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Decay model for biocide treatment of unballasted vessels: Application for the Laurentian Great Lakes
Marine Pollution Bulletin 50 (2005) 1050–1060 www.elsevier.com/locate/marpolbul
Decay model for biocide treatment of unballasted vessels: Application for the Laurentian Great Lakes Larissa L. Sano a, Steven M. Bartell b, Peter F. Landrum a
c,*
Cooperative Institute for Limnology and Ecosystems Research, University of Michigan, 2205 Commonwealth Boulevard, Ann Arbor, MI 48105, USA b E2, Inc., 339 Whitecrest Drive, Maryville, TN 37801, USA c Great Lakes Environmental Research Laboratory, NOAA, 2205 Commonwealth Boulevard, Ann Arbor, MI 48105, USA
Abstract A biocide decay model was developed to assess the potential efficacy and environmental impacts associated with using glutaraldehyde to treat unballasted overseas vessels trading on the Laurentian Great Lakes. The results of Monte Carlo simulations indicate that effective glutaraldehyde concentrations can be maintained for the duration of a vesselÕs oceanic transit (approximately 9–12 days): During this transit, glutaraldehyde concentrations were predicted to decrease by approximately 10% from initial treatment levels (e.g., 500 mg L1). In terms of environmental impacts, mean glutaraldehyde concentrations released at Duluth-Superior Harbor, MN were predicted to be 100-fold lower than initial treatment concentrations, and ranged from 3.2 mg L1 (2 SD: 2.74) in April to 0.7 mg L1 (2 SD: 1.28) in August. Sensitivity analyses indicated that the reballasting dilution factor was the major variable governing final glutaraldehyde concentrations; however, lake surface temperatures became increasingly important during the warmer summer months. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Ballast water; Biocide treatment; Biological invasions; Laurentian Great Lakes; Glutaraldehyde; Decay model
1. Introduction The release of ballast water currently constitutes one of the primary vectors for the global spread of aquatic invasive species (Grosholz, 2002; Leppakoski et al., 2002; Vanderploeg et al., 2002). The impacts of these invaders have been profound and include extirpation of native unionid species in the Laurentian Great Lakes (Nalepa et al., 1996), alterations in the trophic web of the San Francisco Estuary (Feyrer et al., 2003), and potential impacts on important fishery resources in the Black Sea (GESAMP, 1997). To address this pressing environmental issue, several countries either have
*
Corresponding author. Tel.: +1 734 741 2276; fax: +1 734 741 2055. E-mail address: [email protected] (P.F. Landrum).
0025-326X/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpolbul.2005.04.008
implemented or are currently developing standards for ballast water discharge. In addition, the United Nations International Maritime Organization (IMO) recently adopted ballast water management standards to facilitate international coordination for implementing management practices (Christen, 2004). One possible treatment option cited in the IMO standards is the use of ‘‘active substances’’ to eliminate potentially invasive organisms (a.k.a., harmful aquatic organisms and pathogens sensu IMO). The use of chemical biocides (or ‘‘biocides’’) falls under this purview and the acceptability of any given biocide depends partly on efficacy and environmental acceptability. With respect to biocide treatment, however, these two factors present competing demands: In order to be effective, biocide concentrations must be maintained at a high enough concentration and for a sufficient period of time to ensure targeted kill rates, yet the release concentrations
L.L. Sano et al. / Marine Pollution Bulletin 50 (2005) 1050–1060
of residuals must be low enough to protect against undesirable environmental impacts. Because of the vying demands between efficacy and environmental impacts, one of the more promising applications for biocide treatment may be for unballasted vessels trading in the Laurentian Great Lakes (or Great Lakes). Although technically classified as unballasted, overseas vessels that enter the Great Lakes declaring no-ballast-on-board (or NOBOBs) are currently one of the most important vectors for the release of invasive species. Because these vessels enter the Great Lakes carrying cargo, they usually contain only small amounts of residual water and sediments in their tanks (averaging approximately 60 m3 per vessel (Niimi and Reid, 2003)). These residuals, however, pose a risk for species introductions since they contain viable organisms (van Overdijk et al., 2003) and resting stages (Bailey et al., 2003) that are subsequently released into receiving waters during ballasting operations. Since NOBOBs enter carrying cargo, their usual trade pattern is to first visit ports in the lower portion of the lakes, where they unload their cargo and take on ballast water to maintain trim and stability. Most often these vessels then steam to ports in the upper Great Lakes, where they take on outbound cargo. As the vessels take on their outbound cargo load, they must release ballast water from their tanks and it is at this point that non-native species can be released into the Great Lakes. These reballasting operations are potentially advantageous with respect to biocide treatment, since they result in a large dilution of biocide residuals and permit additional decay time prior to release of any residuals into receiving waters. Because these two factors decrease final release concentrations, they mitigate against the potential environmental impacts associated with the higher biocide concentrations required for treatment efficacy. The objective of this study is to assess the potential efficacy and environmental release concentrations for the biocide glutaraldehyde by developing a biocide decay model configured for NOBOBs trading on the Great Lakes. Glutaraldehyde (1,5-pentanedial, CAS Registry no. 111-30-8) has been proposed as a candidate disinfectant for ballast water treatment (NRC, 1996; Sano et al., 2003) because it is both relatively effective against a range of microorganisms and degrades rapidly under natural conditions (Leung, 2001). Since glutaraldehyde decay is primarily a function of temperature and concentration, the decay model is configured to assess variations in degradation throughout different portions of a vesselÕs transit, prior to deballasting at a representative Great Lakes port. To address some of the variability and uncertainty associated with the model parameters, Monte Carlo simulations are used to generate concentration distributions that characterize treatment efficacy and potential release concentrations.
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2. Materials and methods 2.1. Decay equation The decay rate of glutaraldehyde was estimated by fitting an equation to laboratory data generated from previous water-sediment degradation experiments (Landrum et al., 2003). To capture potential conditions in representative NOBOB tanks, these experiments were conducted at a ratio of 1:4 sediment-to-water (by volume) using sediment with an organic carbon content of 2.6 ± 0.4%. Experiments were run at three different temperatures (5 °C, 15 °C, 25 °C), and at three initial glutaraldehyde concentrations (10 mg L1, 100 mg L1, 500 mg L1). The resulting data were fit to a decay function using the software ScientistÒ (Micromath, St. Louis, MO). Model output was then verified against laboratory results to confirm reproducibility. 2.2. Atlantic Ocean transit To estimate the degree of decay during transit across the Atlantic, treatment of NOBOB vessels was assumed to occur at Antwerp, Belgium, a common port of origin for overseas vessels that visit the Great Lakes (Colautti et al., 2003). A hypothetical vessel transit was constructed using a great-circle path from Antwerp to the entrance to the Gulf of St. Lawrence (i.e., at the Cabot Strait). Sea surface temperature observations from the Comprehensive Ocean-Atmosphere Data Set (COADS, for the years of 1950–1992) were used to construct a temperature profile for the transit. These data represent collections of surface marine data observations from a variety of platforms. The data have been trimmed using a procedure that identifies potential outliers based on the climatological 3.5 sigma limits derived from specified data periods (Smith and Reynolds, 2003). Defined by latitude/longitude data, the average monthly temperature for specified segments of the great-circle route was estimated. The weighted mean temperature of each segment was then calculated by dividing the average temperature for a given segment of the great-circle route by the time the vessel spent traversing that segment. This was calculated assuming an average vessel speed of 17.5 miles h1 (or approximately 15 knots). The sum of these weighted averages was used to estimate a mean weighted temperature for the entire transit. These temperatures are assumed to reflect ambient temperatures inside vessel tanks as they transit the Atlantic Ocean. Transit times for the Atlantic crossing were based both on distances for the great-circle path (described above and using a vessel speed of 15 knots) and on published transit times (from Fednav International, www.fednav.com). In terms of the latter, Fednav data for arrival and estimated shipping times were used to
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calculate a range of ship transit times to different Great Lakes ports from Antwerp. 2.3. Great Lakes transit The transition from the Atlantic to the Great Lakes portion of transit was assumed to occur as vessels enter the Gulf of St. Lawrence. For the Great Lakes segment, lake sea surface temperatures (SST) were derived from individual moored buoys, maintained either by the Marine Environmental Data Service (MEDS; Canada Department of Fisheries and Oceans, Ontario, Canada) or by the National Data Buoy Center (NDBC; National Oceanic and Atmospheric Administration, Mississippi, USA). For the MEDS buoys, any data points that were flagged as ‘‘doubtful,’’ ‘‘erroneous,’’ or ‘‘off position’’ were eliminated from analysis. In addition, some of the temperature readings when buoys were first deployed at the start of the season were much higher than subsequent readings; thus the first day of MEDS temperature measurements were also eliminated prior to analysis. In contrast to the temperature data for the Atlantic, the temperature data for the buoys in the Great Lakes transit segment were divided into 10-d increments based on the Julian day (JD). The average 10-d temperatures and the corresponding standard deviations were estimated for each buoy and used to calculate parameters for the decay model. Transit times between different ports within the Great Lakes were derived from three primary sources: nautical mileage charts, the St. Lawrence Seaway Authority (www.greatlakes-seaway.com), and Fednav shipping schedules. The transit times incorporated lockage times for passages through the Welland Canal, the Montreal/ Lake Ontario Locks, and the Sault Ste. Marie Lock system. For transit times based on nautical miles, vessels were assumed to travel at an average of 14 miles h1, ranging from 12 to 18 miles h1. The Duluth-Superior Harbor (Minnesota, USA) was selected as the final port-of-call for estimating potential release concentrations from ballast tanks. This site was selected since Lake Superior receives the majority of discharges from NOBOBs (Holeck et al., 2004) and because Duluth-Superior Harbor is a major traffic site for the NOBOBs that enter Lake Superior (Colautti et al., 2003). Although discharge of ballast water is likely to occur elsewhere in the Great Lakes, sites in Lake Superior are at greater risk of multiple releases from NOBOBs due to vessel trafficking patterns. 2.4. Reballasting dynamics The reballasting dynamics of NOBOB vessels were derived from Colautti et al. (2003), from Fednav shipping schedules, and from surveys collected from
NOBOB ship captains (unpublished data, David F. Reid, Great Lakes Environmental Research Laboratory, NOAA, Ann Arbor, Michigan, USA). The associated reballasting data used for the decay model included the major ports of call for NOBOBs, the average time vessels spent at a port of call, and the amount of dilution involved in reballasting operations (i.e., the ‘‘dilution factor’’ in the model), respectively. 2.5. Monte Carlo simulations Monte Carlo simulations for the biocide decay model were run using Crystal BallÒ software (Version 5.5, Decisioneering, Denver, Colorado, USA). The model generated estimates of glutaraldehyde concentrations for different points in the transit of a NOBOB across the Atlantic, through the Gulf of St. Lawrence and St. Lawrence River, and finally to Duluth-Superior Harbor. The temperature and transit data were defined as distributions that reflect the data uncertainty described in the previous sections. In addition, the water temperature data were assigned the following correlations: the SST for the Atlantic was correlated with the SST of the Gulf of St. Lawrence, and the SST for the different Great Lakes were correlated with each other. All correlations were calculated using SYSTATÒ (Version 10, SPSS, Inc., Chicago, IL, USA). The biocide decay model was run using a ‘‘best-case’’ and a ‘‘worst-case’’ reballasting scenario. The best-case scenario assumes that a vessel reballasts at the port of Oswego (New York, USA: Fig. 1). This site is located on Lake Ontario, and permits more time between reballasting and release at Duluth-Superior Harbor. The worst-case scenario involves reballasting at the port of Detroit (Michigan, USA: Fig. 1). Because most decay occurs at lower concentrations of glutaraldehyde, this represents a conservative estimate of biocide concentration at Duluth-Superior, since less time would elapse between reballasting and release. Based on these two reballasting scenarios, simulations were conducted in 10-d JD increments. The number of trials per time period was 2000, and the Latin Hypercube method (Stein, 1987) was used to sample from the individual distributions. Sensitivity analyses were then performed for each 10-d period.
