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Introduction
When oil is spilled into the sea it undergoes a number of physical
and chemical changes; some of which lead to its removal
from the sea surface, while others cause it to persist. The fate
of spilled oil in the marine environment depends upon factors
such as quantity spilled, the oil’s initial physical and chemical
characteristics, the prevailing climatic and sea conditions and
whether the oil remains at sea or is washed ashore (ITOPF 2002).
Water movement quickly breaks this fi lm up into slicks,
which drift on the water surface, separated by areas of open
water, and, for some of the oil, into droplets which are
dispersed in the fi rst few meters of the water column. The
air, wind, light, swell and the water itself affect these slicks
by a combination of physical and chemical processes that are
known as evaporation, emulsifi cation, dissolution, oxidation,
and sedimentation. Aquatic organisms biologically break up
the molecules of certain hydrocarbons, a process known as
biodegradation. Weathering processes of oil in water are shown
detailed in Fig.1 (CEDRE 2005).
Each process is affected by environmental changes in
different periods of time as shown in Fig. 1. As soon as oil
is released into the environment, it undergoes signifi cant
property changes. For example, oil begins to spread as soon
as it is spilled but it does not spread uniformly. Any shear in
the surface current will cause stretching and even a slight wind
will cause a thickening of the slick in the downwind direction
(Lehr et al. 2002).
Evaporation begins as soon as the oil is released. The rate
of evaporation is highest for light oils. Virtually all components
of C12 and below evaporate within half a day (12 hours). For
most crude oils the rate of evaporation is over 50% in the
same period. Hydrocarbon liquid separate from crude oil due
to changes in temperature and pressure about 98% which
is known as condensation process. Since the most toxic
components (e.g. benzene, toluene and xylene) are among these
more volatile fractions, spilled crude quickly loses its toxicity
– often within a few hours. Evaporation is also accelerated by
high wind speed, turbulence, air, sea and oil temperature (DNV
2011). While evaporation reduces the volume of the surface
slick, emulsifi cation increases it. The turbulent energy in the
surrounding water can cause small droplets of water to get
mixed into the oil, forming a water-in-oil emulsion. The amount
of water and water droplet size distribution affect the viscosity
and temporal stability of the emulsion. A fully emulsifi ed,
stable emulsion may contain eighty to ninety percent water
(Lehr et al. 2002). Biodegradation gradually destroys oil spills
and oil seeps by the sequential metabolism of various classes
of compounds present in the oil. When biodegradation occurs
in an oil reservoir, the process dramatically affects the fl uid
properties and hence the value and producibility of an oil
accumulation (Michael 1990).
Mathematical model
In this section some of available algorithms describing
physical and chemical weathering processes are described.
Short period processes are described individually making use
of available physical and environmental data (e.g. wind speed,
oil composition, etc.).
Archives of Environmental Protection
Vol. 00 no. 0 pp. 0–0
PL ISSN 2083-4772
DOI 10.1515/aep-2016-0037
© Copyright by Polish Academy of Sciences
and Institute of Environmental Engineering of the Polish Academy of Sciences,
Zabrze, Poland 2016
Numerical modelling of oil spill in New York Bay
Ali Cemal Toz*, Burak Koseoglu, Cenk Sakar
Dokuz Eylul University, Turkey
Maritime Faculty
* Corresponding author’s e-mail: ali.toz@deu.edu.tr
Keywords: New York Bay, ADIOS, GNOME, numerical modelling, oil spill.
Abstract: New York Bay is one of the most important transition regions of ships trading to east America. The region
plays an important role in the commerce of the New York metropolitan area. The area is surrounded with the coasts
that have various levels of environmental sensitivity. The area accommodates high diversity of native ecosystems
and species that are rather vulnerable in case of oil spill. Thus getting well informed about the likelihood, or fate,
of oil spills around this region is of great importance so that proactive measures can be taken. The purpose of this
study is to investigate the oil spill and predict the future accidents likely to be encountered around the Bay of New
York. Two trajectory models have been conducted for the study. ADIOS (Automated Data Inquiry for Oil Spills),
has been conducted for natural degradation calculations, and, GNOME (General NOAA Operational Modeling
Environment), has been conducted for surface spread simulation. The results gained through these efforts are
hoped to be useful for many organizations dealing with oil spill response operations and contribute to an effective
and effi cient coordination among the relevant institutions.
