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A MODEL-BASED APPROACH INVESTIGATING KILLER WHALE (ORCINUS ORCA) EXPOSURE TO MARINE VESSEL ENGINE EXHAUST
by
Cara Leah Lachmuth
B.Sc., The University of Calgary, 2000
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
in
THE FACULTY OF GRADUATE STUDIES
(Zoology)
THE UNIVERSITY OF BRITISH COLUMBIA
(Vancouver)
December 2008
Cara Leah Lachmuth, 2008
ii
ABSTRACT
The summer habitat of the southern resident population of killer whales (Orcinus
orca) in British Columbia and Washington experiences heavy traffic by vessels involved in
whale-watching, sport fishing, other recreational activities, and shipping. Behavioural
changes caused by vessel proximity and the impacts of vessel noise have been previously
documented, but this is the first study to assess direct impacts of air pollutant emissions from
vessel traffic. The concentration and composition of air pollutants from whale-watching
vessels that southern resident killer whales are exposed to during the peak tourist season were
estimated, as were the health impacts of the exposure. Specifically, the study a) estimated
the output of airborne pollutants from the whale-watching fleet based on emissions data from
regulatory agencies, b) estimated the vertical dispersion of such pollutants based on air
stability data collected in the field and from climatological sources, c) used a dispersion
model incorporating data on whale, vessel, and atmospheric behaviour to estimate exposure,
and d) examined the likely physiological consequences of this exposure based on allometric
extrapolation of data from other mammalian species. The results of these exercises indicate
that the current whale-watching guidelines are usually effective in limiting pollutant
exposure to levels just at or below those at which adverse health effects would be expected in
killer whales. However, under ‘worst-case’ conditions and even under certain ‘average-case’
conditions the pollutant levels are much higher than those predicted to cause adverse health
effects. With this information, recommendations are made for further studies that would fill
in missing information, and increase confidence in the models, and the predicted impact on
the southern resident killer whales. Recommendations for limiting killer whale exposure to
air pollutants are also provided.
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TABLE OF CONTENTS
Abstract……………………………………………..……………………………………..…ii
Table of Contents……………………………………………………………………………iii
List of Tables……………………………………………………………...…….………..….vi
List of Figures………………………………………………….…………………………...vii
List of Abbreviations…………………………………………………………....……..……ix
Acknowledgements…………………………………………………………...……………xiii
1 INTRODUCTION.……….……………………………..…………………..….……1
1.1 INTRODUCTION………………………………………………………………...1
1.1.1 Frequency and Duration of Whale-Watching…………………………3
1.1.2 Whale-Watching Guidelines…………………………………………..6
1.1.3 Study Site……………………………………………………………...7
1.2 METHODS………………………………………………………………………..9
1.3 REFERENCES…………………………………………………………….…….11
2 EXHAUST EMISSION DISPERSION IN THE MARINE ATMOSPHERIC
BOUNDARY LAYER………..…………………………………………………….14
2.1.1 INTRODUCTION……...………………………………………….………...14
2.1.1.1 Airshed Description and Air Pollution Sources………………………….14
2.1.1.2 Air Quality Objectives and Standards……………………………………21
2.1.1.3 Ambient Air Quality……………………………………………………..23
2.1.1.4 Atmospheric Mixing and Boundary Layer Stability...…………………...27
2.1.2 MARINE ATMOSPHERIC BOUNDARY LAYER MEASUREMENTS.…31
2.1.3 MARINE ATMOSPHERIC BOUNDARY LAYER DATA….…………….33
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2.1.4 MARINE ATMOSPHERIC BOUNDARY LAYER CONCLUSIONS..…...40
2.2.1 MODELING DISPERSION IN THE MARINE ATMOSPHERIC
BOUNDARY LAYER……………………………...………………………..42
2.2.1.1 Emission Dispersion Models…………………………………………….44
2.2.1.2 Marine Engines, Fuel, and Emissions……………………………………46
2.2.1.3 Wet and Dry Exhaust Systems…………………………………………...48
2.2.1.4 The Whale-Watching Fleet………………………………………………50
2.2.1.5 Engine Emission Factors…………………………………………………51
2.2.2 NETLOGO DISPERSION MODEL.………………………………………..54
2.2.3 NETLOGO DISPERSION MODEL RESULTS.……………………………64
2.2.3.1 Results of the Sensitivity Analysis………………………………………65
2.2.3.2 Results of the Average-Case Trials………………………………………69
2.2.3.3 Results of the Worst-Case Trials………………………………………...72
2.2.4 DISPERSION MODEL CONCLUSIONS….....…………………………….73
2.2.5 REFERENCES………………………………………………………………77
3 PHYSIOLOGICAL EFFECTS ASSOCIATED WITH EXPOSURE TO AIR
POLLUTION……………………………………………………………………….84
3.1 INTRODUCTION……………………………………………………………….84
3.1.1 Compounds in Diesel and Gasoline Exhaust………………………...86
3.1.2 Health Effects from Exposure to Diesel and Gasoline Exhaust……..88
3.1.3 Retention and Clearance of Air Pollutants in the Lungs……………..90
3.1.4 Killer Whale Respiratory Anatomy and Physiology………………...95
3.1.5 The Effects of Diving and Breath Holding…………………………..99
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3.1.6 Physiological Models Used to Estimate Internal Pollutant Dose and
Health Effects……………………………………………………….102
3.1.7 Allometric Scaling to Estimate Internal Pollutant Dose and Health
Effects………………………………………………………………104
3.2 METHODS……………………………………………………………………..107
3.3 RESULTS………………………………………………………………………110
3.4 CONCLUSIONS……………………………………………………………….113
3.5 REFERENCES…………………………………………………………………116
4 CONCLUSIONS AND FURTHER STUDIES……………………………….….125
4.1 GENERAL CONCLUSIONS………………………………………………….125
4.2 UNCERTAINTY AND ASSUMPTIONS IN THE MODELS………………...130
4.3 FUTURE RESEARCH…………………………………………………………132
4.4 REFERENCES…………………………………………………………………135
APPENDICES……………………………………………………………………………..138
Appendix A: Be Whale Wise Guidelines…………………………………………..138
Appendix B: Air Pollutant Emissions………………………………………………139
Appendix C: Air Quality Standards………………………………………………...141
Appendix D: Alternative Fuels and Fuel Additives………………………………...142
Appendix E: Programming Code for the NetLogo Dispersion Model …………….144
Appendix F: Sensitivity Analysis Results from the Dispersion Model………..…...149
Appendix G: Classes of Compounds in Diesel Exhaust……………………………156
Appendix H: Health Effects from Exposure to Air Pollutants in Exhaust………....157
Appendix I: University of British Columbia Animal Care Certificate…………..…168
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LIST OF TABLES
Table 2.1 The Canada-Wide Standards for PM2.5 and O3…………………………………..22
Table 2.2 The Metro Vancouver Air Quality Objectives…………………………...……...22
Table 2.3 Monthly average ambient air pollutant concentrations (maximums in parentheses)
at the Christopher Point, BC air quality monitoring station from 2005-2007…...24
Table 2.4 Times of the year when ambient air pollutants reach their maximum and
minimum concentrations in the Georgia Basin Airshed…………..…...…..…….25
Table 2.5 USEPA non-road model emission factors for pre and post-2006 model
recreational marine diesel engines with power ratings less than 175 to 300 hp....52
Table 2.6 USEPA non-road model emission factors for pre and post-2006 model
recreational marine gasoline engines……………………..……………………...52
Table 2.7 Air pollutant multiplication factors for different marine engine configurations...57
Table 2.8 Variables in the dispersion model with their default and range of values……….61
Table 3.1 The dose of CO and NO2 per kg body mass that male and female killer whales
and humans receive during average-case, worst-case, 1-hour, and 8-hour
exposures.…………………………………………..…………………………...110
Table 3.2 CO (1-hour and 8-hour) and NO2 (1-hour) toxicity values (Aw) for male and
female killer whales.………………………………..…………………………..111
Table 3.3 Toxicity doses of CO and NO2 per kg body mass for male and female killer
whales and humans, using toxicity values (Aw).…………………………..……111
Table 3.4 Total dose of CO and NO2 per kg body mass that male and female killer
whales are estimated to receive under average-case or worst-case whale-watching
conditions……………..………………………………………………………...112
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LIST OF FIGURES
Figure 1.1 The number of vessels accompanying southern resident killer whale pods per
month as measured from the Soundwatch vessel 1998-2005.…………………....5
Figure 1.2 Map of the summer habitat of the southern resident killer whales, and inset of the
regional location map……………………….…………………………………….8
Figure 2.1 The Georgia Basin and Lower Fraser Valley Airsheds…………………………16
Figure 2.2 Percentage of air pollutants attributed to all marine vessels in the LFV Airshed in
the year 2000 emission inventory…………………….…………………………17
Figure 2.3 Air pollutant contributions by vessel category in BC outside of Metro Vancouver
and FVRD in the year 2000……….…………….………………………………19
Figure 2.4 Graphs of temperature change with height showing (a) an unstable atmosphere
and (b) a stable atmosphere……………………………………….……………..30
Figure 2.5 Profile view of the thermocouple setup on the zodiac………………………….32
Figure 2.6 Map of southeast Vancouver Island, BC………………………………………..33
Figure 2.7 Results from the first ten 15-minute trials at Oak Bay, BC, demonstrating that the
average temperature increased with height above the water………………..….34
Figure 2.8 Plot of the nine trials at Oak Bay, BC, that had deviations from temperature
increasing with height………………………….........…………………………..34
Figure 2.9 Air-sea surface temperature difference (Tair-Tsea) for all 41 15-min trials, by the
time of day the trial started………...…………..………………………………..35
Figure 2.10 Average monthly air-sea surface temperature difference (Tair-Tsea) at Race
Rocks, BC in 2007, with standard error of the mean bars..………….....……...36
Figure 2.11 Average monthly air-sea surface temperature difference (Tair-Tsea) at Race
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Rocks from 2002-2006, with standard error of the mean bars..……………….37
Figure 2.12 Average monthly air-sea surface temperature difference (Tair-Tsea) at Halibut
Bank Buoy from 2002-2005, with standard error of the mean bars.…….…….39
Figure 2.13 Average monthly air-sea surface temperature difference (Tair-Tsea) at Hein Bank
Buoy from 2004-2007, with standard error of the mean bars.……………...….40
Figure 2.14 Image of the NetLogo interface………………………………………………..56
Figure 2.15 CO and NO2 concentration as a function of wind speed and angle under
average-case whale-watching conditions…….………..……...……………….70
Figure 2.16 CO and NO2 concentration as a function of the vertical mixing height and wind
angle under average-case whale-watching conditions……………..……….….71
Figure 2.17 CO and NO2 concentration as a function of the wind speed and angle under
worst-case whale-watching conditions…………..…….……………………....73
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LIST OF ABBREVIATIONS ACSM American College of Sports Medicine AQO Air Quality Objectives At Toxicity value for a test animal ATSDR Agency for Toxic Substances and Disease Registry Aw Toxicity value for a wildlife species b Allometric scaling factor BC British Columbia, Canada BCLA British Columbia Lung Association BCME British Columbia Ministry of Environment BCPHO British Columbia Provincial Health Officer b m-1 Breaths per minute CAPMoN Canadian Air and Precipitation Monitoring Network °C Degrees Celsius °C m-1 Degrees Celsius per meter CO Carbon monoxide CO2 Carbon dioxide COx Carbon oxides CWS Canada-Wide Standards DALR Dry Adiabatic Lapse Rate DFO Department of Fisheries and Oceans Canada DNA Deoxyribonucleic acid EC Environment Canada
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ELR Environmental Lapse Rate FR Breathing frequency FVRD Fraser Valley Regional District g hp-1 hr-1 Grams per horsepower hour GVRD Greater Vancouver Regional District HC Hydrocarbon HEI Health Effects Institute HNO3 Nitric Acid hp Horsepower IPCS International Programme on Chemical Safety kg Kilogram km Kilometer LFV Lower Fraser Valley L b-1 Litre per breath L m-1 Litre per minute L s-1 Litre per second m Meter Mb Body mass mg Milligram mg m-3 Milligram per meter cubed mg s-1 Milligram per second m s-1 Meter per second MTBE Methyl Tertiary Butyl Ether
xi
MV Metro Vancouver NESCAUM Northeast States for Coordinated Air Use Management NH4 Ammonium nm Nanometer NOAA National Ocean and Atmospheric Association NO Nitrogen oxide NO2 Nitrogen dioxide NO3 Nitrate NOx Nitrogen oxides O2 Oxygen O3 Ozone PAH Polycyclic Aromatic Hydrocarbon PBPK Physiologically-Based Pharmacokinetic model PCB Polychlorinated biphenyl PM Particulate matter PM10 Particulate matter smaller than 10 microns in diameter PM2.5 Particulate matter smaller than 2.5 microns in diameter POP Persistent Organic Pollutant RMR Resting Metabolic Rate RPM Revolutions Per Minute RRER Race Rocks Ecological Reserve SARA Species At Risk Act SEM Standard Error of the Mean
xii
SO2 Sulfur dioxide SO4
2- Sulfate ion SOx Sulfur oxides SRKW Southern Resident Killer Whale SST Sea Surface Temperature TAF Transient Adjustment Factor TLC Total Lung Capacity UF Uncertainty Factor µg m-3 Micrograms per meter cubed µm Micrometer USEPA United States Environmental Protection Agency VA Alveolar volume VC Vital capacity VD Dead space volume
!
V
•
eff Effective ventilation
!
V
•
min Minute volume of respiration VOC Volatile Organic Compound VT Tidal volume WA Washington, USA WDFW Washington Department of Fish and Wildlife WHO World Health Organization WLAP British Columbia Ministry of Water, Land, and Air Protection WWOAN Whale Watch Operators Association Northwest
xiii
ACKNOWLEDGEMENTS
I have tremendous gratitude for the countless people who helped me during the course of this thesis, and I offer my sincere apologies to anyone I missed in this list. First off I would like to thank my supervisors Dr. Lance Barrett-Lennard and Dr. Bill Milsom, for their expertise and support. I could not have done this project without the guidance and assistance from my committee members, Dr. Douw Steyn and Dr. Peter Ross. I am extremely grateful to Dr. Rik Blok, Alistair Blanchford, and Atef Abdelkefi for computer programming support, and their patience with me as I learned. Thanks go to Doug Sandilands for providing wonderful maps, and David Bain for data and advice. I thank Mark Malleson for the generous use of his zodiac to collect temperature data in the field, and Anna Lachmuth and Bo Garrett for being excellent field assistants. Thanks go to Nik Dedeluk and Straitwatch for taking me out on the water to collect data.
I would also like to thank past and present members of the Milsom Lab at UBC (Cosima Ciuhandu, Angelina Fong, Charissa Fung, Stella Lee, Catalina Reyes, Barb Gajda, Emily Coolidge, Colin Sanders, and Graham Scott) and the Cetacean Research Lab at the Aquarium (Charissa Fung, Katie Kuker, Valeria Vergara, Doug Sandilands, and Judy McVeigh) for their inspiration, feedback, and friendship.
Of course this work could not have been done without funding and I am especially thankful to the Vancouver Aquarium Killer Whale Adoption Program, the Zoology Department at UBC, the Dean Fisher Memorial Scholarship in Zoology at UBC, and the Michael A. Bigg Scientist of the Future Award from the Vancouver Aquarium.
I would also like to give special thanks to my family and friends for their remarkable support, encouragement, and ability to keep me balanced.
1
1 INTRODUCTION
1.1 INTRODUCTION
The population of killer whales (Orcinus orca) known as the southern residents (SRKW)
inhabits the waters off southern Vancouver Island, BC during the summer months, and has
been studied extensively since the 1970's. The population experiences intense whale-
watching pressure from May to September every year, and are followed on average by 20
vessels for 12-hours a day (Bain, 2002; Baird, 2001; Erbe, 2002; Koski, 2006; Osborne, et al.
2002; 1999). The population is socially, culturally, and genetically distinct from other
populations (Barrett-Lennard, 2000) and consists of three maternal subgroups called the J, K,
and L pods (Bain, 2002).
In 1995 there were 99 whales in the SRKW community, but poor survival and fecundity
in 1996 initiated a decline in the population that lasted until 2001 with 81 whales remaining
(Krahn et al., 2004; 2002). Because of the population’s small size and genetic isolation from
other killer whale groups, the SRKWs were listed as Endangered under the Canadian Species
at Risk Act (SARA) in 2001, and the United States Endangered Species Act in 2006 (EC,
2005; NOAA, 2005). Since 2001 the population has fluctuated and as of October 2008, the
population numbered 83 whales (Orca Network, 2008). Three anthropogenic factors have
been identified as possible causes of the population’s decline: decreased food availability due
to the decline of salmon stocks (their primary food source), exposure to toxic chemicals such
as polychlorinated biphenyls (PCBs), and vessel disturbance (Bain, 2002; Ross, 2006). The
first two threats will require long term solutions to re-establish salmon stocks and phase out
toxins; however, the third threat of vessel disturbance is one that could be ameliorated
2
relatively quickly and easily to minimize one facet of the negative impact humans have on
this population.
Efforts to quantify vessel disturbance on killer whales has been primarily limited to
studying the killer whale’s behavioural responses. Documented short-term behavioural
responses to whale-watching vessels are increased dive duration, increased swimming speed,
and erratic changes in direction of travel (Jelinski et al., 2002; Kruse, 1991; Williams et al.,
2002). It has also been found that vessel noise can be harmful to whales by potentially
causing temporary threshold shifts in hearing, permanent hearing loss with prolonged
exposure, and by masking their calls and reducing foraging success (Erbe, 2002). While this
research demonstrates that whales often adjust their behaviour when vessels are present, it is
difficult for researchers to quantify the significance and consequence of those adjustments.
Are they seemingly insignificant responses to a mildly irritating stimulus, or are they
significant responses to relatively severe disturbances? The present study investigates more-
directly-measurable effects of whale-watching, exposure to exhaust gases, and examines the
potential physiological impact of this exposure.
The SRKWs are exposed to increasing levels of contaminants in the air they breathe, the
water they inhabit, and the food they eat. Recent studies have found that they are some of the
most contaminated mammals in the world, and the concentration of so-called persistent
organic pollutants (POPs) in their tissues is three times higher than levels that cause damage
to the immune and endocrine systems of harbour seals (Ross et al., 2000; Ross, 2006).
Toxicological research has focused on the organochlorine family of chemicals, which are
found in significantly higher concentrations in SRKWs than St. Lawrence belugas, which
were long considered to be the most contaminated marine mammals in the world (Colborn &
3
Smolen, 1996; Ross et al., 2000). While the majority of POPs originate from the diet of
killer whales, it has been speculated that chemicals such as unburned fuel and exhaust may
be adding to the killer whale’s toxin load through the creation of combustion by-products
such as dioxins, furans, and complex hydrocarbons (Bain et al., 2006). However, the
potential acute and/or chronic risks to the population from these pollutants and other
environmental toxins have not been investigated to date.
Air pollutants are often complex mixtures of numerous gases (e.g. hydrocarbons, nitrogen
oxides, sulfur oxides, ozone) and particulates, and they predominantly pose a chronic health
risk. Air pollutants most commonly affect the lungs, but they also have neurologic, cardiac,
gastrointestinal, renal, hematologic, as well as skin pathology effects (BCPHO, 2004; HEI,
1999). Marine engines produce more air pollutants per kg of fuel burned than automobile
engines, because they usually do not have after-treatment of the exhaust or equivalent
pollution control devices. The proximate air pollutants from whale-watching vessels are not
the only source of airborne contaminants, however, as the SRKWs are also exposed to
regional urban and industrial air pollutants from the large metropolitan centres of Vancouver,
Victoria, and Seattle.
1.1.1 Frequency and Duration of Whale-Watching
The frequency and duration of whale-watching activities must be quantified in order
to determine the concentration of air pollutants the SRKWs are potentially inhaling.
Commercial whale-watching operations in Canada and the U.S. were basically non-existent
prior to 1976 (Koski, 2006), but by the summer of 2005, 39 whale-watching companies
operated 74 vessels in WA and BC, and focused primarily on the SRKWs (Koski, 2006). Of
4
the 74 vessels, Canadian companies owned 55, and 19 were American (Koski, 2006).
Canadian commercial whale-watching companies generally use small outboard motor vessels
that operate at high revolutions per minute (RPM), while American companies generally use
larger inboard motor vessels that operate at a low RPM (Bain, 2002). The vessels used range
from 7 m long open boats carrying 6-16 people to 30 m covered vessels carrying up to 280
people (Wiles, 2004). The vessels make two to six trips a day, based on demand (Wiles,
2004). In addition to commercial vessels, large numbers of recreational boaters participate in
opportunistic whale-watching - approximately 64% of the vessels observing the whales are
commercially operated, while the rest are privately owned (Osborne et al., 2002).
During the summer, commercial whale-watchers and researchers often begin viewing
whales at 6:00 a.m. However, the majority view from 9:00 a.m. to 9:00 p.m., with the
greatest density occurring between 10:00 a.m. and 5:00 p.m. (Bain, 2002). The commercial
season usually starts late April and ends early October, with the peak occurring from May to
September (Figure 1.1); if whales are present, some whale-watching will occur throughout
the winter and early spring (Bain, 2002). Private vessels engaged in whale-watching have
similar seasonal and daily patterns as commercial whale-watchers (Bain, 2002). During peak
summer months, the mean number of whale-watching vessels has increased from five in
1990, to 18-26 vessels within 800 m of the whales from 1996-2002 (Bain, 2002; Baird, 2001;
Erbe, 2002; Koski, 2006; Osborne et al., 2002; 1999). In total, the SRKWs are exposed to
whale-watching vessels approximately 12-hours a day for six months of the year (Trites &
Bain, 2000).
5
Figure 1.1: The number of vessels accompanying southern resident killer whale pods per month as measured from the Soundwatch vessel 1998-2005 (Koski, 2006). The bottom of the box indicates 25th percentile, the mid line indicates the median, and the top of the box indicates the 75 th percentile.
From 1998-2002, the annual maximum number of vessels following the SRKW
population ranged from 72-120 vessels per day, with the majority being private vessels rather
than commercial whale-watching vessels (Wiles, 2004). This annual maximum pales in
comparison to the autumn of 1997, when up to 500 private vessels were counted each
weekend observing a group of killer whales that remained in Dyes Inlet, WA for one month
(Wiles, 2004).
Bain et al. (2006) conducted a theodolite study from San Juan Island, WA on SRKW
exposure to vessels from 2003-2005. They found that approximately 25% of the killer
whale’s time was spent with at least one vessel closer than 100 m, over 50% of their time
was spent with vessels within 400 m, over 75% of their time was spent with vessels within
6
1000 m, and there were vessels at further distances on almost 100% of scans (Bain et al.,
2006). A study by Jelinski et al. (2002) conducted in Johnstone Strait, BC, found that
motorized vessels remained with northern resident killer whales for a longer period of time
than non-motorized vessels. The average length of time a charter vessel remained with a
group of killer whales was 73 minutes, compared with two minutes for a kayak. Head-on
approaches to the whales occurred more often with motorized vessels and there was evidence
of aggressive viewing among both motorized and non-motorized vessels (Jelinski et al.,
2002). This situation is probably similar for motorized and non-motorized vessels viewing
the SRKWs.
1.1.2 Whale-Watching Guidelines
The Department of Fisheries and Oceans Canada in conjunction with the National
Ocean and Atmospheric Administration (NOAA) created voluntary Be Whale Wise
Guidelines (DFO, 2008) for whale-watching vessels to reduce disturbance and manage vessel
traffic (Appendix A). The Whale Watch Operators Association Northwest (WWOAN)
created more comprehensive guidelines for commercial vessel operators observing the
SRKWs, known as the Best Practices Guidelines (WWOAN, 2008). Vessel operators are
strongly encouraged to follow the guidelines, however, enforcement is limited. Several
charges have been laid in Canada under the Marine Mammal Regulations of the Fisheries
Act, with successful prosecutions of both recreational and whale-watching operators being
charged. The guidelines recommend that vessels approach whales no closer than 100 m,
slow their speed to less than seven knots (3.6 m s-1) when within 400 m of whales, and that
they travel parallel to the whales rather than in front or behind (DFO, 2008).
7
Commercial whale-watch operators have been quick to adopt the guidelines,
however, private vessels are often unaware of them, and unfortunately incidents of non-
compliance with the guidelines are common (Koski, 2006; Osborne et al., 1999).
Additionally, compliance with the guidelines strongly depends on whether or not monitoring
and enforcement agencies are on the water (Smith & Bain, 2002). The most frequent
incidents are: vessels stopping in the path of whales, vessels under power while inshore of
whales, and vessels under power within 100 m of whales (Koski, 2006). Osborne et al.
(l999) reported that vessels violated the guidelines in the Haro Strait area 400 times in 1998,
and 560 times in 1999, while in 2005 Koski (2006) reported 957 violations. Of the 957
violations in 2005, 10% were from vessels within 100 m of whales (Koski, 2006). In the
summer of 2007, the Be Whale Wise Guidelines became law in WA, which has resulted in
stronger enforcement in American waters (WDFW, 2008).
1.1.3 Study Site
When assessing air quality, an important factor to consider is the occurrence of
atmospheric conditions conducive to air pollutant accumulation. The time period of concern
is the peak whale-watching season, and the area of concern is the summer habitat of the
SRKWs, mainly Haro Strait, Juan de Fuca Strait, and Boundary Pass (Figure 1.2).
8
Figure 1.2: Map of the summer habitat of the southern resident killer whales, and inset of the regional location map. Stars indicate locations mentioned in the text.
Calm weather conditions are particularly important in air quality assessment because
air pollutant concentrations are greatest under these conditions (EC, 2004). In coastal BC
and WA the highest median wind speeds occur in December, the lowest in August
(Vingarzan & Thomson, 2004). The Gulf and San Juan Islands commonly experience light
winds (Lange, 1998) due to merging airflows from the Straits of Georgia and Juan de Fuca,
and the “wake effect” from Vancouver Island and the Gulf and San Juan Islands (Brook et
al., 2004). This has been called the Wake-Induced Stagnation Effect, and it is instrumental in
the accumulation and photochemical evolution of air pollutants from Vancouver, Victoria,
the Lower Fraser Valley, Whatcom County (WA), and marine vessels. Storms and calm
9
conditions can occur at any time of year along BC’s coast, however, the spring and fall have
the most active weather events, whereas winter and summer are calmer. High atmospheric
pressure dominates during the summer months, and this reduces airflow to local circulations
and produces temperature inversions that can last several days (EC, 2004). Temperature
inversions occur when a layer of cold air is trapped near the surface by a layer of warmer air
on top, and produces stable atmospheric conditions. The worst air quality events occur
during the summer because stagnant air gets trapped close to the surface over large areas of
the airshed. The timing of these elevated air pollution events overlap with the commercial
whale-watching season.
Sea breezes occur most frequently in the summer months (Steyn & Faulkner, 1986),
however, they do not generally disperse air pollution in an efficient manner because their
speeds are usually less than four m s-1, and they are closed circulation systems that
experience reversals in the direction of flow diurnally (EC, 2004). This results in limited air
exchange, with air pollutants moving back and forth from land to sea in a contained volume
of air with the pollutant load increasing from marine sources while over the ocean and from
land sources while over land. The extent of air pollution buildup is determined by the
duration of the sea breeze and the strength of the inversion layer, and for the system to be
flushed, a stronger synoptic system with high winds is required (EC, 2004).
1.2 METHODS
This thesis is presented in four chapters. The present chapter introduces the subject
matter, and summarizes the rationale and background surrounding the objectives of the study.
Chapter two outlines the atmospheric conditions of SRKW habitat, and includes the results
10
from a short study conducted in August 2007 to assess atmospheric stability in SRKW
habitat. This information was included in a computer model used to simulate exhaust
dispersion to determine killer whale exposure to air pollutants from whale-watching vessels.
Data on the number of vessels, engine emission rates, distance of vessels to killer whales,
distance between vessels, and length of exposure were also incorporated in the model.
Chapter three describes the air pollutants emitted in marine engine exhaust, and their
health effects on mammals. An allometric scaling model was used to assess the health
impact of the air pollutant exposures predicted by the dispersion model on killer whales.
Factors such as age, health, and genetic susceptibility make some individuals more sensitive
to air pollution (Van Atten et al., 2004), thus the percentage of sensitive individuals in the
SRKW population was determined. Chapter four summarizes the findings in the previous
chapters, and concludes the study with suggestions for future research that would increase the
confidence of the model predictions. It also provides methods for reducing SRKW exposure
to air pollutants from whale-watching vessels.
My objective in preparing this thesis was to estimate the quantity of specific air
pollutants inhaled by the whales during exposure to whale-watching vessels under various
conditions, and predict the health effects. I also wanted to determine the proportion of
SRKWs that may be extra sensitive to air pollution. While the results of this study may
indicate that vessel exhaust has a negligible physiological impact on killer whales, if the
results show that exposure levels are sufficient to pose a health risk, the findings may be
utilized as an instrument for further development of the whale-watching guidelines to ensure
that whale-watching vessels have the least possible impact on this endangered killer whale
population.
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1.3 REFERENCES Bain, D. E. 2002. A Model Linking Energetic Effects of Whale Watching to Killer Whale
(Orcinus orca) Populations. Friday Harbor, WA: Friday Harbor Laboratories, University of Washington.
