Articles | Volume 24, issue 11
https://doi.org/10.5194/acp-24-6583-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-24-6583-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Technical note
| 
06 Jun 2024
Technical note |
| 06 Jun 2024
| 06 Jun 2024
Technical note: Influence of different averaging metrics and temporal resolutions on the aerosol pH calculated by thermodynamic modeling
Haoqi Wang
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, 300350 Tianjin, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, 300350 Tianjin, China
Xiao Tian
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, 300350 Tianjin, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, 300350 Tianjin, China
Wanting Zhao
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, 300350 Tianjin, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, 300350 Tianjin, China
Jiacheng Li
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, 300350 Tianjin, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, 300350 Tianjin, China
Haoyu Yu
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, 300350 Tianjin, China
Yinchang Feng
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, 300350 Tianjin, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, 300350 Tianjin, China
State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, 300350 Tianjin, China
CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, 300350 Tianjin, China
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Cited articles
Ault, A. P.: Aerosol Acidity: Novel Measurements and Implications for Atmospheric Chemistry, Acc. Chem. Res, 53, 1703–1714, https://doi.org/10.1021/acs.accounts.0c00303, 2020.
Chen, Y., Cheng, Y., Ma, N., Wolke, R., Nordmann, S., Schüttauf, S., Ran, L., Wehner, B., Birmili, W., van der Gon, H. A. C. D., Mu, Q., Barthel, S., Spindler, G., Stieger, B., Müller, K., Zheng, G.-J., Pöschl, U., Su, H., and Wiedensohler, A.: Sea salt emission, transport and influence on size-segregated nitrate simulation: a case study in northwestern Europe by WRF-Chem, Atmos. Chem. Phys., 16, 12081–12097, https://doi.org/10.5194/acp-16-12081-2016, 2016.
Chen, Y., Wolke, R., Ran, L., Birmili, W., Spindler, G., Schröder, W., Su, H., Cheng, Y., Tegen, I., and Wiedensohler, A.: A parameterization of the heterogeneous hydrolysis of N2O5 for mass-based aerosol models: improvement of particulate nitrate prediction, Atmos. Chem. Phys., 18, 673–689, https://doi.org/10.5194/acp-18-673-2018, 2018.
Cheng, Y., Zheng, G., Wei, C., Mu, Q., Zheng, B., Wang, Z., Gao, M., Zhang, Q., He, K., Carmichael, G., Pöschl, U., and Su, H.: Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China, Sci. Adv., 2, e1601530, https://doi.org/10.1126/sciadv.1601530, 2016.
Cui, X., Tang, M., Wang, M., and Zhu, T.: Water as a probe for pH measurement in individual particles using micro-Raman spectroscopy, Anal. Chim. Acta, 1186, 339089, https://doi.org/10.1016/j.aca.2021.339089, 2021.
Ding, J., Zhao, P., Su, J., Dong, Q., Du, X., and Zhang, Y.: Aerosol pH and its driving factors in Beijing, Atmos. Chem. Phys., 19, 7939–7954, https://doi.org/10.5194/acp-19-7939-2019, 2019.
Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+–Ca2+–Mg2+–
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Geng, G., Zhang, Q., Tong, D., Li, M., Zheng, Y., Wang, S., and He, K.: Chemical composition of ambient PM2.5 over China and relationship to precursor emissions during 2005–2012, Atmos. Chem. Phys., 17, 9187–9203, https://doi.org/10.5194/acp-17-9187-2017, 2017.
Guo, H., Sullivan, A. P., Campuzano-Jost, P., Schroder, J. C., Lopez-Hilfiker, F. D., Dibb, J. E., Jimenez, J. L., Thornton, J. A., Brown, S. S., Nenes, A., and Weber, R. J.: Fine particle pH and the partitioning of nitric acid during winter in the northeastern United States, J. Geophys. Res.-Atmos, 121, 10355–10376, https://doi.org/10.1002/2016jd025311, 2016.
Guo, H., Weber, R. J., and Nenes, A.: High levels of ammonia do not raise fine particle pH sufficiently to yield nitrogen oxide-dominated sulfate production, Sci. Rep.-UK, 7, 12109, https://doi.org/10.1038/s41598-017-11704-0, 2017.
Guo, H., Nenes, A., and Weber, R. J.: The underappreciated role of nonvolatile cations in aerosol ammonium-sulfate molar ratios, Atmos. Chem. Phys., 18, 17307–17323, https://doi.org/10.5194/acp-18-17307-2018, 2018.