3. Results 3.1. Governing decay equation The decay function that best fit the laboratory degradation data described a first-order decay function and a power function: .701 Þ k d ¼ ð0.0120Þ expð0.1010T ÞðC 0 x
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Fig. 1. Map of the Great Lakes basin, including the major ports of call referenced in this study. The lines depict the general transit route of NOBOB vessels used to develop the biocide decay model.
where kd is a decay constant (t1), T is water temperature (°C), and Cx is the beginning glutaraldehyde concentration for a given segment of the transit (mg glutaraldehyde L1). The R2 value for this equation was 0.999. One value from the original laboratory data was excluded from analysis, because it was deemed an outlier. The omission of this value did not strongly affect the resulting decay coefficient, but provided a better model fit to the data. The decay constant was then used to estimate the first-order degradation of glutaraldehyde throughout a vesselÕs transit. Up until reballasting, degradation was represented by
of glutaraldehyde are different at higher and lower concentrations. At higher concentrations (e.g., >20 mg L1), glutaraldehyde degrades primarily through abiotic processes, while at lower concentrations (e.g., <10 mg L1), it also decays via biodegradation (Leung, 2001; Landrum et al., 2003). This difference in degradation pathways is reflected through the use of two different glutaraldehyde concentrations, one for the initial (treatment) concentration and one for the reballasted (diluted) concentration.
dC ¼ kdCi dt
The SST data for the different portions of a NOBOB transit are presented in Table 1. The SST observations for the Atlantic segment covered more than four decades, from 1950 to 1992, while the SST for the Great Lakes segment generally ranged from the mid-1980s up through 2003. Because of differences in resolution, temperature data were averaged either monthly (for the Atlantic data and the St. Lawrence River data) or in 10-d JD increments (for the remaining data). In addition, data quality was sufficient for lakes Huron and Superior to use SST readings from two different buoys within each lake; thus, these readings represent averaged data from two different buoys within each lake. For the remaining two lakes (Ontario and Erie), temperature data were limited to one buoy per lake.
where Ci is the initial treatment concentration of glutaraldehyde at time t0 (averaging 500 mg L1), and t is the time period of the first segment of the transit, up until reballasting. The initial treatment concentration was based on the highest 24-h LC90 reported in Sano et al. (2003). After a vessel reballasted, the equation was reset to use the new, diluted glutaraldehyde concentration and the decay equation became: dC ¼ kdCd dt where Cd is the diluted concentration of glutaraldehyde. This approach was necessary, since the decay dynamics
3.2. Temperature data
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Table 1 List of variables and associated values developed for the biocide decay model Variable
Mean
Min
Max
Distribution
Initial treatment concentration Initial glutaraldehyde concentration (mg L1)
500
450
550
Normal
Reballasting factors Dilution factor (expressed as #-fold dilution) Time unloading cargo/reballasting (h)
106 48
36 24
178 72
Triangular Triangular
Temperatures Atlantic temperature during transit (°C) Gulf of St. Lawrence temperature (°C) St. Lawrence River temperature (to Oswego: °C) Lake Ontario temperature (°C) Lake Erie temperature (°C) Lake Huron temperature (°C) Lake Superior temperature (°C)
Varies Varies Varies Varies Varies Varies Varies
Transit times Atlantic transit time (h) Gulf of St. Lawrence transit time (h) St. Lawrence transit time (h) Lake Ontario transit time (h) Lake Erie transit time (h) Lake Huron transit time (h) Lake Superior transit time (h)
168 72 27 24 19 26 28
5.5 1.8 5.4 1.9 4.1 1.9 1.8 156 62 20 18 15 20 22
12.1 14.7 21.6 21.9 23.9 18.9 13.1 216 84 29 28 22 30 32
Normal Normal Normal Normal Normal Normal Normal Triangular Triangular Triangular Triangular Triangular Triangular Triangular
For this model, sea surface temperatures are used as a proxy for ambient ballast tank temperatures. Mean values for lake temperatures are not provided, since water temperatures varied temporally. For all triangular distributions, the ‘‘mean’’ is actually represented by the mode. See accompanying text for additional details.
3.3. Transit data Fig. 1 presents the transit route selected for the biocide decay model. To generate more conservative estimates of glutaraldehyde concentrations, it was assumed that vessels did not visit any ports in Lake Michigan, and instead navigated directly through lakes Huron and Superior to Duluth-Superior Harbor. Table 1 lists the estimated transit times for NOBOBs. Because data were limited, these model parameters were described using triangular distributions. 3.4. Predicted efficacy concentrations Results from the biocide decay model indicate that glutaraldehyde concentrations should not decay substantially prior to reballasting at a Great Lakes port (Tables 2 and 3). Because the conditions for the first portion of the transit were similar, the reballasting scenario did not affect predicted concentrations until the point of reballasting (at port Oswego for the best-case scenario and at Detroit for the worst-case scenario). Examples of the expected distribution of glutaraldehyde concentrations after the Atlantic portion of the transit are provided in Fig. 2. The variation in these concentrations largely reflects the variability in the initial treatment concentration. The amount of decay predicted to occur during the Atlantic segment varied by
month, with the warmer, summer months resulting in slightly higher decay. On average, the predicted mean concentration prior to entering the Gulf of St. Lawrence for both reballasting scenarios was 464 mg L1, representing a 7% decrease from the initial treatment value. Glutaraldehyde concentrations were predicted to continue to decrease after the vessels entered the Gulf of St. Lawrence. At the port of Oswego, the mean glutaraldehyde concentration was predicted to be approximately 439 mg L1 (2 SD: 58), representing a 12% decrease from treatment concentrations. The lowest predicted mean concentration for the port of Oswego was 404 mg L1 (2 SD: 60) and occurred in August (JDs 211–220). For those vessels reballasting in Detroit, treatment concentrations continued to decrease during transit through lakes Ontario and Erie. The mean glutaraldehyde concentration predicted at the port of Detroit was 418 mg L1 (2 SD: 60). Similar to the Atlantic data, there were temporal differences in concentrations, with the predicted mean concentration decreasing during the summer months and reaching minimum values in August. The lowest predicted mean concentration at the port of Detroit was 371 mg L1 (2 SD: 64). Similar to the results for the port of Oswego, this minimum mean concentration was also predicted to occur in August (JD 211–220).
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Table 2 Predicted glutaraldehyde concentrations under the best-case reballasting scenario at different sites specified by the biocide decay model Julian day
Atlantic mean conc. (mg L1)
Gulf of St. Lawrence mean conc. (mg L1)
Port Oswego mean conc. (mg L1)
Welland Canal mean conc. (mg L1)
Port of Detroit mean conc. (mg L1)
Sault Ste. Marie mean conc. (mg L1)
Duluth Harbor mean conc. (mg L1)
91
475.3 55.8
468.5 56.1
462.3 56.9
3.6 2.9
3.2 2.8
2.8 2.7
2.5 2.5
121
472.3 56.2
464.0 57.0
454.0 57.7
3.2 2.8
2.6 2.6
2.3 2.5
2.0 2.3
151
466.3 56.8
454.5 57.3
436.0 58.2
2.2 2.5
1.6 2.1
1.3 1.9
1.2 1.8
181
458.7 56.9
441.4 57.8
413.6 59.6
1.0 1.6
0.5 1.1
0.4 0.9
0.3 0.8
211
454.2 56.8
432.0 58.8
403.8 60.2
0.5 1.1
0.2 0.6
0.1 0.4
0.1 0.4
241
457.3 56.8
440.0 58.2
421.9 60.4
0.7 1.3
0.3 0.8
0.2 0.6
0.1 0.4
271
463.7 57.0
451.9 57.6
439.9 58.3
1.5 2.1
1.0 1.7
0.7 1.3
0.5 1.1
301
469.2 56.7
461.6 56.7
453.7 57.6
2.2 2.5
1.8 2.2
1.4 1.9
1.2 1.7
The estimates represent mean values of glutaraldehyde based on the best-case reballasting scenario, in which ballast water is taken on at port Oswego. The values for port Oswego indicate glutaraldehyde concentration prior to reballasting. Values in italics are for 2 SD based on the output from the Monte Carlo simulations. Table 3 Predicted glutaraldehyde concentrations under the worst-case reballasting scenario at different sites specified by the biocide decay model Julian day
Atlantic mean conc. (mg L1)
Gulf of St. Lawrence mean conc. (mg L1)
Port Oswego mean conc. (mg L1)
Welland Canal mean conc. (mg L1)
Port of Detroit mean conc. (mg L1)
Sault Ste. Marie mean conc. (mg L1)
Duluth Harbor mean conc. (mg L1)
91
474.6 55.98
467.81 56.3
464.57 56.6
462.55 56.6
457.37 56.74
3.68 2.88
3.22 2.74
121
471.59 56.36
463.25 57.14
458.09 57.42
455.44 57.56
446.71 58.2
3.53 2.8
3.09 2.66
151
465.56 57.08
453.74 57.46
444.1 57.82
439.46 58.38
422.81 60.48
3.06 2.64
2.64 2.46
181
457.97 57.2
440.61 58
426.09 58.74
416.86 59.54
389.3 61.88
1.95 2.36
1.64 2.18
211
453.36 57.06
431.18 59.02
416.45 59.58
402.86 60.8
370.8 63.64
0.93 1.48
0.7 1.28
241
456.54 57.1
439.24 58.42
429.81 59.36
417.15 60.64
387.77 63.74
1.04 1.62
0.7 1.3
271
462.93 57.22
451.15 57.78
444.9 58.1
437.09 59.12
417.75 61.16
1.89 2.2
1.43 1.92
301
468.46 56.9
460.8 56.9
456.71 57.28
451.56 57.48
440.78 58.14
2.7 2.54
2.22 2.32
The estimates represent mean values of glutaraldehyde based on the worst-case reballasting scenario, in which ballast water is taken on at the port of Detroit. The values for port of Detroit indicate glutaraldehyde concentration prior to reballasting. Values in italics are for 2 SD based on the output from the Monte Carlo simulations.
