Articles | Volume 17, issue 6
https://doi.org/10.5194/gmd-17-2197-2024
© 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/gmd-17-2197-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
HETerogeneous vectorized or Parallel (HETPv1.0): an updated inorganic heterogeneous chemistry solver for the metastable-state NH4+–Na+–Ca2+–K+–Mg2+–SO42−–NO3−–Cl−–H2O system based on ISORROPIA II
Air Quality Modelling and Integration Section, Air Quality Research Division, Atmospheric Science and Technology Directorate, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, Ontario, M3H 5T4, Canada
Paul A. Makar
CORRESPONDING AUTHOR
Air Quality Modelling and Integration Section, Air Quality Research Division, Atmospheric Science and Technology Directorate, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, Ontario, M3H 5T4, Canada
Colin J. Lee
Air Quality Modelling and Integration Section, Air Quality Research Division, Atmospheric Science and Technology Directorate, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, Ontario, M3H 5T4, Canada
Related authors
Stefan J. Miller and Mark Gordon
Atmos. Meas. Tech., 15, 6563–6584, https://doi.org/10.5194/amt-15-6563-2022, https://doi.org/10.5194/amt-15-6563-2022, 2022
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This research investigates the measurement of atmospheric turbulence using a low-cost instrumented car that travels at near-highway speeds and is impacted by upwind obstructions and other on-road traffic. We show that our car design can successfully measure the mean flow and atmospheric turbulence near the surface. We outline a technique to isolate and remove the effects of sporadic passing traffic from car-measured velocity variances and discuss potential measurement uncertainties.
Christian Hogrefe, Stefano Galmarini, Paul A. Makar, Ioannis Kioutsioukis, Olivia E. Clifton, Ummugulsum Alyuz, Jesse O. Bash, Roberto Bellasio, Roberto Bianconi, Tim Butler, Philip Cheung, Alma Hodzic, Richard Kranenburg, Aurelia Lupascu, Kester Momoh, Juan Luis Perez-Camanyo, Jonathan E. Pleim, Young-Hee Ryu, Roberto San Jose, Martijn Schaap, Donna B. Schwede, and Ranjeet Sokhi
Atmos. Chem. Phys., 25, 12629–12656, https://doi.org/10.5194/acp-25-12629-2025, https://doi.org/10.5194/acp-25-12629-2025, 2025
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Performed under the umbrella of Phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4), this study applies AQMEII4 diagnostic tools to better characterize how dry deposition removes pollutants from the atmosphere in regional-scale models. The results also strongly suggest that improvement and harmonization of the representation of land use in these models would serve the community in their future development efforts.
Chi-Tsan Wang, Patrick C. Campbell, Paul Makar, Siqi Ma, Irena Ivanova, Bok H. Baek, Wei-Ting Hung, Zachary Moon, Youhua Tang, Barry Baker, Rick Saylor, and Daniel Tong
EGUsphere, https://doi.org/10.5194/egusphere-2025-485, https://doi.org/10.5194/egusphere-2025-485, 2025
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Forests influence air quality by altering ozone levels, but most air pollution models overlook canopy effects. Our study improves ozone predictions by incorporating forest canopy shading and turbulence into a widely used model. We found that tree cover reduces near-surface ozone by decreasing photolysis rates and diffusion inside canopy, resulting in lower ozone concentrations in densely forested areas. These findings enhance ozone surface prediction accuracy and improve air quality modeling.
Debora Griffin, Colin Hempel, Chris McLinden, Shailesh Kumar Kharol, Colin Lee, Andre Fogal, Christopher Sioris, Mark Shephard, and Yuan You
EGUsphere, https://doi.org/10.5194/egusphere-2025-1681, https://doi.org/10.5194/egusphere-2025-1681, 2025
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Surface NO2 concentrations are obtained across North America using satellite data and machine learning, and compared to traditional approaches to determine surface NO2 from satellite observations.
Ioannis Kioutsioukis, Christian Hogrefe, Paul A. Makar, Ummugulsun Alyuz, Jessy O. Bash, Roberto Bellasio, Roberto Bianconi, Tim Buttler, Olivia E. Clifton, Philippe Cheung, Alma Hodzic, Richard Kranenburg, Aurelia Lupascu, Kester Momoh, Juan Luis Perez-Camaño, John Pleim, Young-Hee Ryu, Robero San Jose, Donna Schwede, Ranjeet Sokhi, and Stefano Galmarini
EGUsphere, https://doi.org/10.5194/egusphere-2025-1091, https://doi.org/10.5194/egusphere-2025-1091, 2025
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Deposition is a key in air quality modelling. An evaluation of the AQMEII4 models is performed prior to analysing the different deposition schemes in relation to the LULC used. Such analysis is unprecedented. Among the results, LULC masks have to be harmonised and up-to-date information used in place of outdated and too course masks. Alternatively LULC masks should be evaluated and intercom pared when multiple model results are analysed.
Paul A. Makar, Philip Cheung, Christian Hogrefe, Ayodeji Akingunola, Ummugulsum Alyuz, Jesse O. Bash, Michael D. Bell, Roberto Bellasio, Roberto Bianconi, Tim Butler, Hazel Cathcart, Olivia E. Clifton, Alma Hodzic, Ioannis Kioutsioukis, Richard Kranenburg, Aurelia Lupascu, Jason A. Lynch, Kester Momoh, Juan L. Perez-Camanyo, Jonathan Pleim, Young-Hee Ryu, Roberto San Jose, Donna Schwede, Thomas Scheuschner, Mark W. Shephard, Ranjeet S. Sokhi, and Stefano Galmarini
Atmos. Chem. Phys., 25, 3049–3107, https://doi.org/10.5194/acp-25-3049-2025, https://doi.org/10.5194/acp-25-3049-2025, 2025
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The large range of sulfur and nitrogen deposition estimates from air quality models results in a large range of predicted impacts. We used models and deposition diagnostics to identify the processes controlling atmospheric sulfur and nitrogen deposition variability. Controlling factors included the uptake of gases and aerosols by hydrometeors, aerosol inorganic chemistry, particle dry deposition, ammonia bidirectional fluxes, gas deposition via plant cuticles and soil, and land use data.
Sepehr Fathi, Paul Makar, Wanmin Gong, Junhua Zhang, Katherine Hayden, and Mark Gordon
Atmos. Chem. Phys., 25, 2385–2405, https://doi.org/10.5194/acp-25-2385-2025, https://doi.org/10.5194/acp-25-2385-2025, 2025
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Our study explores the influence of water phase changes in plumes from industrial sources on atmospheric dispersion of emitted pollutants and air quality. Employing PRISM (Plume-Rise-Iterative-Stratified-Moist), a new method, we found that considering these effects significantly improves predictions of pollutant dispersion. This insight enhances our understanding of environmental impacts, enabling more accurate air quality modelling and fostering more effective pollution management strategies.
Dane Blanchard, Mark Gordon, Duc Huy Dang, Paul Andrew Makar, and Julian Aherne
Atmos. Chem. Phys., 25, 2423–2442, https://doi.org/10.5194/acp-25-2423-2025, https://doi.org/10.5194/acp-25-2423-2025, 2025
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This study offers the first known evaluation of water-soluble brown carbon aerosols in the Athabasca oil sands region (AOSR), Canada. Fluorescence spectroscopy analysis of aerosol samples from five regional sites (collected during the summer of 2021) identified oil sands operations as a measurable brown carbon source. Industrial aerosol emissions were unlikely to impact regional radiative forcing. These findings show that fluorescence spectroscopy can be used to monitor brown carbon in the AOSR.
Hazel Cathcart, Julian Aherne, Michael D. Moran, Verica Savic-Jovcic, Paul A. Makar, and Amanda Cole
Biogeosciences, 22, 535–554, https://doi.org/10.5194/bg-22-535-2025, https://doi.org/10.5194/bg-22-535-2025, 2025
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Deposition from sulfur and nitrogen pollution can harm ecosystems, and recovery from this type of pollution can take decades or longer. To identify risk to Canadian soils, we created maps showing sensitivity to sulfur and nitrogen pollution. Results show that some ecosystems are at risk from acid and nutrient nitrogen deposition: 10 % of protected areas are receiving acid deposition beyond their damage threshold, and 70 % may be receiving nitrogen deposition that could cause biodiversity loss.
Kerry Anderson, Jack Chen, Peter Englefield, Debora Griffin, Paul A. Makar, and Dan Thompson
Geosci. Model Dev., 17, 7713–7749, https://doi.org/10.5194/gmd-17-7713-2024, https://doi.org/10.5194/gmd-17-7713-2024, 2024
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The Global Forest Fire Emissions Prediction System (GFFEPS) is a model that predicts smoke and carbon emissions from wildland fires. The model calculates emissions from the ground up based on satellite-detected fires, modelled weather and fire characteristics. Unlike other global models, GFFEPS uses daily weather conditions to capture changing burning conditions on a day-to-day basis. GFFEPS produced lower carbon emissions due to the changing weather not captured by the other models.
Debora Griffin, Jack Chen, Kerry Anderson, Paul Makar, Chris A. McLinden, Enrico Dammers, and Andre Fogal
Atmos. Chem. Phys., 24, 10159–10186, https://doi.org/10.5194/acp-24-10159-2024, https://doi.org/10.5194/acp-24-10159-2024, 2024
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Satellite-derived CO emissions provide new insights into the understanding of global CO emission rates from wildfires. We use TROPOMI satellite data to create a global inventory database of wildfire CO emissions. These satellite-derived wildfire emissions are used for the evaluation and improvement of existing fire emission inventories and to examine how the wildfire CO emissions have changed over the past 2 decades.
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
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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+.
Colin J. Lee, Paul A. Makar, and Joana Soares
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-185, https://doi.org/10.5194/gmd-2023-185, 2023
Publication in GMD not foreseen
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Clustering is an analysis technique for finding similarities within datasets. We present a new implementation of the hierarchical clustering algorithm that is able to process much larger datasets than was previously possible, by spreading the program out over many connected computers in a high-performance computing system. We show airshed maps of a high-resolution regional model output domain, and find related air pollution profiles at monitoring stations separated by thousands of kilometers.
Xuanyi Zhang, Mark Gordon, Paul A. Makar, Timothy Jiang, Jonathan Davies, and David Tarasick
Atmos. Chem. Phys., 23, 13647–13664, https://doi.org/10.5194/acp-23-13647-2023, https://doi.org/10.5194/acp-23-13647-2023, 2023
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Measurements of ozone in the atmosphere were made in a forest downwind of oil sands mining and production facilities in northern Alberta. These measurements show that the emissions of other pollutants from oil sands production and processing reduce the amount of ozone in the forest. By using an atmospheric model combined with measurements, we find that the rate at which ozone is absorbed by the forest is lower than typical rates from similar measurements in other forests.
Olivia E. Clifton, Donna Schwede, Christian Hogrefe, Jesse O. Bash, Sam Bland, Philip Cheung, Mhairi Coyle, Lisa Emberson, Johannes Flemming, Erick Fredj, Stefano Galmarini, Laurens Ganzeveld, Orestis Gazetas, Ignacio Goded, Christopher D. Holmes, László Horváth, Vincent Huijnen, Qian Li, Paul A. Makar, Ivan Mammarella, Giovanni Manca, J. William Munger, Juan L. Pérez-Camanyo, Jonathan Pleim, Limei Ran, Roberto San Jose, Sam J. Silva, Ralf Staebler, Shihan Sun, Amos P. K. Tai, Eran Tas, Timo Vesala, Tamás Weidinger, Zhiyong Wu, and Leiming Zhang
Atmos. Chem. Phys., 23, 9911–9961, https://doi.org/10.5194/acp-23-9911-2023, https://doi.org/10.5194/acp-23-9911-2023, 2023
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A primary sink of air pollutants is dry deposition. Dry deposition estimates differ across the models used to simulate atmospheric chemistry. Here, we introduce an effort to examine dry deposition schemes from atmospheric chemistry models. We provide our approach’s rationale, document the schemes, and describe datasets used to drive and evaluate the schemes. We also launch the analysis of results by evaluating against observations and identifying the processes leading to model–model differences.
Mark Gordon, Dane Blanchard, Timothy Jiang, Paul A. Makar, Ralf M. Staebler, Julian Aherne, Cris Mihele, and Xuanyi Zhang
Atmos. Chem. Phys., 23, 7241–7255, https://doi.org/10.5194/acp-23-7241-2023, https://doi.org/10.5194/acp-23-7241-2023, 2023
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Measurements of the gas sulfur dioxide (SO2) were made in a forest downwind of oil sands mining and production facilities in northern Alberta. These measurements tell us the rate at which SO2 is absorbed by the forest. The measured rate is much higher than what is currently used by air quality models, which is supported by a previous study in this region. This suggests that SO2 may have a much shorter lifetime in the atmosphere at this location than currently predicted by models.
Timothy Jiang, Mark Gordon, Paul A. Makar, Ralf M. Staebler, and Michael Wheeler
Atmos. Chem. Phys., 23, 4361–4372, https://doi.org/10.5194/acp-23-4361-2023, https://doi.org/10.5194/acp-23-4361-2023, 2023
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Measurements of submicron aerosols (particles smaller than 1 / 1000 of a millimeter) were made in a forest downwind of oil sands mining and production facilities in northern Alberta. These measurements tell us how quickly aerosols are absorbed by the forest (known as deposition rate) and how the deposition rate depends on the size of the aerosol. The measurements show good agreement with a parameterization developed from a recent study for deposition of aerosols to a similar pine forest.
