General comments

The manuscript titled “Contribution of Cooking Emissions to the Urban Volatile Organic Compounds in Las Vegas, NV” evaluates the contribution of commercial and residential cooking emissions to urban VOCs using mobile laboratory and ground site observations in Las Vegas, NV with supplemental observations made Los Angeles, CA and Boulder, CO. The authors found that long-chain aldehydes were significantly enhanced in restaurant plumes and long-chain fatty acids were also associated with cooking emissions, while the result of the contribution of the cooking source based on observation was not consistent with the result based on emission inventory. The results could contribute to the targeted reduction of the VOCs emissions to a certain extent. However, some statements should be more clearly and precisely, and some adjustments could facilitate the understanding of the paper. Therefore, some major issues need to be revised in the manuscript before it is considered for publication in this journal.

Detailed comments

1. Line 54: The abbreviations tend to be capitalized and the ‘oxygenated VOCs’ are usually abbreviated to ‘OVOCs’.
2. The description of the PTR-ToF-MS could be reduced because it is a common instrument and the technique was not improved in this study.
3. The temporal changes of the mobile sources were irregular and the contribution during period 2 was lower than that during period 1. Could you explain the reason?
4. The results of the inventory may be added a spatial distribution, which could contribute to the confirmation of the accuracy of the inventory.
5. Conclusion should be general and succinct. The quotations may be moved to results or discussion chapters.
6. Some punctuation-mark issues should be paid attention and modified. For example, ‘30 June– 27 July, 2021’ should be ‘30 June – 27 July, 2021’.

I am very sorry that I uploaded comments on other reserch earlier. Here is the refree for this article.

The authors developed a deep learning framework to estimate surface O₃, NO₂, and PM_2.5 concentrations, and investigated urban-nonurban difference and ozone-NOx-VOCs-aerosols sensitivity for ozone pollution in Shandong. This manuscript needs to be revised before it can be published.

1.Theme: There are two logics based on the title, abstract, conclusion, and the last paragraph of the introduction. One theme is "High Resolution Air Pollution Estimation", while the spatial characterization of pollution and urban-rural differences are further investigated in order to illustrate the value of the application of this deep learning framework. The other theme is to study the spatial characteristics of pollution in Shandong, and a deep learning approach is used. Authors should consider the perspective of the writing.

2.The study is divided into two main parts: one on estimating ozone concentrations and studying urban-rural differences, and the other on ozone sensitivity. The logical relationship between the two parts is not very coherent. Ozone sensitivity does not adequately explain the variations and differences in ozone concentrations between urban and rural areas and in different years. In other words, if ozone concentrations are not estimated, it does not seem to affect the results of the ozone sensitivity study.

3.Ozone formation regimes: From Fig.8, the NOx-limited regime dominates, especially Fig.8D shows that the proportion of NOx-limited is almost 1.0, which is not quite consistent with the authors' conclusions.

4.Specification of figures: Do Figures 7A and B share a colorbar to indicate O₃ concentration? In fact, Figure 7B contains the information from Figure 7A. In Figure 7D, what does the arrow next to PM_2.5 mean? Figure 8C is a legend for Figures 8A and B, making it difficult to understand. In Figure 9, it is not appropriate to represent one variable in dots and one in columns as they are of the same kind.

This study developed a novel spatiotemporal deep learning model for concurrent prediction of three air pollutants (ozone, NO2, PM2.5). The authors used the generated fine-scale concentrations to assess urban-nonurban differences and ozone-NOx-VOCs-aerosols sensitivity in Shandong, China. To facilitate the analysis, interpretable machine learning was employed to handle nonlinearity and isolate impacts of drivers relating to ozone photochemistry. The methodology is solid, and the findings are important for the development of ozone control strategies, though a few issues remain.

1. Line 259: Please explain the possible reason why NO2 has significantly lower out-of-site CV-R2 (0.75) than ozone and PM2.5 (>0.9), note that the out-of-sample CV results are comparable across all pollutants?

2. Lines 265-266: In evaluating stability and robustness of the model, it would be interesting to see if the CNEMC-trained model can obtain local concentration variations and interpretation outcomes similar to that from the CNEMC+SDEM-trained model.

3. Lines 322-323: The time span of the training data should be given, as that information is important to understand whether the good agreements between measurements and estimations reflect fitting or prediction performance.

4. How many monitoring stations are there in urban areas? A map highlighting the urban and nonurban areas is recommended for intuitive understanding.

5. There is a lack of validation for the XGBoost model, given that reliability of interpretation outcomes should be based on the model with high accuracy.

6. Please provide more explanations for the SHAP interaction values. The statement “lower NO2 … could diminish the formation of ozone under high PM2.5 concentrations” (Line 456) is difficult to follow. In Figure 7e, lower NO2 and negative PM2.5-NO2 SHAP interaction values are observed at lower PM2.5 levels.

7. Figure 8d shows that the NOx-limited regime dominates in urban areas. Please confirm.

8. Minor typos and grammar errors need to be corrected. For example, Line 355: the upper right area of E, M, and U; Line 370: are shown in Figure 5, etc.