3.5. Predicted Duluth-Superior Harbor concentrations The final predicted concentrations at Duluth-Superior Harbor were substantially lower than treatment
concentrations, primarily due to the dilution factor associated with reballasting. The estimated mean final concentrations at Duluth-Superior ranged from 3.2 mg L1 to 0.1 mg L1 and differed depending on whether
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(a)
(b)
Fig. 3. Predicted mean glutaraldehyde concentrations at DuluthSuperior Harbor, MN. Data are provided for both the worst- and bestcase reballasting scenarios. The error bars represent ±2 standard deviations from the mean.
(a)
Fig. 2. Frequency distribution of predicted glutaraldehyde concentrations after the Atlantic transit, prior to entering the Gulf of St. Lawrence. Data were generated under the worst-case reballasting scenario and are provided for (a) April, Julian days 91–100, and (b) August, Julian days 211–220.
the best-case or worst-case reballasting scenario was modeled (Fig. 3). Equally important was the high degree of right skewness in the final concentrations, which resulted in a wide range of minimum and maximum values. For the August time period of 211–220 JD, for example, the mean estimated glutaraldehyde concentration for the worst-case scenario was 0.70 mg L1 (Fig. 4). The predicted ballast tank concentrations during this time period, however, could be as high as 4.90 mg L1, although this concentration had less than a 1% chance of occurring. The predicted final concentrations at Duluth-Superior also varied temporally: Concentrations were higher in the early spring (JDs 91–121) than in mid-summer (JDs 201–251). Simulated concentrations began to increase again in the fall, with the return of lower water temperatures (see Fig. 3). The magnitude of difference in glutaraldehyde concentrations, however, was again affected by the high degree of data variation. For example, although the predicted mean concentration in April
(b)
Fig. 4. Frequency distributions for predicted glutaraldehyde concentrations at Duluth-Superior Harbor, MN. The black bar graphs are results from the best-case reballasting scenarios, and the gray bar graphs are results from the worst-case reballasting scenarios. Data are presented for (a) April, Julian days 91–100, and (b) August, Julian days 211–220. Note that in graph a, values greater than 7.0 mg L1 are expressed as a single occurrence frequency.
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(JD 91) for the worst-case reballasting scenario was almost five times higher than the mean concentration for August (JD 211), the associated distributions of concentrations overlap, suggesting that comparable minimum and maximum concentrations can occur during these two months. 3.6. Sensitivity analysis For the overall ballast tank decay model, the importance of the different variables depended on the specified endpoint. Under the best-case reballasting scenario (with water taken on at port Oswego), the most influential variable on predicted efficacy was the initial glutaraldehyde treatment concentration (Table 4). This variable consistently had a rank correlation value >0.80 and was much higher than any of the other variables. This trend was similar to that observed for the worst-case reballasting scenario (with reballasting at the port of Detroit): Glutaraldehyde concentrations at
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all points until reballasting were determined mainly by the initial treatment concentration. Other variables, such as Atlantic SST had a small contribution in comparison, with rank correlations values of averaging 0.40. In terms of simulated ballast tank concentrations for vessels arriving at the Duluth-Superior Harbor, the dilution factor had the greatest impact on glutaraldehyde concentrations under both best- and worst-case reballasting scenarios (Table 5). For the best-case scenario, the rank correlation for the dilution factor ranged from 0.95 to 0.71, with the rank correlation decreasing during the summer months. In contrast, both the temperature of Lake Ontario and of Lake Erie had relatively small impacts during early spring, but demonstrated large increases in the rank correlation parameter during the summer months (reaching as high as 0.57). In comparison, other variables such as treatment concentration and transit times had relatively constant rank correlation values.
Table 4 Sensitivity analysis of the variables affecting the treatment concentration (i.e., efficacy) of glutaraldehyde up to the port of Oswego, NY Parameter
April JD 91
May JD 121
June JD 151
July JD 181
August JD 211
September JD 241
October JD 271
November JD 301
Best-case scenario Treatment concentration Atlantic SST Gulf St. Lawrence SST St. Lawrence River SST
0.93 0.33 0.30 0.29
0.92 0.36 0.34 0.32
0.90 0.40 0.38 0.36
0.86 0.45 0.44 0.42
0.84 0.48 0.47 0.45
0.86 0.46 0.45 0.43
0.90 0.40 0.38 0.36
0.92 0.36 0.33 0.32
Worst-case scenario Treatment concentration Atlantic SST Gulf St. Lawrence SST St. Lawrence River SST
0.94 0.35 0.31 0.28
0.93 0.38 0.35 0.31
0.91 0.44 0.42 0.35
0.87 0.49 0.48 0.41
0.85 0.52 0.51 0.44
0.87 0.50 0.49 0.41
0.91 0.44 0.41 0.35
0.93 0.32 0.36 0.31
The data are presented as rank correlation values and provided for both the best- and worst-case reballasting scenarios (i.e., reballasting at port Oswego and port of Detroit, respectively). The time period for the results is the first 10-d Julian day increment per month. Lake sea surface temperature is abbreviated as SST.
Table 5 Sensitivity analysis of the variables affecting final glutaraldehyde release concentrations at the Duluth-Superior Harbor Parameter
April JD 91
May JD 121
June JD 151
July JD 181
August JD 211
September JD 241
October JD 271
November JD 301
Best-case scenario Dilution factor Treatment concentration Lake Ontario temp Lake Erie temp Time at port
0.95 0.19 0.18 0.17 0.13
0.91 0.19 0.30 0.29 0.15
0.80 0.18 0.49 0.47 0.19
0.73 0.17 0.55 0.54 0.24
0.71 0.17 0.51 0.56 0.26
0.71 0.17 0.57 0.56 0.24
0.74 0.17 0.56 0.55 0.21
0.87 0.18 0.36 0.35 0.21
Worst-case scenario Dilution factor Treatment concentration Lake Huron temp Lake Erie temp Atlantic temp
0.96 0.18 0.12 0.12 0.09
0.96 0.18 0.14 0.14 0.11
0.91 0.18 0.30 0.29 0.21
0.71 0.16 0.63 0.58 0.39
0.72 0.17 0.59 0.55 0.38
0.73 0.16 0.59 0.55 0.37
0.81 0.17 0.49 0.46 0.32
0.91 0.18 0.29 0.27 0.20
Reballasting scenarios are presented for both the best-case (reballasting at port Oswego) and worst-case (reballasting at port of Detroit) scenarios. Data are presented as the rank correlation values. The Julian day represents the first day in the 10-d simulation time segment per month.
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The general trend in sensitivity analysis for the bestcase reballasting scenario also applies to the worst-case reballasting scenario: The dilution factor had the largest effect on concentrations at Duluth-Superior Harbor. During the summer months (primarily July and August: Table 5), however, water temperatures in Lake Huron and Lake Erie has an increasing effect on predicted final ballast tank concentrations.
4. Discussion A biocide decay model was developed to represent the potential treatment of Great Lakes NOBOB vessels with glutaraldehyde. The parameters used in developing the model were designed to reflect potential treatment scenarios and, more importantly, to help explore the variability associated with ship trafficking patterns, reballasting dynamics, and biocide degradation rates. As such, this model is meant to characterize potential treatment concentrations and residual release concentrations and to describe the level of uncertainty and variability in the resulting predictions. 4.1. Treatment efficacy In terms of treatment efficacy, the results from these simulations suggest that glutaraldehyde concentrations can be maintained throughout the initial segment of NOBOB transits. From the time the vessel departs its port of origin (selected as Antwerp, Belgium for this application) until it reballasts at a Great Lakes port, the average amount of degradation was predicted to be approximately 20%. Because of the increasing temperatures of the St. Lawrence River and the Great Lakes compared to the Atlantic, more than half of this degradation is expected to occur later in the journey. The ability to maintain initial glutaraldehyde concentrations, ranging from 450 mg L1 to 550 mg L1, during the trans-Atlantic journey is essential for treatment efficacy. The results from this model compare favorably with results from acute toxicity bioassays using glutaraldehyde (Sano et al., 2003). In these experiments, the 24-h 90% lethal concentration of glutaraldehyde for the most resistant species tested (i.e., the amphipod, Hyalella azteca) in a 1:4 sediment–water exposure was estimated to be 563 mg L1 (95% CI: 499–685), and the 50% lethal concentration was estimated to be 323 mg L1 (95% CI: 286–354). If the model predictions are accurate, this indicates that lethal concentrations of glutaraldehyde might be maintained for a much longer period than the 24-h exposure estimates provided from laboratory experiments. This should both help assure predicted kill rates and may allow for a reduction in treatment levels if adequate concentrations are maintained for longer than 24 h.
