the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 03 May 2023
An Updated Modeling Framework to Simulate Los Angeles Air Quality. Part 1: Model Development, Evaluation, and Source Apportionment
Abstract. This study describes a modeling framework, model evaluation, and source apportionment to understand the causes of Los Angeles (LA) air pollution. A few major updates are applied to the Community Multiscale Air Quality (CMAQ) Model with high spatial resolution (1 km × 1 km). The updates include dynamic traffic emissions based on real-time on-road information and recent emission factors and secondary organic aerosol (SOA) schemes to represent volatile chemical products (VCP). Meteorology is well-predicted compared to ground-based observations, and the emission rates from multiple sources (i.e., on-road, volatile chemical product, area, point, biogenic, and sea spray) are quantified. Evaluation of the CMAQ model shows that ozone is well-predicted despite inaccuracies in nitrogen oxide (NOx) predictions. Particle matter (PM) is underpredicted compared to concurrent measurements made with an aerosol mass spectrometer (AMS) in Pasadena. Inorganic aerosol is well-predicted while SOA is underpredicted. Modeled SOA consists of mostly organic nitrates and products from oxidation of alkane-like intermediate volatility organic compounds (IVOCs) and has missing components that behave like less-oxidized oxygenated organic aerosol (LO-OOA). Source apportionment demonstrates that the urban areas of the LA Basin and vicinity are NOx-saturated (VOC-sensitive) with the largest sensitivity of O3 to changes in VOCs in the urban core. Differing oxidative capacities in different regions impact the nonlinear chemistry leading to PM and SOA formation, which is quantified in this study.
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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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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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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-749', Anonymous Referee #1, 26 May 2023
In this manuscript the authors present a comprehensive exploration of air quality in the LA basin, a complex region influenced by unique atmospheric dynamics and diverse emission sources. As a prologue to a planned future submission exploring consequences of the COVID-19 pandemic lockdowns in more detail, the authors here set out to model regional air quality by applying updated and optimized emissions inventories to the chemical transport model CMAQ, and to evaluate the model against a suite of observations including typical gas- and particle-phase pollutants and their precursors.On the whole I find this to be an important and well-composed manuscript, providing a compilation of recently identified emission types and associated chemistry into valuable new model/measurement comparisons. The manuscript text is clear and well-composed, as are key figures. However, I do have just a few concerns, mostly related to the treatment of modeled dynamics and its consequences, which I'd like to see addressed before I recommend publication.
- My primary concern is related to WRF performance in reproducing dynamics and transport. As noted by the authors, wind speed and direction are both crucial components in overall model performance due to their role in determining the transport of pollutants and precursors. The failure of the nested WRF model to adequately represent these features is therefore a pretty big issue in my mind: it's hard to tell how much of the subsequent analyses of chemical composition are impacted by problems in dynamics, casting many of the subsequent conclusions in doubt. I understand that this is a very thorny modeling problem, but I would very much like to see the authors attempt to address this problem in some form, at the very least to try and better understand when and where their WRF simulations are failing, and to quantify the sensitivity of their final results to transport issues.
- On a related note, I was curious about the chosen WRF nesting scheme used here, which employs a 16:4:1 km grid size scheme. My understanding is that WRF best practice is to use an odd ratio less than 7 (typically 3:1 or 5:1) for spatial dimensions of nested grids to minimize interpolation errors. I'd be very curious to see whether a shift to 15:3:1 km grid sizes might help to improve WRF output here.
- Since the intended focus of this paper is not on WRF model performance itself, I also wonder whether it would be better to use meteorological data assimilation techniques (or even established high-resolution meteorology data products such as HRRR) to further improve modeled wind speed and direction, providing a clearer focus on emissions and chemistry.
- Regardless of how well these (or other) techniques might improve model performance, their use would also allow for a kind of sensitivity study to assess the impact of transport on modeled chemistry. It would be helpful and informative to assess CMAQ predictions under alternative (hopefully improved!) meteorological methods such as those described above, and to compare that performance against predictions made using original meteorology, thereby estimating sensitivities.
- Why were lightning NOx and windblown dust sources omitted? While they may not dominate in this urban region, they still seem like sources better included than excluded. (If the omission was due to a lack of trust in modeled wind speeds and convection, this seems like all the more reason to improve these elements.)
- This is a more subjective and minor request, but I also would like to suggest that the authors consider an alternative and consistent color scheme for the panel maps of Fig. 6. Rainbow schemes such as this one produce artificial bands across specific color ranges, making it harder to consistently evaluate differences in the maps across concentration thresholds. It also looks like some of the maps use discretized color bins while others are continuous. Unless there is a specific reason for this, please make this a consistent decision one way or the other.
