- 0.0 EXECUTIVE SUMMARY
- 0.1 Program Objectives and Participants
- 0.1.1 The Pan-Arctic Region: Highlights of the Literature Review
- 0.1.1.1 Behavior and Fate of Oil in the Arctic
- 0.1.1.2 VECs and Ecotoxicity
- 0.1.2 Role of Ecosystem Consequence Analyses in NEBA Applications for the Arctic
- 0.1.2.1 Arctic Population Resiliency and Potential for Recovery
- 0.2 Priority Recommendations to Enhance NEBA Applications in the Arctic
- 0.2.1 Development of ARCAT Matrices
- 0.2.2 Influence of Oil on Unique Arctic Communities
- 0.2.3 Biodegradation in Unique Communities
- 0.2.4 Modeling of Acute and Chronic Population Effects of Exposure to OSRs
- 0.3 Further Information
- 1.0 THE PHYSICAL ENVIRONMENT
- 1.1 Introduction
- 1.1.1 The Arctic Ocean, Marginal Seas, and Basins
- 1.2 Knowledge Status
- 1.2.1 The Circumpolar Margins
- 1.2.2 Arctic Hydrography
- 1.2.3 Ice And Ice-Edges
- 1.2.4 Seasonality: Productivity and the Carbon Cycle in the Arctic
- 1.3 Future Research Considerations
- 1.3.1 Priority Recommendations to Enhance NEBA Applications in the Arctic
- 1.4 Further Information
- 2.0 ARCTIC ECOSYSTEMS AND VALUABLE RESOURCES
- 2.1 Introduction
- 2.2 Knowledge Status
- 2.2.1 Habitats of the Arctic
- 2.2.2 Arctic Food Webs
- 2.2.2.1 Pelagic Communities
- 2.2.2.2 Benthic and Demersal Communities
- 2.2.2.2 Sea-ice Communities
- 2.2.2.4 Mammals and Birds
- 2.2.2.5 Communities of Special Significance
- 2.2.3 Pelagic Realm
- 2.2.3.1 Phytoplankton
- 2.2.3.2 Zooplankton
- 2.2.3.3 Neuston
- 2.2.3.4 Other Pelagic Invertebrates
- 2.2.3.4.1 Krill
- 2.2.3.4.2 Amphipods
- 2.2.3.4.3 Cephalopods
- 2.2.3.4.4 Jellyfish
- 2.2.3.5 Fish
- 2.2.3.5.1 Pelagic Fish
- 2.2.3.5.2 Anadromous Fish
- 2.2.3.5.3 Demersal Fish
- 2.2.3.5.4 Deep-Sea Fish
- 2.2.3.6 Marine Mammals
- 2.2.3.6.1 Bowhead Whale (Balaena mysticetus)
- 2.2.3.6.2 White Whale (Delphinapterus Leucas)
- 2.2.3.6.3 Narwhal (Monodon monoceros)
- 2.2.3.6.4 Ice Seals
- 2.2.3.6.5 Walrus (Odobenus rosmarus)
- 2.2.3.6.6 Orca Whales (Orcinus orca)
- 2.2.3.6.7 Polar Bear (Ursus maritimus)
- 2.2.3.7 Birds
- 2.2.3.7.1 Black-legged kittiwakes (Rissa tridactyla)
- 2.2.3.7.2 Black Guillemots (Cepphus grille)
- 2.2.3.7.3 Thick billed Murres (Uria lomvia)
- 2.2.3.7.4 Northern Fulmar (Fulmarus glacialis)
- 2.2.3.7.5 Common Eider (Somateria mollissima)
- 2.2.3.7.6 Little Auk/Dovekie (Alle alle)
- 2.2.3.7.7 Glaucous gull (Larus glaucescens)
- 2.2.3.7.8 Arctic jaeger (Stercorarius parasiticus)
- 2.2.4 Benthic Realm
- 2.2.4.1 Intertidal Communities
- 2.2.4.2 Shelf and Deepwater Communities
- 2.2.4.3 Mollusca
- 2.2.4.4 Polychaetes
- 2.2.4.5 Amphipods
- 2.2.4.6 Decapod Crustaceans
- 2.2.4.7 Echinoderms
- 2.2.5 Sea-Ice Realm
- 2.2.5.1 Ice Algae
- 2.2.5.2 Sympagic Copepods
- 2.2.5.3 Ice Amphipods
- 2.2.5.4 Pelagic Copepods
- 2.2.5.5 Sympagic Fish
- 2.2.5.6 Mammals
- 2.2.5.7 Birds
- 2.2.6 VECs of Arctic Marine Environments
- 2.2.6.1 Seasonal Distribution Patterns of Arctic Marine Populations
- 2.3 Future Research Considerations
- 2.3.1 Priority Recommendations to Enhance NEBA Applications in the Arctic
- 2.4 Further Information
- 3.0 THE TRANSPORT AND FATE OF OIL IN THE ARCTIC
- 3.1 Introduction
- 3.2 Knowledge Status
- 3.2.1 Weathering of Oil Spilled in Ice
- 3.2.2 Oil in Ice Interactions
- 3.2.3 Oil on Arctic Shorelines
- 3.2.4 Oil-Sediment Interactions
- 3.3 Future Research Considerations
- 3.3.1 Priority Recommendations for Enhanced NEBA Applications in the Arctic
