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A novel spatial disaggregation model of vehicle emission inventory
Urban Climate ( IF 6.4 ) Pub Date : 2024-05-09 , DOI: 10.1016/j.uclim.2024.101947 Haixia Feng , Xingyu Wang , Qiang Jia , Maoxin Zhu
Urban Climate ( IF 6.4 ) Pub Date : 2024-05-09 , DOI: 10.1016/j.uclim.2024.101947 Haixia Feng , Xingyu Wang , Qiang Jia , Maoxin Zhu
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 PM emission and measured PM increased by approximately 10%. The normalized mean deviation and error between the simulated PM 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.
更新日期：2024-05-09
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