4.2. Environmental impacts In terms of environmental releases, the results from these simulations indicate that the final residual glutaraldehyde concentrations in tanks reaching Duluth-Superior Harbor are likely to vary both between different vessels and over the course of the shipping season. For most of the simulation conditions, the mean residual concentration was predicted to range between 1 mg L1 and 3 mg L1. The distributions for these data, however, were skewed, and it is important to consider the possibility of higher residual concentrations. Assuming a worst-case reballasting scenario, for example, there is a predicted 10% chance of residual ballast tank concentrations of 5 mg L1 in April (JDs 91–100). The maximum simulated glutaraldehyde concentrations reach as high as 10 mg L1 during April, but have a low probability of occurring (less than 1%). These high concentrations were limited to the months of April and May, since ballast tank concentrations were predicted to be highest during these two months and lowest during the late summer and early fall. The actual environmental impact of average release concentrations of glutaraldehyde in the range from 1 to 3 mg L1 will depend largely on diffusive and advective processes in the vicinity of the slips and ports, where most deballasting occurs. The results from chronic toxicity bioassays indicate that phytoplankton and fish larvae may be impacted by long-term exposure to glutaraldehyde concentrations as low as 1 mg L1 (Sano et al., 2005). A synthesis of toxicity data by Leung (2001) yielded an environmental probable-no-effect-concentration (PNEC) of 31 lg L1. These data suggest that ballast tank concentrations can reach effect-level concentrations; however, the potential for chronic toxicity associated with glutaraldehyde release into receiving waters will likely be moderated by its rapid decay rate, particularly at lower concentrations. For example, the estimated half-life of 10 mg L1 at 15 °C is approximately 35 h (Landrum et al., 2003). At warmer temperatures (25 °C), the half-life has been estimated to range from 10 h (Leung, 2001) to 23 h (Landrum et al., 2003). Thus, even if effect-level concentrations are reached, they may not be maintained for a sufficiently long period to elicit chronic impacts in most species. Despite these mitigating factors, it is still possible that given the appropriate configuration of events (such as multiple releases from ships containing higher residual concentrations of glutaraldehyde), effect-level concentrations may be attained. The implications of these types of exposures should be addressed in future studies to better characterize the potential for ecological impacts. 4.3. Model limitations The decay model developed here is designed to provide a simplified, yet realistic, representation of ballast
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tank dynamics. In general, this model was developed with relatively conservative assumptions regarding NOBOB dynamics in the Great Lakes. There are several limitations to the application of this model that are worthy of discussion. The first concerns the governing equation used in developing the model. This decay equation was derived from a set of degradation experiments using glutaraldehyde. Because experiments were conducted at a single sediment–water volume and at only three temperatures, the resulting decay equation is limited in its ability to describe glutaraldehyde decay dynamics under a wider range of chemical and physical conditions. For example, glutaraldehyde decay rates will likely be faster than predicted by this model for tanks that contain either larger amounts of sediment or sediments with higher organic carbon content or higher bacterial loads. In contrast, decay rates will likely be slower in tanks with lower sediment–water ratios or with a lower level of bacteria. For ballast tank environments that fall well outside of the test conditions, this fundamental decay equation might not apply. Another model limitation concerns transit routes. The ship routes selected for this model are representative of those taken by NOBOBs, but cannot be considered specific for any given vessel. This is largely due to the inherent variability in the trade routes of overseas vessels: A large percentage of overseas vessels that enter the Great Lakes have neither regular nor defined trade routes (Niimi, 2000). Grigorovich et al. (2003) found that between 1983 and 1998, overseas vessels arriving into the Great Lakes reported 460 different last ports-of-call, from all over the world. The general trend in the data, however, was that most vessels arriving into the Great Lakes came from European ports. Thus, for this model, the port of Antwerp was selected as a representative European port-of-call. This should provide a representative route for a hypothetical NOBOB vessel and yield a more conservative transit time estimate, since Antwerp is more westerly than many other major port cities (such as Copenhagen, Helsinki, Ventspils, or St. Petersburg). The representation of transit routes within the Great Lakes was also limited, since only two ports were used for the reballasting scenarios developed for this model. In reality, the reballasting dynamics of NOBOBs are more complicated, with vessels often unloading (and thus reballasting) at multiple ports throughout the Great Lakes. This complex ballasting pattern will affect the decay dynamics of glutaraldehyde within the ballast tanks and create potential ecological risks at sites other than the Duluth-Superior Harbor. Despite these limitations, the output from this model should provide preliminary estimates of potential ballast tank concentrations under different reballasting scenarios and provide ranges of exposure concentrations at a Great Lakes port that receives a high volume of NOBOB discharges.
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To address the limitations of other elements of the model, it is appropriate to focus on the model parameters that have a disproportionate impact on the predicted ballast tank concentrations. In terms of efficacy, the most important model variable was initial treatment concentration. This variable had the highest rank correlation value (of approximately 0.90), which was much higher than the next most important variable, Atlantic Ocean temperature (with a rank correlation of approximately 0.40). In addition, the importance of this parameter did not differ between the two reballasting scenarios and did not change substantially over the shipping season. Thus, it can be assumed that if the decay equation is representative of the ballast tank conditions, then the biggest variable affecting efficacy will be the initial treatment concentration. As for residual biocide concentrations at the DuluthSuperior Harbor, the most important variable affecting final concentrations was the reballasting dilution. This parameter was derived from surveys filled out by vessel captains. Although a large number of NOBOB vessels reported their ballasting operations, data quality is not verifiable. Because this parameter is essential for predicting final release concentrations, it is imperative that these estimates be verified prior to making decisions regarding the potential for environmental impacts. In summary, the results from this model provide information necessary for a more rigorous assessment of the feasibility of biocide treatment of vessels. Because the continued release of non-native, and potentially harmful, organisms constitutes a large risk to freshwater and marine ecosystems throughout the world, it is imperative that comprehensive ballast water standards be implemented as rapidly as possible. Although several criteria have been proposed for selecting treatment technologies, the efficacy and environmental acceptability of treatment methods are two primary considerations. The output from this decay model provides one method for evaluating glutaraldehyde treatment of unballasted vessels under varying treatment scenarios. These types of data are critical for evaluating treatment options, prior to management implementation.
5. Conclusions The results from this model indicate that for the compound glutaraldehyde, biocidal concentrations can be maintained throughout the initial period of transit, until the point of reballasting. After reballasting, concentrations were predicted to decrease by approximately 100fold, and this dilution should help mitigate against the potential for environmental impacts. At a representative Great Lakes port (Duluth-Superior Harbor), the predicted mean release concentrations ranged between 1 and 3 mg L1, with the higher concentrations tending
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to occur in the early spring and late fall. The predicted concentrations during the summer months averaged less than 1 mg L1. All of these values are hypothetical, as glutaraldehyde has not yet been used to treat any vessel trading on the Great Lakes. Based on this model, the most important variables affecting efficacy and release concentrations were predicted to be treatment concentration and reballasting dilution, respectively. Although a target treatment concentration of 500 mg L1 glutaraldehyde was used in these simulations, the ability to maintain biocidal concentrations longer than 24 h may indicate that lower concentrations could be used without compromising efficacy. In terms of environmental impacts, the simulated release concentrations from this model should pose minimal risk for long-term ecological impacts provided that glutaraldehyde decays relatively rapidly and that advection and diffusion are adequate to flush the slip areas. Despite this, the possibility remains that glutaraldehyde concentrations could be sufficiently high in the immediate vicinity of slips at limited times of the year to cause ecological impacts. Additional studies characterizing the types of, and evaluating the likelihood of, ecological impacts are therefore warranted prior to further consideration of this treatment approach.
Acknowledgments This project was funded in part through the Cooperative Institute for Limnology and Ecosystems Research, University of Michigan, under cooperative agreement (NA67RJ0148) from the Office of Oceanic and Atmospheric Administration, US Department of Commerce. We thank David F. Reid and Stephen Constant for providing data from NOBOB captain surveys that were critical to model development. We also thank David Reid and an anonymous reviewer for their editorial comments. This manuscript constitutes GLERL contribution #1343.
References Bailey, S.A., Duggan, I.C., van Overdijk, C.D.A., Jenkins, P.T., MacIsaac, H.J., 2003. Viability of invertebrate diapausing eggs collected from residual ballast sediment. Limnology and Oceanography 48, 1701–1710. Christen, K., 2004. UN sets treatment standard for ballast water. Environmental Science and Technology 38, 153A–154A. Colautti, R., Niimi, A., van Overdijk, C.D.A., Mills, E.L., Holeck, K., MacIsaac, H.J., 2003. Spatial and temporal analysis of shipping vectors to the Great Lakes. In: Ruiz, G.M., Carlton, J.T. (Eds.), Invasive Species: Vectors and Management Strategies. Island Press, Washington, DC, pp. 227–246. Feyrer, F., Herbold, B., Matern, S.A., Moyle, P.B., 2003. Dietary shifts in a stressed fish assemblage: consequences of a bivalve
invasion in the San Francisco Estuary. Environmental Biology of Fishes 67, 277–288. GESAMP (IMO/FAO/UNESCO-IOC/WMO/IAEA/UN/UNEP Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection), 1997. Opportunistic settlers and the problem of the ctenophore Mnemiopsis leidyi invasion in the Black Sea. GESAMP Reports and Studies, pp. 1–84. Grigorovich, I.A., Colautti, R.I., Mills, E.L., Holeck, K., Ballert, A.G., MacIsaac, H.J., 2003. Ballast-mediated animal introductions in the Laurentian Great Lakes: retrospective and prospective analyses. Canadian Journal of Fisheries and Aquatic Sciences 60, 740–756. Grosholz, E., 2002. Ecological and evolutionary consequences of coastal invasions. Trends in Ecology and Evolution 17, 22–27. Holeck, K.T., Mills, E.L., MacIsaac, H.J., Dochoda, M.R., Colautti, R.I., Ricciardi, A., 2004. Bridging troubled waters: biological invasions, transoceanic shipping, and the Laurentian Great Lakes. Bioscience 54, 919–929. Landrum, P.F., Sano, L.L., Mapili, M.A., Garcia, E., Krueger, A.M., Moll, R.A., 2003. Degradation of chemical biocides with application to ballast water treatment. NOAA Technical Memorandum GLERL-123, June 2003. Available from:. Leppakoski, E., Gollasch, S., Gruszka, P., Ojaveer, H., Olenin, S., Panov, V., 2002. The Baltic—a sea of invaders. Canadian Journal of Fisheries and Aquatic Sciences 59, 1175–1188. Leung, H.-W., 2001. Ecotoxicology of glutaraldehyde: review of environmental fate and effects studies. Ecotoxicology and Environmental Safety 49, 26–39. NRC (National Research Council), 1996. Stemming the tide: Controlling introductions of nonindigenous species by shipsÕ ballast water. Committee on ShipsÕ Ballast Operations and Marine Board Commission on Engineering and Technical Systems for the National Research Council. National Academy Press, Washington, DC. Nalepa, T.F., Hartson, D.J., Gostenik, G.W., Fanslow, D.L., Lang, G.A., 1996. Changes in the freshwater mussel community of Lake St. Clair: From Unionidae to Dreissena polymorpha in eight years. Journal of Great Lakes Research 22, 354–369. Niimi, A.J., 2000. Influence of vessel transit patterns on developing a ballast water treatment strategy for exotic species. Marine Pollution Bulletin 40, 253–256. Niimi, A.J., Reid, D.M., 2003. Low salinity residual ballast discharge and exotic species introductions to the North American Great Lakes. Marine Pollution Bulletin 46, 1334–1340. Sano, L.L., Moll, R.A., Krueger, A.M., Landrum, P.F., 2003. Assessing the potential efficacy of glutaraldehyde for biocide treatment of un-ballasted transoceanic vessels. Journal of Great Lakes Research 29, 545–557. Sano, L.L., Krueger, A.M., Landrum, P.F., 2005. Chronic toxicity of glutaraldehyde: Differential sensitivity of three freshwater organisms. Aquatic Toxicology 71, 283–296. Smith, T.M., Reynolds, R.W., 2003. Extended reconstruction of global sea surface temperatures based on COADS data (1854–1997). Journal of Climate 16, 1495–1510. Stein, M., 1987. Large sample properties of simulations using Latin Hypercube sampling. Technometrics 29, 143–151. Vanderploeg, H.A., Nalepa, T.F., Jude, D.J., Mills, E.L., Holeck, K.T., Liebig, J.R., Grigorovich, I.A., Ojaveer, H., 2002. Dispersal and emerging ecological impacts of Ponto-Caspian species in the Laurentian Great Lakes. Canadian Journal of Fisheries and Aquatic Sciences 59, 1209–1228. van Overdijk, C.D.A., Bailey, S.A., Duggan, I.C., MacIsaac, H.J., 2003. Transoceanic NOBOB vessels entering the Great Lakes: vectors for new invasions. In: Proceedings of the 46th Conference on Great Lakes Research. International Association for Great Lakes Research, pp. 106–107.