- 3.4 Further Information
- 4.0 OIL SPILL RESPONSE STRATEGIES
- 4.1 Introduction
- 4.1.1 Environmental Uniqueness of the Arctic Region in Relation to OSR
- 4.2 Knowledge Status - Impact of OSRs
- 4.2.1 Natural Attentuation
- 4.2.1.1 Potential Environmental Impact of Untreated Oil
- 4.2.1.2 Conclusions on Natural Attenuation
- 4.2.2 Mechanical Recovery and Containment
- 4.2.2.1 Environmental impacts from Mechanical Recovery and Containment
- 4.2.2.2 Conclusions
- 4.2.3 In-Situ Burning and Chemical Herders
- 4.2.3.1 Potential environmental and human health effects of ISB residues and unburnt oil
- 4.2.3.2 Environmental Impact of Herders
- 4.2.3.3 Conclusions on ISB and Herders
- 4.2.4 Improving Dispersion of Oil
- 4.2.4.1 Impact of Chemically Dispersed Oil
- 4.2.4.2 Conclusions on Chemical Dispersion
- 4.2.4.3 Dispersing Oil using Oil Mineral Aggregates (OMA)
- 4.2.4.4 Environmental Impact of OMA formation
- 4.2.4.5 Conclusions on OMA
- 4.3 Future Research Considerations
- 4.3.1 Priority Recommendations for Enhanced NEBA Applications in the Arctic
- 4.4 Further Information
- 5.0 BIODEGRADATION
- 5.1 Introduction
- 5.1.1 The Microbiology of the Arctic Oceans
- 5.1.1.1 Transport routes
- 5.1.1.2 Microbial populations in the Arctic Ocean
- 5.1.2 Microbial Adaptation to Arctic Conditions
- 5.1.2.1 Low temperature and microbial adaptions
- 5.1.2.2 Light and microbial phototrophs
- 5.1.2.3 Marine ice and microbial survival and metabolism
- 5.2 Knowledge Status
- 5.2.1 Biodegradation of Oil in Cold Marine Environments
- 5.2.1.1 Types of Crude Oils
- 5.2.1.2 Surface oil spills
- 5.2.1.2.1 Evaporation
- 5.2.1.2.2 Water solubility
- 5.2.1.2.3 Photooxidation
- 5.2.1.2.4 Sedimentation
- 5.2.1.2.5 Water-in-oil emulsification
- 5.2.1.2.6 Natural dispersion
- 5.2.1.2.7 Oil films
- 5.2.1.3 Microbial Oil-Degrading Populations in Cold Water Environments
- 5.2.1.3.1 Indigenous Microorganism Populations
- 5.2.1.3.2 Population Effects on Oil Degradation
- 5.2.1.4 Hydrocarbon biodegradation in cold marine environments
- 5.2.1.4.1 Seawater
- 5.2.1.4.2 Sediments and soils
- 5.2.1.4.3 Sea ice
- 5.2.1.5 Modeling of biodegradation
- 5.2.1.5.1 Biodegradation in oil spill models
- 5.2.1.5.2 Biodegradation modeling and temperature
- 5.2.1.6 Determination of Biodegradation
- 5.2.1.6.1 Analytical methods for oil compound analyses
- 5.2.1.6.2 Experimental apparatus
- 5.2.1.6.3 Biodegradation data processing
- 5.2.1.7 Persistent Oil Compounds
- 5.2.2 Accelerated Biodegradation
- 5.2.2.1 Biostimulation
- 5.2.2.1.1 Shoreline sediments
- 5.2.2.1.2 Seawater
- 5.2.2.1.3 Marine ice
- 5.2.2.2 Bioaugmentation
- 5.2.2.3 Understanding Processes in Accelerated Biodegradation
- 5.3 Future Research Considerations
- 5.3.1 Priority Recommendations for Enhanced NEBA Applications in the Arctic
- 5.4 Further Information
- 6.0 ECOTOXICOLOGY OF OIL AND TREATED OIL IN THE ARCTIC
- 6.1 Introduction
- 6.1.1 General Methods and Relevant Endpoints in Laboratory Testing
- 6.1.1.1 Test Exposure
- 6.1.1.2 Test Media Preparation
- 6.1.1.2.1 Water Soluble Fractions (WSF)
- 6.1.1.2.2 Water Accommodated Fractions (WAF, CEWAF)
- 6.1.1.2.3 Oil-in-Water Dispersions (Oil Droplets)
- 6.1.1.2.4 Oil Type/Weathering
- 6.1.1.2.5 Exposure Concentrations
- 6.1.1.2.6 Test Organisms
- 6.1.1.2.7 Test Endpoints and Exposures
- 6.1.1.2.8 Data Extrapolation and Population Models
- 6.2 Knowledge Status