Stefan J. Miller and Mark Gordon
Atmos. Meas. Tech., 15, 6563–6584, https://doi.org/10.5194/amt-15-6563-2022, https://doi.org/10.5194/amt-15-6563-2022, 2022
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This research investigates the measurement of atmospheric turbulence using a low-cost instrumented car that travels at near-highway speeds and is impacted by upwind obstructions and other on-road traffic. We show that our car design can successfully measure the mean flow and atmospheric turbulence near the surface. We outline a technique to isolate and remove the effects of sporadic passing traffic from car-measured velocity variances and discuss potential measurement uncertainties.
Mahtab Majdzadeh, Craig A. Stroud, Christopher Sioris, Paul A. Makar, Ayodeji Akingunola, Chris McLinden, Xiaoyi Zhao, Michael D. Moran, Ihab Abboud, and Jack Chen
Geosci. Model Dev., 15, 219–249, https://doi.org/10.5194/gmd-15-219-2022, https://doi.org/10.5194/gmd-15-219-2022, 2022
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A new lookup table for aerosol optical properties based on a Mie scattering code was calculated and adopted within an improved version of the photolysis module in the GEM-MACH in-line chemical transport model. The modified version of the photolysis module makes use of online interactive aerosol feedback and applies core-shell parameterizations to the black carbon absorption efficiency based on Bond et al. (2006) to the size bins with black carbon mass fraction of less than 40 %.
Debora Griffin, Chris A. McLinden, Enrico Dammers, Cristen Adams, Chelsea E. Stockwell, Carsten Warneke, Ilann Bourgeois, Jeff Peischl, Thomas B. Ryerson, Kyle J. Zarzana, Jake P. Rowe, Rainer Volkamer, Christoph Knote, Natalie Kille, Theodore K. Koenig, Christopher F. Lee, Drew Rollins, Pamela S. Rickly, Jack Chen, Lukas Fehr, Adam Bourassa, Doug Degenstein, Katherine Hayden, Cristian Mihele, Sumi N. Wren, John Liggio, Ayodeji Akingunola, and Paul Makar
Atmos. Meas. Tech., 14, 7929–7957, https://doi.org/10.5194/amt-14-7929-2021, https://doi.org/10.5194/amt-14-7929-2021, 2021
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Satellite-derived NOx emissions from biomass burning are estimated with TROPOMI observations. Two common emission estimation methods are applied, and sensitivity tests with model output were performed to determine the accuracy of these methods. The effect of smoke aerosols on TROPOMI NO2 columns is estimated and compared to aircraft observations from four different aircraft campaigns measuring biomass burning plumes in 2018 and 2019 in North America.
Stefano Galmarini, Paul Makar, Olivia E. Clifton, Christian Hogrefe, Jesse O. Bash, Roberto Bellasio, Roberto Bianconi, Johannes Bieser, Tim Butler, Jason Ducker, Johannes Flemming, Alma Hodzic, Christopher D. Holmes, Ioannis Kioutsioukis, Richard Kranenburg, Aurelia Lupascu, Juan Luis Perez-Camanyo, Jonathan Pleim, Young-Hee Ryu, Roberto San Jose, Donna Schwede, Sam Silva, and Ralf Wolke
Atmos. Chem. Phys., 21, 15663–15697, https://doi.org/10.5194/acp-21-15663-2021, https://doi.org/10.5194/acp-21-15663-2021, 2021
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This technical note presents the research protocols for phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). This initiative has three goals: (i) to define the state of wet and dry deposition in regional models, (ii) to evaluate how dry deposition influences air concentration and flux predictions, and (iii) to identify the causes for prediction differences. The evaluation compares LULC-specific dry deposition and effective conductances and fluxes.
Sepehr Fathi, Mark Gordon, Paul A. Makar, Ayodeji Akingunola, Andrea Darlington, John Liggio, Katherine Hayden, and Shao-Meng Li
Atmos. Chem. Phys., 21, 15461–15491, https://doi.org/10.5194/acp-21-15461-2021, https://doi.org/10.5194/acp-21-15461-2021, 2021
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We have investigated the accuracy of aircraft-based mass balance methodologies through computer model simulations of the atmosphere and air quality at a regional high-resolution scale. We have defined new quantitative metrics to reduce emission retrieval uncertainty by evaluating top-down mass balance estimates against the known simulated meteorology and input emissions. We also recommend methodologies and flight strategies for improved retrievals in future aircraft-based studies.
