the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Oct 2024
Population exposure to outdoor NO2, black carbon, particle mass, and number concentrations over Paris with multi-scale modelling down to the street scale
Abstract. This study focuses on mapping the concentrations of pollutants of health interest (NO2, black carbon (BC), PM2.5, number of particles (PN)) down to the street scale to represent as accurately as possible the population exposure. Simulations are performed over the Greater Paris area with the WRF-CHIMERE/MUNICH/SSH-aerosol chain, using either the top-down inventory EMEP or the bottom-up inventory Airparif with correction of the traffic flow. The concentrations of the pollutants are higher in streets than in the regional-scale urban background, due to the strong influence of road-traffic emissions locally. Model-to-data comparisons were performed at urban background and traffic stations, and evaluated using two performance criteria from the literature. For BC, harmonized equivalent BC (eBC) concentrations were estimated from concomitant mea-surements of eBC and elemental carbon. Using the bottom-up inventory with corrected road-traffic flow, the strictest criteria are met for NO2, eBC, PM2.5, and PN. Using the EMEP top-down inventory, the strictest criteria are also met for NO2, eBC and PM2.5, but errors tend to be larger than with the bottom-up inventory for NO2, eBC and PN. Using the top-down inventory, the concentrations tend to be lower along the streets than those simulated using the bottom-up inventory, especially for NO2 con-centrations, resulting in less urban heterogeneities. The impact of the size-distribution of non-exhaust emissions was analyzed at both regional and local scales, and it is higher in heavy-traffic streets. To assess exposure, a french database detailing the number of inhabitants in each building was used. The population-weighted concentration (PWC) was calculated by weighting populations by the outdoor concentrations to which they are exposed at the precise location of their home. An exposure scaling factor (ESF) was determined for each pollutant to estimate the ratio needed to correct urban background concentrations in order to assess exposure. The average ESF in Paris and Paris Ring Road is higher than 1 for NO2, eBC, PM2.5, PN, because the concentrations simulated at the local scale in streets are higher than those modelled at the regional scale. It indicates that the Parisian population exposure is under-estimated using regional-scale concentrations. Although this underestimation is low for PM2.5, with an ESF of 1.04, it is very high for NO2 (1.26), eBC (between 1.22 and 1.24), and PN (1.12). This shows that urban heterogeneities are important to be considered in order to represent the population exposure to NO2, eBC, and PN, but less so for PM2.5.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(4024 KB)
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-2120', Anonymous Referee #1, 22 Oct 2024
Overall Evaluation:
The study presents a valuable approach by using a coupled WRF-CHIMERE/MUNICH/SSH-aerosol model to simulate pollutant concentrations such as NO2, black carbon (BC), PM2.5, and particle number (PN) at the street level, and evaluating population exposure in the Greater Paris region. The topic is timely and important, especially given the underestimation of population exposure to pollutants like NO2, BC, and PN when only using regional-scale models. The study includes an impressive range of input data and models, and the results are potentially impactful. However, there are some significant issues related to the structure, clarity, and depth of analysis that need to be addressed to improve the overall quality of the paper.
Major Comments:
1. Imbalance Between Technical Details and Discussion:
While the technical details are thorough, there is insufficient analysis and discussion of the results. The paper would benefit from a deeper exploration of the trade-offs between traditional regional-scale models and street-level models. Specifically, discuss how much computational resources are required for street-level models and how much additional accuracy is gained in comparison.
2. Introduction Structure:
The introduction lacks a clear, logical flow and does not effectively highlight the key research gap. It would be helpful to reorganize the introduction to show a more coherent development of the research problem and objectives, making it easier for readers to understand the motivation behind the study.
3. Parallel Treatment of PN and Pollutants:
Particle number (PN) is a statistical measure, not a pollutant like BC or PM2.5. It would be clearer to first discuss pollutant concentrations and then evaluate particle characteristics through PN. Avoid listing PN alongside BC and PM2.5 in a parallel manner in the title and main text.
