Characterization of road freight transportation and its impact on the national emission inventory in China

摘要

Diesel trucks are major contributors of nitrogen oxides (NOx) and primary particulate matter smaller than 2.5 μm (PM2.5) in the transportation sector. However, there are more obstacles to existing estimations of diesel-truck emissions compared with those of cars. The obstacles include both inappropriate methodology and missing basic data in China. According to our research, a large number of trucks are conducting long-distance intercity or interprovincial transportation. Thus, the method used by most existing inventories, based on local registration number, is inappropriate. A road emission intensity-based (REIB) approach is introduced in this research instead of registration-population-based approach. To provide efficient data for the REIB approach, 1060 questionnaire responses and approximately 1.7 million valid seconds of onboard GPS monitoring data were collected in China. The estimated NOx and PM2.5 emissions from diesel freight trucks in China were 5.0 (4.8–7.2) million tonnes and 0.20 (0.17–0.22) million tonnes, respectively, in 2011. The province-based emission inventory is also established using the REIB approach. It was found that the driving conditions on different types of road have significant impacts on the emission levels of freight trucks. The largest differences among the emission factors (in g km?1) on different roads exceed 70 and 50% for NOx and PM2.5, respectively. A region with more intercity freeways or national roads tends to have more NOx emissions, while urban streets play a more important role in primary PM2.5 emissions from freight trucks. Compared with the inventory of the Ministry of Environment Protection, which allocates emissions according to local truck registration number and neglects interregional long-distance transport trips, the differences for NOx and PM2.5 are +28 and ?57%, respectively. The REIB approach matches better with traffic statistical data on a provincial level. Furthermore, the different driving conditions on the different roads types are no longer overlooked with this approach.