Aside from these concerns I am confident that this will be an important literature contribution deserving of publication. My hope is that improved model dynamics will allow for a stronger profile of emissions and chemistry, along with the potential to quantify their associated sensitivities.Citation: https://doi.org/10.5194/egusphere-2023-749-RC1 -
AC1: 'Reply on RC1', John H. Seinfeld, 25 Sep 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-749/egusphere-2023-749-AC1-supplement.pdf
- My primary concern is related to WRF performance in reproducing dynamics and transport. As noted by the authors, wind speed and direction are both crucial components in overall model performance due to their role in determining the transport of pollutants and precursors. The failure of the nested WRF model to adequately represent these features is therefore a pretty big issue in my mind: it's hard to tell how much of the subsequent analyses of chemical composition are impacted by problems in dynamics, casting many of the subsequent conclusions in doubt. I understand that this is a very thorny modeling problem, but I would very much like to see the authors attempt to address this problem in some form, at the very least to try and better understand when and where their WRF simulations are failing, and to quantify the sensitivity of their final results to transport issues.
-
RC2: 'Comment on egusphere-2023-749', Anonymous Referee #2, 21 Jul 2023
This work describes a model framework for investigating air quality and sources of pollution in the Los Angeles region. As a region with complex emissions, understanding the sources of air pollution is critical to improving air quality in the area. The model framework and validation against observations is exceptionally well-described and the work fits well within the scope of ACP. While I think the article could essentially be published as is, additional discussion on the limitations of this model framework based on the analysis of model error would provide more context on how to interpret future results. Therefore I would recommend publication and would encourage the authors to consider addressing the following things. I look forward to part 2!
1. While the evaluation of the model against available measurements is quite thorough and well-described in the text, there is little discussion of how this evaluation impacts the interpretation of the results as well as the strengths and weaknesses of the model. A comprehensive assessment of how all the biases in specific pollutants could affect results would be largely speculative and thus is unnecessary, but I believe a brief discussion on how the model’s performance against measurements could affect big picture results would be appropriate. Below I provide a few questions that struck me as meaningful to address, however they need not all be.
- Does the good representation of POA, but poor representation of SOA mean that generally, this model will predict OA better near sources and diminish in its effectiveness further away? Could the poor NOx prediction impact the conclusions surrounding the NOx vs VOC-limited ozone regimes? Because of the large biases in certain species does this model’s strength lie in predictions of relative changes in species (as the results shown in this work are) rather than predicting absolute values or are there certain species the author’s feel confident could be predicted? Do the large errors in wind speed and direction indicate a systematic bias of air from different areas into the domain region (e.g. higher sea spray aerosol from the ocean vs more agricultural or road sources from in-land) or is the spread too large?
2. Are wildfire emissions included? If they are included in one of the emission inventories then that should be made clear.
3. Why were lightning NOx and dust sources not included? Are these just not large emission sources in the area?
Technical comments:
1. Caption Fig 2: The caption refers to 2 different resolution scenarios as “d01.” I assume this is a typo, but as I don’t think this notation is used again, it may be unnecessary.
Citation: https://doi.org/10.5194/egusphere-2023-749-RC2 -
AC2: 'Reply on RC2', John H. Seinfeld, 25 Sep 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-749/egusphere-2023-749-AC2-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-749', Anonymous Referee #1, 26 May 2023
In this manuscript the authors present a comprehensive exploration of air quality in the LA basin, a complex region influenced by unique atmospheric dynamics and diverse emission sources. As a prologue to a planned future submission exploring consequences of the COVID-19 pandemic lockdowns in more detail, the authors here set out to model regional air quality by applying updated and optimized emissions inventories to the chemical transport model CMAQ, and to evaluate the model against a suite of observations including typical gas- and particle-phase pollutants and their precursors.On the whole I find this to be an important and well-composed manuscript, providing a compilation of recently identified emission types and associated chemistry into valuable new model/measurement comparisons. The manuscript text is clear and well-composed, as are key figures. However, I do have just a few concerns, mostly related to the treatment of modeled dynamics and its consequences, which I'd like to see addressed before I recommend publication.
- My primary concern is related to WRF performance in reproducing dynamics and transport. As noted by the authors, wind speed and direction are both crucial components in overall model performance due to their role in determining the transport of pollutants and precursors. The failure of the nested WRF model to adequately represent these features is therefore a pretty big issue in my mind: it's hard to tell how much of the subsequent analyses of chemical composition are impacted by problems in dynamics, casting many of the subsequent conclusions in doubt. I understand that this is a very thorny modeling problem, but I would very much like to see the authors attempt to address this problem in some form, at the very least to try and better understand when and where their WRF simulations are failing, and to quantify the sensitivity of their final results to transport issues.
- On a related note, I was curious about the chosen WRF nesting scheme used here, which employs a 16:4:1 km grid size scheme. My understanding is that WRF best practice is to use an odd ratio less than 7 (typically 3:1 or 5:1) for spatial dimensions of nested grids to minimize interpolation errors. I'd be very curious to see whether a shift to 15:3:1 km grid sizes might help to improve WRF output here.