4. Excessive Abbreviations:
The use of abbreviations is sometimes excessive, making the text difficult to follow. For instance, in Line 44, the abbreviation "PN" is introduced without proper context. It is recommended to reduce the use of abbreviations, particularly for terms like "particle number," to improve readability.
5. Clarification on PN and Ultrafine Particles:
The introduction mentions that ultrafine particles are best characterized by PN concentrations. If PN is being used in this study to represent ultrafine particles, ensure this connection is well-supported in the text. If the simulation does not focus on ultrafine particles, it would be better not to mention them, as they are challenging to model accurately.
6. Model Setup and Emissions Summary:
The description of the model setup and emissions data in Section 2 is too detailed and could be streamlined. Consider summarizing the key aspects in a table and moving the detailed descriptions to the supplementary information (SI) for clarity.
7. Quantitative Differences in Section 3.3:
Instead of using qualitative terms like "higher" or "lower" in Section 3.3, it would be more informative to present the quantitative differences between the results to enhance the clarity of the comparisons.
8. Applicability to Other Cities:
This study focuses on street-level traffic emissions and population distribution in the Greater Paris region. It would be beneficial to discuss whether the findings and conclusions could be extended to other large cities, particularly those with different urban structures or traffic patterns.
Minor Comments:
1.Line 19: Clarify what is meant by "regional scale"—what specific area or distance does this term represent?
2.Line 33: Reword the sentence to clarify that not all particle compounds impact health equally.
3.Line 34: The term "large health effect" is vague; it would be more effective to provide specific examples or references.
4. Line 36: Include a reference or example of the ratio of ultrafine particles in terms of number concentration, such as "XX% of total number concentration is from ultrafine particles."
5. Line 44: I don't think influences of traffic on BC is rare. https://www.sciencedirect.com/science/article/pii/S0269749121014500 https://acp.copernicus.org/articles/23/6545/2023/acp-23-6545-2023.pdf
6. Lines 46-53: The paragraph discussing BC estimation and adjustment methods should be moved to the Methods section for better flow.
7. Line 111: Spell out the full name of "SSH-aerosol" and clarify its role in the study. Similarly, "CAMS" at Line 118 should be defined.
8. Figure 3: To better highlight the changes in NO2 and BC, consider normalizing the data to make the differences clearer.
9. Line 113: Specify whether the WRF-CHIMERE/MUNICH coupling is online or offline.
10. Line 254: The formula presented here should be centered and numbered for clarity and consistency.
Citation: https://doi.org/10.5194/egusphere-2024-2120-RC1 -
AC1: 'Reply on RC1', Karine Sartelet, 18 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2120/egusphere-2024-2120-AC1-supplement.pdf
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AC1: 'Reply on RC1', Karine Sartelet, 18 Nov 2024
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RC2: 'Comment on egusphere-2024-2120', Anonymous Referee #2, 22 Oct 2024
Review on the manuscript of egusphere-2024-2120 titled "Population exposure to outdoor NO2, black carbon, particle mass, and number concentrations over Paris with multi-scale modelling down to the street scale" written by Park et al.
I have thoroughly reviewed this paper, which provides valuable insights by mapping the concentrations of various species down to the street scale. The study addresses an important aspect of air quality management and public health. However, after careful consideration, I regret to say that the manuscript does not meet the standard for publication in ACP at this time. Below, I outline my main concerns:
- Calculation and representativeness for population exposure: The primary concern lies in the calculation of population exposure, as the PWC method used in this study is relatively simple and commonly found in previous studies. As it stands, the study does not offer a sufficiently novel approach. Given that the key objective of this study is to assess the impact of spatial heterogeneities on population exposure from mobile emissions, a more advanced calculation of the PWC (or ESF) is necessary. For example, incorporating a spatiotemporally-weighted approach that accounts for the floating population could significantly enhance the analysis. Moreover, the current calculation of population exposure is limited to a specific temporal range, making it less representative of the broader situation in Paris. Expanding this temporal scope would provide a more comprehensive and accurate representation. Furthermore, returning to the basics, the street-level concentration shown in Figure 9 essentially reflects the PWC, making the conversion to a coarse resolution using Equation (1) less meaningful.
