Articles | Volume 18, issue 18
https://doi.org/10.5194/gmd-18-6479-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-6479-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
26 Sep 2025
Model evaluation paper |
| 26 Sep 2025
| 26 Sep 2025
Implementation of the MOSAIC aerosol module (v1.0) in the Canadian air quality model GEM-MACH (v3.1)
Kirill Semeniuk
CORRESPONDING AUTHOR
Air Quality Research Division, Modeling and Integration Section, Environment and Climate Change Canada, Dorval, Quebec, Canada
Ashu Dastoor
Air Quality Research Division, Modeling and Integration Section, Environment and Climate Change Canada, Dorval, Quebec, Canada
Alex Lupu
Air Quality Research Division, Modeling and Integration Section, Environment and Climate Change Canada, Toronto, Ontario, Canada
Related authors
No articles found.
Wanmin Gong, Stephen R. Beagley, Kenjiro Toyota, Henrik Skov, Jesper Heile Christensen, Alex Lupu, Diane Pendlebury, Junhua Zhang, Ulas Im, Yugo Kanaya, Alfonso Saiz-Lopez, Roberto Sommariva, Peter Effertz, John W. Halfacre, Nis Jepsen, Rigel Kivi, Theodore K. Koenig, Katrin Müller, Claus Nordstrøm, Irina Petropavlovskikh, Paul B. Shepson, William R. Simpson, Sverre Solberg, Ralf M. Staebler, David W. Tarasick, Roeland Van Malderen, and Mika Vestenius
Atmos. Chem. Phys., 25, 8355–8405, https://doi.org/10.5194/acp-25-8355-2025, https://doi.org/10.5194/acp-25-8355-2025, 2025
Short summary
Short summary
This study showed that the springtime O3 depletion plays a critical role in driving the surface O3 seasonal cycle in the central Arctic. The O3 depletion events, while occurring most notably within the lowest few hundred metres above the Arctic Ocean, can induce a 5–7 % loss in the pan-Arctic tropospheric O3 burden during springtime. The study also found enhancements in O3 and NOy (mostly peroxyacetyl nitrate) concentrations in the Arctic due to northern boreal wildfires, particularly at higher altitudes.
Ramina Alwarda, Kristof Bognar, Xiaoyi Zhao, Vitali Fioletov, Jonathan Davies, Sum Chi Lee, Debora Griffin, Alexandru Lupu, Udo Frieß, Alexander Cede, Yushan Su, and Kimberly Strong
Atmos. Meas. Tech., 18, 2397–2423, https://doi.org/10.5194/amt-18-2397-2025, https://doi.org/10.5194/amt-18-2397-2025, 2025
Short summary
Short summary
Nitrogen dioxide (NO2) is a pollutant with a short lifetime and large variability, but there are limited measurements of its distribution in the lower atmosphere. We present a new 3-year dataset of NO2 vertical profiles in Toronto, Canada, and evaluate it using NO2 from satellite and surface monitoring networks and simulations by an air quality forecast model. We quantify and explain the differences among the datasets to provide information that can be used to understand NO2 variability.
Ashu Dastoor, Hélène Angot, Johannes Bieser, Flora Brocza, Brock Edwards, Aryeh Feinberg, Xinbin Feng, Benjamin Geyman, Charikleia Gournia, Yipeng He, Ian M. Hedgecock, Ilia Ilyin, Jane Kirk, Che-Jen Lin, Igor Lehnherr, Robert Mason, David McLagan, Marilena Muntean, Peter Rafaj, Eric M. Roy, Andrei Ryjkov, Noelle E. Selin, Francesco De Simone, Anne L. Soerensen, Frits Steenhuisen, Oleg Travnikov, Shuxiao Wang, Xun Wang, Simon Wilson, Rosa Wu, Qingru Wu, Yanxu Zhang, Jun Zhou, Wei Zhu, and Scott Zolkos
Geosci. Model Dev., 18, 2747–2860, https://doi.org/10.5194/gmd-18-2747-2025, https://doi.org/10.5194/gmd-18-2747-2025, 2025
Short summary
Short summary
This paper introduces the Multi-Compartment Mercury (Hg) Modeling and Analysis Project (MCHgMAP) aimed at informing the effectiveness evaluations of two multilateral environmental agreements: the Minamata Convention on Mercury and the Convention on Long-Range Transboundary Air Pollution. The experimental design exploits a variety of models (atmospheric, land, oceanic ,and multimedia mass balance models) to assess the short- and long-term influences of anthropogenic Hg releases into the environment.
Roya Ghahreman, Wanmin Gong, Paul A. Makar, Alexandru Lupu, Amanda Cole, Kulbir Banwait, Colin Lee, and Ayodeji Akingunola
Geosci. Model Dev., 17, 685–707, https://doi.org/10.5194/gmd-17-685-2024, https://doi.org/10.5194/gmd-17-685-2024, 2024
Short summary
Short summary
The article explores the impact of different representations of below-cloud scavenging on model biases. A new scavenging scheme and precipitation-phase partitioning improve the model's performance, with better SO42- scavenging and wet deposition of NO3- and NH4+.
Mohammad Mortezazadeh, Jean-François Cossette, Ashu Dastoor, Jean de Grandpré, Irena Ivanova, and Abdessamad Qaddouri
Geosci. Model Dev., 17, 335–346, https://doi.org/10.5194/gmd-17-335-2024, https://doi.org/10.5194/gmd-17-335-2024, 2024
Short summary
Short summary
The interpolation process is the most computationally expensive step of the semi-Lagrangian (SL) approach. In this paper we implement a new interpolation scheme into the semi-Lagrangian approach which has the same computational cost as a third-order polynomial scheme but with the accuracy of a fourth-order interpolation scheme. This improvement is achieved by using two third-order backward and forward polynomial interpolation schemes in two consecutive time steps.
