Estimation of the SOA Formation Potential of the National Trunk Highway in Central Plains Urban Agglomeration

Abstract

Secondary organic aerosols (SOAs) are among the main components of air pollution. Accurately estimating SOAs formed from automobile exhaust is crucial for controlling and mitigating traffic air pollution. Sufficient monitoring data is difficult for regional analysis owing to limited monitoring data over a small area or few observation stations. Indirect methods may be used to estimate SOA using data on the number and types of vehicles. A linear reference system of Central Plains urban agglomeration was built from the national trunk line network system and the traffic survey data of transportation. The numbers of different types of vehicles were assigned to road segments as the traffic flow according to the represented length between monitoring stations. Then, VOCs emissions were calculated through the emission coefficient method based on the previous traffic flow data. Moreover, further estimations of the SOA formation potential were made by the fractional aerosol coefficient approach. Through kernel density analysis, discrete point data of the observer station were transformed into line segments and expanded to a continuous spatial distribution for quantitative and spatial variation analysis of the SOA in the study area. The results show that ① toluene has the highest SOA generation potential, 1,4-diethylbenzene has the strongest ability to form SOA, and aromatic hydrocarbons exhibit higher SOA formation capacity than alkanes; ② small and medium gasoline passenger cars generate the most SOA and account for about 1/3 of the total SOA, but small gasoline trucks exhibit the strongest capacity for SOA formation; ③ regarding the capacity of SOA formation for vehicles using different fuel types, gasoline vehicles have a higher capacity than diesel vehicles, and passenger cars have a slightly higher capacity than trucks; ④ in Central Plains urban agglomeration, the spatial distribution of SOA intensity data shows a trend of convergence to the center of Zhengzhou city. Gasoline vehicles exhibit a similar pattern overall, but diesel vehicles exhibit a weaker trend that decreases distinctly. SOA intensity along the north-south direction is much higher than that of the east-west direction around the center of Zhengzhou crossing.