- Lack of validation in modeling performance: Although the study includes some analysis in Tables 3 and 4, the comparisons are spatially limited. Model simulations should be compared spatially and temporally with observations to sufficiently (more intensively) verify the modeling performance. Presently, results are provided for only a few selected points without comprehensive statistical analysis, which diminishes the reliability of the findings. Including time-series analysis for each monitoring site, along with statistical metrics such as mean bias, RMSE, correlation coefficient, and index of agreement, would greatly improve the robustness of the result. Furthermore, there is insufficient evidence to demonstrate that simulations with the REF inventory outperform those with EMEP. In Figure 7, simulations of PM5 and PN using EMEP inventory appear to be more accurate, especially considering the following: i) Excluding the high overestimation of EMEP in the first bin of Figure 6 and ii) The Uncertainty is introduced by applying a mean conversion factor derived from one point to all points. Thus, if EMEP is applied in Figure 8, it seems likely that the performance of PM2.5, eBC, and NO2 would surpass that of the REF inventory.
- Lacks explanation of methodology: Given that the key aim of this study is to develop a method for simulating multiple pollutants down to the street scale, the manuscript would benefit from a more detailed explanation of the street-level model. The current manuscript focuses too much on emission data. This additional clarity would help readers better understand the approach and its applicability.
- Language quality: The English throughout the manuscript requires improvements, including correction of grammatical errors and typos. Many sentences are overly lengthy and would benefit from restructuring for better readability.
Specific comments
- Figure 5 & Lines 185-194: Clearly define the following terms: i) non-traffic emission, ii) Exhaust emission, iii) non-exhaust emission, iv) other-traffic emission, and v) non-road traffic emission, mentioned in the manuscript. Based on the classification, it is unclear how aviation emissions (lines 185 – 194) are categorized under the non-traffic emissions.
- Lines 241-242 & Table 2: The size distribution described in Table 2 is not clearly explained, making it difficult to understand.
- Captions: Review and correct the captions of Figures 4 and B5.
- Section 2.2.5 Title: Clarify the title of Section 2.2.5: Emission sources of NOx (?), BC, PM, and PN.
- Figure order: The figures are not mentioned in the correct order. In the manuscript, Figures B3, B1, B2, B5, and B6 are referenced out of order. Figure B4 is not mentioned.
- Methodology placement: The methodology for population exposure would be better placed in Section 2 (i.e., Section 2.4).
Citation: https://doi.org/10.5194/egusphere-2024-2120-RC2 -
AC2: 'Reply on RC2', Karine Sartelet, 18 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2120/egusphere-2024-2120-AC2-supplement.pdf
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-2120', Anonymous Referee #1, 22 Oct 2024
Overall Evaluation:
The study presents a valuable approach by using a coupled WRF-CHIMERE/MUNICH/SSH-aerosol model to simulate pollutant concentrations such as NO2, black carbon (BC), PM2.5, and particle number (PN) at the street level, and evaluating population exposure in the Greater Paris region. The topic is timely and important, especially given the underestimation of population exposure to pollutants like NO2, BC, and PN when only using regional-scale models. The study includes an impressive range of input data and models, and the results are potentially impactful. However, there are some significant issues related to the structure, clarity, and depth of analysis that need to be addressed to improve the overall quality of the paper.
Major Comments:
1. Imbalance Between Technical Details and Discussion:
While the technical details are thorough, there is insufficient analysis and discussion of the results. The paper would benefit from a deeper exploration of the trade-offs between traditional regional-scale models and street-level models. Specifically, discuss how much computational resources are required for street-level models and how much additional accuracy is gained in comparison.
2. Introduction Structure:
The introduction lacks a clear, logical flow and does not effectively highlight the key research gap. It would be helpful to reorganize the introduction to show a more coherent development of the research problem and objectives, making it easier for readers to understand the motivation behind the study.
