General Comment:

The manuscript of ‘High-resolution modeling the distribution of surface air pollutants and their intercontinental transport by a global tropospheric atmospheric chemistry source-receptor model (GNAQPMS-SM)’ written by Qian Ye presented the source-receptor analysis based on the developed model of GNAQPMS. This topic is highly important to understand the intercontinental transport and toward the emission regulation for better global air quality. At the conclusion section, authors claimed that “We give our opinions on the controversial topic of the intercontinental transport of pollutants. The model that we developed creates a link between the scientific community and policymakers”. Nontheless to this statement, this manuscript mainly focused on the presentation of model evaluation and the source-receptor results within this study. Regarding the source-receptor analysis, comparison with HTAP outcome have been provided, but it is insufficient. Because the result of source-receptor analysis depends on its mathematical configuration, careful interpretation and detailed comparison and its discussion to other relevant studies are essentially requested. I would like to request the major revisions to consider the possible publication of this manuscript from GMD.

Major comment:

Even though this manuscript entitled “source-receptor model”, the discussion in the result of source-receptor relationship with other relevant studies are immature. As we can follow from the configuration presented in Eq. (3), the tagged method will trace the geographical location where produced. This study presented the global-scale source-receptor relationships; however, how can we understand the air pollutants’ production during long-range transport? The presented Figure 14 shows large impact by “OCN” source for O3. For example, NAM was dominated approximately 20% by OCN. In this case, where this O3 transported from and produced? As seen from Figure 15, the contribution of EA is penetrated in NAM region; therefore, direct transport of O3 produced over EA and additional O3 impact produced over Pacific ocean could be found over NAM? If this is true, EA posed only 3.6% contribution over NAM but EA should be considered important source over NAM. Despite this large contribution by OCN, discussion was insufficient. Ultimately, how can we apply this OCN impact on policy making?

Specific comments:

line 67-69, 82, 85: The models name of “STEM”, “CAMx”, “MOZART-4”, “GEOS-Chem”, “CHASER”, “TM5” (ant other models if used) are needed to be explained.

line 89-90: This sentence should be revised to define “GNAQPMS” first.

line 141: “each pollutant” (CT in this context) is “each tagged pollutant”?

line 145: After Eq. (1), the wording of “labeled” is used. Is this same to “tagged”? If this is different, the meaning of “label” should be explained. If this is same, it is better to unify the expression to avoid the confusion.

line 184: It seems to be better to define all abbreviations used in Fig. 1a here, or please prepare the table information (possibly within Table 1). It is confusing to be defined it every time used in discussion section.

line 198: Why NH3 and NMVOC were prepared from different emission inventory? The description of these emissions have been provided; however, there is no reason to conform them. It should be stated. In addition, these emission years are also different from the simulation year (2018). I understood that the time-lag in emission inventories, but do the authors have reasonable reason (e.g, negligible change between 2015 and 2018) to use different emission in this simulation?

line 241-242: The information for MODIS is not sufficient. Which satellite, products and its resolution? In addition, the appropriate reference should be stated.

line 269: What stands for “BCC”?

line 274-275: When did we find this injection? JJA? Figure 3 only represents annual averaged data and where can we trace the seasonality?

line 308: What is the target year of these studies for CO? Remind that the simulation in this study is not consistent to the year of emission inventory.

line 314 and Figure 4: I guess that white color indicated the deficit of measurement. The simulation result did not show such deficit, so are there no treatment to consider the measured deficit grid in the model comparison? There is no available information of AK in TROPOMI retrieval? The comparison methodology was not provided enough.

line 326 and Figure 5: Same comment on Figure 4, but in this case, model simulation was not shown over high-latitude region. Again, in addition to the lack of description on MODIS dataset, the comparison methodology was not given appropriately.

line 373-375: It is ambiguous that whether this is the additional experiment or included as presented study. If modeling results have been presented by including this emission inventory, this statement have to be explained in Section 2.4.

line 453: What means “large-scale”?

line 472-473: Here mentioned on dust and sea-salt, but the analysis is focused on PM2.5. I guess that PM10 could be largely affected by these natural sources whereas PM2.5 would be mainly composed by anthropogenic sources. I can partly understand the following discussion, but for example, approximately half of PM2.5 source over NAM is attributed to natural sources. Are these consistent to or different from other researches? Moreover, the configuration of source-receptor analysis posed “OCN”. Did “OCN” source represent sea-salt sources? Why it was separated as natural sources?

line 502 and Table 2: There is no citation for relevant studies of source-receptor relationship in South Korea and Japan. It is requested to be carefully reviewed other source-receptor studies. What is the consistency and/or difference from the result of this study?

line 615 (caption of Table 3): “The median and range of the annual averages of the 6 models are given below.” Is stated, but where is indicated? Is this statement mention on “reference” column?

General comments:

The manuscript “High-resolution modeling the distribution of surface air pollutants and their intercontinental transport by a global tropospheric atmospheric chemistry source-receptor model (GNAQPMS-SM)” written by Qian Ye, et al. describes a global source-receptor model coupled with the GNAQPMS-SM global chemical transport model. The manuscript contains the model evaluation and assessment of source-receptor relationships of O3, PM2.5, BC, and non-sea-salt-sulfate. The topic of the manuscript is certainly within the scope of GMD. Overall, the manuscript is well written and easy to follow. I would like to consider the publication of the manuscript from GMD, while I have several comments below which should be addressed before publication.

