In:
Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2340, No. 1 ( 2013-01), p. 38-48
Abstract:
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 (NO 2 ), 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 NO 2 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.
Type of Medium:
Online Resource
ISSN:
0361-1981
,
2169-4052
Language:
English
Publisher:
SAGE Publications
Publication Date:
2013
detail.hit.zdb_id:
2403378-9
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