Bain, D. E., Williams, R., Smith, J. C., & Lusseau, D. 2006. Effects of vessels on behavior
of southern resident killer whales (Orcinus spp.) 2003-2005 (NMFS Contract Report No. AB133F05SE3965). Retrieved March 18, 2007, from http://www.nwfsc.noaa.gov/research/divisions/cbd/marine_mammal/documents/bainnmfsrep2003-5final.pdf
Baird, R. W. 2001. Status of killer whales, Orcinus orca, in Canada. Canadian Field-
Naturalist, 115: 676-701. Barrett-Lennard, L. G. 2000. Population structure and mating patterns of killer whales
(Orcinus orca) as revealed by DNA analysis. University of British Columbia Ph.D. Thesis.
British Columbia Provincial Health Officer (BCPHO). 2004. Every Breath You Take:
Provincial Health Officer’s Annual Report 2003. Victoria, BC: Ministry of Health Services.
Brook, J. R., Strawbridge, K. B., Snyder, B. J., Boudries, H., Worsnop, D., Sharma, S.,
Anlauf, K., Lu, G., & Hayden, K. 2004. Towards an understanding of the fine particle variations in the LFV: Integration of chemical, physical and meteorological observations. Atmospheric Environment, 38(34): 5775-5788.
Colborn, T. A., & Smolen, M. J. 1996. Epidemiological analysis of persistent
organochlorine contaminants in cetaceans. Reviews of Environmental Contamination and Toxicology, 146: 91-172.
Department of Fisheries and Oceans Canada (DFO). 2008. Viewing Guidelines. Pacific
Region Marine Mammals and Turtles. Retrieved May 14, 2008, from http://www.pac.dfo-mpo.gc.ca/species/marinemammals/view_e.htm
Environment Canada (EC). 2004. Characterization of the Georgia Basin/Puget Sound
Airshed. Retrieved September 12, 2007, from http://www.pyr.ec.gc.ca/air/gb_ps_airshed/summary_e.htm
Environment Canada (EC). 2005. Killer Whale: Northeast Pacific Southern Resident
Population. Species At Risk. Retrieved on November 20, 2005, from http://www.speciesatrisk.gc.ca/search/speciesDetails_e.cfm?SpeciesID=699
Erbe, C. 2002. Underwater noise of whale-watching boats and potential effects on killer
whales (Orcinus orca), based on an acoustic impact model. Marine Mammal
12
Science, 18: 394-418. Health Effects Institute (HEI). 1999. Diesel Emissions and Lung Cancer: Epidemiology and
Quantitative Risk Assessment. A Special Report of the Institute’s Diesel Epidemiology Expert Panel. N. Andover, MA: Flagship Press.
Jelinski, D. E., Krueger, C. C., & Duffus, D. A. 2002. Geostatistical analyses of interactions
between killer whales (Orcinus orca) and recreational whale-watching boats. Applied Geography, 22: 393-411.
Koski, K. L. 2006. Soundwatch Public Outreach/Boater Education Project 2004-2005
Final Program Report. Friday Harbor, WA: The Whale Museum. Krahn, M. M., Ford, M. J., Perrin, W. F., Wade, P. R., Angliss, R. P., Hanson, M. B., Taylor,
B. L., Ylitalo, G. M., Dahlheim, M. E., Stein, J. E., & Waples, R. S. 2002. Status review of Southern Resident killer whales (Orcinus orca) under the Endangered Species Act (NMFS-NWFSC 54). Washington, DC: United States Department of Commerce.
Krahn, M. M., Ford, M. J., Perrin, W. F., Wade, P. R., Angliss, R. P., Hanson, M. B., Taylor,
B. L., Ylitalo, G. M., Dahlheim, M. E., Stein, J. E., & Waples, R. S. 2004. 2004 Status review of southern resident killer whales (Orcinus orca) under the Endangered Species Act (NMFSNWFSC 62). Washington, DC: United States Department of Commerce.
Kruse, S. 1991. The interactions between killer whales and boats in Johnstone Strait, B.C.
In K. Pryor & K. S. Norris (Eds.), Dolphin Societies: Discoveries and Puzzles (pp. 149-159). Berkeley, CA: University of California Press.
Lange, O. S. 1998. The Wind Came All Ways: A Quest to Understand the Winds, Waves and
Weather in the Georgia Basin (Cat. No. En56-74/1998E). Victoria, BC: Environment Canada.
National Ocean and Atmospheric Association (NOAA). 2005. Final ESA Listing Decision
for Killer Whales. Retrieved on November 20, 2005, from http://www.nwr.noaa.gov/Marine-Mammals/Whales-Dolphins-Porpoise/Killer-Whales/ESA-Act-Status/Listing-Final.cfm
Orca Network. 2008. Southern Resident Orca Community Births and Deaths since 1998.
Retrieved on October 15, 2008, from http://www.orcanetwork.org/news/birthsdeaths.html
Osborne, R., Koski, K., & Otis, R. 2002. Trends in Whale Watching Traffic Around
Southern Resident Killer Whales. Friday Harbor, WA: The Whale Museum. Osborne, R. W., Koski, K. L., Tallmon, R. E., & Harrington, S. 1999. Soundwatch 1999
13
Final Report. Roche Harbor, WA: Soundwatch Boater Education Program. Ross, P. S. 2006. Fireproof killer whales (Orcinus orca): Flame retardant chemicals and the
conservation imperative in the charismatic icon of British Columbia, Canada. Canadian Journal of Fisheries and Aquatic Sciences, 63: 224-234.
Ross, P. S., Ellis, G. M., Ikonomou, M. G., Barrett-Lennard, L. G., & Addison, R. F. 2000.
High PCB concentrations in free-ranging Pacific killer whales, Orcinus orca: Effects of age, sex and dietary preference. Marine Pollution Bulletin, 40(6): 504-515.
Smith, J. C., & Bain, D. E. 2002. Theodolite Study of the Effects of Vessel Traffic on Killer
Whales (Orcinus orca) in the Near-Shore Waters of Washington State, USA. Pages 143-145 in Fourth International Orca Symposium and Workshops, September 23-28, 2002, CEBC-CNRS, France.
Steyn, D. G., & Faulkner, D. A. 1986. The climatology of sea-breezes in the Lower Fraser
Valley, BC. Climatological Bulletin, 20(3): 21-39. Trites, A. W. & Bain, D. E. 2000. Short- and Long-Term Effects of Whale Watching on
Killer Whales (Orcinus orca) in British Columbia. Vancouver, BC: University of British Columbia.
Van Atten, C., Brauer, M., Funk, T., Gilbert, N. L., Graham, L., Kaden, D., Miller, P. J.,
Rojas Bracho, L., Wheeler, A., & White, R. H. 2004. Honing the Methods: Assessing Population Exposures to Motor Vehicle Exhaust. Montreal, QC: Commission for Environmental Cooperation.
Vingarzan, R., & Thomson, B. 2004. Temporal variation in daily concentrations of ozone
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Washington Department of Fish and Wildlife (WDFW). 2008. Wildlife Viewing. Retrieved
July 10, 2008, from http://www.wdfw.wa.gov/ Whale Watch Operators Association Northwest (WWOAN). 2008. 2008 Guidelines and
Best Practices for Commercial Whale Watching Operators. Retrieved July 15, 2008, from http://www.nwwhalewatchers.org/guidelines.html
Wiles, G. J. 2004. Washington State Status Report For The Killer Whale March 2004.
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(Orcinus orca) to whale-watching boats: opportunistic observations and experimental approaches. Journal of Zoology, London, 256: 255-270.
14
2 EXHAUST EMISSION DISPERSION IN THE MARINE ATMOSPHERIC
BOUNDARY LAYER 1
2.1.1 INTRODUCTION
The first part of this chapter contains background information on the airshed in
question, including ambient air quality, air pollution sources, atmospheric processes, and air
quality standards. It also contains the results of a study to assess atmospheric stability off
southeastern Vancouver Island, BC in August 2007, which was compared to air quality and
temperature data from other locations in SRKW habitat. Part two includes background
information on air pollution modeling, exhaust emissions, and the whale-watching fleet. It
also describes an air pollution dispersion model that I developed to simulate exhaust
dispersion from commercial whale-watching vessels, and calculate the killer whale’s
exposure under different conditions.
2.1.1.1 Airshed Description and Air Pollution Sources
The summer habitat of the SRKWs includes the Georgia Basin Airshed, which
comprises the western trans-boundary coastal region of Canada and the U.S., the Georgia
Basin in Canada, Puget Sound in the U.S., and Juan de Fuca Strait’s southern coast (Figure
2.1) (EC, 2004). The Georgia Basin Airshed overlaps with the Lower Fraser Valley (LFV)
Airshed, which stretches from Horseshoe Bay to Hope, BC, and includes Metro Vancouver
(formerly the Greater Vancouver Regional District or GVRD), the south-west portion of the
Fraser Valley Regional District (FVRD), and Whatcom County, WA (Figure 2.1) (GVRD, 1 A version of this chapter will be submitted for publication. Lachmuth, C. L., Barrett-Lennard, L. G., and Milsom, W. K. A model-based approach investigating killer whale (Orcinus orca) exposure to marine vessel engine exhaust.
15
2002). The quantity of pollutants emitted into an airshed is often assumed to determine air
quality, yet other parameters such as topography, atmospheric conditions, and the air
pollution source are very important factors governing air quality (BCME, 2005). The
geography and wind currents in the southern section of the Georgia Strait make it an
important area for the accumulation, re-distribution, and chemical change of air pollutants
(Brook et al., 2004). Atmospheric flow conditions that lead to poor air quality in the LFV
Airshed during the summer occur when light morning winds come from the northwest or
south (because the coastal mountains and Vancouver Island channel the pollutants), and
when there is high-pressure over the eastern Pacific Ocean with a shallow thermal trough
over WA and southwestern BC (Ainsley & Steyn, 2007). Elevated air pollution episodes are
not associated with sea breeze circulation and they end when the high-pressure ridge shifts
eastward, allowing cool marine air into the Juan de Fuca Strait (Ainsley & Steyn, 2007).
16
Figure 2.1: The Georgia Basin and Lower Fraser Valley Airsheds (adapted from EC, 2004).
The Georgia Basin/Puget Sound region is one of the largest metropolitan centres in
North America (EC, 2004). The population of this area was 6.97 million in 2002, and is
expected to grow steadily over the next 20 years to the predicted 9 million by 2020 (EC,
2004). Despite the population growth, average and peak levels of nitrogen dioxide (NO2),
carbon monoxide (CO), and sulfur dioxide (SO2) have substantially decreased over the past
two decades in the LFV, while the average ozone (O3) concentration has increased and the
particulate matter (PM) concentration has remained the same (MV, 2006). However, adverse
human health and environmental effects can occur at ambient concentrations commonly
measured in the LFV (EC, 2004; MV, 2006). The total emissions of air pollutants from all
sources during the year 2000 for the LFV Airshed can be seen in Appendix B Table B1
(GVRD, 2002).
17
The proximate air pollutants emitted by whale-watching vessels are not the only
marine sources of air pollutants the SRKWs are exposed to. Their summer habitat
experiences high levels of commercial vessel traffic, as the Juan de Fuca Strait, Haro Strait,
Boundary Pass, and the Georgia Strait form western Canada’s primary shipping route (The
Chamber of Shipping, 2007). Figure 2.2 displays the percentage of air pollutants attributed
to marine vessels in the LFV Airshed in the year 2000 emission inventory (GVRD, 2002).
Marine vessels in the Georgia Basin Airshed are the largest single source of SO2 emissions,
but this is primarily from ocean-going vessels due to the high sulfur content of marine grade
fuel (EC, 2004). The majority of marine vessel emissions in the LFV Airshed occur in the
coastal waters off Metro Vancouver. Over the next decade it is predicted that pollutant
emissions from automobiles will decrease, while emissions from marine sources will increase
and surpass automobile emissions in the LFV by 2010 (EC, 2004).
Figure 2.2: Percentage of air pollutants attributed to all marine vessels in the LFV Airshed in the year 2000 emission inventory (GVRD, 2002). *Principally NOx, VOC, PM2.5, SOx and NH3.
18
The Port of Vancouver is Canada’s busiest; it handles over 70 million tonnes of cargo
from more than 90 countries, and almost a million passengers per year (Thomson, 2004).
The traffic along the shipping route consists of ocean-going vessels including automobile
carriers, bulk carriers, container ships, cargo ships, tankers, and passenger ships (Quan et al.,
2002a). In addition to ocean-going vessels, other marine vessels contributing emissions to
the Georgia Basin Airshed are: cruise ships, harbour vessels (workboats, tugboats, and
charter vessels), ferries, fishing vessels, and recreational vessels (Quan et al., 2002b). The
percentage of air pollutant emissions produced by different vessel categories is presented in
Figure 2.3 (Quan et al., 2002b). The large percentage of CO and volatile organic compound
(VOC) emissions attributed to recreational vessels is due to the numerous inefficient gasoline
(especially two-stroke) outboard engines in use (Quan et al., 2002b). The total amount of air
pollutant emissions (in tonnes) by ocean-going vessels in BC during 2005-2006 can be seen
in Appendix B Table B2 (The Chamber of Shipping, 2007).
19
Figure 2.3: Air pollutant contributions by vessel category in BC outside of Metro Vancouver and FVRD in the year 2000 (Quan et al., 2002b).
Emissions from ocean-going and fishing vessels have seasonal trends: ocean-going
vessel emissions peak from May to August, when over 40% of their total emissions are
released; fishing vessel emissions peak during July and August, with 40% of their emissions
20
released during those two months (Quan et al., 2002b). Thus the highest emission outputs
from ocean-going and fishing vessels occurs during the peak whale-watching season. Ferry
vessels maintain relatively constant emissions throughout the year, with the BC Ferry
Corporation contributing the majority of emissions to this vessel category (Quan et al.,
2002b). Table B3 in Appendix B presents the estimated quantity of air pollutants emitted by
marine vessels in the year 2000 in coastal areas outside Metro Vancouver and FVRD, as well
as Vancouver Island (Quan et al., 2002b).
The aviation sector also contributes air pollutants to the atmosphere; however, the
year 2000 emission inventory of the LFV Airshed determined that aircraft emissions from the
Vancouver International Airport only accounted for approximately 1% of all greenhouse gas
emissions, and less than 1% of all smog-forming pollutants (GVRD, 2002). However,
contributions from aviation to ambient air pollution may be slightly higher in SRKW habitat
because the Vancouver International Airport, the Victoria International Airport, the
Bellingham International Airport, and the Seattle-Tacoma International Airport are all
located within the population’s airshed.
Most air quality monitoring stations are located in urban centres; however, the
Canadian Air and Precipitation Monitoring Network (CAPMoN) maintains a station on
Saturna Island, BC (48°47’32” N 123°08’35” W), which is in the Georgia Strait between
Vancouver Island and the BC mainland (Vingarzan & Thomsom, 2004). The station is at an
elevation of 178 m and measurements of background gaseous and particulate air pollutants
tend to be low indicating that it is relatively isolated from urban sources of air pollution
(Brook et al., 2004). However, pollution episodes (periodic pollution increases) do occur at
this site and indicate that numerous emission sources from the local to regional are involved.
21
Some of these emission sources are urban pollution from Vancouver (40 km northeast) and
Victoria (38 km southwest), as well as industrial pollution from the Crofton pulp mill on
Vancouver Island (39 km northwest), an oil refinery at Cherry Point in WA (32 km
northeast), two additional oil refineries in Anacortes WA (55 km southeast), and an
aluminum smelter and oil refinery in Ferndale WA (45 km northeast) (Vingarzan &
Thomsom, 2004).
2.1.1.2 Air Quality Objectives and Standards
Government agencies in Canada, the U.S., and the World Health Organization
(WHO) in Europe have set ambient air quality standards and objectives to minimize negative
human health effects and protect the environment from air pollutants (BCLA, 2005). Canada
has adopted Canada-Wide Standards (CWS) for PM2.5 (particulate matter smaller than 2.5
microns in diameter) and O3 (Table 2.1), which are based on the annual 98th percentile
concentration averaged over three consecutive years (BCPHO, 2004). Federal, provincial,
and territorial governments agreed upon the CWSs, and Canadian jurisdictions must meet
these standards by 2010 and show a continuous effort to keep the air clean (BCLA, 2005).
The CWSs are minimum targets for all provinces but the standards may not obtain the level
of air quality that a particular jurisdiction desires, and in certain areas conforming to the
standards may actually lead to air quality deterioration (BCPHO, 2004). Thus Metro
Vancouver (MV) established Air Quality Objectives (AQO) for several air pollutants, which
are similar to the CWSs (Table 2.2) (MV, 2006). The CWSs and MV AQOs are in
milligrams per cubic meter of air (mg m-3), and are for specific averaging periods (durations
of exposure).
22
Table 2.1: The Canada-Wide Standards for PM2.5 and O3 (BCPHO, 2004). Air Pollutant Standard
(mg m-3) Averaging
Period* PM2.5 0.03 24-hour
O3 0.13 8-hour *Annual 98th percentile averaged over three consecutive years. Table 2.2: The Metro Vancouver Air Quality Objectives (MV, 2006).
Air Pollutant Standard (mg m-3)
Averaging Period
30 1-hour CO 10 8-hour 0.2 1-hour NO2 0.04 Annual 0.05 24-hour PM10 0.02 Annual
0.025 24-hour PM2.5 0.012 Annual
O3 0.13 8-hour 0.45 1-hour
0.125 24-hour SO2
0.03 Annual
Air quality standards and objectives are designed to protect human health and are
based on human respiratory rates and breathing patterns. Yet the standards in the U.S. (see
Appendix C, Table C1) are less stringent than those in Canada, California, Europe (see
Appendix C, Table C2), and several other jurisdictions (Cooper & Alley, 2002).
Furthermore, the WHO does not list a standard for PM because they consider there to be no
short or long-term exposure to PM below which no harmful effects are expected (WHO,
2000). Despite the variation in air quality standards and the focus on humans, the Metro
Vancouver Air Quality Objectives for CO and NO2 will be used in this thesis as reference to
determine what concentration of air pollutants may pose a risk to killer whales.
23
2.1.1.3 Ambient Air Quality
The ambient (background or baseline) air pollutant concentrations of an airshed are
not a result of local anthropogenic emissions; instead they arise from a combination of local
natural emissions within the airshed, and the long-range transport of natural and/or
anthropogenic pollutants (McKendry, 2006). Western North America is subjected to the
trans-Pacific transport of Eurasian aerosols and Saharan dust, making background
concentrations highly variable both spatially and temporally (McKendry, 2006). The
Georgia Basin receives polluted air from long-range transport or air masses from Eurasia for
example, and to a lesser extent the Americas. While these pollutants are well diluted on
arrival, they add a measurable amount of PM and O3 to ambient concentrations (EC, 2004).
Larger diameter particles (> 10 µm) tend to settle out of the atmosphere quickly, smaller
diameter particles (< 10 µm) remain suspended for longer durations before deposition, and
very small particles (< 1 µm) can remain in the atmosphere for days and can travel much
further (BCME, 2006). Because long-range pollutants spend extended periods of time in the
atmosphere, they have the opportunity to form secondary pollutants (EC, 2004).
The British Columbia Ministry of Water, Land, and Air Protection (WLAP) Air
Resources Branch maintains an Atmospheric Data and Air Quality Health Index Web Service
for monitoring stations in BC. The data are considered unverified, as they have not been
screened for erroneous values due to equipment malfunction or other sampling anomalies
(WLAP, 2008). The station that best represents SRKW habitat is the Christopher Point
monitoring station (48°18’34” N 123°33’43” W) on the southwestern tip of Vancouver
Island, BC, and has a sampling height of 10 m (Figure 1.2). Table 2.3 provides the average
and maximum ambient air pollutant concentrations from May to September of 2005-2007 for
24
air pollutants at Christopher Point (WLAP, 2008). The ambient air pollutant concentrations
at this site are typical of a remote location and are lower than the CWS and MV AQOs.
Table 2.3: Monthly average ambient air pollutant concentrations (maximums in parentheses) at the Christopher Point, BC air quality monitoring station from 2005-2007*.
Air pollutant (mg m-3)
May June July August September
CO 0.61 (0.94)
0.60 (0.92)
0.91 (1.18)
0.61 (1.18)
0.84 (1.31)
NO2 0.0062 (0.033)
0.0069 (0.043)
0.0078 (0.046)
0.0072 (0.057)
0.012 (0.064)
NO 0.0021 (0.018)
0.0020 (0.034)
0.0020 (0.039)
0.0019 (0.026)
0.0028 (0.037)
O3 0.076 (0.13)
0.059 (0.11)
0.050 (0.12)
0.051 (0.10)
0.043 (0.11)
PM2.5 0.0039 (0.017)
0.0034 (0.018)
0.0033 (0.021)
0.0036 (0.014)
0.0052 (0.018)
SO2 0.0014 (0.016)
0.0015 (0.024)
0.0017 (0.026)
0.0019 (0.032)
0.0027 (0.048)
* The CO data are from May-September of 2006-2007; the NO2 and NO data are from May-September of 2006 and May-July of 2007; and the O3, PM2.5 and SO2 data are from September 2005, and May-September 2006-2007 (WLAP, 2008).
Ambient air pollutant concentrations in the Georgia Basin Airshed display seasonal
trends, as outlined in Table 2.4 (Vingarzan & Thomson, 2004). Air pollutant concentrations
(except SO2) peak during the spring, summer, or fall and this is also when the whale-
watching season peaks. Unfortunately, the SRKWs are usually offshore in the winter when
the ambient air pollutant concentrations are at their lowest in the Georgia Basin Airshed.
25
Table 2.4: Times of the year when ambient air pollutants reach their maximum and minimum concentrations in the Georgia Basin Airshed (Vingarzan & Thomson, 2004).
Air Pollutant Maximum Minimum SO2 November – April Summer
SO42- Spring & summer Winter
HNO3 Summer & fall Winter NO3 Spring & fall Summer NH4 Spring & fall Winter O3 Spring & summer Winter
PM2.5 Summer & fall Winter PM10 September Winter
In the Georgia Basin Airshed, SO2 concentrations are the exception to the seasonal
trend displayed by the other pollutants, as SO2 has a winter maxima between November and
April, and a summer minima (Vingarzan & Thomson, 2004). The other pollutants have the
same general seasonal trend, especially nitric acid (HNO3) and O3, which are strongly
affected by solar radiation. Concentrations of HNO3 peak in late summer/early fall due to
hot and dry conditions that promote the transformation of NO2 to HNO3 (Vingarzan &
Thomson, 2004). O3 is primarily produced under sunny conditions with light winds or
stagnant periods, when O3 and its precursors can become trapped next to the surface (EC,
2004). O3 concentrations are especially high during the month of May due to background
O3, and in the summer due to elevated local emissions (EC, 2004). The weather patterns
over the Georgia Basin are not conducive to O3 production 38% of the time (see Section
2.1.1.1), and the meteorological conditions that produce extreme episodes are usually short
lived and arise only 3% of the time (Ainsley & Steyn, 2007; McKendry, 1994). The mean
background concentration of O3 is estimated to range from 0.039-0.069 mg m-3 in the
Georgia Basin, which is already at 50% of the CWS, thus it is possible that the CWS are
occasionally exceeded by background sources alone (McKendry, 2006). Yet the current
26
ambient health quality standards for O3 do not protect sensitive individuals, as health impacts
occur at levels far below ambient standards (EC, 2004), and the mean background
concentration of O3 is increasing at a rate of 0.5-2% per year (McKendry, 2006). Chemical
transport models show that Asian sources add 6-20 µg m-3 of O3 to background
concentrations in the western U.S. during the spring, and this is estimated to increase due to
rising anthropogenic emissions in Asia (McKendry, 2006).
Air masses with north Pacific trajectories arriving in BC have mean background
concentrations of PM2.5 on the order of 1.5-2 µg m-3 and are associated with marine and
Eurasian sources (McKendry, 2006). The annual average PM2.5 mass concentration in the
Georgia Basin varies from 6-8 µg m-3, except in the urban centres of Victoria and Vancouver
where it averages 9-10 µg m-3 (EC, 2004). However, in 2006, the small town of Saanich,
BC, which is just north of Victoria and borders Haro Strait, had the dubious distinction of
being BC’s air monitoring station with the highest annual PM2.5 concentration of 8.9 µg m-3
(BCLA, 2007). Peak PM2.5 concentrations of 23-27 µg m-3 usually occur in the summer and
fall during the months of October and November, and these concentrations fall just below the
CWS for PM2.5 of 30 µg m-3 for 24-hours exposure (EC, 2004). Due to the implementation
of air pollution standards in urban areas, the background concentrations of PM2.5 from
regional or continental scale transport are decreasing (McKendry, 2006). However, peer-
reviewed evidence indicates that there is no lower threshold limit for PM2.5 effects on human
mortality and morbidity, and it has been suggested that background levels are more
appropriate as CWS (McKendry, 2006).
The annual average concentration of PM10 (particulate matter smaller than 10 microns
in diameter) in the Georgia Basin Airshed varies from 12-16 µg m-3, with peak
27
concentrations of 28-30 µg m-3 usually occurring in September (EC, 2004). During stagnant
summer conditions the daytime concentration of PM10 can reach 50-75 µg m-3, which is
greater than the MV AQO for PM10 of 50 µg m-3 (EC, 2004). The Georgia Basin
meteorological patterns conducive to the production of elevated PM occur 46% of the time,
most often in the spring and winter (EC, 2004).
The United States Environmental Protection Agency (USEPA, 2002a) lists heavily
traveled marinas as diesel PM “hotspots” in air, along with major roadways, bus stations, and
train stations. There is limited data on concentrations of PM measured at hotspots, however,
one study found the concentration of diesel PM at a busy Manhattan bus stop ranged from
13.0-46.7 µg m-3 (USEPA, 2002a). These extremely high PM concentrations indicate that
more research is needed at hotspots, because there is potential for many people to be exposed
in these areas (USEPA, 2002a). However, diesel PM concentrations in urban areas are
usually much lower, ranging from 1.7-3.6 µg m-3 (USEPA, 2002a).
2.1.1.4 Atmospheric Mixing and Boundary Layer Stability
The stability of a layer of air is its tendency to either rise or fall in the atmosphere - a
stable layer resists vertical movement, and an unstable layer easily rises or falls. Static
stability of air pollutants decreases mixing, and leads to higher local pollutant concentrations
(Tjernström et al., 2005). Thus characterizing atmospheric mixing in SRKW habitat during
the peak whale-watching season is essential as it determines whether air pollution will
accumulate or disperse.
The portion of the troposphere affected by marine vessel emissions is called the
boundary layer, and it is directly affected by the Earth’s surface where it responds to surface
28
forcing (pressure and stress forces), such as air pollutant emissions, in an hour or less (Stull,
1988). Because water has a large heat capacity, sea surface temperature (SST) remains
relatively constant in a 24-hour period; thus, the depth of the atmospheric boundary layer
over the ocean changes relatively slowly spatially and temporally compared to boundary
layers over land (Stull, 1988).
Air pollutant accumulation is a function of the emission rate, dispersion rate,
generation rate, and destruction rate, where dispersion rate is a function of local
meteorological conditions (wind speed and direction), humidity, temperature, and
atmospheric stability (Cooper & Alley, 2002; Stull, 1988). As expected, air pollutant
concentrations are proportional to the source strength, and are inversely proportional to wind
speed and the distance to the source (Stull, 1988). The two main differences between
pollution boundary layers over land and water are the vertical mixing height and stability
(Hanna et al., 1985). The vertical mixing height defines the layer above the surface where
mixing due to turbulent motion occurs; above this height turbulent motion is suppressed by a
stable capping layer (BCME, 2006).
In the Georgia Basin Airshed, shallow atmospheric mixing depths of less than 100-
200 m near the shoreline are common due to cool water temperatures (Hoff et al., 1997;
USNSB, 1996). Whenever the SST is lower than the air temperature, the boundary layer can
become stably stratified, with pollutants in the lower layers being effectively unmixed by
turbulence due to turbulent energy damping (Stull, 1988). The greatest static stability occurs
close to the ocean’s surface, and this stability decreases toward neutral (well mixed) with
height in the atmosphere. When the stability near the surface is large enough that
temperature increases with height, then that section of the stable boundary layer is described
29
as having a temperature inversion. Pollutant dispersal is severely limited by temperature
inversions and stable boundary layers because the tendency to move vertically in the
atmosphere is eliminated and turbulence is suppressed (Oke, 1987).
Boundary layer stability can be determined by comparing the air temperature gradient
or the environmental lapse rate (ELR) to the dry adiabatic lapse rate (DALR). The DALR is
the negative rate of temperature change a rising parcel of unsaturated air has under adiabatic
(no heat transfer) conditions, and has a constant value of -9.8x10-3 °C m-1 (Oke, 1987). The
dry adiabatic lapse rate rather than the saturated adiabatic lapse rate is used in the boundary
layer below cloud base height because it has less than 100% relative humidity (Oke, 1987).
The ELR can be determined by the temperature change with height:
!
ELR =T2"T
1
z2" z
1
#
$ %
&
' (
where ELR is the environmental lapse rate (°C m-1), T is the temperature (°C), z is the
altitude (m), with z1 and z2 measurements from two different altitudes.
