【摘要】 为提高网格化排放清单精度，结合AOD（aerosol optical depth）和标准路长提出基于AOD修正系数的空间分配模型，并以青岛市为例进行验证。验证结果表明：基于AOD修正系数的空间分配模型获取的2019年青岛市1 km×1 km分辨率的PM_2.5排放清单与测量数据的相关系数R²为0.55,高于基于标准路长、GDP（gross domestic product）和人口密度的空间分配模型（R²分别为0.47、0.43和0.31）;青岛市中心城区，下辖的即墨、胶州、莱西、平度的中心城区及胶州湾地区是机动车的高排放区。本研究首次将强现实性的遥感数据引入空间分配模型，使每个网格的空间分配系数由固定值变为随真实大气污染状况变化的动态参数，提高了网格化机动车排放清单的精度，对研究机动车排放对大气污染的影响、精细化管控等具有重要意义。更多还原

【Abstract】 To improve the accuracy of gridded emission inventories, this paper proposed a spatial allocation model based on aerosol optical depth（AOD） combined with standard road lengths. The model was validated using Qingdao as a case study. The validation results showed that the PM_2.5 emission inventory for Qingdao in 2019 at a 1 km×1 km resolution, obtained using the spatial allocation model based on AOD correction coefficients, exhibited a higher correlation coefficient（R² was 0.55） with measured data compared to models based on standard road lengths, GDP, and population density（R² was 0.47, 0.43, and 0.31, respectively）. High-emission areas for vehicles in Qingdao include the central urban area, as well as the downtown regions of Jimo, Jiaozhou, Laixi, Pingdu, and the Jiaozhou Bay area. This study was the first to introduce remote sensing data into the spatial allocation model, transforming the allocation coefficient of each grid from a fixed value into a dynamic parameter varing with real atmospheric pollution conditions, thereby enhancing the accuracy of grided vehicle emission inventories. This study was of great significance for studying the impact of vehicle emissions on air pollution and facilitating precise control measures.更多还原