the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
MEIAT-CMAQ v1.0: A Modular Emission Inventory Allocation Tool for Community Multiscale Air Quality Model Version 1.0
Haofan Wang
Jiaxin Qiu
Qi Fan
Yang Zhang
Yifei Xu
Yinbao Jin
Yuqi Zhu
Jiayin Sun
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.
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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
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A Review of
MEIAT-CMAQ v1.0: A Modular Emission Inventory Allocation Tool for Community Multiscale Air Quality Model Version 1.0by Wang Haofan et al.
In the present MS, the authors describe the titular software framework -- a collection of Python scripts and supporting data files -- that transposes gridded emissions inventory from a coarser spatial resolution to a finer resolution, in the so-called spatial allocation process. Additional processes on speciation and vertical grid distribution are subsequently applied to procure model-ready emission input data. As demonstration, an exemplary emission data set prepared using the tool was applied to a WRF-CMAQ run, for which the results are presented.
I recommend this MS be rejected on due to insufficient material on the model development and description presented to justify a model description paper. The title notwithstanding, the authors should make major editing choices to submit a "model description" paper or a "scientific" paper.
The information pertaining to the development of their emission processing framework is cursory at best. Figures are presented effectively without adequate explanation to provide context and coherence. More importantly, the methodology employed in this framework – spatial allocation, speciation, and vertical distribution – are rather routine operations, albeit still challenging to implement. Further, the authors emphasize "modularity" as being a distinctive aspect of this framework without explaining why this modularity is advantageous compared to existing frameworks. I suspect the authors are referring to the implementation each allocation process as a different operating unit. Or the authors might have meant this in a literal sense, in which each of the components has been packaged as a Python module. But these design strategies are merely logical, not novel.
On the other hand, the results of the demonstration model run, as well as the data presented in the supplement, seem better suited for a scientific paper. However, the model run results bear only proximate relevance to the emission processing framework, in which results derived from said framework was used as model input. The authors should concentrate on consistency of the processed emission with respect to the originating gridded data instead.
After going through the model results, the supplemental data, and the source code, I got the impression that this framework is conceived for a certain, application-specific input data infrastructure and workflow in mind, one that is used by the authors’ research group. The authors include detailed comments on portability of this framework to other (WRF-CMAQ) model setups. Ideally there should be very little code change or shoehorning of input data in order to make effective use of this framework. Also, it strikes me that the description section is intended for someone who is already heavily involved in the development of MEIAT.  The authors should instead rewrite it  from point of view of a perspective user who is not familiar with the tool. As a non-exhaustive example, explaining the concept of "spatial surrogate grid" or speciation methodology, would help the reader immensely appreciate what the authors set out to achieve with this tool, as with details on what the expected input and output are for this framework, in an organized and coherent manner.
In addition of the critical issues above, I have the following minor technical comments below:
1) The ISAT framework (Wang et al 2023) is referenced prominently on this MS. This begs the question, whether MEIAT is in any way based on directly ISAT, or developed in as a reaction to address the alleged deficiencies. This point was not made very clear.2) Section 2, paragraph 1. How is MEIAT configured to utilize only 50% of the CPU and why is it scientifically or technically relevant? The same claim can be made quite easily if it is launched as a single thread on a dual-core CPU and can be trivially scaled with higher CPU core counts, assuming MEIAT has been developed specifically for multithreaded operation.
3) How does the grid allocation methodology reconcile the transference of emissions between mutually non-orthogonal gtrids (i.e., lat/long to x/y)?
4) How does MEIAT handle line or point source emissions? There are brief mentions of GIS data (i.e., SHP files) but there is no information on how they are being assigned.
Citation: https://doi.org/10.5194/egusphere-2023-1309-RC1
Haofan Wang et al.
Haofan Wang et al.
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