Final Report 

Chalk Point Oil Spill of April 7, 2000 in Patuxent River, MD: 

Modeling of the Fates and Acute Biological Effects 

of the Spilled Oil on the Water Column 

by 

Deborah French McCay and Jill Jennings 

 Applied Science Associates 

70 Dean Knauss Drive 

Narragansett, RI 02882 

Voc: 401-789-6224 

Fax: 401-789-1932 

dfrench@appsci.com 

Report to: 

Research Planning Inc 

Columbia, SC 

For Submission to: 

NOAA Damage Assessment Center 

Silver Spring, MD 

ASA 00-148 

April 2002 
i

Table of Contents 

SUMMARY ....................................................................................................................... 1

1. INTRODUCTION......................................................................................................... 2

2. MODEL ALGORITHMS ............................................................................................ 4

2.1 Physical Fates Model ................................................................................................ 4 

2.2 Biological Effects Model .......................................................................................... 6 

2.3 Oil Components and Toxicity................................................................................... 8 

2.4 Quantification of Fish and Invertebrate Injury as Lost Production ........................ 11 

3. INPUT DATA.............................................................................................................. 14

3.1 Geographical and Model Grid ................................................................................ 14 

3.2 Environmental Data ................................................................................................ 16 

3.3 Currents................................................................................................................... 16 

3.4 Oil Characteristics and Toxicity ............................................................................. 17 

3.5 Shoreline Oil Retention........................................................................................... 18 

3.6 Scenario................................................................................................................... 18 

3.7 Summary of Fates Model Inputs............................................................................. 18 

3.8 Biological Abundances ........................................................................................... 20 

4. OIL FATES MODEL RESULTS.............................................................................. 23

5. BIOLOGICAL EFFECTS MODEL RESULTS...................................................... 25

6. SUMMARY OF INJURY QUANTIFICATION ..................................................... 27

7. REFERENCES............................................................................................................ 29
1

SUMMARY 

Applied Science Associates’ SIMAP oil fates and biological effects model was used to 

simulate the Chalk Point oil spill of 7 April 2000 into Swanson Creek, a tributary of the 

Patuxent River, MD. The objectives were to provide (1) an assessment of the pathways 

and fate of the oil, and thus estimate exposure to the water surface, shoreline and other 

habitats, water column, and sediments; and (2) an estimate of injuries to aquatic 

organisms that can be used to scale compensatory restoration. 

The model uses incident specific wind data, current data, and transport and weathering 

algorithms to calculate mass balance in various environmental compartments (water 

surface, shoreline, water column, atmosphere, sediments, etc.), oil slick distribution over 

time (trajectory), and concentrations of the oil components in water and sediments. Wind 

data were hourly speed and direction from a nearby recording station. The tidal current 

data used were sketched in vectors based on published (digital) tidal charts (Tides & 

Currents ®). Output maps of oil slicks, water concentrations, shoreline oiling, and 

sediment contamination may be viewed in an animated time-varying Windows interface 

or as snapshots (figures). 

Using the model input data and average species sensitivity to PAHs, and including the 

field collected mummichogs killed in the marsh, the best estimate of total injury to fish 

and invertebrates is 2,464 kg. This total injury includes: (1) the biomass equivalent of 

the direct kill, equal to 1,485 kg; and (2) future growth of the killed animals, had there 

not been a spill, totaling 979 kg (the production foregone). 

The tidal current data used for the simulation is approximated as a rectilinear twice-daily 

high tide of equal tide height. Wind-driven currents are calculated by the model based on 

the local winds. Freshwater river flow was not included in the simulations. With more 

detail in the current data, the simulated fate of the oil would be more accurate spatially 

and temporally (in the detailed distribution). However, the overall distribution and 

concentrations would not vary much from those provided here. 

The accuracy of the biological injury assessment depends primarily on the accuracy of 

(1) the fates model results and (2) the biological abundance data input to the model. With 

more accurate current data, the fates model and percent mortality results would be more 

accurate, but the percent losses would change by less than an order of magnitude. 

Abundance data for fish and invertebrates assumed in the modeling were as follows: (1) 

age classes > one year of age were based on data compiled by ENTRIX (2002); and (2) 

young-of-the-year (< one year of age) were based on the type A model database (French 

et al, 1996c). The biomass losses are directly proportional to the pre-spill abundance 

assumed in the model inputs. Thus, a change in abundance is directly translated to a 

proportional change in the quantified injury. 
2

1. INTRODUCTION 

April 7, 2000, a mixture of No. 2 and No. 6 fuel oils were spilled into Swanson Creek, a 

tributary of the Patuxent River at Chalk Point, from a pipeline break at the PEPCO power 

plant (responsible parties are PEPCO and ST Services). For the first 24 hours, a strong 

southeast wind held the oil in the saltmarsh at the spill site. Late afternoon on April 8 the 

winds became strong from the northwest, blowing the oil out into the Patuxent River. 

Winds remained strong from the northwest and west for two days, entraining the oil into 

the water and moving it downstream. Oil moved further downstream on subsequent days. 

Modeling allows estimation of the fates of the oil to be completed in three-dimensional 

space and over time. Modeling uses incident specific wind and current data, and welltested transport and weathering algorithms, to calculate mass balance in various 

environmental compartments (water surface, shoreline, water column, atmosphere, 

sediments, etc.), oil slick distribution over time (trajectory), and concentrations of the oil 

components in water and sediments. 

The modeling analysis was performed using a model system developed by Applied Science 

Associates (ASA) called SIMAP (Spill Impact Model Analysis Package). SIMAP was 

developed from the oil fates and biological effects submodels in the Natural Resource 

Damage Assessment Model for Coastal and Marine Environments (NRDAM/CME). The 

NRDAM/CME was developed by ASA for the U.S. Department of the Interior over the 

past 12 years. The NRDAM/CME (Version 2.4, April 1996) was published as part of the 

CERCLA type A Natural Resource Damage Assessment (NRDA) Final Rule (Federal 

Register, May 7, 1996, Vol. 61, No. 89, p. 20559-20614; French et al, 1996a). 

This report describes modeling of the fate and biological effects of the oil from the Chalk 

Point spill. Model results are displayed by a Windows graphical user interface that 

animates the trajectory and concentrations over time. The figures included here 

(appendices) are snapshots taken from that output. 

It should be noted that the model runs were made using approximate current data 

sketched in from tidal charts. Freshwater flow in the river was not included (and so the 

downstream movement will be slightly underestimated). The wind data used were hourly 

wind speed and direction from a nearby measurement station, and so quite accurate. 

Wind-driven currents were estimated in the model based on this wind data. Other data 

inputs were also accurate such that results are not sensitive to the uncertainty of these 

parameters. Thus, the uncertainty and detail of the model simulations is a function of the 

current data used. 

Refinement of the current data would provide a more accurate simulation in the detail of 

the oil movements. The most accurate way to refine the current data would be to use a 

hydrodynamic model that interpolates between measurement points and external forcing 

factors (tides, stream flow, etc.). ASA’s hydrodynamic model is capable of using 

coastline and bathymetry data already set up for the SIMAP oil fates model, and it 
3

outputs a current data file that may be read into the SIMAP model directly. However, the 

quantified injury resulting from this analysis would not be substantially different from the 

results reported herein. 

Section 2 describes the physical fates and biological effects models used for this analysis. 

Section 3 describes the model input data and assumptions. Results of the physical fates 

model are described in Section 4. Section 5 describes the biological impacts and injury 

quantification results. Section 6 summarizes the injury quantification. References cited 

are in Section 7. Appendices provide input data and model results, in tables, maps and 

other figures. 
4

2. MODEL ALGORITHMS 

2.1 Physical Fates Model 

The modeling analysis was performed using a model system developed by Applied Science 

Associates (ASA) called SIMAP (Spill Impact Model Analysis Package). SIMAP 

includes (1) an oil physical fates model, (2) a hydrodynamics model for simulation of 

currents, (3) a biological effects model, (4) an oil physical, chemical and toxicological 

database, (5) environmental databases (winds, currents, salinity, temperature), (6) 

geographical data (in a GIS), (7) a biological database, (8) a response module to analyze 

effects of response activities, (9) graphical visualization tools for outputs, and (10) 

exporting tools to produce text format output. 

