the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Aug 2025
Impact of agricultural interventions on ammonia emissions and on PM2.5 concentrations in the UK: a local and regional modelling study
Abstract. The contribution of agricultural emissions of fine particulate matter (PM2.5) poses significant health and environmental challenges, particularly in the UK where intensive farming activities contribute to elevated pollutant levels. This contribution includes direct emissions and PM2.5 formed through chemical reactions from precursors such as ammonia (NH3). The study aims to analyse the impact of series of mitigation measures through emission scenarios (low, medium, high uptake) on dairy, pig and poultry sectors in 2030 and mainly focusing on NH3 emissions. Under the high uptake scenario, NH3 emissions could decrease by up to 13 % nationally, with reductions reaching as high as 20 % in certain regions. The Community Multiscale Air Quality (CMAQ) and the Atmospheric Dispersion Modelling System (ADMS) models were used. CMAQ allows to understand the contribution made by agricultural NH3 to secondary PM2.5 at a regional scale, while ADMS is used to better understand near-field dispersion and dilution of primary pollutants. Despite the impact of the changes in emissions due to the mitigation measures compared to the future baseline scenario, changes are not reflected on regional scale PM2.5 concentrations since the maximum modelled decrease was around 1–1.5 %. This finding is explained by an NH3-rich atmosphere reducing the impact of these reductions in NH3 emissions on mitigating PM2.5 concentrations. Results from ADMS show that the NH3 and PM2.5 concentrations are quickly dispersed near the farms, highlighting the usefulness of local modelling in addressing impact studies on PM2.5 formation near these sources. Indeed, for the five studied livestock farms, it has been found that 50 % of maximum NH3 and PM2.5 concentrations are located within a distance between 100 and 400 m and up to 90 % of concentrations have decreased within 700 m. The study also demonstrates the complementary use of local and regional modelling in understanding PM2.5 dispersion near agricultural areas. The comparison with ground-based measurements might suggest a non-representation of atmospheric processes in the PM2.5 formation by CMAQ (with an underestimation of PM2.5 concentrations by approximately 50 %). It underscores the need for integrated modelling approaches to guide mitigation strategies for both primary and secondary PM2.5, as well as to improve understanding of the chemical atmospheric processes involved in the secondary inorganic aerosols.
Competing interests: All authors were employed by the company Ricardo Energy & Environment. All authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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Status: final response (author comments only)
- RC1: 'Comment on ar-2025-26', Anonymous Referee #1, 26 Sep 2025
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RC2: 'Comment on ar-2025-26', Anonymous Referee #2, 01 Oct 2025
The area of research encompassed by the title of this paper is of global interest and importance. PM2.5 is the air pollutant with the greatest human health burden, and ammonia emissions contribute to a significant proportion of PM2.5 composition, but are proving hard to abate. It is of interest to scientists and policy-makers to determine the extent to which NH3 emissions – from agriculture in particular – might be feasibly reduced, and the effect of these emissions reductions on PM2.5 concentrations.
Unfortunately, however, despite the important research context, this paper ultimately contains little useful new data or depth of insight. The data also appear to be subject to much uncertainty.
The paper is also not helped by writing that, in my opinion, is not of sufficiently good quality for a journal paper. There is much poor or confusing grammar, and long-winded ways of saying things. Scientifically, I found it hard to follow a lot of the description of the methodology, and I also found it very difficult to extract the points being made from a lot of the text in Section 3.2.
There is also a question of prior publication of a lot of the work. Scientifically, the paper reports independent applications of an atmospheric chemistry transport model (CMAQ) and of a local dispersion model (ADMS), to simulate, respectively, annual mean PM2.5 over the UK and rate of PM2.5 concentration fall-off from 5 individual farms. In both cases, model runs are performed using baseline emissions (for 2030) and scenarios corresponding to low, medium and high NH3 emission mitigation measures being applied to NH3 sources on farms nationally, or to the 5 individual farms specifically. The most novel part of the work overall is the methodology used to derive information about potential extent of uptake of various possible NH3 emission mitigation methods and the conversion of this qualitative scenario data into quantitative changes in actual NH3 emissions from farms. (According to what is written in the present paper, that part of the study involved questionnaires and discussions with farmers and other stakeholders, and use of emissions modelling tools.) However, all the scenario and emissions development is contained in other publications: Jenkin & Wiltshire, and Leonard & Wiltshire. The current paper uses these previously derived emissions and emission factors directly.
As well as the emissions methodology and data being published elsewhere it appears that the CMAQ modelling results have also essentially been published elsewhere. There is another recent paper by Pommier et al. with title “The Impact of Farming Mitigation Measures on Ammonia Concentrations and Nitrogen Deposition in the UK” published in Atmosphere 2025, 16(4), 353; https://doi.org/10.3390/atmos16040353. This other paper already describes (i) the development of the same potential mitigation measures as described in this paper, (ii) the results from the CMAQ modelling of these mitigation measures on particulate NH4+ and PM2.5 concentrations across the UK, and (iii) an explanation for the rather low reductions in the latter two entities simulated by the modelling. Whilst it could be argued that the current paper presents a little more data of the authors’ CMAQ modelling of PM2.5, the descriptions of the model limitations and of the authors’ conclusions on the impact of their NH3 mitigation scenarios on UK PM2.5 concentrations is essentially the same.
