| 12 Dec 2025
Seasonal and interannual variability of atmospheric ammonia over Guatemala driven by land use, biomass burning, and meteorological circulation
Abstract. Ammonia (NH3) is a key atmospheric precursor of fine particulate matter and a marker of agricultural and biomass burning emissions. In Central America, NH3 variability remains largely unquantified. This study presents the first integrated spatiotemporal assessment of atmospheric NH3 over Guatemala (2015–2023) using multi-satellite observations (IASI A/B/C), combined with MODIS fire data, Sentinel-2 land cover, ERA5 meteorology, and CAMS reanalysis. Annual median NH3 columns remained relatively stable, reflecting persistent agricultural sources dominated by fertilizer use and livestock. Significant anomalies occurred in 2016, 2020, and 2023, with 2020 showing the highest annual and monthly NH3 levels. Seasonal peaks in April–May coincided with the regional fire season, followed by a sharp decline after rainfall onset. Hotspots were consistently detected in northern (Petén–Quiché) and southern (Escuintla) agricultural regions. The most extreme episode in April 2020 recorded 957 active fires over ~1,486 km2, largely within the Maya Biosphere Reserve. Elevated temperature (+ 0.3 °C above the 2015–2023 mean) and high precipitation (+ 17 % above average) favored NH3 accumulation despite reduced anthropogenic activity during the COVID-19 lockdown. These results indicate that Guatemala’s NH3 variability is shaped by a stable agricultural baseline with superimposed fire-driven peaks, modulated by climatic anomalies. Continuous satellite monitoring is essential to improve emission inventories and support strategies to reduce biomass burning impacts across Central America.
The topic addressed in this manuscript is relevant, and the study's objective is important. However, I have major concerns regarding conceptual inconsistencies and the clarity of the methodology and discussion. Because of the above, I consider that the manuscript cannot be accepted for publication.
MAJOR GENERAL COMMENTS
A major issue throughout the manuscript is the interchangeable use of the terms NH₃ “emissions”, “concentrations”, and “total column”, which are not equivalent. This conceptual confusion seriously undermines the soundness of the data and the interpretation of the results.
Additionally, the document contains internal inconsistencies that raise concerns about its scientific rigor, making it challenging to evaluate its conclusions.
The manuscript is written in a very repetitive style, where similar ideas are repeated both within paragraphs and across sections where they do not belong. Furthermore, the manuscript does not follow the standard structure typically found in scientific writing. These patterns remind me of other manuscripts written using extensive AI assistance. While I do not believe the use of AI is necessarily a reason for rejection, the authors should clearly describe the extent to which AI was used for data retrieval, analysis, interpretation, and writing.
Overall, the manuscript would greatly benefit from a revision that addresses the issues outlined above. Some specific examples of these issues are detailed below.
SPECIFIC MAJOR COMMENTS
Section 2.1 (Data Analysis). This section is an example of not following the common manuscript structure, as it is a summarized narrative of the methodology, mixed with results and conclusions. This type of section is not usually found. Furthermore, results and conclusions are not supposed to be included in a Methodology section.
Section 2.3 (ESRI Land Cover Class Map). “The… LUC dataset… derived from Sentinel-2 satellite imagery…” and “The LULC … dataset, based on data from the European Space Agency’s Sentinel-2 mission”. The above sentences are repetitive and appear in the same paragraph: This is only one example of repetitive writing found throughout the manuscript; there are several more.
Figure 1. Regions mentioned in the text, such as Petén and Quiché, should be indicated on the map to help readers follow the discussion more easily.”
L150 “Atmospheric total column measurements of NH3 were obtained…”. L184 “To identify spatial patterns in atmospheric NH3 emissions…”. Both NH3 observations (total column and emissions) are mentioned in section 2.4, but only the retrieval of the NH3 total column is described. Regarding NH3 emissions, either the authors are using this term as equivalent total column, or they are omitting how they obtained the total column. In the rest of the document, “total column”, “emissions”, and “concentration” are used interchangeably.
L216 “Correlation analyses between these variables and atmospheric NH3 concentrations were performed to better understand the meteorological drivers influencing NH3 variability across Guatemala.” The “Pearson correlation analysis” is also mentioned in L109; however, the manuscript presents no quantitative correlation analysis. There is only a qualitative comparison between the meteorological conditions and the NH3 data described in the following sentence: L407 “… which points to a general inverse relationship where higher NH3 concentrations tend to coincide with lower PBLH values, and vice versa.“
The following paragraph is internally inconsistent (L273 - L280): “The concatenated data from IASI A/B/C data of spatial distribution of annual mean of NH₃ emissions…” “A consistent spatial pattern of elevated NH₃ concentrations was observed…”
L273 “The concatenated data from IASI A/B/C data of spatial distribution of annual mean of NH3 emissions across Guatemala from 2015 to 2023 exhibited a consistent pattern of elevated emissions in the northern and southern regions. A consistent spatial pattern of elevated NH3 concentrations was observed, with discernible hotspots in the northern, north-central, and southern regions of the country. Conversely, the central midlands and highlands consistently displayed lower emission rates throughout the period." Please define terms such as “northern and southern regions”, “north-central and southern regions“, and “north-central and southern regions“; otherwise, it is difficult to corroborate the description in the text. For example, I do not see the “consistently elevated NH3 concentrations in the northern regions” that is mentioned. Also, “NH3 concentrations” are mentioned, but Figure 2 declares to show only “NH3 emissions”. In addition, the methodology section never mentions how the concentrations are determined. It seems, then, that both terms are being incorrectly used interchangeably.
Figure 5. What are the boxplots showing? FRP or fire counts? Units in the vertical axis are missing.
L419 “a monthly means detected by IASI A (2.32 × 1016 molecules/cm2), B (2.48 × 1016 molecules/cm2) and C (2.43 × 1016 molecules/cm2). “ Why are the three values shown? Everywhere else in the manuscript, only one value (I guess it is the average of the three) is shown.
Figure 9. Units on the color scale are inconsistent: ppmv (unit of concentration) is not equivalent to molecules/cm2 (units of total column).
L466: “April 2020 recorded the highest number of active fires across Guatemala throughout the entire study period (957), representing…” The reported number of fires in April 2020 (957) does not match Figure 10a, which shows only 85, approximately.
L474: The term “climatological peak” is illogical, as April 2020 represents an exceptional event.
L475: ”This case study demonstrates that April 2020 was… an expanded spatial footprint compared to other years.” There is no data demonstrating how April 2020 compares to other years.
Figure 11. All the discussion in this section is about April of 2020 as a case study, so why is April combined with May data in this figure?
Section 4 (Discussion) The Discussion section mainly repeats the Results and provides no additional interpretation or new insights. A substantially shorter version of this section could be included as the summary required by ACP in the Conclusions section.
OTHER COMMENTS
L23. The statement “Continuous satellite monitoring is essential to … support strategies to reduce biomass burning” seems unrealistic and is not supported by the analysis shown in the manuscript.
L87 and L141 (maybe more lines). “Environmental Systems Research Institute (ESRI)” The ESRI acronym was defined on line 87, so it does not need to be defined again on line 141. In fact, once the acronym is defined, the full name should not be used again. There are several other cases similar to this in which the acronyms are not used correctly (i.e. next comment).
L249 “Moderate Resolution Imaging Spectroradiometer (MODIS)” The acronym MODIS is defined in L249, but it is used several times before this line.
L261 “The respective boxplots reveal a generally stable NH3 concentration throughout the study period.” L269 “… indicating substantial variability and peak emissions.” Both sentences in the same paragraph are contradictory.
L278 - L281. The information was already stated in the previous paragraph.