Table of Contents
- Section 1
- Section 2
- Section 3
- Section 4
- Section 5
- Section 6
- Section 7
- List of Figures
- List of Tables
The Effect of Zebra Mussels on Cycling and Potential
Bioavailability of PCBs: Case Study of Saginaw Bay
The input data
required to run this modeling framework can be categorized as: 1) system
specific data; 2) forcing functions, loadings, boundary, and initial
conditions; 3) process-related parameters; 4) chemical-specific
parameters. The following section presents a description of the model
input data and the sources of the data for the four categories used in
3.1.1 System-Specific Data
The details on the system specific data are provided in Bierman and Mcllroy (1986). The coupled framework uses the same water column segmentation scheme presented in previously developed coupled phytoplankton zebra mussel model, SAGZM (LTI 1997).
3.1.2 Forcing Functions, Loadings, Boundary, and Initial Conditions
forcing functions used for the present modeling work were the same as they
were used in previous studies (LTI 1995, 1997). However, the following
section presents the description of forcing functions and boundary and
initial conditions for the PCB modeling framework.
PCB loadings originated from tributary inputs, atmospheric deposition, and the input across the Lake Huron boundary. The major input of PCBs is from tributary loadings, principally from Saginaw River. As reported in literature, the predominant source of PCBs within the lower Saginaw River is most likely due to sediment deposition and resuspension processes. Studies also reveal that the presence of an exterior point source within the lower reach of the Saginaw River can not be completely ruled out (Verbrugge et al. 1995).
Saginaw River loading rates were determined by calculating the product of flow and concentration. Daily average flow rates for 1991 were obtained from U.S. Geological Survey (USGS) for Saginaw River. The daily concentrations of PCBs in the dissolved and particulate phases were predicted from the linear regression of concentration of PCBs versus discharge, as reported by Verbrugge et al. (1995). An annual time series for PCB loading from Saginaw River to Saginaw Bay was calculated from flow data and concentration (Figure 3.1).
Integration of the time series resulted in the total annual river loading to the bay, which was approximately 200 kg. The historic trends in mean concentrations of PCBs at the outlet of the Saginaw River are reported in literature (Table 3.1), which clearly shows a decreasing trend in PCB concentration at the outlet of Saginaw River since 1972.
|Table 3.1: PCB Concentration at the Outlet of Saginaw River|
|1972||1,250||Limno-Tech, Inc. 1983|
|1979||175||Richardson et al. 1983|
|1991||31||Verbrugge et at. 1995|
The boundary conditions required for the model was the PCB concentration in Lake Huron waters and PCB air concentrations. The PCB concentrations in Lake Huron since 1979 have been reported in literature (Table 3.2). A value of 0.14 ng/L was taken as boundary concentration from measurements carried out in Spring 1993 by Anderson et al. (1999). Boundary exchange constituted a sink for PCBs because the concentrations were always higher in the bay as compared to the concentration in Lake Huron.
|Table 3.2: PCB Concentration in Lake Huron Water|
|1979||9.0||Stevens and Neilson 1989|
|1986||0.63||Stevens and Neilson 1989|
|1988||0.54||Mullin 1992 (taken from Endicott and Kandt 1994|
|1993||0.14||Anderson et at. 1999|
In this modeling work, an estimated
atmospheric deposition load of PCBs, 12 kg, to Saginaw Bay (Endicott et
al. 1991) was applied.
Zebra mussel densities were estimated using data reported by Nalepa et al. (1995) and additional data provided by the National Oceanic and Atmospheric Administration (NOAA), Great Lakes Environmental Research Laboratory (GLERL). The same data was used in the previous studies (LTI 1995, 1997). The primary data consisted of size frequency distributions, density estimates on hard and soft substrates, and biomass estimates. Zebra mussel numbers were assigned to three year classes based upon reported size-frequency distributions.
