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the Creative Commons Attribution 4.0 License.
| 18 Sep 2024
Source contribution to ozone pollution during June 2021 in Arizona: Insights from WRF-Chem tagged O3 and CO
Abstract. This study reports the contribution of fire emissions on ozone (O₃) pollution in Arizona compared to local and regional anthropogenic emissions. Using the WRF-Chem modeling system with different O₃ and CO tags, we quantified the contributions of these emissions to O₃ levels during June 2021, a period when the region was experiencing both drought conditions and extreme heat. Our findings indicate that background O₃ levels accounted for about 50 % of the total O₃, with local anthropogenic emissions contributing between 24 % and 40 %. During the peak smoky time period, fire-contributed O₃ was significant across the Phoenix metropolitan area, ranging from 5 to 23 ppb or 5 % to 21 % of total O₃ levels, with an average of 15 ppb or 15 %. We verify these O₃ fire tags by conducting a model sensitivity test that excluded fire emissions, which showed strong agreement on the spatiotemporal pattern of O₃ due to fire emissions, although the magnitude of the contribution is underestimated by a factor of 1.4. This further demonstrates that wildfires exacerbate O₃ exceedances over urban areas. Our analysis also showed that the O₃ levels in Yuma are significantly influenced by transboundary pollution from California and Mexico, whereas Phoenix's O₃ levels are mainly driven by local anthropogenic emissions, with much smaller contributions from external sources during the study period. Consistent with previous reports, our findings highlight the role of wildfires and regional emissions in confounding the assessment of local O₃ pollution in urban environments, especially during dry and extremely hot summer in semi-arid/arid regions.
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CC1: 'Comment on egusphere-2024-2617', Mariano Mertens, 24 Sep 2024
Dear Authors,
in your introduction you clearly define the difference between the sensitivity method and a source attribution technique like tagging. Despite the clear definition of the two methods we wonder about the statement “We verify these O₃ fire tags by conducting a model sensitivity test that excluded fire emissions”
The difference between tagging and perturbation has been discussed in many publications. The two methods answer different scientific questions. The perturbation approach calculates the response of a pollutant on an emission change (i.e. the perturbation). The tagging approach calculates the share of an emission sector (and/or region) to a pollutant for one specific state of the atmosphere (i.e. the contribution). The tagging approach provides no information on the sensitivity of an atmospheric chemical constituent on an emission change, and the perturbation approach gives no information about the share of a pollutant for one specific state of the atmosphere (Wang et al., 2009, Clappier et al., 2017, Mertens et al., 2018, Butler et al., 2018, Mertens et al., 2020). Only in a linear system both methods would yield equal results (Grewe et al., 2010, Grewe, 2013), for non-linear systems the results of the different methods are by definition not comparable and might even differ by the sign.
Given the different questions these methods answer, the results of the perturbation approach cannot be used (in a non-linear system) to verify the tagging approach (or vice versa). Both methods give complementary results, but the combination of results from the two methods can be very powerful to fully understand the response of atmospheric chemistry on an emission change (e.g. Mertens et al., 2021, Maruhashi et al., 2024).
This comment is not meant to hinder scientific publication of your results. This comment is also not meant to be regarded as a full scientific review. Our comment is meant to highlight the importance of clearly communicating the difference of the two methods.