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Numerical modelling of oil spill in New York Bay 23
Surface spread
Spread of low pour point oil released on water is probably
the most dominant process in the fi rst stage of a spill.
Since spreading strongly infl uences later processes such as
evaporation and dispersion, it is logical to discuss this process
fi rst (Sebastio and Suares 1995). This process describes the
spreading of oil the fi rst few hours after the start of an oil spill
(Vos 2005). Spreading is important in determining the fate of
spilled oil through evaporation, emulsifi cation, and natural
dispersion. Emulsifi cation and evaporation lead to decreased
oil&water density difference, and increased pour point; these
can be used to estimate the cessation of spreading as described
by the classical gravity&viscous equations of Fay and Hoult
(Reed et al. 1999).
The most widely used spreading model is the one developed
by Fay (1969). In the spreading process Fay distinguishes three
phases, each one being determined by the dominant spreading
and retarding forces involved. The fi rst phase is the gravity-
inertial spreading which lasts only a few minutes except for
large spills. The third phase, tension-viscous phase, occurs when
the slick may be dispersed or broken into separate slicks. So it
is common that spill models consider mainly the second phase,
known as the gravity-viscous spreading, for the simulation of
spreading. The spill model programs divide the slick up into
separate Lagrangian elements that are individually transported
by wind stress, surface currents, Fay gravity-viscous forces,
and random turbulence. Following the suggestion of Ahlstrom
(1975), DFay, is given by;
D
Fay= Δp-w
2/1
3/1
2
−
⋅
¸
¸
¹
·
¨
¨
©
§⋅Δ t
Vg
w
w
ν
(1)
Here, Δp-w is the relative oil water density, g is gravitational
acceleration, V is initial spill volume, vw is the kinematic
viscosity of water, and t is the time after spill release. Added
to this diffusion coeffi cient is a second diffusion coeffi cient
designed to represent eddy diffusion of the surface water. Based
on dye studies, Elliot and Hurford (1989) conclude that such
a process is non-Fickian, and that a time dependent diffusion
parameter better represents empirical results (Lehr et al. 2002).
Drifting of oil due to the advection is mainly due to winds and
Fig. 1. Processes acting on spilled oil
Source: ITOPF 2002
a)
b)
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24 A.C. Toz, B. Koseoglu, C. Sakar
currents. Assuming the placing of oil at the air water interface
would not change the shear stress. Hoult (1972) suggested
the wind driven current speed is approximately 3% of wind
velocity. The drift due to tidal currents was simply taken to
be tidal current velocity. When both wind-driven currents and
tidal currents are present, he suggested simply adding the two
vector quantities as shown in Fig. 2 (Soltanpour et al. 2013).
If the oil slick is close to land and the wind speed is less than
10 km/h, the slick generally moves at a rate that is100% of the
surface current and approximately 3% of the wind speed. If
the wind speed is more than 20 km/h, however, and the slick is
on the open sea, wind predominates in determining the slick’s
movement (Fingas 2015).
Probably the most important cause of long term oil
spreading is wind stress on the slick and surface water.
Observations at past spills have resulted in a rule-of thumb that
the oil slick moves at approximately three percent of the wind
speed measured at ten meters above the water surface. Roughly
two thirds of this movement represents stokes drift of the
surface waves. The remaining one represents the movement of
the slick along the water surface (Lehr et al. 2002).
Evaporation
Evaporation is usually the most important weathering process
in the fi rst days immediately following a spill. Evaporation
may be responsible for the loss of one-third to two-thirds
of an oil spill mass within a few hours or a day (Jordan and
Payne 1980). Rapid initial loss of the more volatile fractions
is followed by progressively slower loss of less volatile
components. A number of parameters contribute to the rate of
evaporation of oil on water (Speight and Arjoon 2012).
*** Properties of the oil: Light oil evaporates more rapidly
than heavier oil.
*** Temperature: Higher temperatures increase the rate of
evaporation.
*** Wind speed: Oil evaporates more rapidly with
increasing wind speed.
*** Area of contact of oil with the atmosphere: The greater
the area, the more rapid the evaporation.
Components of spilled oil evaporate at varying rates and
are transported and diluted by atmospheric processes as shown
in Table 1.