3. Parallel Treatment of PN and Pollutants:
Particle number (PN) is a statistical measure, not a pollutant like BC or PM2.5. It would be clearer to first discuss pollutant concentrations and then evaluate particle characteristics through PN. Avoid listing PN alongside BC and PM2.5 in a parallel manner in the title and main text.
4. Excessive Abbreviations:
The use of abbreviations is sometimes excessive, making the text difficult to follow. For instance, in Line 44, the abbreviation "PN" is introduced without proper context. It is recommended to reduce the use of abbreviations, particularly for terms like "particle number," to improve readability.
5. Clarification on PN and Ultrafine Particles:
The introduction mentions that ultrafine particles are best characterized by PN concentrations. If PN is being used in this study to represent ultrafine particles, ensure this connection is well-supported in the text. If the simulation does not focus on ultrafine particles, it would be better not to mention them, as they are challenging to model accurately.
6. Model Setup and Emissions Summary:
The description of the model setup and emissions data in Section 2 is too detailed and could be streamlined. Consider summarizing the key aspects in a table and moving the detailed descriptions to the supplementary information (SI) for clarity.
7. Quantitative Differences in Section 3.3:
Instead of using qualitative terms like "higher" or "lower" in Section 3.3, it would be more informative to present the quantitative differences between the results to enhance the clarity of the comparisons.
8. Applicability to Other Cities:
This study focuses on street-level traffic emissions and population distribution in the Greater Paris region. It would be beneficial to discuss whether the findings and conclusions could be extended to other large cities, particularly those with different urban structures or traffic patterns.
Minor Comments:
1.Line 19: Clarify what is meant by "regional scale"—what specific area or distance does this term represent?
2.Line 33: Reword the sentence to clarify that not all particle compounds impact health equally.
3.Line 34: The term "large health effect" is vague; it would be more effective to provide specific examples or references.
4. Line 36: Include a reference or example of the ratio of ultrafine particles in terms of number concentration, such as "XX% of total number concentration is from ultrafine particles."
5. Line 44: I don't think influences of traffic on BC is rare. https://www.sciencedirect.com/science/article/pii/S0269749121014500 https://acp.copernicus.org/articles/23/6545/2023/acp-23-6545-2023.pdf
6. Lines 46-53: The paragraph discussing BC estimation and adjustment methods should be moved to the Methods section for better flow.
7. Line 111: Spell out the full name of "SSH-aerosol" and clarify its role in the study. Similarly, "CAMS" at Line 118 should be defined.
8. Figure 3: To better highlight the changes in NO2 and BC, consider normalizing the data to make the differences clearer.
9. Line 113: Specify whether the WRF-CHIMERE/MUNICH coupling is online or offline.
10. Line 254: The formula presented here should be centered and numbered for clarity and consistency.
Citation: https://doi.org/10.5194/egusphere-2024-2120-RC1 -
AC1: 'Reply on RC1', Karine Sartelet, 18 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2120/egusphere-2024-2120-AC1-supplement.pdf
-
AC1: 'Reply on RC1', Karine Sartelet, 18 Nov 2024
-
RC2: 'Comment on egusphere-2024-2120', Anonymous Referee #2, 22 Oct 2024
Review on the manuscript of egusphere-2024-2120 titled "Population exposure to outdoor NO2, black carbon, particle mass, and number concentrations over Paris with multi-scale modelling down to the street scale" written by Park et al.
I have thoroughly reviewed this paper, which provides valuable insights by mapping the concentrations of various species down to the street scale. The study addresses an important aspect of air quality management and public health. However, after careful consideration, I regret to say that the manuscript does not meet the standard for publication in ACP at this time. Below, I outline my main concerns:
- Calculation and representativeness for population exposure: The primary concern lies in the calculation of population exposure, as the PWC method used in this study is relatively simple and commonly found in previous studies. As it stands, the study does not offer a sufficiently novel approach. Given that the key objective of this study is to assess the impact of spatial heterogeneities on population exposure from mobile emissions, a more advanced calculation of the PWC (or ESF) is necessary. For example, incorporating a spatiotemporally-weighted approach that accounts for the floating population could significantly enhance the analysis. Moreover, the current calculation of population exposure is limited to a specific temporal range, making it less representative of the broader situation in Paris. Expanding this temporal scope would provide a more comprehensive and accurate representation. Furthermore, returning to the basics, the street-level concentration shown in Figure 9 essentially reflects the PWC, making the conversion to a coarse resolution using Equation (1) less meaningful.
