Mass balance modelling of priority toxic chemicals within the great lakes toxic chemical decision support system: RateCon model results for Lake Ontario and Lake Erie
Section snippets
Software availability
- Availability:
The Great lakes Toxic Chemical Decision Support System software is currently only available to research partners of Environment Canada. The RAISON for Windows Decision Support System may be acquired through a license with the National Water Research Institute's NWRI Software.
- Contact details:
E-mail: [email protected] or Fax: +1-905-336-4430.
Great lakes toxic chemical decision support system
The RAISON for Windows Decision Support System shell that this system is based upon has evolved over the past decade at the National Water Research Institute of Environment Canada (Lam and Swayne, 1993, Lam et al., 1995, Lam et al., 1998, Lam et al., 2004). Its development has been client driven and its design is devoted to providing generic software tools for fast prototyping and practical implementation of environmental decision support systems. The architecture is open in design. All of the
RateCon model
The RateCon model is a modified version of the model developed by Mackay et al. (1994). It consists of a whole lake model and a food chain model. All expressions for rates of chemical transport and transformation are written in ‘rate constant’ form, which allows for easy comparison of magnitudes of diverse processes. The differential mass balance equations that describe the amount of chemical in water (MW) and in the sediment (MS), in units of kg/year are:
Steady-state model applications
The model may be operated in a number of steady-state modes. There are four options:
- 1.
Forwards, loading to concentration: this is the most common mode and is used to calculate concentrations in all the compartments using loadings input by the user.
- 2.
Backwards, concentrations in both water and sediments to loading: this mode allows the user to determine what the loadings need to be in order to arrive at the water and sediment concentrations observed in the lake.
- 3.
Backwards, concentration in water only
Unsteady-state model applications
Historical loadings as well as projections of loadings (based upon current loadings reductions for each of the load categories) to the year 2020 have been developed for Lake Ontario for PCBs. Historical loadings data have been obtained or calculated from Thomas et al., 1987, Rapaport and Eisenreich, 1988, Halfon and Oliver, 1990. No recent attempts have been found in the literature to determine the historical loads. The projections of loadings to the year 2020 were developed based upon recent
Discussion
It is interesting to compare the 1995 steady-state results for Lake Ontario (Table 4) with those for Lake Erie (Table 13). Total mass of chemical in the system is predicted to be greater in Lake Ontario than in Lake Erie for PCBs, HCB, dieldrin, lead, lead, DDE, fluoranthene, and anthracene The reverse is true for B(a)P, mercury, and DDT, reflecting the different magnitudes and sources of loadings for these chemicals in the two basins. Total mass in the system is predicted to be approximately
Conclusions
Currently there is still insufficient data to allow a simple screening level model such as RateCon to be applied for octachlorostyrene, chlordane, toxaphene, heptachlor, 1,4-dichlorobenzene, 3,3′-dichlorobenzidine, hexachlorocyclohexane, 1,6-dinitropyrene, 1,8-dinitropyrene, and tributyl tin, and many of the PAH's. This includes loadings data, basic physical-chemical properties required as model coefficients, and ambient data required for model calibration and verification. This means that not
Acknowledgements
The authors wish to thank Dr Don Mackay for making available the source code to the rate constant model. We are most grateful to Dr Thomas Tseng (Environmental Protection Branch – Ontario Region, Environment Canada) and Mr Ian Smith (Ontario Ministry of Environment) for their involvement in the creation and development of this study. The Environmental Protection Branch – Ontario Region, Environment Canada, provided partial funding for this study. RAISON team members are thanked for their
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