the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Decreasing trends of ammonia emissions over Europe seen from remote sensing and inverse modelling
Ondřej Tichý
Sabine Eckhardt
Yves Balkanski
Didier Hauglustaine
Abstract. Ammonia (NH3), a significant precursor of particulate matter, is the most important alkaline gas in the atmosphere and directly affects biodiversity, ecosystems, soil acidification. It also indirectly affects climate and human health. In addition, its concentrations are constantly rising because of the increasing feeding needs of the global population accompanied by a larger use of fertilizers and animal farming. The combination of its increasing atmospheric levels with its environmental and human impact has led many countries to adopt abatement strategies in order to conform with respective regulations. While the significance of ammonia is pronounced, its emissions are often associated with large uncertainties, while its atmospheric abundance is difficult to measure. However, during the last decade, several satellite products have been developed that measure ammonia very effectively, with low uncertainty, and most importantly, with a global coverage. Here, we use satellite observations of column ammonia in combination with an inversion algorithm to derive ammonia emissions with a high resolution over Europe for the period 2013–2020.
Ammonia emissions peak in Northern Europe due to agricultural application and livestock management and the local maxima are found over Western Europe (industrial activity) and over Spain (pig farming). Our calculations show that these emissions have decreased by −26 % since 2013 (from 5431 Gg in 2013 to 3994 Gg in 2020) showing that the abatement strategies adopted by the European Union have been very efficient. The slight increase (+4.4 %) reported in 2015 is also reproduced here and is attributed to some European countries exceeding annual emission targets. Ammonia emissions are low in winter (286 Gg) and peak in summer (563 Gg) and are dominated by the temperature dependent volatilization of ammonia from the soil. The largest emission decreases were observed in Central and Eastern Europe (−38 %) and in Western Europe (−37 %), while smaller decreases were recorded in Northern (−17 %) and Southern Europe (−7.6 %). Our results are associated with relatively low uncertainties reaching a maximum of 42 %; when complemented against independent ground-based observations, modelled concentrations using the posterior emissions showed improved statistics, also following the observed seasonal trends. The posterior emissions presented here also agree well with respective estimates reported in the literature and inferred from different methodologies. These results indicate that the posterior emissions of ammonia calculated with satellite measurements and combined with our adapted inverse modelling framework constitute a robust basis for European NH3 estimates and show the de facto evolution of ammonia emissions since 2013.
- Preprint
(2633 KB) -
Supplement
(19875 KB) - BibTeX
- EndNote
Ondřej Tichý et al.
Status: final response (author comments only)
- RC1: 'Comment on egusphere-2023-641', Anonymous Referee #1, 11 May 2023
-
RC2: 'Comment on egusphere-2023-641', Anonymous Referee #2, 11 May 2023
In their paper Tichy et al present the emissions of ammonia derived from CrIS satellite observations, and present the trend in these emissions over the period 2013-2020. To my judgement major revisions are needed before the paper can be published, as detailed in the comments below.
General comments:
In general the description of the method, inputs, filtering, error modeling are incomplete in the paper, and make it impossible to judge the quality of the results, in particular the reported trends, but also the absolute value of the emissions.
Trends in ammonia are presented without discussing other trace gases, in particular NOx and SO2, which have a significant trend over the past decade and influence ammonia concentrations. No NOx/SO2 trend results are shown in the paper, and the authors do not provide evidence that the model used (LMDZ-OR-INCA) provide a realistic description of trends and interaction with other chemicals and aerosols.
How much does the a-priori emission influence the results? The method description in section 2.4 does not provide the information to judge the influence of the prior compared to the impact of the satellite measurements.
The assumptions/modeling of errors of the satellite data (including filtering), in the method (model uncertainty: chemistry, transport) and a-priori emissions are not described. Section 3.3 discusses uncertainties but is very high level and does not provide the details needed to understand the results and related error bars.
Satellite observations are available once per day, but I assume that the emissions are reported as diurnal mean. What uncertainty does the unknown diurnal cycle introduce?
Detailed comments:
The abstract is long and reads like an introduction, especially the first part. I would propose to shorten it and focus on the actual findings in the paper and new results.
The paper has a good introduction with a balanced set of relevant papers.
The paper uses several units for the emissions (per second, per day, per month, per year). This makes it hard to compare the plots. I would suggest to restrict this to one or two choices.
l 40: "Our results are associated with relatively low uncertainties reaching a maximum of 42%" Which result is this? Is it the trend over a region?
l 47: "constitute a robust basis for European NH3 estimates". What does "robust basis" refer to. Do the authors claim that the monitoring of pollution levels as set by the regulations can be performed based on satellite observations and inverse modeling (only)?
l 47: "de facto". Does this mean that the evolution is based on measurements?
l 87: "Greenhouse Gases Observing Satellite". Please add acronym "GOSAT".
l 95: "using alternation between CrIS ammonia retrievals performed with the logarithm of concentrations and linearized retrievals." Please explain more clearly: what does the "alternation" between log and linear retrievals mean?
l 97: "use direct comparisons between the CrIS observations and model retrievals". What are "model retrievals"?
l 118: "total column random measurement error is estimated in the 10–15% range, with total random errors estimates of ~30%". What is the difference between a "total column random" and a "total random" error? Systematic errors are even more important.
l 121: "due to the limited vertical resolution". Please mention a typical degrees of freedom of signal for CrIS NH3.
l 130: "Daily CrIS ammonia (version 1.6.3) was interpolated onto a 0.5°×0.5° grid ". How is this done? How are measurements and kernels, defined in log space, averaged? It seems to me that this needs to be done with great care, so more details are required to convince the reader of the correctness of the approach.
l 132-135: I got lost with the number of observations mentioned. Why does 10000 observations consist of 2920 retrievals? How can 10000 observations be reduced to 12000? This sounds like an increase.
l 144: For GFED it is clear these are biomass burning emissions. But what is ECLIPSE (which source sectors are included)? Is GFED4 the same as GFEDv4? What sectors does "GEIA" add to the other two emission inventories, and why is GEIA not included in option (i)?
l 148: Please explain the difference between option (iii) and (iv). What is the reason that they differ so much?
