Facility-scale quantification and monitoring of ammonia (NH<sub>3</sub>) emissions using ASTER multispectral thermal infrared observations

Tijhuis, Lidewij Beatrijs; Wizenberg, Tyler; Dammers, Enrico

doi:10.5194/egusphere-2026-2893

Preprints

https://doi.org/10.5194/egusphere-2026-2893

Preprints

16 Jun 2026

| 16 Jun 2026

Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Facility-scale quantification and monitoring of ammonia (NH₃) emissions using ASTER multispectral thermal infrared observations

Lidewij Beatrijs Tijhuis, Tyler Wizenberg, and Enrico Dammers

Abstract. Ammonia (NH₃) is an important atmospheric pollutant affecting air quality, ecosystems, and climate, but current satellite observations remain limited in their ability to resolve individual emission sources. Hyperspectral thermal infrared sounders such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-track Infrared Sounder (CrIS) provide broad spatial coverage and high spectral sensitivity, but their kilometer‑scale footprints limit direct facility‑scale source attribution. Here, we investigate whether high‑spatial‑resolution multispectral thermal infrared imaging can detect NH₃plumes at facility scale.

We develop a physically based retrieval framework for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), combining radiative transfer calculations with lookup table inversion. The method exploits the differential sensitivity of ASTER bands 13 and 14 to NH₃absorption in the ν₂ band near 930–970 cm⁻¹ and retrieves NH₃column enhancements at 90 m spatial resolution. Surface emissivity is taken from a long‑term ASTER emissivity climatology, while scene‑level emissivity products are used diagnostically to identify plume‑related band behavior. Sensitivity tests show that NH₃ absorption remains measurable after convolution with the ASTER spectral response functions, but retrieval performance depends strongly on thermal contrast between the surface and the NH₃‑bearing layer.

The retrieval is applied to ASTER observations over three industrial NH₃point sources: Khor Al Zubair, Tolyatti, and Piesteritz. Khor Al Zubair provides the clearest demonstration, with repeated source‑connected plume structures under favorable arid conditions. Tolyatti and Piesteritz show that detection is also possible in more heterogeneous environments, although only under suitable thermal contrast and surface conditions. Source‑rate estimates derived with the Integrated Mass Enhancement method are interpreted as instantaneous effective estimates for successful plume scenes, not as annual mean emissions or continuous facility‑average emissions. Where independent constraints are available, ASTER‑derived source‑rate statistics are consistent in magnitude with published satellite and airborne estimates.

These results demonstrate that multispectral thermal infrared imagers can provide high‑resolution information on NH₃plume structure and source location, complementing coarse‑resolution hyperspectral satellite observations. The approach is best suited for large, persistent sources and episodic plume mapping rather than routine monitoring, because ASTER sampling is limited by revisit frequency, cloud cover, and thermal contrast. The framework supports retrospective analysis of archival ASTER scenes and informs future high‑resolution thermal infrared imaging concepts for NH₃point‑source detection.

Received: 19 May 2026 – Discussion started: 16 Jun 2026

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Lidewij Beatrijs Tijhuis, Tyler Wizenberg, and Enrico Dammers

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RC1: 'Comment on egusphere-2026-2893', Anonymous Referee #1, 25 Jun 2026 reply

This paper reports the first NH3 satellite measurements made from two broadband infrared channels. The authors show that, even though the detection threshold of ASTER is high, under favourable conditions NH3 plumes from industrial facilities can be measured and quantified. The spatial resolution of ASTER is a key element here, as it can probe within-plume airmasses undiluted (unlike CrIS or IASI), pushing the ammonia concentrations above the detection threshold. The results of this paper are very important for the design of future NH3 satellite instruments and the monitoring of mega-emitters.
The science in the paper is of high quality, robust and sound, and I have mainly minor comments and suggestions (see below). My main comment however, and the reason why I recommended major revision, is one of style. Overall, the paper is very repetitive and therefore unnecessarily long. For a reader it is difficult to maintain focus when the same points are repeated over and over; this ultimately dilutes the message rather than reinforcing it. As an example, the first page of the Discussion contains no new information, and is just repeating what was said before, and it is then repeated again in the conclusion. Discussion + conclusion can probably be covered in ~1 page. Some examples of near-identical repetitions are given below. I estimate the paper could be cut by roughly a third without any loss of content, with a gain in clarity and impact.
1. The sentence on effective emissions (x10, even without counting the figure captions)