SIMAP was developed from the oil fates and biological effects submodels in the Natural 

Resource Damage Assessment Model for Coastal and Marine Environments 

(NRDAM/CME). The NRDAM/CME (Version 2.4, April 1996) was published as part of 

the CERCLA type A Natural Resource Damage Assessment (NRDA) Final Rule (Federal 

Register, May 7, 1996, Vol. 61, No. 89, p. 20559-20614). The technical documentation for 

the NRDAM/CME is in French et al. (1996a,b,c). The model algorithms will only be 

briefly summarized here. 

The physical fates model estimates the distribution of oil (as mass and concentrations) on 

the water surface, on shorelines, in the water column and in the sediments. It is threedimensional, using a latitude-longitude grid for environmental and geographical data. 

Algorithms based on state-of-the-art published research include spreading, evaporation, 

transport, dispersion, emulsification, entrainment, dissolution, volatilization, partitioning, 

sedimentation, and degradation. Oil mass is tracked separately for lower molecular weight 

aromatics (1 to 3-ring aromatics) which cause toxicity in the model, other volatiles, and 

non-volatiles. The lower molecular weight aromatics dissolve from the whole oil and are 

partitioned in the water column and sediments according to equilibrium partitioning theory 

(French et al., 1996a, 1999). 

SIMAP includes the physical fates model in the NRDAM/CME, with several changes and 

additions, summarized below. A complete description of the fates model algorithms is in 

French et al. (1996a) and Appendix A of French (1998a), while French et al. (1999) 

contains documentation of the design and sources of the algorithms and data. Most of the 

additions to prepare SIMAP from the NRDAM/CME were made to increase model 

resolution, allow modification and site-specificity of input data, allow incorporation of 

temporally varying two- or three-dimensional current data, and facilitate analysis of results. 

Thus, most of the additions are to enable changes to the input data (from that provided with 

the NRDAM/CME) rather than to model algorithms. 

Differences between the SIMAP model algorithms and those in the NRDAM/CME are: 

• Evaporation algorithm: The NRDAM/CME is based on Mackay et al. (1980) and 

Payne et al. (1984), while SIMAP uses the empirical formulation in Stiver and 
5

Mackay (1984) and Mackay et al. (1982), with the most volatile fractions evaporated 

first. The results are similar with these two algorithms. 

• Entrained oil droplets: In the NRDAM/CME they are assumed to move horizontally 

with the same surface currents as surface slicks. Surface slicks are transported by 

tidal and background currents from a current data file, plus the added vectors of 

surface wind drift. In SIMAP, the surface wind drift is only applied to entrained 

droplets if they are in the surface wind-mixed layer. SIMAP tracks entrained droplets 

separately from surface slicks. Wind drift may be added as a user-specified, constant 

percentage of wind speed (such as the accepted 3-4% of wind speed, ASCE, 1996), 

with the option of including a drift angle clockwise (in the Northern Hemisphere) of 

the down wind direction. Alternatively, the algorithm developed by Youssef and 

Spaulding (Youssef, 1993; Youssef and Spaulding, 1993, 1994) is used for wind 

transport in the surface wave-mixed layer. 

• Sedimentation of entrained oil droplets: This is not included in the NRDAM/CME 

(which assumes it is zero), but is included in SIMAP. 

• Degradation rates: The NRDAM/CME uses two rates, one for water column oil and 

one for sedimented oil. The rates are in the database. SIMAP uses user input for 

these rates, and allows different rates for low molecular weight aromatics and whole 

oil. 

• Entrainment: In both models, the same entrainment algorithm of Delvigne and 

Sweeney (1988) is used to simulate mixing of oil into the water by wind-driven 

waves breaking on the water surface. The NRDAM/CME assumes the spill occurs on 

the water surface. However, a subsurface release and surf entrainment algorithm has 

been added to SIMAP, also based on the algorithm and data of Delvigne and 

Sweeney (1988). The energy and particle size distribution for various assumed levels 

of turbulence during the release are taken from Delvigne and Sweeney (1988). The 

subsurface release is initialized by the model in a user-defined volume and location in 

the water column. The user also sets the turbulence level of the release. When 

applicable, highly turbulent surf entrainment is specified by the user for a window of 

time after the spill. 

• Calculation of water column concentrations: Both models use Lagrangian particles to 

track the center of mass of sublots of entrained or dissolved oil. Each particle has an 

inferred Gausian (normal) spatial distribution of mass around it, calculated from the 

horizontal turbulent diffusion (randomized mixing) coefficient and the time since the 

mass entered the water column. In the NRDAM/CME, when concentrations are 

calculated, the mass is assumed evenly distributed out to one standard deviation from 

the center in the two horizontal directions. In SIMAP, this distribution is truly 

Gausian (a more accurate representation). In both models, the contributions of mass 

from all submerged particles is summed into a “plume grid”, which is a threedimensional concentration grid quantifying the concentrations in the water at any 

given time. 

• Turbulent diffusion (mixing) coefficients: The NRDAM/CME uses the model of 

Okubo (1971) to relate the horizontal turbulent diffusion coefficient to the length 

scale of the grids used. Because of the large sizes of the grids in the NRDAM/CME, 

the horizontal coefficient defaults to a maximum of 100 m2

/sec. The vertical 

turbulent diffusion coefficient is assumed 1 cm2/sec, except in the surface wind-
6

mixed layer (1.5 times wave height), where it is a function of wind speed. SIMAP 

uses user inputs for the horizontal and vertical coefficients to be used below the windmixed layer, and the same surface wind-mixed layer algorithm. 

• Numbers of Lagrangian particles: The NRDAM/CME uses default numbers of 

particles to represent the oil and biota exposed to oil. In SIMAP the user may set 

these values. 

Thus, the fates models are not significantly different in SIMAP and the NRDAM/CME, 

except as noted above regarding subsurface and surf entrainment, turbulent diffusion 

coefficients, and concentration field calculations. Additional model output formats were 

also added to SIMAP. SIMAP may use site-specific data inputs, whereas in the 

NRDAM/CME, default databases are used. The changes in the databases and model 

defaults, from the NRDAM/CME to SIMAP, cause the largest changes in the results 

produced by the two models. 

2.2 Biological Effects Model 

The biological effects model uses habitat-specific and seasonally-varying estimates of 

fish and invertebrate abundances, and productivity of plant and animal communities at 

the base of the food chain, to determine biological effects resulting from the spill. The 

model performs these calculations by first estimating the portion of a stock or population 

affected. The fractional loss is multiplied by abundance or biomass per unit area to 

quantify an injury as number or kg of biomass lost. 

A rectangular grid of habitats represents the area potentially affected by the spill, with 

each grid cell coded for habitat type. Habitats include various water and shoreline 

environments. The habitat grid matches the grid set up for the physical fates model using 

the GIS database. A contiguous grouping of habitat grid cells with the same habitat code 

represents an ecosystem in the biological submodel. Fish and rates of lower trophic level 

productivity are assumed constant and evenly distributed across an ecosystem within 

each of four seasons. Animals are assumed to move at random within each ecosystem 

during a single season (after that they may move on). Fish eggs, larvae, and juveniles 

(i.e., young-of-the-year) are assumed constant and evenly distributed across each 

ecosystem within each month of an annual cycle. Planktonic stages (eggs and larvae in 

the water column) are moved with the currents. 

Fish and their eggs and larvae are affected by dissolved contaminant concentration (in the 

water or sediment). Mortality is calculated using laboratory acute toxicity test data 

(LC50, concentration lethal to 50% of test individuals) corrected for temperature and 

time of exposure, and assuming a log-normal relationship between percent mortality and 

dissolved concentration. LC50s for the most toxic component of oil, dissolved aromatics, 

are used to define the center of that log-normal function. (See next section.) Movements 

of biota, either active or by current transport, are accounted for in determining time and 

concentration of exposure. Organisms killed are integrated over space and time by habitat 

type to calculate a total percentage killed. 
7

The map of percent mortality is multiplied times abundance to estimate fish and 

invertebrates killed as numbers or biomass (kg). Each species and stage is assigned a 

behavior group: planktonic (move with currents), demersal and stationary (on the bottom 

exposed to near bottom water), benthic (in the sediments and stationary), demersal fish 

(on the bottom exposed to near bottom water and moving slowly), small pelagic fish 

(moving randomly and slowly in the water column), or large pelagic fish (moving 

randomly and rapidly in the water column). The percent mortality of the exposure group 

is multiplied times abundance at the time exposed and in the habitat type to calculate the 

species’ mortality. (See French et al., 1996a for details.) 

