AIR POLLUTION – MONITORING MODELLING AND HEALTH
air pollution â monitoring, modelling and health - Ademloos air pollution â monitoring, modelling and health - Ademloos
12 Air Pollution – Monitoring, Modelling and Health VOCs HCMC (Mean) n-Propane 3.7 Changchun (Mean) Karachi (Mean) HongKong (Mean) Hanoi (Mean) Propene 19.5 n-Butane 22.6 Trans-2-Butene 5.2 1-Butene 4 Cis-2-butene 5.2 i-Pentane 80.3 14.7 74 n-Pentane 21.8 Trans-2-Pentene 16.4 1-Pentene 4.6 2-methyl-2-butene 3.8 Cis-2-Pentene 4.2 2,3-Dimethylbutane 8.6 2-Methylpentane 7.7 6.1 39 3-Methylpentane 43.5 n-Hexane 91 1.7 71 4.4 Benzene 14.2 11.9 19.7 8.2 40 total 356.3 Sources: Belalcazar (2009). Table 3. VOCs levels in BTH street (HCMC) and comparison with the VOCs levels in other Asian cities (concentrations are in ppbv). The researches on effective abatement strategies propose solutions to reduce air pollution. It consists mainly of master thesis which is carried out at IER (Institute for Environment and Resources in HCMC). These researches are focused only on some typical aspects of abatement strategies at small scale, due to the lack of the information for research, methodology, etc. Nguyen (2000) proposed some methods to reduce air pollution by traffic in HCMC as well as for developing public transportation system and using unleaded gasoline. Another study proposed technical solutions for reducing air pollution level by traffic activities (Duong, 2004). In conclusion, both the developed and developing cities have air pollution problem, especially in the developing countries where it is related to high level of air pollution. It’s urgent to adapt an air quality management (AQM) system for improving the air quality in these cities. In the next section, the research for improving air quality in HCMC is carried out by using numerical tools.
Urban Air Pollution 13 4.2 Emission inventory of air pollution over HCMC 4.2.1 Traffic source Methodology The following Fig. 2 shows an outline of the process of generating an EI for HCMC where the input data is limited and using the EMISENS model (Bang, 2011). (IV) Revaluation of the most sensible sensitive parameters (I) Evaluation of the inputs parameters and their variability: H i , s Unc , s Spa & s Tem Run EMISENS for 1 cell Evaluation of the variability (III) Average emission (E) and Hierarchy of s E : Input parameters H1: emission fact. H2: vehicle num. H3: temperature … (II) EMISENS model - - - - E _ Unc - - - - E _ Spa - - - - E _ Temp Run for entire domain (j cells) Evaluation of the uncertainties (V) Spatial and temporal repartition of the inputs (if needed) H i,j,t s , i,j,t (VI) EMISENS model (VII) Emission and uncertainties in each cell E & s j E j Fig. 2. Research process outline ( H i is the average value of input parameters. Unc , Spa and Tem are the standard deviation of the input parameters due to the uncertainties, the spatial and temporal variability, respectively. The E_ Unc, E_ Spa and E_ Tem are the standard deviation of the emissions due to variability of the input parameters. The E & are the emission and their standard deviation for each cell. j E j
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Urban Air Pollution 13<br />
4.2 Emission inventory of air pollution over HCMC<br />
4.2.1 Traffic source<br />
Methodology<br />
The following Fig. 2 shows an outline of the process of generating an EI for HCMC where<br />
the input data is limited and using the EMISENS model (Bang, 2011).<br />
(IV) Revaluation of<br />
the most sensible<br />
sensitive<br />
parameters<br />
(I) Evaluation of the inputs parameters and their variability:<br />
H i<br />
, s Unc<br />
, s Spa<br />
& s<br />
Tem<br />
Run EMISENS for 1 cell<br />
Evaluation of the<br />
variability<br />
(III) Average<br />
emission (E) and<br />
Hierarchy of s E<br />
:<br />
Input parameters<br />
H1: emission fact.<br />
H2: vehicle num.<br />
H3: temperature<br />
…<br />
(II) EMISENS<br />
model<br />
<br />
-<br />
-<br />
-<br />
-<br />
E<br />
_<br />
Unc<br />
-<br />
-<br />
-<br />
-<br />
<br />
E<br />
_<br />
Spa<br />
-<br />
-<br />
-<br />
-<br />
E _<br />
Temp<br />
Run for entire domain (j cells)<br />
Evaluation of the uncertainties<br />
(V) Spatial and temporal repartition of the inputs (if needed)<br />
H<br />
i,j,t<br />
s<br />
, i,j,t<br />
(VI)<br />
EMISENS<br />
model<br />
(VII) Emission and uncertainties in each cell<br />
E &<br />
s<br />
j<br />
E j<br />
Fig. 2. Research process outline ( H i is the average value of input parameters. Unc , Spa<br />
and Tem are the standard deviation of the input parameters due to the uncertainties, the<br />
spatial and temporal variability, respectively. The E_<br />
Unc, E_<br />
Spa and E_<br />
Tem are the<br />
standard deviation of the emissions due to variability of the input parameters. The<br />
E & are the emission and their standard deviation for each cell.<br />
j<br />
E j
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