Lines 16-18: "source-rate estimates derived with the Integrated Mass Enhancement method are interpreted as instantaneous effective estimates for successful plume scenes, not as annual mean emissions or continuous facility-average emissions"

Lines 204-205: "the derived source rates should be interpreted as effective emissions over the observed plume extent rather than as total emissions under all transport conditions"

Lines 344–345: "In contrast, the ASTER values reported here are successful-scene instantaneous IME estimates and should not be interpreted as annual mean emissions"

Lines 374–375: "The source-rate statistics reported here describe the distribution of instantaneous source-rate estimates under favorable observing conditions and should not be interpreted as annual mean emissions."

Lines 377–379: "The successful-scene mean and interquartile range should therefore be interpreted as descriptive statistics of ASTER plume snapshots rather than as a long-term facility-average emission estimate."

Lines 392–393: "The resulting values should therefore be interpreted as effective source rates for the observed plume extent, not as continuous annual emissions."

Lines 407–408: "The resulting source-rate statistics should be interpreted as successful-scene instantaneous estimates, not as annual mean emissions."

Lines 427–428: "This value should be interpreted as an instantaneous successful-scene statistic rather than an annual mean emission."

Lines 490–491: "The source rates reported here should therefore be interpreted as instantaneous effective source-rate estimates for the observed plume extent, not as annual mean emissions or continuous facility-average emissions."

Lines 539–540: "The successful-scene means reported here should therefore not be interpreted as annual mean emissions."

2. On the TC

Lines 153–154, 275, 283-284, 303-304, 454-455, 528
3. ERA5 winds

Lines 218–222, 357–360, 483–486 + the caption of figure 8
4. Comparison with published estimates is not validation

Lines 338-339, 385-386, 443-445, 466 + the caption of table 3
5. The band-13/band-14 setup is re-explained six–seven times.

Lines 56-57, 85-88, 92-93, 255-256, 263-265, 448-450, 524-526
etc..
Minor comments:

Line 9: space missing after "resolution."

Line 33: "Emission inventories .. exhibit strong temporal" I think you mean the inventories fail to capture the strong temporal variability, rather than that they exhibit it.

Figure 1: please increase the font size in these figures (especially the numbers)

Figure 2: Specie should be species (always plural). I would not put the TIR bands in the legend, as they are indicated on the figure. This would allow making the figure a bit wider and easier to read.

Line 101: "This can introduce band-correlated variability in emissivity and radiance fields." I would add retrieved emissivity (since emissivity is a property of a surface not from the measurement)

Line 104: Remove the last sentence (implied above)

Line 149: perhaps mention the lapse rate that was used

Line 169/327: No mention is made in the paper of the water vapour continuum, which is apart from clouds and emissivity the next largest contributor to changes in the baseline slope. Did the authors run simulations at different levels of humidity?

Section 3.1.1 and 3.1.2: These sections do not bring a lot of new information. Figure 6 and Figure 7 would probably be enough for the discussion on detectability/TC

Line 266: "This is a general limitation of ASTER TIR...". This is not specific to ASTER, the retrieval sensitivity to TC is general to all IR sounders (e.g. https://doi.org/10.34133/remotesensing.0142 )

Figure 6: are the units correct? As a ratio, is should be dimensionless. I would also add a contour at sqrt(2)sigma/B14, with sigma the radiance noise and B14 calculated at some temperature. This would delimit the detectable TC/NH3 region nicely.

Line 410: IASI box-model estimate used a lifetime of 12 hours, but this can easily be converted to the more realistic lifetime of 2-3 hours, giving 36 kt, well-aligned with the ASTER value. An advantage of ASTER and other high spatial resolution sounders is that they do not require an estimate of the chemical lifetime (I do not think this is mentioned in the manuscript).