When comparing the environmental lapse rate (ELR) to the DALR, three scenarios can
occur (Oke, 1987):
1. The slope of the ELR is more negative than that of the DALR: The layer of air is defined
as unstable because an air parcel would rise vertically in the atmosphere due to buoyancy
(Figure 2.4a). This is typical of sunny days when the ground heats the near-surface air.
2. The slope of the ELR is more positive than that of the DALR: The layer of air is defined
as stable because an air parcel would be colder than the surrounding air and would sink
(Figure 2.4b). The greater the difference between the two slopes, the larger the damping
tendency. This is typical of an inversion layer when warmer air overlies cooler air.
30
3. The slope of the ELR and the DALR are equal: The layer of air is defined as neutral,
because an air parcel would not rise or fall vertically. This is typical of cloudy windy
conditions, which minimizes horizontal temperature stratification.
Figure 2.4: Graphs of temperature change with height showing (a) an unstable atmosphere and (b) a stable atmosphere. The solid line is the ELR or the Environmental Lapse Rate, which is a measured temperature profile; the dashed line is the DALR (-9.8x10-3 °C m-1). The arrows on the right side of each graph demonstrate the motion an air parcel (indicated by a circle) would have if displaced above height z1. If the parcel were displaced downwards, it would be a mirror image of the displacement upwards. Adapted from Oke (1987).
A crude alternative method to determine the stability of the lower marine boundary
layer is to calculate the air-sea surface temperature difference, ΔT = Tair – Tsea. The air
temperature is measured in the boundary layer below mixing height, which is approximately
1-10 m from the surface in a stable atmosphere (D. Steyn, pers. comm., November 2008).
When the air-sea surface temperature difference is negative (air colder than water) the
atmospheric conditions are unstable, when near zero the conditions are neutral, and when
positive (air warmer than water) the conditions are stable.
Both methods of determining the atmospheric stability of SRKW habitat during the
whale-watching season were employed - air temperature gradients were sampled, as were
31
SSTs to obtain the air-sea surface temperature difference. Since SST is highly conservative
in enclosed waters (Steyn & Faulkner, 1986), but air temperatures tend to increase towards
the mainland and Vancouver Island due to overland heating, positive air-sea surface
temperature differences, or stable atmospheric conditions, would be expected at locations
closer to the BC mainland and Vancouver Island.
2.1.2 MARINE ATMOSPHERIC BOUNDARY LAYER MEASUREMENTS
Air and sea surface temperatures were collected from locations offshore of
southeastern Vancouver Island, BC. Dates sampled in 2007 were on August 21 from 10:54
a.m.-4:00 p.m.; on August 22 from 5:21-6:30 p.m.; on August 23 from 12:00-1:00 p.m.; on
August 25 from 6:45-7:45 p.m.; and on August 27 from 1:15-3:30 p.m. During sampling,
cloud cover ranged from 0-100%, and wind speeds ranged from 1-7.7 m s-1. To sample
temperatures, a three-meter aluminum pole was attached vertically to the bow of an 3.35 m
zodiac, and four copper-constantan thermocouple wires were attached at 0.78 m intervals
along the pole, with the first positioned 0.70 m above the waterline (Figure 2.5). Stacked
white plate covers shielded the thermocouples from solar radiation and allowed airflow. A
fifth thermocouple wire entered the upper surface layer of the ocean to obtain SST at
approximately 0.1 m in depth. The exposed wires on the fifth thermocouple were coated
with epoxy to render them waterproof for obtaining SST. The five thermocouple wires were
attached to a micrologger (Campbell Scientific 21X(L)) programmed to record the
temperature from each thermocouple every 5 seconds, and calculate the average temperature
after 15 minutes. During each 15-minute trial the zodiac maintained a speed of two knots
(1.03 m s-1), and traveled approximately 927 m.
32
Figure 2.5: Profile view of the thermocouple setup on the zodiac. Four thermocouple wires covered with stacked white plates were mounted on a vertical aluminum pole to sample the air temperature at evenly spaced heights above the water, and a fifth thermocouple entered the water to obtain sea surface temperature. A horizontal wood plank along the centre line of the zodiac and rigging provided stability for the pole.
Forty-one 15-minute trials were conducted under low, medium, and high
combinations of wind speed and cloud cover. Due to stability issues with the pole mounted
on the bow of the zodiac, trials were not carried out when wind speeds exceeded 15 knots
(7.72 m s-1). All trials occurred near Oak Bay, Vancouver Island, BC (Figures 1.2 and 2.6), a
location where the SRKWs are often sighted. This location was chosen because it is in
33
SRKW habitat, and further distances from shore could not be sampled due to the instability
of the zodiac with the experimental setup.
Figure 2.6: Map of southeast Vancouver Island, BC. The area traversed during temperature sampling is shaded in light grey.
2.1.3 MARINE ATMOSPHERIC BOUNDARY LAYER DATA
To determine atmospheric stability, Oak Bay air temperature gradients (ELRs) were
plotted and compared to the DALR, and the air-sea surface temperature differences were
calculated. All 41 trials displayed the same general trend - an increase in air temperature
with height above the water. The average slope for all 41 trials was 4.72 °C m-1 (SEM =
0.36), which is more positive than that of the DALR (-9.8x10-3 °C m-1). Figure 2.7 shows the
first 10 15-minute trials conducted; for clarity not all trials are shown as they display almost
identical profiles. Only nine trials (of the total 41) deviated from the general trend, as one or
two temperatures did not increase with height, as seen in Figure 2.8. This is likely because
34
the average wind speed of these nine trials was greater (by 1.3 m s-1) than the average wind
speed of all 41 trials, and the average cloud cover of the nine trials was less (by 29%) than
the average of the 41 trials.
Figure 2.7: Results from the first ten 15-minute trials at Oak Bay, BC, demonstrating that the average temperature increased with height above the water. The dry adiabatic lapse rate (DALR) is plotted as a solid black line.
Figure 2.8: Plot of the nine trials at Oak Bay, BC, that had deviations from temperature increasing with height. The dry adiabatic lapse rate (DALR) is plotted as a solid black line.
35
The average difference between the air temperature measured 1.5 m above the water
and SST for all 41 15-minute trials was 2.45 °C (SEM = 0.20); all 41 trials had positive
differences except two (Figure 2.9). The air temperature at 1.5 m above sea surface was used
because that was the air temperature sampling height used at Race Rocks Ecological Reserve
(RRER, 2008). The two trials with negative differences were from consecutive samples
taken on the same day at 14:00 and 14:15 while traveling around the southern tip of Trial
Island. The SSTs of these two trials were approximately 2 °C greater than those of the other
39 trials, making the SST slightly higher than the air temperature, which indicates the zodiac
passed through a water mass with different characteristics (e.g. an eddy).
Figure 2.9: Air-sea surface temperature difference (Tair-Tsea) for all 41 15-min trials, by the time of day the trial started. Air temperatures were measured 1.5 m above the sea surface, and sea surface temperatures were measured approximately 0.1 m below the sea surface.
In order to evaluate the extent to which our pilot study was representative of general
air-sea temperature conditions in the area, and to determine the frequency of stable boundary
36
layers during the rest of the year, a comparison was made to data from the Race Rocks
Ecological Reserve (48°18' N 123°32' W) in the Juan de Fuca Strait, BC (Figure 1.2). The
air-sea surface temperature difference was calculated from monthly air temperature data
gathered in 2007 at the Race Rocks Ecological Reserve (RRER, 2008), and SST data from
the lighthouse at Race Rocks maintained by the Department of Fisheries and Oceans Canada
(DFO, 2008a). The average monthly difference between air temperature and SST at Race
Rocks in 2007 is plotted in Figure 2.10. Air temperature was measured 1.5 m above rock
surface and SST was measured daily off the end of the dock 1-hour before high tide, from a
bucket sample lowered 1-2 m below sea surface.
No air temperature data was available for the month of March; however, based on the
trend seen in Figure 2.10, March would likely have an air temperature similar to the SST.
Figure 2.10: Average monthly air-sea surface temperature difference (Tair-Tsea) at Race Rocks, BC in 2007, with standard error of the mean bars. Air temperature was sampled 1.5 m above sea surface, and sea surface temperature was sampled 1-2 m below sea surface.
37
The average monthly air-sea surface temperature difference from June to September
in 2007 at Race was positive (average = 0.81 °C, SEM = 0.42). The positive difference at
Race Rocks in August is in agreement with the data collected at Oak Bay in August 2007,
and indicates stable atmospheric conditions.
To ensure that 2007 was not an anomalous year, air-sea surface temperature
differences from 2002-2006 were calculated from Race Rocks data (Figure 2.11). The
average monthly air-sea surface temperature difference at Race Rocks was 0.33 °C (SEM =
0.47). From April to October the differences were positive, with an average of 1.53 °C
(SEM = 0.34). Since May 2007 had unstable/neutral atmospheric conditions, a comparison
was made to Race Rocks data from 2002-2006. The unstable/neutral atmospheric conditions
in May of 2007 were due to unusually low air temperatures (about 2 °C lower than average),
as the SSTs in 2007 were consistent with the SST averages from 2002-2006.
Figure 2.11: Average monthly air-sea surface temperature difference (Tair-Tsea) at Race Rocks from 2002-2006, with standard error of the mean bars. Air temperatures were measured 1.5 m above the sea surface, and sea surface temperatures were measured 1-2 m below the sea surface.
38
Air-sea surface temperature differences were calculated for two more locations in
SRKW habitat: the Halibut Bank Buoy (2002-2005 data), and the Hein Bank Buoy (2004-
2007 data). The Halibut Bank Buoy is situated in the middle of the Georgia Strait between
Tsawwassen (on the mainland) and Valdez Island (49°20’2” N 123°43’2” W) (Figure 1.2).
The air temperature at the Halibut Bank Buoy was sampled hourly 2-3 m above the sea
surface, and SST was sampled hourly 1-2 m below the sea surface by a sensor on the buoy
(O. Riche, pers. comm., February 14, 2008). From 2002-2005, the average yearly air-sea
surface temperature difference at the Halibut Bank Buoy was -0.64 °C (SEM = 0.13) (Figure
2.12). The average monthly air-sea surface temperature differences were negative at this
location all year, but occasionally in July, August, and September the differences became
near neutral or positive as indicated by the standard error of the mean bars in Figure 2.12.
The instability (negative air-sea surface temperature difference) at this location may be partly
due to differences in sampling heights; however, the Halibut Bank buoy is in the plume of
Fraser River water as it enters the ocean. The fresh Fraser River water absorbs solar
radiation as it floats on top of denser seawater, and the temperature also increases from the
tidal signal of water warmed while lying over sand at low tide in the Fraser River delta
(Thomson, 1981). Higher SSTs create negative air-sea surface temperature differences, and
result in more neutral or unstable atmospheric conditions. Thus, this location has
confounding factors that affect the atmospheric stability; however, during the time period of
concern the conditions were mostly neutral.
39
Figure 2.12: Average monthly air-sea surface temperature difference (Tair-Tsea) at Halibut Bank Buoy from 2002-2005, with standard error of the mean bars. Air temperatures were measured 2-3 m above the sea surface, and sea surface temperatures were measured 1-2 m below the surface.
The Hein Bank Buoy is situated in Juan de Fuca Strait, southeast of Victoria, BC
(48°20’0” N 123°10’0” W) (Figure 1.2), and the air temperature was sampled hourly 4 m
above the sea surface, and SST was sampled hourly 0.6 m below the sea surface by a sensor
on the buoy (NOAA, 2008). From 2004-2007, the average air-sea surface temperature
difference from May to September was 0.88 °C (SEM = 0.16) (Figure 2.13).
40
Figure 2.13: Average monthly air-sea surface temperature difference (Tair-Tsea) at Hein Bank Buoy from 2004-2007, with standard error of the mean bars. Air temperatures were measured 4 m above the sea surface, and sea surface temperatures were measured 0.6 m below the surface. 2.1.4 MARINE ATMOSPHERIC BOUNDARY LAYER CONCLUSIONS
Two methods were employed to determine the atmospheric stability offshore of Oak
Bay, BC: sampling air temperature profiles (ELRs) and calculating the air-sea surface
temperature difference. Both methods indicated stable atmospheric conditions during the
sampling period, as the ELR slopes (average = 4.72 °C m-1) were more positive than that of
the DALR (-9.8x10-3 °C m-1) for all 41 trials conducted, and positive air-sea surface
temperature differences (average = 2.45 °C) were found in 39 of the trials. Thus, the
atmospheric boundary layer can be classified as stable during peak whale-watching season,
and the increase in temperature with height indicates that a near surface temperature
inversion formed. A stable lower boundary layer creates the worst conditions for pollution
dispersal because turbulence is suppressed and the vertical dispersion of pollutants is
eliminated (Oke, 1987).
41
The results from Oak Bay may not represent other areas in SRKW habitat, since they
were conducted very close to shore, and had limited geographical and temporal scopes.
Therefore, the results from Oak Bay were compared to other locations in SRKW habitat.
The 2007 monthly air-sea surface temperature differences at Race Rocks indicate that stable
boundary layers occurred from June until September. In addition, data from 2002-2006 at
Race Rocks indicates that 2007 was not an anomalous year, as the air-sea surface
temperature differences were positive from April until October, which overlaps with the peak
whale-watching season that runs from May until September. By comparing Figures 2.10 and
2.11, it can be seen that 2007 had monthly atmospheric conditions that were less stable than
those in 2002-2006, and this was mainly due to unusually low air temperatures in 2007.
The Halibut Bank Buoy air-sea surface temperature differences indicate that July,
August, and September tend to have neutral atmospheric conditions, while at other times of
the year atmospheric conditions are unstable. In contrast, the Hein Bank Buoy air-sea
surface temperature differences indicate that stable atmospheric conditions predominate from
May to September, which overlaps with the whale-watching season. This location is closer
to Race Rocks than the Halibut Bank Buoy, and is not influenced by Fraser River water.
Thus, the air-sea surface temperature differences more closely resemble those from Race
Rocks than those from the Halibut Bank Buoy.
Both methods of evaluating atmospheric stability, and all three locations compared
indicate that the atmospheric conditions the SRKWs are exposed to during the commercial
whale-watching season are predominantly stable. This can result in an accumulation of air
pollutants above the surface of the water where the killer whales breathe. Additionally,
42
stable air would quickly dissipate any whale-generated turbulence from exhalation at the
surface, and turbulence from vessel wakes.
2.2.1 MODELING DISPERSION IN THE MARINE ATMOSPHERIC BOUNDARY
LAYER
Empirical roadside studies indicate that dispersion of vehicle exhaust can be rapid -
emissions are concentrated within 100 m of busy roads and then decline rapidly with distance
from the road and reach background levels by 200-300 m downwind (Hitchins et al., 2000;
Roorda-Knape et al., 1998; Tiitta et al., 2002; Zhu et al., 2002a). Hitchins et al. (2000) found
that wind blowing directly from the road caused vehicle emission concentrations to decay to
about 50% of the original at a distance approximately 100-150 m from the road, whereas
wind blowing parallel to the road caused emissions to decay to 50% at 50-100 m from the
road. These studies suggest that the concentration of pollutants from vehicle exhaust can
remain high at distances less than 100 m from the source, but then decline considerably with
increasing distance. Generally at 100 m from a highway vehicle emissions are at
concentrations below air quality standards, yet under certain conditions (e.g. low wind
speeds, limited vertical mixing) the concentrations can remain high (HEI, 1988).
Empirical studies on the dispersal of emissions over coastal waters or open-ocean are
rare due to: the complexities inherent in the emission route; uncertainty regarding the
behaviour and fate of pollutants in water; and the diversity in environmental conditions of
aquatic systems (Rijkeboer et al., 2004). However, a study by Skyllingstad et al. (2007)
found that stratified boundary layers tend to be formed over cool seawater, where air
temperature profiles are quite stable, there is minimal turbulence, and relatively low wind
43
speeds near the sea surface. Angevine et al. (2004) found that ozone was transported over
coastal waters in stable boundary layers at the surface, and while intermittent turbulence
occurred, the chemical constituents and concentrations in the layers remained strong because
there was limited deposition and shallow vertical mixing, which minimized dilution. It was
concluded that pollution transport over water is different than over land due to several
factors: vertical mixing and dilution are reduced and plume shearing increases; the deposition
of O3 and other precursors are reduced because they are deposited at a much slower rate to
water surfaces than to vegetation; local emissions are reduced because there are no fresh
inputs for reactions; wind speeds are greater; and pollutants are carried long distances (20-
200 km) without major losses (Angevine et al., 2004). Smedman et al. (1997) also found a
stratified stable boundary near the sea surface, but with increasing altitude a transition
occurred to a near-neutral layer capped by an inversion.
A literature search did not reveal published air quality monitoring studies focusing on
output from recreational marine engines, and the air pollutant concentrations the SRKWs are
exposed to have never been quantified. Real-time air quality monitoring has inherent
limitations and uncertainties, such as obtaining adequate sample sizes due to the numerous
variables involved. Additionally, the equipment required for a marine air quality study is
extremely expensive, and the extensive data required would necessitate an entire team of
assistants for collection. Instead I developed an emission dispersal model to determine killer
whale exposure to air pollutants produced by whale-watching vessels by running numerous
computer simulations with different combinations of parameters.
44
2.2.1.1 Emission Dispersion Models
Air quality dispersion models use empirically based equations that simulate the
behaviour of gases and/or particles emitted into the atmosphere to estimate exposures at
receptors. Models are especially important in situations where direct measurement is
impractical, as they produce a cause-effect link between emissions and the resulting ambient
pollutant concentrations (BCME, 2006). Computers are used to run numerous scenarios that
provide an unbiased, reproducible, and inexpensive method for assessing existing or future
air quality. Because the models are based on numerous inputs and assumptions, they are
subject to many possible inaccuracies (Cooper & Alley, 2002); however, variance from
reality is expected due to inherent chaotic processes in the atmosphere (BCME, 2006). Often
the predictions made by peer reviewed air quality dispersion models are considered to be the
“best estimate” available for decision-making, and the results are used extensively for air
quality management worldwide (BCME, 2006).
A wide range of air quality dispersion models with different levels of complexity and
combinations of parameters are available, and there are mobile-source models where the
object emitting pollutants is mobile (as is the case with whale-watching vessels), and fixed-
source models where the object emitting is stationary. All mobile-source models require the
following parameters: the engine types in the fleet; the number of operating engines; the
engine emission rates; the atmospheric conditions; and the geophysical characteristics
(BCME, 2005; Van Atten et al., 2004). The British Columbia Ministry of Environment
(BCME, 2006) recommends the use of extensively tested dispersion models; however, the
recommended models are not appropriate for the whale-watching scenario because they are
either: designed for terrestrial situations; designed for single point, area, or volume sources;
45
designed for urban locations, highways, or industrial complexes; and/or require hour-by-hour
meteorological data. Due to the complex atmospheric layers over coastal areas, the
development of marine models is far behind models of land dispersal (BCME, 2005). One
marine model called the Offshore and Coastal Dispersion Model was developed for offshore
oil and gas platforms; however, it is also inappropriate for the whale-watching scenario
because the pollutant transport distance is on the scale of kilometers, it requires extensive
data input (e.g. hourly meteorological data), and does not handle sea surface sources well, if
at all (BCME, 2006).
Multi-agent or agent-based modeling is becoming increasingly popular for simulating
the dynamics of complex systems over time (Anwar et al., 2007). Multi-agent models
typically include: an environment, objects in the environment, agents (the active entities in
the system), links between agents, operations for the agents, and operators that modify
behaviour of the agents (Bousquet & Le Page, 2004). The use of multi-agent models for
human-wildlife interactions are rare; however, a multi-agent system developed by Anwar et
al. (2007) was used to simulate whale-watching tours, and to calculate the “happiness factor”
(the ratio of whale observation time over the trip duration). Here, I used a programmable
multi-agent based modeling environment called NetLogo (Wilensky, 1999) to simulate the
behaviour of exhaust gases emitted from whale-watching vessels, and estimate the
concentration of exhaust gases SRKWs are exposed to under varying conditions. I used a
sensitivity analysis to determine which variables had the greatest impact on the predicted air
pollutant concentration.
46
2.2.1.2 Marine Engines, Fuel, and Emissions
The gaseous and particulate phase of exhaust from marine diesel and gasoline engines
contains hundreds of chemical compounds, the most abundant of which are carbon oxides
(COx), sulfur oxides (SOx), nitrogen oxides (NOx), hydrocarbons (HC), and PM. There are
also small amounts of known (e.g. benzene and toluene) and unknown chemical substances
that may be relevant due to their persistence and/or toxicity (Rijkeboer et al., 2004). HC’s in
the exhaust are composed of unburned fuel (aromatics, alkanes, and alkenes), and partially
oxidized HC’s (phenols and carbonyls) (Rijkeboer et al., 2004). The majority of PM from
combustion engines is in the submicrometer range (0.02-0.5 µm), and is composed of
elemental carbon, adsorbed organic compounds from the fuel and oil used, sulfates from the
sulfur in the fuel, and trace metals (IPCS, 1996).
Several factors influence the emissions produced by marine engines: the age of the
engine, the engine type, the fuel characteristics, the engine maintenance, the performance of
the engine’s pollution control systems, the engine load, the engine temperature, the engine
speed, and the RPM (Van Atten et al., 2004). However, Frey and Bammi (2003) found that
engine exhaust emissions depend more on fuel type (diesel or gasoline), and engine
technology (i.e. two or four-stroke) than engine size, age, or type of aspiration. Automobile
engines have catalytic converters that reduce tailpipe emissions, but due to salt-water
corrosion of the catalyst they are uncommon in marine engines. Gasoline engines lacking
catalytic converters produce quantities of polycyclic aromatic hydrocarbons (PAHs) similar
to diesel engines of equivalent power output (Frumkin & Thun, 2001).
The two categories of marine engines are outboards and inboards, and they have very
different emission characteristics. Outboard engines are mounted on the stern of the vessel,
47
have self-contained drive units, and are typically used in smaller vessels (Coates &
Lassanske, 1990). Inboard engines tend to be used on larger vessels because they usually
develop greater power than outboards, they are mounted inside the vessel, and the drive can
be relayed through different propulsion systems, most commonly a straight shaft and a
swiveling propeller unit referred to as a stern drive (Coates & Lassanske, 1990). Inboard
engines are usually fuelled by diesel due to safety issues with gasoline, as it is much more
flammable and has a greater explosion risk than diesel. Both diesel and gasoline fuel contain
possible and known neurotoxic agents (Kirrane et al., 2006). The organic compounds found
in diesel and gasoline exhaust are qualitatively similar, yet there are quantitative differences
(Frumkin & Thun, 2001). Diesel engines are more fuel-efficient than gasoline engines, and
they produce less carbon dioxide (CO2), CO, and HCs; however, they produce more NOx
(HEI, 1999) and PM at a rate approximately 20 times greater than gasoline engines (IPCS,
1996).
Inboard and outboard engines can be either two or four-stroke, and are typically
fuelled by a gasoline-oil mix or gasoline respectively. Older model two-stroke gasoline
engines are notorious for producing more airborne volatiles, toxic organics, and PM than
four-stroke engines (Kado et al., 2000). Incomplete combustion in the two-stroke engine
produces unburned residual oil and partially burnt oil that enters the marine environment
through the exhaust, and leaves a sheen of oil on the water. Four-stroke engines generally
produce more CO, CO2, and NOx, but fewer aromatic HCs than two-stroke engines. Fuels
other than gasoline and diesel, such as natural gas, petroleum gas, and biodiesel, are
increasingly being used in marine engines. Alternative fuels (Appendix D1) and fuel
additives (Appendix D2) affect exhaust emissions but they were not considered in this study.
48
2.2.1.3 Wet and Dry Exhaust Systems
Marine engines can have either a dry exhaust system that emits exhaust directly into
the air, or a wet exhaust system that combines the exhaust with water or directs the exhaust
into the water to cool, silence, and minimize human exposure (Gabele & Pyle, 2000; Kado et
al., 2000). The first type of wet exhaust system is found in sailboats and small inboard
powerboats that mix sea water with the exhaust to cool it before it is ejected from the hull,
usually just above the water line (Rijkeboer et al., 2004). The second type is found in many
modern outboard and outdrive engines which emit below the water line through the propeller
hub, and the majority of the exhaust is in a gas phase that is directly bubbled out of the water
column into the atmosphere (Rijkeboer et al., 2004). The proportion of wet and dry exhaust
systems in the whale-watching fleet is unknown, thus it was assumed there were equal
numbers of each.
Volatile exhaust gases with poor water solubility bubble out of the water and are
introduced into the air; however, less volatile gases with greater water solubility remain
primarily in the water (Juttner et al., 1995). Exhaust components in the water condense and
either remain suspended in the water column or form an emulsion layer on the water’s
surface. These suspended and surface emissions have several routes of atmospheric release
or degradation through a mixture of chemical, physical, and biological processes (Rijkeboer
et al., 2004). Marine vessels can deteriorate water quality by introducing exhaust gases into
the water at levels that exceed water quality criteria (Juttner et al., 1995), and killer whales
swimming through the emulsion layer to breathe could potentially ingest the pollutants. The
method a toxin enters the body – through inhalation, ingestion or skin contact – determines
the percent absorption of the toxin and the resulting organ toxicity (NESCAUM, 1999). It is
49
unlikely that a killer whale’s skin would absorb pollutants in the emulsion layer since they
have a very thick epidermis (Geraci & St. Aubin, 1990); however, the emulsion layer may
affect their mucus membranes (e.g. eyes), and/or could potentially be ingested and inhaled.
Fuel spilled during re-fueling or sloshing from vents can also add oil and gas to the surface of
the ocean, which can then be mixed by the wind and other disturbances to form a toxic
emulsion layer. While this would most likely occur near shore and at marinas, the emulsion
layer can disperse on currents and potentially affect killer whales in the vicinity. Toxic
emulsion layers were not quantified in this study due to the number of unknowns involved,
yet they may prove to be more harmful to killer whales than exhaust emissions in heavily
trafficked waters. Further studies are required to determine to extent of emulsion layer
formation and dispersion, and their potential health impacts.
Marine engines are usually tested/certified with the engine out of the water, which
does not consider exhaust retained in the water from wet exhaust systems (Kado et al., 2000),
and quantifying emissions retained in the water has proven to be difficult and often
unrepeatable (Coates & Lassanske, 1990). For both two and four-stroke engines with wet
exhaust systems, Juttner et al. (1995) found that the VOCs in the water were almost
exclusively aromatic HCs, and the amount present in the water was only 10% of that emitted
in the exhaust. However, others have found that for both gasoline and diesel fuels,
approximately 40% of the HC’s emitted end up being retained in the water phase and
accumulate on the water surface, while the remaining 60% escapes in gas bubbles to the
surface (Clark et al., 2000; Rijkeboer et al., 2004; Warrington, 1999). For modeling
purposes, a 40% reduction was applied to the dry HC emissions to obtain wet HC emissions.
50
CO is poorly soluble in water and more than 80% emitted is immediately lost to the
atmosphere from vessels with wet exhaust systems (Rijkeboer et al., 2004); thus for the
dispersion model, a 20% reduction was applied to the dry CO emissions to obtain wet CO
emissions. Clark et al. (2000) found that NOx emissions in air for dry exhaust tests were
21% higher than in wet exhaust tests, thus I factored in a 21% loss of NOx to the water to
obtain wet NOx emissions. The PM and PM-associated toxic compounds produced by engine
combustion are hydrophobic, thus can potentially re-enter the air from the water (Kado et al.,
2000). When diesel engine exhaust is expelled beneath the water surface, about 40% of the
PM is deposited in the water (Clark et al., 2000). Since a literature search did not produce
information on wet exhaust PM retention for gasoline engines, it was assumed that gasoline
exhaust experiences the same 40% reduction as diesel. Thus for modeling emissions, a 40%
reduction was applied to the dry PM emissions to obtain wet PM emissions.
2.2.1.4 The Whale-Watching Fleet
Information on marine engines used by the commercial whale-watching companies
that target the SRKWs was obtained from the Soundwatch Boater Education Program in
2005 (unpubl.), and from personal communication with whale-watching companies in 2006.
This provided the number of engines per vessel, the horsepower (hp) of each engine, the fuel
type, and the engine type (inboard/outboard, two, or four-stoke) for 23 out of 46 companies
that operated during the 2005 season. It was assumed that the missing half of the fleet
operated similar engines. Air pollution modelers use emission rate averages of the mixed
fleet of vehicles in use (L. Frank, pers. comm., February 13, 2007); thus, from the
Soundwatch data it was determined that the “average” whale-watching vessel had either twin
51
200 hp (total = 400 hp) inboard four-stroke diesel engines or twin 200 hp outboard four-
stroke gasoline engines.
Even though 36% of the vessels engaged in whale-watching are recreational (Osborne
et al., 2002), no information is available on their engine configurations. Thus only
commercial whale-watching vessel engine configurations were considered in the model.
2.2.1.5 Engine Emission Factors
When measured engine emission rates are not available, published engine emission
factors can be used (BCME, 2006). The USEPA has published emission factors for HCs,
CO, NOx, and PM in grams per horsepower-hour (g hp-1 hr-1) for zero-hour (i.e. new engine),
steady-state, non-road gasoline and diesel engines of varying hp (USEPA, 2004a; 2004b).