Robin Stevens, Andrei Ryjkov, Mahtab Majdzadeh, and Ashu Dastoor
Atmos. Chem. Phys., 22, 13527–13549, https://doi.org/10.5194/acp-22-13527-2022, https://doi.org/10.5194/acp-22-13527-2022, 2022
Short summary
Short summary
Absorbing particles like black carbon can be coated with other matter. How much radiation these particles absorb depends on the coating thickness. The removal of these particles by clouds and rain depends on the coating composition. These effects are important for both climate and air quality. We implement a more detailed representation of these particles in an air quality model which accounts for both coating thickness and composition. We find a significant effect on particle concentrations.
Ashu Dastoor, Andrei Ryjkov, Gregor Kos, Junhua Zhang, Jane Kirk, Matthew Parsons, and Alexandra Steffen
Atmos. Chem. Phys., 21, 12783–12807, https://doi.org/10.5194/acp-21-12783-2021, https://doi.org/10.5194/acp-21-12783-2021, 2021
Short summary
Short summary
An assessment of mercury levels in air and deposition in the Athabasca oil sands region (AOSR) in Northern Alberta, Canada, was conducted to investigate the contribution of Hg emitted from oil sands activities to the surrounding landscape using a 3D process-based Hg model in 2012–2015. Oil sands Hg emissions are found to be important sources of Hg contamination to the local landscape in proximity to the processing activities, particularly in wintertime.
Cited articles
Adebiyi, A., Kok, J. F., Murray, B. J., Ryder, C. L., Stuut, J.-B. W., Kahn, R. A., Knippertz, P., Formenti, P., Mahowald, N. M., Pérez García-Pando, C., Klose, M., Ansmann, A., Samset, B. H., Ito, A., Balkanski, Y., Di Biagio, C., Romanias, M. N., Huang, Y., and Meng, J.: A review of coarse mineral dust in the Earth system, Aeolian Res., 60, 100849, https://doi.org/10.1016/j.aeolia.2022.100849, 2023.
Adebiyi, A. A. and Kok, J. F.: Climate models miss most of the coarse dust in the atmosphere, Sci. Adv., 6, eaaz9507, https://doi.org/10.1126/sciadv.aaz9507, 2020.
Akingunola, A., Makar, P. A., Zhang, J., Darlington, A., Li, S.-M., Gordon, M., Moran, M. D., and Zheng, Q.: A chemical transport model study of plume-rise and particle size distribution for the Athabasca oil sands, Atmos. Chem. Phys., 18, 8667–8688, https://doi.org/10.5194/acp-18-8667-2018, 2018.
Baklanov, A. and Zhang, Y.: Advances in air quality modeling and forecasting, Glob. Transit., 2, 261–270, https://doi.org/10.1016/j.glt.2020.11.001, 2020.
Bechtold, P., Bazile, E., Guichard, F., Mascart, P., and Richard, E.: A mass-flux convection scheme for regional and global models, Q. J. Roy. Meteor. Soc., 127, 869–886, https://doi.org/10.1002/qj.49712757309, 2001.
Bohren, C. F. and Huffman, D. R.: Absorption and Scattering of Light by Small Particles, Wiley and Sons, 530 pp., ISBN 047105772X, 1983.
Buehner, M., McTaggart-Cowan, R., Beaulne, A., Charette, C., Garand, L., Heilliette, S., Lapalme, E., Laroche, S., Macpherson, S. R., Morneau, J., and Zadra, A.: Implementation of Deterministic Weather Forecasting Systems Based on Ensemble – Variational Data Assimilation at Environment Canada. Part I: The Global System, Mon. Weather Rev., 143, 2532–2559, https://doi.org/10.1175/MWR-D-14-00354.1, 2015.
Cao, J. and Lin, J.: A study on formulation of objective functions for determining material models, Int. J. Mech. Sci., 50, 193–204, https://doi.org/10.1016/j.ijmecsci.2007.07.003, 2008.
Capaldo, P. K., Pilinis, C., and Pandis, S. N.: A computationally efficient hybrid approach for dynamic gas/aerosol transfer in air quality models, Atmos. Environ., 34, 3617–3627, https://doi.org/10.1016/S1352-2310(00)00092-3, 2000.
Carter, W. P. L.: Development of a condensed SAPRC-07 chemical mechanism, Atmos. Environ., 44, 5336–5345, https://doi.org/10.1016/j.atmosenv.2010.01.024, 2010.
Carter, W. P. L. and Heo, G.: Development of revised SAPRC aromatics mechanisms, Atmos. Environ., 77, 404–414, https://doi.org/10.1016/j.atmosenv.2013.05.021, 2013.
Chang, W. L., Bhave, P. V., Brown, S. S., Riemer, N., Stutz, J., and Dabdub, D.: Heterogeneous Atmospheric Chemistry, Ambient Measurements, and Model Calculations of N2O5: A Review, Aerosol Sci. Tech., 45, 665–695, https://doi.org/10.1080/02786826.2010.551672, 2011.
de Grandpré, J. , Tanguay, M., Qaddouri, A., Zerroukat, M., and McLinden, C. A.: Semi-Lagrangian Advection of Stratospheric Ozone on a Yin–Yang Grid System, Mon. Weather Rev., 144, 1035–1050, https://doi.org/10.1175/MWR-D-15-0142.1, 2016.
Doner, H. E. and Lynn, W. C.: Carbonate, Halide, Sulfate, and Sulfide Minerals, in: Minerals in Soil Environments, John Wiley & Sons, Ltd, 279–330, https://doi.org/10.2136/sssabookser1.2ed.c6, 1989.
ECCC: GEM version 5.1 source code, Zenodo [code], https://doi.org/10.5281/zenodo.17100109, 2021.
