AIR POLLUTION – MONITORING MODELLING AND HEALTH

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34 Air Pollution – Monitoring, Modelling and Health Delta O where: i is the number of simulation 3 O 3 100 i1 i i 3 _ Base case O3 _ 2015 ( O ) 100 (4) i 3_ Base case O is the O 3 concentration of base case (2006) for the ith simulation i O 3 _ 2015 is the O 3 concentration of strategy in 2015 for the ith simulation, Standard deviation of O 3 100 i1 O3 i 3 2 3 ( O O ) 99 (5) i Where: O3 is the difference in O 3 concentration between the base case and the strategy in 2015 for the ith simulation. The highest uncertainties of O 3 reduction appear at the same time of the highest O 3 reduction at 1200LT - 1400LT of each day (Fig .17). The highest reduction of O 3 concentration at MA, D2 and HB stations during 6th - 8th Feb are 14ppb, 6.7ppb and 3.9ppb, respectively. However, the highest uncertainties of O 3 reduction at MA, D2 and HB stations are 3ppb, 9ppb and 3.5ppb respectively. Therefore, the uncertainties of O 3 reduction are in general similar to the O 3 reduction. We can not conclude that the change in O 3 concentration is due to the impact of the emission control plan, because the change can probably be due to the impact of uncertainties of input parameters. For the evolution of primary pollutants of the strategy in 2015, the concentrations of NOx in simulations will increase 7% than those were in 2006. However, the concentrations of CO and CH 4 decrease around 10% and 8%, respectively than those were in 2006. In conclusion, there is very little impact of the emission control plan in 2015. Strategy in 2020: Figure 17 (d, e, f)-lower panels shows that the highest uncertainties of O 3 reduction ( ) also appear at the same time of the highest O O 3 reduction ( O3 ) at 1200LT - 3 1400LT of each day. In general, the O 3 concentration in 2020 is lower than the O 3 concentration in 2006. The highest reduction of O 3 concentration at MA, D2 and HB stations during 6th – 8th Feb are 23.5ppb, 13.4ppb and 7.8ppb, respectively. While, the highest uncertainties of O 3 reduction at MA, D2 and HB stations are 8.2ppb, 11ppb and 5.1ppb, respectively. The O 3 reduction is higher than the uncertainty of O 3 reduction in all stations. It means that the change in O 3 concentration is due to the change in emissions from the emission control plan. For the evolution of primary pollutants of the strategy in 2020, the concentrations of NOx in simulations will increase 16% than those were in 2006. However, the concentrations of CO decrease around 7% than those were in 2006. In conclusion, this emission control plan in 2020 has strong impact on the primary and secondary pollutants in HCMC. 5. Summary and key recommendations In this chapter, the state of the art for studying urban air pollution was reviewed and discussed. Air in the large cities is polluted, especially in developing cities. Road traffic is the

Urban Air Pollution 35 main source of pollution in cities. The environmental managers strongly need for air quality management systems to help them control air quality in the cities. The available air quality management system is discussed. Emission inventory is the most important part in air quality management system as it is the mostly used tool to identify the pollution source. However there are a lot of difficulties to be faced when we generate EIs. In this chapter, we also present an example of generation an EI for developing cities where there are not enough available data. The results of EI are used as input parameters for air quality modeling. Then we present many Air pollution abatement strategies over HCMC as a case study of developing cities. 6. References - Print Books Dinh, X.T., 2003. Air pollution, VNU-HCM edition. 399p. Ermakov, X.M., 1997. Monte Carlo methodology and its relations. Translated by Pham,T.N. Sciences and Technology publishers. 271p. Molina, L., Molina, M., 2002. Air Quality in the Mexico Megacity. An integrated assessment. Kluwer Academic Publishers, Dordrecht, The Netherlands. ISBN 1-4020-045204. Rainer, Friedrich., Stefan, Reis., 2004. Emissions of Air Pollutants, Springer. 333p Moussiopoulos, Nicolas., 2003. Air Quality in Cities (book). Springer, Heidelberg, Germany. ISBN 3-540-00842-x. 298 p. - Papers in Journals Bang Q. HO, Clappier, A., 2011. Road traffic emission inventory for air quality modelling and to evaluate the abatement strategies: a case of Ho Chi Minh City, Vietnam. Atmospheric Environment Vol 45, Issue 21 (2011) pp. 3584-3593. ISSN: 1352-2310. Bang Q. HO, Clappier, A., Golay F., 2011. Air pollution forecast for Ho Chi Minh City, Vietnam in 2015 and 2020. Air Quality, Atmosphere & Health, Volume 4, Number 2, p.145-158. ISSN: 1873-9318. Bang Q, HO, Clappier, A., Zarate, E., Hubert, V.D.B., Fuhrer, O., 2006. Air quality mesoscale modeling in Ho Chi Minh City: evaluation of some strategies’ efficiency to reduce pollution. Journal Science and Technology Development. Vol 9, N° 5, 2006. Belalcazar, L., Fuhrer, O., Ho. D., Zarate, E., Clappier, A., 2009. Estimation of road traffic emission factors from a long term tracer study in Ho Chi Minh City (Vietnam), atmospheric environment, vol. 43 (2009) 5830–5837. Bougeault, P., Lacarrere, P., 1989. Parameterization of orography-induced turbulence in a mesobeta-scale model. Monthly Weather Review 117, 1872–1890. Clappier, A., Perrochet, P., Martilli, A., Muller, F., Krueger, B.C., 1996. A new non-hydrostatic mesoscale model using a control volume finite element (CVFE) discretisation technique. In: Borrell, P.M., et al. (Ed.), Proceedings of the EUROTRAC Symposium ’96. Computational Mechanics Publications, Southampton, pp. 527–553. Clappier, A., 1998. A correction method for use in multidimensional time-splitting advection algorithms: application to two- and three-dimensional transport. Monthly Weather Review 126, 232–242. Collella, P., Woodward, P., 1984. The piecewise parabolic method (PPM) for gas dynamical simulations. Journal of Computational Physics 54, 174–201. Gurjar, B.R., J. Lelieveld., 2005. New Directions: Megacities and global change. Atmospheric Environment 39 (2), 391-393. Generoso, S and Bey, I., 2007: A satellite and model-based assessment of the 2003 Russian fires: Impact on the Arctic region. J. Geophys.Res.,vol 112, D15302

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