Gridded mobile source emissions with multiple processes and pollutants from 2011 to 2020 in China

作者：Junchao Zhao, Zhaofeng Lv, Zhenyu Luo, Zhining Zhang, Zhe Sun, Wenxin Cao, Wen Yi, Yongyue Wang, Hezhong Tian, Yan Ding, Kebin He, Huan Liu · 发表于：Scientific Data · 年份：2025 · DOI：10.1038/s41597-025-05690-6 · 被引用次数：8 · 研究领域：Vehicle emissions and performance、Atmospheric chemistry and aerosols、Air Quality and Health Impacts

Accurate and comprehensive estimation of mobile source (MS) emissions is essential for air quality management and atmospheric research. However, existing emission inventories often lack sufficient coverage of MS categories, emerging pollutants, and newly developed model parameterizations. Here, we present the Gridded Mobile-source Emission Dataset (GMED), a model-ready emission inventory with detailed source classification and multi-pollutant coverage. GMED provides monthly emissions from 2011 to 2020 at a spatial resolution of 36 km × 36 km, covering eight MS categories and including both tailpipe and non-exhaust emissions. It incorporates several methodological improvements and integrates localized measurements from multiple sources, including emission factors, organic compound speciation, spatiotemporal proxies, and activity data. Validation involves comparisons with established inventories and cross-checks of key model parameters, both showing good agreement across multiple metrics. GMED outputs also align well with high-resolution emission inventories derived from big traffic data. As a validated, long-term dataset, GMED supports emission assessments, air quality modelling, and policy evaluation, helping to fill a critical gap in differentiated, multi-year MS emission inventories.