Lost production of plants and animals at the base of the food chain is also integrated in 

space and over time using EC50s, the effective concentration to reduce growth to 50% of 

normal, to parameterize a log-normal function of the same form as the mortality function. 

Lost production of fish and shellfish due to reduction or contamination of food supply is 

estimated using a simple food web model. In practice, the direct losses from acute 

mortality are much greater than the food web losses, which typically account for less than 

1% of the estimated injury. 

The biological effects model computes reduction of fish and shellfish population size and 

catch in the present and future years using standard fisheries models. The injury includes 

losses due to mortality of adults, juveniles and young-of-the-year due to the spill. 

Relatively high natural mortality rates of fish eggs and larvae are considered in the 

model, since a high number killed at the time of the spill would have died anyway. 

Young-of-the-year (eggs, larvae, and juveniles less than one year old) of each fish species 

category are tracked as percents of the one-year-old population. Young-of-the-year and 

older age classes are not assumed to necessarily inhabit the same environment 

concurrently, and their losses are calculated separately. 

The biological effects model in SIMAP is the same as for the NRDAM/CME, with the 

following updates: 

• The SIMAP biological effects model accepts site- and event-specific geographical 

and environmental data. Habitat and depth grids may be of fine resolution, as 

needed to accurately map the area of the spill. 

• Current data used to transport planktonic forms is updated as for the fates model, 

i.e., the ability to use hydrodynamic model output in two or three dimensions, 

constant or time-varying. 

• The toxicity parameters (i.e., LC50s) and algorithm is updated to that described in 

French McCay (2001), as summarized in Section 2.3. 

• The biological database may be updated from the default available in the 

NRDAM/CME to reflect site- and event-specific abundances. 

• The injury is quantified as total production lost because of the spill. (See Section 

2.4) 
8

2.3 Oil Components and Toxicity 

Oil is a mixture of hydrocarbons of varying physical, chemical, and toxicological 

characteristics, and therefore, varying fates and impacts on organisms. In a model, oil 

needs to be represented by component categories, and the fate of each tracked separately. 

The “pseudo-component” approach (Kirstein et al. 1987; Payne et al. 1984; French et al. 

1996; Jones 1997) is used, where chemicals in the oil mixture are grouped by physicalchemical properties and the resulting component category’s behavior is modeled as if it 

were a single chemical with characteristics typical of the chemical group. 

The following summarizes the oil toxicity model, OilToxEx, used in SIMAP. The full 

development of OilToxEx and data upon which it is based are in French McCay (2001). 

The most toxic components of oil to water-column and benthic organisms are lowermolecular-weight compounds, which are both volatile and soluble in water, especially the 

aromatic compounds (French et al. 1996; French 1998a, 2000; French McCay 2001). It 

has been shown that toxicity of narcotic organic compounds, such as these lowermolecular-weight aromatics in oil (MAHs and PAHs), is related to the octanol-water 

partition coefficient (Kow), a measure of hydrophobicity (Nirmalakhandan and Speece 

1988; Hodson et al. 1988; Blum and Speece 1990; McCarty 1986; McCarty et al. 1992; 

Mackay et al. 1992a; McCarty and Mackay 1993; Varhaar et al. 1992; Swartz et al. 1995; 

French et al. 1996; French 1998, 2000, French McCay 2001). Chemicals that have a 

narcotic mode of action impact organisms by accumulating in lipids (such as in the cell 

membranes) and disrupting cellular and tissue function. The more hydrophobic the 

compound, the more accumulation in the tissues and the more severe the impact. 

However, the more hydrophobic the compound, the less soluble it is in water, and so the 

less available it is to aquatic organisms. Compounds of log(Kow)>5.6 are considered 

insoluble and so unavailable to aquatic biota (French 2000; French McCay 2001). Thus, 

impact is the result of a balance between bioavailability (dissolved-component exposure) 

and toxicity once exposed. 

The acute toxic effects of narcotic chemicals, including lower molecular weight 

aromatics, is additive (Swartz et al. 1995; French et al. 1996; French 1998, 2000, DiToro 

et al., 2000; DiToro and McGrath, 2000; French McCay 2001). The Toxic Unit (TU) 

model is used to estimate the toxicity of a mixture of narcotic chemicals. A TU is defined 

as the exposure concentration divided by the LC50 (lethal concentration to 50% of 

exposed organisms). For a mixture, the toxic units are additive. When Σ TU = 1, the 

mixture is lethal to 50% of exposed organisms. 

It may be shown (French et al., 1996a, French 2000; French McCay 2001) that the LC50 

of the mixture (LC50mix) is related to the LC50 of each chemical i in the mixture and the 

fractional concentration of chemical i in the total mixture, Fi = Cw,i /( Σ Cw,i), where Cw,i

is the dissolved concentration of chemical i in the water. 

(1) LC50mix = 1 / Σ ( Fi / LC50i ) 
9

The values of Fi may be measured in the field, or if field samples are not available, Fi

may be estimated from the source oil composition. It has been shown that for surface 

waters, where turbulent entrainment of oil has occurred, the values of Fi are nearly 

proportional to the source oil aromatic composition. The values of LC50i can be 

estimated using regression models relating LC50 to Kow (French McCay, 2001). The 95% 

confidence range of this regression provides LC50s for average (50th percentile), 

sensitive (2.5th percentile), and insensitive (97.5th percentile) species. This oil toxicity 

model is used to estimate the LC50 for the dissolved aromatic mixture originating from 

the spilled oil. Only compounds of log(Kow)<5.6 are included in the additive toxicity 

model. 

Toxicity varies with time of exposure, the LC50 decreasing as exposure time increases 

(Sprague, 1969; Kooijman, 1981; McAuliffe, 1987; Anderson et al., 1987; French and 

French, 1989; French, 1991; McCarty et al., 1989, 1992a,b; Mackay et al., 1992a; French 

et al.1996). This is due to the accumulation of toxicant over time up to a critical body 

residue (tissue concentration) that causes mortality. The accumulation is more rapid at 

higher temperature, such that LC50 at a given (short) exposure time decreases with 

increasing temperature. 

The LC50 of an aromatic in the oil mixture varies with exposure time and temperature 

according to: 

(2) LC50∞ = LC50t (1- e-εt

) 

(3) log10(ε) = ε1– ε2 log10(Kow) 

(4) dε / dT = τ T 

where t is time of exposure, LC50t is LC50 at time t, LC50∞ is LC50 at infinite time of 

exposure, Kow is the octanol-water partition coefficient, ε1 =1.47 and ε2 = 0.414, T = 

temperature (C), and τ = 0.11 (French McCay, 2001). 

LC50s for monoaromatics and PAHs from the literature were corrected for time and 

temperature of exposure to calculate LC50∞. The QSAR (Quantitative Structure Activity 

Relationship) regression for narcotic chemicals, specifically the aromatics in oil, was 

developed: 

(5) log10(LC50∞) = log10(φ ) + γ log10(Kow) 

For 278 bioassays on individual aromatics, the slope and intercept of the regression are: 

log10(φ ) = 4.8926 and γ = -1.0878. This QSAR describes the mean response for all 

species (i.e., the response of the average species). The slope of this relationship is 

constant for all species (see DiToro et al., 2000 for theory). The intercept varies by 

species, with 95% of species falling within the range log10(φ ) = 3.9704 (sensitive 

species) and log10(φ ) = 5.8147 (insensitive species). Equation (5) may be used to 
10

estimate LC50∞ for any aromatic, assuming an appropriate intercept for the species of 

concern. 

The SIMAP model takes into account the time and temperature of exposure, using the 

rearrangement of equation (2): LC50t = LC50∞ / (1- e-εt) to correct the LC50. Time of 

exposure is evaluated by tracking movements of organisms relative to toxic 

concentrations (greater than the concentration lethal to 1% of exposed organisms, LC1, 

approximated as 1% of LC50∞). Stationary or moving Lagrangian tracers that represent 

organisms record the concentrations of exposure over time. Exposure time is the total 

time concentration exceeds LC1. The concentration is the average over that time. The 

percent mortality is then calculated using the log-normal function centered on LC50t. 

