Hot and Cold: Photochemical Weathering Mediates Oil Properties and Fate Differently Depending on Seawater Temperature.

Photochemical weathering transforms petroleum oil and changes its bulk physical properties, as well as its partitioning into seawater. This transformation process is likely to occur in a cold water marine oil spill, but little is known about the behavior of photochemically weathered oil in cold water. We quantified the effect of photochemical weathering on oil properties and partitioning across temperatures. Compared to weathering in the dark, photochemical weathering increases oil viscosity and water-soluble content, decreases oil-seawater interfacial tension, and slightly increases density. Many of these photochemical changes are much larger than changes caused by evaporative weathering. Further, the viscosity and water-soluble content of photochemically weathered oil are more temperature-sensitive compared to evaporatively weathered oil, which changes the importance of key fate processes in warm versus cold environments. Compared to at 30 °C, photochemically weathered oil at 5 °C would have a 16× higher viscosity and a 7× lower water-soluble content, resulting in lower entrainment and dissolution. Collectively, the physical properties and thus fate of photochemically weathered oil in a cold water spill may be substantially different from those in a warm water spill. These differences could affect the choice of oil spill response options in cold, high-light environments.

Recommendations for the advancement of oil-in-water media and source oil characterization in aquatic toxicity test studies.

During toxicity testing, chemical analyses of oil and exposure media samples are needed to allow comparison of results between different tests as well as to assist with identification of the drivers and mechanisms for the toxic effects observed. However, to maximize the ability to compare results between different laboratories and biota, it has long been recognized that guidelines for standard protocols were needed. In 2005, the Chemical Response to Oil Spills: Ecological Effects Research Forum (CROSERF) protocol was developed with existing common analytical methods that described a standard method for reproducible preparation of exposure media as well as recommended specific analytical methods and analyte lists for comparative toxicity testing. At the time, the primary purpose for the data collected was to inform oil spill response and contingency planning. Since then, with improvements in both analytical equipment and methods, the use of toxicity data has expanded to include their integration into fate and effect models that aim to extend the applicability of lab-based study results to make predictions for field system-level impacts. This paper focuses on providing a summary of current chemical analyses for characterization of oil and exposure media used during aquatic toxicity testing and makes recommendations for the minimum analyses needed to allow for interpretation and modeling purposes.

Adopting a toxic unit model paradigm in design, analysis and interpretation of oil toxicity testing.

The lack of a conceptual understanding and unifying quantitative framework to guide conduct and interpretation of laboratory oil toxicity tests, has led investigators to divergent conclusions that can confuse stakeholders and impede sound decision-making. While a plethora of oil toxicity studies are available and continue to be published, due to differences in experimental design, results between studies often cannot be compared. Furthermore, much resulting data fails to advance quantitative effect models that are critically needed for oil spill risk and impact assessments. This paper discusses the challenges posed when evaluating oil toxicity test data based on traditional, total concentration-based exposure metrics and offers solutions for improving the state of practice by adopting a unifying toxic unit (TU) model framework. Key advantages of a TU framework is that differences in test oil composition, sensitivity of the test organism/endpoint, and toxicity test design (i.e., type of test) can be taken into quantitative account in predicting aquatic toxicity. This paradigm shift is intended to bridge the utility of laboratory oil toxicity tests with improved assessment of effects in the field. To illustrate these advantages, results from literature studies are reassessed and contrasted with conclusions obtained based on past practice. Using instructive examples, model results are presented to explain how dissolved oil composition and concentrations and resulting TUs vary in WAFs prepared using variable loading or dilution test designs and the important role that unmeasured oil components contribute to predicted oil toxicity. Model results are used to highlight how the TU framework can serve as a valuable aid in designing and interpreting empirical toxicity tests and provide the data required to validate/refine predictive toxicity models. To further promote consistent exposure and hazard assessment of physically and chemically dispersed oil toxicity tests recommendations for advancing the TU framework are presented.

Bridging the lab to field divide: Advancing oil spill biological effects models requires revisiting aquatic toxicity testing.

