Preprints
https://doi.org/10.5194/egusphere-2023-749
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2023-749
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
03 May 2023
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
An Updated Modeling Framework to Simulate Los Angeles Air Quality. Part 1: Model Development, Evaluation, and Source Apportionment
Elyse A. Pennington
Department of Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
Yuan Wang
CORRESPONDING AUTHOR
Department of Earth, Atmosphere, and Planetary Sciences, Purdue University, West Lafayette, IN 47907, USA
Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA 91125, USA
Benjamin C. Schulze
Department of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA 91125, USA
Karl M. Seltzer
Office of Air and Radiation, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Jiani Yang
Department of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA 91125, USA
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing 100084, China
Zhe Jiang
Carbon Neutrality Research Center, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Hongru Shi
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
Melissa Venecek
Modeling and Meteorology Branch, California Air Resources Board, Sacramento, CA 95814, USA
Daniel Chau
Modeling and Meteorology Branch, California Air Resources Board, Sacramento, CA 95814, USA
Benjamin N. Murphy
Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Christopher M. Kenseth
Department of Chemistry, California Institute of Technology, Pasadena, CA 91125, USA
Ryan X. Ward
Department of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA 91125, USA
Havala O. T. Pye
Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
Department of Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
Department of Environmental Science and Engineering, California Institute of Technology, Pasadena, CA 91125, USA
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.
How to cite. Pennington, E. A., Wang, Y., Schulze, B. C., Seltzer, K. M., Yang, J., Zhao, B., Jiang, Z., Shi, H., Venecek, M., Chau, D., Murphy, B. N., Kenseth, C. M., Ward, R. X., Pye, H. O. T., and Seinfeld, J. H.: An Updated Modeling Framework to Simulate Los Angeles Air Quality. Part 1: Model Development, Evaluation, and Source Apportionment, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-749, 2023.
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Elyse A. Pennington et al.
Status: open (until 16 Jun 2023)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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RC1: 'Comment on egusphere-2023-749', Anonymous Referee #1, 26 May 2023
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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 - 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.
Elyse A. Pennington et al.
Elyse A. Pennington et al.
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Short summary
To assess the ozone and particulate matter pollution in LA, we improved the CMAQ model by employing dynamic traffic emissions and new secondary organic aerosol (SOA) schemes to represent volatile chemical products (VCP). Source apportionment demonstrates that the urban areas of the LA Basin and vicinity are NOx-saturated with the largest sensitivity of O3 to changes in VOC in the urban core. The improvement and remaining issues shed light on the future direction of the model development.
To assess the ozone and particulate matter pollution in LA, we improved the CMAQ model by...