In the SIMAP fates model (as in the NRDAM/CME, French et al. 1996, 1999), crude oils 

and petroleum products are represented by five components: 

1. MAHs: BTEX (benzene, toluene, ethylbenzene, and xylene) and some of the 

substituted benzenes, which are volatile and soluble in water; 

2. PAHs: 2-4 ring PAHs (and the more hydrophobic substituted benzenes), semivolatile and moderately soluble in water; 

3. non-aromatic volatiles (boiling points <180o

C = 356oF); 

4. non-aromatic semi-volatiles (boiling points 180-380oC); and 

5. a residual fraction, which is neither volatile nor soluble in water (boiling points 

>380oC = 716oF). 

BTEX is very soluble in water, and so exposure concentrations in water can be high. 

However, BTEX is only moderately hydrophobic and so relatively low in toxicity. It is 

also very volatile. Thus, the BTEX rapidly volatilizes reducing exposure concentrations. 

For these reasons, the impact of BTEX after a spill is typically low and of short duration. 

PAHs and many of the alkyl-substituted benzenes are less soluble than BTEX, but do 

dissolve in significant quantities into the water. Thus, they are bioavailable. Because 

they are more hydrophobic than BTEX, they more strongly partition into the lipids in 

membranes and tissues. Thus, they are more toxic and can have significant impacts on 

aquatic organisms. 

Lower-molecular-weight aliphatic hydrocarbons (e.g., alkanes and cycloalkanes with 

boiling points less than about 380o

C) may also contribute to toxicity after an oil spill. 

However, the aliphatics are more volatile (have higher vapor pressure) and less soluble 

than aromatics of the same molecular weight (Mackay et al. 1992b,c,d) and would be 

more readily lost to the atmosphere from surface waters. They are also less toxic than the 

aromatics of similar molecular weight (French 2000; French McCay, 2001). 

The residual fraction in the model is composed on non-volatile and insoluble compounds 

that remain in the “whole oil” that spreads, is transported on the water surface, strands on 

shorelines, and disperses into the water column as oil droplets or remains on the surface 

as tar balls. This is the fraction that comprises black oil, mousse, and sheen. 
11

The LC50mix of the aromatic mixture is calculated using equation (1), including those 

aromatics that are measured in the oil and dissolved in the water (with log(Kow)<5.6) for 

long enough for exposure to aquatic organisms to be significant. Typically, only the 

PAHs are dissolved in sufficient quantity and remain in the water long enough for their 

TU values to be significant. The biological effects model uses the calculated ΣPAH (or 

ΣBTEX + ΣPAH if BTEX is significant) and the estimated LC50mix, corrected for time 

and temperature of exposure, to estimate mortality to aquatic biota. Typically, the 

appropriate LC50mix is for average sensitivity for most species, as specific data are not 

available for all species. However, for certain sensitive species the 2.5th or 97.5th

percentile LC50mix is more appropriate. Categorization of species as sensitive, average or 

insensitive is based on bioassay data reviewed in French McCay (2001).

2.4 Quantification of Fish and Invertebrate Injury as Lost Production 

The biomass (kg) of animals killed represents biomass that had been produced before the 

spill. In addition to this injury, if the spill had not occurred, the killed organisms would 

have continued to grow until they died naturally or to fishing. This lost future (somatic) 

production is estimated and added to the direct kill injury. The total is the total production 

lost. 

The loss is expressed in present day (i.e., present year) values using a 3% annual discount 

rate for future losses. Restoration should compensate for this loss. The scale of 

restoration needed is equivalent to production lost when both are expressed in values 

indexed to the same year (i.e., the present year). 

 

The injury to be compensated is the interim loss. Interim losses are injuries sustained in 

future years (pending recovery to baseline abundance) resulting from the direct kill at the 

time of the spill. Interim losses potentially include the following: 

Lost future uses (ecological and human services) of the killed organisms themselves; 

Lost future (somatic) growth of the killed organisms (i.e., production foregone, which 

provides additional services);Lost future reproduction, which would otherwise recruit to 

the next generation. 

The approach here is that the injury includes the direct kill, plus the lost somatic growth 

of the killed organisms, both of which would have provided services. Because the impact 

on each species, while locally significant, is relatively small compared to the scale of the 

total population in the area (e.g., as estimated in French et al, 1996c), it is assumed that 

density-dependent changes in survival rate are negligible, i.e., changes in natural and 

fishing mortality of surviving animals are assumed not to compensate for the killed 

animals during the natural life span of the animals killed. 

It is also assumed that the injuries were not large enough to significantly affect future 

reproduction and recruitment in the long term. It is assumed that sufficient eggs will be 

produced to replace the lost animals in the next generation. The numbers of organisms 

affected, while locally significant, are assumed to be relatively small portions of the total 

reproductive stock. Given the reproductive strategy of the species involved to produce 
12

large numbers of eggs, of which only a few survive, it is assumed that density-dependent 

compensation for lost reproduction occurs naturally. 

The services provided by the injured organisms are measured in terms of production, i.e., 

biomass (kg wet weight) directly lost or not produced. Among other factors, services of 

biological systems are related to the productivity of the resources, i.e., to the amount of 

food produced, the usage of other resources (as food and nutrients), the production and 

recycling of wastes, etc. Particularly in aquatic ecosystems, the rate of turnover 

(production) is a better measure of ecological services than standing biomass (Odum, 

1971). Thus, the sum of the standing stock killed (which resulted from production 

previous to the spill) plus lost future production is a more appropriate scaler, as opposed 

to standing stock alone (as number or kg), for measuring ecological services. 

This injury estimation method was developed and used previously in the injury 

quantification for the North Cape spill of January 1996 (French, 1998a,b,c; NOAA, 

1998). The method makes use of the population model in the NRDAM/CME and 

SIMAP. Injuries are calculated in three steps: 

 

1. The direct kill is quantified by age class using a standard population model used 

by fisheries scientists. 

2. The net (somatic) growth normally to be expected of the killed organisms is 

computed and summed over the remainder of their life spans (termed lifetime 

production). 

3. Future interim losses are calculated in present day values using discounting at a 

3% annual rate. 

The normal (natural in local waters) survival rates per year and length-weight by age 

relationships are used to construct a life table of numbers and kg for each annual age 

class. Lifetime production is estimated as the sum of the net (somatic) growth normally to 

be expected of the killed individual over the remainder of its life span. The age-class 

specific weight gain per year times percent expected to be left alive by the end of that 

year is summed over all years to calculate total lifetime production. Growth in future 

years is discounted 3% annually. 

More specifically, lifetime production is calculated as follows. The number of animals 

that would have lived the following year (percent survival times initial number killed) is 

multiplied times average weight gain for that age class to the next annual age class; those 

surviving to the following year are multiplied times that next year’s weight gain; and so 

on, until the end of the species life span. In addition, discounting of future losses is 

applied at a 3% annual discount rate. Thus, next year’s values are 97% of present-year 

value, the following year’s value is 94% of present value, etc. 

The equations to calculate lifetime production are as follows. The lifetime production 

expected from a single age class i is: 

(6) ∑ Ni,y Si (Wi+1 – Wi) / (1+d)y
13

 y 

and for all age classes is 

(7) ∑ ∑ Ni,y Si (Wi+1 – Wi) / (1+d)y

 i y 

with number by age being calculated as 

(8) Ni+1,y+1 = Ni,y Si = Ni,y e-(Mi

+F

i

)

Ni,y is the number of age class i remaining alive at the beginning of year y, Si is the 

portion of age class i surviving to class i+1, Wi is the weight per individual for age class 

i, and d is the discount rate (=0.03). 

Survival rates include accounting for natural and fishing mortality. Fishing mortality is 

applied (at 100%) beginning at the age of recruitment to the fishery. Annual survival is 

calculated as e-(M+F), where M = instantaneous annual natural mortality rate and F = 

instantaneous annual fishing mortality rate. Before the age of recruitment, fishing 

mortality F=0. 

