08 Aug 2023
 | 08 Aug 2023
Status: this preprint is open for discussion.

MEIAT-CMAQ v1.0: A Modular Emission Inventory Allocation Tool for Community Multiscale Air Quality Model Version 1.0

Haofan Wang, Jiaxin Qiu, Yiming Liu, Qi Fan, Xiao Lu, Yang Zhang, Kai Wu, Ao Shen, Yifei Xu, Yinbao Jin, Yuqi Zhu, Jiayin Sun, and Haolin Wang

Abstract. The Modular Emission Inventory Allocation Tool for Community Multiscale Air Quality Model (MEIAT-CMAQ) v1.0 is a resource that enables the refinement of coarse emission inventories by delivering complete temporal, species, and vertical allocations. This tool generates model-ready emission files for CMAQ, and it effectively addresses the challenges concerning the pinpointing of grid information and the spatial allocation for spatial surrogates with specific shapes. These features significantly enhance the accuracy of the allocation of emissions from transportation sectors. Additionally, MEIAT-CMAQv1.0 features an efficient operational algorithm and a modular design, thus conferring greater flexibility and making it suitable for both gridded and tabulated emissions inventories. By inputting pre-assessment and post-assessment emissions separately into the CMAQ model, we observe that post-allocation inventory has a significant positive effect on both O3 and PM2.5 simulations. The development of MEIAT-CMAQv1.0 provides valuable insights into the automated operation of air quality models and the development of emission inventory allocation tools.

Haofan Wang et al.

Status: open (until 25 Oct 2023)

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  • RC1: 'Comment on egusphere-2023-1309', Anonymous Referee #1, 22 Aug 2023 reply

Haofan Wang et al.


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Short summary
This tool improves existing methods by providing temporal and species allocation modules and enabling emission file generation in advance for any time period. The tool is also capable of vertical plane distribution for high altitude emissions and accurately tracks road-related emissions, an area overlooked by other tools. It utilizes 50 % of the CPU capacity for efficient parallel processing during allocation.