Xinxin Ye, Pargoal Arab, Ravan Ahmadov, Eric James, Georg A. Grell, Bradley Pierce, Aditya Kumar, Paul Makar, Jack Chen, Didier Davignon, Greg R. Carmichael, Gonzalo Ferrada, Jeff McQueen, Jianping Huang, Rajesh Kumar, Louisa Emmons, Farren L. Herron-Thorpe, Mark Parrington, Richard Engelen, Vincent-Henri Peuch, Arlindo da Silva, Amber Soja, Emily Gargulinski, Elizabeth Wiggins, Johnathan W. Hair, Marta Fenn, Taylor Shingler, Shobha Kondragunta, Alexei Lyapustin, Yujie Wang, Brent Holben, David M. Giles, and Pablo E. Saide
Atmos. Chem. Phys., 21, 14427–14469, https://doi.org/10.5194/acp-21-14427-2021, https://doi.org/10.5194/acp-21-14427-2021, 2021
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Wildfire smoke has crucial impacts on air quality, while uncertainties in the numerical forecasts remain significant. We present an evaluation of 12 real-time forecasting systems. Comparison of predicted smoke emissions suggests a large spread in magnitudes, with temporal patterns deviating from satellite detections. The performance for AOD and surface PM2.5 and their discrepancies highlighted the role of accurately represented spatiotemporal emission profiles in improving smoke forecasts.
Paul A. Makar, Craig Stroud, Ayodeji Akingunola, Junhua Zhang, Shuzhan Ren, Philip Cheung, and Qiong Zheng
Atmos. Chem. Phys., 21, 12291–12316, https://doi.org/10.5194/acp-21-12291-2021, https://doi.org/10.5194/acp-21-12291-2021, 2021
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Vehicle pollutant emissions occur in an environment where upward transport can be enhanced due to the turbulence created by the vehicles as they move through the atmosphere. An approach for including these turbulence effects in regional air pollution forecast models has been derived from theoretical, observation, and higher-resolution modeling. The enhanced mixing, which occurs in the immediate vicinity of roadways, changes pollutant concentrations on the regional to continental scale.
Zhiyong Wu, Leiming Zhang, John T. Walker, Paul A. Makar, Judith A. Perlinger, and Xuemei Wang
Geosci. Model Dev., 14, 5093–5105, https://doi.org/10.5194/gmd-14-5093-2021, https://doi.org/10.5194/gmd-14-5093-2021, 2021
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A community dry deposition algorithm for modeling the gaseous dry deposition process in chemistry transport models was extended to include an additional 12 oxidized volatile organic compounds and hydrogen cyanide based on their physicochemical properties and was then evaluated using field flux measurements over a mixed forest. This study provides a useful tool that is needed in chemistry transport models with increasing complexity for simulating an important atmospheric process.
Paul A. Makar, Ayodeji Akingunola, Jack Chen, Balbir Pabla, Wanmin Gong, Craig Stroud, Christopher Sioris, Kerry Anderson, Philip Cheung, Junhua Zhang, and Jason Milbrandt
Atmos. Chem. Phys., 21, 10557–10587, https://doi.org/10.5194/acp-21-10557-2021, https://doi.org/10.5194/acp-21-10557-2021, 2021
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We have examined the effects of airborne particles on absorption and scattering of incoming sunlight by the particles themselves via cloud formation. We used an advanced, combined high-resolution weather forecast and chemical transport computer model, for western North America, and simulations with and without the connections between particles and weather enabled. Feedbacks improved weather and air pollution forecasts and changed cloud behaviour and forest-fire pollutant amount and height.
Katherine Hayden, Shao-Meng Li, Paul Makar, John Liggio, Samar G. Moussa, Ayodeji Akingunola, Robert McLaren, Ralf M. Staebler, Andrea Darlington, Jason O'Brien, Junhua Zhang, Mengistu Wolde, and Leiming Zhang
Atmos. Chem. Phys., 21, 8377–8392, https://doi.org/10.5194/acp-21-8377-2021, https://doi.org/10.5194/acp-21-8377-2021, 2021
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We developed a method using aircraft measurements to determine lifetimes with respect to dry deposition for oxidized sulfur and nitrogen compounds over the boreal forest in Alberta, Canada. Atmospheric lifetimes were significantly shorter than derived from chemical transport models with differences related to modelled dry deposition velocities. The shorter lifetimes suggest models need to reassess dry deposition treatment and predictions of sulfur and nitrogen in the atmosphere and ecosystems.
Cited articles
Amundson, N. R., Caboussat, A., He, J. W., Martynenko, A. V., Savarin, V. B., Seinfeld, J. H., and Yoo, K. Y.: A new inorganic atmospheric aerosol phase equilibrium model (UHAERO), Atmos. Chem. Phys., 6, 975–992, https://doi.org/10.5194/acp-6-975-2006, 2006.