Haskins, J. D., Jaeglé, L., Shah, V., Lee, B. H., Lopez-Hilfiker, F. D., Campuzano-Jost, P., Schroder, J. C., Day, D. A., Guo, H., Sullivan, A. P., Weber, R., Dibb, J., Campos, T., Jimenez, J. L., Brown, S. S., and Thornton, J. A.: Wintertime Gas-Particle Partitioning and Speciation of Inorganic Chlorine in the Lower Troposphere Over the Northeast United States and Coastal Ocean, J. Geophys. Res.-Atmos, 123, 12897–12916, https://doi.org/10.1029/2018JD028786, 2018.
Hennigan, C. J., Izumi, J., Sullivan, A. P., Weber, R. J., and Nenes, A.: A critical evaluation of proxy methods used to estimate the acidity of atmospheric particles, Atmos. Chem. Phys., 15, 2775–2790, https://doi.org/10.5194/acp-15-2775-2015, 2015.
Jia, S., Wang, X., Zhang, Q., Sarkar, S., Wu, L., Huang, M., Zhang, J., and Yang, L.: Technical note: Comparison and interconversion of pH based on different standard states for aerosol acidity characterization, Atmos. Chem. Phys., 18, 11125–11133, https://doi.org/10.5194/acp-18-11125-2018, 2018.
Li, M., Kan, Y., Su, H., Pöschl, U., Parekh, S. H., Bonn, M., and Cheng, Y.: Spatial homogeneity of pH in aerosol microdroplets, Chem, 9, 1036–1046, https://doi.org/10.1016/j.chempr.2023.02.019, 2023.
Li, W. and Kuwata, M.: Detecting pH of Sub-Micrometer Aerosol Particles Using Fluorescent Probes, Environ. Sci. Technol., 57, 8701–8707, https://doi.org/10.1021/acs.est.3c01517, 2023.
Lippmann, M.: Toxicological and epidemiological studies of cardiovascular effects of ambient air fine particulate matter (PM2.5) and its chemical components: Coherence and public health implications, Crit. Rev. Toxicol., 44, 299–347, https://doi.org/10.3109/10408444.2013.861796, 2014.
Liu, S., Geng, G., Xiao, Q., Zheng, Y., Liu, X., Cheng, J., and Zhang, Q.: Tracking Daily Concentrations of PM2.5 Chemical Composition in China since 2000, Environ. Sci. Technol., 56, 16517–16527, https://doi.org/10.1021/acs.est.2c06510, 2022.
Liu, Y. C., Wu, Z. J., Qiu, Y. T., Tian, P., Liu, Q., Chen, Y., Song, M., and Hu, M.: Enhanced Nitrate Fraction: Enabling Urban Aerosol Particles to Remain in a Liquid State at Reduced Relative Humidity, Geophys. Res. Lett., 50, e2023GL105505, https://doi.org/10.1029/2023GL105505, 2023.
Meskhidze, N., Chameides, W. L., Nenes, A., and Chen, G.: Iron mobilization in mineral dust: Can anthropogenic SO2 emissions affect ocean productivity?, Geophys. Res. Lett., 30, 2085, https://doi.org/10.1029/2003GL018035, 2003.
Möller, D. and Zierath, R.: On the composition of precipitation water and its acidity, Tellus B, 38B, 44–50, https://doi.org/10.1111/j.1600-0889.1986.tb00086.x, 1986.
Nah, T., Guo, H., Sullivan, A. P., Chen, Y., Tanner, D. J., Nenes, A., Russell, A., Ng, N. L., Huey, L. G., and Weber, R. J.: Characterization of aerosol composition, aerosol acidity, and organic acid partitioning at an agriculturally intensive rural southeastern US site, Atmos. Chem. Phys., 18, 11471–11491, https://doi.org/10.5194/acp-18-11471-2018, 2018.
Peng, X., Vasilakos, P., Nenes, A., Shi, G., Qian, Y., Shi, X., Xiao, Z., Chen, K., Feng, Y., and Russell, A. G.: Detailed Analysis of Estimated pH, Activity Coefficients, and Ion Concentrations between the Three Aerosol Thermodynamic Models, Environ. Sci. Technol., 53, 8903–8913, https://doi.org/10.1021/acs.est.9b00181, 2019.
Pye, H. O. T., Liao, H., Wu, S., Mickley, L. J., Jacob, D. J., Henze, D. K., and Seinfeld, J. H.: Effect of changes in climate and emissions on future sulfate-nitrate-ammonium aerosol levels in the United States, J. Geophys. Res.-Atmos, 114, D01205, https://doi.org/10.1029/2008JD010701, 2009.
