Accuracy of models

Models can be used to study human exposure to air pollutants and to identify cost-effective control strategies. In many instances, the primary limitation on the accuracy of model results is not the model formulation, but the accuracy of the available input data (93). Another limitation is the inabiUty of models to account for the alterations in the spatial distribution of emissions that occurs when controls are appHed. The more detailed models are currendy able to describe the dynamics of unreactive pollutants in urban areas. 

Required Sensitivity This is difficult to establish a priori. It is important to recognize that no matter the sophistication, the model will not be an absolute representation of the unit. The confidence in the model is compromised by the parameter estimates that, in theoiy, represent a limitation in the equipment performance but actually embody a host of limitations. Three principal limitations affecting the accuracy of model parameters are ... 

A low accuracy of models for prediction of log D at any pH would not encourage the use of these models for practical applications in industry. Thus, it is likely that the methods for log D prediction at fixed pH that are developed in house by pharmaceutical companies will dominate in industry. However, log D measurements... 

H. van der Voet, Comparing the predictive accuracy of models using a simple randomization test. Chemom. Intell. Lab. Syst., 25 (1994) 313-323. 

The way in which models estimate PET and AET values, plant growth, root growth and distribution, and other parameters can have profound effects on the accuracy of model estimates for ET landfill cover design. For example14 ... 

In chemical-specific parameters used for PBPK modeling, the metabolic rate constant is crucial to the accuracy of modeling results in many cases. For some pyrethroids, hydrolysis in intestine and serum has a significant role in the metabolism of the compound in mammals besides oxidation and ester cleavage in liver, which is the most important organ for detoxification of many chemicals. 

Iterative interpretive methods allow a more accurate location of the optimum and do not rely on the accuracy of models. However, the global optimum may not always be found. 

In the above equations the symbols A, B, C, D designate phenol, hydrogen, cyclohexanone and cyclohexanol. Table 5.7 presents the model parameters at 423 K and 1 atm. The model takes into account the effect of the products on the reaction rate in the region of higher conversion. This feature is particularly useful for describing the product distribution in consecutive catalytic-type reactions. Note that the adsorption coefficients are different in the two reactions. Following the authors, this assumption, physically unlikely, was considered only to increase the accuracy of modeling. 

Analysis of in-house data in pharma companies frequently demonstrates a low prediction ability of current models for both lipophilicity and aqueous solubility. The calculated errors of these models are often around or higher than 1 log-unit, which is not sufficient for screening purposes. Thus, despite relatively large amounts of data for physicochemical properties and their simplicity compared to more complex ADME/T properties, the accuracy of prediction remains low. Let us consider factors that limit the prediction accuracy of models. 

Substituting the values for the electric field obtained from a finite difference computation scheme into the right hand part of equation (12.52) and then integrating numerically, we can compare the result obtained E " with the finite difference solution at the observation points, and establish a criterion for the accuracy of modeling ... 

The accuracy of models will ultimately be limited by practical considerations. These include uncertainties both in the input data (time and precise location of release, initial plume characteristics, or interior dispersion characteristics) and in the local meteorological conditions. 

There were several reports that estimated the accuracy of the aqueous solubility data. Analysis of experimental solubility values for 411 compounds provided an average standard deviation of 0.6 units [21], In a more recent study [22] data for 1031 compounds that had at least two or more experimental values from the AQUASOL database [15] had a standard deviation of 0.5log units for measurements at the same temperature. The large interlaboratory error masked the influence of temperature on the solubility. Differences as large as AT = 30°C did not increase the error. Thus the accuracy of models for diverse sets of compounds can be hardly below 0.5 to 0.6 log units. 

Several methods considered in this article, such as the quantum-chemical approaches developed by Clark et al. [51,113,114] or the ALOGPS program [99], provide an estimation of the accuracy of model predictions. Other approaches to estimate accuracy of model predictions were reviewed elsewhere [99,141],... 

Theoretical isotherms obtained using DFT method rival the accuracy of model isotherms constructed from molecular simulation, as shown in the comparison in Fig. 12 for N2 adsorption results in model graphitic slit pores at 77 K [22]. Tlie DFT model correctly predicts the capillary condensation pressure, the most prominent feature of the subcritical isotherm, relative to the exact computer simulation results shown in Fig. 11. DFT also provides a good description of the secondary structure of the mesopore isotherm (e.g., H = 42.9 A in Fig. 12), in which capillary condensation may be preceded by one or more wetting transitions. 

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