There are large spatial-temporal differences in total zebra mussel densities and in distributions of zebra mussel numbers among different cohort groups. Four different zebra mussel density conditions were used in the predictive simulations: (1) zero zebra mussels; (2) 1991-1995 average zebra mussel densities; (3) 1991-1995 average zebra mussel densities plus 100%; and (4) 1991-1995 average zebra mussel densities minus 50%. Figure 3.2 depicts estimated inner bay average zebra mussel densities from 1991 to 1995.
The range of zebra mussel densities in the predictive simulations was not centered about 1991 conditions because these conditions were not fully representative of zebra mussel densities over the period 1991 to 1995. Zebra mussels in 1991 consisted primarily of large numbers of young-of-year (YOY), with the first large recruitment not occurring until late summer (Nalepa et al. 1995). Zebra mussels in other years consisted of much larger numbers of adults that were present during the entire growing season.
For a dynamic model, knowledge of initial conditions is required, which is often hard to obtain. For this study, PCB concentration in the sediments at the beginning of simulation period was not known. So to generate the initial conditions, the model was run for a long period of time to steady-state and the PCB concentration at the steady-state were assumed as the initial PCB concentration in the sediments.
For different zebra mussel age classes, the initial PCB concentration in the mussels were calculated in the following fashion: at the steady-state year, the initial PCB concentration in <1 year old class was assumed to be zero. The PCB concentration of this class of mussels at the end of the year served as the initial PCB conditions of 1-2 year old mussels and PCB concentration at the end of the year in 1-2 year old class delivered initial conditions of >2 year olds. The underlying assumption of this approach was: each mussel of respective class moves to the next higher age group, at the end of a year. For example, <1 year old mussels are moved to 1-2 year old mussels and similarly, 1-2 year old mussels are moved to >2 year old class. Therefore, the associated PCBs in lower age class at the end of a year become the initial conditions for PCBs in one-step higher age group mussels.
The developed model was used to gain insights into the PCB dynamics of the system with and without zebra mussel. The analysis was carried out at a process level to gain understanding of various processes affecting the contaminant dynamics. In this study, the loss processes for PCBs in the water column include settling associated with different type of solids, volatilization, and uptake of solids associated and dissolved PCBs by zebra mussels. The diffusive exchange between PCBs of the water column and interstitial sediment waters serve as a gain or a loss of contaminants depending on their relative concentration.
In the presence of mussels, the loading of PCBs to the sediments includes the settling of PCBs associated with 1) biotic, abiotic, and detritus solids and 2) feces and pseudofeces. This creates a potential transfer of PCBs to the benthic food web, thereby affecting the pelagic food web contamination. For the sediments, the loss processes for PCBs include resuspension and burial to the deeper layers.
Again all the process related parameters were kept the same as those in LTI (1995, 1997) studies. The settling, resuspension, and burial rates for PCBs used in the model have the same values as those for solids since solids are the carriers of PCBs (Tables A1 and A2 in Appendix). However, all these rates were different for different types of solids.
3.1.4 Chemical Specific Parameters
- Partition Coefficient and Henry’s Law Constant
The dissolved and solid-normalized particulate PCB average homolog profiles for Lake Huron were used for calculating weighted average molecular weight and Henry’s Law constant for total PCBs. The dissolved phase homolog profiles as presented by Anderson et al. (1999) showed tetrachlorobiphenyl congeners were the most abundant, followed by trichlorobiphenyls and pentachlorobiphenyls. The particulate phase homolog profiles showed increased proportions of higher chlorinated PCBs as compared to the dissolved phase. The dominant homolog groups for particulate phase were the tetrachlorobiphenyls through heptachlorobiphenyls (Anderson et al. 1999).
- Partition Associated with Bioaccumulation Modeling Framework
The bioaccumulation modeling framework contains two broad classes of parameters:
- those associated with the specific chemicals: uptake and excretion rates and chemical assimilation efficiency
- those associated with organism physiology and behavior: growth rates and feeding preferences
Some of the
parameters were taken from the original modeling framework, which were
either internally calculated or estimated from literature values. For the
unavailable parameters, estimates were obtained from other independent
studies in the literature.
