ABSTRACT
Vehicle pollution in Sichuan Province is becoming increasingly serious. Here, based on specific inventory calculation methods and multi-caliber activity level data, this study calculated vehicle exhaust emissions from 2010 to 2017 in Sichuan Province. The results show that the average growth rate of vehicle ownership in Sichuan is higher than the national trend. In 2017, vehicle emissions of CO, NOx, SO2, NH3, HC, PM2.5, PM10, BC, and OC were 706.9, 275.3, 0.3, 5.7, 164.8, 8.1, 8.9, 4.1, and 1.4 kt, respectively. Except for NH3, showed a downward trend, peaking in 2014-2016. Diesel vehicle ownership showed a strong correlation with the emission of NOx. Based on these observations, stricter vehicle emission standards offer the greatest potential for emissions reductions, with early implementation offering the greatest reduction potential. The improvement of fuel quality will also have more than a 6% emission reduction effect on pollutant emission each year. HC and NOx emissions reductions will be an important control on vehicle pollution in Sichuan Province in the future.
ABSTRACT
A method for developing a high-resolution emission inventory for road vehicles based on traffic flow monitoring data is proposed in this study. The characteristics of road traffic flow were analyzed and a high-resolution emission inventory of vehicle in Chengdu was established. The results showed that the traffic flow and emissions in Chengdu exhibited an obvious "double peak" distribution, and that the traffic volume of vehicles during peak hours accounted for 39.85% of the total. China IV vehicles, small vehicles, and gasoline vehicles were the main types of road vehicles classified. The daily emissions of SO2, NOx, CO, PM10, PM2.5, BC, OC, and VOCs from road vehicles were 3.89, 162.08, 324.11, 4.79, 4.36, 1.89, 0.78, and 44.37 t, respectively. The overall spatial distribution showed a decreasing trend from the city center to the periphery, and the time distribution essentially presented a "double peak" distribution. The related indicators of particulate matter were greatly affected by the number of trucks. The main source of NOx, PM10, PM2.5, BC, and OC was large diesel vehicles, and the main source of CO was small gasoline vehicles. NOx emissions from large vehicles accounted for up to 80% of the total. The method based on registered vehicles led to an overestimation of the emissions from road vehicles in Chengdu, with a proportion between 1% and 30%.
