Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission.

Vehicle emission has been the major source of air pollution in urban areas in the past two decades. This article proposes an artificial neural network model for identifying the taxi gross emitters based on the remote sensing data. After carrying out the field test in Guangzhou and analyzing various factors from the emission data, the artificial neural network modeling was proved to be an advisable method of identifying the gross emitters.

A remote sensing system of vehicle emissions based on tunable diode laser technology.

As being an effective real-time method of monitoring vehicle emissions on-road, a remote sensing system based on the tunable diode laser (TDL) technology was presented, and the key technologies were discussed. A field test in Guangzhou (Guangdong, China) was performed and was found that the factors, such as slope, instantaneous speed and acceleration, had significant influence on the detectable rate of the system. Based on the results, the proposal choice of testing site was presented.