Authors emphasize advantages of S-R calculation in their models (i.e., tagged tracer method) over sensitivity simulation analyses with perturbed emissions on the aspects of errors introduced from non-linear chemistry and computational resources in abstract, introduction, and summary. I generally agree them, whereas I miss more detailed analysis and discussion of effects of differences in S-R calculation methods. For example, the HTAP-like sensitivity simulations using GNAQPMS-SM in which emissions are perturbed for a few key regions would help isolate the effects of S-R calculation methods from the effects of the other model representations (e.g., chemical and transport processes, horizontal resolution, emission inventories, etc.).

Also, authors compare their results of S-R relationship assessment for BC and sulfate aerosols with the HTAP report. Why do not authors compare S-R relationships of surface ozone with the HTAP report? I suppose that an impact of errors introduced from non-linear chemistry on ozone S-R relationship is larger than that on aerosol S-R relationships.

Specific comments

P. 2, L. 58: Does “research area of observations” means coverage areas of field observations?

P.2-3, third paragraph in Introduction section: 

This paragraph describes background information on S-R calculation methods and model resolution impacts on model performance and is mix of distinct topics. For readability, these topics need to be separated in individual paragraph.

P. 2, L. 63-64: Klich and Fuelberg (2014) is not global modeling study. Please add the appropriate reference.

P. 5, L. 134: What does “sensitivity analysis” mean?

P. 7, L. 192: Why do authors choose the year 2018 for simulation, even though emission for 2015 were used?

P. 7, Section 2.4: Please clarify treatments of soil NOx emissions and volcanic SO2 emissions in the simulation.

P. 11, L. 283-284: “In the model, stratospheric O3 is constrained by relaxation towards zonally and monthly averaged values from ozone climatologies from Logan (1999).” Is it consistent with the description in section 2.3: “After calculation of tropospheric height, the monthly stratospheric ozone above the troposphere is taken from the climatic mean output from MOZART v2.4 (Horowitz et al., 2003).” (P. 7, L. 194-195)?

P. 11, L. 286: “The coarse vertical resolution…” What vertical resolutions do the GNAQPMS have in the UTLS regions? Are these errors introduced by model physical processes or tracer advection scheme?

P. 11, L. 290-292: “Note that the model overestimation in the upper troposphere does not affect our analysis of surface O3 and its origins because O3 in the upper troposphere has little effect on the surface O3 concentration.” Is it confirmed by authors’ tagged tracer calculation?

P. 13, Section 3.1.3: There are no explanation how authors compare the model results with TROPOMI tropospheric NO2 data (e.g., spatial and temporal sampling from model outputs, application of averaging kernels).

P. 13, Section 3.1.3: TROPOMI low biases (Verhoelst et al., 2021) may also be possible reason for model positive biases.

P. 14, L. 330-331: “since there is higher biomass and fossil fuel burning for heating during DJF” Do seasonal cycles of precipitation affect seasonal cycles of simulated PM2.5 concentrations?

P. 16, L. 383: What is the definition of “SNA”?

P. 17, L. 389: “heterogeneous chemistry on aerosol surfaces” Please add the references which support this speculation.

P. 17, L. 401: “a trough in DJF” Is this trough over eastern China also influenced by NOx titration?

P. 18, Figure 8: What is a possible reason why model PM2.5 biases are larger in MAM than in other seasons?

P. 19, Figure 9: This figure shows “Validation of GNAQPMS simulations against EMEP observations.”

P. 23, L. 467: The title of section 4 is confusing. Section 3 also contains results of this study.

P. 23, L. 472: How do biogenic secondary organic aerosols account for?

P. 27, L. 537: “, while the contribution of the top boundary is small in other receptor regions and …” Is it correct? In Figure 14, “Boundary” have second or third most contributions in most receptor regions.

P. 28, Section 4.2: Why do authors evaluate S-R relationships of BC and sulfate in the PBL only in this section?

P. 33, L. 673: Vertical resolution is also important for resolving intercontinental transport plumes (Eastham et al., 2017). Do authors plan to investigate the sensitivity of S-R relationships to different vertical resolutions?

Reference

Eastham, S. D. and Jacob, D. J.: Limits on the ability of global Eulerian models to resolve intercontinental transport of chemical plumes, Atmos. Chem. Phys., 17, 2543–2553, https://doi.org/10.5194/acp-17-2543-2017, 2017.

Verhoelst, T., Compernolle, S., Pinardi, G., Lambert, J.-C., Eskes, H. J., Eichmann, K.-U., Fjæraa, A. M., Granville, J., Niemeijer, S., Cede, A., Tiefengraber, M., Hendrick, F., Pazmiño, A., Bais, A., Bazureau, A., Boersma, K. F., Bognar, K., Dehn, A., Donner, S., Elokhov, A., Gebetsberger, M., Goutail, F., Grutter de la Mora, M., Gruzdev, A., Gratsea, M., Hansen, G. H., Irie, H., Jepsen, N., Kanaya, Y., Karagkiozidis, D., Kivi, R., Kreher, K., Levelt, P. F., Liu, C., Müller, M., Navarro Comas, M., Piters, A. J. M., Pommereau, J.-P., Portafaix, T., Prados-Roman, C., Puentedura, O., Querel, R., Remmers, J., Richter, A., Rimmer, J., Rivera Cárdenas, C., Saavedra de Miguel, L., Sinyakov, V. P., Stremme, W., Strong, K., Van Roozendael, M., Veefkind, J. P., Wagner, T., Wittrock, F., Yela González, M., and Zehner, C.: Ground-based validation of the Copernicus Sentinel-5P TROPOMI NO2 measurements with the NDACC ZSL-DOAS, MAX-DOAS and Pandonia global networks, Atmos. Meas. Tech., 14, 481–510, https://doi.org/10.5194/amt-14-481-2021, 2021.