Application monte-carlo method to estimate uncertainties in the results of air quality modelling: the case of Ho Chi Minh city

Abstract

Air pollution has deteriorated considerably the health of millions of people in Ho Chi Minh City (HCMC) due to high levels emissions. It has been showed that more than 90% of children under the age of 5 years old suffered from different respiratory illnesses in the city. The objectives of this research include: (1) studying the formation of the pollution plume over the city during a 3-day episode in February 2006 and (2) estimating uncertainties in the results of air quality simulations for HCMC. Research results show evidence of the development of a Ozone (O₃) pollution plume in the north-western part of the city. A scenario for reduction of air pollution is studied in this work to help the local government to make decisions for managing air quality in HCMC. For making a better-informed decision, the probabilistic estimate for the photochemical model is carried out in this research. The Monte-Carlo method that is applied in this research for the uncertainty analyses is an efficient method of producing a probabilistic output from the photochemical model. The results of abatement strategy showed that if the local government follows the emission control plan: by 2020, the O₃ concentration will be decreased by 30% compared to the recent O₃ concentration.