the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Mar 2024
The Co-benefits of a Low-Carbon Future on Air Quality in Europe
Abstract. There is considerable academic interest in the potential for air quality improvement as a co-benefit of climate change mitigation. Few studies use regional air quality models for simulating future co-benefits, but many use global chemistry-climate model output. Using regional atmospheric chemistry could provide a better representation of air quality changes than global chemistry-climate models, especially by improving the representation of elevated urban concentrations. We use a detailed regional atmospheric chemistry model (WRF-Chem v 4.2) to model European air quality in 2050 compared to 2014 following three climate change mitigation scenarios. We represent different climate futures by using air pollutant emissions and chemical boundary conditions (from CESM2-WACCM output) for three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0; a high, medium and low mitigation pathway).
We find that in 2050, following SSP1-2.6, mean population-weighted PM2.5 concentrations across European countries reduces by 52 % compared to 2014. Whilst under SSP2-4.5, this average reduction is 34 %. The smallest average reduction was 18 % by following SSP3-7.0. Maximum 6-monthly-mean daily-maximum 8 h (6mDM8h) ozone (O3) is reduced across Europe by 15 % following SSP1-2.6, and 3 % following SSP2-4.5, but increases by 13 % following SSP3-7.0. This demonstrates clear co-benefits of climate mitigation. The additional resolution allows us to analyse regional differences and identify key sectors. We find that mitigation of agricultural emissions will be key for attaining meaningful co-benefits of mitigation policies, evidenced by the importance of changes in NO3 aerosol mass to determining future PM2.5 air quality and changes in CH4 emissions to future O3 air quality.
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RC1: 'Comment on egusphere-2024-755', Anonymous Referee #1, 09 Apr 2024
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This work describes the impact of three different SSP scenarios on future PM2.5 and O3 concentrations in Europe. The WRF-Chem model is applied over Europe on 30 x 30 km for the year 2050 and the SSP scenarios are compared to the Base Case year 2014. The WRF-Chem model is first evaluated by comparing calculated gas and aerosol species with observations followed by the analysis of the SSP scenarios on calculated PM2.5 and O3 concentrations.
The overall structure of the paper is good and the topic is meaningful, but it lacks a profound explanation for the results. For example, the authors make no attempt to physically and chemically relate the changes in emissions (SSP scenarios) to the observed changes in PM2.5 and O3 concentrations. The manuscript amounts to little more than a statement that where emissions of aerosol precursors have decreased in 2050, i.e. a decrease/increase in calculated PM2.5 and O3 are found.
Therefore, the article needs substantial revisions before it can be accepted for publication.
Major comments.
1. The Introduction is quite long. Please try to shorten it.
2. Recent studies (e.g. Thunis et al. (2021, 2022) and Clappier et al. (2021)) showed that the relationship between changing the emissions (e.g. 10%, 25% and 50%) and resulting concentration changes on PM2.5 can be non-linear. Furthermore, they also showed contrasting chemical regimes over a distance of a few hundreds of kilometers. PM2.5 chemical regimes are mainly determined by the relative importance of NOx versus NH3 responses to emission reductions and show large variations seasonally and spatially. For example, in the Po Valley, responses of PM2.5 concentrations to NOx emission reductions beyond 25% become non-linear mainly during wintertime. The seasonality of emission reductions is not well addressed in the paper. Also, the size of the receptor (city definition) is important on the sensitivity of the emission reductions (especially in city centres) and 30 x 30 km could be too coarse to address this issue properly.
Therefore, I would recommend to add to this study 6 simulations (3 SSP scenarios for PM2.5 and 3 for O3) on a higher resolution over a smaller domain (e.g. Po Valley area, Poland, Benelux or Ruhr area) to investigate the impact of the SSP scenarios on PM25 and O3, and describe the underlying chemical mechanisms on the calculated PM2.5 and O3 concentrations and the differences between the three scenarios.
Minor comments:
The paper describes the impact of the different SSP scenarios on PM2.5 and O3 in Europe. Therefore, I suggest to change the title that captures these two pollutants.
Line 25: Be consistent with the naming of the species Ammonia, Sulfur, Nitrogen (non-capitals) throughout the whole text.
Line 30: remove the text between brackets.
Line 40: Remove the dot after m3.