To obtain emission factors for the “average” whale-watching vessel engine, this study
utilized the USEPA emission factors for non-road diesel engines with a power rating of 175-
300 hp, and for four-stroke outboard gasoline engines with a power rating greater than 175
hp (USEPA, 2004a; 2004b). The USEPA states that all PM emissions are assumed to be
smaller than 10 µm, and 92-97% of the PM is assumed to be smaller than 2.5 µm.
Since 1998 the USEPA has been gradually phasing in emissions standards for marine
diesel and gasoline engines to reduce their emissions over pre-control levels. In Canada, new
engines beginning with the model-year 2001 must comply with the USEPA emission
standards (EC, 2005). To account for the effect of federal emission standards, the USEPA
produced emission factors for both pre-pollution control recreational marine engines (pre-
2006), and post-pollution control recreational marine engines (post-2006) (USEPA, 2004a;
2004b). The USEPA emission factors for pre-2006, twin 200-hp diesel, gasoline, wet and
52
dry exhaust engines were averaged to provide the air pollution emission factors for an
“average” whale-watching vessel. The post-2006 pollution control emission factors were
also calculated to determine how pollution control devices on the engines would affect the air
pollutant concentrations the killer whales are exposed to. The diesel and gasoline emission
factors used in the dispersion model can be seen in Tables 2.5 and 2.6 respectively.
Table 2.5: USEPA non-road model emission factors for pre and post-2006 model recreational marine diesel engines with power ratings less than 175 to 300 hp.
Emission factors Air pollutant (g hp-1 hr-1) Pre-2006 Post-2006
HC 0.22 0.14 CO 0.95 0.95 PM 0.16 0.11 NOx 6.67 4.78
Table 2.6: USEPA non-road model emission factors for pre and post-2006 model recreational marine gasoline engines.
2-stroke outboard (> 175 hp)
4-stroke outboard (> 175 hp)
Inboard (all power ratings)
Air pollutant (g hp-1 hr-1)
Pre-2006
Post-2006 (with
carburator & ignition alterations)
Post-2006 (with
alterations & catalyst)
Post-2006 (with
electronic fuel
injection)
Pre-2006
Post-2006
Pre-2006
Post-2006 (with
electronic fuel
injection) HC 128.7 115.6 62.7 18.7 7.5 n/a 5.9 3.0 CO 313.3 289.4 246.2 242.5 258.1 n/a 153.7 71.8 PM 7.7 7.7 7.7 7.7 0.06 n/a 0.06 0.06 NOx 4.5 8.2 3.7 8.2 9.0 n/a 5.4 8.5
Transient adjustment factors (TAFs) are applied to emission factors to take into
account different engine loads and/or speeds; however, the USEPA did not apply TAFs to the
recreational marine engine category due to lack of information, thus it is assumed that marine
engines operate at steady-state (USEPA, 2004a; USEPA, 2004b). Steady-state engine
operating conditions can produce emissions that are quite different from those produced
53
under transient operating conditions, for example acceleration dramatically increases PM
emissions (Graskow, 2001; USEPA, 2002a). Thus the PM emissions calculated in the
dispersion model may be lower than real world situations where engines operate under
transient conditions.
The NOx emitted by marine engines is made up of nitrogen oxide (NO) and NO2
(Quan et al., 2002b). However, ambient air quality objectives only apply to NO2; thus, a
conversion factor is required (BCME, 2006). The British Columbia Ministry of Environment
(BCME, 2006) states that NO2 is typically 5-10% of the NOx concentration, while the
USEPA (2000) uses a mean emission rate of NO to NOx ratio of 0.94 (SD = 0.03), as does
Lloyd’s Register of Shipping (Lloyd’s, 1995). Thus NO accounts for approximately 94%
and NO2 6% of the total NOx emitted, and this conversion factor was used for the NOx
concentrations predicted by the dispersion model.
Air quality dispersion models are usually used to estimate the incremental change in
pollutant concentrations resulting from specific sources (BCME, 2006). However, the
atmosphere always has an ambient/background concentration of air pollutants from natural
and anthropogenic sources not included in the model because they add unnecessary
complexity (BCME, 2006). Thus a simple equation is used to determine total air quality
where, Total = Background + Predicted Increment (contribution from modeled emission)
(BCME, 2006).
Typically a single background value is used, and when modeling worst-case
situations (where a conservative estimate of the impacts are preferred) a conservative
background concentration rather than the maximum should be used (BCME, 2006). The
background concentrations used in the dispersion model were from the Christopher Point
54
ambient air quality monitoring station on Vancouver Island, BC, during May to September of
2005-2007 (Table 2.3) (WLAP, 2008). Average ambient concentrations of CO (0.71 mg m-3)
and NO2 (0.008 mg m-3) simply need to be summed with the concentrations predicted by the
dispersion model to obtain the total air pollution exposure.
2.2.2 NETLOGO DISPERSION MODEL
The NetLogo program interface consists of two main components: space and agents
(Wilensky, 1999). The space (also called the world) is the physical environment where the
agents are situated and interact; in this case it represents the ocean surface. The space is
automatically split into positive and negative x and y quadrants, and I further divided the
quadrants into patches 2 m by 2 m square. World wrapping was allowed, so that the vessels
and whales were replaced on one side of the space as they disappeared on the other; however,
pollution did not wrap and the patches on the edge of the domain removed the pollution as if
on an infinite plane. The programming code for the NetLogo dispersion model can be found
in Appendix E.
The two types of agents in the model were the whale-watching vessels and the whale.
The movements of the whale and whale-watching vessels were not based on empirical
trajectories; instead the whale was instructed to swim in a straight-line trajectory towards the
right of the world at a constant speed of 2.85 m s-1, which is the published average swimming
speed of an adult male killer whale (Kriete, 2002). Since each patch was 2 m x 2 m, it
resulted in time-steps (tick) of 0.7 seconds. Straight-line trajectories are representative of
northern resident killer whale behaviour when more than three vessels are present within
55
1000 m of whales (Williams & Ashe, 2007). It is expected that the SRKWs would behave in
a similar manner (Williams & Ashe, 2007).
Since the whale and whale-watching vessels were all moving to the right of the space,
the vessels were placed above and below the whale in uniformly spaced rows to simulate
paralleling, which is the method of whale-watching recommended by the Be Whale Wise
Guidelines (DFO, 2008b). As seen in Figure 2.14, the first row of vessels on either side of
the whale were set at the buffer distance variable (the distance vessels maintained from the
whale), and the distance between vessels was set by the inter-vessel distance variable. The
vessels remained in the same position relative to each other and the whale for the duration of
the simulation, and the number of vessels remained constant. The vessels moved at the same
speed as the whale, 2.85 m s-1 (5.5 knots); thus, the vessel speed was slower than the Be
Whale Wise Guideline that recommends a vessel speed less than 3.6 m s-1 (7 knots) when
within 400 m of whales (DFO, 2008b).
56
Figure 2.14: Image of the NetLogo interface. The whale is at the center of the world and is colored green. The 20 whale-watching vessels are shaped as colored triangles, arranged in two uniformly spaced rows on either side of the whale. The vessels emit blue pollution plumes that are moved downwind at an angle of 240˚. North (0˚) is at the top of the image, and during simulations the whale and vessels moved to the right, or East (90˚).
Each vessel moved forward one patch per time-step and had its air pollution emission
rate set at 70.2 mg per time-step (equivalent to 100 mg s-1). The model was programmed to
calculate the air pollutant concentration (equation below) in the whale’s patch at each time-
step. Because ambient concentrations scale linearly with emission rate the 100 mg s-1 is
essentially a dummy pollutant emission rate, which can be multiplied by the USEPA
emission factors to obtain specific pollutant concentrations. To calculate the emission rate
from the emission factor provided by the USEPA, the emission factor (in g hp-1 hr-1) was
multiplied by the rated horsepower of the engine, which provided the grams of pollutant
emitted per hour (g hr-1). This was then converted to milligrams emitted per second (mg s-1)
57
and divided by the dummy air pollutant emission rate of 100 mg s-1 to obtain the
multiplication factor for each air pollutant. The multiplication factors for different marine
engine configurations can be seen in Table 2.7. These multiplication factors simply need to
be multiplied by the dummy air pollutant concentrations predicted by the dispersion model to
find modeled air pollutant concentrations from different engine configurations.
Table 2.7: Air pollutant multiplication factors for different marine engine configurations. Engine
configuration* CO NO2 HC PM
1 129.50 0.47 3.41 0.10 2 129.50 0.41 3.38 0.08 3 1.06 0.44 0.24 0.18 4 286.78 0.60 8.29 0.07 5 0.84 0.35 0.15 0.11 6 229.42 0.47 4.97 0.04 7 Same as pre-2006 0.32 0.16 0.12 8 288.22 0.45 218.89 Same as pre-2006 9 79.78 0.57 3.33 0.07
* Engine configurations: 1 = “Average” vessel: pre-2006 twin 200-hp 4-stroke diesel and gasoline, wet and dry exhaust engines 2 = “Average” vessel: post-2006 twin 200-hp 4-stroke diesel and gasoline, wet and dry exhaust engines 3 = Diesel, pre-2006 twin 200-hp, inboard engine, dry exhaust only 4 = Gasoline, pre-2006 twin 200-hp, 4-stroke engine, dry exhaust only 5 = Diesel, pre-2006 twin 200-hp, inboard engine, wet exhaust only 6 = Gasoline, pre-2006 twin 200-hp, 4-stroke engine, wet exhaust only 7 = Diesel, post-2006 twin 200-hp, inboard engine, dry exhaust only 8 = Gasoline, post-2006 twin 200-hp, 2-stroke engine, dry exhaust only 9 = Gasoline, post-2006, twin 200-hp inboard engine, dry exhaust only
The most widely used air dispersion models are based on the Gaussian dispersion
equation, which has a number of assumptions and limitations. Gaussian models calculate
hourly air pollutant concentrations with uniform meteorological conditions within the
modeling domain; they do not account for curved plume trajectories or variable wind
conditions; they assume the emission plume originates from a point source rather than a
mobile source; they assume that exposure occurs at the plume centerline; they have an
58
inverse dependency on wind speed, thus a low wind speed limit that imposes unacceptable
bias during stagnant atmospheric conditions; they overestimate air pollutant concentrations
under stable atmospheric conditions; and the calculated concentrations are only within ± 50%
of actual values due to the restrictive conditions under which the dispersion parameters for
the equation were developed (Arya, 1999; BCME, 2006; Cooper & Alley, 2002; Mohan &
Siddiqui, 1997).
Thus instead of using a Gaussian dispersion equation, the exhaust emissions were
dispersed in the model by diffusion and advection, which can be treated separately. The
“diffuse” function in NetLogo captures isotropic molecular diffusion, and programs each
patch containing pollution to share a percent of its pollution with its eight neighboring
patches. The percent of pollution shared is called the diffusion constant variable, and varies
between zero and one, thus small values produced narrow concentrated pollution plumes and
large values produced fanning diluted plumes. The diffuse function spread the pollutants
perpendicular to the wind direction, and the amount of diffusion into the neighboring patches
was conserved (except the edge patches where the pollution was removed). Advection was
captured in the model by adding wind that moved the pollution downwind, and advection and
diffusion created a crosswind plume that grew in width over time. A pollution deposition
function was not included in the model because deposition is insignificant for time periods of
1-hour or less (D. Steyn, pers. comm., January 2008), and deposition over water for the
pollutants under consideration has not been quantified empirically.
The dispersion model contained an equation to calculate the dummy air pollutant
concentration in the patch of the whale each time-step, expressed as (D. Steyn, pers. comm.,
October 2007):
59
!
C =e
g" zm " s
where C is the concentration in mg m-3, e is the emission rate in mg s-1 (equal to 100 mg s-1),
g is the patch size that the pollutant disperses into each time-step (set at 2 m, which is the
transom length of an average whale-watching vessel), zm is the vertical pollutant mixing
height in m, and s is the vessel speed in m s-1 (equal to 2.85 m s-1). Thus g, s, and zm define
the volume the pollutant disperses into.
The formula to calculate air pollutant exposure is simply (NRC, 1991):
!
E = c " t
where E is the exposure (mg hr m-3), c is the concentration (mg m-3), and t is time (hours).
The length of time was 1-hour since most air pollutant standards (e.g. the MV AQOs for CO
and NO2) are based on 1-hour exposures.
A sensitivity analysis was conducted on all the variables included in the model, and
two versions of the dispersion model were employed: a completely deterministic version
without any random elements, and a stochastic version where the wind angle randomly
changed every time-step around a normally distributed mean wind angle. The deterministic
version was run until the pollutant concentration the whale experienced reached a steady
state, usually around the 47th time-step, and for consistency the concentration at the 100th
time-step was taken as the value for each simulation. The world size for the deterministic
simulations was 500 by 500 patches (equal to 1,000 m by 1,000 m), and because the
simulations were of short duration the agents did not wrap around to the other side of the
world, and to obtain 1-hour exposures the predicted air pollutant concentrations were
multiplied by one. The stochastic simulations ran for 1-hour of real time (2520 time-steps),
thus the predicted air pollutant concentrations at each time step were averaged to obtain 1-
60
hour exposures. The world size in the stochastic simulations was set at 600 by 400 patches
(1,200 m by 800 m), and because of the longer duration of the stochastic simulations (2520
time-steps versus 100 time-steps) the agents did wrap around to the other side of the world.
While the whale was wrapping around the world it was not exposed to pollution because the
edge patches deleted the pollution, and this was taken into account when calculating the
average concentration by running the simulations for 2870 time-steps and removing the 60
time-step concentrations that occurred during the transitions (the whale wrapped around five
times during the 1-hour simulations). Additionally the first 50 time-steps were removed
because it took approximately that long for the pollutants to reach the whale once the
simulations began. Multiple repetitions of each variable setting were not required for the
deterministic version of the model since there were no random elements. A repetition of a
stochastic simulation was conducted and the absolute difference between the two trials was
0.00247 mg m-3 while the relative difference was 1.5%; because this was such a small
difference further repetitions were not conducted.
There are two types of variables in the model: model structural variables (i.e.
diffusion constant) and model representations of real world qualities (i.e. wind speed, wind
angle, the vertical mixing height of pollutants in the atmosphere, the buffer distance, the
inter-vessel distance, and the number of vessels). Each variable was fixed at a default value,
except for the variable being analyzed, which experienced a realistic range of values with at
least six increments within that range. The default and range of values used in the sensitivity
analysis for each variable are listed in Table 2.8. In addition to the sensitivity analysis,
simulations were run with the variables at average-case and worst-case whale-watching
values.
61
Table 2.8: Variables in the dispersion model with their default and range of values. Model variables Default Range
Wind speed (m s-1) 5.7 0.71 – 13.54 Wind angle (°) 180 90 - 270 Pollutant diffusion constant 0.5 0 - 1 Vertical mixing height of pollutant (m) 3 0.5 - 9.5 Buffer distance to whale (m) 60 2 - 122 Number of vessels 20 1 - 121 Inter-vessel distance (m) 50 4 - 104
Meteorological data are necessary in all air quality dispersion models, and this can be
in the form of hourly data or a matrix of combinations of realistic meteorological conditions,
and in this dispersion model a matrix was used (BCME, 2006). Wind angles coming from
one direction produced identical pollutant concentrations as the wind angle they mirrored
(i.e. 30° was equivalent to 150°); thus, only angles from one side of the compass rose were
considered for the deterministic simulations (i.e. 90°, 120°, 150°, 180°, 210°, 240°, 270°). A
constant wind speed and direction can be assumed for time periods equal to 1-hour or less
(D. Steyn, pers. comm., January 2008), thus the deterministic simulations kept wind speed
and direction constant. However, the effect of a changing wind angle was explored in the
stochastic simulations, as the wind angle randomly changed every time-step around a
normally distributed mean wind angle and within a standard deviation of the wind direction
fluctuations (sigma theta, σθ) equal to 22.5°. This value for the standard deviation of the
wind direction fluctuations is expected in stable to slightly stable atmospheres for time
periods of 1-hour or less (Hanna et al., 1982). Three mean wind angles were considered in
the stochastic simulations, the angle that typically produced the lowest concentrations in the
deterministic simulations (90°), the angle that typically produced medium concentrations
(150°), and the angle that typically produced the highest concentrations (210°). The British
Columbia Ministry of Water, Land, and Air Protection (WLAP) Air Resources Branch Web
62
Service provides measured wind direction fluctuations at the Christopher Point monitoring
station on Vancouver Island, BC. In 2007 from May to September, the average wind
direction fluctuation (σθ) was 15°, with the highest value of 19.4° occurring in September
(WLAP, 2008). Thus the wind direction fluctuation value of 22.5° used in the dispersion
model was higher than the average observed at Christopher Point. The average wind
direction at Christopher Point ranged from 220°-253° (winds from the southwest) from May
to September 2005-2007 (WLAP, 2008). The same was true for the Hein Bank Buoy from
May to September from 2004-2007, as the average direction the wind came from varied from
225°-238° (winds from the southwest) (NOAA, 2008).
Wind was programmed in the model to move the pollution from all patches with the
same direction and speed. The minimum wind speed was 0.71 m s-1, the approximate stall
speed of anemometers, and the maximum was 13.54 m s-1 (equivalent to 26.32 knots or
Beaufort scale 6); at this maximum wind speed the wind is described as a strong breeze that
produces large 3 m waves on the sea surface (Barnes-Svarney, 1995). At wind speeds
greater than 10 m s-1 the lower atmosphere will no longer be stable but wind driven mixing
will render it neutral. In these wind and wave conditions it is likely that whale-watching
vessels would be unable to find and follow killer whales, thus greater wind speeds were not
considered. At Christopher Point the average wind speed during the peak whale-watching
months in 2007 was 8.55 m s-1, with the highest wind gust of 35 m s-1 occurring in May
(WLAP, 2008). Because the Christopher Point station is at the interface between the land
and water, the wind speeds may be higher than those over water. At the Hein Bank Buoy the
average wind speed from May to September from 2004-2007 was 4.7 m s-1 and the daily
average maximum wind speed was 17.2 m s-1 (NOAA, 2008). Thus, the default wind speed
63
in the dispersion model (5.7 m s-1) was slightly higher than the average wind speed at the
Hein Bank Buoy, but less than the average wind speed at the Christopher Point station.
Under most conditions it is expected that the vertical air pollutant concentration
profile within the first few meters of the surface is reasonably homogeneous due to
mechanical mixing (BCME, 2006), and the model assumed that the profile of vertical
pollution diffusion initially decreased rapidly with height and then leveled off. The vertical
mixing height in the dispersion model was independent of patch size, and instead was 1-10
times the average vessel transom length (2-20 m) due to the aerodynamic drag of the moving
vessel. Drag from the vessel produces a turbulent wake in which the pollutants mix, and is
influenced by the shape and speed of the vessel (HEI, 1988). Because of the reduced vertical
mixing over cool water bodies, the maximum mixing height considered was only 9.5 m since
heights greater than 5 m are unlikely (D. Steyn, pers. comm., January 2008). Elevated
vertical mixing heights quickly disperse air pollutants into large volumes of air, and produce
low downwind air pollutant concentrations; whereas low mixing heights can trap pollutants,
giving rise to high pollutant concentrations (BCME, 2006).
The Be Whale Wise Guidelines (DFO, 2008b) advise vessel operators to maintain at
least 100 m from whales, thus the buffer distance variable ranged from 2-122 m. The
number of vessels variable ranged from 1-121, as the maximum number of vessels ever
observed around the SRKWs is 120 (Osborne et al., 2002). The default number of vessels
was set at 20 as that is the average number of vessels around the SRKWs during the summer
months (Bain, 2002; Baird, 2001; Erbe, 2002; Koski, 2006; Osborne et al., 2002; 1999). The
inter-vessel distance varied from 4-104 m, as vessel operators try to maintain a 100 m
distance between vessels (M. Malleson, pers. comm., February 1, 2008), and due to vessel
64
size it was assumed that vessels would not approach each other closer than 4 m. Vessel
count data was gathered from the Straitwatch Boater Education vessel during June, July, and
August 2008, and was analyzed for inter-vessel distances at the Marine Communications and
Traffic Services radar station. However, the resolution of the radar system was not accurate
enough to provide this detailed information.
Most air quality dispersion models also take surface roughness into account, due to
the complex turbulence patterns created by topography; however, models recommended by
the British Columbia Ministry of Environment (2006) set the surface roughness over water to
0.0001 m, and because that is such a small value it was not included in the dispersion model.
Additionally, average wave heights recorded at Hein Bank from May to September in 2004-
2007 only varied from 0.29-0.48 m (NOAA, 2008).
Since direct validation of the air pollutant concentrations calculated by the dispersion
model was not feasible, the assumptions were made as close to reality as possible. Sources
of uncertainty in the model were minimized by error checking, and by correcting odd model
behaviour. However, it must be recognized that air dispersion model predictions have
“inherent uncertainty” from unknown turbulent atmospheric processes that cannot be
resolved (BCME, 2006).
2.2.3 NETLOGO DISPERSION MODEL RESULTS
The air pollution concentrations predicted by the dispersion model were standardized
as 1-hour exposures, and are from the patch the whale was in. The graphs that follow depict
the results of the average-case and worst-case dispersion model simulations, and have been
scaled to the engine emission factors for the “average” whale-watching vessel previously
65
described. To see how different engine configurations affect the exposure concentrations
refer to the multiplication factors in Table 2.7. All the graphs have the CO concentration on
the left y-axis, and the NO2 concentration on the right y-axis, along with reference lines that
indicate the MV AQOs for 1-hour of exposure to CO (30 mg m-3) and NO2 (0.2 mg m-3) for
comparison. The MV AQOs for other air pollutants have longer exposures (8-hour, 24-hour,
and annual), thus they were not compared. Human air quality standards were included to put
the exposures into context, and are converted into killer whale equivalents in Chapter three.
2.2.3.1 Results of the Sensitivity Analysis
All graphs depicting the results of the sensitivity analysis simulations can be found in
Appendix F. The main difference between the deterministic and stochastic versions of the
dispersion model was that adding random wind angle fluctuations tended to smooth out the
peaks and valleys in the air pollutant concentrations (i.e. lower maximum concentrations and
higher minimum concentrations). If the whale was exposed to a direct exhaust plume from a
vessel in the deterministic version, the plume would remain directly over the whale for the
entire simulation, resulting in a high exposure. Whereas in the stochastic version, the wind
angle fluctuations caused the exhaust plumes to move every time-step so that the whale never
experienced a direct plume for long, resulting in lower average exposures. The
concentrations obtained in the stochastic and deterministic versions of the models were
within the same order of magnitude.
The wind angle variable produced the greatest extremes in air pollution concentration
that the killer whale was exposed to, for both the deterministic and stochastic versions of the
dispersion model. However, after wind angle, the ranking of variables that had the greatest
66
effect were slightly different for the deterministic and stochastic simulations. The second
most important variable was either buffer distance or mixing height, followed by the number
of vessels or the inter-vessel distance, and finally the wind speed.
When the whale was downwind of the vessels it received the highest air pollutant
exposures. Wind angles of 210° and 240° consistently produced the highest air pollutant
concentrations, with 210° being the highest 67% of the time and 240° 33% of the time.
When the wind came from angles of 210° and 240°, the air pollution was pushed downwind
in roughly the same direction as the whale (to the right of the world). Thus at these wind
angles the whale often experienced both direct exhaust plumes from whale-watching vessels
and indirect diffused pollution, which resulted in the whale receiving the highest air pollutant
concentrations.
The wind angles that produced the highest air pollutant concentrations after 210°
were ranked: 240° was second highest 67% of the time; 180° was third highest 67% of the
time; 150° was fourth highest 83% of the time; 120° was fifth highest 67% of the time; 270°
was sixth highest 83% of the time; and 90° was the lowest 83% of the time. In the
deterministic model, when the wind came from directly ahead of the whale (90°) or directly
behind the whale (270°), the whale was not exposed to direct exhaust plumes from vessels,
and very limited pollution from diffusion reached the whale, thus the air pollutant
concentration the whale experienced was virtually zero at these angles. However, when the
wind angle randomly changed in the stochastic version of the model, it resulted in higher air
pollutant concentrations at wind angles of 90° and 270°, because diffused pollution ended up
reaching the whale.
67
As wind speed increased it generally produced a roughly unimodal trend in the
modeled air pollution concentration in the patch the whale was in (Figures F1 and F2 in
Appendix F). The lowest wind speed of 0.7 m s-1 produced air pollutant concentrations that
were essentially zero; however, by 2 m s-1 the air pollution concentration was often much
higher, except for wind angles of 90°, 120° and 270°, which had air pollution concentrations
that were essentially zero at all wind speeds. The highest air pollution concentrations
occurred at wind speeds between 2-7 m s-1, and as the wind speed increased above 7 m s-1 the
air pollution concentration decreased (except at a wind angle of 150°, which had a maximum
concentration at approximately 11 m s-1). Increasing the wind speed caused the air pollution
plumes to bend over as the pollution got swept down wind at a faster rate, which changed the
angle of plume spreading and resulted in higher air pollutant concentrations. Thus the peaks
in air pollutant concentration at the different wind angles were due to exhaust plumes from
vessels moving directly into the whale’s path as wind speed increased.
The diffusion constant variable had opposing effects on the air pollutant
concentration the whale experienced, depending whether or not the whale received air
pollution from a direct vessel exhaust plume or from diffusion alone (Figures G3 and G4 in
Appendix F). A large diffusion constant resulted in a high air pollutant concentration if the
whale was not in a direct exhaust plume (i.e. at wind angles of 90°, 120°, 150°, 270°),
because a large diffusion constant spread the pollution so that some eventually reached the
whale. The opposite was true if the whale was in a direct exhaust plume from a vessel: a low
diffusion constant (e.g. 0.2) exposed the whale to highly concentrated plumes and as the
diffusion constant increased the plume was diluted, exposing the whale to lower
concentrations.
68
The air pollutant concentration initially displayed a rapid decline as the vertical
mixing height increased, but it then leveled off without approaching zero (Figure F5 and F6
in Appendix F). The amount of air pollution the whale received was dependent on whether
the whale was in a direct exhaust plume from a vessel or if it was only receiving pollution
that diffused toward it.
The air pollution concentration tended to be inversely proportional to the buffer
distance (Figure F7 and F8 in Appendix F). Oscillations in the pollution concentration
occurred because the whale was either in a direct exhaust plume or not due to changing
vessel positions and spacing as the buffer distance increased. This variable was unusual
because the 270° wind angle produced the second highest air pollutant concentration,
whereas with the other variables that angle produced one of the lowest air pollution
concentrations. The 150° wind angle was also unusual as it produced the lowest air pollutant
concentration, but with the other variables it produced mid-range concentrations.
The air pollution concentration generally increased with the number of vessels
(Figure F9 and F10 in Appendix F). The stepwise increases in concentration were due to the
addition of rows of vessels as the number of vessels increased.
The inter-vessel distance variable did not produce a consistent trend at all wind
angles; however, the air pollution concentration generally decreased with increasing inter-
vessel distance (Figure F11 and F12 in Appendix F). At inter-vessel distances less than 10 m
the air pollutant concentration was very low because the wind moved the pollution away
before it had time to reach the whale. The oscillations in air pollution concentration occurred
because the whale was either in a direct exhaust plume or not due to changing vessel
positions and spacing as the inter-vessel distance increased.
69
The results of the sensitivity analysis illustrated that the dispersion model captured
important dynamics of air pollutant dispersion, and the following sections (2.2.3.2 and
2.2.3.3) will investigate specific phenomena/situations of interest.
2.2.3.2 Results of the Average-case Trials
Further deterministic simulations were run with the variables set at average-case
whale-watching values, rather than the default values used in the previous sensitivity analysis
simulations. The buffer distance, inter-vessel distance, diffusion constant, and number of
vessels variables remained constant, while the wind speed and mixing height variables
experienced their range of values listed in Table 2.8. The buffer distance was set at 100 m,
the distance recommended by the Be Whale Wise Guidelines (DFO, 2008b). The inter-
vessel distance was also set at 100 m, which is the distance whale-watch vessel operators try
to maintain between vessels (M. Malleson, pers. comm., February 1, 2008). The diffusion
constant was set at 0.5, and the number of vessels was set at 20, which is the average number
of vessels around the SRKWs during the summer months (Bain, 2002; Baird, 2001; Erbe,
2002; Koski, 2006; Osborne et al., 2002; 1999). This average-case scenario set-up caused
the furthest vessels from the whale to be at a distance of 316 m, which is much closer than
the average real-world whale-watching conditions of 20 vessels within 800 m (Bain, 2002;
Baird, 2001; Erbe, 2002; Koski, 2006; Osborne et al., 2002; 1999). Thus the average-case
simulations may have overestimated exposures since the vessels were closer to the whale.
Under average-case whale-watching conditions the wind speed variable produced CO
concentrations that exceeded the MV AQO only at wind angles of 210° and 240°; while the
MV AQO for NO2 was never exceeded (Figure 2.15). The CO MV AQO was only exceeded
70
when the air pollution concentrations peaked, generally at wind speeds between
approximately 1-9 m s-1, thus it is the mid-range wind speeds that are most problematic. The
highest dummy air pollutant concentration of 0.33 mg m-3 occurred at a wind angle of 240°,
and wind speed of 2.1 m s-1. This maximum concentration is lower than both the
deterministic and stochastic versions of the model, when run with default values rather than
average values. The mean CO concentration predicted by the average-case simulations was
11.98 mg m-3 (SEM = 3.63), and the mean NO2 concentration was 0.04 mg m-3 (SEM =
0.01). These means are well under the MV AQOs for both CO and NO2.