Since the rate of spreading depends on the viscosity
of the oil, light oils evaporate more rapidly, due both to an
increase in exposed area and their higher percentage of lighter
components. Estimates of evaporative losses are required in
order to assess the persistence (lifetime) of the spill, and are
also the basis for estimates of changes in oil properties with
time. Simple methods have been widely used, mainly based on
an analytical model proposed by Stiver and Mackay (1984).
If a liquid, of vapor pressure P (Pa), is spilled over an
area of a (m2), the rate of evaporation is given by (Stiver and
Mackay 1984);
N = kaP/(RT) (2)
where N is the molar fl ux (mol/s), ka is the mass transfer
coeffi cient under the prevailing wind conditions (m/s), R is the
gas constant [8.314 Pa·m3/(mol·K)], and T is the environmental
temperature (K). Equation 1 can be arranged to give (Fingas
2013);
Fig. 2. The resultant oil movement’s surface current and wind drift vectors*
Source: Fingas 2015
*Note: The vectors are shown in ‘velocity’
Table 1. Approximate evaporation for various classes of oil
Oil Type 12-Hour Evaporation* 48-Hour Evaporation Total Fraction Evaporated
Group 1 (Gasoline) 50–100% 100% 100%
Group 2 (Diesel) 10–40% 25–80% 100%
Group 3 (Medium Crude) 5–15% 10–25% 35%
Group 4 (Heavy Oils) 1–3% 5–10% 15%
(*) Lower limits are for 5°C and the upper limit for 30°C and a moderate wind speed of 5 m/s
Source: ITOPF 1987 Unauthenticated
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Numerical modelling of oil spill in New York Bay 25
dFv/dt = kaPv/(V0RT) (3)
where Fv is the volume fraction evaporated, t is time (s),v is the
liquid’s molar volume (m3/mol), and Vo is the initial volume
of spilled liquid (m3). Rearranging gives (Jordan and Payne
1980);
dFv = [Pv / (RT)] (kadt / Vo) (4)
or
dFv = H·dθ (5)
The right-hand side of Eq. 4 has been separated into
two dimensionless groups. The group kat/Vo is termed the
“evaporative exposure” and is denoted as θ. The evaporative
exposure is a function of time, the spill area and volume
(or thickness), and the mass transfer coeffi cient (which is
dependent on the wind speed). The evaporative exposure can
be viewed as the ratio of exposed vapor volume to the initial
liquid volume (Fingas 2013).
The group Pv/(RT) or H is a dimensionless Henry’s law
constant or ratio of the equilibrium concentration of the
substance in the vapor phase [P/(RT)] to that in the liquid (l/v).
It is a function of temperature but not of other environmental
conditions (Fingas 2013).
The product Hθ is thus the ratio of the amount which has
evaporated (oil concentration in vapor times vapor volume)
to the amount originally present. If the liquid is pure, H is
independent of Fv, and Eq. 5 can be integrated directly to give
(Stiver and Mackay, 1984);
F
v = Hθ (6)
If ka and temperature are constant, the evaporation rate
is constant and evaporation is complete (Fv, is unity) when θ
achieves a value of 1/H. If the liquid is a mixture, H depends
on Fv and Eq. 5 can only be integrated if H is expressed as
a function of Fv; i.e., the principal variable of vapor pressure is
expressed as a function of composition. The evaporation rate
slows as evaporation proceeds in such cases. If the liquid is
pure, the resistance to mass transfer must lie entirely in the
air phase because there is no necessity for the substance to
diffuse in the liquid phase to the interface. The mass transfer
coeffi cient k is then entirely an air-phase resistance term. If
the liquid is a mixture, it is possible that there is a contributing
liquid-phase resistance, especially if the substance has a high
air-liquid partition coeffi cient (i.e., a high vapor pressure)
or if the liquid is viscous. We assume here that the air-phase
resistance dominates. The other approach is to use a gas
stripping technique with an exit gas rate G (m3/s). If the exit
gas is saturated, the evaporation rate will be GP/(RT) (molls)
and (Fingas 2015);
dFv/dt = [GP/(RT)](v/Vo) (7)
or
dFv = Hdθ (8)
The evaporative exposure θ is now defi ned as Gt/Vo and is
the actual ratio of vapor volume to liquid volume. The identical
nature of Eq.4 and Eq.7 suggests that if surface (tray) and
stripping experimental data are plotted as Fv vs. θ, the points
should lie on a common line, θ being defi ned either as kat/Vo or
Gt/V0 (Stiver and Mackay 1984).
