Air quality is a global issue with significant impacts on human health. Understanding the likely long-term trend for air quality and the potential utility of different policy interventions is an important use of our scientific understanding as manifested in a range of chemical transport models. As the authors argue, the computational burden of such models make the development and use of reduced complexity models useful. In this paper the authors present a methodology of generating a reduced complexity model for surface PM2.5 concentrations based on the GCHP model driven by a somewhat complex series of climate and emissions models.

In general, I don’t think there is anything significantly wrong with the methodology outlined here. However, it is hard to make that evaluation as I found the methodology difficult to follow and understand. Both the modelling methodology to generate the training data and then Gaussian process emulation are not explained clearly and given the information provided it would be difficult to replicate. The paper needs to be significantly re-written to make it easier to better explain what has been done. I have not indicated every problem that I see in the paper. I’ve highlighted the major problems as I see it and have indicated some others as I went through the text.

I would suggest that the editor consider either “major corrections,” or “reject and re-submit,” and the authors substantially re-write the paper, and it is then re-reviewed by a different set of reviewers.

Major problems.

Modelling tools

One major concern is the clarity on how the base modelling is described. Ultimately the output comes from GCHP but the meteorology for this comes from CAM which is provided with climate information from the MIT IGSM. There are emissions from GAINS via TAPs etc. It is unclear what the inputs and outputs are from each of these models and the configurations that are being used. This ultimately raises several questions revolving around the consistency between the emissions assumptions used for the driving climate / meteorology and the air pollution?  This section of the methods needs to be significantly clarified.

Some of this is laid out in Figure 2 which should come earlier in the methodology section but there needs to be a significant effort to re-write the methodology is a more coherent and straight forwards way. I would start with the MIT IGSM, explain the configuration for that and the time-period of the model runs. Then what are the outputs from that and how are they used by the subsequent modelling tools. Then move onto the next model and explain its inputs and outputs.

Reduced form modelling

There is also a lack of clarity about how the gaussian process emulation is being undertaken. What is being predicted? Is it the PM2.5 or the individual tracers needed to calculate PM2.5? Is it all the GCHP tracers? In some places it suggests its PM2.5 but then there are discussions of the anthropogenic PM2.5 which would suggest that individual components have been predicted and then in other places its seem that the individual components are being predicted. What exactly are the input / output variables to make the prediction? A table of some sort would be useful here. It would also be useful to know more explicitly what the training data consisted of. Data from what period to what period. Again, this isn’t clear. How is this a “geographically weighted” approach. This isn’t clear in the description. What is meant by this?

Improvement of the methodology

There are many ways a reduced form model can be produced. This paper describes one way. However, there is only real utility in the methods if it is “better” than other approaches. Table 3 includes columns describing the results from a Multilinear regression emulator approach. It would be useful if this could be described in the methodology more and highlighted in more detail. How does Figure 6 look like with the MLR approach? Is the additional burden of the Gaussian Process system “worth it” compared to the simpler MLR approach?

Overall, it is difficult to evaluate the work here as I can’t really understand exactly what has been done. The paper is long and covers the development of the reduced form model and then some application. It would be worth thinking about whether these applications are useful. The health effects between the full model and the emulator are identical. This isn’t a surprise given Figure 6. Similarly, it’s not obvious to me that there is much utility in the work on the AerChemMIP comparison. The numbers calculated appear to be sensible, but do we learn much here? Given the performance of the emulator in Figure 17, this work appears to be just a statement about the performance of the GCHP/CAM/MIT IGCM system compared to the AerChemMIP models rather than the emulator. There is a lot of work done here but the paper feels long. The main conclusions get lost in this, both by the range of topics discussed and the way that they are explained.

General comment.

The word “level” does a lot of work in this paper. It is used to mean “concentration” (pollution level), the vertical coordinate of the model grid (vertical level), the “degree” of global warming (level of global warming), a spatial scale (global level). It would be useful if there could be some specificity in the different words used here.

The words “high fidelity” is used in several places. It’s not clear to me what this means. An alternative set of words should be used of more context given to what the authors mean. At a resolution of ~200km this is not a “high resolution” model.  

I include specific comments here.

Title: The paper title suggests an emulation of the whole model, but I think only the PM2.5 concentrations have been emulated. GCHP at this spatial resolution (~200km by 250km) isn’t really an “Air quality” model, it’s an “atmospheric composition” model or something like that but most people would think an “air quality” model would have a substantially higher spatial resolution.

Abstract

Line 19 “Widely adopted.” I don’t think any of these techniques have been “widely” adopted. I would remove this comment.

Line 37 “Uncertainties resulting from both chemistry and climate variability” I understand what climate variability is. I’m not sure what chemistry variability. However, I’m not sure that the methodology used here addresses these issues. This should either be expanded to be clearer or removed.

Introduction

Line 36. Sustainable development goals. I don’t think air quality has been explicitly stated as part of the SDGs. There isn’t an AQ SDG which is surprising. The AQ goals are given as sub, sub, SDGs (3.9.1 and 11.6.2).

Line 37. This suggests that the only way to evaluate the future air quality is through offline models. However, there are online ESM approaches which are in general the more used for this kind of long-term projections.

Line 54. “Frequently applied in recent science and policy studies” References should be given.

Line 60. “Chemical regimes” What do the authors mean here? Ozone NOx-VOC limitations? Aerosol SO4-NO3-NH4 regimes?