Anlauf, K., Li, S.-M., Leaitch, R., Brook, J., Hayden, K., Toom–Sauntry, D., and Wiebe, A.: Ionic composition and size characteristics of particles in the lower fraser valley: Pacific 2001 field study, Atmos. Environ., 40, 2662–2675, https://doi.org/10.1016/j.atmosenv.2005.12.027, 2006.
Ansari, A. S. and Pandis, S. N.: Prediction of multicomponent inorganic atmospheric aerosol behavior, Atmos. Environ., 33, 745–757, https://doi.org/10.1016/s1352-2310(98)00221-0, 1999a.
Ansari, A. S. and Pandis, S. N.: An analysis of four models predicting the partitioning of semivolatile inorganic aerosol components, Aerosol Sci. Technol., 31, 129–153, https://doi.org/10.1080/027868299304200, 1999b.
Atkinson, R. W., Mills, I. C., Walton, H. A., and Anderson, H. R.: Fine particle components and health – a systematic review and meta-analysis of epidemiological time series studies of Daily Mortality and hospital admissions, J. Expo. Sci. Env. Epid., 25, 208–214, https://doi.org/10.1038/jes.2014.63, 2014.
Bromley, L. A.: Thermodynamic properties of strong electrolytesin aqueous solutions, AIChE J., 19, 313–320, 1973.
Burden, R. L. and Faires, J. D.: Numerical analysis (9th ed.), Cengage Learing, Boston, MA, USA, 861 pp., ISBN 978-0-538-73351-9, 2011.
Capps, S. L., Henze, D. K., Hakami, A., Russell, A. G., and Nenes, A.: ANISORROPIA: the adjoint of the aerosol thermodynamic model ISORROPIA, Atmos. Chem. Phys., 12, 527–543, https://doi.org/10.5194/acp-12-527-2012, 2012.
Clegg, S. L. and Pitzer, K. S.: Thermodynamics of multicomponent, miscible, ionic solutions: Generalized equations for symmetrical electrolytes, J. Phys. Chem., 96, 3513–3520, https://doi.org/10.1021/j100187a061, 1992.
Community Modeling and Analysis System (CMAS, 2016): https://www.airqualitymodeling.org/index.php/CMAQv5.1_Isorropia, last access: 17 July 2023.
Denbigh, K.: The principles of chemical equilibrium, 4th edn., Cambridge University Press, Cambridge, 520 pp., ISBN 978-0521281508, 1981.
Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+–Ca2+–Mg2+–NH –Na+–SO –NO –Cl−–H2O aerosols, Atmos. Chem. Phys., 7, 4639–4659, https://doi.org/10.5194/acp-7-4639-2007, 2007.
Fountoukis, C., Nenes, A., Sullivan, A., Weber, R., Van Reken, T., Fischer, M., Matías, E., Moya, M., Farmer, D., and Cohen, R. C.: Thermodynamic characterization of Mexico City aerosol during MILAGRO 2006, Atmos. Chem. Phys., 9, 2141–2156, https://doi.org/10.5194/acp-9-2141-2009, 2009.
GEOS-Chem 14.0.0, Zenodo [code], https://doi.org/10.5281/zenodo.7254288, 2022.
Harrison, R. M. and Pio, C. A.: Major ion composition and chemical associations of Inorganic Atmospheric Aerosols, Environ. Sci. Technol., 17, 169–174, https://doi.org/10.1021/es00109a009, 1983.
Heintzenberg, J.: Fine particles in the global troposphere a review, Tellus B, 41B, 149–160, https://doi.org/10.1111/j.1600-0889.1989.tb00132.x, 1989.
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.
Irwin, J. G. and Williams, M. L.: Acid rain: Chemistry and transport, Environ. Pollut., 50, 29–59, https://doi.org/10.1016/0269-7491(88)90184-4, 1988.
Jacobson, M. Z.: Studying the effects of calcium and magnesium on size–distributed nitrate and ammonium with EQUISOLV II, Atmos. Environ., 33, 3635–3649, https://doi.org/10.1016/s1352-2310(99)00105-3, 1999.
Jacobson, M. Z.: Global direct radiative forcing due to multicomponent anthropogenic and natural aerosols. J. Geophys. Res.-Atmos., 106, 1551–1568, https://doi.org/10.1029/2000jd900514, 2001.