- 6.2.1 Species represented in the data set
- 6.2.2 Arctic ecosystem compartments in the dataset
- 6.2.2.1 Pack ice
- 6.2.2.2 Pelagic
- 6.2.2.3 Benthic
- 6.2.3 Review by Taxa
- 6.2.3.1 Phytoplankton and seaweed
- 6.2.3.2 Mysids
- 6.2.3.3 Copepods
- 6.2.3.4 Amphipods
- 6.2.3.5 Benthic organisms
- 6.2.3.6 Fish
- 6.3 Discussion
- 6.3.1 Petroleum related components
- 6.3.1.1 Crude oil
- 6.3.1.2 Single PAH
- 6.3.2 Chemically dispersed oil versus physically dispersed oil
- 6.3.3 Are Arctic species more sensitive than temperate species?
- 6.4 Future Research Considerations
- 6.4.1 Priority Recommendations to Enhance NEBA Applications in the Arctic
- 6.5 Further Information
- 7.0 POPULATION EFFECTS MODELING
- 7.1 Introduction
- 7.2 Knowledge Status
- 7.2.1 Parameters Needed to Assess Potential Responses of VECs to Environmental Stressors
- 7.2.1.1 Transport and fate / exposure potential
- 7.2.1.2 Oil toxicity evaluations / sensitivity
- 7.2.1.3 Population distributions, stressors, and mortality rates
- 7.2.2 Copepod Population Ecology
- 7.2.2.1 Copepod Growth and Development
- 7.2.2.2 Summary of Arctic and Sub-Arctic Copepod Species
- 7.2.3 Copepod Populations
- 7.2.4 Arctic Fish Population Ecology
- 7.2.4.1 Arctic Fish Species Diversity
- 7.2.4.2 Representative Fish Species
- 7.2.5 Application of Population Models
- 7.3 Future Research Considerations
- 7.3.1 Priority Recommendations to Enhance NEBA Applications in the Arctic
- 7.4 Further Information
- 8.0 ECOSYSTEM RECOVERY
- 8.1 Introduction
- 8.2 Knowledge Status
- 8.2.1 Resilience and Potential for Recovery
- 8.3 Future Research Considerations
- 8.3.1 Priority Recommendations for Enhanced NEBA Applications in the Arctic
- 8.4 Further Information
- 9.0 NET ENVIRONMENTAL BENEFIT ANALYSES FOR OIL SPILL
- 9.1 Introduction
- 9.2 Knowledge Status
- 9.2.1 Importance of NEBA Development for Arctic Regions
- 9.2.2 Scope and Applicability
- 9.2.3 Information Required to Utilize the NEBA Process
- 9.2.3.1 Potential oil spill scenarios
- 9.2.3.2 Response resources available
- 9.2.4 Ecological Resources at Risk
- 9.2.5 Social and Economic Relevance
- 9.2.6 Historical uses of NEBA and Case Studies
- 9.2.6.1 Assessing response strategy effectiveness and estimating oil fate and transport
- 9.2.6.2 Assessing the potential impacts and resource recovery rates
- 9.2.7 Historical Spills that Used or Informed NEBA Processes
- 9.2.7.1 A. Experimental: Baffin Island tests in northern Canada
- 9.2.7.2 B. Experimental: TROPICS study
- 9.2.7.3 C. Tanker: Braer Spill
- 9.2.7.4 D. Tanker: Sea Empress spill
- 9.2.7.5 E. Well Blowout: Montara spill (also known as the West Atlas Spill)
- 9.2.8 Potential Challenges to Applying NEBA Processes in the Arctic Environment
- 9.3 Future Research Considerations
- 9.3.1 Priority Recommendations for Enhanced NEBA Applications in the Arctic
- 9.4 Further Information
- APPENDIX: USE OF NEDRA IN CONNECTION TO OIL SPILL CONTINGENCY PLANNING IN NORWAY
- 10.0 SUPPORTING REPORTS
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5.2.1.6 Determination of Biodegradation
5.2.1.6.1 Analytical methods for oil compound analyses
