly affected by oil sufficient to impact bears. However, 27-38% of polar bears in the region were potentially exposed to oil. Oil consistently had the highest probability of reaching Wrangel and Herald islands, important areas of denning and summer terrestrial habitat. Oil did not reach polar bears until approximately 3 weeks after the spills. Our study found the potential for significant impacts to polar bears under a worst case discharge scenario, but suggests that there is a window of time where effective containment efforts could minimize exposure to bears. Our study provides a framework for wildlife managers and planners to assess the level of response that would be required to treat exposed wildlife and where spill response equipment might be best stationed. While the size of spill we simulated has a low probability of occurring, it provides an upper limit for planners to consider when crafting response plans

Validation of Oil Trajectory and Fate Modeling of the Deepwater Horizon Oil Spill

Trajectory and fate modeling of the oil released during the Deepwater Horizon blowout was performed for April to September of 2010 using a variety of input data sets, including combinations of seven hydrodynamic and four wind models, to determine the inputs leading to the best agreement with observations and to evaluate their reliability for quantifying exposure of marine resources to floating and subsurface oil. Remote sensing (satellite imagery) data were used to estimate the amount and distribution of floating oil over time for comparison with the model’s predictions. The model-predicted locations and amounts of shoreline oiling were compared to documentation of stranded oil by shoreline assessment teams. Surface floating oil trajectory and distribution was largely wind driven. However, trajectories varied with the hydrodynamic model used as input, and was closest to observations when using specific implementations of the HYbrid Coordinate Ocean Model modeled currents that accounted for both offshore and nearshore currents. Shoreline oiling distributions reflected the paths of the surface oil trajectories and were more accurate when westward flows near the Mississippi Delta were simulated. The modeled movements and amounts of oil floating over time were in good agreement with estimates from interpretation of remote sensing data, indicating initial oil droplet distributions and oil transport and fate processes produced oil distribution results reliable for evaluating environmental exposures in the water column and from floating oil at water surface. The model-estimated daily average water surface area affected by floating oil \u3e1.0 g/m2 was 6,720 km2, within the range of uncertainty for the 11,200 km2 estimate based on remote sensing. Modeled shoreline oiling extended over 2,600 km from the Apalachicola Bay area of Florida to Terrebonne Bay area of Louisiana, comparing well to the estimated 2,100 km oiled based on incomplete shoreline surveys

Critical Behavior of the 3d Random Field Ising Model: Two-Exponent Scaling or First Order Phase Transition?

In extensive Monte Carlo simulations the phase transition of the random field Ising model in three dimensions is investigated. The values of the critical exponents are determined via finite size scaling. For a Gaussian distribution of the random fields it is found that the correlation length $\xi$ diverges with an exponent $\nu=1.1\pm0.2$ at the critical temperature and that $\chi\sim\xi^{2-\eta}$ with $\eta=0.50\pm0.05$ for the connected susceptibility and $\chi_{\rm dis}\sim\xi^{4-\bar{\eta}}$ with $\bar{\eta}=1.03\pm0.05$ for the disconnected susceptibility. Together with the amplitude ratio $A=\lim_{T\to T_c}\chi_{\rm dis}/\chi^2(h_r/T)^2$ being close to one this gives further support for a two exponent scaling scenario implying $\bar{\eta}=2\eta$. The magnetization behaves discontinuously at the transition, i.e. $\beta=0$, indicating a first order transition. However, no divergence for the specific heat and in particular no latent heat is found. Also the probability distribution of the magnetization does not show a multi-peak structure that is characteristic for the phase-coexistence at first order phase transition points.Comment: 14 pages, RevTeX, 11 postscript figures (fig9.ps and fig11.ps should be printed separately

Oil fate and mass balance for the Deepwater Horizon oil spill

Based on oil fate modeling of the Deepwater Horizon spill through August 2010, during June and July 2010, ~89% of the oil surfaced, ~5% entered (by dissolving or as microdroplets) the deep plume (\u3e900 m), and ~6% dissolved and biodegraded between 900 m and 40 m. Subsea dispersant application reduced surfacing oil by ~7% and evaporation of volatiles by ~26%. By July 2011, of the total oil, ~41% evaporated, ~15% was ashore and in nearshore (\u3c10 m) sediments, ~3% was removed by responders, ~38.4% was in the water column (partially degraded; 29% shallower and 9.4% deeper than 40 m), and ~2.6% sedimented in waters \u3e10 m (including 1.5% after August 2010). Volatile and soluble fractions that did not evaporate biodegraded by the end of August 2010, leaving residual oil to disperse and potentially settle. Model estimates were validated by comparison to field observations of floating oil and atmospheric emissions