Section 2.2: What is the reason why the authors created such an elaborate a-priori emission as a average of four estimates? Is the final a-posteriori emission very sensitive to the a-priori?
Section 2.2: The a-priori emissions show a very large range of values. So it is unlikely that all of them are realistic. Is there an estimate of typical uncertainties for the four priors individually, or some knowledge of biases? Which one is supposedly the most accurate one?
Section 2.3: Please comment on the (non-)linearity of the SRMs. How much do they depend on the accurate knowledge of other species, e.g. NOx, SO2. Trends in NH3 will be influenced by trends in concentrations of such species, determining the loss timescale of NH3. Is this accounted for in the study? Without such information it is impossible to judge the quality of the reported trends.
l 193: "This is a useful technique.." Which technique?
l 204: "iterative minimization of distance". Normally a cost function is introduced with terms describing the distance between model and measurement, and model and a-priori emission, or, alternatively, a regularization to avoid the under-determined ill-posed problem. The way it is introduced here it seems there is no penalty for moving away from the a-priori emission? Please explain more clearly what is done, e.g. by introducing the cost function, and specify all terms in detail, including how error covariances are modelled.
l 222; "however, the bounds are large enough to allow for new sources, as well as for attenuation of
223 old sources. For this reason, the choice of prior emission is of great importance in the method." These two sentences seem to contradict each other. If the range is large enough the impact of the a-priori emission should be negligible!? How does the a-priori emission affect the results? This was not clear to me after looking at all the results presented.l 224: "for some spatiotemporal elements are missing in the dataset." Which percentage of the gridcells is missing on average (on a daily basis)? Please explain the quality filtering for the CrIS data. What quality flag filtering is used here? Does the filtering remove cloud-covered scenes (is the cloud flag used)? I would say that interpolation is very tricky for large areas without observations.
Fig 2, panel a: Please use the same color scale as for Figure 1. It is important to see how much this differs from the combined prior avgEENV.
Fig 2: What does the box plot show? Is it the range of values for the 8 years (8 points)?
Fig 2: Why is there no uncertainty range specified for the posterior emissions in this figure? E.g. in the top-right and bottom-right panels. The uncertainty analysis is presented at the end of the paper.
l 294: "due to bias created by the decrease of NOx and SO2 " This small sentence is the only mention of NOx/SO2 in the whole paper. The impact of these species on the NH3 emission (concentration) trend should be discussed in much more detail. How are the trends in NOx/SO2 accounted for in this work (e.g. including the impact of COVID-19)? What is the evidence that these trends are described in a realistic way?
l 328: "model likely underperforms" Please also comment on the quality of the CrIS data in wintertime Northern Europe. Are there enough constraints from the satellite observations? How much coverage does CrIS provide after filtering?
Fig. 3: "decreases in ammonia" How are the numbers on the figure determined. Is it (2020-2013)/2013, or is it derived from a trend line analysis, or something else?
Fig. S3: (top-left) The order of the trend legends does not match the order of the lines, and a string is missing for central-east. Please update the figure.
l 334: "due to the strong prior that we use there" This is one example where it is difficult for the reader to understand the impact of the prior on the posterior results.
Section 3.3: I propose that uncertainties are discussed before the results, e.g. as part of section 2.
Section 3.3, line 347-356. I could not understand this discussion on how uncertainties are computed. The standard deviation mentioned in line 353 is only valid when the errors in both variables are uncorrelated. Assuming uncorrelated errors in general underestimates the real uncertainty. Equation 5 does not make sense to me, and the variables are not defined (U_elem and u_elem,t).
The discussion in this section is very high level and does not provide any detail. What needs to be added is a quantitative analysis of the various contributions to the uncertainty: the a-priori emission uncertainty, the uncertainty in the model linking satellite concentrations to emission, and the satellite uncertainties. The discussion should also include the systematic contributions to the error. The posterior uncertainties are very low, especially compared to the range of prior values shown in Fig.1, and this does not give me confidence that the error analysis is conducted in a proper way.page 13: How are the comparisons done? Are surface observations available hourly? Are hourly values compared (or daily-mean, or weekly ..)?
I had a hard time linking Fig. 6 with Fig S 7. Figure 6 seems to indicate that at many stations there is a very major improvement in the correlation. But the time series in S7 and the statistical quantities presented do not seem to confirm this. S7 indicates only minor improvements in MAE, RMSE, RMSLE. The difficulty to represent individual measured peaks is not really different in the posterior run, so how can the correlations have improved so much, as indicated in Fig. 6?Citation: https://doi.org/10.5194/egusphere-2023-641-RC2
Ondřej Tichý et al.
Data sets
Decreasing trends of ammonia emissions over Europe seen from remote sensing and inverse modelling Ondřej Tichý, Sabine Eckhardt, Yves Balkanski, Didier Hauglustaine, and Nikolaos Evangeliou https://doi.org/10.5281/zenodo.7646462
Ondřej Tichý et al.
Viewed
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
222 | 81 | 11 | 314 | 24 | 3 | 2 |
- HTML: 222
- PDF: 81
- XML: 11
- Total: 314
- Supplement: 24
- BibTeX: 3
- EndNote: 2
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1