The relationships of weight and length to age are based on standard fisheries models, as 

used in French et al. (1996a). For growth by individuals, the von Bertalanfy equation (a 

variation of the Brody growth equation, Ricker, 1975) is used: 

(9) Lt = L∞ [ 1 – exp( -K (t- to)) ] 

where Lt is length (cm) at age t, L∞ is length at ∞ (at an infinite age, i.e., the asymptotic 

maximum length), K is a constant called the Brody growth coefficient, and to is a constant 

representing the age at zero length. To calculate weight from length, 

(10) Wt = a Lt

b 

where Wt is weight (g, wet) at age t years and a and b are constants. 

The result of these calculations is the present-day value of the direct kill plus future 

interim losses. Total injuries are measured as kg (wet tissue weight) of kill and 

production lost. It should be noted that compensation is needed for lost production of 

each of the individual species injured, and that losses are additive. 

Discounting at 3% per year is included to translate losses in future years (interim loss) to 

present-day values. The discounting multiplier for translating value n years after the spill 

to present value is calculated as (1+d)-n = 1/(1+d)n

, where d=0.03. Thus, the losses in 

future years have a discounted value in the present. In this report, all discounting is 

calculated based on the number of years from the year of the spill. The present day is 

considered the year of the spill. 
14

3. INPUT DATA 

3.1 Geographical and Model Grid 

For geographical reference, SIMAP uses a rectilinear grid to designate the location of the 

shoreline, the water depth (bathymetry), and the shore or habitat type. The grid is 

generated from a digital coastline using the ESRI Arc/Info compatible Spatial Analyst 

program. The cells are then coded for depth and habitat type. Note that the model 

identifies the shoreline using this grid. Thus, in model outputs, the coastline map is only 

used for visual reference; it is the habitat grid that defines the actual location of the 

shoreline in the model. 

The digital shoreline, shore type, and habitat mapping was obtained from the ArcView 

Environmental Sensitivity Index (ESI) coverage for Patuxent River area. The gridded 

habitat type data are shown in Appendix A-2. The grid scale resolution is a cell size of 33 

m E-W by 20 m N-S. 

Within a grid, habitats are designated as landward or seaward. Landward portions are the 

inlets or shallower portions of the grid. The seaward portion is the main part of the water 

body. This designation allows different biological abundances to be simulated in landward 

and seaward zones of the same habitat type (e.g., open water with sand bottom). The 

biological database is coded to landward or seaward by species (see French et al., 1996a, c). 

(See Section 3.8 below for explanation of assignment of species abundances to seaward = 

open water and landward = shoal waters.) 

Ecological habitat types (Table 3-1) are broadly categorized into two zones: intertidal and 

subtidal. Intertidal habitats are those above spring low water tide level, with subtidal being 

all water areas below that level. Intertidal areas may be extensive, such that they are wide 

enough to be represented by an entire grid cell at the resolution of the grid. These are 

typically either mud flats or wetlands, and are coded 20 (seaward mudflat), 21 (seaward 

wetland), 50 (landward mudflat) or 51 (landward wetland). All other intertidal habitats are 

typically much narrower than the size of a grid cell. Thus, these fringing intertidal types 

(indicated by F in Table 3-1) have typical (for the region, French et al., 1996a) widths 

associated with them in the model. Boundaries between land and water are fringing 

intertidal habitat types. On the waterside of fringing intertidal grid cells, there may be 

extensive intertidal grid cells if the intertidal zone is extensive. Otherwise, subtidal habitats 

border the fringing intertidal.
15

Table 3-1. Classification of habitats. Seaward (Sw) and landward (Lw) system codes are 

listed. (Fringing types indicated by (F) are only as wide as intertidal zone in that 

province. Others (W = water) are a full grid cell wide and must have a fringing type on 

the land side.) 

Habitat 

Code 

(Sw,lw) 

 Zone Ecological Habitat F or W 

1,31 Intertidal Rocky Shore F 

2,32 Gravel Beach F 

3,33 Sand Beach F 

4,34 Fringing Mud Flat F 

5,35 Fringing Wetland (Saltmarsh) F 

12,42 Subtidal Sand, silt, and/or mud bottom W 

18,48 Intertidal Man-made, Artificial F 

20,50 Extensive Mud Flat W 

21,51 Extensive Wetland (Saltmarsh) W 

The intertidal habitats were assigned based on the shore types in digital Environmental 

Sensitivity Index (ESI) maps distributed by NOAA HAZMAT (CD-ROM) and supplied 

to ASA by Research Planning Inc. (RPI). This data was gridded using the ESRI Arc/Info 

compatible Spatial Analyst program. Open water areas were defaulted to general soft 

bottom, as open water bottom type has no influence on the model results. Where data are 

missing, shore types are defaulted as in Table 3-2. Habitats in the shallower portions of 

the river and creeks are designated as landward. 

Table 3-2. Default fringing intertidal habitat type, given adjacent subtidal or extensive 

intertidal habitat type. 

Subtidal or Extensive 

Intertidal Habitat 

Fringing Intertidal Habitat 

Seagrass Bed (47) Sand Beach (33) 

Subtidal Sand Bottom (41) Sand Beach (33) 

Extensive Mudflat (50) Fringing Mudflat (34) 

Extensive Wetland (51) Fringing wetland (35) 

Depth data were obtained from Hydrographic Survey Data supplied on CD-ROM by the 

U.S. Department of Commerce, National Oceanic and Atmospheric Administration, 

National Geophysical Data Center. Hydrographic survey data consist of large numbers 
16

of individual depth soundings. The depth soundings were gridded using the ESRI 

Arc/Info compatible Spatial Analyst program. The gridded depth data are shown in 

Appendix A-3. 

3.2 Environmental Data 

The model uses hourly wind speed and direction for the time of the spill and simulation. 

Wind data (hourly speed and direction) are from Thomas Pt. MD near Annapolis (NOAA 

station TPLM2). Wind speed and direction data are listed in Appendix C. 

Temperature is 14o

C and salinity is 8 ppt, based on measurements in the Patuxent River 

on Apr. 4, 2000, east of mouth of Indian Creek (data provided by the Trustees). Water 

temperature affects evaporation rate, and so surface oil volume, but not the trajectory of 

the spill. The effect of water temperature within the range of a few degrees Celsius is 

insignificant. Salinity has little influence on the fate of the oil. 

Suspended sediment is assumed 10 mg/l, a typical value for coastal waters (French et al., 

1996a). The sedimentation rate is set at 1 m/day. These default values have no 

significant affect on the model trajectory, so their values are not explored further. 

 The horizontal diffusion (randomized mixing) coefficient is assumed 1 m2

/sec. The 

vertical diffusion (randomized mixing) coefficient is assumed 0.0001 m2/sec. These are 

reasonable values based on Okubo (1971) and modeling experience. The vertical 

diffusion coefficient used keeps the water column well mixed, and so variation of this 

parameter has no significant impact on the results. 

3.3 Currents 

3.3.1 Tidal and Other Currents 

Currents have significant influence on the trajectory, and are important data inputs. Tidal 

currents were manually interpolated from tidal chart vectors in a commercial (digital) 

tidal data program (Tides & Currents ®). They are assumed rectilinear (in and out equal 

and opposite) and with two equal high tides per 25 hrs (M2). Downstream freshwaterdriven river flow was not included. Appendix B contains maps of the resulting current 

data used in the oil model simulations. 

3.3.2 Wind-driven Surface Currents 

Wind-driven surface currents are calculated within the SIMAP fates model, based on 

local wind speed and direction. Surface wind drift of oil has been observed in the field to 

be 1-6% (average 3-4%) of wind speed in a direction 0-30 degrees to the right (in the 

northern hemisphere) of the down-wind direction (ASCE, 1996). 

Wind drift speed and angle were studied in detail by Youssef and Spaulding (Youssef, 

1993; Youssef and Spaulding, 1993, 1994). Wind drift speed is a percentage of wind 
17

speed over the water, highest at low wind speed and decreasing as wind speed increases. 

The range of drift speed for winds up to 20 kts (averaged over time) is 2-4% of wind 

speed. At 10 kts or less, which prevailed during the spill event, the percent of wind speed 

is about 3.5-4% at the water surface, decreasing to 2% at 0.1m below the surface. The 

angle to the right of down wind is highest at low wind speed, on the water surface 

ranging from about 20o

-30o at 10 kts or less. The drift speed decreases, and the drift angle 

increases, deeper into the water column. 

