In China, motor vehicle emissions have been identified as the major source of urban air pollution. Thus, estimation of emissions and their impact on air quality is necessary. Vehicle emissions vary strongly with region and depend on local vehicle operation and emission performance. This paper uses the MOVES (Motor Vehicle Emission Simulator) model, released by the U.S. Environmental Protection Agency, for the estimation of vehicle emission factors in Shanghai, China. To achieve a convincing emission result, vehicle operation is extracted from massive taxi Global Positioning System (GPS) data, and the emission inventory from MOVES is revised according to China's vehicle emission standards. In addition, deterioration factors are calculated on the basis of vehicle condition. Comprehensive emission factors are generated for Shanghai light-duty vehicles at various average speed levels. The results indicate that emission factors for hydrocarbons, carbon monoxide, and oxides of nitrogen of in-use light vehicles in China are 0.1 to 0.25 g/km, 4 to 7 g/km, and 0.4 to 0.8 g/km, respectively. These amounts are 15, 1.9, and 5.9 times higher than those in the United States, respectively. By 2012, the Environmental Protection Bureau had established 10 monitoring sites in Shanghai and released data for real-time concentrations of particulate matter less than 2.5 μm in diameter, particulate matter less than 10 μm in diameter, nitrogen dioxide (NO₂), and sulfur dioxide to the public. On the basis of the new release of data, a field study estimating near-road air quality was conducted with MOVES and the air dispersion model AERMOD. The concentration result shows that the accuracy of near-road NO₂ estimation is improved with taxi GPS data and the revised MOVES emission inventory. The study explores the extended application of MOVES by offering a procedure for applying MOVES in non-U.S. regions.

Estimating transportation network emissions requires multiplying estimates of on-road vehicle activity (by source type and operating mode) by applicable emission rates for the observed source type and operating conditions. Coupling microsimulation model runs with emissions modeling can make fast assessments possible in transportation air quality planning. This research developed a tool with automated linkage between the Vissim microsimulation model and the Motor Vehicle Emission Simulator (MOVES) model. To link the two models, the research team used MOVES-Matrix, which was prepared by iteratively running MOVES across all possible iterations of vehicle source type, fuel, environmental and operating conditions, and other parameters (hundreds of millions of model runs) to create a multidimensional emission rate lookup matrix. A Vissim simulation of the major arterial roads and freeways at I-85 and Jimmy Carter Boulevard in Gwinnett County, Georgia, provided the case study for this MOVES-matrix application. The researchers present predicted emissions and the results of a sensitivity analysis to identify the potential impacts of various internal Vissim modeling parameters (such as minimum headway, maximum deceleration rate for cooperative braking, and emergency stop distance) on a case study’s emissions outputs. The sensitivity analysis found that internal Vissim parameters impacted emissions and that proper care should be taken in using Vissim for emissions analysis at the corridor and link level. The case study demonstrates that Vissim coupled with MOVES-Matrix can be an effective tool for emissions analysis.