| 22 Jul 2026
CO emissions and NOx/CO ratios from the extreme 2025 Iberian wildfires: TROPOMI-based estimates and comparison with inventories
Abstract. In summer 2025 the Iberian Peninsula experienced exceptional wildfire activity, with nearly 0.5 Mha of land burned and carbon emissions that broke European records since 2003. In this work, we quantify CO emission rates from 15 wildfires in northwestern Spain and northern Portugal during August 2025 using satellite observations from the Tropospheric Monitoring Instrument (TROPOMI). Through a plume masking method that exploits the synergy between collocated NO2 and CO TROPOMI observations in complex multi-fire scenes, we infer CO emission rates for 46 individual hotspots. The area studied represents about 70 % of the total burned area in the Iberian Peninsula during that period. The inferred TROPOMI-based emission rates are compared against three fire emission inventories: the Global Fire Assimilation System (GFAS), the Global Fire Emissions Database (GFED) and the Fire Inventory from NCAR (FINN). Our results indicate that inventories systematically underestimate CO emissions by about a factor of 3 relative to TROPOMI-derived estimates, consistent with findings from previous studies on other extreme wildfire events. Additionally, we also investigate the NOx/CO mole density ratio (MDR; NOx = NO + NO2) as a proxy for combustion efficiency, finding that TROPOMI-derived MDR values are consistently lower than those from inventories, with regional variability suggesting differences in combustion phase and fuel type across the study domain. These results highlight the potential of combined satellite observations to assess wildfire emissions in complex fire scenes, while also pointing to limitations in current fire emission inventories under such conditions.
This paper addresses the 2025 fires at the Iberian Peninsula using TROPOMI CO and NO2 data.
The main development presented in the paper is the “synergy” method to quantify CO and NO2 emissions from individual fires. These top-down estimates are compared to bottom-up estimates from GFAS, GFED, and FINN.
A main finding is that the CO emissions from the top-down method are much higher (3x) than the bottom-up estimates. Concerning the NOx/CO mole density ratio (MDR: NOx emissions relative to CO emissions: a metric for combustion efficiency), the paper finds variability in time and space, which is sometimes consistent with bottom-up estimates.
Overall, the paper presents a new “satellite data only” methodology that holds some promise to independently quantify wildfire emissions in complex fire scenes.
The paper is well written and clearly presents the results. I have a number of small comments, which I add as annotated manuscript and supplement. I have some major remarks that could improve the quality of the manuscript further. These points are listed below.
1. Satellite data quality
TROPOMI columns are derived from fingerprints in the absorbed spectrum (UV/vis for NO2, Short-wave IR for CO). Specifically, for NO2, the sensitivity to near-surface NO2 depends on the amount of scattering. Specifically, in the quoted Wang et al. (2026) paper it is written: “ However, due to the lack of explicit fire-related a priori information in current satellite NO2 retrieval algorithms, the resulting data products exhibit large uncertainties under fire conditions.”, and “Retrieved tropospheric NO2 VCDs increase by up to 100 % at locations greatly impacted by fires and by about 80 % in surrounding areas. “. I am a bit surpised that this source is error is not discussed at all. These errors will both affect the plume mask and the MDR. For CO TROPOMI, errors related to fire scenes are likely smaller, because of the vertically flat averaging kernel and limited effects of aerosol on the CO retrieval.
2. Plume masking algorithm
The main issue in plume masking is to determine the CO and NOx added to the background. Thus, a background is proposed (section 4.1.1). However, in the case of overlapping plumes, the background might be influenced by upwind plumes (which is OK, because you want to analyse the amount that is added by the new plume). Although this becomes clear from the paper (e.g. figure 5) and the supplement, it would be worthwhile to explain this better in the manuscript. Moreover, in case of an upwind plume, the size of the background (0.8 degree by 0.6 degree) will encapsulate likely only part of the upwind plume and hence lead to uncertainty. Since the “synergy” methods is central to the paper, it would be helpful to present a more elaborate error analysis for background selection (now figure 3 presents a plume-free background, while figure 5 presents a more complicated case). Concerning figure 5 and potential biases, it remains a bit unclear how the “total” result from the “standard threshold” method differs from the newly developed “synergy” method. Mentioning these numbers would be valuable information.
In the Supplement additional information is provided. However, the “standard”, “threshold”, and “synergy” methods are not very clearly explained. It would be good to start with the “standard threshold” method (e.g. used for NO2) and then explain how the synergy method applies the NO2 mask to CO satellite data, i.e. define the “standard threshold” method as standard.
3. Plume chemistry
CO is relatively long lived, and emissions are estimated using the IME method. For NOx the application of the IME method is questionable. Apart from the NO (emitted) to NO2 conversion, also the lifetime of NOx (~ hours) might bias the IME method. In line 536 “plume chemistry” is explicitly mentioned, so it is important to quantify potential errors due to (lack of) chemistry.