- Since the intended focus of this paper is not on WRF model performance itself, I also wonder whether it would be better to use meteorological data assimilation techniques (or even established high-resolution meteorology data products such as HRRR) to further improve modeled wind speed and direction, providing a clearer focus on emissions and chemistry.
- Regardless of how well these (or other) techniques might improve model performance, their use would also allow for a kind of sensitivity study to assess the impact of transport on modeled chemistry. It would be helpful and informative to assess CMAQ predictions under alternative (hopefully improved!) meteorological methods such as those described above, and to compare that performance against predictions made using original meteorology, thereby estimating sensitivities.
- Why were lightning NOx and windblown dust sources omitted? While they may not dominate in this urban region, they still seem like sources better included than excluded. (If the omission was due to a lack of trust in modeled wind speeds and convection, this seems like all the more reason to improve these elements.)
- This is a more subjective and minor request, but I also would like to suggest that the authors consider an alternative and consistent color scheme for the panel maps of Fig. 6. Rainbow schemes such as this one produce artificial bands across specific color ranges, making it harder to consistently evaluate differences in the maps across concentration thresholds. It also looks like some of the maps use discretized color bins while others are continuous. Unless there is a specific reason for this, please make this a consistent decision one way or the other.
Aside from these concerns I am confident that this will be an important literature contribution deserving of publication. My hope is that improved model dynamics will allow for a stronger profile of emissions and chemistry, along with the potential to quantify their associated sensitivities.Citation: https://doi.org/10.5194/egusphere-2023-749-RC1 -
AC1: 'Reply on RC1', John H. Seinfeld, 25 Sep 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-749/egusphere-2023-749-AC1-supplement.pdf
- My primary concern is related to WRF performance in reproducing dynamics and transport. As noted by the authors, wind speed and direction are both crucial components in overall model performance due to their role in determining the transport of pollutants and precursors. The failure of the nested WRF model to adequately represent these features is therefore a pretty big issue in my mind: it's hard to tell how much of the subsequent analyses of chemical composition are impacted by problems in dynamics, casting many of the subsequent conclusions in doubt. I understand that this is a very thorny modeling problem, but I would very much like to see the authors attempt to address this problem in some form, at the very least to try and better understand when and where their WRF simulations are failing, and to quantify the sensitivity of their final results to transport issues.
-
RC2: 'Comment on egusphere-2023-749', Anonymous Referee #2, 21 Jul 2023
This work describes a model framework for investigating air quality and sources of pollution in the Los Angeles region. As a region with complex emissions, understanding the sources of air pollution is critical to improving air quality in the area. The model framework and validation against observations is exceptionally well-described and the work fits well within the scope of ACP. While I think the article could essentially be published as is, additional discussion on the limitations of this model framework based on the analysis of model error would provide more context on how to interpret future results. Therefore I would recommend publication and would encourage the authors to consider addressing the following things. I look forward to part 2!
1. While the evaluation of the model against available measurements is quite thorough and well-described in the text, there is little discussion of how this evaluation impacts the interpretation of the results as well as the strengths and weaknesses of the model. A comprehensive assessment of how all the biases in specific pollutants could affect results would be largely speculative and thus is unnecessary, but I believe a brief discussion on how the model’s performance against measurements could affect big picture results would be appropriate. Below I provide a few questions that struck me as meaningful to address, however they need not all be.
- Does the good representation of POA, but poor representation of SOA mean that generally, this model will predict OA better near sources and diminish in its effectiveness further away? Could the poor NOx prediction impact the conclusions surrounding the NOx vs VOC-limited ozone regimes? Because of the large biases in certain species does this model’s strength lie in predictions of relative changes in species (as the results shown in this work are) rather than predicting absolute values or are there certain species the author’s feel confident could be predicted? Do the large errors in wind speed and direction indicate a systematic bias of air from different areas into the domain region (e.g. higher sea spray aerosol from the ocean vs more agricultural or road sources from in-land) or is the spread too large?
2. Are wildfire emissions included? If they are included in one of the emission inventories then that should be made clear.
3. Why were lightning NOx and dust sources not included? Are these just not large emission sources in the area?
Technical comments:
1. Caption Fig 2: The caption refers to 2 different resolution scenarios as “d01.” I assume this is a typo, but as I don’t think this notation is used again, it may be unnecessary.
Citation: https://doi.org/10.5194/egusphere-2023-749-RC2 -
AC2: 'Reply on RC2', John H. Seinfeld, 25 Sep 2023
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2023/egusphere-2023-749/egusphere-2023-749-AC2-supplement.pdf
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Discussed
Elyse A. Pennington
Benjamin C. Schulze
Karl M. Seltzer
Jiani Yang
Zhe Jiang
Hongru Shi
Melissa Venecek
Daniel Chau
Benjamin N. Murphy
Christopher M. Kenseth
Ryan X. Ward
Havala O. T. Pye
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(5146 KB) - Metadata XML
-
Supplement
(5408 KB) - BibTeX
- EndNote
- Final revised paper