AIR POLLUTION – MONITORING MODELLING AND HEALTH

Urban Air Pollution 7
developing countries. The time of generating EIs of this approach is longer than top-down
approach
Examples of using bottom - up approach for generating EIs from the literature are:
- Erika combined the traffic fluxes obtained from a traffic model and emission factors per
vehicles to calculate the traffic emissions in Bogota, Colombia (Erika, 2007).
- Molina measured emissions of industry and used GPS to locate the position of factory
on the map to calculate the industrial emissions for Mexico City (Molina et al.2001).
- Brulfert calculated the EIs for Maurienne and Chamonix valley, France (G.Brulfert et al.
2004).
- Mattai calculated the EIs for London, England (J. Mattai et al. 2002)
The bottom-up approach is in general impossible due to lack of available information, while
top-down approach might lead to poor accuracy level for urban EIs. So several researchers
used top-down approach to generate EIs for point sources and area sources because data for
these sources are not easy to access, while they used bottom-up approach to generate EIs for
road traffic sources. An example from the literature is:
Sturnm generated EIs for urbanized triangle Antwerp-Brussels-Ghent which is located in
Northwest Europe. He used the top-down approach for generating EIs for point sources
(based on statistic data and emission factors from literatures) and area sources (based on
collective data per km2). However for road traffic emissions he used bottom-up approach
using road traffic emission measurements and an urban traffic flow model (Moussiopoulos
et al., 2004).
Combination of top-down and bottom-up approaches
In the cities of developing countries, the data for generating EIs for traffic sources are
generally not available, so it is difficult or even impossible to use bottom-up approach for
generating EIs. In this chapter, we present a new road traffic emission model which
combines top-down and bottom-up approaches (Bang et al., 2011).
The strengths and weaknesses of the combination of the two approaches are as follows:
Strengths: By using this method (1) the computation time is rapid; (2) less input data is
needed to generate EIs; (3) we have control on the uncertainty in EIs due to input data; (4)
we can generate EIs both for developed cities and developing cities and (5) the accuracy in
the results of EIs is improved.
Weaknesses: the limitation of this method is that (1) we have to follow many steps to
generate EIs and (2) we have to study the input parameters which generate most uncertainty
in EIs.
3.3 Modeling of air pollution
The European Directive on evaluation ambient air quality (EU-N°96/62/CE 1996) requires
the use of modeling tools to define high pollutant concentration zones. Modeling tools use
mathematical formula to simulate all the atmospheric processes over various time and space