Emission Patterns under Alternative Congestion and Motor Vehicle Pollution Mitigation Policies in Shanghai

As a megacity with thriving economy, Shanghai is experiencing rapid motorisation and confronted with traffic congestion problems despite its low car ownership. It is of value to look into the policies on emission control of motor vehicle and congestion reduction in such a city to explore how to reconcile mobility enhancement with the environment. Results of a dynamic simulation displayed time paths of emissions from motor vehicles in Shanghai over the period from 2000 to 2020. The simulation results showed that early policies on emission control of motor vehicle could bring about far-reaching effects on emission reduc- tion, and take advantage of available low-polluting technologies and technical innovation over time. Travel demand management would play an important role in curbing congestion and reducing motor vehicle pollution by calming down car ownership rise and deterring inefficient trips as well as reducing fuel waste caused by congestion.

Comparison of the Mutagenicity of Exhaust Emissions From Motor Vehicles Using Leaded and Unleaded Gasoline as Fuel

While Unleaded gasoline has the advantage of eliminating lead from automobile exhaust, its potential to reduce the exhaust gas and particles, merits further examination. In the present studies,the concentrations of hydrocarbons (HC) and earbon monoxides (CO) in emissions were analyzed on Santana engine Dynamometer under a standard test cycle, and total exhaust particles were collected from engines using leaded and unleaded gasoline. It was found that unleaded gasoline reduced the emissions of CO and HC, and decreased the quantity of vehicle exhaust particulate matters by 60%.With the unlead gasoline, only 23 kinds of organic substances, adsorbed in the particles, were identified by gas chromatography/mass spectrometer (GC/MS) while 32 components were detected using the leaded gasoline. The results of in vitro Salmonella/ microsomal test and micronucleus induction assay in CHL cells indicated that both types of gasoline increased the number of histidine-independent colonies and the frequencies of micronucleus induction; no significant differellce was found in their mutagenicity.

Prediction of Spatiotemporal Evolution of Urban Traffic Emissions Based on Taxi Trajectories

With the rapid increase of the amount of vehicles in urban areas,the pollution of vehicle emissions is becoming more and more serious.Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making.Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning.Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions,however,they are not effective and efficient enough in the combination and utilization of different inputs.To address this issue,we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator(MOVES)model.Specifically,we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms.These matrices can reflect the attributes related to the traffic status of road networks such as volume,speed and acceleration.Then,our multi-channel spatiotemporal network is used to efficiently combine three key attributes(volume,speed and acceleration)through the feature sharing mechanism and generate a precise prediction of them in the future period.Finally,we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states,road networks and the statistical information of urban vehicles.We evaluate our model on the Xi′an taxi GPS trajectories dataset.Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.