Figure 2.15: CO and NO2 concentration as a function of wind speed and angle under average-case whale-watching conditions.
71
Under average-case whale-watching conditions the vertical mixing height variable
produced CO concentrations that exceeded the MV AQO at wind angles of 150°, 180°, 210°,
and 240° when the mixing heights were less than approximately 1.5, 2.3, 6, and 1 m
respectively (Figure 2.16). The NO2 concentrations exceeded the MV AQO at wind angles
of 150°, 180°, and 210° when the mixing heights were less than approximately 1, 1.5, 3.7 m
respectively. The highest dummy air pollutant concentration of 2.87 mg m-3 occurred at a
wind angle of 210° and mixing height of 0.5 m. This maximum concentration is lower than
that of the default deterministic version in the model, but higher than the stochastic version.
Figure 2.16: CO and NO2 concentration as a function of the vertical mixing height and wind angle under average-case whale-watching conditions.
72
2.2.3.3 Results of the Worst-Case Trials
Additional deterministic simulations were conducted with reasonable “worst-case”
values for the variables, which were within the bounds of whale, vessel, and atmospheric
behaviour. For these simulations the buffer distance was set at 50 m, which is half the
distance recommended by the Be Whale Wise Guidelines (DFO, 2008b). The inter-vessel
distance was also set at 50 m, which is half the distance whale-watching vessel operators try
to maintain from each other (M. Malleson, pers. comm., February 1, 2008). The number of
vessels was set at 40, which is double the average number of vessels (Bain, 2002; Baird,
2001; Erbe, 2002; Koski, 2006; Osborne et al., 2002; 1999). The vertical mixing height was
set at 2 m, and the diffusion constant was set at 0.5, and the wind speed varied from 2.6-6.7
m s-1. The two wind angles that produced the highest air pollutant concentrations in the
deterministic version of the model, 210° and 240°, were used in the worst-case simulations.
The MV AQOs for CO and NO2 were exceeded at all wind speeds, and both wind
angles in the worst-case simulations (Figure 2.17). The highest dummy air pollutant
concentration of 1.35 mg m-3 occurred at a wind angle of 210° and wind speed of 5.4 m s-1.
The mean CO concentration predicted by the worst-case simulations was 76.28 mg m-3 (SEM
= 15.44), and the mean NO2 concentration was 0.27 mg m-3 (SEM = 0.06). Both of these
means (especially that for CO) are above the MV AQOs.
73
Figure 2.17: CO and NO2 concentration as a function of the wind speed and angle under worst-case whale-watching conditions. 2.2.4 DISPERSION MODEL CONCLUSIONS
The results from the sensitivity analysis simulations suggest that the wind angle had
the largest effect on the concentration of air pollutants the killer whale was exposed to, with
downwind angles (210° and 240°) producing the highest concentrations. The second most
important variable was either buffer distance or mixing height, followed by the number of
vessels or the inter-vessel distance, and finally the wind speed. The deterministic version of
the model produced higher maximum concentrations than the stochastic version, except for
the inter-vessel distance variable.
Under average-case conditions when the 20 vessels maintained the recommended 100
m distance from the whale and each other, the MV AQOs for CO and NO2 could be
74
exceeded. While in worst-case simulations the MV AQOs for CO and NO2 were always
exceeded. The average CO concentration predicted by the average-case simulations was
11.98 mg m-3, with the highest concentrations ranging from 42.6-372 mg m-3. This average
concentration of CO is five times greater than the 2.0-2.5 mg m-3 range of CO concentrations
measured 30 m from a busy Los Angeles highway (Zhu et al., 2002b). The average
concentration of CO predicted by the worst-case simulations was 76.28 mg m-3, which is 34
times greater than the average concentration of CO measured by Zhu et al. (2002b). These
results are not that surprising since the zone around non-road engines often have exhaust
emissions that are the same or double those measured around busy roadways (USEPA,
2002b; 2004c).
The average NO2 concentration predicted by the average-case simulations was 0.04
mg m-3, with the highest concentrations ranging from 0.15-1.34 mg m-3. This average NO2
concentration is just above the range of NO2 concentrations (0.032-0.037 mg m-3) measured
at a distance of 115 m from busy motorways (Roorda-Knape et al., 1998). The average NO2
concentration predicted by the worst-case simulations was 0.27 mg m-3, which is 7.8 times
greater than the NO2 concentrations measured by Roorda-Knape et al. (1998). This elevated
level of NO2 is not that surprising since 22% of NOx in the LFV Airshed in the year 2000
emission inventory was attributed to marine vessels. However, it indicates that under worst-
case whale watching conditions, the whales are experiencing very poor air quality episodes
that are potentially harming their health.
Therefore, even the average-case simulations predicted air quality (as indicated by
CO and NO2) to be worse (especially for CO) than that measured along busy highways, with
worst-case predictions indicating extreme pollution episodes. Generally the MV AQOs for
75
CO and NO2 were exceeded in the average-case simulations when: the wind came from an
angle of 150°, 180°, 210° or 240°; the wind speed was between 1-9 m s-1; and the mixing
height was less than 6 m. However, the average-case simulations were run with only 20
vessels, and they all maintained 100 m from the whale and each other. The sensitivity
analysis indicated that the MV AQOs for CO and NO2 were generally exceeded when the
buffer distance was less than 20 m; the number of vessels was greater than 27 on average;
and the inter-vessel distance was less than 50 m.
The simulations showed that shallow mixing heights produced high air pollutant
concentrations, and stable atmospheric conditions (shallow mixing heights) predominate
during the whale-watching season. Additionally, the average wind speed measured at the
Hein Bank Buoy and Christopher Point during the summer months was 4.7 m s-1 and 8.55 m
s-1 respectively (NOAA, 2008; WLAP, 2008), which is within the range of wind speeds that
produced the highest air pollutant concentrations in the average-case simulations. Since the
atmospheric conditions during the whale-watching season are highly conducive to air
pollutant accumulation, the potential to exceed the MV AQOs is high. Therefore, to ensure
air pollutant concentrations are below the MV AQOs, vessel operators need to maintain
adequate distances from each other and the whales, the number of vessels around the whales
should not exceed the average too often, and vessels should not be positioned upwind of the
whales, especially at wind angles of 210° and 240° to the whale.
Empirical roadway studies have shown that air pollutant concentrations decrease to
approximately 50% of the original 100 m from the roadway (as explained in section 2.2.1).
Therefore, the predicted air pollutant concentrations from the deterministic simulations that
manipulated buffer distance were compared at distances of 2 m and 102 m. When the whale
76
was downwind (210° and 240°), the concentration at 100 m decayed to approximately 38%
of the original. When the wind was parallel to the whale (90° and 180°), the concentration at
100 m decayed to 24% of the original. Thus the model predicted emissions to decrease to a
greater extent (on average to 31% of original) than those found in empirical roadway studies
(~50%). This indicates that the dispersion model may have underestimated concentrations,
as higher concentrations were expected because of the low mixing heights used. Obviously
improved modeling or on the water air sampling would determine the accuracy of the
calculated concentrations.
In the dispersion model, the whale-watching vessels paralleled a single whale rather
than a pod of whales, which is more representative of watching a lone transient killer whale
rather than a pod of SRKWs. The average number of vessels following the SRKWs could
have been divided by the number of individuals to find how many vessels watch a single
whale on average; however, the number of vessels was a variable that was manipulated
during the simulations, therefore this issue was taken into account.
77
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3 PHYSIOLOGICAL EFFECTS ASSOCIATED WITH EXPOSURE TO AIR
POLLUTION2
3.1 INTRODUCTION
Air pollution is an issue that currently receives a lot of attention, but the 1952 London
Smog was the first air pollution episode to cause major public concern because of its causal
link with a sharp rise in human mortality (Bell & Davis, 2001). The association between air
pollution and human mortality is generally independent of location or climate, and by 1956
the United Kingdom established clean air legislation, and open coal burning was banned
(Bates, 1994). The statistics concerning air pollution are troubling - it is estimated that air
pollutants kill approximately three million people worldwide each year, and 50% of chronic
respiratory illnesses are associated with air pollutants (Pimentel et al., 2007). In Canada
approximately 21,000 deaths will be attributed to air pollution in 2008, with 88% due to the
chronic effects of long-term exposure, and 12% due to acute short-term recent exposure
(Canadian Medical Association, 2008).
The compounds in engine exhaust can produce short-term, reversible effects as well
as long-term irreversible effects (NESCAUM, 1999). The majority of direct health effects
attributed to air pollution are respiratory illness (e.g. asthma, acute respiratory infection, and
lung cancer), cardiovascular illness, as well as premature death (BCPHO, 2004; Pimentel et
al., 2007). Adverse health effects from long-term exposure to air pollution usually originate
in the alveolar region of the lung (USEPA, 2002); however, increases in mortality from air
pollution exposure often arise from heart conditions rather than respiratory disorders 2 A version of this chapter will be submitted for publication. Lachmuth, C. L., Barrett-Lennard, L. G., and Milsom, W. K. A model-based approach investigating killer whale (Orcinus orca) exposure to marine vessel engine exhaust.
85
(BCPHO, 2004). Positive associations have been found between respiratory symptoms, such
as asthma, and traffic volume (USEPA, 2004), and the concentration and duration of
exposure correlates with the start and severity of adverse health effects to some extent, but
the relationship is not linear (Kalberlah et al., 2002). There is a statistically significant risk
of mortality for individuals living within 200 m of a busy road, with the risk decreasing with
distance from the road (USEPA, 2004).
Epidemiology, human laboratory research, and animal and in-vitro toxicology are
used to study the effects of air pollution on human health (Koenig, 2000). Human
epidemiological studies show that exposure to low levels of air pollution produce chronic
adverse health effects, but exposure measurements are often vague (Bates, 1994). Studies on
acute human exposure to single gases establish minimum-effects levels; however, animal
studies are currently the only way long-term effects are currently studied (Bates, 1994). All
known human carcinogens also cause cancer in one or more animal species, and this
similarity in response is used as the basis for extrapolation of animal studies to humans
(Goddard & Krewski, 1992). Animal-to-human extrapolations have given assumptions and
mathematical methods, including route of exposure, dose, and response (BCPHO, 2004).
An individual’s exposure to air pollution depends on their pattern of activity in
relation to the source of the air pollution, and their response to air pollution depends on their
age, health, and genetic predisposition (Van Atten et al., 2004). Individuals that tend to be
especially sensitive to air pollution are infants, children, the elderly, individuals already
suffering from respiratory or cardiovascular illness, and individuals who engage in frequent
exercise outdoors (HEI, 1988; Koenig, 2000). Thus this chapter includes an estimate of the
proportion of SRKWs that may be extra sensitive to air pollution.
86
3.1.1 Compounds in Diesel and Gasoline Exhaust
Both diesel and gasoline fuels are mixtures of highly toxic chemicals that when
combusted produce air pollutants such as CO, CO2, NOx, hydrocarbons (HCs; composed of
over 150-260 aliphatic and aromatic HC compounds including potential neurotoxicants such
as benzene, n-hexane, toluene, xylenes, naphthalene, and 1,3-butadiene), formaldehyde, and
PM (composed of elemental carbon, sulfates, trace metals, and adsorbed organic compounds
that usually comprise 10-30% of the particle mass) (BCPHO, 2004; IPCS, 1996; Ritchie et
al., 2001; Yu et al., 1991). For a list of all the classes of compounds in diesel exhaust, see
Appendix G. Unburned HCs and NOx in gasoline and diesel exhaust can combine and
undergo photochemical reactions to form secondary pollutants in the atmosphere, such as O3
and smog, which often lead to shortness of breath, chest pain, wheezing, and coughing in
humans (Frumkin & Thun, 2001).
Gasoline contains 25-30% aromatic HCs and is very volatile; diesel contains no
aromatic HCs because it is distilled, and has very low volatility (Kirrane et al., 2006). Diesel
PM is 75% elemental carbon and has a lower fraction of organic matter than gasoline PM,
which is 25% elemental carbon (USEPA, 2002). Diesel engines also emit more
benzo[a]pyrene, but less VOC, CO, and HC than gasoline engines (USEPA, 2002).
The six principle air pollutants referred to as Criteria Air Pollutants by the USEPA
are PM, ground level O3, CO, SOx, NOx, and lead (Koenig, 2000). There are also over 189
Hazardous Air Pollutants (e.g. benzene, mercury, asbestos), but little is known of their health
effects or ambient concentrations (Koenig, 2000). PM in diesel exhaust is a pollutant of
primary concern because it is easily inhaled due to its small size (the mass median
aerodynamic diameter is approximately 0.2 µm), and because it has a high capacity to bind to
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harmful chemicals (HEI, 1999; Yu et al., 1991). O3 and PM have been identified as the two
Criteria Air Pollutants associated with the most serious health effects (MV, 2006); however,
the most carcinogenic compounds in engine exhaust are 1,3-butadiene, benzene, and
formaldehyde (NESCAUM, 1999). Animal studies have shown that there is potential for
synergistic, additive, and/or antagonistic interactions between the individual components in
fuel exhaust (Ritchie et al., 2001).
The zone around non-road diesel engines (e.g. all terrain vehicles, lawnmowers, and
marine vessels) frequently has air pollutant concentrations that are the same or nearly double
those found along busy roads, and full-time operators of non-road engines have a
significantly increased incidence of lung cancer (USEPA, 2002; 2004). Therefore, the health
effects from on-road diesel engine exhaust can be applied to non-road engine exhaust
(USEPA, 2004). Non-road engine technology usually lags behind that of on-road engines,
which have had emission standards since 1988 (USEPA, 2004). While new technology often
reduces exhaust emissions, some research has shown that there may be unintended
consequences. Increases in fuel injection pressure creates higher rates of fuel atomization
and evaporation, which can produce PM in the exhaust with much smaller diameters, in the
nanometer size range (USEPA, 2002). This is very worrisome since ultrafine PM produces
the most adverse health effects (USEPA, 2002). Very little information exists on ultrafine
particles, and only PM with a diameter less than or equal to 10 µm (PM10) and less than or
equal to 2.5 µm (PM2.5) are currently regulated with air quality standards.
A study by Kirrane et al. (2006) found that fishermen exposed to vessel exhaust
received higher concentrations of benzene from vessels with two-stroke gasoline engines
compared to four-stroke gasoline or diesel engines. However, even though the benzene
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exposure was greater than ambient levels, the effects of benzene and other gasoline
constituents at these concentrations on human health is unknown, but they are below levels to
which health effects are reliably attributed (Kirrane et al., 2006). The simple presence of
pollutants does not equal a health risk, and this issue will be explored further below.
3.1.2 Health Effects from Exposure to Diesel and Gasoline Exhaust
The toxicological database for diesel exhaust is considerable, especially when
compared to other toxins, and critical health effects have been derived from numerous long-
term chronic exposure studies (USEPA, 2002). Research on diesel exhaust exposure has
focused on PM, which has historically been used as a proxy for exposure to the entire
mixture of diesel exhaust; however, there may be additive or synergistic effects from the
vapor-phase of the exhaust (USEPA, 2002). It has been suggested that diesel PM is likely no
more toxicologically potent than other constituents of ambient PM2.5 (USEPA, 2002). Long-
term animal studies show that diesel exhaust poses a chronic respiratory hazard to humans
(USEPA, 2002). Diesel exhaust has little acute toxicity (IPCS, 1996), but has been identified
as a probable human carcinogen due to inhalation from environmental exposure by several
national and international agencies (i.e. National Institute for Occupational Safety and
Health, International Agency for Research on Cancer, World Health Organization, California
USEPA, and U.S. Department of Health and Human Services); this classification is based on
broad evidence from epidemiologic, toxicological, and experimental studies on animals and
humans (HEI, 1999; USEPA, 2002). For a description of the health effects attributed to
specific pollutants in exhaust see Appendix H.
The database on the health effects unique to gasoline exhaust is far sparser than that for
89
diesel, and there are few epidemiology studies on gasoline exhaust (USEPA, 2002). There
have been limited animal studies (again, not as comprehensive as the numerous and lengthy
diesel exhaust studies) on gasoline exhaust done in prior years. These studies suggest that
gasoline exhaust may have similar health effects as diesel exhaust (USEPA, 2002). For
example, a study by Seagrave et al. (2002) evaluated the toxicity of diesel and gasoline
exhaust from engines that were either normal or high emitters, by using a panel of assays that
incorporated several categories of biological responses. They found that normal-emitter
diesel and gasoline engines had very similar toxicity per unit of mass, but high emitters were
much more toxic; cold conditions produced emissions that were more toxicologically potent;
and adverse responses occurred even at low doses (Seagrave et al., 2002).
The engine emission rates used in the dispersion model in Chapter two were from both
diesel and gasoline engines. But diesel and gasoline PM is not equivalent in terms of
toxicity, so instead of using PM as a proxy for exposure to the mixture of exhaust, I will use
the predicted exposures of CO and NO2 from the dispersion model.
Adverse health effects from exposure to exhaust observed in animals are: formation
of DNA adducts; lung mass increases up to 400%; pulmonary inflammation; impairment of
lung mechanics; presence of PM-laden macrophages; increasing changes in epithelial cells;
and a high incidence of lung tumors (Bond et al., 1984; 1990; IPCS, 1996; Kaplan et al.,
1982; Nikula et al., 1995; Steerenberg et al., 1998; USEPA, 2002; Yu et al., 1991). The
mode of action for exhaust-induced lung cancer in humans is not fully understood, and it is
possible that there is a non-threshold mode of action involving mutagenic events (USEPA,
2002). In addition, the latent period for lung cancer development in humans is 20-30 years or
more, making the causal link difficult to prove (Brüske-Hohlfeld et al., 1999). The lower end
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of human occupational exposure concentrations overlap with engine exhaust and lower
environmental exposure concentrations, thus current environmental exposures have the
potential to be hazardous to human health (USEPA, 2002). Since there is no known level of
carcinogen exposure that is risk-free, exposure to diesel and gasoline exhaust should be
minimized as much as possible.
Experimental data from one species is often used to predict effects in another, and this
is prone to a level of uncertainty, because parameters such as uptake, deposition,
biotransformation, mode of toxic action, and clearance can differ among species in
qualitative and quantitative ways (Blaauboer, 1996). Additional uncertainties arise with:
extrapolations from high-dose animal studies to low-dose human exposures; differences in
health effects that occur in short-term versus long-term studies; similarities in toxicological
response between animals and humans; and knowledge gaps regarding human exposure
levels (Kalberlah et al., 2002; USEPA, 2002). Furthermore, laboratory studies are conducted
on small, homogeneous groups of animals, and the results are usually extrapolated to the
heterogeneous human population (Blaauboer, 1996). Even though human occupational
exposure studies on exhaust usually lack detailed exposure information, the observed health
effects support animal-to-human extrapolation (USEPA, 2002). The uncertainties with
extrapolation present difficulties in risk estimation, yet animal studies further the
understanding of biological plausibility and mechanisms of action (Bates, 1994).
3.1.3 Retention and Clearance of Air Pollutants in the Lungs
Quantifying the impact of air pollution exposure requires an understanding of the
processes involved, such as: accumulation, partitioning, and vertical transfer (Klanjscek et
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al., 2007). Lipid-soluble pollutants are stored in fat, and this is the primary way pollutants
deemed to be persistent and bioaccumulative are transferred vertically in marine mammal
food webs (Klanjscek et al., 2007). However, most air pollutants do not bioaccumulate, as
they can be cleared or metabolized, making vertical pollutant transfer negligible (Klanjscek
et al., 2007).
For terrestrial mammals the nose is the first line of defence against air pollution as it
cleans inhaled air by removing water-soluble gases. However, it has a low capacity to filter
PM from 0.1-0.5 µm in diameter (Koenig, 2000). Inhaled gases quickly come into contact
with airway surfaces via molecular diffusion, but there is limited uptake for compounds that
are insoluble in water (i.e. O3), and the greatest uptake often occurs in the peripheral areas of
the lung because of longer residence times and larger surface areas (Lippmann, 2000).
Compounds that are more water-soluble dissolve in and/or react with fluids on the surface of
the airways thus removing them from the air. In this manner very water-soluble compounds
(i.e. SO2) barely penetrate into the lung because they are almost completely removed by
airways in the head (Lippmann, 2000). Thus, the solubility of the compound can affect
where regional health effects will occur. The majority of organic compounds are reasonably
soluble in lung tissue, thus can be cleared from the lungs by direct absorption into the blood
(Gerde et al., 1991; HEI, 1988). From the blood, compounds can be retained in
extrapulmonary tissues, excreted, or biotransformed (by metabolic activation) in the nose,
lung, skin, intestine, placenta, kidney, testes, adrenals, and liver (HEI, 1988).
When rodents and humans inhale diesel PM, approximately 15-20% of it is initially
deposited in the lungs and respiratory tract (HEI, 1988). PM deposition is affected by the
geometry of the respiratory tract in several ways, the diameter of the airway determines the
92
displacement necessary for a particle to contact a surface, the cross-section of the airway
determines the airflow velocity, and differences in branch lengths affects regional deposition
(HEI, 1988). However, particle size and the convective flow of inhaled air are the most
important parameters for inhaled PM deposition (Lippmann, 2000). The total deposition of
PM increases with decreasing particle size, and deposition strongly depends on breathing
frequency (FR) and weakly depends on tidal volume (VT) (Tu & Knutson, 1984). High
inspiratory flow rates produce more turbulence and flatter velocity profiles in the large
conducting airways, which decreases axial dispersion (Palmes et al., 1973). Alveoli with the
shortest connecting branch lengths receive higher deposition of PM with diameters greater
than 1 µm (HEI, 1988). Submicrometer PM tends to deposit evenly in all lobes and is not
affected by branch length (HEI, 1988), but there can be preferential deposition at airway
bifurcations (Lippmann, 2000).
Inertial impaction is the primary deposition mechanism in the nasal, extrathoracic,
and tracheobronchial regions of the lungs, especially for PM with a diameter greater than 2.5
µm, due to high airflow velocities and abrupt changes in airflow direction in these regions
(Stöber et al., 1993). Sedimentation is deposition of PM due to gravity, and increases with
increasing residence time in the airways, increasing particle size, and density, but decreases
with increasing FR (HEI, 1988). Sedimentation occurs to PM exceeding 0.5 µm in diameter
in airways with relatively low air velocity; however, diffusion dominates for PM less than 0.2
µm (HEI, 1988). PM with a diameter less than 0.5 µm (ultrafine) tends to penetrate the lungs
past the extrathoracic and tracheobronchial regions, and is subject to diffusive deposition in
the alveolar region of the respiratory tract (Stöber et al., 1993).
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The following discussion of air pollutant clearance from the lungs assumes that
exposure is an acute event. Removal of PM from the lungs occurs by either dissolution or
mechanical clearance (Yu et al., 1991). Compounds that dissolve in the conducting airway
mucus or alveolar surfactant can quickly diffuse into epithelial cells and into the blood, thus
can be spread to other organs and tissues (Lippmann, 2000). If the mucus thickness lining
the trachea is halved, the dose is increased by a factor of 10 (HEI, 1988). Inhaled compounds
can also undergo chemical and metabolic processes in the fluids and cells of the lung, which
can limit their entrance into the blood and can create products that differ in solubility and
toxicity (Lippmann, 2000). Mechanical clearance occurs by either mucociliary transport (in
the nasal passages, ciliated airways, and tracheobronchial region), or macrophage
phagocytosis and migration in nonciliated airways (Yu et al., 1991).
Mucociliary transport is accomplished by rhythmically beating cilia lining the
respiratory tract from the terminal bronchioles to the trachea, which move poorly soluble PM
and alveolar macrophages in a mucous layer toward the larynx (Robertson, 1980). The rate
of mucociliary transport is species dependent and is determined by the flow of mucous,
which is slow in the distal airways and increases proximally (Stöber et al., 1993). Clearance
mechanisms in the lungs are believed not to be especially particle-sized dependent (Snipes et
al., 1983). Studies on large diameter PM (> 2.5 µm) have found that it is quickly eliminated
by mucociliary transport (almost complete within 24-hours in humans) and dissolution
(requires a few hours in humans), however some long-term retention occurs (HEI, 1988;
IPCS, 1996; USEPA, 2004; Yu et al., 1991).
Macrophages act as a defence mechanism by engulfing foreign matter in the lungs
and secreting inactivating enzymes (Koenig, 2000); however, organic chemicals in exhaust
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emissions can produce toxic effects in alveolar macrophages (HEI, 1988). PM in the alveolar
region is primarily cleared by macrophage phagocytosis (approximately 6-hours in humans)
and migration (several weeks in humans) to the tracheobronchial area, lymph nodes, and
blood (Yu et al., 1991). PM not removed by macrophages is incorporated into epithelial and
interstitial cells that clear PM slowly by dissolution and/or lymphatic drainage (half-time is
approximately 30-1000 days or more), but particles can be retained indefinitely in interstitial
sites (HEI, 1988; IPCS, 1996; Lippmann, 2000; USEPA, 2004; Yu et al., 1991).
Electrostatic charges on PM can affect regional deposition, due to repulsion and attraction,
and this effect is inversely proportional to PM size and airflow velocity (Cohen et al., 1998).
For humans, deposition and clearance rates are affected by age. PM deposition is
higher in infants and children than in adults while nasal breathing at rest, with maximum
deposition occurring at approximately two years of age (HEI, 1988). Clearance rates for the
elderly and children are slower than clearance rates for adults (Yu et al., 1991).
Clearance mechanisms and patterns in the respiratory tract are similar for humans and
most other mammals, as diffusion across epithelial barriers occurs at approximately the same
rate, but they may differ if mediated by mucous transport or macrophages (HEI, 1988;
USEPA, 2002). However, the clearance rate of insoluble particles is species dependent and
not fully understood, with the retention half-time for rats, mice, and hamsters about 50-100
days, and several hundred days for dogs, guinea pigs, and humans (Yu et al., 1991). Humans
have a greater lung burden of PM than rats because they inhale greater quantities of PM and
have slower clearance rates (Yu et al., 1991). Evolutionary selection for PM clearance is
likely higher for terrestrial than marine mammals because of inherent differences in exposure
rates, which would suggest that retention times may be greater in killer whales than humans.
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The discussion of clearance mechanisms above is mainly based on studies that
exposed experimental animals to acute doses of air pollutants. When animals (Yu et al.,
1991) and humans (Stöber et al., 1967) are chronically exposed to high doses of diesel
exhaust the clearance of particles deep in the lungs can be impaired, a pattern referred to as
the overload effect. Because killer whales are chronically exposed to whale-watching vessels
for 12-hours a day for six months of the year, they are potentially experiencing the overload
effect and accumulating PM at a rate faster than it can be removed. Accumulation may be
especially severe if their clearance rates are significantly slower than humans.
3.1.4 Killer Whale Respiratory Anatomy and Physiology
While little has been documented on the respiratory physiology of killer whales,
lessons may be learned from another better-studied dephinid, the bottlenose dolphin
(Tursiops truncatus). The Delphinidea are nested in the suborder Odontoceti (toothed
whales), and they have the most extreme lung modifications of all marine mammals (Perrin
et al., 2002). Delphinidae lungs are larger relative to body size than most other marine
mammals (Perrin et al., 2002). Their lungs have no external lobulation, and are pyramidal in
shape with the base situated dorsally and caudally (Fanning & Harrison, 1974). Like other
cetaceans, the peripheral airways of delphinids are highly reinforced with cartilage and
smooth muscle to keep the conducting airways open during deep dives while allowing the
alveoli to collapse (Kooyman, 1989a). Delphinid lungs have lost respiratory bronchioles,
they have bronchial sphincters, and they have very flexible (compliant) chest walls (Perrin et
al., 2002). The trachea is lined with microvillous surface cells, while distal parts of the
trachea and bronchi are lined with ciliated and goblet cells (Fanning, 1977). The terminal
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airways and alveoli typical of the mammalian pattern have additional connective tissue
support, have an abundance of free macrophages (Fanning, 1974; Fanning & Harrison,
1974), and the alveolar surfactant has high fluidity and rapid expansion capabilities (Foot et
al., 2006). In the terminal bronchus, the thickness of the blood-air barrier averages 150-250
nm, with the thinnest recorded barrier being 120 nm (Fanning & Harrison, 1974).
Cetaceans are obligate nasal breathers, and the nostrils of Odontocetes have joined to
form one blowhole (Perrin et al., 2002). Cetaceans do not have facial sinuses or conchae
(turbinate bones in the nasal cavity), which aids in accelerating inhalation and exhalation
(Perrin et al., 2002). The lack of facial sinuses in cetaceans may render them more
vulnerable to water-soluble air pollutants like SO2, because instead of being removed by
airways in the head (Lippmann, 2000), they would penetrate further into the lungs.
