enclosed house with pit recharge and irrigation of supernatant onto forage land; model farm S2).
The utility of this kind of model farm construct depends on the following:
defining models in which the dependent variable—the amount of each air emission per unit of time—is closely related to independent variables that accurately depict real feeding operations and that explain a substantial share of the variation in the dependent variable;
providing accurate estimates of the relationship between the dependent and independent variables; and
having estimates of the relationships between dependent and independent variables that clearly distinguish among the kinds of AFOs being modeled.
A critical requirement for estimating the appropriate emission factors is a statistically representative survey of emissions from a class of AFOs over several iterations of the time period to be represented. The size of the sample required to estimate the mean emission rate with a given degree of accuracy increases with the variability in the dependent variable to be measured (e.g., the average emission rate) across the set of independent variables that affect it. Independent variables that have been discussed include animal type and age, diet, local climate, building type, land application method, and management practices. To the extent that some of these variables change over time (e.g., trends in farm organization, location, practices, and technology), updating of estimates and estimates of trends may be required.
The model farm construct is represented by Equation 5-1:
E = Σi (wi • ei)
in which the total emission (E) of a particular pollutant from an AFO during a period of time is the product of the emission (ei) from each unit (i) on the model farm and the number of units (wi) of that type, summed over the farm.
One use of model farms might be to predict emission rates and local effects of a single AFO or a cluster of AFOs in a small area. This use differs from that described by EPA (2001a), and it would require a detailed model or models describing the effects of selected variables on the rates of emissions and their downwind concentrations. An example is an odor dispersion model that predicts odor intensity as a function of time at various locations, given information on odor sources and local meteorological conditions. More data (perhaps hourly) and statistical analyses of the relationships between various explanatory variables and pollutant concentrations or impacts are required.
The committee believes that EPA’s proposal (2001a) is inadequate to meet these standards. It does not provide a method to adequately determine air emis-