the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Apr 2025
A Framework for Gridded Estimates of Ammonia Emissions from Agriculture in South Asia
Abstract. Emissions of ammonia (NH3) from agricultural activities are a major threat to ecosystems and human health. Its quantification via emissions inventories is vital to the understanding of mitigation strategies and policy formation. South Asia, specifically the South Asian Association for Regional Cooperation (SAARC), is a global hotspot of NH3 emissions from agriculture but also an area of great uncertainty due to a lack of data that are representative of local practices. This study presents a framework into which indigenous data can be ingested to adjust such estimates, to provide spatially distributed (0.1° × 0.1°) emissions in five agricultural sectors for improved input data for atmospheric chemistry transport models, by moving away from Tier 1 methods for emission inventories. Results incorporate data such as lower emission factors of NH3 following the application of Urea (13 % of total nitrogen lost as NH3-N) to provide a total estimated emission of NH3 in the SAARC of ~6 Tg, with high values (> 5 g NH3 m-2 a-1) in the Indian states Haryana, Punjab and Uttar Pradesh in the Indo-Gangetic Plain (IGP).
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Status: open (until 30 Oct 2025)
- RC1: 'Comment on essd-2025-75', Anonymous Referee #1, 13 Aug 2025 reply
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RC2: 'Comment on essd-2025-75', Anonymous Referee #2, 08 Oct 2025
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This is a strong and valuable paper that successfully produces a improved, high-resolution ammonia inventory for South Asia by incorporating crucial regional data. Its major strengths are the application of a lower, region-specific urea EF and the use of a more appropriate N-flow approach for livestock. The main weaknesses are the under-specification of the proposed "framework," the lack of a formal uncertainty analysis, and the relatively high uncertainty in the crop residue sectors. Addressing the major comments, particularly regarding the livestock discrepancy and uncertainty, would significantly enhance the paper's impact. The minor comments are primarily suggestions for improving clarity and rigor.
Major Comments
The paper's title and abstract position it as presenting a "framework" for integrating indigenous data. However, the manuscript reads more like a single implementation of that framework for the year 2015. The "framework" itself is not explicitly detailed as a standalone, reusable methodology. It is embedded within the specific calculations for SAARC.Strengthen the "framework" aspect. Consider adding a flow diagram or a more explicit, generalized description of the data ingestion and adjustment process in the Methods section (Section 2). This would make it clearer how other researchers or inventory compilers could apply the same steps with their own local data.
The adjustment of the urea EF from ~16-17% (EEA, 2023) down to 13% is a major driver of the study's results (a ~20% reduction in total emissions). While this is justified by referencing Bhatia et al. (2023) and IARI (2016), the treatment is somewhat brief.
Provide more context. How many of the 29 South Asian studies from Bhatia et al. were specifically on urea? What was the range of EFs found? A brief discussion on why EFs might be lower in South Asian conditions (e.g., different application practices, soil types, climate) would bolster this critical decision. Furthermore, stating that this adjusted EF was applied uniformly across the domain, despite having pH data, is a significant simplification that deserves a more explicit justification.
The paper identifies a massive discrepancy between its livestock emissions and those in REAS/HTAP, primarily attributed to differences in Typical Animal Mass (TAM). This is a crucial finding.
This point is so critical that it warrants a more in-depth discussion. A simple comparative table showing the TAM, N excretion, and resulting implicit EFs from this study versus the assumptions in EEA (2019)/REAS would be highly effective. This would visually underscore the source of the disparity and strongly support the paper's argument for region-specific parameters.
The paper correctly states that "Estimated uncertainty... was not estimated." For a study aiming to improve upon existing inventories, this is a significant omission. The discussion of uncertainties is qualitative (e.g., "highly uncertain," "large uncertainty").
A more formal, even if semi-quantitative, discussion of uncertainty is needed. The authors could adopt a tiered approach, discussing the likely direction and relative magnitude of uncertainty for key parameters (e.g., ±X% for Activity Data, ±Y% for EFs, especially for crop residues and burning). This would provide readers with a much better understanding of the confidence bounds around the final estimate of ~6 Tg.
Crop Residue Burning (CRB) and Left in Fields (CRR): These sectors are acknowledged to be highly uncertain, and the methods seem less refined than those for synthetic fertilizer and livestock. The use of a single, mean EF for all crop burning and the reliance on the generic Yevich and Logan (2003) FB value for many cases are notable weaknesses.
The discussion here is good, but the methods could be more transparent about the limitations. A clearer summary table in the main text (not just the appendix) showing which country-specific FB values were used and which fell back to the default would be helpful. The conclusion that these sectors are a small part of the total NH₃ budget should be tempered with the statement that they might be a more significant source of other pollutants (PM2.5).
Minor Comments
The text references "equation 1 (Eq. 1)" but the equation itself is not numbered in the provided text. All equations should be clearly numbered.In Section 2.4, Equation 9 is used twice (for E.grz and E.h). This is confusing and should be corrected (e.g., Eq. 9 and Eq. 10).
The description of Eq. 2 is unclear. The term H(E,N) is not well-defined in the text.
Define SAARC at its first occurrence in the abstract.
The acronym "GRM" (Livestock grazing & manure spreading) is introduced in Table 1 but is not defined in the methods section, where the components are treated separately. It should be defined where it first appears in the results.
"Tier 1" methods are mentioned but not briefly explained for a broader audience.
The use of gridded crop data from the year 2000 (EarthStat) for a 2015 inventory is a potential source of error. This should be explicitly acknowledged as a limitation in the discussion, though the scaling to 2015 national totals mitigates this.
The statement that ~103% of total N use was estimated before rescaling suggests the bottom-up method was reasonably accurate; this is a good result and could be emphasized.
Figure 3 and 4 are crucial but are described in the text before they are presented. Ensure the figures are correctly positioned in the final manuscript.
The caption for Figure 2 should more clearly state that the five maps are the spatial distribution for each of the five sectors, and the single legend applies to all.
Page 1, Abstract: "oil acidification" should be "soil acidification."
Page 5: "Azhzar et al., 2019" is likely a typo for "Azhar et al., 2019".
Page 14: "THIS_STUDY_NRU in Figure 2" should probably be "Figure 3".
Citation: https://doi.org/10.5194/essd-2025-75-RC2
Data sets
Gridded emissions of ammonia (NH3) from agricultural sources in South Asia at 0.1 degrees resolution, 2015 S. J. Tomlinson et al. https://doi.org/10.5285/e0114a4f-32c2-41d9-9c2a-c46f365d4c30
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- 1
This manuscript presents a comprehensive and regionally tailored framework for estimating agricultural ammonia (NH₃) emissions in South Asia, an area critical for understanding reactive nitrogen pollution, yet underrepresented in high-resolution emission inventories. The proposed framework uses a hybrid Tier 1/2 methodology, incorporating national and subnational data (e.g., fertilizer use, livestock systems, crop distribution) at a high spatial resolution of 0.1° x 0.1°. The dataset offers significant value due to its spatial granularity, region-specific emission factors (particularly for urea), and transparent methodology. This makes it a promising tool for atmospheric modeling, policy planning, and future updates. However, several issues need to be addressed to ensure that the dataset meets the standards of accessibility, reproducibility, and robustness required by ESSD.
Key Concerns:
Given the manuscript's valuable methodological contribution and the importance of the estimates, major revisions are required to align with ESSD's standards for data accessibility, reproducibility, and rigorous uncertainty quantification.
List of Major Revisions Required:
Specific Comments: