Please refer to the Supplement for detailed comments.

The work “High-resolution Emission Inventory Development and Co-emission Hotspot Identification of Air Pollutants and Greenhouse Gases in Central Plains Region, China” works to compute emissions of various air pollutants and greenhouse gasses in Henan Province. Henan province is an under-looked area in terms of global emissions research, due to its rapidly increasing economy, large number of people fed through its agricultural production, and amount of support for industry both inside of China as well as around the world. For this reason, understanding emissions and how they are changing in Henan is of vital importance for understanding the global atmospheric balance of many relevant chemical species. However, this work uses a very traditional set of approaches. It is clear from the results that there are substantial spatial and temporal mismatches with top-down emissions datasets created for NO2, CO, CH4, and Black Carbon, based on research work with which I am familiar and has been published extensively over the past few years. At the present time, such datasets offer similar spatial resolution as provided herein, in addition to daily temporal resolution, while this approach herein only offers monthly average resolution. The fact that these top-down datasets are not compared against means that rationales for the spatial and temporal differences are not explored. Rapid industrial development and expansion of biomass burning sources on the ground in places like Luoyang, Sanmenxia, Jiaozuo, Anyang, and Shangqiu are a reality which should be addressed in more depth. Very high pollution levels observed (and published elsewhere) demonstrates that November and December should have much higher emission than the rest of the year, something this work does not seem to reproduce. One possibility is that the uncertainty of various inputs are too small. Another possibility is that there are geospatial discrepancies in some of the underlying data used. I am not sure. However, this work should clearly investigate these issues, so that the community can understand which of these factors is leading to the differences between the bottom-up and top-down perspectives, making the results more realistic. Given the importance of the region discussed, this should be a top priority, since the ultimate publication of this work would certainly aid various atmospheric studies both in China as well as in other regions throughout the developing Global South which contain similar situations of rapid economic, industrial, and agricultural development.

Specific details:

1. BC is mentioned in Figure 2, but I do not see any maps of its emissions such as in Figures 4-8. What happened?
2. The equation used in 2.3.1 is not sufficiently robust to predict BC within a reasonable amount of uncertainty, since BC mass is a very small fraction of PM2.5 yet has a substantial impact on the climate system. One such issue has been moving beyond mass-based approaches to include number and particle size-based approaches. Please see work published more than 1 decade ago raising this issue such as https://doi.org/10.1029/2007JD009756 and https://doi.org/10.1002/2013JD019912, as well as more modern work at high spatial resolution which offers solutions to this issue such as https://doi.org/10.1038/s41467-022-35147-y and https://doi.org/10.1021/acs.estlett.5c00340
3. It seems your spatial distributions of sources relating to new industrial growth (Jiaozuo, Sanmenxia, Anyang), new urban growth in in Tier 3 urban areas (i.e., Luoyang, Shangqiu), and biomass burning (many places) do not necessarily match with on-the ground observations of air pollution and multiple recently published top-down studies. Please make such comparisons in general.
4. When making comparisons, it would be best to demonstrate skill of your work on a grid-by-grid and day-by-day basis between your 3km x 3km and similar 5km x 5km gridded datasets. This comparison would ideally be done at high temporal frequency, so that any uncertainties and/or biases can be clearly identified. If there are underlying reasons for these differences, they can be explored in more detail. Adding in this step is critical to demonstrate that the paper is sufficiently innovative and that the results are reasonable.