Decay model for biocide treatment of unballasted vessels: Application for the Laurentian Great Lakes Larissa L. Sano a, Steven M. Bartell b, Peter F. Landrum a
c,*
Cooperative Institute for Limnology and Ecosystems Research, University of Michigan, 2205 Commonwealth Boulevard, Ann Arbor, MI 48105, USA b E2, Inc., 339 Whitecrest Drive, Maryville, TN 37801, USA c Great Lakes Environmental Research Laboratory, NOAA, 2205 Commonwealth Boulevard, Ann Arbor, MI 48105, USA
Abstract A biocide decay model was developed to assess the potential efficacy and environmental impacts associated with using glutaraldehyde to treat unballasted overseas vessels trading on the Laurentian Great Lakes. The results of Monte Carlo simulations indicate that effective glutaraldehyde concentrations can be maintained for the duration of a vesselÕs oceanic transit (approximately 9–12 days): During this transit, glutaraldehyde concentrations were predicted to decrease by approximately 10% from initial treatment levels (e.g., 500 mg L1). In terms of environmental impacts, mean glutaraldehyde concentrations released at Duluth-Superior Harbor, MN were predicted to be 100-fold lower than initial treatment concentrations, and ranged from 3.2 mg L1 (2 SD: 2.74) in April to 0.7 mg L1 (2 SD: 1.28) in August. Sensitivity analyses indicated that the reballasting dilution factor was the major variable governing final glutaraldehyde concentrations; however, lake surface temperatures became increasingly important during the warmer summer months. Ó 2005 Elsevier Ltd. All rights reserved. Keywords: Ballast water; Biocide treatment; Biological invasions; Laurentian Great Lakes; Glutaraldehyde; Decay model
1. Introduction The release of ballast water currently constitutes one of the primary vectors for the global spread of aquatic invasive species (Grosholz, 2002; Leppakoski et al., 2002; Vanderploeg et al., 2002). The impacts of these invaders have been profound and include extirpation of native unionid species in the Laurentian Great Lakes (Nalepa et al., 1996), alterations in the trophic web of the San Francisco Estuary (Feyrer et al., 2003), and potential impacts on important fishery resources in the Black Sea (GESAMP, 1997). To address this pressing environmental issue, several countries either have
*
Corresponding author. Tel.: +1 734 741 2276; fax: +1 734 741 2055. E-mail address: [email protected] (P.F. Landrum).
0025-326X/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.marpolbul.2005.04.008
implemented or are currently developing standards for ballast water discharge. In addition, the United Nations International Maritime Organization (IMO) recently adopted ballast water management standards to facilitate international coordination for implementing management practices (Christen, 2004). One possible treatment option cited in the IMO standards is the use of ‘‘active substances’’ to eliminate potentially invasive organisms (a.k.a., harmful aquatic organisms and pathogens sensu IMO). The use of chemical biocides (or ‘‘biocides’’) falls under this purview and the acceptability of any given biocide depends partly on efficacy and environmental acceptability. With respect to biocide treatment, however, these two factors present competing demands: In order to be effective, biocide concentrations must be maintained at a high enough concentration and for a sufficient period of time to ensure targeted kill rates, yet the release concentrations
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of residuals must be low enough to protect against undesirable environmental impacts. Because of the vying demands between efficacy and environmental impacts, one of the more promising applications for biocide treatment may be for unballasted vessels trading in the Laurentian Great Lakes (or Great Lakes). Although technically classified as unballasted, overseas vessels that enter the Great Lakes declaring no-ballast-on-board (or NOBOBs) are currently one of the most important vectors for the release of invasive species. Because these vessels enter the Great Lakes carrying cargo, they usually contain only small amounts of residual water and sediments in their tanks (averaging approximately 60 m3 per vessel (Niimi and Reid, 2003)). These residuals, however, pose a risk for species introductions since they contain viable organisms (van Overdijk et al., 2003) and resting stages (Bailey et al., 2003) that are subsequently released into receiving waters during ballasting operations. Since NOBOBs enter carrying cargo, their usual trade pattern is to first visit ports in the lower portion of the lakes, where they unload their cargo and take on ballast water to maintain trim and stability. Most often these vessels then steam to ports in the upper Great Lakes, where they take on outbound cargo. As the vessels take on their outbound cargo load, they must release ballast water from their tanks and it is at this point that non-native species can be released into the Great Lakes. These reballasting operations are potentially advantageous with respect to biocide treatment, since they result in a large dilution of biocide residuals and permit additional decay time prior to release of any residuals into receiving waters. Because these two factors decrease final release concentrations, they mitigate against the potential environmental impacts associated with the higher biocide concentrations required for treatment efficacy. The objective of this study is to assess the potential efficacy and environmental release concentrations for the biocide glutaraldehyde by developing a biocide decay model configured for NOBOBs trading on the Great Lakes. Glutaraldehyde (1,5-pentanedial, CAS Registry no. 111-30-8) has been proposed as a candidate disinfectant for ballast water treatment (NRC, 1996; Sano et al., 2003) because it is both relatively effective against a range of microorganisms and degrades rapidly under natural conditions (Leung, 2001). Since glutaraldehyde decay is primarily a function of temperature and concentration, the decay model is configured to assess variations in degradation throughout different portions of a vesselÕs transit, prior to deballasting at a representative Great Lakes port. To address some of the variability and uncertainty associated with the model parameters, Monte Carlo simulations are used to generate concentration distributions that characterize treatment efficacy and potential release concentrations.