Line 43: For reducing O3 levels in cities, this is not so ‘feasible’. Please rephrase.
Line 47-49: Note that for some locations the PM10 and O3 concentrations increased, see Putaud et al., (2021, 2023). Please rephrase.
Line 52: Also background CH4 concentrations have increased over the years.
Line 55: Is mentioned earlier.
Line 70: Add references here.
Line 74-77: This is a repetition of line 30.
Line 79: “Modelling”
Line 90: Can the authors address if future land-use/cover changes are included in the SSP scenarios? If not included, what would be the impact of future land cover changes on the outcome of the scenarios?
Line 114-119: This part fits better in a section Discussion, not in the Introduction.
Section 2.1
Are the simulations performed with feedback switched on in WRF-Chem? If so, could you please describe if the meteorology changes between the scenarios and how much this affects the air pollutants? This might be important for SO2 oxidation in clouds (SO4= formation) and O3 photochemistry.
Line 166: Remove “The emissions… of futures” and replace by something like that you performed three different emission reduction scenarios.
Line 175: Can you name the countries where NH3 has different trajectories between SSP2-4.5 and SSP3-7.0?
Page 7: Can you please elaborate more what the differences are between the numbers in Figure 1 and Table 2. The numbers in Table 2 do not correspond with the number is Figure 1. You can also put the relative reductions between brackets in each column.
Page 7: Table 2 should be “2014 emissions”.
It would be nice to have this information for the different countries in the model domain. Or only for the bigger countries, e.g. UK, FR, ES, DE, I. Not every country reduces in the same manner their emissions. This would help to understand the emission ratios and the corresponding chemical regimes (e.g. NOx vs. NH3 for PM2.5 formation & VOC/NOX ratios for O3) for the different scenarios.
Also, it would be interesting to see the difference in spatial distribution in the emissions between the SSP scenarios for NOx, NH3 and PM2.5.
Line 197: what do you mean by “For some analysis..” ?
Line 203: Add “other” between “and aerosol”.
Line 206: Please provide reference.
Line 207: Add dot at the end of the sentence.
Line 209: Use the correct syntax for Figure(s) -> Fig.
Line 210: Indicate which panels (a) and (b).
Figure 2: Please provide Titles of the plots, plus add to the plot for example the Bias.
Table 3: 5th Column can be named “Bias”.
Table 3: Why is O3 missing in the evaluation?
Line 218: I don’t see the added value of Figure 3.
Also I don’t see the relevance for Figure 4a. Knowing that the emissions are different, source sector allocation, temporal and spatial profiles.
Line 249: PM2.5 is overestimated by 8 ug/m3 on average, but I wonder if that’s true for the Eastern part of Europe. Can the author say something about the inclusion of condesables in the emissions? I guess they aren’t. The condensables have a large fraction to the PM2.5 emissions for the residential heating sector, especially in Eastern Europe. How is the model performing in that area with respect to PM2.5 concentrations?
Line 250: It’s a classical problem that models overestimate ammonium nitrate aerosol when compared to quartz filters, due to the evaporation of ammonium nitrate aerosol when temperatures are higher than 20 degrees Celsius (Schaap et al., 2003, De Meij et al., 2006). Did the authors investigate if for these measurement sites quartz filters are used? This could explain the bias.
Figure 5 caption, remove “of” form “of the annual”.
Also, I don’t see the added value of Fig.5a. For Fig5. b-d, I propose to show the differences between 2014 and the scenarios in absolute terms. You can put the relative difference to the Appendix. Changes in low concentrations might lead to large relative differences, while larger differences in absolute values in lower relative differences. Changes in absolute values would make more sense.
The same applies to Figure 8.
Line 265-276: You describe the changes in PM2.5 concentrations for the different scenarios and provide possible explanations for that. Please quantify the emission reductions for PM, NOx and SO2 and describe the chemical mechanisms for the areas that you mention.
Line 285: Can you please clarify the effect of NH3 emission reductions in NH3-sensitive regions, i.e. NOx abundant and NOx/NH3 ratios?
Line 290. Can you indicate for which source sector the largest reductions are found for Slovenia?
Line 297: I understand that sea salt is important for coastal sites, but does sea salt travel so much inland that it has a significant contribution to PM2.5? If so, please quantify it.
