Species distribution model

By using the fundamental. principles of conservation of mass and charge, and chemical equilibrium expressions, the concentration of individual species in solution (sometimes called the "species distribution") can be calculated. The available equilibrium models for lime or limestone based FGD are the Bechtel-modified Radian equilibrium program (BMREP) (4) and the species distribution model (SDM)... 

Stockwell D.R.B., Peterson A.T. 2002. Effects of sample size on accuracy of species distribution models. Ecol. Model. 148 1-13. 

Faist, M. B., Riese, C. E., and Gevirtzman, L., 1981, Species Distribution Model A General Computer Program to Calculate the Distribution of Chemical Species Among Several Multicomponent Phases, submitted to U.S. D.O.E., Morgantown Energy Technology Center, October. (Cited by Benson, 1985.)... 

In general, it is easier to use models such as these to predict the distribution of chemicals (i.e., relationship between exposure and tissue concentration) than it is to predict their toxic action. The relationship between tissue concentration and toxicity is not straightforward for a diverse group of compounds, and depends on their mode of action. Even with distribution models, however, the picture can be complicated by species differences in metabolism, as in the case of models for bioconcentration and bioaccumulation (see Chapter 4). Rapid metabolism can lead to lower tissue concentrations than would be predicted from a simple model based on values. Thus, such models need to be used with caution when dealing with different species. 

When they calculated the species distribution in seawater, Garrels and Thompson (1962) were probably the first to apply chemical modeling in the field of geochemistry. Modern chemical analyses give the composition of seawater in terms of... 

Garrels and Thompson s calculation, computed by hand, is the basis for a class of geochemical models that predict species distributions, mineral saturation states, and gas fugacities from chemical analyses. This class of models stems from the distinction between a chemical analysis, which reflects a solution s bulk composition, and the actual distribution of species in a solution. Such equilibrium models have become widely applied, thanks in part to the dissemination of reliable computer programs such as SOLMNEQ (Kharaka and Barnes, 1973) and WATEQ (Truesdell and Jones, 1974). 

The examples in the previous section demonstrate that nonunique solutions to the equilibrium problem can occur when the modeler constrains the calculation by assuming equilibrium between the fluid and a mineral or gas phase. In each example, the nonuniqueness arises from the nature of the multicomponent equilibrium problem and the variety of species distributions that can exist in an aqueous fluid. When more than one root exists, the iteration method and its starting point control which root the software locates. 

Reliable chronic toxicity data were available for 21 species of plants (13 phytoplankton and 8 macrophytes) and 15 species of animals. The species sensitivity distributions (SSDs) for atrazine chronic toxicity (no observed effect concentrations [NOECs]) to plants and animals are shown in Figure 4.4. A log-normal distribution model was fitted to each SSD by least-squares regression. 

Calculations of the variations expected in the fluorescent-yield (FY) profiles as a function of the distribution model parameters are shown in Figure 7.19. When the species of interest resides predominantly at the solid surface, the FY profile shows a maximum at the critical angle for total external reflection. As the ratio of the surface-bound species to the total number of species in the solution volume adjacent to the surface decreases, the FY distribution broadens at the low angles. A similar effect is noted when a diffuse layer accumulation arises due to an interfacial electrostatic potential. 

Process-scale models represent the behavior of reaction, separation and mass, heat, and momentum transfer at the process flowsheet level, or for a network of process flowsheets. Whether based on first-principles or empirical relations, the model equations for these systems typically consist of conservation laws (based on mass, heat, and momentum), physical and chemical equilibrium among species and phases, and additional constitutive equations that describe the rates of chemical transformation or transport of mass and energy. These process models are often represented by a collection of individual unit models (the so-called unit operations) that usually correspond to major pieces of process equipment, which, in turn, are captured by device-level models. These unit models are assembled within a process flowsheet that describes the interaction of equipment either for steady state or dynamic behavior. As a result, models can be described by algebraic or differential equations. As illustrated in Figure 3 for a PEFC-base power plant, steady-state process flowsheets are usually described by lumped parameter models described by algebraic equations. Similarly, dynamic process flowsheets are described by lumped parameter models comprising differential-algebraic equations. Models that deal with spatially distributed models are frequently considered at the device... 

The species distributions shown in Table 12.2 depict results from one of the earliest (Garrels and Thompson, 1962) and one of the most recent (Millero and Schreiber, 1982) seawater ion pairing models. These results and others (Kester, 1975a) are consistent with the following general characteristics of major ion speciation in seawater ... 

At present, modeling simulations of both laboratory experiments (2) and measured atmospheric trace species distributions ( ) are unable to reproduce observed concentrations of DMS and its oxidation products. It is clear that direct studies aimed at identifying reactive intermediates, and a better understanding of the chemistry via which these intermediates are converted to end products, is required. 

Besides meeting its assumptions, other problems in the application of SSD in risk assessment to extrapolate from the population level to the community level also exist. First, when use is made of databases (such as ECOTOX USEPA 2001) from which it is difficult to check the validity of the data, one does not know what is modeled. In practice, a combination of differences between laboratories, between endpoints, between test durations, between test conditions, between genotypes, between phenotypes, and eventually between species is modeled. Another issue is the ambiguous integration of SSD with exposure distribution to calculate risk (Verdonck et al. 2003). They showed that, in order to be able to set threshold levels using probabilistic risk assessment and interpret the risk associated with a given exposure concentration distribution and SSD, the spatial and temporal interpretations of the exposure concentration distribution must be known. 

Maltby et al. (2002) and Van den Brink et al. (2006a) compared SSDs based on acute and chronic laboratory toxicity data for aquatic test species exposed to pesticides. The SSDs were constructed with toxicity data for the most sensitive taxonomic group, because of the specific toxic mode of action of the pesticides selected. The SSDs were used to calculate the hazardous concentration to 5% of the species (HC5) by means of a log-normal distribution model, and comparisons were performed for 2 insecticides and 7 herbicides (Table 6.4). The log-normal model did not fit the diuron (herbicide) short-term L(E)C50 data or the atrazine (herbicide) long-term NOEC data. Consequently, the L(E)C50 HC5 value for diuron and the NOEC HC5 value for atrazine should be interpreted with caution, as well as their acute HC5-chronic... 

Case 3 Both temperature and species variation - In this case, additional information is required. This could be obtained from another diagnostic or a mathematical model. Smith (10) used an extensive mathematical model of a laminar hydrogen diffusion flame to predict the species distribution throughout the flame having this, the temperature could be inferred from the Rayleigh scattering intensity. 

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