Kakavas, S., Pandis, S. N., and Nenes, A.: ISORROPIA–Lite: A comprehensive atmospheric aerosol thermodynamics module for Earth System Models, Tellus B, 74, 1, https://doi.org/10.16993/tellusb.33, 2022.
Kim, Y. P. and Seinfeld, J. H.: Atmospheric gas–aerosol equilibrium: III. Thermodynamics of crustal elements Ca2+, K+, and Mg2+, Aerosol Sci. Technol., 22, 93–110, https://doi.org/10.1080/02786829408959730, 1995.
Kim, Y. P., Seinfeld, J. H., and Saxena, P.: Atmospheric gas–aerosol equilibrium I. Thermodynamic model, Aerosol Sci. Technol., 19, 157–181, https://doi.org/10.1080/02786829308959628, 1993a.
Kim, Y. P., Seinfeld, J. H., and Saxena, P.: Atmospheric gas–aerosol equilibrium II. Analysis of common approximations and activity coefficient calculation methods, Aerosol Sci. Technol., 19, 182–198, https://doi.org/10.1080/02786829308959629, 1993b.
Kusik, C. L. and Meissner, H. P.: Electrolyte activity coefficients in inorganic processing, AIChE Symp. Series, 173, 14–20, 1978.
Lovett, G. M., Tear, T. H., Evers, D. C., Findlay, S. E. G., Cosby, B. J., Dunscomb, J. K., Driscoll, C. T., and Weathers, K. C: Effects of air pollution on ecosystems and biological diversity in the Eastern United States, Annals of the New York Academy of Sciences, 1162, 99–135, https://doi.org/10.1111/j.1749-6632.2009.04153.x, 2009.
Makar, P. A.: Fast use chemical numerics methods: the use of “vectorization by gridpoint”, in: Air Pollution III, Vol. 1, edited by: Moussiopoulos, H. N. and Brebbia, C. A., Computational Mechanics Publications, Southampton, 434 pp., 1995.
Makar, P. A., Wiebe, H. A., Staebler, R. M., Li, S. M., and Anlauf, K: Measurement and modeling of particle nitrate formation, J. Geophys. Res.-Atmos., 103, 13095–13110, https://doi.org/10.1029/98jd00978, 1998.
Makar, P. A., Bouchet, V. S., and Nenes, A.: Inorganic Chemistry calculations using HETV – a vectorized solver for the SO –NO –NH system based on the ISORROPIA algorithms, Atmos. Environ., 37, 2279–2294, https://doi.org/10.1016/s1352-2310(03)00074-8, 2003.
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.
Martin, S. T., Hung, H.-M., Park, R. J., Jacob, D. J., Spurr, R. J. D., Chance, K. V., and Chin, M.: Effects of the physical state of tropospheric ammonium-sulfate-nitrate particles on global aerosol direct radiative forcing, Atmos. Chem. Phys., 4, 183–214, https://doi.org/10.5194/acp-4-183-2004, 2004.
Meissner, H. P. and Peppas, N. A.: Activity coefficients – aqueous Solutions of polybasic acids and their salts, AIChE Journal, 19, 806–809, 1973.
Meng, Z., Seinfeld, J. H., Saxena, P., and Kim, Y. P.: Atmospheric gas–aerosol equilibrium: IV. Thermodynamics of carbonates, Aerosol Sci. Technol., 23, 131–154, https://doi.org/10.1080/02786829508965300, 1995.
Metzger, S., Mihalopoulos, N., and Lelieveld, J.: Importance of mineral cations and organics in gas-aerosol partitioning of reactive nitrogen compounds: case study based on MINOS results, Atmos. Chem. Phys., 6, 2549–2567, https://doi.org/10.5194/acp-6-2549-2006, 2006.
Miller, S.: HETP: An updated inorganic heterogeneous chemistry solver for metastable state based on ISORROPIA II, Zenodo [code], https://doi.org/10.5281/zenodo.8164704, 2024.
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.
Oliveira, I. F. and Takahashi, R. H.: An enhancement of the bisection method average performance preserving Minmax optimality, ACM Transactions on Mathematical Software, 47, 1–24, https://doi.org/10.1145/3423597, 2021.
Press, W. H., Teukolsky, S. A., Vetterling, W. T., Flannery B. P.,: Numerical Recipes The Art of Scientific Computing, 3rd edn., Cambridge University Press, Cambridge, UK, 1235 pp., ISBN 978-0-511-33555-6, 2007.
Pye, H. O., 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., 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.
Quan, J., Liu, Q., Li, X., Gao, Y., Jia, X., Sheng, J., and Liu, Y.: Effect of heterogeneous aqueous reactions on the secondary formation of inorganic aerosols during haze events, Atmos. Environ., 122, 306–312, https://doi.org/10.1016/j.atmosenv.2015.09.068, 2015.
