jco is the background (i.e., clean air) concentra-

Here BC(

tion of CO, which must be taken into account.

For a nonreactive pollutant such as CO, linear rollback provides a reasonable control strategy, providing the location, temporal distribution, and relative strengths of the sources do not change. However, for a secondary pollutant such as 03 that is formed through chemical reactions of primary pollutants, the complexity of the chemistry makes this approach inappropriate. In addition, since both VOC and NOx are involved in oxidant formation, a decision must be made as to which pollutant—or both—linear rollback should be applied.

(2) Simple empirical models Simple empirical models have been developed for application to specific areas, primarily for forecasting purposes on a short-term basis. For example, in areas such as the Los Angeles region, daily air quality forecasts are made that are based primarily on past correlations of meteorological parameters with peak ozone concentrations (e.g., Zeldin and Thomas, 1975). Such models have proven useful for adjusting observed trends in measured pollutant concentrations to similar meteorological conditions, which is critical in assessing whether control strategies are having the intended effect.

Because such empirical models are based on historical relationships for a particular region, they may not be valid if significant changes occur, e.g., if the spatial or temporal distribution of the sources, or the chemical composition, changes. In addition, they are specific to the airshed for which they were developed.

b. Mathematical Models

(1) Components Clearly, the best approach to understanding, and ultimately predicting, the relationship between emissions of primary pollutants and the concentrations of secondary pollutants formed is to develop mathematical models that describe all of the inputs and factors that affect this relationship. The major components are (1) emissions of the primary pollutants, including their specific composition and spatial and temporal variations; (2) the meteorological and topographical features of the region, including such parameters as temperature, relative humidity, wind speed and direction in three dimensions, atmospheric stability, inversion height, and surface elevation and other terrain features; and (3) the chemistry, including both the kinetics and mechanisms of the reaction converting primary pollutants into secondary pollutants.

Figure 16.17a schematically shows the individual subcomponents of these three major modules (McRae et al., 1982a), and Fig. 16.17b shows a typical chemical submodel in more detail (Dabdub and Seinfeld, 1996). In the following sections, we shall briefly refer to some of the individual subcomponents of these three modules, particularly with respect to effects on predicted concentrations of secondary species. However, consistent with the focus of this book on atmospheric chem istry, some important aspects of the chemistry module are discussed in more detail.

(2) Chemical component of models As we have seen from the examination of kinetics and mechanisms of atmospheric reactions thus far, the chemistry of even relatively simple organics can be quite complex. This chemistry has been described in terms of explicit chemical mechanisms, that is, a listing of the individual chemical reactions. The oxidation of even one organic in air includes hundreds of reactions.

In a VOC-NOx mixture containing many different organics, the number of reactions becomes unmanageable for application in models used to describe an air basin or region. Thus the amount of computer time required for numerical integration of the rate equations associated with the thousands of individual species found in ambient air is prohibitive. Furthermore, even as computing power increases, in practice, the kinetics and mechanisms required as input are not all known.

As a result, these explicit chemical mechanisms are generally not used in such models, except to describe the inorganic NO. chemistry, which by comparison is relatively straightforward (see Chapter 7). Rather, the mechanisms are condensed in various ways to reduce the number of organic reactions substantially. In these lumped mechanisms, the chemistry of the organics is treated by grouping or "lumping" together a number of reactions and/or chemical species. The overall rate constant and products of the lumped reactions are chosen to be representative of that group of reactions or reactants. Examples of how this can be accomplished for a group of non-aromatic VOCs are discussed in detail by Jenkin et al. (1997) and, for a group of alkanes, by Wang et al. (1998).

Two approaches have been used in developing condensed mechanisms for organics. The first groups organics for the most part by their traditional classifications, e.g., alkanes, alkenes, and aromatics. There are some exceptions, usually for the first member of a class, which often shows unique properties. For example, methane is not lumped together with other alkanes because of its low reactivity (vide infra). Two major chemical submodels that use this approach are those of Lurmann et al. (1986), often referred to as the LCC mechanism (named for the developers, Lurmann, Carter, and Coyner), and the RADM mechanism (Regional ^4cid Deposition Model) (Stockwell et al., 1990). The latter has been updated by Stockwell et al. (1997) in a version known as RACM (Regional yftmospheric Chemistry Mechanism). Table 16.1 shows a typical classification of organics used in the RADM and RACM submodels for gas-phase chemistry (Stockwell et al.,

Meteorological fields

Gas-phase initial and boundary conditions

Emissions VOC NOx S02



Heterogeneous processes

Homogeneous processes

Photochemical reactions



1. Anthropogenic

2. Biogenic

Pollutants transported into region

Surface deposition


Aerosol processes

Chemical processes


Mathematical model


Vertical diffusion and dry deposition


Sink processes

Emissions -primary particles

3-D, time-dependent gas-phase concentrations and dry deposition

Inorganic species gas-aerosol equilibrium

Secondary organic aerosol


Computed concentrations

Gas-to-particle conversion

Nucleatlon Coagulation

3-D, tme-dependent aerosol size-combustion distribution and dry deposition

Liquid water field


Inversion height



Topography and surface roughness


Cloud cover


Gas-phase — concentrations from gas-phase model

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