Oil fate and exposure modeling addresses the complexities of oil composition, weathering, partitioning in the environment, and the distributions and behaviors of aquatic biota to estimate exposure histories, i.e., oil component concentrations and environmental conditions experienced over time. Several approaches with increasing levels of complexity (i.e., aquatic toxicity model tiers, corresponding to varying purposes and applications) have been and continue to be developed to predict adverse effects resulting from these exposures. At Tiers 1 and 2, toxicity-based screening thresholds for assumed representative oil component compositions are used to inform spill response and risk evaluations, requiring limited toxicity data, analytical oil characterizations, and computer resources. Concentration-response relationships are employed in Tier 3 to quantify effects of assumed oil component mixture compositions. Oil spill modeling capabilities presently allow predictions of spatial and temporal compositional changes during exposure, which support mixture-based modeling frameworks. Such approaches rely on summed effects of components using toxic units to enable more realistic analyses (Tier 4). This review provides guidance for toxicological studies to inform the development of, provide input to, and validate Tier 4 aquatic toxicity models for assessing oil spill effects on aquatic biota. Evaluation of organisms' exposure histories using a toxic unit model reflects the current state-of the-science and provides an improved approach for quantifying effects of oil constituents on aquatic organisms. Since the mixture compositions in toxicity tests are not representative of field exposures, modelers rely on studies using single compounds to build toxicity models accounting for the additive effects of dynamic mixture exposures that occur after spills. Single compound toxicity data are needed to quantify the influence of exposure duration and modifying environmental factors (e.g., temperature, light) on observed effects for advancing use of this framework. Well-characterized whole oil bioassay data should be used to validate and refine these models.

An algorithm for modeling entrainment and naturally and chemically dispersed oil droplet size distribution under surface breaking wave conditions.

A surface oil entrainment model and droplet size model have been developed to estimate the flux of oil under surface breaking waves. Both equations are expressed in dimensionless Weber number (We) and Ohnesorge number (Oh, which explicitly accounts for the oil viscosity, density, and oil-water interfacial tension). Data from controlled lab studies, large-scale wave tank tests, and field observations have been used to calibrate the constants of the two independent equations. Predictions using the new algorithm compared well with the observed amount of oil removed from the surface and the sizes of the oil droplets entrained in the water column. Simulations with the new algorithm, implemented in a comprehensive spill model, show that entrainment rates increase more rapidly with wind speed than previously predicted based on the existing Delvigne and Sweeney's (1988) model, and a quasi-stable droplet size distribution (d<~50µm) is developed in the near surface water.

Development of a unified oil droplet size distribution model with application to surface breaking waves and subsea blowout releases considering dispersant effects.

An oil droplet size model was developed for a variety of turbulent conditions based on non-dimensional analysis of disruptive and restorative forces, which is applicable to oil droplet formation under both surface breaking-wave and subsurface-blowout conditions, with or without dispersant application. This new model was calibrated and successfully validated with droplet size data obtained from controlled laboratory studies of dispersant-treated and non-treated oil in subsea dispersant tank tests and field surveys, including the Deep Spill experimental release and the Deepwater Horizon blowout oil spill. This model is an advancement over prior models, as it explicitly addresses the effects of the dispersed phase viscosity, resulting from dispersant application and constrains the maximum stable droplet size based on Rayleigh-Taylor instability that is invoked for a release from a large aperture.

Oil spill impact modeling: development and validation.

A coupled oil fate and effects model has been developed for the estimation of impacts to habitats, wildlife, and aquatic organisms resulting from acute exposure to spilled oil. The physical fates model estimates the distribution of oil (as mass and concentrations) on the water surface, on shorelines, in the water column, and in the sediments, accounting for spreading, evaporation, transport, dispersion, emulsification, entrainment, dissolution, volatilization, partitioning, sedimentation, and degradation. The biological effects model estimates exposure of biota of various behavior types to floating oil and subsurface contamination, resulting percent mortality, and sublethal effects on production (somatic growth). Impacts are summarized as areas or volumes affected, percent of populations lost, and production foregone because of a spill's effects. This paper summarizes existing information and data used to develop the model, model algorithms and assumptions, validation studies, and research needs. Simulation of the Exxon Valdez oil spill is presented as a case study and validation of the model.

Development and application of an oil toxicity and exposure model, OilToxEx.

An oil toxicity and exposure model (OilToxEx) was developed and validated for estimation of impacts to aquatic organisms resulting from acute exposure to spilled oil. Because oil exposure is shorter than the time required for equilibrium between the organism and the water to be reached, the time and temperature dependence of toxicity is addressed. Oil toxicity is a function of aromatic composition and the toxicity of individual aromatics in the mixture. Lethal concentration to 50% of exposed organisms (LC50), as a function of octanol-water partition coefficient (Kow), and an additive model are used to estimate the toxicity of monoaromatic and polycyclic aromatic hydrocarbon mixtures in water-soluble fractions (WSF) and oil-in-water dispersions (OWD) of oil. The toxicity model was verified by comparison with oil bioassay data where the exposure concentrations of aromatics were measured. The observed toxicity in the bioassays could be accounted for by the additive narcotic effects of the dissolved aromatics in the exposure media. Predicted LC50s were compared to those calculated from measured concentrations after spills to verify the exposure model for field conditions. These results indicate that the additive toxicity and exposure model may be used to estimate toxicity of untested oils and spill conditions.