Natural dispersion
Natural dispersion is the dispersion of oil, under the infl uence of
waves, into fi nely divided droplets below the slick. This increases
the total surface area of the oil, and so speeds biodegradation
(Blaikley et. al. 1977). It is expected that the dispersion rate is a
function of the slick thickness, the oil-water interfacial tension, the
oil density and viscosity, the sea state and, particularly, the fraction
of sea covered by breaking waves (Papadimitrakis et al. 2011).
In nearly all of the above references and in various simulation
models of oil slick behavior at sea, use is made of an empirical
expression proposed by Delvinge and Sweeney (1988) for the
rate of oil mass dispersion in the water column, per unit surface
area, caused by the breaking of surface waves. That relationship
is, mainly, characterized by its dependence on the oil type, the
energy of breaking waves lost into turbulence, and the fraction of
sea surface covered by whitecaps, per unit time; the latter (two)
quantities are estimated empirically (Paradimitrakis et al. 2011).
F
WC = Cb (Uw–Uwi)/ Tw (9)
Where the local wind speed Uw is measured at the height
of 10 m above MWL, Uwi represents a wind speed necessary
for the “initiation” of breaking (≈ 5 ms-1), Tw is a characteristic
wave period, and Cb (≈ 0.032 s m-1) is a constant; A semi-
-imperical relation for the energy dissipation per unit surface
area in a breaking event given by:
D
ba≈ 0.0034pwgH2
rms (10)
Where Hrms represents root mean square (rms) value of
the wave height in the wave fi eld (m), pw is water density in
(kg/m3) and g represents acceleration as a result of gravity
(m/s2) (Delvigne 1993).
The close interdependence of oil spill weathering processes
is well known. Many of the advances in our understanding of
weathering over the past decade are rejected in an increased
awareness of these interactions.
Materials and method
The purpose of this study is to provide immediate trajectory
and fate predictions in the event of a real spill incident, and
thereby help priorities oil spill response activities in New
York Bay. This study also aims to perform risk assessments
for important resources (beaches, fi sheries, marine wildlife,
marine parks and other protected coastal areas) in affected
region, and to help develop coastal planning and management.
In this study two softwares have been used to simulate
weathering process of oil. For the analysis of sea circulation
General NOAA Operational Modeling Environment (GNOME)
model, which predicts the fate of past and current oil spills,
has been operated for surface spread simulation. Automated
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26 A.C. Toz, B. Koseoglu, C. Sakar
Data Inquiry for Oil Spills (ADIOS) model has been conducted
for natural dispersion and evaporation calculations. ADIOS
integrates a library of approximately one thousand oils with
a short-term oil fate and cleanup model to help estimate the
amount of time that spilled oil will remain in the marine
environment, and to develop cleanup strategies (NOAA 2012).
ADIOS calculations combine real-time environmental data, such
as wind speed, with chemical and physical property information
in its oil library. The program provides output on oil weathering
parameters such as evaporation, dispersion into the water
column, and changes in oil density and viscosity. Aside from the
oil characteristics, winds and currents are the main factors that
have effects on the spill trajectory (Lighthill 1978).
Study site and experimental design
The New York Bay was selected for model area in this study.
The area is surrounded with the coasts that have different
levels of environmental sensitivity. From ocean beaches to
a maritime forest, freshwater ponds, and salt water marshes,
the area contains many environments with a large variety
of wildlife. For example Raritan Bay is the habitat for over
90 species of fi shes. The shorelines of Raritan Bay host
migratory shorebirds and Neotropical migrant landbirds.
The area accommodates high diversity of native ecosystems
and species that are rather vulnerable in case of oil spill.
Environmental sensitivity map of study area and spill point
are shown in Fig 3 and Fig 4.
Fig. 3. Study Area Map: New York Bay, USA
Source: Google Maps 2014
Fig. 4. Environmental Sensitivity Map: Sandy Hook / New York Bay, USA
Source: NOAA 2001 Unauthenticated
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Numerical modelling of oil spill in New York Bay 27
Heavy industry accelerates development to satisfy the
needs of population for the region, and many industries have
increased their capacities. This development has affected
marine transportation in the region. The ship traffi c in New
York Bay has been increasing day by day. Thus, the risk of
marine pollution is increasing. The one considerable accident
resulted in marine pollution have been noticed in New
York Bay. In 1995 the single-hulled ship almost completed
a voyage from St. Croix in the Virgin Islands to the Port
Reading Terminal, about 20 miles northwest of Sandy Hook,
when she struck a sand ledge two miles east of the region
tearing a hole in the hull and spilling at least 300 barrels of
light oil into water.