Robinson R. A. and Stokes R. H.: Electrolyte solutions (revised 2nd ed.), Butterworths, London, 608 pp., ISBN 0486422259, 1965.
Rood, M. J., Shaw, M. A., Larson, T. V., and Covert, D. S.: Ubiquitous nature of ambient metastable aerosol, Nature, 337, 537–539, https://doi.org/10.1038/337537a0, 1989.
Saiz-Lopez, A., Plane, J. M., Baker, A. R., Carpenter, L. J., von Glasow, R., Gómez Martín, J. C., McFiggans, G., and Saunders, R. W.: Atmospheric chemistry of iodine, Chem. Rev., 112, 1773–1804, https://doi.org/10.1021/cr200029u, 2011.
Sander, R., Keene, W. C., Pszenny, A. A. P., Arimoto, R., Ayers, G. P., Baboukas, E., Cainey, J. M., Crutzen, P. J., Duce, R. A., Hönninger, G., Huebert, B. J., Maenhaut, W., Mihalopoulos, N., Turekian, V. C., and Van Dingenen, R.: Inorganic bromine in the marine boundary layer: a critical review, Atmos. Chem. Phys., 3, 1301–1336, https://doi.org/10.5194/acp-3-1301-2003, 2003.
Savoie, D. L. and Prospero, J. M.: Particle size distribution of nitrate and sulfate in the marine atmosphere, Geophys. Res. Lett., 9, 1207–1210, https://doi.org/10.1029/gl009i010p01207, 1982.
Schmale, J., Zieger, P., and Ekman, A. M.: Aerosols in current and future arctic climate, Nat. Clim. Change, 11, 95–105, https://doi.org/10.1038/s41558-020-00969-5, 2021.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and physics: From air pollution to climate change, Wiley & Sons, 1152 pp., ISBN 978-1-118-94740-1, 2016.
Shaw, M. A. and Rood, M. J.: Measurement of the crystallization humidities of ambient aerosol particles, Atmos. Environ. A, 24, 1837–1841, https://doi.org/10.1016/0960-1686(90)90516-p, 1990.
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.
Spiegel, M. R., Lipschutz, S., and Liu, J.: Schaum's outlines – Mathematical Handbook of Formulas and Tables, 3rd Edn., The McGraw-Hill Companies, USA, 289 pp., ISBN 0-07-154856-4, 2009.
Tang, I. N., Fung, K. H., Imre, D. G., and Munkelwitz, H. R.: Phase transformation and metastability of hygroscopic microparticles, Aerosol Sci. Technol., 23, 443–453, https://doi.org/10.1080/02786829508965327, 1995.
United States Environmental Protection Agency (USEPA): CMAQ (Version 5.4), Zenodo [code], https://doi.org/10.5281/zenodo.7218076, 2022.
Wang, G., Wang, H., Yu, Y., Gao, S., Feng, J., Gao, S., and Wang, L.: Chemical characterization of water–soluble components of PM10 and PM2.5 atmospheric aerosols in five locations of Nanjing, China, Atmos. Environ., 37, 2893–2902, https://doi.org/10.1016/S1352-2310(03)00271-1, 2003.
Wang, K., Zhang, Y., Nenes, A., and Fountoukis, C.: Implementation of dust emission and chemistry into the Community Multiscale Air Quality modeling system and initial application to an Asian dust storm episode, Atmos. Chem. Phys., 12, 10209–10237, https://doi.org/10.5194/acp-12-10209-2012, 2012.
Wexler, A. S. and Clegg, S. L.: Atmospheric aerosol models for systems including the ions H+, NH , Na+, SO , NO , Cl−, Br−, and H2O, J. Geophys. Res., 107, ACH-14, https://doi.org/10.1029/2001jd000451, 2002.
Zhang, Y., Seigneur, C., Seinfeld, J. H., Jacobson, M., Clegg, S. L., and Binkowski, F. S.: A comparative review of inorganic aerosol thermodynamic equilibrium modules: Similarities, differences, and their likely causes, Atmos. Environ., 34, 117–137, https://doi.org/10.1016/s1352-2310(99)00236-8, 2000.
Short summary
This work outlines a new solver written in Fortran to calculate the partitioning of metastable aerosols at thermodynamic equilibrium based on the forward algorithms of ISORROPIA II. The new code includes numerical improvements that decrease the computational speed (compared to ISORROPIA II) while improving the accuracy of the partitioning solution.
This work outlines a new solver written in Fortran to calculate the partitioning of metastable...