URBAN AIR POLLUTION MODELING 1175
The mixing layer is the lower region of the troposphere
in which pollutants are relatively free to circulate and dis-
perse vertically as well as horizontally because of the pre-
ponderance of small-scale turbulence. It may extend to a
height as small as 50 m or as great as 5 km above the surface
(Deardorff, 1975). More typically, it extends about 1 to 2 km
during the day and a few hundred meters at night, although
day-to-day variations can be quite large (Smith, 1982). This
turbulence promotes intimate contact between vapor-phase
and aerosol-associated pollutants.
Such direct contact is an important step in the chain of
events that ultimately results in chemical transformations
of pollutants near their source before extensive dilution has
occurred and while their air concentrations are still relatively
high. The consequences of limited vertical mixing may be
exacerbated at northern latitudes, where air pollutants released
close to the ground may disperse only to a very limited extent
because of the extreme stability of air brought about by inver-
sion layers characteristic of the Arctic, especially in winter.
This situation can give rise to elevated ambient-air concentra-
tion of noxious contaminants in those regions.Pollutant MeasurementsIt is necessary to remember that the distribution of any con-
taminants is a function of space and time. With a heteroge-
neous distribution of sources in space and the pronounced
variation in source strength with time, one can hardly
expect a few stations to describe adequately the distribu-
tion of the contaminant sources. Also, the averaging time
is important. A 1-hour sample or even a 2-hour sample will
bring out the diurnal effects quite well; however, 24-hour
samples do not.Photochemical TransformationsSchroeder and Lane (1988) have also discussed the reactions
occurring in the atmosphere thus: “During transport and dif-
fusion through the atmosphere, all but the most inert toxic
pollutants are likely to participate in complex chemical or
photochemical reactions. These processes can transform a
pollutant from its primary state (the physical and chemical
form in which it first enters the atmosphere) to another state
that may have similar or very different characteristics.”^
Transformation products can differ from their precur-
sors in chemical stabilities, toxic properties, and various
other characteristics. For example, pyrene, a nontoxic, non-
carcinogenic organic molecule, can react with NO x and
nitric acid in the air to form various nitropyrenes, which are
highly potent, direct-acting mutagens. Secondary pollutants
may be removed from the atmosphere in a manner different
from that of their parent substances as a result of charac-
teristic chemical and photochemical degradation or physi-
cal-removal mechanisms. It is difficult to formulate general
statements regarding atmospheric transformations of Toxic
Air Pollutants^ because the contributing chemical processes
are numerous and complex.
The Earth’s atmosphere is an efficient oxidizing medium
even though most of its mass is composed of either relativelyinert molecules or chemically reducing gases such as N 2 , H 2 ,
and CH 4. Nevertheless, the atmosphere acts as an oxidative
system because of its overall composition and the relative
chemical reactivity of natural atmospheric constituents or
contaminants. Some of the more chemically reactive species
known to be present in ambient air are atomic oxygen, ozone,
hydroxyl and other free radicals (HO 2 , CH 3 O 2 ), peroxides
(H 2 O 2 , CH 3 O 2 H), nitrogen oxides, sulfur oxides, and a wide
variety of acidic and basic species. Consequently, contami-
nants of environmental interest, once emitted into ambient
air, are converted at various rates into substances character-
ized by higher chemical oxidation states than their parent
substances.FUTURE CONSIDERATIONSIf mathematical modeling is to be effective, it is essential
that information be available on the space-time distribution
of both the pollutant and the necessary meteorological vari-
ables. By considering concentration changes with time and
using the receptor-oriented approach, one may minimize the
influence of the source inventory, but in the last analysis,
source-inventory information is necessary for any model. In
the validation procedure, one must consider the occurrence
of systematic errors, i.e., readings consistently too high or
too low compared to the calculated values. Similarly, large
deviations between calculated and observed values should
be carefully investigated. The selection of an appropriate
sampling time is very important.
In conclusion, although the efforts made to date on the
problem are commendable, the results can still be improved.
Continued effort in the development of an urban air pollution
model is necessary, and hopefully will provide the needed
tools for handling urban air pollution problems.REFERENCES^Addison, P.S., Currie, J.I., Low, D.J., and McCann, J.M. (2000), An inte-
grated approach to street canyon pollution modelling, Environmental
Monitoring and Assessment, 65, pp. 333–342.
Affum, J.K., Brown, A.L., and Chan, Y.C. (2003), Integrating air pollution
modeling with scenario testing in road transport planning: the TRAEMS
approach, Science of the Total Environment, 312, pp. 1–14.
Aloyan, A.E., Arutyunyan, V., Haymet, A.D., He, J.W., Kuznetsov, Y., and
Lubertino, G. (2003), Air quality modeling for Houston-Galveston-
Brazoria area, Environment International, 29, pp. 377–383.
Altshuller, A.P. (1989), Nonmethane organic compound to nitrogen oxide
ratios and organic composition in cities and rural areas, Journal of the
Air and Waste Management Association, 39, pp. 936–943.
Barna, M.G. and Gimson, N.R. (2002), Dispersion modeling of a wintertime
particulate pollution episode in Christchurch, New Zealand, Atmo-
spheric Environment, 36, pp. 3531–3544.
Bauer, G., Deistler, M., and Scherrer, W. (2001), Time series models for
short term forecasting of ozone in the eastern part of Austria, Environ-
metrics, 12, pp. 117–130.
Beiruti, A.A. and Al-Omishy, H.K. (1985), Traffic atmospheric diffusion
model, Atmospheric Environment, 19, No. 9, pp. 1519–1524.
Berge, E., Walker, S., Sorteberg, A., Lenkopane, M., Eastwood, S., Jablonska,
H.I., and Køltzow, M.Ø. (2002), A real-time operational forecast model
for meteorology and air quality during peak air pollution episodes in Oslo,
Norway, Water, Air, and Soil Pollution: Focus, 2, pp. 745–757.C021_001_r03.indd 1175C021_001_r03.indd 1175 11/18/2005 1:31:36 PM11/18/2005 1:31:36 PM