Countercurrent heat exchange and induced turbulence in the nares and nasal sac system of
bottlenose dolphins extracts most of the water vapor in the exhalation, and results in a 70%
reduction in water loss compared to terrestrial mammals (Perrin et al., 2002). In Odontocetes
there is complete regression of the olfactory system by birth (Marino, 2004). A consequence
of this in the context of this study is that killer whales are unable to smell, and thereby avoid,
engine exhaust.
The adaptations in the respiratory system of marine mammals provide greater elastic
recoil of the lungs, chest cavity, and diaphragm (Perrin et al., 2002), and the larger
conducting airways allow extremely fast ventilation rates, with most of the VT exchanged
within a fraction of a second (Kooyman, 1989a). Marine mammals are able to use a much
greater proportion of their total lung capacity (TLC) while breathing than terrestrial
mammals, and their VT is usually greater than 75% of TLC, and their vital capacity (VC) can
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exceed 90% of TLC (Perrin et al., 2002). This means that they exchange almost all of their
respiratory gases every breath, but the large VT means they also have a reduced reserve lung
volume and prevents them from significantly increasing VT further during strenuous exercise
(Perrin et al., 2002).
A killer whale’s respiratory cycle begins with a rapid active expiration or blow (~
0.38-0.59 s) that starts just before surfacing and clears the upper airway of water, followed by
a slower passive inhalation (~ 0.75-0.78 s), and a variable period of apnea that typically lasts
between 2-10 minutes (Kooyman & Cornell, 1981; Kooyman & Sinnett, 1979; Kooyman et
al., 1975; Milsom, 1989). A high flow rate is maintained for the entire expiration, whereas in
humans and other terrestrial mammals the expiratory flow rate peaks early and then
decreases substantially throughout the end of the breath (Perrin et al., 2002). Kooyman and
Cornell (1981) measured the peak expiratory flow rate of a 4.4 m killer whale to be 180 L s-1
(approximately 2.5 VC s-1), while that for a normal human male is approximately 10 L s-1
(1.5-2.0 VC s-1) (Gregg & Nunn, 1973; Kooyman & Cornell, 1981). Breathing patterns are
affected by behaviour, and while resting at the surface the FR of killer whales is about 0.9
breaths per minute (b m-1) (Mortola & Limoges, 2006), and during strenuous exercise is
approximately 7.3 b m-1 (Kriete, 1995). When traveling at low speed the blowhole barely
clears the water during exhalation and inhalation, but at higher speeds there is greater
clearance as the animal porpoises above the sea surface (Perrin et al., 2002).
The anatomy and physiology of the respiratory system of cetaceans affects particle
retention in the lungs. Due to the structure of the blowhole and nasal cavities, the epithelium
of the proximal trachea is exposed to higher levels of PM in the air (Fanning, 1977). The
large VT of cetaceans allows PM and gases to penetrate deep into the lungs, and increases
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deposition by sedimentation in smaller conducting airways and alveolar regions where
airway diameters and velocities are smaller (HEI, 1988; Martin & Finlay, 2006). For
example, a rat that doubles its VT increases aerosol deposition seven times (HEI, 1988).
Deposition by impaction does not depend on VT because impaction mainly occurs in the
upper and central airways (Martin & Finlay, 2006). Fast inhalation velocities, however, do
increase deposition by impaction, but decrease deposition by sedimentation and diffusion,
and high velocities may also create turbulence, which increases PM deposition by impaction
(HEI, 1988). Increasing the minute volume (
!
V
•
min ) of respiration causes significantly greater
absorption of gases in the pulmonary region, but the absorption in the tracheobronchial
region does not increase as much (HEI, 1988). Thus, compared to humans and other
terrestrial mammals, killer whales would be expected to have increased PM deposition and
absorption of gases in the distal airways.
Simpson and Gardner (1972) examined cetacean lungs (including those of killer
whales) from whaling vessels worldwide and found that the animals very rarely suffered
from overt lung disease or pulmonary histopathology, which was attributed to their relatively
clean and microbe-free environment. However, lung disease in porpoises was reasonably
common, and usually a result of nematode infestation (Simpson & Gardner, 1972). In
contrast, a recent study by Raverty et al. (unpubl.), obtained data on killer whale strandings
worldwide, which included 222 post mortem findings from 1944-2003, and 309 tissue
samples from 48 killer whales. They found that 50% of the 46 animals had histopathological
abnormalities in the lung tissue (Raverty et al., unpubl.). Some of the effects observed by
Raverty et al. (unpubl.) are not inconsistent with the likely effects of air pollutants (i.e.
disease processes in the endocrine and metabolic systems, and neoplastic tissue changes) but
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other important factors affect killer whale health in the same manner, such as persistent
organic pollutants, macroparasites, disease, nutrition/condition, and age. However, the
results from Simpson and Gardner (1972) are very different than those of Raverty et al.
(unpubl.), and may reflect the deterioration in air quality since the 1970’s.
3.1.5 The Effects of Diving and Breath Holding
Every 10 m in depth under water adds one atmosphere of hydrostatic pressure. This
increase in pressure with depth decreases lung volume, causing a higher partial pressure of
gasses in the lungs, which increases gas solubility, and the amount of gas dissolved in tissues
(Fahlman et al., 2006; Kooyman, 1973). Blood flow through tissue determines when gas
equilibration/saturation is reached, and vasoconstriction and bradycardia increase the time to
saturation; thus the heart saturates rapidly, and fat saturates slowly (Fahlman et al., 2006).
To avoid nitrogen narcosis and decompression sickness, seals and dolphins experience lung
collapse while diving at approximately 40-80 m, and air in the lungs is pushed into
conducting airways where no gas exchange occurs (Fahlman et al., 2006; Ridgway &
Howard, 1979). However, the animal is vulnerable to decompression hazards when
repeatedly diving to depths shallower than those at which lung collapse occurs (Ridgway &
Howard, 1979).
Many marine mammals exhibit adaptations for diving to depth, and the “mammalian
diving reflex” allows maintenance of a constant blood pressure, and reduced metabolic rate
while diving (Hastie et al., 2006; Kooyman, 1989b; Noren et al., 2004). Upon submergence
killer whales reduce their resting heart rate by 50% (Spencer et al., 1967). Since killer
whales live their entire lives in water they likely have “normal” heart rate and blood flow
100
distribution while shallow diving, and an “abnormal” or elevated distribution while surfacing
(Bill Milsom, pers. comm., October 2008). Thus killer whales would only be expected to
experience depressed metabolic rates and bradycardia during very deep dives, and this would
result in slower rates of pollutant conversion to metabolites. Since circulation to the skin and
splanchnic organs virtually stops during deep dives, O2 is channeled to the organs that
require it most (i.e. heart and brain), and during very deep dives killer whales may shift to
anaerobic metabolism (Perrin et al., 2002). When the lungs are collapsed at depth no gas
uptake occurs, but the shift of blood flow from organs that detoxify blood could allow toxins
already in systemic circulation to concentrate in sensitive tissues like the heart and brain
(Reynolds et al., 2005).
Diving mammals have large blood volumes with a high O2 carrying capacity,
allowing them to store more O2 in their blood, and they are very tolerant to low arterial O2
tensions (Kooyman, 1989b). Compared to terrestrial mammals, cetaceans tend to have fewer
red blood cells but those cells have a higher hemoglobin concentration (Dhindsa et al., 1974),
they have muscle myoglobin concentrations 10-30 times greater for additional O2 transport
(Kooyman, 1989b), and they generally have a greater blood O2 affinity (Dhindsa et al.,
1974). Killer whale blood also has a high buffering capacity, which restricts the
development of respiratory and metabolic acidosis during dives (Lenfant et al., 1968), and
reduces their ventilatory response to inhaled CO2 (Mortola & Limoges, 2006). Delphinids
dive with 22% of their total oxygen stores in the air in their lungs, with the rest sequestered
in muscle myoglobin and blood hemoglobin (Perrin et al., 2002).
All cetaceans have rapid, high velocity breathing, allowing them to exchange a large
percentage of their lung volume during the brief period of inhalation and exhalation at the
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surface (Reed et al., 2000). Hyperventilation often occurs before and after diving to increase
O2 tension and decrease CO2 tension (Perrin et al., 2002). Cetaceans are able to fully restore
O2 stores in the first three to four breaths after a long dive; however, built-up levels of CO2
must also be eliminated, and a further two to three breaths are often required (Reed et al.,
2000). Since it takes longer to eliminate CO2 than to replenish O2, there may be occasions
when cetaceans do not fully readjust their CO2 stores before beginning a dive sequence and
have to manage with an increased CO2 load (Reed et al., 2000). Just before surfacing, there
is often an increase in heart rate, which decreases O2 levels in the blood and increases CO2
and allows rapid O2 uptake and CO2 elimination by the lungs (Perrin et al., 2002). Deep
dives produce less CO2 exhaled in the first breath upon surfacing than shallow dives, because
there is no gas exchange while the lungs are collapsed (Perrin et al., 2002). Killer whales
often react to vessels by increasing their dive duration and speed (Kruse, 1991; Williams et
al., 2002) and during this time they may accumulate CO2. If CO2 concentrations are high in
the air they are inhaling due to the exhaust from whale watching vessels and if they just take
one breath on surfacing, their CO2 load may become exaggerated. This could have serious
impacts on gas exchange and recovery time, and may result in future health problems.
The SRKWs primarily occupy near-surface waters and only 2.4% of their time is
spent below 30 m in depth, but deep dives last for much longer than shallow dives (Baird et
al., 2005; 2003). Baird et al. (2003) suggested that deep dives are primarily for foraging,
while time at the surface is for breathing, socializing, resting, traveling, and some foraging.
Adult males make significantly more deep dives than adult females, but both sexes dive
equally deep (Baird et al., 2005). Greater dive rates and swim speeds occur during the day
compared to night, and dive depths greater than 150 m occur regularly, with 264 m the
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maximum recorded depth for a SRKW (Baird et al., 2005). Since the SRKWs spend most of
their time in the first 30 m of the water column (Baird et al., 2005; 2003), they are likely not
experiencing lung collapse (Ridgway & Howard, 1979). However, at 30 m in depth the
atmospheric pressure is three times that at the surface, thus the whales have greater amounts
of gas dissolving into their tissues (Fahlman et al., 2006; Kooyman, 1973).
When an aerosol is inhaled, its probability of being exhaled decreases exponentially
with time (Goldberg & Smith, 1981; Palmes et al., 1973); thus as breath holding time
increases, deposition in the lungs increases exponentially (Goldberg & Smith, 1981; Tu &
Knutson, 1984). Aerosol persistence decreases as the lung volume of the held breath
decreases, due to smaller intrapulmonary air spaces in smaller lungs (Palmes et al., 1973).
Therefore, even though non-breath holders breathe more frequently, killer whales experience
greater levels of deposition in the distal areas of lungs because of breath holding, low FR,
large VT, and high air flow rates (Goldberg & Smith, 1981; HEI, 1988; Palmes et al., 1973;
Tu & Knutson, 1984). In addition, their large lung size (which is greater than expected
allometrically) increases the area for deposition (Perrin et al., 2002). Because of these issues,
it appears that killer whales are likely more sensitive to air pollution than humans.
3.1.6 Physiological Models Used to Estimate Internal Pollutant Dose and Health Effects
Many different models are used to predict the effect of a pollutant dose on the
physiology of a species, and simplified mathematical formulas, correlation of data,
assumptions, simplifications, and extrapolation all play a role (HEI, 1988). Several methods
are used to overcome uncertainties with extrapolation, such as the introduction of safety
factors, uncertainty factors, and default values (Blaauboer, 1996). Uncertainty factors are
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used to take into account inter-individual variability, and animal-to-human variability. The
uncertainty factor (UF) for inter-individual variability is usually set at 10, as is that for
animal-to-human extrapolation (Renwick & Lazarus, 1998). This implies that the effects
seen in animals would occur in humans at concentrations 10 times lower because of higher
doses and/or greater sensitivity of human tissue (Renwick & Lazarus, 1998). The sensitivity
of killer whales is currently unknown, but can be estimated by using extrapolation and a
weight of evidence approach for pollutant effects in marine mammals (Ross, 2000).
However, based on the previous information one would assume that killer whales could
tolerate a concentration ten times higher than humans. However, the UF of 10 is applied
when scaling up from rodent data to humans and may not be applicable when scaling up from
humans to killer whales.
Lung compartment models are the most basic, and are used to predict gas uptake by the
blood and tissues (HEI, 1988). Dosimetric models (either mathematical or experimental) are
used to predict gas uptake and distribution in specific regions of the respiratory system for
different exposure levels in different species, and to estimate toxicological effects (HEI,
1988). Sophisticated physiologically-based pharmacokinetic (PBPK) models can be used to
predict ultimate health effects by simulating concentrations of pollutants in the blood and
tissues that result as a function of duration and exposure (Béliveau et al., 2005; NRC, 1991).
However, all of these models require detailed information, such as: the anatomy/geometry of
the airways, tissue and blood spaces; ventilation and perfusion limitations; pulmonary
function parameters (e.g. respiratory and pulmonary blood flows); convection in respiratory
tract fluids (i.e. blood and mucus); lipid and water content of blood and tissues; enzyme
concentrations; metabolic constants; material balance equations that describe time-dependent
104
and spatial distributions of the pollutant; thermodynamic equilibration, diffusional flux, and
chemical reaction rate equations; particle characteristics (i.e. size, shape, density, and
electrostatic attraction); partition coefficients; and binding capacities (Béliveau et al., 2005;
HEI, 1988; USEPA, 2002).
It would be possible to use one of the above model frameworks to develop a model
for killer whales by estimating parameters with virtually no experimental data. However, the
use of a sophisticated model would result in a level of uncertainty in proportion to the
accuracy of the parameters included (Preston, 2005). Unfortunately much of the necessary
input information required is lacking for killer whales, rendering the level of uncertainty
unacceptably high. I have therefore opted for using a simple allometric scaling model to
predict the effect of air pollutants on killer whale physiology.
3.1.7 Allometric Scaling to Estimate Internal Pollutant Dose and Health Effects
Allometric scaling is a commonly used extrapolation method that allows quantitative
comparison of function between or within species, since physiological functions (such as
respiratory mechanics) are often related to body size or mass (Mb) (HEI, 1988; West et al.,
1999). A routinely used simple allometric conversion modifies the external dose
concentration based on the quantity of pollutant inhaled per unit body mass per day, whereas
more complicated conversions can include body surface area, which accounts for differences
in metabolism, biotransformation, and degradation (McColl et al., 2000). Allometric scaling
occurs most often between rodents and humans, and these extrapolations have many potential
difficulties and uncertainties, but when dose-response assessment includes adequate
allometric conversions, the interspecies UF can be reduced (McColl et al., 2000).
105
Allometric scaling uses parameters that are either constant with body size or are
related to body size by a proportion (HEI, 1988; West et al., 1999). Metabolic rate (O2
consumption) in vertebrates tends to scale with a three-quarters power of body mass (Kleiber,
1961; Sample & Arenal, 1999; West et al., 1999). Respiratory variables related to gas
exchange (i.e. the respiratory minute volume, which is the volume of air that is inhaled or
exhaled in one minute) also tend to scale to Mb0.75, but size-related variables of the
respiratory system (i.e. VT) tend to scale to Mb1 (Milsom, 1989; West et al., 1999).
Breathing frequency (FR) scales to Mb-0.25, while the ratio of dead space volume (VD) to VT is
independent of body size (HEI, 1988). The FR of most aquatic mammals is below the
allometric curve of terrestrial mammals, and this difference increases in larger aquatic
mammals (Mortola & Limoges, 2006). In addition, the resting metabolic rate (RMR) of
marine mammals is about 1.5-3 times greater than that predicted by Mb0.75 (Kooyman,
1989b); however, others have found no real difference in RMR for marine mammals
(Lavigne et al., 1982; 1986).
The USEPA recommends scaling to Mb0.75 for the extrapolation of test animal
carcinogenicity data to humans (USEPA, 1992), and for wildlife risk assessment Sample et
al. (1996) also recommend scaling to Mb0.75 using mammalian toxicity data. Since
respiratory rates also scale to Mb0.75, species-specific carcinogenicity and toxicity scale
directly with respiratory rate (Schneider et al., 2004). If the toxicity value for a test animal
(At), an allometric scaling exponent (b), and the body mass of the test animal (Mbt) and
wildlife species (Mbw) are known, one can calculate the toxicity value for the wildlife species
(Aw) by the equation (Sample et al., 1996):
!
Aw
= At
Mbt
Mbw
"
# $
%
& '
1(b
106
Sample and Arenal (1999) investigated the allometric relationships for acute
mammalian toxicity data for a number of chemicals, and concluded that unless a chemical-
specific scaling exponent is known, scaling to Mb0.94 should be used for mammals.
However, the 0.94 scaling exponent was for acute rather than chronic toxicity data, and since
the modes of action for acute and chronic effects differ for many chemicals the 0.94 scaling
exponent is not appropriate for the whale-watching scenario. Schneider et al. (2004) found
that for the majority of toxins, caloric demand scaling (Mb0.75) is much more accurate than
body mass scaling predictions (Mb1), even for readily metabolized substances. Caloric
demand scaling predicts that smaller species are less susceptible to toxins than larger species
if the dose is per kg body mass (Schneider et al., 2004). Thus, I used a scaling exponent of
0.75 since it accounts for uncertainty in interspecies extrapolation with allometric rules
(Schneider et al., 2004).
Differences in pollutant sensitivity between humans and other animals was evaluated
by Kalberlah et al. (2002), who used data from the Agency for Toxic Substances and Disease
Registry (ATSDR). For gases and liquids, humans were more sensitive in 62% of the cases,
and for particles, humans were again more sensitive in 53% of the cases (Kalberlah et al.,
2002). However, humans and animals had no quantitative differences in sensitivity to NO2,
and sensitivity levels were similar for O3 (Kalberlah et al., 2002). The study highlighted the
fact that there is only limited data on quantitative interspecies comparison even for well-
known respiratory toxicants, but on average humans tend to be more sensitive (Kalberlah et
al., 2002). The studies evaluated by Kalberlah et al. (2002) primarily utilized body mass
scaling from rodents to humans to estimate sensitivity, which suggests that the larger the
animal the more sensitive it is. When this logic is applied to killer whales, it suggests that
107
they would be even more sensitive than humans to air pollutants.
3.2 METHODS
An individual’s ultimate exposure to a pollutant is a function of the concentration they
are exposed to, their breathing pattern, and their particle retention pattern (USEPA, 2002).
All sources of exposure need to be considered (i.e. ingestion, skin penetration, inhalation), as
does the individual’s activity level because ventilation rate is used to determine dose
(Koenig, 2000). Studies investigating the effects of oil spills on cetaceans have found that
their epidermis is relatively impermeable to petroleum, because of tight intercellular bridges,
vitality of the superficial cells, and extreme epidermal thickness (Geraci & St. Aubin, 1990).
Therefore, it is unlikely that skin contact is a significant source of toxicity (O’Shea &
Aguilar, 2001; Wiles, 2004). Killer whales could be exposed to exhaust pollutants such as
HCs by ingestion while feeding, but marine mammals can generally metabolize and excrete
HCs (Wiles, 2004). Thus, it was assumed that inhalation was the only route of exposure to
exhaust pollutants for the SRKWs.
A basic estimate of a pollutant dose is: dose = concentration (in mg m-3) * duration
(in minutes) * ventilation rate (in litres per minute-1), which assumes the pollutant is
completely absorbed internally (Koenig, 2000). The biologically effective dose is the
amount of pollutant or its metabolites that interacts with a target organ during a given time
period to alter physiological functioning (NRC, 1991). It is often not practical to use the
biologically effective dose when determining the overall effect of exposure to air pollution
because of a lack of information about uptake, distribution, metabolism, and modes of action
of contaminants (NRC, 1991). Thus I used the basic dose equation, and converted the
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concentration to mg per litre, which resulted in a dose in mg during 60 minutes. The mean
CO and NO2 concentrations predicted by the dispersion model in Chapter two for average-
case simulations (11.98 mg m-3 and 0.04 mg m-3 respectively), and worst-case simulations
(67.91 mg m-3 and 0.24 mg m-3 respectively) were used in the basic dose equation. The dose
was then divided by the mass of a male or female killer whale to obtain the dose per kg of
body mass for each gender.
A free-swimming breathing rate for killer whales was used, because in the dispersion
model the killer whale was traveling at a constant speed of 2.85 m s-1 after Kriete (2002).
Kriete (2002) measured the respiratory variables of free-ranging SRKWs, and the FR for
males was 1.63 b min-1, and for females was 1.74 b min-1. Kriete (1995) estimated the
maximum VT of captive adult killer whales, and found that males exchange approximately
210 litres per breath (L b-1), and females exchange approximately 100 L b-1. Thus the
predicted
!
V
•
min for males is 342.3 L min-1, and for females is 174 L min-1. Kriete (1995) also
estimated the mass of four female and two male killer whales in captivity, by measuring their
body lengths and using Bigg and Wolman’s (1975) equation for the relationship between
body length and mass for killer whales. The average mass for female killer whales was
2,427.5 kg, and for males was 3,766.5 kg. Effective ventilation (
!
V
•
eff ) is the ventilation in the
alveolar region (VA = VT - VD) multiplied by FR, which provides a more accurate internal
dose estimate than simply calculating dose using
!
V
•
min , which includes VD. There are no
published values of
!
V
•
eff or VD for killer whales; however, VD can be calculated by using the
allometric equation formulated by Stahl (1967), where VD = 2.76 Mb0.96. Thus the VD for a
3,766.5 kg male killer whale is predicted to be 7.5 L with a
!
V
•
eff of 330.08 L min-1, and for a
109
2,427.5 kg female killer whale the VD is 4.9 L, with a
!
V
•
eff of 165.47 L min-1. These values
for male and female killer whales were used to determine the dose per kg body mass, by
using the basic dose equation. For comparison, the basic dose of CO and NO2 per kg body
mass was calculated for a 70 kg human during mild exercise (requiring less than 60%
maximum O2 uptake), with a
!
V
•
eff of 18.0 L min-1 (ACSM, 2006).
The allometric equation of Sample et al. (1996) was used to calculate CO and NO2
toxicity values (Aw) for male and female killer whales. The MV AQO for 1-hour exposure to
CO (30 mg m-3), NO2 (0.2 mg m-3), and 8-hour exposure to CO (10 m g m-3) were used as the
test species toxicity values (At), rendering humans the test species. The MV AQOs are based
on expected actual exposure conditions of human populations in British Columbia, and if
humans are exposed long-term to mean concentrations above the standards they would
potentially exhibit premature mortality, increased admissions to hospitals, respiratory
symptoms, and decreased lung function (USEPA, 2002).
The calculated toxicity values for killer whales were then used in the basic dose
equation, to calculate a toxicity dose for killer whales. This toxicity dose is the dose below
which no adverse health effects would be expected in an average adult killer whale, but
above it adverse health effects would be expected. Thus, the toxicity dose can be compared
to the basic dose that was calculated by using the average-case and worst-case exposures
predicted by the dispersion model. For comparison a toxicity dose for humans was also
calculated by using the MV Air Quality Objectives for CO and NO2 as the exposure values in
the equation.
110
3.3 RESULTS
The basic dose per kg body mass for male and female killer whales and humans using
1-hour and 8-hour exposures to CO and 1-hour exposure to NO2 predicted by the dispersion
equation are presented in Table 3.1.
Table 3.1: The dose of CO and NO2 per kg body mass that male and female killer whales and humans receive during average-case, worst-case, 1-hour, and 8-hour exposures.
Species & gender
Average 1-hour
CO (mg kg-1)
Average 8-hour
CO (mg kg-1)
Worst-case 1-hour
CO (mg kg-1)
Average 1-hour
NO2 (mg kg-1)
Worst-case 1-hour
NO2 (mg kg-1)
Killer whale, male
0.063 0.50 0.40 0.00021 0.0014
Killer whale, female
0.049 0.39 0.31 0.00016 0.0011
Human 0.19 1.48 1.17 0.00062 0.0042
Even though killer whales have a much lower FR, much greater
!
V
•
eff , and extremely
large VT compared to humans, the doses of CO and NO2 per kg body mass that male and
female killer whales receive were much lower than that of humans (Table 3.1). The average-
case and worst-case doses of CO and NO2 for male killer whales were three times lower than
those for humans, and 3.8 times lower for female killer whales than humans. The difference
in values between killer whales and humans is because dose was calculated on a per kg body
mass basis, and because killer whales have a much greater body mass, their dose is much
smaller. The worst-case doses for 1-hour exposure to CO were very similar to the average-
case doses for 8-hours exposure to CO. Male killer whales had doses slightly higher than
female killer whales (Table 3.1).
The CO and NO2 toxicity values (Aw) for male and female killer whales can be seen
in Table 3.2, and they were calculated by using the equation derived by Sample et al. (2006).
111
Table 3.2: CO (1-hour and 8-hour) and NO2 (1-hour) toxicity values (Aw) for male and female killer whales.
Killer whale gender CO (mg m-3) 1-hour
CO (mg m-3) 8-hour
NO2 (mg m-3) 1-hour
Male 11.13 3.69 0.074 Female 12.38 4.12 0.082
The toxicity values (Aw) for male and female killer whales (Table 3.2) are much
lower than the human MV AQOs for CO (30 mg m-3 for a 1-hour exposure and 10 mg m-3 for
an 8-hour exposure) and NO2 (0.2 mg m-3 for a 1-hour exposure). The toxicity values for
killer whales in Table 3.2 were then used in the basic dose equation (Koenig, 2000), calculate
the toxicity dose (Table 3.3). Table 3.3 also includes the toxicity dose for humans.
Table 3.3: Toxicity doses of CO and NO2 per kg body mass for male and female killer whales and humans, using toxicity values (Aw).
Species & gender CO (mg kg-1) 1-hour
CO (mg kg-1) 8-hour
NO2 (mg kg-1) 1-hour
Killer whale, male 0.058 0.16 0.00039 Killer whale, female 0.049 0.13 0.00034 Human 0.46 1.23 0.0031
The toxicity doses (Table 3.3), which are based on the MV AQOs, are very similar to
those predicted by the average-case simulations in the dispersion model (Table 3.1). For
male killer whales the average-case dose of CO for 1-hour of exposure is slightly higher than
the CO toxicity dose, but for female killer whales the doses were equal. The average-case 8-
hour CO dose was approximately 3 times higher than the 8-hour CO toxicity dose for both
male and female killer whales. The average-case doses of NO2 for both male and female
killer whales were slightly lower than the NO2 toxicity doses.
The worst-case doses of CO and NO2 for male and female killer whales were much
higher than the toxicity doses. The worst-case CO dose was 6.9 and 6.3 times higher than the
112
CO 1-hour toxicity dose, for male and female killer whales respectively. The worst-case
NO2 dose was 3.6 and 3.2 times higher than the NO2 toxicity dose, for male and female killer
whales respectively.
The 1-hour toxicity doses of CO and NO2 for male killer whales were on average
33% that of humans; whereas the 1-hour toxicity doses of CO and NO2 for female killer
whales were on average 11% that of humans.
The ambient average concentrations (from May to September) of CO (0.71 mg m3)
and NO2 (0.008 mg m3) measured at Christopher Point, BC were added to the average-case
and worst case concentrations to obtain the total exposure and total dose (Table 3.4).
Table 3.4: Total dose of CO and NO2 per kg body mass that male and female killer whales are estimated to receive under average-case or worst-case whale-watching conditions.
Gender Average CO (mg kg-1)
Worst-case CO (mg kg-1)
Average NO2 (mg kg-1)
Worst-case NO2 (mg kg-1)
Male 0.068 0.40 0.00025 0.0015 Female 0.053 0.31 0.0002 0.0011
Because the average ambient concentrations of CO and NO2 are relatively low
compared to the exposure doses, the total doses of CO and NO2 that male and female killer
whales receive are almost identical to the predicted doses in Table 3.1.
Infants, children, and the elderly are especially sensitive to air pollution (HEI, 1998;
Koenig, 2000), and current urban levels of air pollution result in chronic, adverse effects on
lung development in children (Gauderman et al., 2004). Gauderman et al. (2004) found that
children living in communities exposed to numerous air pollutants like O3, acid vapor, NO2,
elemental carbon, and PM had significant reductions in respiratory system growth, resulting
in smaller lung volume. As of October 2008, 36 members or 43% of the SRKW population
113
were post-reproductive females, calves or juveniles, and may therefore have had lower
threshold toxicities than those calculated above.
3.4 CONCLUSIONS
The model simulations based on average whale-watching conditions predicted a CO
concentration of 11.98 mg m-3 for 1-hour of exposure, which is lower than the female killer
whale’s threshold toxicity value for CO but higher than the male’s (Table 3.2). If the
average-case simulation exposure to CO lasts for up to 8-hours, then the dose the killer whale
receives (Table 3.1) would be three times greater than the 8-hour toxicity value (Table 3.3).
This suggests that under average whale-watching conditions, the doses of CO that killer
whales receive are just at levels predicted to cause adverse health effects for 1-hour of
exposure, but if the exposure lasts 8-hours then the threshold for adverse health effects is
greatly exceeded. Considering that a typical cigarette emits 67 mg of CO (Löfroth et al.,
1989), during 1-hour of average whale-watching conditions the male killer whale would
experience the equivalent of 3.5 cigarettes, and the female 1.8 cigarettes. The worst-case
doses of CO that male and female killer whales receive are much higher, by factors of 6.9
and 6.3 respectively, than those expected to cause adverse health effects in killer whales.
