Probabilistic Health Risk Assessment of Vehicular Emissions as an Urban Health Indicator in Dhaka City

Abstract

Emissions modelling is an important tool for assessing the urban health status of any city, but often the assessments are affected by the uncertainty of the data used for the modelling. Therefore, a Monte Carlo simulation technique was used for a probabilistic emissions modelling of Dhaka City by simulating 20,000 scenarios for the highest and lowest values of traffic volume and speed profiles for each of the major road links. Only nitrogen oxide (NOx) emissions from on-road vehicles were considered, as vehicular sources are major contributors. Each dataset included two peak periods and an offpeak period of the day to cover the diurnal variation within each road link. Using the probability of the magnitude of emissions along with the corresponding health risk, a series of spatial urban health risk severity scenarios was generated for 2018 and 2024, suggesting that transportation and environmental planning is required for urban sustainability.