Encyclopedia of Environmental Science and Engineering, Volume I and II

URBAN AIR POLLUTION MODELING 1173

FIGURE 2 Predicted spatial distribution of the yearly means of PM 10 in central Helsinki

in 1998 (mg/m^3 ). The white star indicates the predicted maximum concentration in the

area (40.4 mg/m^3 ) (From Kukkonen et al., 2001, with permission from Elsevier).

shown in Figure 3^ (Berkowicz, 2000a). The Danish opera-

tional street pollution model (OSPM) has been used by sev-

eral researchers to model dispersion of pollutants at street

level. Several studies have assessed the validity of the model

by using data for different cities in Europe.

New techniques such as Fuzzy Logic and Neural

Networks^ have been used with great results (Kukkonen,

Partanen, Karppinen, Ruuskanen, et al., 2003; Viotti et al.,

2002, Pokrovskya et al., 2002; Pelliccioni and Poli, 2002).

Schlink and Volta (2000) used grey box stochastic models

and extended autoregressive moving average models to

predict ozone formation. Other approaches have been time-

series analysis, regression analysis, and statistical modeling,

among others.

For additional information, the reader is referred to the

references at the end of the chapter.

CONCLUDING REMARKS

Inadequacies and shortcomings exist in our assessment of

each of the components of the mathematical urban air pollu-

tion model. In this section these difficulties are discussed.

The Source Inventory

For large metropolitan areas, one finds that the inventory

obtained by the usual methods, such as questionnaires, is

often out of date upon its completion. Continuous updat-

ing is necessary. However, in a receptor-oriented model,

the requirement for a detailed source inventory is relaxed.

Further, by developing a receptor-oriented “anomaly” model,

one may further reduce the error resulting from inadequate

source information. In the anomaly-type model, changes in

the dependent variable over a given time period are calcu-

lated. This interval may be 1, 2, 4, or 6 hours.

Initial Mixing

According to Schroeder and Lane (1988), “Initial mixing

refers to the physical processes that act on pollutants imme-

diately after their release from an emission source. The

nature and extent of the initial interaction between pollutants

and the ambient air depend on the actual configuration of the

source in terms of its area, its height above the surrounding

terrain, and the initial buoyancy conditions.”^

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