Youssef and Spaulding (Youssef, 1993; Youssef and Spaulding, 1993, 1994) developed a 

set of equations to describe the percent of wind speed and angle as functions of wind 

speed and depth in the water. This algorithm has been incorporated into SIMAP. The 

wind drift is applied to the upper 5 meters of the water column. This Youssef and 

Spaulding algorithm was used in model runs for surface wind drift. 

3.4 Oil Characteristics and Toxicity 

The spill was of a mix of No. 6 and No. 2 fuel. Oil characteristics were specified using 

measurements of the source oil, as available. Other characteristics were assumed to be as 

for defaults from the NRDAM/CME database (French et al., 1996b). As a mix of No. 6 

and No. 2 fuel would be closest to No. 5 fuel (of those available in the NRDAM/CME 

database), characteristics of No. 5 fuel were used as default data. Only the percentage of 

soluble aromatics in the fuel has a significant impact on the model results. 

The needed evaporation constants are not from the NRDAM/CME database (French et 

al., 1996b) (nor were they measured on the source oil). The A, B, TG and To constants for 

the Stiver and Mackay (1984) evaporation algorithm (equations 10-11 therein) were 

calculated from boiling curve data for the most similar fuel (bunker) obtained from 

Environment Canada’s oil catalog (Whiticar et al., 1992). TG is the gradient of the 

distillation curve; To is the initial boiling point of the distillation curve; and A and B are 

constants (see Stiver and Mackay, 1984). 

The percentage of aromatic volatiles has a significant influence on the model results. The 

volatiles evaporate and dissolve, decreasing the surface oil volume over time and 

inducing water column contamination. The aromatic content was as measured and 

reported by ENTRIX for the source oil sample: 3.348% PAH with log(kow)<5.6 (which 

is bioavailable and so the toxic fraction). BTEX was not measured in the source oil, but 

is assumed to have negligible impact on water column organisms because of its high 

volatility. (See discussion in Section 2). 

To estimate LC50mix values for dissolved PAHs in the water, equation (1) is used with 

LC50s calculated from QSARs (given in Section 2.3) and Fi values calculated from 

dissolved concentrations of aromatics (with log(Kow<5.6) in water samples taken in the 

field after the spill. Table 3-3 summarizes the measurements of parent and alkylsubstituted PAHs in the water samples. The LC50mix values based on the water samples 

are similar to those in Table 3-3 where dissolved aromatics are assumed in the same 

proportions as in the source oil. PAH concentrations in the source oil are based on an 
18

April 19, 2000 sample from the pipeline, as reported by ENTRIX (2000, Appendix D, 

Table D-2). If dissolved concentrations are assumed to be at equilibrium with the source 

oil, the calculated LC50s (223 ppb for average and 27 ppb for sensitive species) are not in 

agreement with those based on the measured dissolved samples (Appendix D, Table D3). Thus, equilibrium is not indicated, rather the dissolved concentrations are in similar 

proportions to the source oil concentrations. 

The geometric mean of the LC50mix values for species of average sensitivity is 75 ppb. 

The lowest LC50 for sensitive species (2.5th percentile species) is about 5 ppb. Thus, two 

LC50s were run in the biological effects model: 5 ppb and 75 ppb to estimate injuries for 

species of average sensitivity to PAHs and for sensitive species. 

3.5 Shoreline Oil Retention 

Retention of oil on a shoreline depends on the shoreline type, width and angle of the 

shoreline, viscosity of the oil, the tidal amplitude, and the wave energy. In the 

NRDAM/CME (French et al., 1996a,b), shore holding capacity was based on 

observations from the Amoco Cadiz spill in France and the Exxon Valdez spill in Alaska 

(based on Gundlach (1987) and later work summarized in French et al., 1996a). These 

data are used here. The shore width (intertidal zone width) was assumed 1 m. 

3.6 Scenario 

The following conditions were assumed. The spill site was at the Pepco pipeline in 

Swansons Creek at 76o

 42.097’ W, 38o 32.6555’ N. The release occurred at the water 

surface. The spill was instantaneous on April 7, 2000 at 17:30 EDT. The volume 

released is estimated at 120,000 gallons. 

3.7 Summary of Fates Model Inputs 

Appendix D contains a list of model inputs for the SIMAP physical fates model. Note 

that some of the inputs are either not applicable to this case or are not used for the 

trajectory and fate analysis of this spill. 
19

Table 3-3. Measured PAH concentrations in water samples [PAH], calculated LC50s using QSARs, and TU = toxic units = PAH concePAHs with log(Kow) < 5.6 are included in [PAH] and in calculation of LC50 (for infinite exposure time) using QSARs. The range of sshown: ave.spp. is the mean QSAR including data for all species. Sensitive and insensitive are the extremes for 95% of species. LC50s oil are also given, assuming high entrainment such that oil-in-water dispersions were formed (as indicated by the high winds after the rSample Date Time Location [PAH] μg/L LC50 (μg/L) 

Ave. sp. 

LC50 (μg/L) 

Sensitive 

LC50 (μg/L) 

Insensitive 

TU Ave. sp. T

SW-2 4/10/00 1400 Swanson Creek 167.007 54 6 453 3.078 SW-17 4/10/00 1120 Swanson Creek 0.087 110 13 915 0.001 SW-18 4/10/00 1745 Swanson Creek 1.003 84 10 700 0.012 SW-19 4/10/00 1040 Swanson Creek 458.376 43 5 357 10.742 SW-20 4/10/00 1530 Swanson Creek 1.851 57 7 476 0.033 SW-21 4/10/00 1610 Swanson Creek 16.935 92 11 772 0.183 SW-24-3 4/12/00 1420 Cat Creek 0.187 87 10 724 0.002 SW-25-6 4/12/00 1436 Cat Creek 0.221 101 12 842 0.002 SW-26-9 4/12/00 1453 Cat Creek 1.146 135 16 1128 0.008 SW-27-12 4/12/00 1557 Persimmon Creek 6.436 52 6 437 0.123 SW-28-15 4/12/00 1614 Persimmon Creek 7.096 67 8 557 0.106 SW-29-18 4/12/00 1643 Persimmon Creek 3.897 75 9 629 0.052 SW-30-21 4/12/00 1710 Washington 

Creek 

0.612 90 11 753 0.007 

SW-31-24 4/12/00 1731 Washington 

Creek 

0.612 90 11 753 0.007 

SW-32-27 4/12/00 1746 Washington 

Creek 

2.628 69 8 574 0.038 

SW-33-30 4/12/00 1756 Washington 

Creek 

2.278 67 8 557 0.034 

SW-34-33 4/12/00 1814 Washington 

Creek 

2.973 69 8 573 0.043 

PR-SC-01-A 4/13/00 1610 Patuxent R. for 

bioassay 

25.616 60 7 502 0.427 

All Geometric mean 2.958 75 9 624Source oil 4/19/00 Pepco Pipeline 

μg/kg = 

 33,479,400 45 5 378 
20

3.8 Biological Abundances 

The model uses average number or biomass per unit area (#/km2

) in appropriate habitats 

(French et al., 1996a,c). The species is assigned to a behavior category according to its 

location and movements in the water column (i.e., small pelagic, large pelagic or 

demersal). The species is assumed uniformly distributed across its preferred habitats and 

vertically in the zone it occupies. Thus, the habitat grid defines the habitat map, and so 

the abundance of each species. Demersal species remain within 1 m of the bottom, while 

pelagic species move throughout the water column at slow (small pelagics) or fast (large 

pelagics) swimming speeds. 

In the model, fish and invertebrates abundance varies by landward open water, seaward 

open water, and structured habitat (i.e., wetlands, reefs, and macroalgal beds, Table 3-1). 

In the biological database input to the model, the abundances are for fished stocks and the 

biomass includes those animals greater than the age of recruitment to fishing (or one year 

of age for small fishes such as killifish and silversides). Within the biological effects 

model, the age/size distribution is computed from fishery modeling parameters (natural 

and fishing instantaneous mortality rates, length as a function of age, and weight-length 

relationships), such that the mortality is calculated for all age classes from age 1 year up 

(and assuming the various age classes live in the same habitat in that age structure). 

Young-of-the-year mortality is quantified separately. The biological database includes 

number of age 1-year (365 day old) individuals per km2

. Thus, young-of-the-year 

mortality is for only those that would have survived their first year if not for the spill. 