Line 63. SOA is an important component of PM2.5

Line 64. What are the direct vs indirect impacts of climate change on air pollution? Changes in the meteorology? Increased temperatures? Can this be more specific.

Line 73. What is meany by “geographically weighted.” This is used a lot in the paper but there isn’t a definition of what this means.

Why was Gaussian Process Regression chosen over other methods? What is it and why is an appropriate tool to use for this problem?

Methods

As indicated earlier I found this difficult to understand. In the first paragraph the authors talk about GCHP but this study uses a chain of models to generate the PM2.5 concentrations under several climate and emissions scenarios. It is very hard to understand what they have done. This section should be re-written with an introduction to explain the system being used and then details of each model used given in turn. What information is being used by which models? How is the data transferred between these models. Figure 2 is a start for this. But the textual description should be clearer and more specific. What are the inputs into the MIT GCM? What are its configurations? What are the outputs? What are the inputs into CAM? What is the CAM configuration? And then then what are the outputs? What emissions is it using? etc

This whole section should be rewritten in a much more coherent way. Some more section headings to describe the MIT GCM, CAM, TAPS, GAINS, GCHP etc and the flow of information between them. Once the model framework has been outlined the experiments performed to develop the training data can be explained.

Line 215. The choice of L appears somewhat arbitrary. Presumably it has something to do with the lifetime of the compound and some mean wind-speed component. Given a ~250km gridbox and say a ~5ms-1 wind. The timescale for air to be blown out of the box is ~12 hours. Thus L=1 seems appropriate for NO and potentially for some of the shorter-lived VOCs. L=3 (36 hours) seems very short for CO. The authors should provide more of a chemical interpretation of this in their text or identify this as a weakness of their approach.

Section 2.3

What is a random variable? Is this a normally distributed variable? This is a bit confusing as it could be construed as a variable containing random numbers, but I don’t think this is what is meant. What is N in equation (1)?

Are the PM2.5 surface concentrations and the input variables normally distributed? My guess is that they are not and many of them are likely log normally distributed. Does this matter?

What bits of information are are being used here? Exactly what is being predicted and with what information?

Section 2.4

I might move this description to be in the section 4 as it feels disjointed in the flow of the text.

Section 3.2

It would be useful there could be some description at the start of how the evaluation is going to take place, what is going to be contained in this section. As described early the metrics are only useful if they are compared to an alternative method of reduced model generation. It seems like this has been done but it would be useful if this could be the basis for the evaluation? Is the new approach better than the old, rather than providing metrics of the performance of the new model in isolation.

Line 293. This says that Figure 6 shows the comparison with the Delta Anthropogenic PM2.5 but the figure caption text says that it just Delta PM2.5. It’s not clear over what time period this is run for? What is the calculated delta between?

Line 300. Why does the standard deviation of the prediction and the MAE between the prediction and GCHP indicate that emulator SD is an appropriate measure of chemical and climate uncertainty. This should be explained in more detail.

Section 3.

Figure 9. This is very small and hard to read. I can’t see any dots in this figure, but they are described in the caption. The colour scale isn’t very useful as to my eye as everywhere seems to show a reduction other than a possibly over Bangladesh.

Table 3. The description of the multilinear approach is pages ahead in the document. It should be in the methods section and explained properly.

Is it clear why Section 3.3.2 is that rather than Section 3.4?

Discussion

I think the workflow has been applied to a number of applications outside of Engineering. There are a number of examples of similar approaches in atmospheric composition research.

In Wong et al. 2025, the authors use geographically weighted gaussian process regression to emulate changes in anthropogenic PM2.5 under different climate and air pollution control scenarios.  The authors demonstrate that their methodology greatly improves computation times while accurately estimating bulk annual PM2.5 and capturing non-linear chemistry.  This paper is ready for publication pending the following changes:

Major Comments:

1. The authors make several assumptions in generating training data with GCHP-CAM including using CO2 levels instead of total radiative forcing to parameterize total radiative forcing in the 21st century.  Uncertainty that arises from these assumptions is not addressed when presenting ΔPM2.5 and Δmortality results.
2. In the Gas Ratio subsection of 3.3.1 it is unclear how Gas Ratio indicates level of linearity vs non-linearity until the end of the subsection.  It could help clarify the point to address this towards the beginning of the subsection.
3. Many of the figures (e.g., Fig. 1, 4, 5) do not have very descriptive captions, making them rather difficult to understand. Consistent detail in the captions (e.g., Fig 6 can be easily understood as a fully standalone figure and caption) would improve the manuscript. 
4.  

Minor Comments:

1. Use colorblind friendly colors for figures 3, 4, 8, 10.
2. Use consistent regional colors between figures 3 & 4.
3. Line 134: Iman-Conover Transform is not described well enough for a reader to understand what it is. 
4. Figure 11: Remove uncoupled right parentheses “)” from colorbar label.
5. Line 198: Change “points” to “point”.
6. Line 228: Change “mode” to “made”.
7. Line 231:  Remove “to” at the end of the line.
8. Line 333: Range of results within 2 standard deviations are presented in a confusing way (potential fix “(82.9%)-(96%)”).
9. Line 345: Change “agrees” to “agree”.
10. Line 360: Change “expect” to “except”.
11. Line 428: Remove “of”.