In recent years, air pollutant levels in China have been effectively controlled, with a particular focus on reducing emissions from stationary sources. However, the contribution of mobile source pollutants is on the rise, especially in some of megacities. The increasing number of motor vehicles and the pollutants emit are having a detrimental impact on the health of residents. Addressing motor vehicle emissions is currently a key priority for the government.

This study combines data from road detectors to estimate the activity cycles of different vehicle types in Hong Kong. Since the activity cycles of vehicles can affect traffic flow and significantly impact the calculation of vehicle air pollutant emissions, we refer to these as ""temporal allocation factors"". These distribution fac...[ Read more ]

In recent years, air pollutant levels in China have been effectively controlled, with a particular focus on reducing emissions from stationary sources. However, the contribution of mobile source pollutants is on the rise, especially in some of megacities. The increasing number of motor vehicles and the pollutants emit are having a detrimental impact on the health of residents. Addressing motor vehicle emissions is currently a key priority for the government.

This study combines data from road detectors to estimate the activity cycles of different vehicle types in Hong Kong. Since the activity cycles of vehicles can affect traffic flow and significantly impact the calculation of vehicle air pollutant emissions, we refer to these as ""temporal allocation factors"". These distribution factors, when combined with existing traffic flow data, can predict the standard hourly traffic flow information for Hong Kong. A bottom-up approach was used to estimate the vehicle emission rates in Hong Kong for the year 2023, and these pollutant emission rates were then applied to the air quality model (ADMS-RML) to simulate the dispersion of various pollutants at the street level. The results show that the simulated NO₂ value during the noon period is underestimated, while the simulated O₃ value is overestimated. In order to gain a deeper understanding of the behavior of the ADMS-Urban, I conducted a sensitivity analysis to examine the impact of various dynamic variables on the ADMS-Urban simulation results. The findings indicate that the NO₂ simulation results from ADMS-Urban are significantly influenced by NO₂, NO_x, and O₃ concentrations. The background concentration emerged as the most sensitive parameter, followed by wind speed. Therefore, the CMAQ results and the chemical schemes occurring in the atmosphere are the key elements in improving the accuracy of the ADMS-RML simulation.