The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Why do models overestimate surface ozone in the Southeast United States?
Open accessAbstract
Abstract. Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NOx ≡ NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC4RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25° × 0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30–60 %, dependent on the assumption of the contribution by soil NOx emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NOx oxidation products. However, the model is still biased high by 6 ± 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.
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Cites
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Self-citationParameterization of surface resistances to gaseous dry deposition in regional-scale numerical models
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Self-citationGlobal simulation of tropospheric O3‐NOx‐hydrocarbon chemistry: 1. Model formulation
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Self-citationHudman, R. C., Moore, N. E., Mebust, A. K., Martin, R. V., Russell, A. R., Valin, L. C., & Cohen, R. C. (2012) · Atmospheric Chemistry and Physics · doi:10.5194/acp-12-7779-2012 · Cited by 538
Self-citationMultimodel estimates of intercontinental source‐receptor relationships for ozone pollution
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Hoesly, R. M., Smith, S. J., Feng, L., Klimont, Z., Janssens-Maenhout, G., Pitkanen, T., Seibert, J. J., Vu, L., Andres, R. J., Bolt, R. M., et al. (2018) · Geoscientific Model Development · doi:10.5194/gmd-11-369-2018 · Cited by 2,203
Anthropogenic drivers of 2013–2017 trends in summer surface ozone in China
Li, K., Jacob, D. J., Liao, H., Shen, L., Zhang, Q., & Bates, K. H. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1812168116 · Cited by 1,804
Self-citationA two-pollutant strategy for improving ozone and particulate air quality in China
Li, K., Jacob, D. J., Liao, H., Zhu, J., Shah, V., Shen, L., Bates, K. H., Zhang, Q., & Zhai, S. (2019) · Nature Geoscience · doi:10.1038/s41561-019-0464-x · Cited by 874
Self-citationMcDuffie, E. E., Smith, S. J., O'Rourke, P., Tibrewal, K., Venkataraman, C., Marais, E. A., Zheng, B., Crippa, M., Brauer, M., & Martin, R. V. (2020) · Earth System Science Data · doi:10.5194/essd-12-3413-2020 · Cited by 714
Self-citationThe Chemistry Mechanism in the Community Earth System Model Version 2 (CESM2)
Emmons, L. K., Schwantes, R. H., Orlando, J. J., Tyndall, G., Kinnison, D., Lamarque, J., Marsh, D., Mills, M. J., Tilmes, S., Bardeen, C., et al. (2020) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2019ms001882 · Cited by 613
Metadata
- Authors
- Travis, K. R., Jacob, D. J., Fisher, J. A., Kim, P. S., Marais, E. A., Zhu, L., Yu, K., Miller, C. C., Yantosca, R. M., Sulprizio, M. P., Thompson, A. M., Wennberg, P. O., Crounse, J. D., St. Clair, J. M., Cohen, R. C., Laughner, J. L., Dibb, J. E., Hall, S. R., Ullmann, K., Wolfe, G. M., Pollack, I. B., Peischl, J., Neuman, J. A., & Zhou, X.
- Venue
- Atmospheric Chemistry and Physics
- Publisher
- Copernicus GmbH
- Published
- 2016-11-01
- Volume
- 16
- Issue
- 21
- Pages
- 13561-13577
- ISSN
- 1680-7316 (ISSN-L)
- 1680-7324
- OpenAlex
- W2546736492
- Cited by
- 470
- References
- 78
- FWCI
- 29.38
- Type
- journal article
- Language
- en
- Open access
- gold
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- Travis, K. R., Jacob, D. J., Fisher, J. A., Kim, P. S., Marais, E. A., Zhu, L., Yu, K., Miller, C. C., Yantosca, R. M., Sulprizio, M. P., Thompson, A. M., Wennberg, P. O., Crounse, J. D., St. Clair, J. M., Cohen, R. C., Laughner, J. L., Dibb, J. E., Hall, S. R., Ullmann, K., . . . Zhou, X. (2016). Why do models overestimate surface ozone in the Southeast United States? Atmospheric Chemistry and Physics. https://doi.org/10.5194/acp-16-13561-2016
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