The bioaccumulation model required rates of chemical uptake and elimination, and the efficiency of dietary chemical assimilation for two zooplankton groups (herbivorous and carnivorous zooplankton) and zebra mussels (3 cohorts). The chemical uptake rate, ku, is related to the respiration rate of the organism and efficiency of transfer to the chemical across the organism membrane (Thomann 1989; Thomann et al. 1992). The uptake rate parameterizes the transport of chemical across the gill to the blood and is determined from the uptake rate of oxygen. Using the ratio of mass transfer coefficients for the chemical and oxygen, the expression for uptake rate, ku, is given by (Connolly 1991; Thomann et al. 1992):
where aoxc is the oxygen-to-carbon ratio, ac is the carbon-to-dry-weight ration, awd is the wet-dry ratio, r is the organism oxygen respiration rate [g(wet)/g(wet)-d], fL is the lipid fraction, and [O2] is the oxygen concentration (mg/L). The ratio of the mass transfer coefficient, β, is equal to the ratio of chemical transfer efficiency to oxygen transfer efficiency when the body burden is zero.
Uptake rate for zebra mussels were calculated based on the filtration rate and efficiency of chemical uptake. Filtration rate for zebra mussels was calculated internally in the bioenergetics model, which depends on temperature and zebra mussel weight. Chemical uptake efficiency (E), which is a function of Kow, was applied. For the present study, the regression developed by Endicott et al. (1998) based on mussels of size 21 mm with high lipid content has been adopted:
The chemical assimilation efficiency, α, through dietary transfer was calculated based on following correlation provided by Thomann (1989):
The chemical elimination rate, K, for all the organisms was calculated from uptake rate, lipid content, and Kow. It was assumed that the gill is the major site of elimination, so that the uptake rate and elimination define the rates of chemical transfer for a reversible diffusion process at the gills. Under the condition of zero growth, the lipid-based bioconcentration factor (BCF) is assumed to be equal to the chemical Kow (Thomann 1989). Therefore, the excretion rate may be calculated as:
Tables A1 and A2 contain model parameters and their values in Appendix.
3.2 Temporal and Spatial Scales
The developed model was applied to the same seven water column spatial segments as used in SAGZM model (LTI 1997) (Figure 3.3).
The arrows in Figure 3.3 show the principal adjective flow field for prevailing wind conditions during 1991. Mean depth, surface area, and water volume of the seven segments are given in Table 3.3.
|Table 3.3:||Mean Depth, Surface Area, and Water Volume of the Seven Segments of the Saginaw Bay (LTI 1997)|
|Segment #||Mean Dept (m)||Surface Area (m2)||Volume (m3)|
|1||3.37||2.50 x 108||8.42 x 108|
|2||2.60||2.20 x 108||5.70 x 108|
|3||7.97||6.46 x 108||5.15 x 108|
|4||3.78||2.37 x 108||8.95 x 108|
|5||2.59||2.18 x 108||5.65 x 108|
|6||13.60||6.62 x 108||9.01 x 109|
|7||14.50||5.57 x 108||8.08 x 109|
Table 3.4 describes the sediment properties. Major areas of segments 3, 6, and 7 are depositional while all other segments are non-depositional (Richardson et al. 1983).
|Table 3.4: Sediment Properties|
|1||0.05||0.125 x 108||0.70||2.45|
|2||0.05||0.110 x 108||0.86||2.45|
|3||0.05||0.323 x 108||0.95||2.10|
|4||0.05||0.118 x 108||0.80||2.40|
|5||0.05||0.109 x 108||0.90||2.40|
|6||0.05||0.331 x 108||0.95||2.10|
|7||0.05||0.279 x 108||0.94||2.10|
External forcing functions were specified in the form of daily time series (see Appendix). These functions included external constituent loadings, water temperature, incident solar radiation and boundary conditions between Saginaw Bay and Lake Huron. The values for the forcing functions used in the present study are the same as used in SAGZM (LTI 1995, 1997). Based on the assumptions made in the conceptual framework of the model, the simulated results represent the PCB concentration in various compartments of the system in a monthly to seasonal time scales.