Pye, H. O. T., Pinder, R. W., Piletic, I. R., Xie, Y., Capps, S. L., Lin, Y., Surratt, J. D., Zhang, Z., Gold, A., Luecken, D. J., Hutzell, W. T., Jaoui, M., Offenberg, J. H., Kleindienst, T. E., Lewandowski, M., and Edney, E. O.: Epoxide Pathways Improve Model Predictions of Isoprene Markers and Reveal Key Role of Acidity in Aerosol Formation, Environ. Sci. Technol., 47, 11056–11064, https://doi.org/10.1021/es402106h, 2013.
Pye, H. O. T., Nenes, A., Alexander, B., Ault, A. P., Barth, M. C., Clegg, S. L., Collett Jr., J. L., Fahey, K. M., Hennigan, C. J., Herrmann, H., Kanakidou, M., Kelly, J. T., Ku, I.-T., McNeill, V. F., Riemer, N., Schaefer, T., Shi, G., Tilgner, A., Walker, J. T., Wang, T., Weber, R., Xing, J., Zaveri, R. A., and Zuend, A.: The acidity of atmospheric particles and clouds, Atmos. Chem. Phys., 20, 4809–4888, https://doi.org/10.5194/acp-20-4809-2020, 2020.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric chemistry and physics: from air pollution to climate change, John Wiley & Sons, Inc., Hoboken, New Jersey, USA, ISBN 978-1118947401, 2016.
Shah, V., Jacob, D. J., Moch, J. M., Wang, X., and Zhai, S.: Global modeling of cloud water acidity, precipitation acidity, and acid inputs to ecosystems, Atmos. Chem. Phys., 20, 12223–12245, https://doi.org/10.5194/acp-20-12223-2020, 2020.
Song, S.: shaojiesong/GC14.1.1_output_for_pH: First release (publish), Zenodo [data set], https://doi.org/10.5281/zenodo.11480367, 2024.
Song, S., Gao, M., Xu, W., Shao, J., Shi, G., Wang, S., Wang, Y., Sun, Y., and McElroy, M. B.: Fine-particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models, Atmos. Chem. Phys., 18, 7423–7438, https://doi.org/10.5194/acp-18-7423-2018, 2018.
Straub, D. J., Hutchings, J. W., and Herckes, P.: Measurements of fog composition at a rural site, Atmos. Environ., 47, 195–205, https://doi.org/10.1016/j.atmosenv.2011.11.014, 2012.
Sun, M., Wang, Y., Wang, T., Fan, S., Wang, W., Li, P., Guo, J., and Li, Y.: Cloud and the corresponding precipitation chemistry in south China: Water-soluble components and pollution transport, J. Geophys. Res.-Atmos, 115, D22303, https://doi.org/10.1029/2010JD014315, 2010.
The International GEOS-Chem User Community: geoschem/geoschem: GEOS-Chem 14.1.1 (Version 14.1.1), Zenodo [code], https://doi.org/10.5281/zenodo.7696632, 2023.
Tilgner, A., Schaefer, T., Alexander, B., Barth, M., Collett Jr., J. L., Fahey, K. M., Nenes, A., Pye, H. O. T., Herrmann, H., and McNeill, V. F.: Acidity and the multiphase chemistry of atmospheric aqueous particles and clouds, Atmos. Chem. Phys., 21, 13483–13536, https://doi.org/10.5194/acp-21-13483-2021, 2021.
Vasilakos, P., Russell, A., Weber, R., and Nenes, A.: Understanding nitrate formation in a world with less sulfate, Atmos. Chem. Phys., 18, 12765–12775, https://doi.org/10.5194/acp-18-12765-2018, 2018.
Wang, S., Su, H., Chen, C., Tao, W., Streets, D. G., Lu, Z., Zheng, B., Carmichael, G. R., Lelieveld, J., Pöschl, U., and Cheng, Y.: Natural gas shortages during the “coal-to-gas” transition in China have caused a large redistribution of air pollution in winter 2017, P. Natl. Acad. Sci. USA, 117, 31018–31025, https://doi.org/10.1073/pnas.2007513117, 2020.