Emerson, E. W., Hodshire, A. L., DeBolt, H. M., Bilsback, K. R., Pierce, J. R., McMeeking, G. R., and Farmer, D. K.: Revisiting particle dry deposition and its role in radiative effect estimates, P. Natl. Acad. Sci. USA, 117, 26076–26082, https://doi.org/10.1073/pnas.2014761117, 2020.
Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.: Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67, https://doi.org/10.5194/gmd-3-43-2010, 2010.
Feng, Y. and Penner, J. E.: Global modeling of nitrate and ammonium: Interaction of aerosols and tropospheric chemistry, J. Geophys. Res.-Atmos., 112, https://doi.org/10.1029/2005JD006404, 2007.
Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+–Ca2+–Mg2+–NH
Fuchs, N. A. and Sutugin, A. G.: HIGH-DISPERSED AEROSOLS, in: Topics in Current Aerosol Research, edited by: Hidy, G. M. and Brock, J. R., Pergamon, 1, https://doi.org/10.1016/B978-0-08-016674-2.50006-6, 1971.
Galmarini, S., Makar, P., Clifton, O. E., Hogrefe, C., Bash, J. O., Bellasio, R., Bianconi, R., Bieser, J., Butler, T., Ducker, J., Flemming, J., Hodzic, A., Holmes, C. D., Kioutsioukis, I., Kranenburg, R., Lupascu, A., Perez-Camanyo, J. L., Pleim, J., Ryu, Y.-H., San Jose, R., Schwede, D., Silva, S., and Wolke, R.: Technical note: AQMEII4 Activity 1: evaluation of wet and dry deposition schemes as an integral part of regional-scale air quality models, Atmos. Chem. Phys., 21, 15663–15697, https://doi.org/10.5194/acp-21-15663-2021, 2021.
Ghahreman, R., Gong, W., Makar, P. A., Lupu, A., Cole, A., Banwait, K., Lee, C., and Akingunola, A.: Modeling below-cloud scavenging of size-resolved particles in GEM-MACHv3.1, Geosci. Model Dev., 17, 685–707, https://doi.org/10.5194/gmd-17-685-2024, 2024.
Girard, C., Plante, A., Desgagné, M., McTaggart-Cowan, R., Côté, J., Charron, M., Gravel, S., Lee, V., Patoine, A., Qaddouri, A., Roch, M., Spacek, L., Tanguay, M., Vaillancourt, P. A., and Zadra, A.: Staggered Vertical Discretization of the Canadian Environmental Multiscale (GEM) Model Using a Coordinate of the Log-Hydrostatic-Pressure Type, Mon. Weather Rev., 142, 1183–1196, https://doi.org/10.1175/MWR-D-13-00255.1, 2014.
Gong, S. L., Barrie, L. A., and Lazare, M.: Canadian Aerosol Module (CAM): A size-segregated simulation of atmospheric aerosol processes for climate and air quality models 2. Global sea-salt aerosol and its budgets, J. Geophys. Res.-Atmos., 107, AAC 13-1-AAC 13-14, https://doi.org/10.1029/2001JD002004, 2002.
Gong, S. L., Barrie, L. A., Blanchet, J.-P., Salzen, K. von, Lohmann, U., Lesins, G., Spacek, L., Zhang, L. M., Girard, E., Lin, H., Leaitch, R., Leighton, H., Chylek, P., and Huang, P.: Canadian Aerosol Module: A size-segregated simulation of atmospheric aerosol processes for climate and air quality models 1. Module development, J. Geophys. Res.-Atmos., 108, AAC 3-1–AAC 3-16, https://doi.org/10.1029/2001JD002002, 2003.
Gong, W., Dastoor, A. P., Bouchet, V. S., Gong, S., Makar, P. A., Moran, M. D., Pabla, B., Ménard, S., Crevier, L.-P., Cousineau, S., and Venkatesh, S.: Cloud processing of gases and aerosols in a regional air quality model (AURAMS), Atmos. Res., 82, 248–275, https://doi.org/10.1016/j.atmosres.2005.10.012, 2006.
Gong, W., Makar, P. A., Zhang, J., Milbrandt, J., Gravel, S., Hayden, K. L., Macdonald, A. M., and Leaitch, W. R.: Modelling aerosol–cloud–meteorology interaction: A case study with a fully coupled air quality model (GEM-MACH), Atmos. Environ., 115, 695–715, https://doi.org/10.1016/j.atmosenv.2015.05.062, 2015.
Gorkowski, K., Preston, T. C., and Zuend, A.: Relative-humidity-dependent organic aerosol thermodynamics via an efficient reduced-complexity model, Atmos. Chem. Phys., 19, 13383–13407, https://doi.org/10.5194/acp-19-13383-2019, 2019.
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.
Hand, J. L., Gill, T. E., and Schichtel, B. A.: Urban and rural coarse aerosol mass across the United States: Spatial and seasonal variability and long-term trends, Atmos. Environ., 218, 117025, https://doi.org/10.1016/j.atmosenv.2019.117025, 2019.
Hänel, G.: The Properties of Atmospheric Aerosol Particles as Functions of the Relative Humidity at Thermodynamic Equilibrium with the Surrounding Moist Air, in: Advances in Geophysics, vol. 19, edited by: Landsberg, H. E. and Mieghem, J. V., Elsevier, 73–188, https://doi.org/10.1016/S0065-2687(08)60142-9, 1976.
Hodzic, A. and Jimenez, J. L.: Modeling anthropogenically controlled secondary organic aerosols in a megacity: a simplified framework for global and climate models, Geosci. Model Dev., 4, 901–917, https://doi.org/10.5194/gmd-4-901-2011, 2011.
Ibusuki, T. and Takeuchi, K.: Sulfur dioxide oxidation by oxygen catalyzed by mixtures of manganese(II) and iron(III) in aqueous solutions at environmental reaction conditions, Atmos. Environ., 21, 1555–1560, https://doi.org/10.1016/0004-6981(87)90317-9, 1987.
