Development of a High-Resolution Integrated Emission Inventory of Air Pollutants for China

排放清单 污染物 中国 环境科学 空气污染物 高分辨率 分辨率(逻辑) 空气污染 计算机科学 遥感 地理 化学 考古 人工智能 有机化学
作者
Nanping Wu,Guannan Geng,Ruibo Xu,Shigan Liu,Xiaodong Liu,Qinren Shi,Ying Zhou,Yu Zhao,Huan Liu,Yu Shi,Junyu Zheng,Qiang Zhang
标识
DOI:10.5194/essd-2024-3
摘要

Abstract. Constructing a highly-resolved comprehensive emission dataset for China is challenging due to limited availability of refined information for parameters in a unified bottom-up framework. Here, by developing an integrated modeling framework, we harmonized multi-source heterogeneous data including several up-to-date emission inventories at national and regional scale, and for key species and sources in China, to generate a 0.1° resolution inventory for 2017. By source mapping, species mapping, temporal disaggregation, spatial allocation and spatial-temporal coupling, different emission inventories are normalized in terms of source categories, chemical species, and spatiotemporal resolutions. This achieves the coupling of multi-scale, high-resolution emission inventories with the MEIC (Multi-resolution Emission Inventory for China), forming a high-resolution INTegrated emission inventory of Air pollutants for China (i.e., INTAC). We find that the INTAC provides more accurate representations for emission magnitudes and spatiotemporal patterns. In 2017, China’s emissions of SO2, NOx, CO, NMVOC, NH3, PM10, PM2.5, BC, and OC are 12.3, 24.5, 141.0, 27.9, 9.2, 11.1, 8.4, 1.3 and 2.2 Tg, respectively. The proportion of point source emissions for SO2, PM10, NOx, PM2.5 increases from 7–19 % in MEIC to 48–66 % in INTAC, resulting in improved spatial accuracy, especially mitigating overestimations in densely populated areas. Compared to MEIC, INTAC reduced mean biases in simulated concentrations of major air pollutants by 2–14 μg/m³ across 74 cities against ground observations. The enhanced model performance by INTAC was particularly evident at finer grid resolutions. Our new dataset is accessible at https://doi.org/10.5281/zenodo.10459198 (Wu et al., 2024), and it will provide a solid data foundation for fine-scale atmospheric research and air quality improvement.
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