For this assessment, the species abundances for April 2000 (Table 3-4) were assumed to 

be as described in ENTRIX (2002). Open water abundances apply to >1.6 m water, 

designated as “seaward” in the model. Shoreline/shoal abundances apply to <1.6 m 

water, designated as “landward” or “structured” (=wetland) in the model. 

The life history parameters (growth, mortality) for these species were assumed to be as 

included in the NRDAM/CME (French et al., 1996c). The life history parameters are 

specified by species category (generally family or similar taxa, identified by codes with 

the same value in hundreds), using data for one representative species in the group. 

Thus, for example, life history parameters for white perch are assumed to be as for 

striped bass, and Atlantic silverside life history parameters are used for all species in their 

group (inland silverside, striped killifish, mummichog, spottail shiner, and less common 

finfish). 
21

Table 3-4. Abundance estimates of fish and invertebrates for April 2000 (ENTRIX, 

2002). 

Model 

Code 

Species Open Water 

Standing Stock 

kg/km2

Shoreline/Shoal 

Standing Stock

kg/km2

102 Bay anchovy .04 .04

302 Blueback herring .01 .01

405 Atlantic menhaden 470 470

1001 Atlantic silverside 5.4 5.4

1009 Striped killifish - 1.2

1010 Mummichog - 4.5

1011 Spottail shiner - .07

1012 Inland silverside - .02

1013 Less common finfish species 54 6.7

3101 Striped Bass (adult) 269 269

3101 Striped Bass (subadult) 1.8 1.8

3102 White Perch 2,271 341

3303 Atlantic Croaker 1,369 -

3401 American eel 432 7.4

3530 Hogchoker 1,932 32

4504 Brown bullhead 11 11

4601 Blue crab 351 351

4822 Horseshoe crab 163 163

5901 Oysters (ash free dry weight) 437 -

Young-of-the-year abundance estimates were assumed to be as those derived in the 

NRDAM/CME database (French et al., 1996c). The NRDAM/CME contains mean 

monthly abundances for 77 biological provinces in US coastal and marine waters. The 

data for the upper Chesapeake Bay province (16) in April were used. Presence of youngof-the-year in the Patuxent River in April was confirmed based on Stone et al. (1994). 

Table 3-5 shows the species that were used in the young-of-year calculations, and the 

stages in which these species have been found to occur in April in the Patuxent River. 

Those species noted by Stone et al. (1994) as present as juveniles are assumed to include 

individuals less than 1 year in age, based on the duration of the larval stages for the 

species of concern (French et al, 1996c). Abundance and weight per individual are in 

Table 3-6. Note that the young-of-the-year abundances are for those animals that would 

survive to 365 days of age, but the model treats them as behaving according to their age 

and stage in April (see French et al., 1996a for further explanation). 

White perch young-of-the-year abundance estimates were not included in the 

NRDAM/CME database (French et al., 1996c). Thus, white perch young-of-the-year 
22

were estimated from striped bass young-of-the-year, using the ratio of abundance for the 

two species in the ichthyoplankton surveys performed in the Patuxent River in 2000 

(ENTRIX, 2002). The ratio was 4.481 based on 16,310 white perch collected versus 

3,640 striped bass young-of-the-year. 

Table 3-5. Young-of-the-year species used in the SIMAP biological model, and the 

stages in which these species occur in April in the Patuxent River (Stone et al., 1994). 

Species Present as 

Eggs 

Present as 

Larvae 

Present as 

Juveniles 

River 

herring 

(blueback) 

* * 

Striped bass * * * 

White perch * * * 

Atlantic 

croaker 

 * 

Hogchoker * * * 

Blue crab * 

Table 3-6. Young-of-the-year abundance (as age 365-day equivalents) and weight per 

individual at 365 days of age (French et al., 1996c; province 16 therein) used as input to 

the model runs. 

Species Abundance 

in Water 

<1.6m 

(#/km2) 

Abundance 

in Water 

>1.6m 

(#/km2) 

g per 

individual 

River 

herring 

(blueback) 

3.36 0 47 

Striped bass 14.27 7.84 12 

White perch 63.94 35.11 12 

Atlantic 

croaker 

1265 1265 120 

Hogchoker 2.51 2.43 50 

Blue crab 34,470 0 215 
23

4. OIL FATES MODEL RESULTS 

Modeling of the trajectory and fate of the oil was performed using SIMAP, varying the 

horizontal turbulent diffusion coefficient (1, or 5 m2

/sec) to evaluate sensitivity to this 

assumption. The fates model results of surface oil were visually compared to observed 

slick locations (e.g., from overflights), scat reports, shoreline oiling maps, and other field 

data, as available (ENTRIX, 2000). It was found that the model was not sensitive to this 

assumption, but that 1 m2

/sec provided a better fit to the observations. 

Appendix F contains trajectory plots. The oil trajectory snapshots show the modeled 

“spillet” centers of mass for surface oil (black), subsurface entrained oil droplets (blue), 

and dissolved aromatics (PAHs, green). These figures do not weigh the dots for mass, 

and the dots are of varying mass. 

Total hydrocarbon concentrations, including the slicks on the water surface, are plotted in 

Appendix G. The maps show total hydrocarbons on and in the water after the spill. 

Concentrations are calculated for a grid (50 X 50 cells horizontally, 5 layers vertically) 

sized to just cover the plume at the time of the output. The surface layer of that grid 

contains the surface slicks and entrained oil immediately under the slick (near the water 

surface). The maps show the vertical maximum concentration at each (latitudelongitude) location, and thus the surface slicks. The cross-section inserts on the maps 

show subsurface concentrations along the dotted line in the planar view map. 

In this case, each of the 5 vertical layers is 0.2m thick. Thus, 1 ppb = 1 mg/m3 = 0.2 

mg/m2 for slicks in the surface layer. Table 4-1 gives approximate thickness ranges for 

surface oil of varying appearance. Dull brown sheens are about 1000 mg/m2

 thick. 

Rainbow sheen is about 200-800 mg/m2 and silver sheens are 50-800 mg/m2 thick (NAS, 

1985). Thus, only >1000 ppb would be visible. 

Table 4-1. Oil thickness (microns ~ g/m2

) and appearance on water (NAS, 1985). 

Minimum Maximum Appearance 

0.05 0.2 Colorless and silver sheen 

0.2 0.8 Rainbow sheen 

1 4 Dull brown sheen 

10 100 Dark brown sheen 

1000 10000 Black oil 

Appendix H shows the amount of oil accumulated on shorelines and sediments for the 

simulation, as mass of total hydrocarbons per unit area (averaged in each habitat grid 

cell). No shoreline cleanup was simulated in the model. Thus, oil simply accumulates 

and remains on the shore. 
24

The oil trajectory (Appendix F) agrees with the field observations, as described in several 

documents and indicated by the shoreline oiling maps (ENTRIX, 2000). The following 

description of the fate of the oil is based on the model trajectory and driving forces 

(wind). From April 7 at 17:30 until April 8 at 15:30, the wind was strong and from the 

southeast, holding the oil in the marsh near the pipeline break. From April 8 at 15:30 until 

April 10 at dusk, strong northwest and west blew the oil out of Swanson Creek across the 

Patuxent River to Buena Vista. The strong winds also entrained oil into the water (based 

on the entrainment data from Delvigne and Sweeney, 1988), increasing the aromatic 

concentrations in the water. The aromatics (PAHs, Appendix I) were swept downstream 

and up and down the river with the tides. Overnight April 10-11 and through April 11, the 

oil was blown to the west bank and downstream by north and northeast winds. The 

trajectory shows more oil in Indian Creek than Trent Hall Creek. However, the addition 

of some freshwater flow to the simulation would move the oil downstream slightly and 

more into Trent Hall Creek and southward. By the afternoon of April 12, the simulation 

shows oil reaching Cats Creek and Jack’s Bay. 

Dissolved PAH concentrations in the water are shown in Appendices I and J. The modelpredicted PAH concentrations (Appendix I) generally agree with the data in Table 3-3 in 

magnitude of concentration. The only concentrations >10 ppb are in Swanson's Creek 

(before April 10) and spottily in the Patuxent River (10-100 ppb). Persimmon and 

Washington Creek areas are 1-10 ppb on April 12. The details of the PAH peaks in the 

simulation will not line up with sample measurements exactly, as the current data used in 

the modeling are not of sufficient accuracy to predict concentrations in fine resolution. 

