AIR POLLUTION – MONITORING MODELLING AND HEALTH
air pollution â monitoring, modelling and health - Ademloos air pollution â monitoring, modelling and health - Ademloos
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
Urban Air Pollution 9 In this example the uncertainty on E is equal to: E( H H ) ( H H ) ( H H ) 1 2 2 1 1 2 As we can see in this example the calculation of the emissions is non-linear and generates several uncertainty terms. All these terms rapidly become impossible to calculate analytically when the parameters used in the emission calculation are more complex. In such situation, the most oftenly used approach is the Monte-Carlo method (Hanna et al. 1998). The detailed of Monte-Carlo method is explained as follows: The Monte Carlo methodology has been used to evaluate the uncertainties in previous air quality studies (Hanna et al., 1998; Sathya., 2000; Hanna et al., 2001; Abdel-Aziz and Christopher Frey., 2004). In the emission inventory model, the EIs are generated as the combination of different input parameters: ( ,... ) (2) E f H1 H n where H i are the input parameters (i=1, n). H i can change due to the uncertainties. Each parameter H is distributed around an average H and a standard deviation . i The Monte Carlo method (Ermakov, 1977) generates for each input parameters a H pseudorandom normally distributed numbers ( 1 and mean= 0) which can be used to compute several values of H : i k i i i H H H (3) The percentage of standard deviation could be calculated as: i 100 i i Hi H i i The equation (3) becomes: H k i H Hi Hi 1 100 k i These parameters H are used to calculate several values of E : E f ( H1 ,... H n ). The k average value E and the standard deviation E are deduced from the distribution of E . The percentage of standard deviation E could be calculated as: E 100 E E The classification of standard deviation E is based on the standard deviation of the input parameters Hi : k k k k If Hi is the standard deviation of input parameters due to spatial and temporal repartition, the values E( x, y, t) are the standard deviation of emission for all input parameters but in space and time. If Hi (the standard deviation of each input parameters) is constant in the entire domain, then the values of E are the standard deviations of emission for all the domain but for Hi each input parameter. Examples from the literature for uncertainties in emission inventory and Monte-Carlo application to air quality model are:
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Urban Air Pollution 9<br />
In this example the uncertainty on E is equal to:<br />
E( H H ) ( H H ) ( H H )<br />
1 2 2 1 1 2<br />
As we can see in this example the calculation of the emissions is non-linear and generates<br />
several uncertainty terms. All these terms rapidly become impossible to calculate<br />
analytically when the parameters used in the emission calculation are more complex. In<br />
such situation, the most oftenly used approach is the Monte-Carlo method (Hanna et al.<br />
1998). The detailed of Monte-Carlo method is explained as follows:<br />
The Monte Carlo methodology has been used to evaluate the uncertainties in previous air<br />
quality studies (Hanna et al., 1998; Sathya., 2000; Hanna et al., 2001; Abdel-Aziz and<br />
Christopher Frey., 2004). In the emission inventory model, the EIs are generated as the<br />
combination of different input parameters:<br />
( ,... )<br />
(2)<br />
E f H1<br />
H n<br />
where H i are the input parameters (i=1, n). H i can change due to the uncertainties. Each<br />
parameter H is distributed around an average H and a standard deviation .<br />
i<br />
The Monte Carlo method (Ermakov, 1977) generates for each input parameters a<br />
H<br />
pseudorandom normally distributed numbers ( 1 and mean= 0) which can be used<br />
to compute several values of H :<br />
i<br />
k<br />
i<br />
i<br />
i<br />
H<br />
H H <br />
(3)<br />
The percentage of standard deviation could be calculated as: i 100<br />
<br />
i<br />
i<br />
Hi<br />
H i<br />
i<br />
The equation (3) becomes:<br />
H<br />
k<br />
i<br />
H<br />
Hi<br />
<br />
Hi<br />
<br />
1 <br />
100 <br />
<br />
k<br />
i<br />
These parameters H are used to calculate several values of E : E f ( H1<br />
,... H n ). The<br />
k<br />
average value E and the standard deviation E are deduced from the distribution of E .<br />
The percentage of standard deviation E could be calculated as: E 100<br />
E <br />
E<br />
The classification of standard deviation E is based on the standard deviation of the input<br />
parameters Hi :<br />
k<br />
k k k<br />
If Hi is the standard deviation of input parameters due to spatial and temporal repartition,<br />
the values E( x, y, t)<br />
are the standard deviation of emission for all input parameters but in<br />
space and time.<br />
If Hi (the standard deviation of each input parameters) is constant in the entire domain,<br />
then the values of E are the standard deviations of emission for all the domain but for<br />
Hi<br />
each input parameter.<br />
Examples from the literature for uncertainties in emission inventory and Monte-Carlo<br />
application to air quality model are:
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