the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Mar 2024
Development And Application of WRF(v4.1.2)-uEMEP(v5) Model at the City with the Highest Industrial Density: A Case Study of Foshan
Abstract. The study aims to develop and apply the WRF-uEMEP model to simulate air quality at the city scale, with a focus on Foshan, the city with the highest industrial density. The interaction between atmospheric diffusion and chemical reactions in different regions further complicates the modeling process. Therefore, this study proposes a multi-scale approach to build an urban air quality model with a resolution of 250 meters by integrating different models. The model takes into account the effects of urban structure and takes into account atmospheric diffusion and chemical reactions in different regions. The research process included model development, calibration, and validation using existing air quality data in Foshan. The study shows that the WRF-uEMEP model effectively captures the impact of urban structure on air pollutant processes. The simulation results reveal the spatial and temporal distribution of air pollutants in Foshan, providing valuable insights for urban air quality management.
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RC1: 'Comment on egusphere-2024-28', Anonymous Referee #1, 18 Apr 2024
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General Comments:
Yang and coauthors propose a multi-scale approach to build an urban air quality model with higher resolution, with urban structure, atmospheric diffusion and chemical reactions in different regions being taken into account. The model performance is acceptable and the WRF-uEMEP model effectively captures the impact of urban structure on air pollution. However, I think the innovation of this study has not been highlighted. In addition, the writing is not acceptable and there are many problems with grammatical mistakes, such as sentence structure, verb tense, and clause construction, etc. This makes it really difficult for us to read the text and harder to understand the author's intention. We strongly suggest that your manuscript needs careful editing by someone with expertise in technical English editing so that the methods and results of the study are clear to reader. Below are some suggestions that could help improve the presentation of the work. It should be noted that I have only discuss the most glaring ones, and more specific comments, such as the appropriate words to express and the grammars, cannot be listed one by one due to too many. Please check and revise by own.Specific Comments
- Abstract: The abstract is repetitive and provides what I would consider unnecessary details for an abstract, but does not specify the main conclusions of this study. I'd recommend only including the most important context in the abstract.
- Introduction: The author mentioned many models in this part. It would be helpful to provide their full names for us to understand the application of each model, especially the EMEP that mainly used in this study. Please provide its full name, and what does the “u” in “uEMEP” represent?
- line 99: Please provide the website address for “data GLC2020 the European Space Agency (ESA)”. Additionally, there is a grammar issue with this sentence.
- Figure 2: It is difficult to see whether the content represented by the second legend “Districts in Foshan” has already been displayed in the figure. Grey outline or black outline?
- line 115-123: Please improve the description of these two methods and what are their respective characteristics? What are the similarities and differences between them, and what’s the meaning of the sentence “the proxy data is given in the form of emissions and summarized into the CTM grid emissions, and the two methods are equivalent”? How do we understand the meaning of “equivalent”? Why did the author choose the first method and what are its advantages?
- line 116: “contaminants” or “pollutants”?
- line 141: What is the spatiotemporal resolution of the MEIC used in this study? If the monthly mean emission was used in this study, and how to allocate emissions to reflect daily or hourly variation during the study period? In addition, MEIC inventory has been updated to the year 2020, and compared to 2017, the emissions might have changed significantly, especially after COVID-19. It is obvious that using the emissions from 2017 is no longer appropriate for the simulated period of 2021 in this study.
- line 143: What’s the “SNAP” method? Please provide the full name and relevant references and describe this method in detail.
- line 150: How to obtain the “allocation coefficient”?
- line 157-165: In my opinion, the emissions in downscaling models should be remapped based on total emissions and higher resolution data, such road network, population, or industry. I do not understand what the process of "replace (line 162)" and "reduce (line 165)" the author mentioned during inventory processing. Please review and describe the inventory processing in detail. The current description is not very clear.
- line 179: Is the headline appropriate? Can you consider using the expression of “polluted periods” or others?
- line 180-190: Some descriptions of meteorological conditions in this part are inconsistent with those in Table 2, for example, “high-pressure out-of-sea” and “High-pressure going to Sea” in L2, “High voltage control” in L3, “high-pressure out of the sea” and “High-pressure access to the sea” in L4. These make me feel really confused.
- Section 3.1: What is the number of simples for the model validation in each case? Has the confidence test been passed? What is the reason for the poor performance of simulated wind speed? Is it related to the selection of parameterization schemes in WRF model? Please explain.
- Figure 5: What’s the meaning of the “Observation-Standard Deviation”? How to calculate this? And there are no units in the Figure and caption, please check and revise. Additionally, shouldn't the validation of simulation results be compared with observations? There is no relevant description in the caption. If there are other comparison methods, please explain.
- Figures 6,7, 8: There also no units for the special distribution figures.
16. Section 3.4: What methods are used for the “Analysis of NO2 traceability characteristics”? By using the model results?
Citation: https://doi.org/10.5194/egusphere-2024-28-RC1 -
RC2: 'Comment on egusphere-2024-28', Anonymous Referee #2, 19 Apr 2024
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Comment on « Development And Application of WRF(v4.1.2)-uEMEP(v5) Model at
the City with the Highest Industrial Density: A Case Study of Foshan »
by Liting Yang and co-authors.
The authors have applied a hierarchy of models (EMEP and uEMEP) to the modelling of the pollution in Foshan. The analyze their results in terms of accuracy compared to observations, for both meteorology and pollution (regarding pollution, their focus is NO2 and PM2.5). The methodology used by the authors looks appropriate for the problem they address (simulation air quality at high resolution in a highly industrial and complex urban area).
However, I think the authors do not explain sufficiently the shortcomings in their results.
For example, there is a general underestimation of about two thirds in both PM2.5 and NOx by both their models, which is hardly commented at all, and in my opinion questions the relevance of the entire study. The others deliver very optimistric conclusions on the fact that the street-model uEMEP gives much better results than EMEP, but these conclusions are not supported by their results.
My critical comments are here. Other comments will be found in the pdf annotations (attached) ; including many sentences that are very unclear/problematic in their formulation.
I do not recommend this article for publication in ACP/GMD.
Critical comments :
1. The authors say in several places that uEMEP results bring added values compared to EMEP (l. 325…, 400…, 405...). This is not supported by the figures they give in Appendix B. Regarding Normalized Mean Bias for example, uEMEP performs marginally better than EMEP for L1-L2, much worse for L3-L4 (Table B2). The same is true for PM2.5 (Table B4). Therefore, the authors seem to be discussing what the wanted to find (strong added value with uEMEP) rather than what they actually found (no added value / degradation). This appeare to me as a major flaw in the scientific method.
2. Tables B2 and B4 show a general and massive underestimation by the simulations in both NO2 and PM2.5. This is not discussed in the article, and questions all the results.
3. The tracing methodology in Figure 9 is not explained, and I find the results very questionable. The authors mention that « regional transmission » (meaning not clearly defined) represents up to 99.4 % of the total NO2 quantity for two pollution peaks. How could this possibly happen in a city like Foshan which is presented by the authors as extremely industrialized and with strong trafic ? Here again, the authors seem to lack a critical analysis of their results.
4. What I see in their results, with such a massive underestimation of PM2.5 and NO2 is probably either a massive underestimation in emissions (which the authors seem to consider in their conclusions). The point of Gaussing grid modelling with tools such as uEMEP being to better evaluate benefit of a good knowledge of local emissions to improve street-level results, the fact that the emissions seem to be so massively underestimated questions the entire point of the article.
Citation: https://doi.org/10.5194/egusphere-2024-28-RC2
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