Im, U., Bianconi, R., Solazzo, E., Kioutsioukis, I., Badia, A., Balzarini, A., Baró, R., Bellasio, R., Brunner, D., Chemel, C., Curci, G., Flemming, J., Forkel, R., Giordano, L., Jiménez-Guerrero, P., Hirtl, M., Hodzic, A., Honzak, L., Jorba, O., Knote, C., Kuenen, J. J. P., Makar, P. A., Manders-Groot, A., Neal, L., Pérez, J. L., Pirovano, G., Pouliot, G., San Jose, R., Savage, N., Schroder, W., Sokhi, R. S., Syrakov, D., Torian, A., Tuccella, P., Werhahn, J., Wolke, R., Yahya, K., Zabkar, R., Zhang, Y., Zhang, J., Hogrefe, C., and Galmarini, S.: Evaluation of operational on-line-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part I: Ozone, Atmos. Environ., 115, 404–420, https://doi.org/10.1016/j.atmosenv.2014.09.042, 2015a.
Im, U., Bianconi, R., Solazzo, E., Kioutsioukis, I., Badia, A., Balzarini, A., Baró, R., Bellasio, R., Brunner, D., Chemel, C., Curci, G., Denier van der Gon, H., Flemming, J., Forkel, R., Giordano, L., Jiménez-Guerrero, P., Hirtl, M., Hodzic, A., Honzak, L., Jorba, O., Knote, C., Makar, P. A., Manders-Groot, A., Neal, L., Pérez, J. L., Pirovano, G., Pouliot, G., San Jose, R., Savage, N., Schroder, W., Sokhi, R. S., Syrakov, D., Torian, A., Tuccella, P., Wang, K., Werhahn, J., Wolke, R., Zabkar, R., Zhang, Y., Zhang, J., Hogrefe, C., and Galmarini, S.: Evaluation of operational online-coupled regional air quality models over Europe and North America in the context of AQMEII phase 2. Part II: Particulate matter, Atmos. Environ., 115, 421–441, https://doi.org/10.1016/j.atmosenv.2014.08.072, 2015b.
Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., and Suttie, M.: The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019, 2019.
Jacobson, M. Z.: Development and application of a new air pollution modeling system – II. Aerosol module structure and design, Atmos. Environ., 31, 131–144, https://doi.org/10.1016/1352-2310(96)00202-6, 1997.
Jacobson, M. Z., Turco, R. P., Jensen, E. J., and Toon, O. B.: Modeling coagulation among particles of different composition and size, Atmos. Environ., 28, 1327–1338, https://doi.org/10.1016/1352-2310(94)90280-1, 1994.
Jaeglé, L., Quinn, P. K., Bates, T. S., Alexander, B., and Lin, J.-T.: Global distribution of sea salt aerosols: new constraints from in situ and remote sensing observations, Atmos. Chem. Phys., 11, 3137–3157, https://doi.org/10.5194/acp-11-3137-2011, 2011.
Jekel, C. F., Venter, G., Venter, M. P., Stander, N., and Haftka, R. T.: Similarity measures for identifying material parameters from hysteresis loops using inverse analysis, Int. J. Mater. Form., 12, 355–378, https://doi.org/10.1007/s12289-018-1421-8, 2019.
Jiang, T., Gordon, M., Makar, P. A., Staebler, R. M., and Wheeler, M.: Aerosol deposition to the boreal forest in the vicinity of the Alberta Oil Sands, Atmos. Chem. Phys., 23, 4361–4372, https://doi.org/10.5194/acp-23-4361-2023, 2023.
Jiang, W.: Instantaneous secondary organic aerosol yields and their comparison with overall aerosol yields for aromatic and biogenic hydrocarbons, Atmos. Environ., 37, 5439–5444, https://doi.org/10.1016/j.atmosenv.2003.09.018, 2003.
Kain, J. S. and Fritsch, J. M.: A One-Dimensional Entraining/Detraining Plume Model and Its Application in Convective Parameterization, J. Atmos. Sci., 47, 2784–2802, https://doi.org/10.1175/1520-0469(1990)047<2784:AODEPM>2.0.CO;2, 1990.
Karamchandani, P., Lurmann, F., and Venkatram, A.: ADOM/TADAP model development program, volume 8: central operator, Enviornmental Research and Technology, Inc., Newbury Park, CA, 1985.
Karydis, V. A., Tsimpidi, A. P., Pozzer, A., and Lelieveld, J.: How alkaline compounds control atmospheric aerosol particle acidity, Atmos. Chem. Phys., 21, 14983–15001, https://doi.org/10.5194/acp-21-14983-2021, 2021.
Kasibhatla, P., Sherwen, T., Evans, M. J., Carpenter, L. J., Reed, C., Alexander, B., Chen, Q., Sulprizio, M. P., Lee, J. D., Read, K. A., Bloss, W., Crilley, L. R., Keene, W. C., Pszenny, A. A. P., and Hodzic, A.: Global impact of nitrate photolysis in sea-salt aerosol on NOx, OH, and O3 in the marine boundary layer, Atmos. Chem. Phys., 18, 11185–11203, https://doi.org/10.5194/acp-18-11185-2018, 2018.
Kerminen, V.-M. and Kulmala, M.: Analytical formulae connecting the “real” and the “apparent” nucleation rate and the nuclei number concentration for atmospheric nucleation events, J. Aerosol Sci., 33, 609–622, https://doi.org/10.1016/S0021-8502(01)00194-X, 2002.
Kim, Y. J., Spak, S. N., Carmichael, G. R., Riemer, N., and Stanier, C. O.: Modeled aerosol nitrate formation pathways during wintertime in the Great Lakes region of North America, J. Geophys. Res.-Atmos., 119, 12420–12445, https://doi.org/10.1002/2014JD022320, 2014.