Thus the male killer whale potentially experiences the equivalent of 22.5 cigarettes, and the
female 11.2 cigarettes during 1-hour of worst-case whale-watching conditions.
The average-case doses of NO2 that male and female killer whales receive are just
below levels that are predicted to cause adverse health effects in killer whales. Yet peaks in
ambient NO2 concentrations (Table 2.3) can approach levels predicted to cause adverse
health effects in killer whales (Table 3.2). The worst-case doses of NO2 that male and female
114
killer whales receive are also higher, by 3.6 and 3.2 times respectively, than those expected
to cause adverse health effects in killer whales.
Even though the modeled average-case and worst-case doses of CO and NO2 that
killer whales receive are on average 3.4 times lower than what a human would receive, their
toxicity values for CO and NO2 are much lower. When these toxicity values were used to
calculate the 1-hour toxicity doses of CO and NO2, it was determined that the toxicity doses
for killer whales are on average 12% of those for humans. This suggests that killer whales
are potentially much more sensitive to air pollutants in the atmosphere than humans, because
humans require much higher concentrations to produce adverse health effects. This is
primarily due to the effect of allometric scaling with an exponent equal to 0.75 - as it
produces an inverse relationship between body size and toxicity value/dose.
In addition killer whales may be more sensitive to air pollution due to their
respiratory anatomy and physiology alone. Compared to humans and other terrestrial
mammals, killer whales would experience increased PM deposition, persistence, and
absorption of gases in the distal airways because of breath holding, low FR, large VT, and fast
respiratory rates (Goldberg & Smith, 1981; HEI, 1988; Palmes et al., 1973; Tu & Knutson,
1984). As breath holding time during a dive increases, deposition in the lungs increases
exponentially (Goldberg & Smith, 1981; Tu & Knutson, 1984), and their large lung size
increases the surface area for deposition (Perrin et al., 2002).
Even though the SRKWs spend the majority of their time in the first 30 m of the
water column, 30 m in depth is three times the atmospheric pressure at the surface. The
increase in pressure with depth increases gas solubility, thus the killer whales experience
greater amounts of gasses dissolving into tissues while diving above depths at which lung
115
collapse occurs (Fahlman et al., 2006; Kooyman, 1973). Since the calculated toxicity doses
for CO and NO2 do not account for the effect of increased gas solubility during shallow
dives, they may be misleading, and much lower concentrations may actually pose adverse
health effects for killer whales. However, during deep dives when lung collapse occurs,
blood is re-directed to O2 sensitive tissues causing the heart and brain to saturate rapidly
(Fahlman et al., 2006), and the reduced metabolic rate would result in slower rates of
pollutant conversion to metabolites. Thus the calculated doses per kg body mass for killer
whales do not apply to deep diving situations. These results are cause for concern as whale-
watching is potentially having a greater effect on the SRKWs than previously recognized by
behavioural and acoustic studies alone.
Life history also plays a role in an individual’s sensitivity to pollutants. Almost half
of the SRKW population (43%) is comprised of calves, juveniles, and post-reproductive
females, which are members of the population considered sensitive to air pollution. This
underscores the importance of regulating emissions in their vicinity since air quality
standards only consider the general population and certain individuals may be more
susceptible to adverse health effects (USEPA, 2002). When air quality standards are set, the
size and nature of sensitive populations are considered (USEPA, 2002). Thus the large
proportion of sensitive individuals in the SRKW population indicates that strict standards are
required to protect the population’s health. Safety and uncertainty factors applied to air
pollution thresholds may provide a level of protection to more sensitive individuals;
however, differences in the sensitivity of individual killer whales has not been quantified,
and these differences may be larger than what safety and uncertainty factors account for.
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4 CONCLUSIONS AND FURTHER STUDIES
4.1 GENERAL CONCLUSIONS
The summer habitat of the southern resident killer whale (SRKW) population is
subject to heavy vessel traffic, including commercial shipping, fishing, recreation, tourism,
and ferries, and whales are almost always in the presence of some type of vessel (Bain et al.,
2006). These vessels not only affect the behaviour of the animals (Jelinski et al., 2002;
Kruse, 1991; Williams et al., 2002), but they also impact their acoustic abilities (Erbe, 2002).
Prior to this study there was no information on how vessels may be affecting the air quality
near the SRKWs or on potential adverse health effects resulting from this exposure. The
present study used modeling techniques based on highly-predictable dispersal properties of
gases to estimate exposures of killer whales to exhaust pollution under average and worst-
case weather and vessel scenarios. These exposures were used to predict internal pollutant
doses in killer whales, which were compared with toxic threshold estimates extrapolated
from humans. This summary chapter provides recommendations for limiting killer whale
exposure to harmful levels of exhaust pollutants and identifies areas for further research.
Chapter two established that the atmospheric conditions the SRKWs are exposed to
during the commercial whale-watching season are predominantly stable, which can cause an
accumulation of air pollutants above the surface of the water where the whales breathe. The
results from the dispersion model indicated that the wind angle had the largest effect on killer
whale exposure to air pollutants, with downwind angles (210° and 240°) producing the
highest concentrations. The ranking of variables that followed was: the distance of the
vessels to the whale (buffer distance), which was equally as important as the mixing height,
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followed by the number of vessels, which was equally as important as the inter-vessel
distance, and finally the wind speed. Generally the Metro Vancouver Air Quality Objectives
(MV, 2006) for CO and NO2 were exceeded when: the wind came from an angle of 150°,
180°, 210° or 240°; the wind speed was between 1-9 m s-1; and the mixing height was less
than 6 m; the buffer distance was less than 20 m; the number of vessels was greater than 27;
and the inter-vessel distance was less than 50 m.
Even under average-case conditions with 20 vessels maintaining the recommended
100 m distance from the whale and each other, the MV AQOs for CO and NO2 could be
exceeded. The mean CO concentration predicted by the average-case simulations (11.98 mg
m-3) at 100 m from the source was five times greater than that measured 30 m from a busy
Los Angeles highway (Zhu et al., 2002). Based on measurements of cigarette emissions by
Löfroth et al. (1989), during 1-hour of average whale-watching conditions the male killer
whale would potentially experience the equivalent of 3.5 cigarettes, and the female 1.8
cigarettes. The mean NO2 concentration predicted by the average-case simulations (0.04 mg
m-3) at 100 m from the source was just above that measured at a distance of 115 m from busy
motorways (Roorda-Knape et al., 1998). Therefore, the model predicted that even average-
case whale watching scenarios can produce worse air quality than along busy highways, and
worst-case scenarios can produce extreme pollution episodes that greatly exceeded the MV
AQOs.
The average-case dispersion model simulations in Chapter two were essentially “best-
case” whale-watching conditions, and the mean exposure concentrations of CO and NO2
from these simulations were used to predict the internal pollutant dose male and female killer
whales receive. It was determined that the doses of CO (Table 3.1) from average-case
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whale-watching conditions were equal to or slightly higher than those predicted to cause
adverse health effects in killer whales (Table 3.3), while average-case doses of NO2 were
slightly lower than those predicted to cause adverse health effects. Unfortunately, the Be
Whale Wise Guideline (DFO, 2008) that vessels must maintain 100 m from the whales is
violated frequently (Koski, 2006), thus this “best-case” scenario does not often hold true.
Since the atmospheric conditions during the whale-watching season are highly conducive to
air pollutant accumulation, the whale-watching conditions can easily become “worst-case”.
The doses of CO and NO2 calculated from worst-case exposure simulations (Table 3.1) were
on average 6.6 and 3.4 times greater respectively than those expected to cause adverse health
effects in killer whales. This suggests the male killer whale can potentially experience the
equivalent of 22.5 cigarettes, and the female 11.2 cigarettes during 1-hour of worst-case
whale-watching conditions.
The monthly average and maximum ambient concentrations of CO (Table 2.3)
measured at the Christopher Point, BC air quality monitoring station from 2005-2007 are all
well below the calculated toxicity values for male and female killer whales (Table 3.2).
However, the monthly maximum ambient concentrations of NO2 are just below the NO2
toxicity values for both male and female killer whales. Thus occasionally peak ambient NO2
concentrations alone are almost at the level predicted to be a health hazard to killer whales.
The dispersion model estimated 1-hour exposures to air pollutants, but whale
watching occurs on average 12-hours a day during the peak season (Bain, 2002). When
exposed to pollutants for longer than 1-hour, the exposure concentration considered safe is
lowered. For example the MV AQO for an 8-hour exposure to CO is 10 mg m-3, which is
much lower than the 1-hour exposure of 30 mg m-3 (MV, 2006). However, it is unlikely that
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the whales would be exposed to the situation modeled for 12-hours per day, but the situation
may apply for several hours during the day. Thus a toxicity dose for 8-hour exposure to CO
was calculated (Table 3.3), and it was much lower than the average-case 8-hour dose of CO
(Table 3.1), which suggests that long exposures are a health hazard for the whales. Since the
SRKWs are being exposed to air pollutants for longer durations than were modeled, the
calculated 1-hour toxicity doses are likely conservative.
Chapter three also included factors that may affect killer whale sensitivity to air
pollutants. The SRKWs spend 97.6% of their time within 30 m of the surface (Baird et al.,
2005; 2003), which is above the depth that lung collapse occurs (Ridgway & Howard, 1979).
However, even shallow dives increase pressure in the lungs, and would cause greater gas
solubility and uptake into the systemic circulation. During deep dives (which do not occur
that often) metabolic rate is depressed and would result in a slower rate of pollutant
conversion to metabolites. Furthermore, during deep dives blood flow is preferentially
directed to O2 sensitive tissues such as the heart and brain, and if pollutants are present in the
systemic circulation, then those tissues could potentially receive higher doses (Fahlman et al.,
2006; Kooyman, 1973). In addition, 43% of the SRKW population is post-reproductive
females, juveniles, and calves that are likely to experience adverse health effects at
concentrations lower than the calculated toxicity values (Table 3.2). The dose equation did
not take the effects of diving or extra-sensitive individuals into account, which also makes
the calculated toxicity values and doses conservative. However, it may be that safety and
uncertainty factors make up for this deficiency.
The main results from this thesis are summarized in bullet form below.
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• The atmospheric conditions in SRKW habitat during the commercial whale-watching
season are predominantly stable, which can cause an accumulation of air pollutants
above the surface of the water where the whales breathe.
• Wind angle had the largest effect on killer whale exposure to air pollutants, and the
highest exposures occurred when the whale was downwind of vessels.
• Average-case whale-watching simulations produced CO and NO2 concentrations that
occasionally exceeded the Metro Vancouver Air Quality Objectives (i.e. with low
wind speeds, and mixing heights).
• Worst-case whale-watching simulations produced extreme CO and NO2
concentrations that greatly exceeded the Metro Vancouver Air Quality Objectives.
• Doses of CO and NO2 calculated from average-case whale-watching conditions were
approximately equal to and lower respectively than those predicted to cause adverse
health effects in killer whales.
• Doses of CO and NO2 calculated from worst-case exposure simulations were on
average 6.6 and 3.4 times greater respectively than those expected to cause adverse
health effects in killer whales.
• Peak ambient NO2 concentrations in SRKW habitat are just below the level predicted
to be a health hazard to killer whales.
• SRKWs are exposed to air pollutants for longer than the 1-hour duration modeled,
thus the calculated 1-hour toxicity doses are likely conservative.
• During shallow dives there would be greater gas uptake into the systemic circulation
than while at the surface, due to increases in gas solubility.
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• During deep dives there may be slower rates of pollutant conversion to metabolites
due to metabolic depression, and the heart and brain could potentially receive higher
doses of pollutants because of the re-direction of blood flow.
• 43% of the SRKW population can be considered sensitive to air pollution, and would
likely experience adverse health effects at concentrations lower than the calculated
toxicity values.
4.2 UNCERTAINTY AND ASSUMPTIONS IN THE MODELS
Mathematical models never describe the real world completely, and the dispersion
and allometric models included in this thesis have many simplifying assumptions. In some
cases the model parameters could not be measured, and were calculated using formulae with
their own simplifying assumptions (e.g. using the equation by Stahl (1967) to estimate
respiratory dead space volume), further increasing uncertainty. The central assumptions and
most significant sources of uncertainty in the models are described below.
This study helped identify data gaps and lack of suitable models necessary for
characterizing marine recreational vessel emissions. No published information exists on the
emissions of recreational marine engines engaged in whale-watching, which prevented their
inclusion in the dispersion model. Therefore only commercial whale-watching vessel engine
configurations were considered, yet recreational marine engines may be very different on
average than those used by commercial whale-watching companies. The engine
configurations for half of the whale-watching fleet were unknown and were assumed to be
identical to those that were known, and this assumption may not be valid in the real world.
The emission rates for the vessels were not adjusted for age or other factors. The percent
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retention of wet exhaust constituents in the water column was obtained from a few published
studies, which were prone to inaccuracies. The dispersion model only considered a very
specific situation where the whales and vessels were continuously moving at a constant speed
(steady-state engine operation), yet whale-watching vessels commonly shut down their
engines when viewing the whales. However, transient operating conditions (during arrival,
repositioning, and exiting) often have increased emissions compared to steady-state operation
(Graskow, 2001; USEPA, 2002a). The dispersion model assumed that the profile of vertical
pollution diffusion initially decreased rapidly with decreasing height in the atmosphere and
then reached a plateau close to the surface. The dispersion model predicted that air pollutant
concentrations decreased to 31% of the original at a distance of 100 m from the source,
whereas empirical roadway studies have measured a 50% decrease over the same distance.
Thus the dispersion model may have underestimated exposure concentrations.
As discussed in Chapter three, the uncertainty factor from the allometric model alone
is 100, due to inter-individual variability and animal-to-human extrapolation (Renwick &
Lazarus, 1998). It was assumed that the health effects from exposure to air pollutants that
occur in small mammals and humans also occur in killer whales. It was also assumed that
inhalation was the only route of exposure to exhaust pollutants, which may not be the case
(especially with wet exhaust systems). Some of the respiratory rates, volumes, and body
masses for male and female killer whales used to calculate dose were obtained from
allometric scaling of data from captive killer whales, and are likely inappropriate for all
animals in the SRKW population.
In typical risk assessments, when there is a high degree of uncertainty with the model
and/or data included, there is a corresponding low exposure limit (toxicity value) set as a
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safeguard against any underestimation of the potential health effects from the pollutant in
question (NRC, 1991). This reasoning should also apply to the SRKWs, and only improved
information, data, or models should result in raising the exposure limit. Furthermore the
SRKWs are not only being exposed to air pollution from whale-watching vessels, as there are
numerous pollutant sources in their summer habitat, which could produce additive or
synergistic effects. For example, when diesel exhaust is inhaled along with O3, there is a
significant increase in lung inflammation in rats (USEPA, 2002b).
4.3 FUTURE RESEARCH
Future research to improve this study would include a more comprehensive
dispersion model that captures: vessels exiting and entering the scene, vessels operating
independently of one another so that the distances from the whale and each other can vary
during the simulation, vessels shutting down their engines, and changing killer whale and
vessel speeds. It would also be helpful to obtain more accurate representations of time-spent
whale-watching from whale-watching vessel operator logs. Information on all the engines
used by whale-watching companies would also help, however, that could not be done for
recreational vessels engaged in whale-watching because those vessels are constantly
changing. Measurements of all the variables included in the model would also be helpful,
such as determining emission rates from the fleet of whale-watching vessels, calculating the
amount of time killer whales are downwind of vessels (especially at the worst wind angles of
exposure 210° and 240°), and measuring actual vertical mixing heights. To validate the
dispersion model, empirical measurements of air pollutant concentrations around the SRKWs
are necessary.
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Improved estimates and measurements of killer whale respiratory anatomy and
physiology would allow the use of more sophisticated physiological models to determine
more accurate pollutant doses and toxicity values. Future studies to empirically determine
killer whale pollutant doses are biopsy sampling to determine if pollutant metabolites are
present, and collection of exhaled air to determine presence of pollutants.
Lowering engine emissions standards reduces air pollutant emissions (Table 2.6), but
the full benefits of these standards are not realized until there has been a significant turnover
in the fleet, and this takes about 12 years for a 90% turnover to occur (NESCAUM, 1999).
There is potential for alternative fuels to lower air pollutant emissions (Appendix D1), and
the benefits begin immediately upon use of the fuel and apply to all engines, regardless of
their age, technology, or operating conditions (NESCAUM, 1999). However, new
technology and alternative fuels can have unintended consequences on air quality as
discussed in Appendix D2 and section 3.1.1 in Chapter three. Thus relying on engine and
fuel improvements to reduce killer whale exposure to air pollutants may not be an effective
solution, and further research in this area is required.
This study is the first investigation of whale-watching vessel exhaust emissions, and
has demonstrated that in certain situations the SRKWs may be inhaling concentrations of air
pollutants that have the potential to cause serious health effects. It can be argued that the
“average” whale-watching conditions modeled may not arise frequently in the real world,
and may not be very realistic due to the number of assumptions involved. However, these
conditions were based upon published averages of whale-watching and atmospheric
behaviour. The sensitivity analysis of the dispersion model determined that the wind angle
had the largest effect on the whale’s air pollution exposure, and this variable can be
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examined in further detail. In average-case simulations with wind approaching the whale
from the worst angles of 210˚ and 240˚, only emission plumes from the vessels paralleling
the whale on one side had the potential to reach the whale (i.e. from ten vessels), and of
those, only vessel emissions upwind of the whale had the potential to reach the whale (i.e.
from five vessels). This suggests that only five vessels can deteriorate air quality to a point
where it is harmful to the whale, thus any whale-watching situation with at least five vessels
upwind of whales with their engines running is potentially problematic. Furthermore, the
worst-case simulations only considered 40 vessels, whereas the highest number of vessels
counted around the SRKWs is 120, thus the worst-case simulations may be conservative.
Until further studies are conducted that provide more reliable estimates of exposures
and health effects in killer whales, the precautionary principle should be adhered to.
However, it is up to decision makers (in Canada, the Ministry of Fisheries and Oceans) to
determine if the SRKWs involuntary exposure to exhaust emissions are an acceptable or
tolerable risk to the population, based on the probability of harmful health effects, the means
of controlling emissions, and the expected costs and benefits of doing so (McColl et al.,
2000). Yet, this is one threat to the population that can be easily managed by imposing limits
on the number of vessels whale-watching, limits on the amount of time whale-watching
vessels are allowed to remain with the whales, critical habitat areas where vessels are not
allowed, air pollution standards on the engines in use, and tougher enforcement of the Be
Whale Wise Guidelines (DFO 2008). In addition, it must be recognized that air pollution
from the vessels is not only a health threat to this endangered species, but it also threatens the
health of vessel operators, naturalists, and tourists on board the vessels.
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4.4 REFERENCES Bain, D. E. 2002. A Model Linking Energetic Effects of Whale Watching to Killer Whale
(Orcinus orca) Populations. Friday Harbor, WA: Friday Harbor Laboratories, University of Washington.
Bain, D. E., Williams, R., Smith, J. C., & Lusseau, D. 2006. Effects of Vessels on Behavior
of Southern Resident Killer Whales (Orcinus spp.) 2003-2005 (NMFS Contract Report No. AB133F05SE3965). Retrieved March 18, 2007, from http://www.nwfsc.noaa.gov/research/divisions/cbd/marine_mammal/documents/bainnmfsrep2003-5final.pdf
Baird, R. W., Hanson, M. B., Ashe, E. E., Heithaus, M. R., & Marshall, G. J. 2003. Studies
of Foraging in “Southern Resident” Killer Whales During July 2002: Dive Depths, Bursts in Speed, and the Use of a “Crittercam” System for Examining Sub-Surface Behavior (Report Order Number AB133F-02-SE-1744). Seattle, WA: National Marine Fisheries Service, National Marine Mammal Laboratory.
Baird, R. W., Hanson, M. B., & Dill, L. M. 2005. Factors influencing the diving behaviour
of fish-eating killer whales: Sex differences and diel and interannual variation in diving rates. Canadian Journal of Zoology, 83: 257-267.
Department of Fisheries and Oceans Canada (DFO). 2008b. Viewing Guidelines. Pacific
Region Marine Mammals and Turtles. Retrieved May 14, 2008, from http://www.pac.dfo-mpo.gc.ca/species/marinemammals/view_e.htm
Erbe, C. 2002. Underwater noise of whale-watching boats and potential effects on killer
whales (Orcinus orca), based on an acoustic impact model. Marine Mammal Science, 18: 394-418.
Fahlman, A., Olszowka, A., Bostrom, B., & Jones, D. R. 2006. Deep diving mammals: Dive
behavior and circulatory adjustments contribute to bends avoidance. Respiratory Physiology and Neurobiology, 153: 66-77.
Graskow, B. R. 2001. Design and Development of a Fast Aerosol Size Spectrometer.
University of Cambridge Ph.D. Thesis. Jelinski, D. E., Krueger, C. C., & Duffus, D. A. 2002. Geostatistical analyses of interactions
between killer whales (Orcinus orca) and recreational whale-watching boats. Applied Geography, 22: 393-411.
Kooyman, G. L. 1973. Respiratory adaptations in marine mammals. American Zoologist,
13(2): 457-468. Koski, K. L. 2006. Soundwatch Public Outreach/Boater Education Project 2004-2005
Final Program Report. Friday Harbor, WA: The Whale Museum.
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Kruse, S. 1991. The interactions between killer whales and boats in Johnstone Strait, B.C. In K. Pryor & K. S. Norris (Eds.), Dolphin Societies: Discoveries and Puzzles (pp. 149-159). Berkeley, CA: University of California Press.
McColl, S., Hicks, J., Craig, L., & Shortreed, J. 2000. Environmental Health Risk
Management: A Primer for Canadians. Waterloo, ON: Network for Environmental Risk Assessment and Management.
Metro Vancouver (MV). 2006. 2006 Air Quality Report for the Lower Fraser Valley.
Burnaby, BC: Metro Vancouver. National Research Council (NRC). 1991. Human Exposure Assessment for Airborne
Pollutants. Washington, DC: National Academy of Sciences. Northeast States for Coordinated Air Use Management (NESCAUM). 1999. The Health
Effects of Gasoline Constituents – Attachment 1. Boston, MA: Northeast States for Coordinated Air Use Management.
Renwick, A. G., & Lazarus, N. R. 1998. Human variability and noncancer risk assessment –
an analysis of the default uncertainty factor. Regulatory Toxicology and Pharmacology, 27: 3-20.
Ridgway, S. H., & Howard, R. 1979. Dolphin lung collapse and intramuscular circulation
during free diving: evidence from nitrogen washout. Science, 206: 1182-1183. Roorda-Knape, M., Janssen, N., De Harthog, J., Van Vliet, P., Harssema, H., & Brunekreef,
B. 1998. Air pollution from traffic in city districts near major motorways. Atmospheric Environment, 32(11): 1921-1930.
Stahl, W. R. 1967. Scaling of respiratory variables in mammals. Journal of Applied Physiology, 22: 453-460. United States Environmental Protection Agency (USEPA). 2002a. A Comprehensive
Analysis of Biodiesel Impacts on Exhaust Emissions (EPA420-P-02-001). Washington, DC: United States Environmental Protection Agency, Office of Air and Radiation.
United States Environmental Protection Agency (USEPA). 2002b. Health Assessment
Document for Diesel Engine Exhaust (EPA/600/8-90/057F). Washington, DC: United States Environmental Protection Agency, Office of Transportation and Air Quality.
Williams, R., Trites, A. W., & Bain, D. E. 2002. Behavioural responses of killer whales
(Orcinus orca) to whale-watching boats: Opportunistic observations and experimental approaches. Journal of Zoology, London, 256: 255-270.
137
Zhu, Y., Hinds, W. C., Kim, S., & Sioutas, C. 2002. Concentration and size distribution of ultrafine particles near a major highway. Journal of the Air & Waste Management Association, 52(9): 1032-1042.
138
APPENDICES3
Appendix A: Be Whale Wise Guidelines (DFO 2008).
1. BE CAUTIOUS and COURTEOUS: approach areas of known or suspected marine
wildlife activity with extreme caution. Look in all directions before planning your
approach or departure.
2. SLOW DOWN: reduce speed to less than 7 knots when within 400 metres/yards of the
nearest whale. Avoid abrupt course changes.
3. KEEP CLEAR of the whales’ path. If whales are approaching you, cautiously move out
of the way.
4. DO NOT approach whales from the front or from behind. Always approach and depart
whales from the side, moving in a direction parallel to the direction of the whales.
5. DO NOT approach or position your vessel closer than 100 metres/yards to any whale.
6. If your vessel is not in compliance with the 100 metres/yards approach guideline (#5),
reduce your speed and cautiously move away from the whales
7. STAY on the OFFSHORE side of the whales when they are traveling close to shore.
8. LIMIT your viewing time to a recommended maximum of 30 minutes. This will
minimize the cumulative impact of many vessels and give consideration to other viewers.
9. DO NOT swim with, touch or feed marine wildlife.
3 References cited within appendices are listed in the reference section of the chapter they were mentioned in.
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Appendix B: Air Pollutant Emissions
Table B1: Total emissions of air pollutants from all sources during the year 2000 for the Lower Fraser Valley Airshed (GVRD, 2002).
Air Pollutant Emissions (metric tonnes) PM10 15,228 PM2.5 9,021 NOx 100,090 SOx 19,015 VOC 112,360 CO 483,083 CO2 23,022,272 N2O 2,754 Principal smog-forming pollutants (NOx, VOC, PM2.5, SOx, NH3)
258,508
Table B2: Total emissions of air pollutants from ocean-going vessels during 2005-2006 in British Columbia* (The Chamber of Shipping, 2007).
Air Pollutant Emissions (tonnes per year) PM10 1,604 PM2.5 1,438 NOx 26,500 SOx 18,413 CO 2,236 CO2 1,278,084 N2O 36 HC 934 CH4 128 NH3 28
*This area includes all inland and territorial waters along the BC coast, the U.S. and Canadian portions of the Strait of Juan de Fuca, and oceanic waters extending 50 nautical miles offshore. Results were also compiled for the LFV region, which includes all of the MV and the southwestern portion of the Fraser Valley Regional District (FVRD).
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Table B3: Emissions (tonnes per year) for ocean-going vessels, harbour vessels, ferries, fishing vessels and recreational vessels in BC outside of Metro Vancouver and FVRD* and Vancouver Island for the year 2000 (Quan et al., 2002b).
Air Pollutant B.C. and WA Emissions (tonnes per year)
Vancouver Island Emissions (tonnes per year)
CO 4,145 2,095 CO2 1,350,024 840,930 VOC 1,332 649 Total NOx 38,404 23,867 NO 28,047 14,944 NO2 1,535 954 PM10 & PM2.5 1,592 1,037
*Canadian portion included Vancouver Island and the coast of BC, and the U.S. portion included Washington State, Whatcom County, Puget Sound, and the coast of Washington.
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Appendix C: Air Quality Standards Table C1: The United States USEPA National Ambient Air Quality Standards (Cooper & Alley, 2002).
Air Pollutant Standard (mg m-3) Averaging Period 10 8-hour CO 40 1-hour
NO2 0.1 Annual mean 0.05 Annual mean PM10 0.15 24-hour
0.015 Annual mean PM2.5 0.065 24-hour
Table C2: The World Health Organization Air Quality Guidelines for Europe (WHO 2000).
Air Pollutant Standard (mg m-3) Averaging Period 60 30-minute CO 30 1-hour
NO2 0.2 1-hour PM Dose response n/a
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Appendix D: Alternative Fuels and Fuel Additives
Appendix D1: The Effect of Alternative Fuels on Engine Emissions
The advantages of using natural and petroleum gasses are that they emit
approximately 50% less CO and VOCs, very few photochemically active VOCs, and no
toxins like benzene, while the disadvantages of natural and petroleum gasses are problems
with on-board fuel storage, handling, and refueling (Cooper & Alley, 2002). Biodiesel is
made from vegetable or animal fats; it is non-toxic and biodegradable and is increasingly
being used in marine engines as it can be blended with regular diesel fuel (BCLA, 2005).
Compared to conventional diesel fuel, biodiesel is relatively clean burning as it produces
fewer emissions of HCs, CO2, PM, and has less O3 forming potential, but produces
equivalent or slightly higher emissions of NOx (USEPA, 2002a).
Appendix D2: The Effect of Fuel Additives on Engine Emissions
Fuel additives are used to reduce engine emissions of HCs, CO and PM, and often
benzene, formaldehyde, and 1,3-butadiene are reduced as well (Zhu et al., 2003). However,
additives often decrease emissions of certain pollutants while increasing emissions of others
(Zhu et al., 2003). Methyl Tertiary Butyl Ether (MTBE) is a gasoline oxygenate and octane
enhancer additive used in automobile and marine engines; however, due to water
contamination issues its use is declining in the United States (NESCAUM, 1999). The use of
MTBE decreases emissions of benzene, 1,3-butadiene, and acetaldehyde, but it increases
formaldehyde emissions (NESCAUM, 1999). MTBE in air and water has harmful health
effects: it is an animal (and possibly human) carcinogen; it produces harmful neurological
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effects in sensitive individuals; it has harmful reproductive and developmental effects; and at
high concentrations it can be toxic to the kidney, liver, and endocrine system (NESCAUM,
1999). However, MTBE is one of the least toxic compounds in gasoline and is much less
toxic than either 1,3-butadiene or benzene, yet even at extremely low concentrations humans
can smell and taste it in air and water (NESCAUM, 1999).