In relation to the CMAQ modelling part of this work, Section 3.1.1 of the present paper presents rather poor model simulation of present day (2019) annual mean PM2.5 at rural PM2.5 monitoring sites. The model values underestimate measurements by a factor of two on average, and the spatial correlation across 48 measurement sites is only 0.58, which implies an explanation of variation of only 36%, i.e. well under half of the measured spatial variation is captured by the model. The authors do not describe further diagnostic model-measurement comparisons. I checked the previous Pommier et al. paper referred to above, and found that its Supplementary Material section does contain some other model-measurement comparisons, for NH4+ and for NH3; but the model-measurement comparisons for both these species essentially have no correlation at all. It is surely a concern for a study whose focus is on the link between NH3 emissions and the effect on NH4+ and PM2.5 that the model simulations are so poor. The author state that their model performance statistics “are not fully satisfactory” but then carry on with presentation of the results from this “not fully satisfactory” modelling. The reader requires far more convincing from the authors that data from their CMAQ modelling yields reliable insight.
The one aspect of the current paper that doesn’t appear to have been published elsewhere is the ADMS dispersion modelling of fall-off of NH3 and PM2.5 concentrations in a few km distance from individual farms. As one would expect, the concentrations fall-off rapidly, down to 10% of max concentration within 1000 meters or shorter. However, these results are caveated by the authors with a long list of sources of uncertainty or difficulty with the input emissions data (that are published elsewhere) so again I was left wondering about the reliability of these results. Regardless of the accuracy or otherwise of these dispersion modelling results I was left wondering what would catch the international reader’s attention from the fact that emissions from a point source dilute quite rapidly. Instead, the main message appears to be that more data and research are needed.
Some specific comments:
L46: Sentence starting “Resulting of” is not grammatically correct. It is also not clear to me what the point is that this sentence is making.
L59: Sentence starting “Results confirmed” is not grammatically correct.
L63: The statement here that Ge et al. (2022) suggests NH3 reduction only has minor improvement on PM2.5 in the UK contradicts what the sentence starting in L59 says.
L123: There is reference here and in Appendix A to the investigation of 19 mitigation measures to reduce NH3 emissions, yet Table A1 lists 38 different measures that it is stated were used in each of the three scenarios. To confuse the reader further, Table B1 lists 20 mitigation measures. How many, and which, mitigation measures were actually incorporated in the model emission scenarios?
L162: What is the word “This” at the start of this sentence referring to?
L215, and Figure 2: The explanation of why CO emissions decrease in the agricultural NH3 emission mitigation scenarios is not at all clear. Surely the NH3 emission reductions measures have essentially no impact on CO emissions, or if they do have some impact – such as account being taken of how combustion emissions may be reduced through the process of enacting the NH3 mitigation measures – then surely there would be reductions in NOx emissions (and possibly also in primary PM emissions) alongside the reductions in CO emissions? If there is some form of bias in the modelling of CO emissions, as the text seems to imply, then why weren’t the CO emissions for the mitigation scenarios set to be the same as for the base2030 run?
L231: Simply stating that “filling” was used where data capture was poor is not helpful: what quantitative approach was used for data filling? Also, start a new sentence at “Where”.
L234: This sentence mentions “Each farm…” but nothing has been mentioned about individual farms so far in this paper. What farms? There are positions of some farms marked on a map in Fig 2b but this map is never referred to in the text.
L275-L297: it is very difficult to follow what nature of temporality in emission profiles was actually developed and applied in the ADMS model for which particular sources on each farm.
L275-297: It appears that no seasonality in NH3 emissions were applied to most, or all, of the outdoor sources of NH3, yet surely outdoor NH3 emissions are very temperature, i.e. season, dependent.
L290: The sentence starting “Loafing areas” cannot be understood.
L315: Presumably the authors mean that the value 0.58 they quote here is the ratio between PM2.5 and PM10 concentrations, but they write it the other way around.
L340-L344: The fact that increasing NH3 emissions by 50% doesn’t increase PM2.5 doesn’t automatically imply that present-day NH3 emissions are not limiting to PM2.5. To address that question requires simulating with lower NH3 emissions than present-day NH3 emissions.
L357: I’m sure the authors quote a MRE value here that is too small by a factor of 100. As their NMB value is -51%, I presume there MRE value should read ~-50% here, rather than ~-0.5%. It would not be possible to have NMB and MRE values for the same dataset that differ by a factor 100.
L563: There is an error here, the mean relative error does not have unit of concentration. If expressed as a ratio then it is unitless.
Citation: https://doi.org/10.5194/ar-2025-26-RC2
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This manuscript presents different ammonia emission scenarios’ impact on PM2.5 mitigation through both a chemical-transport model and a dispersion model. A major revision is recommended before acceptance.
Major comments:
Minor comments:
Tabel S1: no SO42-?