Kuenen, J., Dellaert, S., Visschedijk, A., Jalkanen, J.-P., Super, I., and Denier van der Gon, H.: CAMS-REG-v4: a state-of-the-art high-resolution European emission inventory for air quality modelling, Earth Syst. Sci. Data, 14, 491–515, https://doi.org/10.5194/essd-14-491-2022, 2022.
Kulmala, M., Laaksonen, A., and Pirjola, L.: Parameterizations for sulfuric acid/water nucleation rates, J. Geophys. Res.-Atmos., 103, 8301–8307, https://doi.org/10.1029/97JD03718, 1998.
Li, G., Wang, Y., and Zhang, R.: Implementation of a two-moment bulk microphysics scheme to the WRF model to investigate aerosol-cloud interaction, J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD009361, 2008.
Li, Y., Tong, D., Ma, S., Freitas, S. R., Ahmadov, R., Sofiev, M., Zhang, X., Kondragunta, S., Kahn, R., Tang, Y., Baker, B., Campbell, P., Saylor, R., Grell, G., and Li, F.: Impacts of estimated plume rise on PM2.5 exceedance prediction during extreme wildfire events: a comparison of three schemes (Briggs, Freitas, and Sofiev), Atmos. Chem. Phys., 23, 3083–3101, https://doi.org/10.5194/acp-23-3083-2023, 2023.
Liggio, J., Li, S.-M., Vlasenko, A., Stroud, C., and Makar, P.: Depression of Ammonia Uptake to Sulfuric Acid Aerosols by Competing Uptake of Ambient Organic Gases, Environ. Sci. Technol., 45, 2790–2796, https://doi.org/10.1021/es103801g, 2011.
Lou, S., Liao, H., and Zhu, B.: Impacts of aerosols on surface-layer ozone concentrations in China through heterogeneous reactions and changes in photolysis rates, Atmos. Environ., 85, 123–138, https://doi.org/10.1016/j.atmosenv.2013.12.004, 2014.
Lyons, W. A., Pielke, R. A., Tremback, C. J., Walko, R. L., Moon, D. A., and Keen, C. S.: Modeling impacts of mesoscale vertical motions upon coastal zone air pollution dispersion, Atmos. Environ., 29, 283–301, https://doi.org/10.1016/1352-2310(94)00217-9, 1995.
Mailhot, J., Bélair, S., Benoit, R., Bilodeau, B., Delage, Y., Filion, L., Garand, L., Girard, C., and Tremblay, A.: Scientific Description of RPN Physics Library – Version 3.6, Recherche en Prevision Numerique, Atmospheric Environment Service, Dorval, Quebec, Canada, https://collaboration.cmc.ec.gc.ca/science/rpn/physics/physic98.pdf (last access: 5 May 2010), 1998.
Majdzadeh, M., Stroud, C. A., Sioris, C., Makar, P. A., Akingunola, A., McLinden, C., Zhao, X., Moran, M. D., Abboud, I., and Chen, J.: Development of aerosol optical properties for improving the MESSy photolysis module in the GEM-MACH v2.4 air quality model and application for calculating photolysis rates in a biomass burning plume, Geosci. Model Dev., 15, 219–249, https://doi.org/10.5194/gmd-15-219-2022, 2022.
Makar, P. A., Bouchet, V. S., and Nenes, A.: Inorganic chemistry calculations using HETV – a vectorized solver for the SO
Makar, P. A., Moran, M. D., Zheng, Q., Cousineau, S., Sassi, M., Duhamel, A., Besner, M., Davignon, D., Crevier, L.-P., and Bouchet, V. S.: Modelling the impacts of ammonia emissions reductions on North American air quality, Atmos. Chem. Phys., 9, 7183–7212, https://doi.org/10.5194/acp-9-7183-2009, 2009.
Makar, P. A., Gong, W., Hogrefe, C., Zhang, Y., Curci, G., Žabkar, R., Milbrandt, J., Im, U., Balzarini, A., Baró, R., Bianconi, R., Cheung, P., Forkel, R., Gravel, S., Hirtl, M., Honzak, L., Hou, A., Jiménez-Guerrero, P., Langer, M., Moran, M. D., Pabla, B., Pérez, J. L., Pirovano, G., San José, R., Tuccella, P., Werhahn, J., Zhang, J., and Galmarini, S.: Feedbacks between air pollution and weather, part 2: Effects on chemistry, Atmos. Environ., 115, 499–526, https://doi.org/10.1016/j.atmosenv.2014.10.021, 2015.
Makar, P. A., Akingunola, A., Aherne, J., Cole, A. S., Aklilu, Y.-A., Zhang, J., Wong, I., Hayden, K., Li, S.-M., Kirk, J., Scott, K., Moran, M. D., Robichaud, A., Cathcart, H., Baratzedah, P., Pabla, B., Cheung, P., Zheng, Q., and Jeffries, D. S.: Estimates of exceedances of critical loads for acidifying deposition in Alberta and Saskatchewan, Atmos. Chem. Phys., 18, 9897–9927, https://doi.org/10.5194/acp-18-9897-2018, 2018.
Makar, P. A., Stroud, C., Akingunola, A., Zhang, J., Ren, S., Cheung, P., and Zheng, Q.: Vehicle-induced turbulence and atmospheric pollution, Atmos. Chem. Phys., 21, 12291–12316, https://doi.org/10.5194/acp-21-12291-2021, 2021.
McLinden, C. A., Olsen, S. C., Hannegan, B., Wild, O., Prather, M. J., and Sundet, J.: Stratospheric ozone in 3-D models: A simple chemistry and the cross-tropopause flux, J. Geophys. Res.-Atmos., 105, 14653–14665, https://doi.org/10.1029/2000JD900124, 2000.