Best regards,
Mariano Mertens, Patrick Jöckel, Volker Grewe
References:
Butler, T., Lupascu, A., Coates, J., and Zhu, S.: TOAST 1.0: Tropospheric Ozone Attribution of Sources with Tagging for CESM 1.2.2, Geosci. Model Dev., 11, 2825–2840, https://doi.org/10.5194/gmd-11-2825-2018, 2018
Clappier, A., Belis, C. A., Pernigotti, D., and Thunis, P.: Source apportionment and sensitivity analysis: two methodologies with two different purposes, Geosci. Model Dev., 10, 4245–4256, https://doi.org/10.5194/gmd-10-4245-2017, 2017
Grewe, V., Tsati, E., Hoor, P., On the attribution of contributions of atmospheric trace gases to emissions in atmospheric model applications, Geosci. Model Dev., 3, 487-499, 2010
Grewe, V.: A generalized tagging method, Geosci. Model Dev., 6, 247–253, https://doi.org/10.5194/gmd-6-247-2013, 2013
Maruhashi, J., Mertens, M., Grewe, V. and Dedoussi, I. C.: A multi-method assessment of the regional sensitivities between flight altitude and short-term O3 climate warming from aircraft NOx emissions, Environ. Res. Lett. 19 054007, https://doi.org/10.1088/1748-9326/ad376a, 2024
Mertens, M., Grewe, V., Rieger, V. S., & Jöckel, P.: Revisiting the contribution of land transport and shipping emissions to tropospheric ozone, Atmospheric Chemistry and Physics, 18, 5567–5588, https://doi.org/10.5194/acp-18-5567-2018, 2018
Mertens, M., Kerkweg, A., Grewe, V., Jöckel, P., & Sausen, R.: Attributing ozone and its precursors to land transport emissions in Europe and Germany, Atmospheric Chemistry and Physics, 20, 7843–7873, https://doi.org/10.5194/acp-20-7843-2020, 2020
Mertens, M., Jöckel, P., Matthes, S., Nützel, M., Grewe, V., & Sausen, R.: COVID-19 induced lower-tropospheric ozone changes, Environmental Research Letters, https://doi.org/10.1088/1748-9326/abf191, 2021
Wang, Z. S., C.-J. Chien, and G. S. Tonnesen, Development of a tagged species source apportionment algorithm to characterize three-dimensional transport and transformation of precursors and secondary pollutants, J. Geophys. Res., 114, D21206, doi:10.1029/2008JD010846, 2009
Citation: https://doi.org/10.5194/egusphere-2024-2617-CC1 -
CC3: 'Reply on CC1', Yafang Guo, 26 Sep 2024
Publisher’s note: this comment is a copy of AC1 and its content was therefore removed on 27 September 2024.
Citation: https://doi.org/10.5194/egusphere-2024-2617-CC3 -
CC4: 'Reply on CC1', Yafang Guo, 26 Sep 2024
Publisher’s note: this comment is a copy of AC1 and its content was therefore removed on 27 September 2024.
Citation: https://doi.org/10.5194/egusphere-2024-2617-CC4 -
CC5: 'Reply on CC1', Yafang Guo, 26 Sep 2024
Publisher’s note: this comment is a copy of AC1 and its content was therefore removed on 27 September 2024.
Citation: https://doi.org/10.5194/egusphere-2024-2617-CC5 -
AC1: 'Reply on CC1', Avelino F. Arellano, 26 Sep 2024
Dear Reviewers,
Thank you very much for your thoughtful comments and for highlighting the key distinctions between the sensitivity and tagging methods. We appreciate your insight and the references provided, which contribute to a more robust discussion of these two approaches.
We would like to clarify that we are not attempting to verify the tagging method using the sensitivity test. Our intention was to compare the results of the two methods, acknowledging that they answer different scientific questions. We agree that these methods are not interchangeable. Indeed, we have found that their results differ significantly in magnitude and even in sign.
In light of your feedback, we will rephrase our statement to more accurately reflect the purpose of the sensitivity test in our study. In the abstract and discussion section we have mentioned “verify”. Rather than suggesting any form of verification, we will emphasize that we use the sensitivity test for comparative purposes only, recognizing the distinct contributions of both approaches to understanding the impact of fire emissions. We will also revise Figures 11 and 12 accordingly to better show the negative values from perturbation method which will illustrate the non-linearity better. We will include discussion on these comparisons.
Additionally, we fully agree that combining these two methods can be powerful. In this study, the comparison between them offers a more comprehensive understanding of the effects of fire emissions.
Thank you again for your valuable feedback. We will make the necessary revisions to ensure that the differences between these two methods are communicated clearly.
Sincerely,
Yafang Guo
Citation: https://doi.org/10.5194/egusphere-2024-2617-AC1
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CC3: 'Reply on CC1', Yafang Guo, 26 Sep 2024
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CC2: 'Comment on egusphere-2024-2617', Mariano Mertens, 24 Sep 2024
Publisher’s note: this comment is a copy of CC1 and its content was therefore removed on 26 September 2024.
