A novel spatial disaggregation model of vehicle emission inventory

Abstract

An accurate spatial disaggregation method is crucial to ensure the accuracy of gridding vehicle emissions inventories. In the present study, we proposed a novel spatial disaggregation model that combines standard road length with aerosol optical depth (AOD) remote sensing data. Qingdao, a typical coastal city in China, was selected as the study area. We used the new model to obtain a gridded vehicle emissions inventory with a resolution of 1 × 1 km. In the novel model, the square of the correlation coefficient between the disaggregated PM2.5 emission and measured PM2.5 increased by approximately 10%. The normalized mean deviation and error between the simulated PM2.5 determined using the Community Multi-scale Air Quality (CMAQ) model decreased by 35% and 27%, respectively, compared with those determined using the standard road length method. The focus of urban traffic control is to address the hotspot formation of high-emission areas in the Jiaozhou Bay region. This study expands the potential application of remote sensing data and improves the accuracy of vehicle emissions inventories.