McTaggart-Cowan, R., Vaillancourt, P. A., Zadra, A., Chamberland, S., Charron, M., Corvec, S., Milbrandt, J. A., Paquin-Ricard, D., Patoine, A., Roch, M., Separovic, L., and Yang, J.: Modernization of Atmospheric Physics Parameterization in Canadian NWP, J. Adv. Model. Earth Sy., 11, 3593–3635, https://doi.org/10.1029/2019MS001781, 2019.
Merikanto, J., Napari, I., Vehkamäki, H., Anttila, T., and Kulmala, M.: New parameterization of sulfuric acid-ammonia-water ternary nucleation rates at tropospheric conditions, J. Geophys. Res.-Atmos., 112, https://doi.org/10.1029/2006JD007977, 2007.
Miller, S. J., Makar, P. A., and Lee, C. J.: HETerogeneous vectorized or Parallel (HETPv1.0): an updated inorganic heterogeneous chemistry solver for the metastable-state NH
Mircea, M., Grigoras, G., D'Isidoro, M., Righini, G., Adani, M., Briganti, G., Ciancarella, L., Cappelletti, A., Calori, G., Cionni, I., Cremona, G., Finardi, S., Larsen, B. R., Pace, G., Perrino, C., Piersanti, A., Silibello, C., Vitali, L., and Zanini, G.: Impact of Grid Resolution on Aerosol Predictions: A Case Study over Italy, Aerosol Air Qual. Res., 16, 1253–1267, https://doi.org/10.4209/aaqr.2015.02.0058, 2016.
Mohs, A. J. and Bowman, F. M.: Eliminating Numerical Artifacts When Presenting Moving Center Sectional Aerosol Size Distributions, Aerosol Air Qual. Res., 11, 21–30, https://doi.org/10.4209/aaqr.2010.06.0046, 2011.
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015.
Nakaegawa, T.: High-Performance Computing in Meteorology under a Context of an Era of Graphical Processing Units, Computers, 11, 114, https://doi.org/10.3390/computers11070114, 2022.
Nenes, A., Pandis, S. N., and Pilinis, C.: ISORROPIA: A New Thermodynamic Equilibrium Model for Multiphase Multicomponent Inorganic Aerosols, Aquat. Geochem., 4, 123–152, https://doi.org/10.1023/A:1009604003981, 1998.
Odum, J. R., Hoffmann, T., Bowman, F., Collins, D., Flagan, R. C., and Seinfeld, J. H.: Gas/Particle Partitioning and Secondary Organic Aerosol Yields, Environ. Sci. Technol., 30, 2580–2585, https://doi.org/10.1021/es950943, 1996.
Park, S. H., Gong, S. L., Gong, W., Makar, P. A., Moran, M. D., Zhang, J., and Stroud, C. A.: Relative impact of windblown dust versus anthropogenic fugitive dust in PM2.5 on air quality in North America, J. Geophys. Res.-Atmos., 115, D16210, https://doi.org/10.1029/2009JD013144, 2010.
Pierce, T. E., Kinnee, E. J., and Geron, C. D.: Development of a 1-km vegetation database for modeling biogenic fluxes of hydrocarbons and nitric oxide, https://www.epa.gov/sites/default/files/2015-08/beld3_web.ppsx (last access: 10 March 2024), 2000.
Pleim, J. E., Ran, L., Appel, W., Shephard, M. W., and Cady-Pereira, K.: New Bidirectional Ammonia Flux Model in an Air Quality Model Coupled With an Agricultural Model, J. Adv. Model. Earth Sy., 11, 2934–2957, https://doi.org/10.1029/2019MS001728, 2019.
Pleim, J. E., Ran, L., Saylor, R. D., Willison, J., and Binkowski, F. S.: A New Aerosol Dry Deposition Model for Air Quality and Climate Modeling, J. Adv. Model. Earth Sy., 14, e2022MS003050, https://doi.org/10.1029/2022MS003050, 2022.
Polavarapu, S. M., Neish, M., Tanguay, M., Girard, C., de Grandpré, J., Semeniuk, K., Gravel, S., Ren, S., Roche, S., Chan, D., and Strong, K.: Greenhouse gas simulations with a coupled meteorological and transport model: the predictability of CO2, Atmos. Chem. Phys., 16, 12005–12038, https://doi.org/10.5194/acp-16-12005-2016, 2016.
Polvani, L. M. and Esler, J. G.: Transport and mixing of chemical air masses in idealized baroclinic life cycles, J. Geophys. Res.-Atmos., 112, https://doi.org/10.1029/2007JD008555, 2007.
Reff, A., Bhave, P. V., Simon, H., Pace, T. G., Pouliot, G. A., Mobley, J. D., and Houyoux, M.: Emissions Inventory of PM2.5 Trace Elements across the United States, Environ. Sci. Technol., 43, 5790–5796, https://doi.org/10.1021/es802930x, 2009.
Roger, J.-C., Vermote, E., Skakun, S., Murphy, E., Dubovik, O., Kalecinski, N., Korgo, B., and Holben, B.: Aerosol models from the AERONET database: application to surface reflectance validation, Atmos. Meas. Tech., 15, 1123–1144, https://doi.org/10.5194/amt-15-1123-2022, 2022.
Rosanka, S., Tost, H., Sander, R., Jöckel, P., Kerkweg, A., and Taraborrelli, D.: How non-equilibrium aerosol chemistry impacts particle acidity: the GMXe AERosol CHEMistry (GMXe–AERCHEM, v1.0) sub-submodel of MESSy, Geosci. Model Dev., 17, 2597–2615, https://doi.org/10.5194/gmd-17-2597-2024, 2024.
Schafer, J. S., Eck, T. F., Holben, B. N., Thornhill, K. L., Ziemba, L. D., Sawamura, P., Moore, R. H., Slutsker, I., Anderson, B. E., Sinyuk, A., Giles, D. M., Smirnov, A., Beyersdorf, A. J., and Winstead, E. L.: Intercomparison of aerosol volume size distributions derived from AERONET ground-based remote sensing and LARGE in situ aircraft profiles during the 2011–2014 DRAGON and DISCOVER-AQ experiments, Atmos. Meas. Tech., 12, 5289–5301, https://doi.org/10.5194/amt-12-5289-2019, 2019.