Since oil consists of thousands of different compounds (Marshall and Rogers 2003) measurements of individual compounds is a challenge. Bulk oil biodegradation may be determined by traditional gravimetric analyses (e.g. Horowitz and Atlas 1977), while broader groups of oil components (saturates, aromatics, resins and asphaltenes = SARA) may be determined by Iatroscan thin-layer chromatography with flame ionization detection (TLC-FID; Stevens 2004). Using this method, crude oil components are determined according to their polarity. The saturate fraction consists of nonpolar material including linear, branched, and cyclic saturated hydrocarbons (paraffins). Aromatics, which contain one or more aromatic rings, are slightly more polarizable. The remaining two fractions, resins and asphaltenes, have polar substituents. Additional bulk oil analytical methods include Fourier Transform Infrared (FTIR) spectroscopy and Nuclear Magnetic Resonance (NMR) spectroscopy. FTIR is an absorption technique that uses infrared (IR) electromagnetic radiation to examine the identity of chemical bonds within the substance of interest. As microbial degradation of the oil is expected to result in the addition of oxygen atoms into the structure of oil compounds this method may be a method for measuring bulk changes in composition, although the resolution and sensitivity is poor compared to other methods. NMR is a nondestructive technique that is well-suited for identifying and quantifying different hydrocarbon classes and can provide information on the relative content of aliphatic, olefinic, and aromatic components. Studies have shown that NMR spectra in conjunction with multivariate statistical analysis can be correlated to a number of physicochemical properties and standard distillation cut yields (Molina et al. 2007). Mass spectrometry (MS) has become one of the most important detection principles in modern analytical chemistry. The principle behind MS is that molecules can be identified through their molecular weight and fragmentation patterns. MS is very often connected to a separation step, usually gas (GC) or liquid (LC) chromatography. These methods may be used to identify and quantify targeted oil compounds or for fingerprinting of complex chemical mixtures. To separate between different oil compound groups gas chromatographic analyses (GC-FID and GC-MS) are the standards today, but these methods favor detection of nonpolar compounds. The common use of these methods therefore limits our knowledge of oil biodegradation, mainly to some compound groups, such as the C10-C40 saturates, cyclic saturates (decalines), BTEX, phenols, 2-6 ring PAHs, and a variety of biomarkers. LC-MS analyses may therefore be an important supplement to the gas chromatographic analyses for more polar compound groups. In addition, biodegradation studies of compounds like naphthenic acids have been of interest in specific areas like Canada. Several high-resolution instruments, like time-of-flight mass spectrometers (ToF-MS) coupled to GCxGC systems (GCxGC-ToF-MS)(e.g. Tran et al. 2010) and Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer (e.g. Hughey et al. 2008) provide powerful techniques for the analytical separation of complex mixtures combined with methods for characterizing the resolved compounds. Minor components hidden in the large background can be detected by these instruments, and both resolution and sensitivity allow for searching of spectra from very narrow peaks. For instance FT-ICR MS can separate masses of <0.002 Dalton of compounds that contain heteroatoms such as N, O, S and other elements, identifying oil compounds by mass and molecular formula at high resolution.
