AIR POLLUTION – MONITORING MODELLING AND HEALTH

8
Air Pollution – Monitoring, Modelling and Health
scales. These processes are complex and nonlinear so that modeling is the only tool which
takes into account all the processes.
Concerning the space scales of modeling, there are many scales such as global scale, regional
or continental scale, mesoscale (city or country) and microscale (street canyons). There are
many mesoscale models that are used to simulate urban air quality, such as METPOMOD,
TVM-Chem, CIT, CHIMERE, CMAQ, TAPOM, etc. Input parameters of these air quality
models are meteorological conditions, EIs, landuse, topography, boundary and initial
conditions.
In recent decades, computer technology has rapidly developed; so many new mesoscale
models are developed. With the increased power of computer technology, the time scale of
modeling more refined. The new models can now simulate air quality for long periods and
on temporal resolution from few hours to few months.
One of the most important functions of air quality modelling is to evaluate the effective of
abatement strategies to reduce air pollution in cities. Modeling tools are also used to study
the impact of different activities on urban air quality and to evaluate the methodology for
generating EIs, such as Erika used TAPOM model to evaluate the accuracy of different
methodology for generating EIs for Bogota city, Colombia (Erika et al., 2007).
3.4 Analysis of uncertainties
Uncertainty in emission inventory
One of the most important strengths of the emission inventory is generate the uncertainties
of EIs due to the input parameters. There are many methods used to calculate uncertainty.
One of these methods called analytical method is as follows:
Emissions are calculated as the combination of different parameters:
Where, E is the emission
H1 is the parameter to quantify the activities
H2 is an emission factor per unit of activities
E H1
H2
H1 and H2 can depend on many other factors like the number of vehicles, road parameters,
etc.
When H1 and H2 are simple enough it is possible to compute directly the uncertainties. For
example when H1 and H2 are constants:
E( H H ) ( H H )
1 1 2 2
Where, H 1 , H 2 is the average of H1, H2 respectively
H 1 , H2
is the uncertainty of H1, H2 respectively.
E( H H ) ( H H ) ( H H ) ( H H ) and also: E E
E
1 2 1 2 2 1 1 2