the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Sep 2024
Unchanged PM2.5 levels over Europe during COVID-19 were buffered by ammonia
Abstract. The coronavirus outbreak in 2020 had devastating impact on human life, albeit a positive effect for the environment reducing emissions of primary aerosols and trace gases and improving air quality. In this paper, we present inverse modelling estimates of ammonia emissions during the European lockdowns of 2020 based on satellite observations. Ammonia has a strong seasonal cycle and mainly originates from agriculture. We further show how changes in ammonia levels over Europe, in conjunction with decreases in traffic-related atmospheric constituents modulated PM2.5. The key result of this study is a -9.8 % decrease in emissions in the first half of 2020 compared to the same period in 2016–2019 attributed to restrictions related to the global pandemic. We further calculate the delay in the evolution of the emissions in 2020 before, during and after lockdowns, by an sophisticated comparison of the evolution of ammonia emissions during the same time periods for the reference years (2016–2019). Our analysis demonstrates a clear delay in the evolution of ammonia emissions of -77 kt, that was mainly observed in the countries that suffered the strictest travel, social and working measures. Despite the general drop in emissions during the first half of 2020 and the delay in the evolution of the emissions during the lockdown period, satellite and ground-based observations showed that European levels of ammonia increased. On one hand, this was due to the reduction of SO2 and NOx (precursors of the atmospheric acids with which ammonia reacts) that caused less binding and thus less chemical removal of ammonia (smaller loss – higher lifetime); on the other, the majority of the emissions persisted, because ammonia mainly originates from agriculture, a primary production sector that was not influenced by the lockdown restrictions, as practically agricultural activity never ceased. Despite the projected drop in various atmospheric aerosols and trace gases, PM2.5 levels stayed unchanged or even increased in Europe due to a number of reasons attributed to the complicated NH3 - H2SO4 - HNO3 system. Higher water vapour during the European lockdowns favoured more sulfate production from SO2 and OH (gas phase) or O3 (aqueous phase). Ammonia first neutralised sulfuric acid (due to higher atmospheric abundance) also producing sulfate. Then, the continuously accumulating free ammonia reacted with nitric acid shifting the equilibrium reaction towards particulate nitrate. In high free ammonia atmospheric conditions such as those in Europe during the 2020 lockdowns, a small reduction of NOx levels drives faster oxidation toward nitrate and slower deposition of total inorganic nitrate causing high secondary PM2.5 levels.
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RC1: 'Comment on ar-2024-22', Anonymous Referee #1, 24 Sep 2024
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Evangeliou and co-authors have modelled ammonia emissions and concentrations over Europe during the 2020 COVID-19 lockdown, and further assessed its effect on aerosol particle concentrations (in particular PM2.5). The topic is interesting and timely, and the paper is certainly worth publishing after some rewriting. I am an atmospheric chemist, but do not have expertise in either remote sensing or bayesian modelling. I am thus unfortunately unable to comment in detail on the technical aspects of the manuscript.
The main issue I have with the manuscript is that it is rather sloppily written - there is much repetition (e.g. the fact that agricultural activity continued during lockdown is repeated very many times) , unclear sentences and even completely incorrect statements (e.g. on line 572 in the conclusion, “ambient pollution levels did not INCREASE as expected” should presumable read “decrease”). As an extreme example of sloppiness, the caption of Figure 1 contains the phrase “Error! Reference source not found”. How did something like this pass by both the authors and the copy-editors? Overall the text gives the impression of a very promising but rather unfinished draft, not a submitted ready manuscript. While most of this sloppy writing is just irritating, rather than scientifically problematic, it also makes it hard to understand exactly what the authors are arguing. When they talk about various “decreases” (or increases), for example, it is often unclear if they mean absolute decreases/increases, decreases/increases compared to some trend or average, or something else.
The discussion on atmospheric chemistry (around for example lines 460-747 or 527 and onward) is also somewhat confusing or confused. For example, ammonium sulfate is certainly not formed directly in the gas phase (even the notation used, “(s)”, indicates SOLID ammonium sulfate, thus emphatically NOT gas phase). The net effect of NH3 + H2SO4 clustering may indeed be particles with the net composition of ammonium sulfate, so for the purposes of this study (focusing on PM2.5, not gas-phase chemistry), the distinction may be irrelevant, but to a gas-phase chemist such as myself it almost hurts to read this. Similarly, it is certainly not generally true that sulfuric acid is more abundant in the atmosphere than nitric acid, at least not if gas-phase H2SO4 and HNO3 are compared (total sulfate may well be larger than total nitrate though). Also, I would say that after e.g. the CLOUD experiments, the “effect” of NH3 on promoting H2SO4 nucleation IS quite well understood (though since the authors seem to base their understanding on references from 1999 and 2000, their confusion is quite understandable).