Line 310: Figure 7. I wonder if this is relevant, knowing that in Europe, the spatial and sectoral distribution of the emissions varies a lot (e.g. Po Valley vs central France or Poland vs Ireland). I would focus over smaller areas such as the southern part of Poland, Po Valley, Benelux or Ruhr area to highlight the impact of the three SSP scenarios on the yearly distribution of the PM2.5 concentrations and its components.
Line 350: Link this work to Air Quality legislation, it would be interesting to see the impact of these scenarios on the new WHO AQ limit values. The authors can select a few cities to show the impact of the SSP scenarios on WHO AQ limit values.
Line 354, “such as”
Figure 8 caption: Move the Figs. 8c and d to a new Figure.
Line 361. It’s true that changes in O3 concentrations caused by changes in NOx emissions over urban areas are small. Reducing NOx increases O3 in city centres. However, the model resolution is 30 x 30km, which is quite coarse. Could the authors perform (as indicated before) additional simulations over a smaller area and see if the statement holds?
References:
Clappier, P. Thunis, M. Beekmann, J.P. Putaud, A. de Meij, Impact of SOx, NOx and NH3 emission reductions on PM2.5 concentrations across Europe: Hints for future measure development, Environment International, Volume 156, 2021, 106699, ISSN 0160-4120, https://doi.org/10.1016/j.envint.2021.106699.
De Meij, A., Krol, M., Dentener, F., Vignati, E., Cuvelier, C., and Thunis, P.: The sensitivity of aerosol in Europe to two different emission inventories and temporal distribution of emissions, Atmos. Chem. Phys., 6, 4287–4309, https://doi.org/10.5194/acp-6-4287-2006, 2006.
Putaud, J.-P., Pozzoli, L., Pisoni, E., Martins Dos Santos, S., Lagler, F., Lanzani, G., Dal Santo, U., and Colette, A.: Impacts of the COVID-19 lockdown on air pollution at regional and urban background sites in northern Italy, Atmos. Chem. Phys., 21, 7597–7609, https://doi.org/10.5194/acp-21-7597-2021, 2021.
Putaud, J.-P., Pisoni, E., Mangold, A., Hueglin, C., Sciare, J., Pikridas, M., Savvides, C., Ondracek, J., Mbengue, S., Wiedensohler, A., Weinhold, K., Merkel, M., Poulain, L., van Pinxteren, D., Herrmann, H., Massling, A., Nordstroem, C., Alastuey, A., Reche, C., Pérez, N., Castillo, S., Sorribas, M., Adame, J. A., Petaja, T., Lehtipalo, K., Niemi, J., Riffault, V., de Brito, J. F., Colette, A., Favez, O., Petit, J.-E., Gros, V., Gini, M. I., Vratolis, S., Eleftheriadis, K., Diapouli, E., Denier van der Gon, H., Yttri, K. E., and Aas, W.: Impact of 2020 COVID-19 lockdowns on particulate air pollution across Europe, Atmos. Chem. Phys., 23, 10145–10161, https://doi.org/10.5194/acp-23-10145-2023, 2023.
Schaap, M., Van Loon, M., Ten Brink, H. M., Dentener, F., and Builtjes, P. J. H.: The nitrate aerosol field over Europe: simulations with an atmospheric chemistry-transport model of intermediate complexity, Atmos. Chem. Phys., 3, 5919–5976, 2003, http://www.atmos-chem-phys.net/3/5919/2003/.
Thunis, P., Clappier, A., de Meij, A., Pisoni, E., Bessagnet, B., and Tarrason, L.: Why is the city's responsibility for its air pollution often underestimated? A focus on PM2.5, Atmos. Chem. Phys., 21, 18195–18212, https://doi.org/10.5194/acp-21-18195-2021, 2021.
Thunis, P., Clappier, A., Beekmann, M., Putaud, J. P., Cuvelier, C., Madrazo, J., and de Meij, A.: Non-linear response of PM2.5 to changes in NOx and NH3 emissions in the Po basin (Italy): consequences for air quality plans, Atmos. Chem. Phys., 21, 9309–9327, https://doi.org/10.5194/acp-21-9309-2021, 2021.
Citation: https://doi.org/10.5194/egusphere-2024-755-RC1
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Cclayton_WRFChem_SSP_pres Connor Clayton https://zenodo.org/records/10781398
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