Semeniuk, K.: Implementation of the MOSAIC Aerosol Module in the Canadian Air Quality Model GEM-MACH, Zenodo [code], https://doi.org/10.5281/zenodo.13787463, 2024.
Shrivastava, M., Fast, J., Easter, R., Gustafson Jr., W. I., Zaveri, R. A., Jimenez, J. L., Saide, P., and Hodzic, A.: Modeling organic aerosols in a megacity: comparison of simple and complex representations of the volatility basis set approach, Atmos. Chem. Phys., 11, 6639–6662, https://doi.org/10.5194/acp-11-6639-2011, 2011.
Shrivastava, M., Cappa, C. D., Fan, J., Goldstein, A. H., Guenther, A. B., Jimenez, J. L., Kuang, C., Laskin, A., Martin, S. T., Ng, N. L., Petaja, T., Pierce, J. R., Rasch, P. J., Roldin, P., Seinfeld, J. H., Shilling, J., Smith, J. N., Thornton, J. A., Volkamer, R., Wang, J., Worsnop, D. R., Zaveri, R. A., Zelenyuk, A., and Zhang, Q.: Recent advances in understanding secondary organic aerosol: Implications for global climate forcing, Rev. Geophys., 55, 509–559, https://doi.org/10.1002/2016RG000540, 2017.
Silvern, R. F., Jacob, D. J., Kim, P. S., Marais, E. A., Turner, J. R., Campuzano-Jost, P., and Jimenez, J. L.: Inconsistency of ammonium–sulfate aerosol ratios with thermodynamic models in the eastern US: a possible role of organic aerosol, Atmos. Chem. Phys., 17, 5107–5118, https://doi.org/10.5194/acp-17-5107-2017, 2017.
Simmel, M. and Wurzler, S.: Condensation and activation in sectional cloud microphysical models, Atmos. Res., 80, 218–236, https://doi.org/10.1016/j.atmosres.2005.08.002, 2006.
Sinyuk, A., Holben, B. N., Eck, T. F., Giles, D. M., Slutsker, I., Korkin, S., Schafer, J. S., Smirnov, A., Sorokin, M., and Lyapustin, A.: The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2, Atmos. Meas. Tech., 13, 3375–3411, https://doi.org/10.5194/amt-13-3375-2020, 2020.
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, D., Wang, W., Huang, X.-Y., and Duda, M.: A Description of the Advanced Research WRF Version 3, NCAR Technical Note TN-475+STR, https://doi.org/10.5065/D68S4MVH, 2008.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X.-Y.: A Description of the Advanced Research WRF Model Version 4, NCAR Technical Note TN-475+STR, https://doi.org/10.5065/1DFH-6P97, 2019.
Spada, M., Jorba, O., Pérez García-Pando, C., Janjic, Z., and Baldasano, J. M.: Modeling and evaluation of the global sea-salt aerosol distribution: sensitivity to size-resolved and sea-surface temperature dependent emission schemes, Atmos. Chem. Phys., 13, 11735–11755, https://doi.org/10.5194/acp-13-11735-2013, 2013.
Stokes, R. H. and Robinson, R. A.: Interactions in Aqueous Nonelectrolyte Solutions. I. Solute-Solvent Equilibria, J. Phys. Chem., 70, 2126–2131, https://doi.org/10.1021/j100879a010, 1966.
Sun, T., Zarzycki, C. M., and Bond, T. C.: Effect of discrepancies caused by model resolution on model-measurement comparison for surface black carbon, Atmos. Environ., 247, 118178, https://doi.org/10.1016/j.atmosenv.2020.118178, 2021.
Tang, D. F. and Dobbie, S.: iGen 0.1: a program for the automated generation of models and parameterisations, Geosci. Model Dev., 4, 785–795, https://doi.org/10.5194/gmd-4-785-2011, 2011.
Tian, P., Yu, Z., Cui, C., Huang, J., Kang, C., Shi, J., Cao, X., and Zhang, L.: Atmospheric aerosol size distribution impacts radiative effects over the Himalayas via modulating aerosol single-scattering albedo, Npj Clim. Atmos. Sci., 6, 1–9, https://doi.org/10.1038/s41612-023-00368-5, 2023.
Turnock, S. T., Mann, G. W., Woodhouse, M. T., Dalvi, M., O'Connor, F. M., Carslaw, K. S., and Spracklen, D. V.: The Impact of Changes in Cloud Water pH on Aerosol Radiative Forcing, Geophys. Res. Lett., 46, 4039–4048, https://doi.org/10.1029/2019GL082067, 2019.
Tzivion (Tzitzvashvili), S., Feingold, G., and Levin, Z.: An Efficient Numerical Solution to the Stochastic Collection Equation, J. Atmos. Sci., 44, 3139–3149, https://doi.org/10.1175/1520-0469(1987)044<3139:AENSTT>2.0.CO;2, 1987.
van Donkelaar, A., Martin, R. V., Li, C., and Burnett, R. T.: Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors, Environ. Sci. Technol., 53, 2595–2611, https://doi.org/10.1021/acs.est.8b06392, 2019.
Vehkamäki, H., Kulmala, M., Napari, I., Lehtinen, K. E. J., Timmreck, C., Noppel, M., and Laaksonen, A.: An improved parameterization for sulfuric acid–water nucleation rates for tropospheric and stratospheric conditions, J. Geophys. Res.-Atmos., 107, AAC 3-1–AAC 3-10, https://doi.org/10.1029/2002JD002184, 2002.
