Measurement and dispersion prediction of gaseous and particle emissions from on-road vehicles in Hong Kong

xix, 179 leaves : ill. (some col.) ; 30 cm.PolyU Library Call No.: [THS] LG51 .H577M ME 2005 NingIn the present study, the calculation procedures of vehicle gaseous emission factors of carbon monoxide (CO), hydrocarbon (HC) and nitric oxide (NO) has been developed based on the measured gaseous emission concentration data using the remote sensing vehicle emissions testing system at several local sites in Hong Kong. The results show that the vehicle driving speed profiles have direct effect on the characterization of vehicle emission factors (EF). Finally, a unique database of gaseous emission factors from the aggregate on-road petrol/gasoline and diesel vehicles for urban driving conditions in Hong Kong has been established. On the other hand, six local representative in-use vehicles (i.e., diesel light duty vehicle, diesel taxi, diesel light bus, two gasoline/petrol passenger cars and liquefied petroleum gas, LPG taxi) have been selected to further investigate their characteristics of gaseous and particle emissions for different driving conditions (i.e., low and high idle, and steady state of constant vehicle speed tests). The average EFCO, EFHC and EFNO of the different fuelled in-use vehicles decrease with increasing of vehicle speed. The average vehicle emission factors of selected in-use diesel vehicles are higher than that of selected in-use petrol and LPG vehicles. A correlation of the calculated vehicle gaseous emission factors in terms of the vehicle driving speed with good regression coefficient, R² values has been established for both chassis dynamometer (CD) and remote sensing (RS) testing systems. Furthermore, a correlation of the calculated vehicle gaseous emission factors for selected different fuelled in-use vehicles with good regression coefficient, R² value from the CD and RS tests has also established successfully. It demonstrates the highly correlated relationship between the CD and RS testing systems.The total particle number and mass concentrations of the selected local representative in-use vehicles for different driving conditions have been investigated using the chassis dynamometer testing system. A general increasing trend of the particle number and mass concentrations for PM₀.₁, PM₂.₅ and PM₁₀ has also been observed for all the selected in-use vehicles with increasing of vehicle speed. The results also show that the particle emission factors of the selected in-use diesel vehicles are much higher than that of the selected in-use petrol and LPG vehicles. A correlation between the individual selected in-use diesel vehicle emission factors of PM₀.₁, PM₂.₅, PM₁₀ and EFHC in mgkm⁻¹ or gkm⁻¹ with good regression coefficient, R² values has been established. It indicates a highly correlated relationship between the vehicle particle emission factors and HC emission factors for individual vehicle type of the selected in-use diesel vehicles. However, when all the selected in-use diesel vehicles data are combined together and correlated, the regression coefficient, R² values of the correlation equations decrease significantly from higher than 0.9 to 0.6. It implies that the correlation between the particle emission factors and HC emission factors is very sensitive to the vehicle types, vehicular engine cylinder sizes, vehicle model years and maintenance conditions etc. The characteristics of the traffic-induced gaseous and particle emissions dispersion in urban atmosphere have also been investigated at the selected local urban sites using a developed Gaussian line source emission dispersion (GLSED) model based on the urban traffic conditions in Hong Kong. A general decreasing trend of both measured and predicted CO, PM₂.₅ concentrations with increasing distance from the road at the selected local urban sites has been obtained. The comparison of the measured and predicted gaseous emission of CO and particle emission of PM₂.₅ shows that the developed GLSED model has a reasonable prediction performance for both gaseous and particle emissions with an average prediction error of 10% and 13% for CO and PM₂.₅, respectively. The statistical analysis of the developed GLSED model has been carried out to evaluate the prediction performance of the developed model. The index of agreement ranges from 0.68 to 0.92 and 0.6 to 0.75 for CO concentrations and PM₂.₅, respectively. It demonstrates a fairly good agreement between the predicted and measured emissions concentrations. This developed GLSED model has provided a reliable and convenient tool to evaluate the urban air quality at the roadside in Hong Kong.Department of Mechanical EngineeringM.Phil., Dept. of Mechanical Engineering, The Hong Kong Polytechnic University, 200