Articles | Volume 26, issue 13
https://doi.org/10.5194/acp-26-9997-2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
Alkaline dust deposition to foliage surfaces likely enhances the dry deposition velocity of SO2: an investigation in the Alberta Oil-Sands Region using the GEM-MACH air-quality model
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- Final revised paper (published on 16 Jul 2026)
- Supplement to the final revised paper
- Preprint (discussion started on 30 Dec 2025)
- Supplement to the preprint
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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- RC1: 'Comment on egusphere-2025-6392', Anonymous Referee #1, 04 Feb 2026
- RC2: 'Comment on egusphere-2025-6392', Anonymous Referee #2, 07 Apr 2026
- AC1: 'Comment on egusphere-2025-6392: Response to Reviewers' Comments', Paul Makar, 29 Apr 2026
Peer review completion
AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload
AR by Paul Makar on behalf of the Authors (29 Apr 2026)
Author's response
Author's tracked changes
Manuscript
ED: Reconsider after major revisions (08 May 2026) by Joshua Fu
ED: Referee Nomination & Report Request started (15 Jun 2026) by Joshua Fu
RR by Anonymous Referee #2 (26 Jun 2026)
ED: Publish as is (26 Jun 2026) by Joshua Fu
AR by Paul Makar on behalf of the Authors (02 Jul 2026)
Manuscript
Review: Miller et al., 2026
“Alkaline dust deposition to foliage surfaces likely enhances the dry deposition velocity of SO2: An investigation in the Alberta Oil-Sands Region using the GEM-MACH air-quality model”
Summary
This manuscript describes work to improve process representation of SO2 dry deposition in the GEM-MACH regional air quality model. Dry deposition is an important process to capture in chemistry transport models, but process realism is difficult to achieve due the sub-grid scale nature of the contributing processes. The authors address this problem by targeting dry deposition on wet vegetation surfaces. They expand GEM-MACH’s dry deposition mechanism to include a scheme that calculates pH on the thin films of water that accumulate on foliage. They hypothesize that this will improve SO2 dry deposition in regions like the Athabasca Oil Sands, where high atmospheric concentrations of acidic and alkaline gases and aerosols can influence foliage pH, and therefore deposition of acidic and basic gases. The authors find that GEM-MACH can better predict SO2 dry deposition velocities in the Athabasca Oil Sands Region when this scheme is included, although the impacts on atmospheric SO2 concentrations is small.
Overall, the manuscript is well written in clear language. However, I think that the manuscript is over-long relative to the limited scope of the results. It is unclear what the wider impacts, if any, the new foliage water pH scheme would have on e.g. deposition velocities of other acidic and basic gases like NH3 or HNO3 and what impact it might have a dusty or wildfire affected region. While I recommend the manuscript for publication, I have some General and Technical comments below that should be addressed first.
General Comments
Technical Comments
Abstract
Suggest “We examine here the potential for simultaneous…”
Introduction
Section 2.2
Section 3 – Dry deposition algorithms
Here, is “M” mol L-1?
Section 4 - simulating foliage pH
Suggest pointing to the specific section/subsection.
Section 5.1
Section 5.2
Section 5.3
Figures 11 and 12 show that there are only small changes in SO2 concentrations between the base and CALCCO3_Lw_high simulations at most of the sites and only five sites where CALCCO3_Lw_high substantially reduces bias against the observations. This is despite the better agreement between CALCCO3_Lw_high and observed VdSO2 shown in Figure 9. Is there anything linking the five WBEA sites where CALCCO3_Lw_high reduces bias against the observations and Oski-otin, YAJP and DWEF? E.g. location or vegetation type?
I also found Figure 12 hard to interpret due to the small font and symbol sizes. Perhaps the authors could consider i) including just Figure 12b (the results from June that are also presented in Figure 11) and ii) summarizing the results from Figures 12a, c and d?
Can the authors suggest a reason/s why CALCCO3_Lw_high reduces bias against the atmospheric SO2 observations at some sites and months, but not others? Are these sites linked by location or vegetation type? Further, is there anything linking the five WBEA sites where CALCCO3_Lw_high reduces bias against the observations and Oski-otin, YAJP and DWEF? Again, location or vegetation type?