I recommend a thorough rewriting of the manuscript to avoid unnecessary repetition, and to make the key arguments and results clearer. After that, I have no objection to the manuscript being published.
Citation: https://doi.org/10.5194/ar-2024-22-RC1 -
RC2: 'Comment on ar-2024-22', Anonymous Referee #2, 11 Oct 2024
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Evangeliou et al. investigated how COVID-19 impacted ammonia (NH3) emissions in Europe. They used the CrIS satellite to constrain NH3 and conducted inverse modeling to calculate the emissions rates. They found that the NH3 emissions during COVID-19 were lower than prior years. However, NH3 concentration in the atmosphere was higher. They suggest it is due to lower SO2 and NOx emissions, leading to less acidic gases. The paper is of potential interest to Aerosol Research; however, there are many concerns the authors need to address prior to publication.
1) I agree with Reviewer #1 that the paper as presented is confusing and needs both reorganized as well as edited for understanding. One major reorganization is that the errors in the emission estimates needs to be presented much sooner (Section 3.1 ideally) as understanding these errors is necessary to understand if the differences in the results are statistically different or not (more on this later).
2) A major concern, similar to Reviewer #1, is that the authors do not appears to understand the thermodynamics that is controlling inorganic aerosol chemistry. First, it takes a large concentration of NH3 in the atmosphere to charge neutralize sulfate (Weber et al., 2016). Charge is emphasized as the term neutralization, which is used, is assumed to reference the pH of the system. Most environments rarely achieve pH neutralization (Pye et al., 2020; Weber et al., 2016). Further, the sulfate does not need to be charge neutralized by NH3 in order to start forming ammonium nitrate (NH4NO3). Instead, the formation of NH4NO3 is controlled by a combination of aerosol pH, temperature, and aerosol liquid water (Guo et al., 2016), and can happen when ammonium bisulfate is a dominant species in the aerosol phase. I highly recommend the authors review Pye et al. (2020) to improve the discussions about the thermodynamics controlling ammonium (NH4) in the aerosol phase as well as the discussions throughout the paper.
3) Further, it is not clear what thermodynamic model was used for the inverse modeling/chemical transport model. A description of this is important, as it entails what species are included, how well it performs at high or low relative humidity/temperature, and whether it was operated with constraints on all species (gas + particle) or just particle, which leads to higher uncertainty (Hennigan et al., 2015).
4) Important controlling knobs of NH3 were never discussed and compared throughout the manuscript. This includes temperature and relative humidity (both for emissions and thermodynamics), precipitation and other meteorology (wet/dry deposition), and comparisons of NOx, SO2, sulfate (SO4), and nitrate (NO3). Without these comparisons, it is not clear if the amount of emissions needed for the different years are being driven by the correct mechanisms or not. Clouds are important both for the lifetime of NH3 but also retrievals of NH3. How were observations with clouds dealt with, as optically thick clouds block any retrieval of NH3 (Shephard and Cady-Pereira, 2015)? Were there areas with optically thick clouds during any of these time periods that introduce more uncertainty?
5) Overall, the results are confusing as they are currently written. The authors argue that agriculture was not impacted by lockdowns, and thus why NH3 remained high; however, they then discuss how lockdowns reduced the NH3 in some different countries. The authors discuss how lockdowns reduced travel and human activity in urban areas; however, the results indicate that the urban areas may have had minimal changes with NH3 emissions. So, did the lockdowns impact agriculture, or was there other influences such as weather (delayed application due to rain/snow/temperature vs temperature/relative humidity reducing releases)? Lines 351 - 363, among others, make this very confusing, especially as the authors state that fertilizer application is tightly regulated but then give a wide range of months to apply.