Wang, S. W., Zhang, Q., Streets, D. G., He, K. B., Martin, R. V., Lamsal, L. N., Chen, D., Lei, Y., and Lu, Z.: Growth in NOx emissions from power plants in China: bottom-up estimates and satellite observations, Atmos. Chem. Phys., 12, 4429–4447, https://doi.org/10.5194/acp-12-4429-2012, 2012.
Wang, X., Jacob, D. J., Eastham, S. D., Sulprizio, M. P., Zhu, L., Chen, Q., Alexander, B., Sherwen, T., Evans, M. J., Lee, B. H., Haskins, J. D., Lopez-Hilfiker, F. D., Thornton, J. A., Huey, G. L., and Liao, H.: The role of chlorine in global tropospheric chemistry, Atmos. Chem. Phys., 19, 3981–4003, https://doi.org/10.5194/acp-19-3981-2019, 2019.
Wang, Y., Chen, Y., Wu, Z., Shang, D., Bian, Y., Du, Z., Schmitt, S. H., Su, R., Gkatzelis, G. I., Schlag, P., Hohaus, T., Voliotis, A., Lu, K., Zeng, L., Zhao, C., Alfarra, M. R., McFiggans, G., Wiedensohler, A., Kiendler-Scharr, A., Zhang, Y., and Hu, M.: Mutual promotion between aerosol particle liquid water and particulate nitrate enhancement leads to severe nitrate-dominated particulate matter pollution and low visibility, Atmos. Chem. Phys., 20, 2161–2175, https://doi.org/10.5194/acp-20-2161-2020, 2020.
Weber, R. J., Guo, H., Russell, A. G., and Nenes, A.: High aerosol acidity despite declining atmospheric sulfate concentrations over the past 15 years, Nat. Geosci., 9, 282–285, https://doi.org/10.1038/ngeo2665, 2016.
Wu, D., Zheng, H., Li, Q., Wang, S., Zhao, B., Jin, L., Lyu, R., Li, S., Liu, Y., Chen, X., Zhang, F., Wu, Q., Liu, T., Jiang, J., Wang, L., Li, X., Chen, J., and Hao, J.: Achieving health-oriented air pollution control requires integrating unequal toxicities of industrial particles, Nat. Commun., 14, 6491, https://doi.org/10.1038/s41467-023-42089-6, 2023.
Yuan, A. E. and Shou, W.: Data-driven causal analysis of observational biological time series, eLife, 11, e72518, https://doi.org/10.7554/eLife.72518, 2022.
Yuan, J., Stein, M. L., and Kopp, R. E.: The Evolving Distribution of Relative Humidity Conditional Upon Daily Maximum Temperature in a Warming Climate, J. Geophys. Res.-Atmos, 125, e2019JD032100, https://doi.org/10.1029/2019JD032100, 2020.
Zhang, B., Shen, H., Liu, P., Guo, H., Hu, Y., Chen, Y., Xie, S., Xi, Z., Skipper, T. N., and Russell, A. G.: Significant contrasts in aerosol acidity between China and the United States, Atmos. Chem. Phys., 21, 8341–8356, https://doi.org/10.5194/acp-21-8341-2021, 2021.
Zhang, T., Shen, Z. X., Su, H., Liu, S. X., Zhou, J. M., Zhao, Z. Z., Wang, Q. Y., Prévôt, A. S. H., and Cao, J. J.: Effects of Aerosol Water Content on the formation of secondary inorganic aerosol during a Winter Heavy PM2.5 Pollution Episode in Xi'an, China, Atmos. Environ., 252, 118304, https://doi.org/10.1016/j.atmosenv.2021.118304, 2021.
Zheng, G., Su, H., Wang, S., Andreae, M., Pöschl, U., and Cheng, Y.: Multiphase buffer theory explains contrasts in atmospheric aerosol acidity, Science, 369, 1374–1377, https://doi.org/10.1126/science.aba3719, 2020.
Zheng, G. J., Su, H., and Cheng, Y. F.: Revisiting the Key Driving Processes of the Decadal Trend of Aerosol Acidity in the U.S., ACS Environ. Au, 2, 346–353, https://doi.org/10.1021/acsenvironau.1c00055, 2022.
Short summary
pH is a key property of ambient aerosols, which affect many atmospheric processes. As aerosol pH is a non-conservative parameter, diverse averaging metrics and temporal resolutions may influence the pH values calculated by thermodynamic models. This technical note seeks to quantitatively evaluate the average pH using varied metrics and resolutions. The ultimate goal is to establish standardized reporting practices in future research endeavors.
pH is a key property of ambient aerosols, which affect many atmospheric processes. As aerosol pH...
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