5.2.1.6.2 Experimental apparatus
Advances in microbial sampling capabilities, in particular sampling of the ocean in drilling areas, came with advances in drilling technology. The Ocean Drilling Project (ODP) and subsequent Integrated Deep Ocean Drilling Program (IODP 2003-2013) and planned International Ocean Discovery Program (IODP 2013-2023) provide a framework for these activities (Edwards et al. 2012). Each of the named programs includes or will include a sampling component for microbial ecology research. In particular, the 2003 IODP included extensive evaluations of seafloor and sub-seafloor microbial communities (Cyranoski 2003). In conjunction with new microbiological techniques, these samples provided new perspectives on deep ocean microbial community composition and function (D'Hondt et al. 2004; Schippers et al. 2005; Inagaki et al. 2006; Biddle et al. 2008; Kobayashi et al. 2008; Forschner et al. 2009; Lomstein et al. 2012). Understanding native populations in Arctic drilling fields requires sampling such as has been carried out in these programs.
Much of the sampling that is associated with drilling activities focuses on the deep subsea floor while water column and sediment samples can be collected with remote samplers or can also be collected by autonomous underwater vehicles (AUVs). The Chemosynthetic Ecosystem Science (ChEss) project of the Census of Marine Life (2002-2010) was one such project that generated a substantial amount of new information about marine microbial communities. Much of the success of the ChEss project was attributed to the development of improved deep-ocean AUVs (German et al. 2011) that allowed systematic exploration of previously understudied areas, including cold seeps. Modern AUVs are capable of rapid deployment and operation at a range of depths. They have been effectively deployed to sample in response to events such as the Deepwater Horizon spill of 2010 (Camilli et al. 2010). These vehicles contribute to the ability to observe natural processes and conduct in situ experiments, particularly at depth in harsh marine environments.
Another strategy is to employ microbial observatories in marine environments that incorporate real-time sensors, time-lapse cameras, and other experimental devices. These observatories, along with autonomous and cabled sensors, allow direct measurement of microbial processes in the deep ocean. In particular, beginning some 20 years ago, circulation obviation retrofit kits (CORKs) came into use to study connectivity of hydraulics and biogeochemistry at the interface of the ocean bottom and open water (www.corkobservatories.org; Cowen et al. 2003) rather than relying on extrapolation from controlled laboratory experiments (e.g. Bartlett 2002; Tapilatu et al. 2010) or inference from population composition (Simonato et al. 2006) as is more commonly done. Another type of observatory, the Microbial Methane Observatory for Seafloor Analysis (MIMOSA), is an autosampler that collects and archives microbial material for later recovery and analysis. Two of these devices recently were deployed in the Gulf of Mexico to evaluate petroleum seeps and spills as they affect microbial population structure (Balinski 2012). This type of observatory may be useful to implement in situ experiments to monitor biodegradation rates and processes and further advance knowledge of petroleum hydrocarbon degradation processes in the deep ocean environment.
Finally, another tool that is directly relevant to petroleum biodegradation and carbon utilization is the so-called “bug trap,” in which hydrophobic beads or woven matrix is dosed with petroleum hydrocarbons to evaluate in situ degradation potential and analyzed to characterize degrading community composition. Because petroleum-degrading microorganisms can be chemotaxic to suitable substrates, these experimental devices can be used to attract and study degraders in the laboratory (Brakstad and Bonaunet 2006) and in situ experiments (Raloff 2010; DeAngelis et al. 2011).
5.2.1.6.3 Biodegradation data processing
In standard laboratory studies, oil degradation is usually determined by comparison of depletion in normal seawater or cultures to depletion in sterile (killed) controls. In this way processes like evaporation, wall effects, dissolution of compounds from the oil phase etc. may be accounted for and separated from the biodegradation process. However, in field and meso-/large-scale studies biodegradation is determined by normalization of degradable compounds to less degradable (recalcitrant) compounds. Common compounds for this internal normalization are pentacyclic triterpane biomarkers (e.g.C3017α(H),21β(H)-hopane) and the isoprenoids pristane and phytane (Prince et al. 1994; Douglas et al. 1996; Page et al. 1996). The isoprenoids have proven to be biodegradable themselves, although at slower rates than their corresponding n-alkanes (e.g. Douglas et al. 1996). Hopanes also have limitations if used to determine biodegradation of compounds with low boiling points, since it may be difficult to separate biodegradation from evaporation. In addition, determination of biodegradation as ratios between biodegradable and more persistent compounds has also been suggested using other compounds, like 2-methylphenanthrene/1-methylphenanthrene, and C3-phenanthrene/C3-dibenzothiophene (Fedorak and Westlake 1981; Christensen and Larsen 1993; Wang et al. 1998; Lamberts et al. 2008).