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2. Materials and methods 2.1. Decay equation The decay rate of glutaraldehyde was estimated by fitting an equation to laboratory data generated from previous water-sediment degradation experiments (Landrum et al., 2003). To capture potential conditions in representative NOBOB tanks, these experiments were conducted at a ratio of 1:4 sediment-to-water (by volume) using sediment with an organic carbon content of 2.6 ± 0.4%. Experiments were run at three different temperatures (5 °C, 15 °C, 25 °C), and at three initial glutaraldehyde concentrations (10 mg L1, 100 mg L1, 500 mg L1). The resulting data were fit to a decay function using the software ScientistÒ (Micromath, St. Louis, MO). Model output was then verified against laboratory results to confirm reproducibility. 2.2. Atlantic Ocean transit To estimate the degree of decay during transit across the Atlantic, treatment of NOBOB vessels was assumed to occur at Antwerp, Belgium, a common port of origin for overseas vessels that visit the Great Lakes (Colautti et al., 2003). A hypothetical vessel transit was constructed using a great-circle path from Antwerp to the entrance to the Gulf of St. Lawrence (i.e., at the Cabot Strait). Sea surface temperature observations from the Comprehensive Ocean-Atmosphere Data Set (COADS, for the years of 1950–1992) were used to construct a temperature profile for the transit. These data represent collections of surface marine data observations from a variety of platforms. The data have been trimmed using a procedure that identifies potential outliers based on the climatological 3.5 sigma limits derived from specified data periods (Smith and Reynolds, 2003). Defined by latitude/longitude data, the average monthly temperature for specified segments of the great-circle route was estimated. The weighted mean temperature of each segment was then calculated by dividing the average temperature for a given segment of the great-circle route by the time the vessel spent traversing that segment. This was calculated assuming an average vessel speed of 17.5 miles h1 (or approximately 15 knots). The sum of these weighted averages was used to estimate a mean weighted temperature for the entire transit. These temperatures are assumed to reflect ambient temperatures inside vessel tanks as they transit the Atlantic Ocean. Transit times for the Atlantic crossing were based both on distances for the great-circle path (described above and using a vessel speed of 15 knots) and on published transit times (from Fednav International, www.fednav.com). In terms of the latter, Fednav data for arrival and estimated shipping times were used to
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calculate a range of ship transit times to different Great Lakes ports from Antwerp. 2.3. Great Lakes transit The transition from the Atlantic to the Great Lakes portion of transit was assumed to occur as vessels enter the Gulf of St. Lawrence. For the Great Lakes segment, lake sea surface temperatures (SST) were derived from individual moored buoys, maintained either by the Marine Environmental Data Service (MEDS; Canada Department of Fisheries and Oceans, Ontario, Canada) or by the National Data Buoy Center (NDBC; National Oceanic and Atmospheric Administration, Mississippi, USA). For the MEDS buoys, any data points that were flagged as ‘‘doubtful,’’ ‘‘erroneous,’’ or ‘‘off position’’ were eliminated from analysis. In addition, some of the temperature readings when buoys were first deployed at the start of the season were much higher than subsequent readings; thus the first day of MEDS temperature measurements were also eliminated prior to analysis. In contrast to the temperature data for the Atlantic, the temperature data for the buoys in the Great Lakes transit segment were divided into 10-d increments based on the Julian day (JD). The average 10-d temperatures and the corresponding standard deviations were estimated for each buoy and used to calculate parameters for the decay model. Transit times between different ports within the Great Lakes were derived from three primary sources: nautical mileage charts, the St. Lawrence Seaway Authority (www.greatlakes-seaway.com), and Fednav shipping schedules. The transit times incorporated lockage times for passages through the Welland Canal, the Montreal/ Lake Ontario Locks, and the Sault Ste. Marie Lock system. For transit times based on nautical miles, vessels were assumed to travel at an average of 14 miles h1, ranging from 12 to 18 miles h1. The Duluth-Superior Harbor (Minnesota, USA) was selected as the final port-of-call for estimating potential release concentrations from ballast tanks. This site was selected since Lake Superior receives the majority of discharges from NOBOBs (Holeck et al., 2004) and because Duluth-Superior Harbor is a major traffic site for the NOBOBs that enter Lake Superior (Colautti et al., 2003). Although discharge of ballast water is likely to occur elsewhere in the Great Lakes, sites in Lake Superior are at greater risk of multiple releases from NOBOBs due to vessel trafficking patterns. 2.4. Reballasting dynamics The reballasting dynamics of NOBOB vessels were derived from Colautti et al. (2003), from Fednav shipping schedules, and from surveys collected from
NOBOB ship captains (unpublished data, David F. Reid, Great Lakes Environmental Research Laboratory, NOAA, Ann Arbor, Michigan, USA). The associated reballasting data used for the decay model included the major ports of call for NOBOBs, the average time vessels spent at a port of call, and the amount of dilution involved in reballasting operations (i.e., the ‘‘dilution factor’’ in the model), respectively. 2.5. Monte Carlo simulations Monte Carlo simulations for the biocide decay model were run using Crystal BallÒ software (Version 5.5, Decisioneering, Denver, Colorado, USA). The model generated estimates of glutaraldehyde concentrations for different points in the transit of a NOBOB across the Atlantic, through the Gulf of St. Lawrence and St. Lawrence River, and finally to Duluth-Superior Harbor. The temperature and transit data were defined as distributions that reflect the data uncertainty described in the previous sections. In addition, the water temperature data were assigned the following correlations: the SST for the Atlantic was correlated with the SST of the Gulf of St. Lawrence, and the SST for the different Great Lakes were correlated with each other. All correlations were calculated using SYSTATÒ (Version 10, SPSS, Inc., Chicago, IL, USA). The biocide decay model was run using a ‘‘best-case’’ and a ‘‘worst-case’’ reballasting scenario. The best-case scenario assumes that a vessel reballasts at the port of Oswego (New York, USA: Fig. 1). This site is located on Lake Ontario, and permits more time between reballasting and release at Duluth-Superior Harbor. The worst-case scenario involves reballasting at the port of Detroit (Michigan, USA: Fig. 1). Because most decay occurs at lower concentrations of glutaraldehyde, this represents a conservative estimate of biocide concentration at Duluth-Superior, since less time would elapse between reballasting and release. Based on these two reballasting scenarios, simulations were conducted in 10-d JD increments. The number of trials per time period was 2000, and the Latin Hypercube method (Stein, 1987) was used to sample from the individual distributions. Sensitivity analyses were then performed for each 10-d period.
3. Results 3.1. Governing decay equation The decay function that best fit the laboratory degradation data described a first-order decay function and a power function: .701 Þ k d ¼ ð0.0120Þ expð0.1010T ÞðC 0 x
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Fig. 1. Map of the Great Lakes basin, including the major ports of call referenced in this study. The lines depict the general transit route of NOBOB vessels used to develop the biocide decay model.
where kd is a decay constant (t1), T is water temperature (°C), and Cx is the beginning glutaraldehyde concentration for a given segment of the transit (mg glutaraldehyde L1). The R2 value for this equation was 0.999. One value from the original laboratory data was excluded from analysis, because it was deemed an outlier. The omission of this value did not strongly affect the resulting decay coefficient, but provided a better model fit to the data. The decay constant was then used to estimate the first-order degradation of glutaraldehyde throughout a vesselÕs transit. Up until reballasting, degradation was represented by
of glutaraldehyde are different at higher and lower concentrations. At higher concentrations (e.g., >20 mg L1), glutaraldehyde degrades primarily through abiotic processes, while at lower concentrations (e.g., <10 mg L1), it also decays via biodegradation (Leung, 2001; Landrum et al., 2003). This difference in degradation pathways is reflected through the use of two different glutaraldehyde concentrations, one for the initial (treatment) concentration and one for the reballasted (diluted) concentration.
dC ¼ kdCi dt
The SST data for the different portions of a NOBOB transit are presented in Table 1. The SST observations for the Atlantic segment covered more than four decades, from 1950 to 1992, while the SST for the Great Lakes segment generally ranged from the mid-1980s up through 2003. Because of differences in resolution, temperature data were averaged either monthly (for the Atlantic data and the St. Lawrence River data) or in 10-d JD increments (for the remaining data). In addition, data quality was sufficient for lakes Huron and Superior to use SST readings from two different buoys within each lake; thus, these readings represent averaged data from two different buoys within each lake. For the remaining two lakes (Ontario and Erie), temperature data were limited to one buoy per lake.
where Ci is the initial treatment concentration of glutaraldehyde at time t0 (averaging 500 mg L1), and t is the time period of the first segment of the transit, up until reballasting. The initial treatment concentration was based on the highest 24-h LC90 reported in Sano et al. (2003). After a vessel reballasted, the equation was reset to use the new, diluted glutaraldehyde concentration and the decay equation became: dC ¼ kdCd dt where Cd is the diluted concentration of glutaraldehyde. This approach was necessary, since the decay dynamics
3.2. Temperature data
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Table 1 List of variables and associated values developed for the biocide decay model Variable
Mean
Min
Max
Distribution
Initial treatment concentration Initial glutaraldehyde concentration (mg L1)
500
450
550
Normal
Reballasting factors Dilution factor (expressed as #-fold dilution) Time unloading cargo/reballasting (h)
106 48
36 24
178 72
Triangular Triangular
Temperatures Atlantic temperature during transit (°C) Gulf of St. Lawrence temperature (°C) St. Lawrence River temperature (to Oswego: °C) Lake Ontario temperature (°C) Lake Erie temperature (°C) Lake Huron temperature (°C) Lake Superior temperature (°C)
Varies Varies Varies Varies Varies Varies Varies
Transit times Atlantic transit time (h) Gulf of St. Lawrence transit time (h) St. Lawrence transit time (h) Lake Ontario transit time (h) Lake Erie transit time (h) Lake Huron transit time (h) Lake Superior transit time (h)
168 72 27 24 19 26 28
5.5 1.8 5.4 1.9 4.1 1.9 1.8 156 62 20 18 15 20 22
12.1 14.7 21.6 21.9 23.9 18.9 13.1 216 84 29 28 22 30 32
Normal Normal Normal Normal Normal Normal Normal Triangular Triangular Triangular Triangular Triangular Triangular Triangular
For this model, sea surface temperatures are used as a proxy for ambient ballast tank temperatures. Mean values for lake temperatures are not provided, since water temperatures varied temporally. For all triangular distributions, the ‘‘mean’’ is actually represented by the mode. See accompanying text for additional details.
3.3. Transit data Fig. 1 presents the transit route selected for the biocide decay model. To generate more conservative estimates of glutaraldehyde concentrations, it was assumed that vessels did not visit any ports in Lake Michigan, and instead navigated directly through lakes Huron and Superior to Duluth-Superior Harbor. Table 1 lists the estimated transit times for NOBOBs. Because data were limited, these model parameters were described using triangular distributions. 3.4. Predicted efficacy concentrations Results from the biocide decay model indicate that glutaraldehyde concentrations should not decay substantially prior to reballasting at a Great Lakes port (Tables 2 and 3). Because the conditions for the first portion of the transit were similar, the reballasting scenario did not affect predicted concentrations until the point of reballasting (at port Oswego for the best-case scenario and at Detroit for the worst-case scenario). Examples of the expected distribution of glutaraldehyde concentrations after the Atlantic portion of the transit are provided in Fig. 2. The variation in these concentrations largely reflects the variability in the initial treatment concentration. The amount of decay predicted to occur during the Atlantic segment varied by
month, with the warmer, summer months resulting in slightly higher decay. On average, the predicted mean concentration prior to entering the Gulf of St. Lawrence for both reballasting scenarios was 464 mg L1, representing a 7% decrease from the initial treatment value. Glutaraldehyde concentrations were predicted to continue to decrease after the vessels entered the Gulf of St. Lawrence. At the port of Oswego, the mean glutaraldehyde concentration was predicted to be approximately 439 mg L1 (2 SD: 58), representing a 12% decrease from treatment concentrations. The lowest predicted mean concentration for the port of Oswego was 404 mg L1 (2 SD: 60) and occurred in August (JDs 211–220). For those vessels reballasting in Detroit, treatment concentrations continued to decrease during transit through lakes Ontario and Erie. The mean glutaraldehyde concentration predicted at the port of Detroit was 418 mg L1 (2 SD: 60). Similar to the Atlantic data, there were temporal differences in concentrations, with the predicted mean concentration decreasing during the summer months and reaching minimum values in August. The lowest predicted mean concentration at the port of Detroit was 371 mg L1 (2 SD: 64). Similar to the results for the port of Oswego, this minimum mean concentration was also predicted to occur in August (JD 211–220).