General: Are all three point sources associated with the production of fertilizers? Did you have a look for NH3 emissions from livestock housings/feedlots? It would be good to address both questions in the revised manuscript.

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Citation: https://doi.org/10.5194/egusphere-2026-2893-RC1
RC2:
'Comment on egusphere-2026-2893', Anonymous Referee #2, 06 Jul 2026 reply
This study by Tijhuis et al. demonstrates the use of a multispectral thermal infrared imaging instrument, ASTER, to map facility-scale ammonia emissions. Using the high-resolution radiative transfer model TFIT and the ASTER spectral response functions, the authors construct a lookup table (LUT) of ASTER-simulated radiances, varying the ammonia column, surface emissivity, air temperature, and surface temperature. Given a multi-year mean ASTER-derived emissivity product, a reanalysis air temperature, and the ASTER-retrieved surface temperature, the ammonia column is taken from the LUT entry that produces a band ratio (L13-L14/L14) closest to that of the observations (which are corrected with an RMA regression using the LUT radiances on a scene-by-scene basis). These retrievals are carried out over three industrial sites, followed by emission retrieval via the IME method. The authors are cautious with their emission estimates, for example because they are dependent on an assumed ammonia vertical distribution. This is a nice study, and, to my knowledge, the first demonstration of ammonia retrievals for this class of instrument, which serves as a nice contribution to the field. Though I strongly encourage the authors to edit the paper for conciseness and clarity throughout, my primary comments as follows are minor and for the authors to consider at their discretion.
General comment:
From the perspective of emissions monitoring… how well known is the thermal contrast considered to be? This would be of relevance for interpreting the scenes where ammonia is not seen (e.g., in the “no Q” scenes of Figure 9). For these, is there a class of scenes where the thermal contrast and thus sensitivity is believed to be high such that the scene can be confidently labeled a null detect? Would the studied sources be expected to be intermittent?

Specific comments:
In the scene-based radiance correction described in section 2.3.2, which LUT-simulated radiances are selected to match with the observed radiances?

Equation 5: where does knowledge of the near-surface air temperature come from? Is the ammonia column the only free parameter?

Line 214: what do you use for L in the IME equation?

Line 225: are there specific potential co-emitted species to be concerned about with the sources studied here?

Line 229: if it is known, consider saying what types of industrial processes are occurring at each of these sources

Table 2: why are the total number of scenes less than would be expected for a 25-year data record with the stated 16-day revisit time (line 76)?

Figure 6: can you add an explanation of the contour behavior on the left side of the plot (at less than 1e17 molecules/cm2 of NH3)? Also, is this not unitless?

Line 435: why use only the Spring estimates from Noppen et al. (2023)?

Technical corrections:
Line 9: missing space before “Surface”

Line 47: please check that Varon et al. (2019) and Cusworth et al. (2022) are appropriate references here for the use of Sentinel-2 data

Line 57: please check the wavenumber to wavelength conversion for 970 cm-1

Figure 1: subplots are inconsistent with source figures in the manuscript (e.g., inset emission rate in the middle subplot; mean emission rate in the right subplot)

Figure 2: “Specie” to “Species”

Figure 3: please check that the magnitude of radiance values is correct given the stated units

Table 1: consider removing TC as it is not independently varied

Line 200: I believe this is an unintended sentence break

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Citation: https://doi.org/10.5194/egusphere-2026-2893-RC2

Lidewij Beatrijs Tijhuis, Tyler Wizenberg, and Enrico Dammers

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Latest update: 06 Jul 2026

Short summary

Ammonia is an air pollutant that affects human health, ecosystems, and climate, but satellite measurements usually cannot identify emissions from individual facilities. We show that high‑resolution thermal infrared images can detect ammonia plumes at a much finer scale than existing satellites. This enables clearer identification of industrial sources and provides new opportunities for improved emission estimates and the design of future satellite missions.

Facility-scale quantification and monitoring of ammonia (NH3) emissions using ASTER multispectral thermal infrared observations

Facility-scale quantification and monitoring of ammonia (NH₃) emissions using ASTER multispectral thermal infrared observations