6) Why did the authors only select a subset of stationary measurements to compare the NH3 from the new emission inventory? E.g., they did not select any stations that were near points of interest they discussed (e.g., northern Italy/Switzerland, the Netherlands, etc.) that also show hotspots that need to be validated before discussion. Also, one of the highest hot spots, Belgium, does not have any ground observations to verify the new emissions. How certain are the authors in the new emissions for this location, especially as the other emission inventories in the supplement do not show a similar hot spot? Should this be an area of higher uncertainty as there is nothing to constrain it, even though Fig. 5 shows relatively lower relative uncertainty?
7) Furthermore, the time series of these ground observations for the old vs new emissions do not validate that the new emission inventory is work. The root mean square error and mean absolute error do not look significantly improved between the old and new emission inventory and beyond the "peaks" the authors mentioned in the manuscript, there are many instances that the new inventory is phased-shifted (high when observations are low and vice versa) and/or shows high bias/baseline.
8) The authors argue there is no meteorological impacts as 2016 - 2019 NH3 emissions (average) were similar year to year. However, at the time of lockdown (March - April) for those years, the largest variation in the averages is seen. Thus, how much is meteorology (temperature, rain, etc.) is impacting the application and/or release of NH3. Also, as the values shown in Fig. 2e & f is the average, what is the standard deviation of those means? What is the median? Are the values really statistically different or not (more on this later). Finally, the authors skim over month of May where suddenly the NH3 emissions match prior years--what led to this jump?
9) Statistical analysis. As suggested in comment 1), the error analysis for the emissions needs to be presented at the beginning of the results. With it being there, it can be analyzed if the values in Fig. 2, 3, and 4 are statistically different or within the error of the emissions. If Fig. 5 is taken at face value, the combined uncertainty for many locations is 400 - 1000 mg m-2. The differences between lockdown and non-lockdown years and after the lockdown period are then all within this uncertainty, meaning that there was no statistically different changes in the NH3 emissions during 2020. If Fig. 5c is an error for scale, it is still confusing what is the error as it is stated as 11% in Line 438 and 48% in line 1118. If it's 48%, then that would mean there is no statistical difference in Fig. 2, 3, and 4. If it's 11%, then there may be statistical difference, but depends what the actual year-to-year variation (spread of observations about the mean, comment 8)).
10) Discussion. This goes with the discussions above about needing a better discussion about the thermodynamics controlling NH3 to make this section more easily understood. In addition, Fig. 6a does not provide any evidence that there should be more NH3 in the atmosphere, as the modeled predicted NH3 lifetime increased by ~0.02 days, which is well within the combined uncertainty of the emissions and chemistry as well as the spread of lifetimes for the region. Further, as discussed in Weber et al. (2006), there needs to be large reductions in SO2 in order to observe noticeable differences in NH3 and aerosol pH. Finally, the model was never shown for validation of aerosol composition. The model shows that most of the PM is primary (even during lockdown). However, as shown in Chen et al. (2022), most of the aerosol is secondary in nature. Thus, there is overall concern in the models ability to predict the aerosol composition and that it is getting PM2.5 correct for the incorrect reasons.
References
Weber et al. High aerosol acidity despite declining atmospheric sulfate concentrations over the past 15 years. Nature Geoscience. 2016
Pye et al. The acidity of atmospheric particles and clouds. Atmospheric Chemistry and Physics. 2020.
Hennigan et al. A critical evaluation of proxy methods used to estimate the acidity of atmospheric particles. Atmospheric Chemistry and Physics. 2015.
Shephard and Cady-Pereira. Cross-track Infrared Sounder (CrIS) satellite observations of tropospheric ammonia. Atmospheric Measurement Techniques. 2015.
Chen et al. European aerosol phenomenology - 8: Harmonised source apportionment of organic aerosol using 22 year-long ACSM/AMS datasets. Environment International. 2022.
Guo et al. Fine particle pH and the partitioning of nitric acid during winter in the northeastern United States. Journal of Geophysical Research-Atmosphere. 2016.
Citation: https://doi.org/10.5194/ar-2024-22-RC2
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Unchanged PM2.5 levels over Europe during COVID-19 were buffered by ammonia Nikolaos Evangeliou, Ondřej Tichý, Marit Svendby Otervik, Sabine Eckhardt, Yves Balkanski, and Didier Hauglustaine https://datadryad.org/stash/share/Wgbc9UiXwtMH44366myWh2bt7MQc92JKhJBz7UwQlgY
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