L.L. Sano et al. / Marine Pollution Bulletin 50 (2005) 1050–1060
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Table 2 Predicted glutaraldehyde concentrations under the best-case reballasting scenario at different sites specified by the biocide decay model Julian day
Atlantic mean conc. (mg L1)
Gulf of St. Lawrence mean conc. (mg L1)
Port Oswego mean conc. (mg L1)
Welland Canal mean conc. (mg L1)
Port of Detroit mean conc. (mg L1)
Sault Ste. Marie mean conc. (mg L1)
Duluth Harbor mean conc. (mg L1)
91
475.3 55.8
468.5 56.1
462.3 56.9
3.6 2.9
3.2 2.8
2.8 2.7
2.5 2.5
121
472.3 56.2
464.0 57.0
454.0 57.7
3.2 2.8
2.6 2.6
2.3 2.5
2.0 2.3
151
466.3 56.8
454.5 57.3
436.0 58.2
2.2 2.5
1.6 2.1
1.3 1.9
1.2 1.8
181
458.7 56.9
441.4 57.8
413.6 59.6
1.0 1.6
0.5 1.1
0.4 0.9
0.3 0.8
211
454.2 56.8
432.0 58.8
403.8 60.2
0.5 1.1
0.2 0.6
0.1 0.4
0.1 0.4
241
457.3 56.8
440.0 58.2
421.9 60.4
0.7 1.3
0.3 0.8
0.2 0.6
0.1 0.4
271
463.7 57.0
451.9 57.6
439.9 58.3
1.5 2.1
1.0 1.7
0.7 1.3
0.5 1.1
301
469.2 56.7
461.6 56.7
453.7 57.6
2.2 2.5
1.8 2.2
1.4 1.9
1.2 1.7
The estimates represent mean values of glutaraldehyde based on the best-case reballasting scenario, in which ballast water is taken on at port Oswego. The values for port Oswego indicate glutaraldehyde concentration prior to reballasting. Values in italics are for 2 SD based on the output from the Monte Carlo simulations. Table 3 Predicted glutaraldehyde concentrations under the worst-case reballasting scenario at different sites specified by the biocide decay model Julian day
Atlantic mean conc. (mg L1)
Gulf of St. Lawrence mean conc. (mg L1)
Port Oswego mean conc. (mg L1)
Welland Canal mean conc. (mg L1)
Port of Detroit mean conc. (mg L1)
Sault Ste. Marie mean conc. (mg L1)
Duluth Harbor mean conc. (mg L1)
91
474.6 55.98
467.81 56.3
464.57 56.6
462.55 56.6
457.37 56.74
3.68 2.88
3.22 2.74
121
471.59 56.36
463.25 57.14
458.09 57.42
455.44 57.56
446.71 58.2
3.53 2.8
3.09 2.66
151
465.56 57.08
453.74 57.46
444.1 57.82
439.46 58.38
422.81 60.48
3.06 2.64
2.64 2.46
181
457.97 57.2
440.61 58
426.09 58.74
416.86 59.54
389.3 61.88
1.95 2.36
1.64 2.18
211
453.36 57.06
431.18 59.02
416.45 59.58
402.86 60.8
370.8 63.64
0.93 1.48
0.7 1.28
241
456.54 57.1
439.24 58.42
429.81 59.36
417.15 60.64
387.77 63.74
1.04 1.62
0.7 1.3
271
462.93 57.22
451.15 57.78
444.9 58.1
437.09 59.12
417.75 61.16
1.89 2.2
1.43 1.92
301
468.46 56.9
460.8 56.9
456.71 57.28
451.56 57.48
440.78 58.14
2.7 2.54
2.22 2.32
The estimates represent mean values of glutaraldehyde based on the worst-case reballasting scenario, in which ballast water is taken on at the port of Detroit. The values for port of Detroit indicate glutaraldehyde concentration prior to reballasting. Values in italics are for 2 SD based on the output from the Monte Carlo simulations.
3.5. Predicted Duluth-Superior Harbor concentrations The final predicted concentrations at Duluth-Superior Harbor were substantially lower than treatment
concentrations, primarily due to the dilution factor associated with reballasting. The estimated mean final concentrations at Duluth-Superior ranged from 3.2 mg L1 to 0.1 mg L1 and differed depending on whether
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(a)
(b)
Fig. 3. Predicted mean glutaraldehyde concentrations at DuluthSuperior Harbor, MN. Data are provided for both the worst- and bestcase reballasting scenarios. The error bars represent ±2 standard deviations from the mean.
(a)
Fig. 2. Frequency distribution of predicted glutaraldehyde concentrations after the Atlantic transit, prior to entering the Gulf of St. Lawrence. Data were generated under the worst-case reballasting scenario and are provided for (a) April, Julian days 91–100, and (b) August, Julian days 211–220.
the best-case or worst-case reballasting scenario was modeled (Fig. 3). Equally important was the high degree of right skewness in the final concentrations, which resulted in a wide range of minimum and maximum values. For the August time period of 211–220 JD, for example, the mean estimated glutaraldehyde concentration for the worst-case scenario was 0.70 mg L1 (Fig. 4). The predicted ballast tank concentrations during this time period, however, could be as high as 4.90 mg L1, although this concentration had less than a 1% chance of occurring. The predicted final concentrations at Duluth-Superior also varied temporally: Concentrations were higher in the early spring (JDs 91–121) than in mid-summer (JDs 201–251). Simulated concentrations began to increase again in the fall, with the return of lower water temperatures (see Fig. 3). The magnitude of difference in glutaraldehyde concentrations, however, was again affected by the high degree of data variation. For example, although the predicted mean concentration in April
(b)
Fig. 4. Frequency distributions for predicted glutaraldehyde concentrations at Duluth-Superior Harbor, MN. The black bar graphs are results from the best-case reballasting scenarios, and the gray bar graphs are results from the worst-case reballasting scenarios. Data are presented for (a) April, Julian days 91–100, and (b) August, Julian days 211–220. Note that in graph a, values greater than 7.0 mg L1 are expressed as a single occurrence frequency.
L.L. Sano et al. / Marine Pollution Bulletin 50 (2005) 1050–1060
(JD 91) for the worst-case reballasting scenario was almost five times higher than the mean concentration for August (JD 211), the associated distributions of concentrations overlap, suggesting that comparable minimum and maximum concentrations can occur during these two months. 3.6. Sensitivity analysis For the overall ballast tank decay model, the importance of the different variables depended on the specified endpoint. Under the best-case reballasting scenario (with water taken on at port Oswego), the most influential variable on predicted efficacy was the initial glutaraldehyde treatment concentration (Table 4). This variable consistently had a rank correlation value >0.80 and was much higher than any of the other variables. This trend was similar to that observed for the worst-case reballasting scenario (with reballasting at the port of Detroit): Glutaraldehyde concentrations at
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all points until reballasting were determined mainly by the initial treatment concentration. Other variables, such as Atlantic SST had a small contribution in comparison, with rank correlations values of averaging 0.40. In terms of simulated ballast tank concentrations for vessels arriving at the Duluth-Superior Harbor, the dilution factor had the greatest impact on glutaraldehyde concentrations under both best- and worst-case reballasting scenarios (Table 5). For the best-case scenario, the rank correlation for the dilution factor ranged from 0.95 to 0.71, with the rank correlation decreasing during the summer months. In contrast, both the temperature of Lake Ontario and of Lake Erie had relatively small impacts during early spring, but demonstrated large increases in the rank correlation parameter during the summer months (reaching as high as 0.57). In comparison, other variables such as treatment concentration and transit times had relatively constant rank correlation values.
Table 4 Sensitivity analysis of the variables affecting the treatment concentration (i.e., efficacy) of glutaraldehyde up to the port of Oswego, NY Parameter
April JD 91
May JD 121
June JD 151
July JD 181
August JD 211
September JD 241
October JD 271
November JD 301
Best-case scenario Treatment concentration Atlantic SST Gulf St. Lawrence SST St. Lawrence River SST
0.93 0.33 0.30 0.29
0.92 0.36 0.34 0.32
0.90 0.40 0.38 0.36
0.86 0.45 0.44 0.42
0.84 0.48 0.47 0.45
0.86 0.46 0.45 0.43
0.90 0.40 0.38 0.36
0.92 0.36 0.33 0.32
Worst-case scenario Treatment concentration Atlantic SST Gulf St. Lawrence SST St. Lawrence River SST
0.94 0.35 0.31 0.28
0.93 0.38 0.35 0.31
0.91 0.44 0.42 0.35
0.87 0.49 0.48 0.41
0.85 0.52 0.51 0.44
0.87 0.50 0.49 0.41
0.91 0.44 0.41 0.35
0.93 0.32 0.36 0.31
The data are presented as rank correlation values and provided for both the best- and worst-case reballasting scenarios (i.e., reballasting at port Oswego and port of Detroit, respectively). The time period for the results is the first 10-d Julian day increment per month. Lake sea surface temperature is abbreviated as SST.
Table 5 Sensitivity analysis of the variables affecting final glutaraldehyde release concentrations at the Duluth-Superior Harbor Parameter
April JD 91
May JD 121
June JD 151
July JD 181
August JD 211
September JD 241
October JD 271
November JD 301
Best-case scenario Dilution factor Treatment concentration Lake Ontario temp Lake Erie temp Time at port
0.95 0.19 0.18 0.17 0.13
0.91 0.19 0.30 0.29 0.15
0.80 0.18 0.49 0.47 0.19
0.73 0.17 0.55 0.54 0.24
0.71 0.17 0.51 0.56 0.26
0.71 0.17 0.57 0.56 0.24
0.74 0.17 0.56 0.55 0.21
0.87 0.18 0.36 0.35 0.21
Worst-case scenario Dilution factor Treatment concentration Lake Huron temp Lake Erie temp Atlantic temp
0.96 0.18 0.12 0.12 0.09
0.96 0.18 0.14 0.14 0.11
0.91 0.18 0.30 0.29 0.21
0.71 0.16 0.63 0.58 0.39
0.72 0.17 0.59 0.55 0.38
0.73 0.16 0.59 0.55 0.37
0.81 0.17 0.49 0.46 0.32
0.91 0.18 0.29 0.27 0.20
Reballasting scenarios are presented for both the best-case (reballasting at port Oswego) and worst-case (reballasting at port of Detroit) scenarios. Data are presented as the rank correlation values. The Julian day represents the first day in the 10-d simulation time segment per month.