Note that the magnitude of concentrations and water volumes contaminated at each level 

are the influential factors in the injury quantification. The exact locations of PAH peaks 

do not influence the results, as the aquatic biota are assumed uniformly distributed across 

the habitats they occupy. Thus, the distribution of contamination between habitat 

categories where abundance varies is the important factor to the resulting quantification 

of injuries. 

Appendix E shows the mass balance of oil in the simulation. The graph shows, as a 

function of time since the release start, percent of total mass spilled on the water surface, 

in the water column, on shorelines, in the sediment, in the atmosphere, and degraded. 

Quantitative measurements of oil mass cleaned up (not including water and debris) are 

not available. Thus, cleanup was not included in the model simulations. 
25

5. BIOLOGICAL EFFECTS MODEL RESULTS 

Fish and invertebrate injuries were estimated using the biological effects model. The 

percent loss figures (Appendix K) show the model’s percent mortality estimates for 

organisms stationary on the bottom and exposed to bottom water. Two LC50s were used: 

(1) that for a species of average sensitivity (75 μg/L) and (2) that for a sensitive species 

(2.5 percentile) for the sample with the most toxic mixture (5 μg/L). 

The points on the maps are locations of Lagrangian tracers (or “particles” or elements) 

used to record the exposure concentrations over time for the animals they represent (see 

Section 2.3). For the average species, impacts are slight and restricted to shallow waters. 

However, for the sensitive species impacts are estimated as covering a significant area. 

Most of the impact is in Swanson Creek, Indian Creek and Trent Hall Creek. 

Tables 5-1 to 5-3 list the estimated kills of fish and invertebrates, assuming the 

abundances described in Section 3.8 and using the two assumptions for LC50: (1) species 

of average sensitivity where LC50∞ for the oil PAH mixture = 75 μg/L and (2) sensitive 

species where LC50∞ for the oil PAH mixture = 5 μg/L. As none of the species of 

concern have been shown to be sensitive to PAHs (French McCay, 2001), the results for 

the LC50 = 75 mg/L are the best estimates. Table 5-1 is for young-of-the-year, as number 

of animals that would have survived to 365 days (1 year) of age otherwise. Young-of-theyear kills are included in the kills of Tables 5-2 and 5-3. The biomass equivalent of the 

direct kill of YOY is 757 kg. 

Tables 5-2 and 5-3 also list the calculated production foregone and total injury (= direct 

kill plus production foregone) in kg. Assuming the model input data and average species 

sensitivity to PAHs, future growth of the killed animals, had there not been a spill, would 

total 979 kg (the production foregone). Thus, the model-estimated total injury to fish and 

invertebrates (assuming average species sensitivity, Table 5-2) is 2,460 kg. 

Table 5-1. Model estimates of kills of eggs, larvae and juveniles (young-of-the-year) as 

age one-year equivalents (#, kg), assuming average (75 μg/L) or sensitive (5 μg/L) 

species LC50s. 

 

LC50 = 75 μg/L LC50 = 5 μg/L 

Species # kg # kg 

Blueback herring 0.4 0.02 5.9 0.28

Striped bass 2.2 0.03 46 0.54

White perch 10 0.12 205 2.43

Atlantic croaker 1,844 221 4,104 492

Hogchoker 0.4 0.02 7.2 0.36

Blue crab 2,494 535 40,396 8671

Total 4,351 757 44,764 9167
26

Table 5-2. Model estimates of fish and invertebrate losses totaled for all age classes, 

assuming average species LC50 = 75 μg/L. 

Fishery species Kill (kg) 

Production 

Forgone (kg) Total Injury (kg) 

Bay anchovy 0.01 0.0 0.01

Blueback herring 0.02 0.10 0.12

Atlantic menhaden 120 50 170

Silversides, etc. 4.6 0.7 5.2

Striped bass 60 81 141

White perch 252 343 595

Atlantic croaker 329 317 645

American eel 17 20 38

Hogchoker 84 70 154

Brown bullhead 1.7 0.7 2.4

Blue crab 579 44 623

Horseshoe crabs 32 51 83

Oysters, dry weight 2.13 0.75 2.87

Total 1,481 979 2,460

Table 5-3. Model estimates of fish and invertebrate losses totaled for all age classes, 

assuming sensitive species LC50 = 5 μg/L for all species (a worst case assumption). 

Fishery species Kill (kg) 

Production 

Forgone (kg) 

Total Injury 

(kg) 

Bay anchovy 0.09 0.01 0.1

Blueback herring 0.3 1.6 1.9

Atlantic menhaden 1,845 765 2,610

Silversides, etc. 83 12 95

Striped bass 990 1,360 2,350

White perch 5,148 7,002 12,150

Atlantic croaker 2,476 1,365 3,841

American eel 303 359 661

Hogchoker 1,470 1,226 2,696

Brown bullhead 29 11 40

Blue crab 9,364 713 10,077

Horseshoe crabs 503 818 1,321

Oysters, dry weight 4.8 1.7 6.4

Total 22,216 13,635 35,851
27

6. SUMMARY OF INJURY QUANTIFICATION 

Table 6-1 summarizes the total injuries to fish and invertebrates, with the “silversides, 

etc.” category broken down to individual species. This includes the data in Table 5-2 

calculated by the model (assuming average sensitivity to PAHs for all species) and an 

additional 200 mummichogs collected in the marsh after the spill (USFWS, 2000, Animal 

Mortality Log). The 200 mummichogs were assumed killed in the marsh in areas where 

exposure was not evaluated by the model. Thus, these animals are in addition to the 

model injury. They are assumed to be of the same size and result in the same injury per 

animal as the modeled injury of mummichogs. 

Table 6-1 summary of the total injuries to fish and invertebrates. 

Fishery species Kill (kg) 

Production 

Forgone (kg) 

Total Injury 

(kg) 

Bay anchovy 0.01 0.0 0.01

Blueback herring 0.02 0.1 0.12

Atlantic menhaden 120 50 170

Atlantic silverside 1.39 0.21 1.6

Striped killifish 0.30 0.05 0.35

Mummichog (model) 1.1 0.2 1.3

Mummichog (marsh) 3.3 0.5 3.8

Spottail shiner 0.02 0.00 0.02

Inland silverside 0.01 0.00 0.01

Less common finfish 1.7 0.3 2.0

Striped bass 60 81 141

White perch 252 343 595

Atlantic croaker 329 317 645

American eel 17 20 38

Hogchoker 84 70 154

Brown bullhead 1.7 0.7 2.4

Blue crab 579 44 623

Horseshoe crabs 32 51 83

Oysters, dry weight 2.1 0.8 2.9

Total 1,485 979 2,464
28

Assuming the model input data and average species sensitivity to PAHs, and including 

the field collected mummichogs killed in the marsh, the best estimate of total injury to 

fish and invertebrates is 2,464 kg. This total injury includes: (1) the biomass equivalent 

of the direct kill, equal to 1,485 kg; and (2) future growth of the killed animals, had there 

not been a spill, totaling 979 kg (the production foregone). 

Restoration should provide 2, 464 kg of equivalent quality fish and invertebrate biomass 

to compensate for the lost fish and invertebrate production. Equivalent quality implies 

same or similar species with equivalent ecological role and value for human uses. The 

equivalent production should be discounted to present-day values to account for the 

interim loss between the time of the injury and the time restoration provides equivalent 

ecological and human services. 

The accuracy of the biological injury assessment depends primarily on the accuracy of 

(1) the fates model results and (2) the biological abundance data input to the model. With 

more accurate current data and details of the fates model simulations, the percent losses 

would change by less than an order of magnitude. Since most of the injuries were in 

shoal waters (<1.6m) where abundance was assumed uniformly distributed, the quantified 

injury would not change significantly if the details of the oil hydrocarbon movements 

were more closely simulated. 

The injuries quantified are sensitive to the assumed LC50. However, none of the species 

involved have been shown to be particularly sensitive to PAHs. Thus, the injuries are 

unlikely to be underestimated by this analysis based on toxicity to average species. 

The biomass losses are directly proportional to the pre-spill abundance assumed in the 

model inputs. Thus, a change in abundance is directly translated to a proportional change 

in the quantified injury. 
29

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