Venkatram, A., Karamchandani, P. K., and Misra, P. K.: Testing a comprehensive acid deposition model, Atmos. Environ., 22, 737–747, https://doi.org/10.1016/0004-6981(88)90011-X, 1988.
Vukovich, J. M. and Pierce, T. E.: The implementation of BEIS3 within the SMOKE modeling framework, in: 11th International Emissions Inventory Conference, 11th International Emissions Inventory Conference, Atlanta, Georgia, USA, 1–7, https://www.epa.gov/sites/default/files/2015-10/documents/vukovich.pdf (last access: 22 September 2025), 2002.
Wang, W., Nowlin Jr., W. D., and Reid, R. O.: Analyzed Surface Meteorological Fields over the Northwestern Gulf of Mexico for 1992–94: Mean, Seasonal, and Monthly Patterns, Mon. Weather Rev., 126, 2864–2883, https://doi.org/10.1175/1520-0493(1998)126<2864:ASMFOT>2.0.CO;2, 1998.
Wesely, M. L.: Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models, Atmos. Environ., 23, 1293–1304, https://doi.org/10.1016/0004-6981(89)90153-4, 1989.
Wexler, A. S., Lurmann, F. W., and Seinfeld, J. H.: Modelling urban and regional aerosols – I. model development, Atmos. Environ., 28, 531–546, https://doi.org/10.1016/1352-2310(94)90129-5, 1994.
Whaley, C. H., Makar, P. A., Shephard, M. W., Zhang, L., Zhang, J., Zheng, Q., Akingunola, A., Wentworth, G. R., Murphy, J. G., Kharol, S. K., and Cady-Pereira, K. E.: Contributions of natural and anthropogenic sources to ambient ammonia in the Athabasca Oil Sands and north-western Canada, Atmos. Chem. Phys., 18, 2011–2034, https://doi.org/10.5194/acp-18-2011-2018, 2018.
Wu, M., Wang, H., Easter, R. C., Lu, Z., Liu, X., Singh, B., Ma, P.-L., Tang, Q., Zaveri, R. A., Ke, Z., Zhang, R., Emmons, L. K., Tilmes, S., Dibb, J. E., Zheng, X., Xie, S., and Leung, L. R.: Development and Evaluation of E3SM-MOSAIC: Spatial Distributions and Radiative Effects of Nitrate Aerosol, J. Adv. Model. Earth Sy., 14, e2022MS003157, https://doi.org/10.1029/2022MS003157, 2022.
Xausa, F., Paasonen, P., Makkonen, R., Arshinov, M., Ding, A., Denier Van Der Gon, H., Kerminen, V.-M., and Kulmala, M.: Advancing global aerosol simulations with size-segregated anthropogenic particle number emissions, Atmos. Chem. Phys., 18, 10039–10054, https://doi.org/10.5194/acp-18-10039-2018, 2018.
Yang, W., Ma, J., Yang, H., Li, F., and Han, C.: Photoenhanced sulfate formation by the heterogeneous uptake of SO2 on non-photoactive mineral dust, Atmos. Chem. Phys., 24, 6757–6768, https://doi.org/10.5194/acp-24-6757-2024, 2024.
Ye, C., Zhou, X., Pu, D., Stutz, J., Festa, J., Spolaor, M., Tsai, C., Cantrell, C., Mauldin, R. L., Campos, T., Weinheimer, A., Hornbrook, R. S., Apel, E. C., Guenther, A., Kaser, L., Yuan, B., Karl, T., Haggerty, J., Hall, S., Ullmann, K., Smith, J. N., Ortega, J., and Knote, C.: Rapid cycling of reactive nitrogen in the marine boundary layer, Nature, 532, 489–491, https://doi.org/10.1038/nature17195, 2016.
Zaveri, R. A., Easter, R. C., and Peters, L. K.: A computationally efficient Multicomponent Equilibrium Solver for Aerosols (MESA), J. Geophys. Res.-Atmos., 110, https://doi.org/10.1029/2004JD005618, 2005.
Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for Simulating Aerosol Interactions and Chemistry (MOSAIC), J. Geophys. Res.-Atmos., 113, https://doi.org/10.1029/2007JD008782, 2008.
Zdanovskii, A. B.: New methods for calculating solubilities of electrolytes in multicomponent systems, Zh Fiz Khim, 22, 1478–1485, 1948.
Zhang, L., Gong, S., Padro, J., and Barrie, L.: A size-segregated particle dry deposition scheme for an atmospheric aerosol module, Atmos. Environ., 35, 549–560, https://doi.org/10.1016/S1352-2310(00)00326-5, 2001.
Zhou, X., Gao, H., He, Y., Huang, G., Bertman, S. B., Civerolo, K., and Schwab, J.: Nitric acid photolysis on surfaces in low-NOx environments: Significant atmospheric implications, Geophys. Res. Lett., 30, https://doi.org/10.1029/2003GL018620, 2003.
Zieger, P., Väisänen, O., Corbin, J. C., Partridge, D. G., Bastelberger, S., Mousavi-Fard, M., Rosati, B., Gysel, M., Krieger, U. K., Leck, C., Nenes, A., Riipinen, I., Virtanen, A., and Salter, M. E.: Revising the hygroscopicity of inorganic sea salt particles, Nat. Commun., 8, 15883, https://doi.org/10.1038/ncomms15883, 2017.
Short summary
The Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) inorganic aerosol sub-model has been implemented in the Global Environmental Multiscale – Modeling Air Quality and Chemistry (GEM-MACH) air quality model. MOSAIC includes metal cation reactions and is a non-equilibrium, double-moment scheme that conserves aerosol number. Compared to the current aerosol sub-model, MOSAIC produces a more accurate size distribution and aerosol number concentration. It also improves the simulated nitrate and ammonium distribution. This work serves to expand the capacity of GEM-MACH for chemistry and weather coupling.
The Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) inorganic aerosol sub-model...