Ethanol and ether are often used instead of MTBE to oxygenate gasoline as they
reduce emissions of CO, NOx and photochemically reactive compounds (Cooper & Alley,
2002; NESCAUM, 1999; Rice et al., 1991). Unfortunately ethanol and ether increase the
amount of acetaldehyde and formaldehyde in engine emissions, which are both probable
human carcinogens (NESCAUM, 1999). Ethanol added to gasoline may also increase the
volatility of other compounds like benzene (NESCAUM, 1999). In addition, two studies by
Environment Canada (unpubl.) found that as ethanol content in fuel increased: CO2 and CH4
emission rates remained essentially unchanged; decreases in CO were not always statistically
significant; there were consistent increases in NOx emissions; HC emissions increased in
some engines but decreased in others; and VOC emissions decreased.
While fuel additives play a role in decreasing certain emissions, the health effects
from other compounds produced by the additives must be considered. MTBE, formaldehyde
and acetaldehyde are probable human carcinogens, and this almost certainly applies to
marine mammals; therefore, the impacts of using fuel additives in the marine environment
should be carefully examined.
144
Appendix E: Programming Code for the NetLogo Dispersion Model breed [ whales whale ] breed [ boats boat ] ;; adapted from "Models > Code Examples > Diffuse Off Edges Example" ;------------------------------------------------------------------- patches-own [ pollution ;; this is the quantity we will be diffusing new-pollution ;; this is the quantity diffused/moved by wind ] ;------------------------------------------------------------------- globals [ edge-patches ;; border patches where pollution should remain 0 main-patches ;; patches not on the border polluter-patches ;; patches creating pollution num-boat-in-a-row ;; number of boats in a row before stacking to next row factor ;; multiplication factor for row length yboat ;; equal to buffer distance index ;; multiplication factor for row length whale-direction ;; the random angle that the whale moved at ] ;------------------------------------------------------------------- to setup ;; Create boats & whales (both are turtle agents) clear-all set-default-shape whales "shark" create-whales 1 [ set color green set size 4 set heading 90 ;; start facing east ] create-boats boatnumber [ ;; number of boats is set by the slider set size 2 set heading 90 ;; start facing east like the whale ] ;; the following determines the distribution of boats on either ;; side of the whale (in rows), so that the boats are at the buffer ;; distance from the whale and at the interboat-dist from each other set factor 0 set num-boat-in-a-row 0 set yboat buffer set index 1 repeat boatnumber [ if num-boat-in-a-row / 2 >= ( number-of-buffers * buffer ) / interboat-dist [ set yboat ( yboat + interboat-dist ) set num-boat-in-a-row 0
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set factor 0 ] set num-boat-in-a-row num-boat-in-a-row + 1 if num-boat-in-a-row mod 4 = 1 [ set factor factor + 1 ] if index mod 4 = 1 [ if-else factor = 1 [ set [ xcor ] of boat index 0 ][ set [ xcor ] of boat index ( interboat-dist * ( factor - 1 ))] set [ ycor ] of boat index yboat set [ color ] of boat index white ] if index mod 4 = 2 [ set [ xcor ] of boat index ( interboat-dist * ( - factor )) set [ ycor ] of boat index yboat set [ color ] of boat index orange ] if index mod 4 = 3 [ if-else factor = 1 [ set [ xcor ] of boat index 0 ][ set [ xcor ] of boat index ( interboat-dist * ( factor - 1 ))] set [ ycor ] of boat index ( - yboat ) set [ color ] of boat index blue ] if index mod 4 = 0 [ set [ xcor ] of boat index ( interboat-dist * ( - factor )) set [ ycor ] of boat index ( - yboat ) set [ color ] of boat index red ] set index index + 1 ] ;; identify edge and non-edge patches. ;; original code - only worked with world wrap checkboxes cleared ;; otherwise all patches have 8 neighbors ;; set edge-patches patches with [count neighbors != 8] ;; set main-patches patches with [count neighbors = 8] ;; this new code works independently of world wrap conditions as long as ;; origin is at center. With world wrapping on turtles are allowed to wrap ;; around boundaries (so they don't get stuck on edges) but pollution won't. ;; Can think of it as a "conserved" system: as one boat leaves one side, another ;; comes in on the opposite edge. set edge-patches patches with [ abs pxcor = max-pxcor or abs pycor = max-pycor ] set main-patches patches with [ abs pxcor < max-pxcor and abs pycor < max-pycor ]
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set polluter-patches n-of boatnumber boats ;; only patches w/boats produce pollution recolor end ;------------------------------------------------------------------- to go move-whales move-boats blow-wind diffuse-off-edges tick end ;------------------------------------------------------------------- to move-whales ask whales [ set whale-direction random ( 2 * max-degrees + 1 ) - max-degrees right whale-direction ;; movement of whale is random forward 1 ] end ;------------------------------------------------------------------- to move-boats ask boats [ right whale-direction forward 1 ;; by the same speed of the whale set pollution pollution + 70.2 ;; dummy pollution emission rate set at ] ;; 70.2 mg/tick = 100 mg/second end ;------------------------------------------------------------------- to blow-wind ;; moves 'pollution' from all patches in the same direction. ;; Works by finding the source square (where is the wind moving ;; air from?) and depositing it in variable new-pollution. Once all ;; new-pollutions are set then it's safe to replace old 'pollutions'. ;; Kind of tricky because source "square" will probably overlap 4 ;; source patches so we have to sum over all parts of overlapping source ;; patches. ask patches [ ;; find center of square where wind is coming from let source-x pxcor + ( wind-speed + 1e-6 ) * sin wind-angle let source-y pycor + ( wind-speed + 1e-6 ) * cos wind-angle set new-pollution weighted-mean-pollution source-x source-y ] ask patches [ set pollution new-pollution ]
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if random-wind [ change-wind ] end ;------------------------------------------------------------------- to diffuse-off-edges ;; diffuse pollution but remove any that has reached the edges diffuse pollution diffusion-constant ask edge-patches [ set pollution 0 ] recolor end ;------------------------------------------------------------------- to recolor ;; color patches proportionately to pollution ask patches [ set pcolor scale-color blue pollution 0 100 ] end ;------------------------------------------------------------------- to-report weighted-mean-pollution [ x y ] ;; reports mean 'pollution' in unit square with top-left corner at (x,y) let sum-pollution contributed-pollution x y -0.5 0.5 ;; top left corner set sum-pollution sum-pollution + contributed-pollution x y 0.5 0.5 ;; top right corner set sum-pollution sum-pollution + contributed-pollution x y 0.5 -0.5 ;; bottom right corner set sum-pollution sum-pollution + contributed-pollution x y -0.5 -0.5 ;; bottom left corner report sum-pollution end ;------------------------------------------------------------------- to-report contributed-pollution [ x y deltax deltay ] ;; find how much area of unit square at (x,y) overlaps into ;; patch at offset (deltax,deltay) and calculate contribution ;; of 'pollution' to unit square from overlap ;; find corner of square let corner-x x + deltax let corner-y y + deltay let corner-patch patch corner-x corner-y ;; override edge-wrapping if ( [ pxcor ] of corner-patch != round corner-x ) or ( [ pycor ] of corner-patch != round corner-y ) [ ;; this condition will only be true if edge wrapping has ;; occurred in finding corner-patch report 0 ] if-else corner-patch = nobody [ report 0
148
][ ;; find opposite corner of patch that square corner lands on let patch-x [ pxcor ] of corner-patch - deltax let patch-y [ pycor ] of corner-patch - deltay let area ( abs ( corner-x - patch-x )) * ( abs ( corner-y - patch-y )) report area * [ pollution ] of corner-patch ] end ;------------------------------------------------------------------- to change-wind ;; randomly shift wind speed and direction set wind-speed wind-speed + 0.01 * ( random 3 - 1 ) set wind-speed median lput wind-speed [ 0 1 ] ; must be >= 0 and <= 1 set wind-angle ( wind-angle + random 3 - 1 ) mod 360 end ;------------------------------------------------------------------- to-report concentration ;; of the whale's patch ;; 2 is the patch size (2m x 2m) ;; mixing-height set by slider ;; 2.85 is the boat speed in m/s report [ ( new-pollution / ( 2 * mixing-height * 2.85 )) ] of whale 0 end ;-------------------------------------------------------------------
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Appendix F: Sensitivity Analysis Results from the Dispersion Model.
Figure F1: Deterministic simulation results with CO and NO2 concentrations as a function of wind speed and wind angle.
150
Figure F2: Stochastic simulation results with CO and NO2 concentrations as a function of the wind speed and random mean wind angle of either 90°, 150° or 210°.
Figure F3: Deterministic simulation results with CO and NO2 concentrations as a function of the pollutant diffusion constant and wind angle.
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Figure F4: Stochastic simulation results with CO and NO2 concentration as a function of the diffusion constant and random mean wind angle of either 90°, 150° or 210°.
Figure F5: Deterministic simulation results with CO and NO2 concentration as a function of the vertical mixing height of the air pollutant and wind angle.
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Figure F6: Stochastic simulation results with CO and NO2 concentration as a function of the vertical pollutant mixing height and random mean wind angle of either 90°, 150° or 210°.
Figure F7: Deterministic simulation results with CO and NO2 concentration as a function of the buffer distance the vessels maintained from the whale, and wind angle.
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Figure F8: Stochastic simulation results with CO and NO2 concentration as a function of the buffer distance and random mean wind angle of either 90°, 150° or 210°.
Figure F9: Deterministic simulation results with CO and NO2 concentration as a function of the number of vessels and the angle the wind came from.
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Figure F10: Stochastic simulation results with CO and NO2 concentration as a function of the number of vessels and random mean wind angle of either 90°, 150° or 210°.
Figure F11: Deterministic simulation results with CO and NO2 concentration as a function of the inter-vessel distance and the angle the wind came from.
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Figure F12: Stochastic simulation results with CO and NO2 concentration as a function of the inter-vessel distance and random mean wind angle of either 90°, 150° or 210°.
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Appendix G: Classes of Compounds in Diesel Exhaust (from Mauderly, 1992). Particulate Phase: • Elemental carbon • Heterocyclics, hydrocarbons (C14-C35), polycyclic aromatic hydrocarbons and
derivatives (acids, alcohols, aldehydes, anhydrides, esters, ketones, nitriles, quinones, sulfonates, halogenated and nitrated compounds)
• Inorganic sufates and nitrates • Metals
Gas and Vapor Phases: • Acrolein • Ammonia • Carbon dioxide • Carbon monoxide • Benzene • 1,3-Butadiene • Formaldehyde • Formic Acid • Heterocyclics, hydrocarbons (C1-C18), and derivatives (as listed above) • Hydrogen cyanide • Hydrogen sulfide • Methane • Methanol • Nitric and nitrous acids • Nitrogen oxides • Sulfur dioxide • Toluene • Water
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Appendix H: Health Effects from Exposure to Air Pollutants in Exhaust.
Particulate Matter (PM)
Particulate matter represents a broad class of chemically and physically diverse
substances that form discrete particles that exist in the condensed phase (USEPA, 2004). PM
is of great concern because it is easily inhaled, chemicals such as sulphates, nitrates, and
heavy metals (e.g. chromium, manganese, mercury, and nickel) easily attach to the surface,
and often the attached chemicals are known or suspected mutagens and carcinogens that
persist in the environment (BCPHO, 2004; HEI, 1999; USEPA, 2004). Many of the attached
chemicals (and other carcinogens associated with engine exhaust) are not directly toxic and
only produce harmful effects when activated metabolically (HEI, 1988).
Even short-term elevations in the ambient concentration of PM contributes to
numerous harmful health effects such as: cough; lower respiratory symptoms; decrements in
lung function; chronic bronchitis; asthma; chronic obstructive pulmonary disease; lung
cancer; increases in cardio-respiratory mortality; impairment of lung clearance mechanisms;
cough; labored breathing; chest tightness; wheezing; inflammation; cell proliferation;
ischemic heart disease; heart failure; and metaplasia (cell transformation from a normal to an
abnormal state) (Bates, 1994; HEI, 1999; Invernizzi et al., 2004; USEPA, 2004). Whenever
the ambient PM concentration increases by 10 mg m-3 there is an observable short-term
health burden, which can be calculated via human morbidity and mortality (Invernizzi et al.,
2004). Acute or short-term exposure to diesel exhaust can result in acute irritation to the
eyes, throat, and bronchioles, can cause neurophysiological symptoms such as
lightheadedness and nausea, and can cause respiratory symptoms like cough and phlegm
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(USEPA, 2004). Immunologic effects are also produced, such as the aggravation of
allergenic responses to allergens, and asthma-like symptoms (USEPA, 2004).
Land-based engines emit 95% of their PM in the fractions of PM10 and PM2.5, and
marine vessels also emit small diameter PM mostly below 1 µm (Quan et al., 2002). Several
studies have found a consistent relationship between inhalation of PM2.5 at concentrations of
15 µg m-3 and higher with hospitalizations from cardiac and respiratory diseases like asthma,
bronchitis, emphysema, and even death (BCPHO, 2004). Zhu et al. (2002) conducted a
literature survey on the toxicity of PM, and found that PM with a diameter less than 100 nm
(ultrafine) is more toxic than larger particles of the same chemical composition and
concentration, and the risk of adverse health effects increases proportionately with exposure
(BCPHO, 2004). However, a no-effects threshold for PM has never been achieved, thus even
concentrations below air quality standards require precaution (BCPHO, 2004).
Nitrogen Oxides (NOx)
Nitrogen oxides are byproducts of fuel combustion and are composed primarily of
NO that reacts quickly to form NO2 (BCPHO, 2004). NO2 is chemically reactive, water
soluble, corrosive gas, and when combined with water vapor forms nitric acid (HNO3) and
nitrous acid (HNO2), that can in turn form carcinogenic nitrosamines (BCPHO, 2004; HEI,
1988; Koenig, 2000). HNO3 is reactive with other organic chemicals and produces
secondary particles like ammonium nitrate, which bind to PM contributing to its toxicity
(BCPHO, 2004). NO2 can also react with HCs in sunlight, producing O3 and other
photochemical byproducts that form smog (BCPHO, 2004). NOx damages cells that line the
159
lungs, reducing lung function and intensifying health problems like asthma, bronchitis,
coughing, and chest pain (BCPHO, 2004).
NO is not as water-soluble as NO2, thus it can penetrate further into the lungs, where
it diffuses quicker than NO2 into tissue but is not transported far due to its reaction with
oxyhemoglobin (Lippmann, 2000). NO has low direct toxicity, but reactions with other
compounds can produce potent toxic oxidants (Lippmann, 2000). When NOx enters the
blood, it binds to hemoglobin to produce nitrosylhemoglobin (NOHb) because hemoglobin
has a higher affinity for NO than O2 (Lippmann, 2000). Enzymes quickly oxidize NOHb,
and as long as the enzyme activity is maintained, potential toxicity from NO-related oxygen
transport effects are eliminated, at least with NO concentrations less than 12.3 mg m-3
(Lippmann, 2000).
When NO2 is inhaled up to 90% can be removed, with the majority taken up by the
lungs and the rest by the upper respiratory tract (Lippmann, 2000). Increased ventilation
causes more NO2 to be delivered and absorbed in the alveolar region (Lippmann, 2000).
Inhaled NO2 is absorbed into the blood where it is likely converted to nitrite (Koenig 2000).
Ambient concentrations of NO2 have been related to increased mortality, but generally only
concentrations greater than 1880 µg m-3 are problematic (Lippmann, 2000). Animal studies
have demonstrated that exposure to NO2 at concentrations of less than 1880 µg m-3 over
several weeks to months causes numerous effects to the lungs, spleen, liver and blood
(WHO, 2000). In the lungs, the effects are both reversible and irreversible, but structural
changes can occur in cell types with emphysema-like effects (WHO, 2000). Increased
susceptibility to bacterial and viral lung infections occurs at exposure levels as low as 940 µg
m-3 of NO2 (WHO, 2000).
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Carbon Monoxide (CO)
Carbon monoxide is odorless, colorless, tasteless, nonirritating and is produced by the
incomplete combustion of fossil fuels (USEPA, 2004). High levels of CO are recorded near
roadways, and peak in city centers and at intersections during rush hour (BCPHO, 2004).
However, the respiratory system is not considered the primary target for CO effects, as it
mostly affects the cardiovascular system (Koenig, 2000).
When CO is inhaled, it enters the bloodstream through the lungs and inhibits
hemoglobin’s capacity to carry O2 to the tissues and organs due to the preferential binding of
CO over O2, (hemoglobin’s affinity for CO is 240-250 times that of O2) (Lippmann, 2000;
USEPA, 2004). This has a significant negative impact on human health because it interferes
with O2 release at the tissues (Lippmann, 2000), which causes toxic effects in the blood and
tissues, impairs organ functioning, and if exposure is long enough it results in death (USEPA,
2004). Because the heart and brain require more blood flow than other organs and tissues,
they experience the hypoxic effects of CO much more rapidly (Lippmann, 2000). CO can
also modify electron transport in nerve cells (Lippmann, 2000), thus exposure to CO can
impair exercise capacity, visual perception, manual dexterity, learning functions, and the
ability to perform complex tasks (USEPA, 2004). The human circulatory system takes 8-12
hours to eliminate CO due to its high hemoglobin affinity (Lippmann, 2000).
Epidemiological studies have found a link between CO exposure and premature
morbidity such as angina, congestive heart failure, and other cardiovascular diseases
(USEPA, 2004). Even ambient CO exposure has been associated with increased hospital
admissions due to cardiovascular issues, especially congestive heart failure (USEPA, 2004).
Chronic exposure to CO can induce adaptations such as increases in the number of red blood
161
cells, blood volume, heart size, heart rate, stroke volume, and systolic blood pressure
(Lippmann, 2000).
Sulfur Dioxide (SO2)
Sulfate emissions are approximately proportional to the quantity of sulfur in the fuel,
and most of the sulfur is oxidized to SO2, but 1-4% is oxidized to sulfuric acid in the exhaust
(USEPA, 2002). Exposure to SO2 can produce chronic bronchitis and potentially asthma
(Bates, 1994). However, SO2 is highly water-soluble, thus is easily removed by the upper
airways, which is the primary site of respiratory defence.
Hydrocarbons (HC)
Volatile Organic Compounds (VOCs) consist mainly of HCs and other organic
gasses, many of which are potentially toxic at ambient levels (i.e. benzene, 1,3-butadiene,
acetaldehyde, and formaldehyde) (Bates et al., 2003). Aromatic HCs are the most toxic
major class of compounds in fuel exhaust, with acute toxicity correlated to light aromatic
HCs, and chronic effects correlated to four- and five-ring aromatic and heteroaromatic HCs
(Geraci & St. Aubin, 1990). HCs evaporate slower in cooler waters than warmer waters, due
to the inverse relationship between temperature and vapor pressure (Geraci & St. Aubin,
1990).
HC vapors can irritate and damage the sensitive membranes of the eyes, mouth, and
respiratory tract (Geraci & St. Aubin, 1990). HCs with high volatility (e.g. benzene and
toluene) are easily inhaled, can displace oxygen in the lungs, are quickly transferred to the
blood from the lungs, can accumulate in tissues like the brain and liver, which can lead to
162
neurological disorders and liver damage (Geraci & St. Aubin, 1990). Chemical pneumonitis
can be caused by small amounts of insoluble HCs that easily penetrate deep into the
bronchopulmonary tree, resulting in bronchospasm and inflammatory response (Le Tertre et
al., 2002). Acute and chronic central nervous system and peripheral nervous system toxicity
can arise from the systemic absorption of HCs (Le Tertre et al., 2002). Inhalation of HCs can
also cause inflammation of mucous membranes, lung congestion, pneumonia, neurological
damage, and liver disorders (Matkin & Saulitis, 1997). Therefore, the inhalation of HCs can
result in direct mortality or indirect mortality and morbidity.
Oil spill research has shown that volatile HCs can cause sudden death in cetaceans
that inhale them while traveling quickly or when stressed because their breathing is rapid and
explosive (Matkin & Saulitis, 1997). However, for death to occur the HCs must be present in
high concentrations (100 mg m-3 and greater) or the time of exposure needs to be significant
(Matkin & Saulitis, 1997). Exposure to chronic moderate concentrations of HCs could pose
a health risk to killer whales; however, marine mammals have liver enzymes that can
metabolize and excrete HCs, which limits their accumulation and potential harm (Geraci &
St. Aubin, 1990).
Polycyclic aromatic hydrocarbons (PAHs)
Polycyclic aromatic hydrocarbons (PAHs) are derived from condensed benzene rings,
and are formed via combustion processes (Godard et al., 2006). The USEPA has classified
several PAH compounds as carcinogens, and probable human carcinogens based on animal
studies (i.e. fish, amphibians, rats) and in human cell culture assays (USEPA, 2004). Many
PAHs have carcinogenic or mutagenic potential via their metabolites, which affect the
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reproductive system, developmental systems, immunological systems, the endocrine system,
and can enhance plaque formation in the arteries (Frumkin & Thun, 2001; Godard et al.,
2006; USEPA, 2004). The most widely studied PAH is Benzo[a]pyrene; it is the only PAH
that has been used in inhalation experiments to test for carcinogenicity, and in these
inhalation experiments it produced lung tumors in hamsters (WHO, 2000).
PAHs present in diesel soot strongly bind to the surface of PM, which greatly slows
their clearance rate (IPCS, 1996). The human lung can clear approximately 50% of the
PAHs on diesel PM in one day, but the remaining PAHs have retention half times of 18-36
days (IPCS, 1996). Pulmonary macrophages can metabolize some PAHs by oxidation
(IPCS, 1996).
PAHs have many routes into the marine environment: oil/fuel spills, ship discharge,
oil seepage, road runoff, industrial effluent, forest fires, and atmospheric deposition from the
incomplete combustion of fossil fuels (SETAC, 1996). After entering the marine
environment PAHs are weathered by physical, chemical, and biological processes, yet the
highest PAH concentrations within the water column are right at the surface of the water
(SETAC, 1996). PAHs and PAH-DNA adducts have been measured in brain and liver tissue
of belugas in the St. Lawrence River estuary (Godard et al., 2006). PAHs taken up by
cetaceans are biotransformed/metabolized by oxidation and conjugation to form more polar
and water-soluble metabolites that are either retained or excreted (Law & Whinnett, 1992;
SETAC, 1996). PAHs are stored short-term in the liver, and in the long-term they
bioaccumulate in muscle tissue and this represents the portion retained (Law & Whinnett,
1992; SETAC, 1996). However, in harbour porpoises (Phocoena phocoena), accumulation
of PAH seems to be low, and most likely primarily comes from their food (Law & Whinnett,
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1992). Fish in areas polluted with PAHs usually have low concentrations of PAH, as they
can quickly convert them to metabolites such as dihydrodiols and phenols (Law & Whinnett,
1992).
Benzene
Benzene is a volatile aromatic HC, and makes up one to two percent of the exhaust
emitted from gasoline and diesel engines (USEPA, 2004). Benzene is a known human
carcinogen and causes leukemia in humans via all routes of exposure (USEPA, 2004).
Benzene also causes chromosomal changes in human and animal cells, and blood disorders
such as aplastic anemia (USEPA, 2004). However, benzene is volatile and short-lived, thus
it is likely involved more with acute toxicity rather than chronic (Geraci & St. Aubin, 1990).
Aldehydes
Aldehydes make up an important fraction of the gas phase of exhaust emissions, they
are probable human carcinogens, and produce non-cancer health effects (USEPA, 2002).
Formaldehyde in exhaust is a result of the incomplete combustion of gasoline and diesel, it is
the most abundant aldehyde present in engine exhaust, making up over 10% of the total HC
emissions (USEPA, 2004), and makes up 65-80% of the aldehyde emissions in diesel exhaust
(USEPA, 2002). Formaldehyde is very reactive (even with itself), but is non-toxic in small
quantities as it is a by-product of metabolism (HEI, 1988). Methanol added to gasoline
significantly increases formaldehyde emissions, while ethanol increases acetaldehye
emissions (Lippmann, 2000).
165
Formaldehyde is very water-soluble, and when inhaled essentially 100% of it is
absorbed/deposited in the upper and lower respiratory tract (HEI, 1988; Lippmann, 2000).
The long-term inhalation of formaldehyde can produce tumors in the sinuses and nasal cavity
of humans (USEPA, 2004). Although formaldehyde is a probable human carcinogen since it
causes cancer in animals, there is limited evidence of carcinogenicity in humans (USEPA,
2004). Studies have also found that formaldehyde causes mutagenic activity in cell cultures,
it is toxic to the kidney at moderately high doses, it has harmful neurological effects, it can
reduce pulmonary function, and can initiate skin sensitization (Lippmann, 2000; USEPA,
2004). Other aldehydes can be genotoxic and produce tumors in vivo, and can also produce
similar respiratory effects as formaldehyde (Lippmann, 2000).
1,3-Butadiene
1,3-Butadiene is a colorless gas found in small quantities in gasoline vapor and in
engine exhaust (ATSDR, 1993). Even though it breaks down quickly in air, especially in the
presence of sunlight, the average concentration of 1,3-butadiene in urban air is 0.67 µg m-3
(ATSDR, 1993). Exposure to 1,3-butadiene usually occurs from breathing contaminated air,
and low levels are not expected to result in adverse health effects (ATSDR, 1993). However,
when humans inhale 1,3-butadiene at high concentrations for short periods of time it results
in central nervous system damage, blurred vision, nausea, fatigue, headache, decreased blood
pressure, decreased pulse rate, and unconsciousness (ATSDR, 1993). Studies with
experimental animals have found that inhaling 1,3-butadiene can increase the number of birth
defects, cause kidney and liver disease, produce tumors, damage the lungs, and even cause
death of some individuals (ATSDR, 1993). In the United States, 1,3-butadiene has been
166
listed as a probable carcinogen based on animal studies, and the occupational exposure limit
is 2,212 mg m-3 (ATSDR, 1993).
Ozone (O3)
Ozone occurs naturally in the environment at low levels; however, when engines emit
air pollutants such as NO2 and VOCs, they can react photochemically to produce O3 when
exposed to sunlight (BCPHO, 2004). Ozone is typically found several km downwind from
the source of primary pollutants, because O3 in ambient air tends to lag the emissions of the
primary pollutants required for its formation (Koenig, 2000).
Out of all the oxidizing air pollutants, O3 is the most toxic due to its oxidative
properties (HEI, 1988). O3 is a highly reactive gas of moderate solubility, thus it can
penetrate into the tracheobronchial tree and will react with the mucous layer of the small
bronchioles, damaging the tissue underneath (HEI, 1988). O3 is an intense irritant and
damages lung epithelial cells, which reduces lung function and aggravates other health
problems like: asthma, bronchitis, coughing, pneumonia, and chest pain (BCPHO, 2004).
The CWS for O3 is 0.13 mg m-3 during an 8-hour average (BCPHO, 2004). When young
normal exercising humans are exposed to 0.16 mg m-3 of O3 for 6-hours, it adversely affects
their lung functioning and causes inflammation in the lung (Bates, 1994).
Other Compounds in Exhaust
Diesel and gasoline engines emit numerous compounds that are known or suspected
to be human or animal carcinogens, and/or have other non-carcinogenic effects when inhaled
(USEPA, 2004). Examples are: acetaldehyde, acrolein, dioxin, furans, polychlorinated
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biphenyls (PCBs), and polycyclic organic matter (USEPA, 2004). While exposure to these
compounds usually occurs via food consumption, inhalation and absorption through the skin
can also occur (BCPHO, 2004). Some dioxins have been classified as carcinogenic, and
some of the other effects of exposure are: skin lesions, liver enzyme changes, damage to the
immune system, and damage to the reproductive system (BCPHO, 2004).
file:///E|/Animal%20Care%20Certificate.html
THE UNIVERSITY OF BRITISH COLUMBIA
ANIMAL CARE CERTIFICATE
Application Number: A06-1548
Investigator or Course Director: Lance Barrett-Lennard
Department: Zoology
Animals:
Whales Southern resident killer whale (Orcinus orca) 85
Start Date: May 1, 2007 Approval Date: March 20, 2007
Funding Sources:
Funding Agency: Vancouver Aquarium Marine Science Centre
Funding Title: BC Wild Killer Whale Adoption Program
Unfunded title: Assessing the health implications of marine engine exhaust gas on southern resident killer whales (Orcinus orca)
The Animal Care Committee has examined and approved the use of animals for the above experimental
file:///E|/Animal%20Care%20Certificate.html (1 of 2) [16/12/2008 2:04:21 PM]
file:///E|/Animal%20Care%20Certificate.html
project.
This certificate is valid for one year from the above start or approval date (whichever is later) provided there is no change in the experimental procedures. Annual review is required by the CCAC and some granting agencies.
A copy of this certificate must be displayed in your animal facility.
Office of Research Services and Administration
102, 6190 Agronomy Road, Vancouver, BC V6T 1Z3 Phone: 604-827-5111 Fax: 604-822-5093
file:///E|/Animal%20Care%20Certificate.html (2 of 2) [16/12/2008 2:04:21 PM]