- Lack of validation in modeling performance: Although the study includes some analysis in Tables 3 and 4, the comparisons are spatially limited. Model simulations should be compared spatially and temporally with observations to sufficiently (more intensively) verify the modeling performance. Presently, results are provided for only a few selected points without comprehensive statistical analysis, which diminishes the reliability of the findings. Including time-series analysis for each monitoring site, along with statistical metrics such as mean bias, RMSE, correlation coefficient, and index of agreement, would greatly improve the robustness of the result. Furthermore, there is insufficient evidence to demonstrate that simulations with the REF inventory outperform those with EMEP. In Figure 7, simulations of PM5 and PN using EMEP inventory appear to be more accurate, especially considering the following: i) Excluding the high overestimation of EMEP in the first bin of Figure 6 and ii) The Uncertainty is introduced by applying a mean conversion factor derived from one point to all points. Thus, if EMEP is applied in Figure 8, it seems likely that the performance of PM2.5, eBC, and NO2 would surpass that of the REF inventory.
- Lacks explanation of methodology: Given that the key aim of this study is to develop a method for simulating multiple pollutants down to the street scale, the manuscript would benefit from a more detailed explanation of the street-level model. The current manuscript focuses too much on emission data. This additional clarity would help readers better understand the approach and its applicability.
- Language quality: The English throughout the manuscript requires improvements, including correction of grammatical errors and typos. Many sentences are overly lengthy and would benefit from restructuring for better readability.
Specific comments
- Figure 5 & Lines 185-194: Clearly define the following terms: i) non-traffic emission, ii) Exhaust emission, iii) non-exhaust emission, iv) other-traffic emission, and v) non-road traffic emission, mentioned in the manuscript. Based on the classification, it is unclear how aviation emissions (lines 185 – 194) are categorized under the non-traffic emissions.
- Lines 241-242 & Table 2: The size distribution described in Table 2 is not clearly explained, making it difficult to understand.
- Captions: Review and correct the captions of Figures 4 and B5.
- Section 2.2.5 Title: Clarify the title of Section 2.2.5: Emission sources of NOx (?), BC, PM, and PN.
- Figure order: The figures are not mentioned in the correct order. In the manuscript, Figures B3, B1, B2, B5, and B6 are referenced out of order. Figure B4 is not mentioned.
- Methodology placement: The methodology for population exposure would be better placed in Section 2 (i.e., Section 2.4).
Citation: https://doi.org/10.5194/egusphere-2024-2120-RC2 -
AC2: 'Reply on RC2', Karine Sartelet, 18 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2120/egusphere-2024-2120-AC2-supplement.pdf
Peer review completion
Journal article(s) based on this preprint
Data sets
ACROSS_LCE_PRG_SMPS_5 min_L2 Julien Kammer et al. https://doi.org/10.25326/658
ACROSS_LISA_PRG_AETH-eBC_PM1_1-Min_L2 Ludovico Di Antonio et al. https://doi.org/10.25326/575
Model code and software
Population exposure to NO2, black carbon, particle mass and number concentrations over Paris with multi-scale modelling down to the street scale. The multi-scale air-quality modeling toolchain: WRF-CHIMERE/MUNICH/SSH-aerosol Soo-Jin Park et al. https://doi.org/10.5281/zenodo.12639507
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Soo-Jin Park
Lya Lugon
Oscar Jacquot
Youngseob Kim
Alexia Baudic
Barbara D’Anna
Ludovico Di Antonio
Claudia Di Biagio
Fabrice Dugay
Olivier Favez
Véronique Ghersi
Aline Gratien
Julien Kammer
Jean-Eudes Petit
Olivier Sanchez
Myrto Valari
Jérémy Vigneron
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(4024 KB) - Metadata XML