Photochemical oxidation of oil reduced the effectiveness of aerial dispersants applied in response to the Deepwater Horizon spill

Author Posting. © American Chemical Society, 2018. This is an open access article published under an ACS AuthorChoice License. The definitive version was published in Environmental Science and Technology Letters 5 (2018): 226–231, doi:10.1021/acs.estlett.8b00084.Chemical dispersants are one of many tools used to mitigate the overall environmental impact of oil spills. In principle, dispersants break up floating oil into small droplets that disperse into the water column where they are subject to multiple fate and transport processes. The effectiveness of dispersants typically decreases as oil weathers in the environment. This decrease in effectiveness is often attributed to evaporation and emulsification, with the contribution of photochemical weathering assumed to be negligible. Here, we aim to test this assumption using Macondo well oil released during the Deepwater Horizon spill as a case study. Our results indicate that the effects of photochemical weathering on Deepwater Horizon oil properties and dispersant effectiveness can greatly outweigh the effects of evaporative weathering. The decrease in dispersant effectiveness after light exposure was principally driven by the decreased solubility of photo-oxidized crude oil residues in the solvent system that comprises COREXIT EC9500A. Kinetic modeling combined with geospatial analysis demonstrated that a considerable fraction of aerial applications targeting Deepwater Horizon surface oil had low dispersant effectiveness. Collectively, the results of this study challenge the paradigm that photochemical weathering has a negligible impact on the effectiveness of oil spill response and provide critical insights into the “window of opportunity” to apply chemical dispersants in response to oil spills in sunlit waters.This work was supported, in part, by National Science Foundation Grant OCE-1333148, Gulf of Mexico Research Initiative Grants 015, SA 16-30, the DEEP-C consortium, and the Clark Family Foundation, Inc. EPA funding was provided to R.N.C. from the Oil Spill Liability Trust Fund

Discovering the Unfindable: The Tension Between Findability and Discoverability in a Bookshop Designed for Serendipity

Serendipity is a key aspect of user experience, particularly in the context of information acquisition - where it is known as information encountering. Unexpectedly encountering interesting or useful information can spark new insights while surprising and delighting. However, digital environments have been designed primarily for goal-directed seeking over loosely-directed exploration, searching over discovering. In this paper we examine a novel physical environment - a bookshop designed primarily for serendipity - for cues as to how information encountering might be helped or hindered by digital design. Naturalistic observations and interviews revealed it was almost impossible for participants to find specific books or topics other than by accident. But all unexpectedly encoun-tered interesting books, highlighting a tension between findability and discoverability. While some of the bookshop’s design features enabled information en-countering, others inhibited it. However, encountering was resilient, as it occurred despite participants finding it hard to understand the purpose of even those features that did enable it. Findings suggest the need to consider how transparent or opaque the purpose of design features should be and to balance structure and lack of it when designing digital environments for findability and discoverability

A Pose-Based Feature Fusion and Classification Framework for the Early Prediction of Cerebral Palsy in Infants

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of automating these processes may improve accessibility of the assessment and also enhance the understanding of movement development of infants. Previous works have established the viability of using pose-based features extracted from RGB video sequences to undertake classification of infant body movements based upon the GMA. In this paper, we propose a series of new and improved features, and a feature fusion pipeline for this classification task. We also introduce the RVI-38 dataset, a series of videos captured as part of routine clinical care. By utilising this challenging dataset we establish the robustness of several motion features for classification, subsequently informing the design of our proposed feature fusion framework based upon the GMA. We evaluate our proposed framework’s classification performance using both the RVI-38 dataset and the publicly available MINI-RGBD dataset. We also implement several other methods from the literature for direct comparison using these two independent datasets. Our experimental results and feature analysis show that our proposed pose-based method performs well across both datasets. The proposed features afford us the opportunity to include finer detail than previous methods, and further model GMA specific body movements. These new features also allow us to take advantage of additional body-part specific information as a means of improving the overall classification performance, whilst retaining GMA relevant, interpretable, and shareable features

CHESTNUT: Improve serendipity in movie recommendation by an Information Theory-based collaborative filtering approach

The term serendipity has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT , a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous runtime system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness , and the results show that it is fast, scalable and improves serendip-ity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-idl-ucc/CHESTNUT/