Citation: https://doi.org/10.5194/egusphere-2024-2617-CC2 -
RC1: 'Comment on egusphere-2024-2617', Anonymous Referee #1, 08 Oct 2024
Review of “Source contribution to ozone pollution during June 2021 in Arizona: Insights from WRF-Chem tagged O3 and CO” by Guo et al.
This is a study on the source attribution of surface and tropospheric O3 and CO in Arizona during one month (June 2021) when fire emissions impacted the area. The authors use WRF-Chem tagged simulations as well as WRF-Chem sensitivity simulations (with and without fire) to determine the budget of O3 for the region, including the fire contribution. They also investigate the meteorological, diurnal, and chemical conditions that influence O3 production for two case studies in detail. While the study covers only a small spatial and temporal region/period, this kind of detailed case study is very interesting as there remains a high level of uncertainty and variability in O3 production during fire events. My comments below are mainly related to some missing information and discussion that, if added, would make the results clearer and put into better context.
Line by line comments:
Lines 60-64: The wording could be improved since it’s not the vehicles or industrial buildings that are combusting. Better to say fossil fuel combustion by vehicles, industry, and power plants. Also add “natural” in “as well as the natural biogenic emissions” since the start of the sentence says anthropogenic activities.
Lines 75-86: I realize this isn’t supposed to be an exhaustive list, but you could add GEOS-Chem, which has had O3 tagging options for quite some time. Described originally in Wang et al (1998) and updates in Zhang et al (2008) and used in several studies, including Whaley et al (2015), for example.
Wang, Y., and D. J. Jacob (1998), Anthropogenic forcing on tropospheric ozone and OH since preindustrial times, J. Geophys. Res., 103, 31,123–31,135, doi:10.1029/1998JD100004.
Zhang, L., et al. (2008), Transpacific transport of ozone pollution and the effect of recent Asian emission increases on air quality in North America: An integrated analysis using satellite, aircraft, ozonesonde, and surface observations, Atmos. Chem. Phys., 8, 6117–6136, doi:10.5194/acp-8-6117-2008.
Whaley, C. H., K. Strong, D. B. A. Jones, T. W. Walker, Z. Jiang, D. K. Henze, M. A. Cooke, C. A. McLinden, R. L. Mittermeier, M. Pommier, et al. (2015), Toronto area ozone: Long-term measurements and modeled sources of poor air quality events, J. Geophys. Res. Atmos., 120, 11,368–11,390, doi:10.1002/2014JD022984.
Section 2: can you include information about what kinds of vegetation are in this region that burned in the wildfires?
Figure 1a: Can an outline of the city of Pheonix be marked on the left panel? The caption implies that this whole area is considered Phoenix.
Section 2.2: Does the WRF-Chem model include the radiative effects of smoke on the air temperature and the photochemical production of O3? E.g., when smoke is heavy and shading the surface, does O3 production and surface temperature go down? If so, please say explicitly, as not all models do this. And if not, then this is a caveat to all of your O3 results that come in Section 3.2, as it’s an important missing process.
Figure 2: how come you don’t include TROPOMI O3 and CO as well?
Figure 4: Similarly, why is TROPOMI O3 not shown in Figure 4?
Fig 4 vs Fig S2: These two figures should either (a) have the same colour scales per species, so that we can more easily see the difference between the two case studies, or (b) have colour scales that highlight the spatial distribution of the enhancements. The HCHO row does both (a&b), the CO row does the latter (just b), but the NO2 row seems to do neither. Can you please adjust the colour bars of the NO2 row in these figures to at least do one or the other?
Fig 5 & Fig S3: Ok, now I see that the colour scales above were set to match those for these model figures which follow. If that’s the priority, then I suggest that you re-jig these figures so that the TROPOMI and WRFChem tropospheric columns appear right on top of each other (e.g. one row for TROPOMI HCHO, and next row for modelled HCHO). You could potentially also add a model-minus-satellite row as well to better see the differences..
Line 351: I believe you mean Figures S3-S4 here.
Lines 390-395: This background information seems out of place. You could have included this when you first mention CO in the paper. Some of it (e.g., that CO is emitted when there is incomplete combustion) you can presume the reader already knows.
Line 402-404: Is the background O3 really an “absence of local sources”? Or are local natural fluxes (e.g., isoprene, soil NOx, lightning NOx, stratospheric O3 decent) included to contribute to this high background O3 during the heatwave? I think you probably mean absence of local anthropogenic sources... and maybe you can clarify whether biogenic and natural sources are included in panel (b). Line 404 says that the background O3 is due only to “regional and global influences on a monthly basis”, which the reader could interpret as only long-range anthropogenic sources.
Line 410-411: A few additional words can help clarify this sentence: “Mexico's anthropogenic contributions *to O3* (Figure 8f) have a larger impact than *they do for* CO (Figure 7f)…”
Figure 8: I notice here and in other O3 figures, there is never a negative contribution to O3 concentrations, which could occur if, for example, the emission source caused O3 titration or a transition on the O3 formation chemical regime, or if the fire emissions caused shading that reduced photochemical production of O3. Do those circumstances really never happen in the model in your study? Or have they just been averaged out in the June mean? Perhaps the authors could include in the discussion the fact that there is no reduction in modelled O3 from any source at any part of the time series and why that may be (missing process in the model?).
Line 431: “Here” should instead be “In Figure 9”.
Line 439-440: I’m not sure I see this in Figure 9. June 17th only seems remarkable in that it’s the day when there is the least good match between the model and measured O3 in the time series, and so I don’t think it should be emphasized. Also, the background O3 contribution, which may or may not include local natural sources (see comment above) is the dominant O3 source throughout this time series. The red, which represents the Arizona anthro contribution is also significant throughout, and in particular on June 15th, so I’m not sure why Mexico is emphasized in the text.
Lines 460-469: As per the comment above about what fire processes the model is including: Does it include the shading effects of the smoke on the O3 production? If not, then please include that in the discussion here as a caveat. If it were, the O3 increase with fires may not be so high (and may even decrease O3).
Figure 11: Can you please make the colour scales the same between panels (a) and (b). Similarly, make the colour bars the same for panels (c) and (d), as the purpose of the figure is to compare the O3 diff technique with the O3 tag technique.
Lines 532-535: could you please add here what causes the negative values in Figure 13c? While I was expecting/looking for negative values to appear for O3, I don’t know why negative values would appear for the fire influence on CO.
Lines 591-593: Don’t you mean “…the influx of VOCs from the fires can shift the chemical regime from VOC-limited to NOₓ-limited, altering…”?
Figure 16c and f: could you add in the caption which chemical regime red represents a move towards and which chemical regime blue is a move toward?
Citation: https://doi.org/10.5194/egusphere-2024-2617-RC1 -
RC2: 'Comment on egusphere-2024-2617', Anonymous Referee #2, 28 Oct 2024
Guo et al. used WRF-Chem model to identify the source of ozone in summer of Arizona. Overall, the method is robust and the results are reliable. However, the novelty of this study is not fully revealed. Some conclusions seem to be well known. I suggest the authors should stress the major findings and the novelty of this study. The detailed comments are as follows:
- The authors only introduced the importance of source attribution techniques, while this study lacks of the introduction of the novelty in Arizona compared with previous studies in California and many other regions.
- Section 3.1 The authors should simply explain the reasons for the selection of this period.
- Line 359: FNR often shows large uncertainties. Why threshold could you use to distinguish the VOC- or NOx-limited regions.
- The authors used H2O2/HNO3 to identify the oxidizing capacity of the atmosphere and the relative contributions of different chemical pathways to O₃ Please examine the predictive accuracy of H2O2 and HNO3 firstly. Besides, the threshold also shows large uncertainties. Please explain the detailed reasons.
- The limitations of this study should be added in the conclusion.
Citation: https://doi.org/10.5194/egusphere-2024-2617-RC2 - AC2: 'Authors' responses to all comments on egusphere-2024-2617', Yafang Guo, 26 Nov 2024
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