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Environmental Modeling



Last updated: September 22, 2016




AQUATOX is the only general ecological risk model that represents the combined environmental fate and effects of conventional pollutants, such as nutrients and sediments, and toxic chemicals in aquatic ecosystems. It considers several trophic levels, including attached and planktonic algae and submerged aquatic vegetation, invertebrates, and forage, bottom-feeding, and game fish; it also represents up to 20 associated organic toxicants. It has been implemented for streams, ponds, lakes, and reservoirs.

The fate portion of the model, which is applicable especially to organic toxicants, includes: partitioning among organisms, suspended and sedimented detritus, suspended and sedimented inorganic sediments, and water; volatilization; hydrolysis; photolysis; ionization; and microbial degradation. The effects portion of the model includes: acute toxicity to the various organisms modeled; and indirect effects such as release of grazing and predation pressure, increase in detritus and recycling of nutrients from killed organisms, dissolved oxygen sag due to increased decomposition, and loss of food base for animals.

AQUATOX is the latest in a long series of models, starting with the aquatic ecosystem model CLEAN (Park et al., 1974) and subsequently improved in consultation with numerous researchers at various European hydrobiological laboratories, resulting in the CLEANER series (Park et al., 1975, 1979, 1980; Park, 1978; Scavia and Park, 1976) and LAKETRACE (Collins and Park, 1989). The MACROPHYTE model, developed for the U.S. Army Corps of Engineers (Collins et al., 1985), provided additional capability for representing submersed aquatic vegetation. Another series started with the toxic fate model PEST, developed to complement CLEANER (Park et al., 1980, 1982), and continued with the TOXTRACE model (Park, 1984) and the spreadsheet equilibrium fugacity PART model. AQUATOX combined algorithms from these models with an ecotoxicological construct borrowed from the FGETS model (Suárez and Barber, 1992); and additional code was written as required for a truly integrative fate and effects model (Park, 1990, 1993). The current version has been restructured and linked to Microsoft Windows interfaces to provide even greater flexibility, capacity for additional compartments, and user friendliness.

AQUATOX continues to be improved. Currently, it is being modified under contract with the Exposure Assessment Branch of the EPA Standards and Applied Science Division. EPA is interested in using the AQUATOX model to perform quantitative environmental risk assessments as required by the Clean Water Act.

Mechanistic Details

AQUATOX represents the aquatic ecosystem by simulating the changing concentrations (in mg/L or g/m3) of organisms, nutrients, chemicals, and sediments in a unit volume of water. As such, it differs from population models, which represent the changes in numbers of individuals. As O'Neill et al. (1986) stated, ecosystem models and population models are complementary; one cannot take the place of the other. Population models excel at modeling individual species at risk and modeling fishing pressure and other age/size-specific aspects; but recycling of nutrients, the combined fate and effects of toxic chemicals, and other interdependencies in the aquatic ecosystem are important aspects that AQUATOX represents and that cannot be addressed by a population model.

AQUATOX is written in object-oriented Pascal. This enables the code to represent different aspects of the ecology as distinct Pascal objects. Each of these coded objects has similar characteristics as the object that it is representing in the aquatic ecosystem does. Objects also include equations as to how they interact with each other in each timestep. Each timestep these equations are fed into a differential equations solver that calculates their values in the next timestep. AQUATOX uses a very efficient fourth- and fifth-order Runge-Kutta integration routine with adaptive stepsize to solve the differential equations. The routine uses the fifth-order solution to determine the error associated with the fourth-order solution; it decreases the stepsize when rapid changes occur and increases the stepsize when there are slow changes, such as in winter. If the stepsize is greater than one day, the model interpolates and reports the results with a daily reporting step.


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