Pollutants
Two different types of pollutants have been selected for the
study. The pollutants have different weathering characteristics
while spilled on water, so the risks have a great variety of
different physical and chemical properties. The specifi c
material data of pollutants are shown in Table 2.
It is clear that specifi c properties of pollutants are quite
different as shown in the table. The main weathering process
determinants are quite different and have strong relationship
with each other. For instance, the specifi c gravity which is
the main determinant for spreading, and fl oating process
has a strong relationship with viscosity. Also evaporation
and dispersion processes are interlinked with boiling point,
viscosity and vapor pressure. This relation is specifi ed in terms
of an initial liquid phase boiling point temperature and the
gradient of this boiling point temperature versus the fraction
evaporated (Reed et al. 1999). Evaporation and emulsifi cation
increase the density and viscosity of the slick. Even when
freshly spilled, most oils and oil products are more viscous
than water (Lehr et al. 2002).
Environmental data
The regional environmental data is obtained from long
term observation statistics which was prepared by local
meteorological institution. Two main environmental
determinants (wind and sea current) have been considered
for the study. The speed and direction of regional wind are
identifi ed in accordance with wind frequencies of which
W direction for the year S direction for month of September
have the highest frequency values as shown in Fig. 5.
Fig. 5. Wind direction distribution in (%), (1995–2007)
Source: WF 2014
Table 2. Physical and chemical properties of pollutants
Gasoline Fuel Oil No:6
Specifi c Gravity 0.66–0.75 (Water=1) (60°F) >0.9 to 1.2 g/mL
Boiling Point 26.7–226.7°C 154–372°C
Viscosity at 40°C 0.64 to 0.88 mm²/s >300 cST
Vapor Pressure 345– 1,034 hPa at 37,8°C 210 Pa at 25°C
Source: IARC 1989
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28 A.C. Toz, B. Koseoglu, C. Sakar
GNOME has a location fi le for New York harbor which
contains information about local oceanographic condition. The
wind characteristics at study area between the years of 1995
and 2007 were considered for the model. Two different wind
characteristics (S/8kts-W/9kts) have been considered in the
model. The wind data was put into the model manually and
then software started simulation with the combination of other
related components.
Scenarios and model
Four different scenarios have been identifi ed and conducted
in the model as shown in Table 3. Gasoline and Fuel Oil
have been selected as pollutant in this study due to different
weathering characteristics. The scenarios run with hourly
period with particular environmental data and spill amount.
Every spill scenario starts at the geographical point of Long:
74°00΄W/Lat: 40°30΄N. The model has a run which is based on
the actual hourly weather data. Every scenario is divided into
two sub scenarios regarding the spill amount and run period.
The fi rst and the third scenarios run for gasoline spill and the
second and the fourth scenarios run for fuel oil spill. As for
running scenarios, meteorological data and amount of spill
have been put into the model by user in order to simulate the
weathering process. The summary of studied scenarios with
spill information and meteorological data set are shown in
Table 3.
The GNOME and the ADIOS software have been used to
run scenarios to simulate fate and trajectory of oil spilled from
the tanker located near the Sandy Hook. Main oil characteristics
and related information already installed in the software have
been selected by the user. The GNOME has been operated for
surface spread simulation and the ADIOS has been conducted
for natural dispersion and evaporation process.
Results and discussion
The printouts of each scenario show that the movement of
spill was mostly affected by tidal current and surface wind
forces. Tracking route of oil spill present that area affected
from spill is changing for both scenarios. Most of the Gasoline
evaporates during the fi rst hours due to low viscosity. As fuel
oil is more viscose, the fl oating part is greater than evaporation
and dispersed.
Coney Island is under the risk of contamination in case of
fuel oil spill with westerly wind (S:2a) within 8 hour periods.
However, for a longer period of time spill moves back to the
entrance of Raritan Bay (S:2b) due to southerly tidal current
forces. The scenarios with gasoline spill show that Raritan
Bay is under the risk of contamination due to surface spread
motion. The short period scenario (S:1a) indicates that most
of oil is evaporated and dispersed naturally. However, longer
period model (S:1b) shows that the coasts of the model area is
riskless due to quick natural degradation process.