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The general trend in sensitivity analysis for the bestcase reballasting scenario also applies to the worst-case reballasting scenario: The dilution factor had the largest effect on concentrations at Duluth-Superior Harbor. During the summer months (primarily July and August: Table 5), however, water temperatures in Lake Huron and Lake Erie has an increasing effect on predicted final ballast tank concentrations.
4. Discussion A biocide decay model was developed to represent the potential treatment of Great Lakes NOBOB vessels with glutaraldehyde. The parameters used in developing the model were designed to reflect potential treatment scenarios and, more importantly, to help explore the variability associated with ship trafficking patterns, reballasting dynamics, and biocide degradation rates. As such, this model is meant to characterize potential treatment concentrations and residual release concentrations and to describe the level of uncertainty and variability in the resulting predictions. 4.1. Treatment efficacy In terms of treatment efficacy, the results from these simulations suggest that glutaraldehyde concentrations can be maintained throughout the initial segment of NOBOB transits. From the time the vessel departs its port of origin (selected as Antwerp, Belgium for this application) until it reballasts at a Great Lakes port, the average amount of degradation was predicted to be approximately 20%. Because of the increasing temperatures of the St. Lawrence River and the Great Lakes compared to the Atlantic, more than half of this degradation is expected to occur later in the journey. The ability to maintain initial glutaraldehyde concentrations, ranging from 450 mg L1 to 550 mg L1, during the trans-Atlantic journey is essential for treatment efficacy. The results from this model compare favorably with results from acute toxicity bioassays using glutaraldehyde (Sano et al., 2003). In these experiments, the 24-h 90% lethal concentration of glutaraldehyde for the most resistant species tested (i.e., the amphipod, Hyalella azteca) in a 1:4 sediment–water exposure was estimated to be 563 mg L1 (95% CI: 499–685), and the 50% lethal concentration was estimated to be 323 mg L1 (95% CI: 286–354). If the model predictions are accurate, this indicates that lethal concentrations of glutaraldehyde might be maintained for a much longer period than the 24-h exposure estimates provided from laboratory experiments. This should both help assure predicted kill rates and may allow for a reduction in treatment levels if adequate concentrations are maintained for longer than 24 h.
4.2. Environmental impacts In terms of environmental releases, the results from these simulations indicate that the final residual glutaraldehyde concentrations in tanks reaching Duluth-Superior Harbor are likely to vary both between different vessels and over the course of the shipping season. For most of the simulation conditions, the mean residual concentration was predicted to range between 1 mg L1 and 3 mg L1. The distributions for these data, however, were skewed, and it is important to consider the possibility of higher residual concentrations. Assuming a worst-case reballasting scenario, for example, there is a predicted 10% chance of residual ballast tank concentrations of 5 mg L1 in April (JDs 91–100). The maximum simulated glutaraldehyde concentrations reach as high as 10 mg L1 during April, but have a low probability of occurring (less than 1%). These high concentrations were limited to the months of April and May, since ballast tank concentrations were predicted to be highest during these two months and lowest during the late summer and early fall. The actual environmental impact of average release concentrations of glutaraldehyde in the range from 1 to 3 mg L1 will depend largely on diffusive and advective processes in the vicinity of the slips and ports, where most deballasting occurs. The results from chronic toxicity bioassays indicate that phytoplankton and fish larvae may be impacted by long-term exposure to glutaraldehyde concentrations as low as 1 mg L1 (Sano et al., 2005). A synthesis of toxicity data by Leung (2001) yielded an environmental probable-no-effect-concentration (PNEC) of 31 lg L1. These data suggest that ballast tank concentrations can reach effect-level concentrations; however, the potential for chronic toxicity associated with glutaraldehyde release into receiving waters will likely be moderated by its rapid decay rate, particularly at lower concentrations. For example, the estimated half-life of 10 mg L1 at 15 °C is approximately 35 h (Landrum et al., 2003). At warmer temperatures (25 °C), the half-life has been estimated to range from 10 h (Leung, 2001) to 23 h (Landrum et al., 2003). Thus, even if effect-level concentrations are reached, they may not be maintained for a sufficiently long period to elicit chronic impacts in most species. Despite these mitigating factors, it is still possible that given the appropriate configuration of events (such as multiple releases from ships containing higher residual concentrations of glutaraldehyde), effect-level concentrations may be attained. The implications of these types of exposures should be addressed in future studies to better characterize the potential for ecological impacts. 4.3. Model limitations The decay model developed here is designed to provide a simplified, yet realistic, representation of ballast
L.L. Sano et al. / Marine Pollution Bulletin 50 (2005) 1050–1060
tank dynamics. In general, this model was developed with relatively conservative assumptions regarding NOBOB dynamics in the Great Lakes. There are several limitations to the application of this model that are worthy of discussion. The first concerns the governing equation used in developing the model. This decay equation was derived from a set of degradation experiments using glutaraldehyde. Because experiments were conducted at a single sediment–water volume and at only three temperatures, the resulting decay equation is limited in its ability to describe glutaraldehyde decay dynamics under a wider range of chemical and physical conditions. For example, glutaraldehyde decay rates will likely be faster than predicted by this model for tanks that contain either larger amounts of sediment or sediments with higher organic carbon content or higher bacterial loads. In contrast, decay rates will likely be slower in tanks with lower sediment–water ratios or with a lower level of bacteria. For ballast tank environments that fall well outside of the test conditions, this fundamental decay equation might not apply. Another model limitation concerns transit routes. The ship routes selected for this model are representative of those taken by NOBOBs, but cannot be considered specific for any given vessel. This is largely due to the inherent variability in the trade routes of overseas vessels: A large percentage of overseas vessels that enter the Great Lakes have neither regular nor defined trade routes (Niimi, 2000). Grigorovich et al. (2003) found that between 1983 and 1998, overseas vessels arriving into the Great Lakes reported 460 different last ports-of-call, from all over the world. The general trend in the data, however, was that most vessels arriving into the Great Lakes came from European ports. Thus, for this model, the port of Antwerp was selected as a representative European port-of-call. This should provide a representative route for a hypothetical NOBOB vessel and yield a more conservative transit time estimate, since Antwerp is more westerly than many other major port cities (such as Copenhagen, Helsinki, Ventspils, or St. Petersburg). The representation of transit routes within the Great Lakes was also limited, since only two ports were used for the reballasting scenarios developed for this model. In reality, the reballasting dynamics of NOBOBs are more complicated, with vessels often unloading (and thus reballasting) at multiple ports throughout the Great Lakes. This complex ballasting pattern will affect the decay dynamics of glutaraldehyde within the ballast tanks and create potential ecological risks at sites other than the Duluth-Superior Harbor. Despite these limitations, the output from this model should provide preliminary estimates of potential ballast tank concentrations under different reballasting scenarios and provide ranges of exposure concentrations at a Great Lakes port that receives a high volume of NOBOB discharges.
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To address the limitations of other elements of the model, it is appropriate to focus on the model parameters that have a disproportionate impact on the predicted ballast tank concentrations. In terms of efficacy, the most important model variable was initial treatment concentration. This variable had the highest rank correlation value (of approximately 0.90), which was much higher than the next most important variable, Atlantic Ocean temperature (with a rank correlation of approximately 0.40). In addition, the importance of this parameter did not differ between the two reballasting scenarios and did not change substantially over the shipping season. Thus, it can be assumed that if the decay equation is representative of the ballast tank conditions, then the biggest variable affecting efficacy will be the initial treatment concentration. As for residual biocide concentrations at the DuluthSuperior Harbor, the most important variable affecting final concentrations was the reballasting dilution. This parameter was derived from surveys filled out by vessel captains. Although a large number of NOBOB vessels reported their ballasting operations, data quality is not verifiable. Because this parameter is essential for predicting final release concentrations, it is imperative that these estimates be verified prior to making decisions regarding the potential for environmental impacts. In summary, the results from this model provide information necessary for a more rigorous assessment of the feasibility of biocide treatment of vessels. Because the continued release of non-native, and potentially harmful, organisms constitutes a large risk to freshwater and marine ecosystems throughout the world, it is imperative that comprehensive ballast water standards be implemented as rapidly as possible. Although several criteria have been proposed for selecting treatment technologies, the efficacy and environmental acceptability of treatment methods are two primary considerations. The output from this decay model provides one method for evaluating glutaraldehyde treatment of unballasted vessels under varying treatment scenarios. These types of data are critical for evaluating treatment options, prior to management implementation.
5. Conclusions The results from this model indicate that for the compound glutaraldehyde, biocidal concentrations can be maintained throughout the initial period of transit, until the point of reballasting. After reballasting, concentrations were predicted to decrease by approximately 100fold, and this dilution should help mitigate against the potential for environmental impacts. At a representative Great Lakes port (Duluth-Superior Harbor), the predicted mean release concentrations ranged between 1 and 3 mg L1, with the higher concentrations tending
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to occur in the early spring and late fall. The predicted concentrations during the summer months averaged less than 1 mg L1. All of these values are hypothetical, as glutaraldehyde has not yet been used to treat any vessel trading on the Great Lakes. Based on this model, the most important variables affecting efficacy and release concentrations were predicted to be treatment concentration and reballasting dilution, respectively. Although a target treatment concentration of 500 mg L1 glutaraldehyde was used in these simulations, the ability to maintain biocidal concentrations longer than 24 h may indicate that lower concentrations could be used without compromising efficacy. In terms of environmental impacts, the simulated release concentrations from this model should pose minimal risk for long-term ecological impacts provided that glutaraldehyde decays relatively rapidly and that advection and diffusion are adequate to flush the slip areas. Despite this, the possibility remains that glutaraldehyde concentrations could be sufficiently high in the immediate vicinity of slips at limited times of the year to cause ecological impacts. Additional studies characterizing the types of, and evaluating the likelihood of, ecological impacts are therefore warranted prior to further consideration of this treatment approach.
Acknowledgments This project was funded in part through the Cooperative Institute for Limnology and Ecosystems Research, University of Michigan, under cooperative agreement (NA67RJ0148) from the Office of Oceanic and Atmospheric Administration, US Department of Commerce. We thank David F. Reid and Stephen Constant for providing data from NOBOB captain surveys that were critical to model development. We also thank David Reid and an anonymous reviewer for their editorial comments. This manuscript constitutes GLERL contribution #1343.
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