The study shows that spill amount has also strong
relationship with degradation process. The third and the fourth
scenarios show that greater amount of pollutants are more
resistant to degradation process compared to smaller spills.
The spills with greater amount show that entrance of New
York Bay is under the risk of contamination for short period
model (S:3a/S:4a). The longer period model shows that Coney
Island, South Beach and Breezy Point are under the risk of
contamination in case of greater amount of spill.
Weathering process for each pollutant is quite different
concerning different physical and chemical properties. This
process is directly affected by environmental conditions. Oil
degradation starts instantly when spilt on water. The fi gures
show time dependent weathering process of each pollutant
with different amount.
The fi gures show that only 82% of gasoline (Fig.7a)
and 1% of fuel oil (Fig.7b) is evaporated after 1 hour period.
Gasoline needs approximately 3 hours to evaporate completely
and disappear but fuel oil survives for longer period of time.
After 6 hours’ period, only 1% of fuel oil is naturally dispersed
and 4% of fuel oil is evaporated. Finally, after 6 hours’ period
gasoline is completely degraded but 95% of fuel oil is remained
on water.
The fi gures show that only 76% of gasoline (Fig.7c) and
1% of fuel oil (Fig.7d) is evaporated after 1 hour period. After
2 hours’ period gasoline is completely degraded but 99% of
fuel oil is remained on water. After 6 hours’ period only 1% of
fuel oil is naturally dispersed and 3% of fuel oil is evaporated.
Finally, after 6 hours’ period gasoline is completely degraded
but 96% of fuel oil is remained on water.
Conclusion
In this study it is clearly identifi ed that environmental
factors (wind, current, air temperature, etc.) and pollutant
characteristics (viscosity, boiling point, specifi c gravity, etc.)
are the main determinants of weathering process. The results
Table 3. Summary of studied scenarios
Scenario Oil Type Amount (m3) Wind Data Model Run Period
1a GASOLINE 50 W 9 kts 8 hour
1b 24 hour
2a FUEL OIL 50 W 9 kts 8 hour
2b 24 hour
3a GASOLINE 100 S 8 kts 8 hour
3b 24 hour
4a FUEL OIL 100 S 8 kts 8 hour
4b 24 hour
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Numerical modelling of oil spill in New York Bay 29
Fig. 6a. Scenario 1a (Gasoline/8 hrs) Fig. 6b. Scenario 2a (Fuel Oil/8 hrs)
Fig. 6c. Scenario 1b (Gasoline/24 hrs) Fig. 6d. Scenario 2b (Fuel Oil/24hrs)
Fig. 6e. Scenario 3a (Gasoline/8 hrs) Fig. 6f. Scenario 4a (Fuel Oil/8 hrs)
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30 A.C. Toz, B. Koseoglu, C. Sakar
Fig. 6g. Scenario3b (Gasoline/24 hrs) Fig. 6h. Scenario4b (Fuel Oil/24 hrs)
Fig. 7a. (S:1). Gasoline degradation chart Fig. 7b. (S:2). Fuel Oil degradation chart
Fig. 7c. (S:3). Gasoline degradation chart Fig. 7d. (S:4). Fuel Oil degradation chart
reveal that in case of gasoline spill, with average environmental
conditions, there is no risk of contamination for model area
due to rapid evaporation process. The study also reveals that
in case of fuel oil spill there is big risk of contamination for
Coney Island, Breezy Point and South Beach. In case of big
spillage with S directed wind, the spill reaches a larger area,
and the results reveal that local current conditions are much
more effective than the wind conditions.
Beaching amount of fuel oil is higher than beaching
amount of gasoline so the negative impact on environment is
different. Not only the quantity beached but also type of oil
is the main factor of contamination density. Contamination
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Numerical modelling of oil spill in New York Bay 31
density determines response strategy and cleaning operations.
In this study it is identifi ed that the degradation life time is
dependent on the oil type and viscosity.
This study investigated oil spill fate, which is released
around New York Bay and predicted the fate of future
accidents. The results will be useful for many organizations
related to oil spill response operations. The information can be
used to improve the emergency management systems in order
to protect the human health, coastal management, and marine
environment.
Acknowledgements
The authors would like to thank Dokuz Eylul University
Maritime Faculty and Maine Maritime Academy of USA for
fi nancial support, providing data and supporting this project.
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