The IASI-PCA-GE method was applied to several volcanic eruptions. Here, we illustrate the findings for the eruption in Ubinas, Peru, on 20 July 2019 (Venzke, 2019). The Instituto Geofísico del Perú (IGP; Geophysics Institute of Peru) mentioned that seismic activity suddenly increased during June 2019 and remained high during July 2019, with important ash emissions causing the evacuation of the population in some areas affected by ashfall. Figure 4 illustrates the normalized GMI pseudo-residual obtained during this volcanic eruption, which corresponds to a granule taken in the area of the plume during daytime. A large difference between the reconstructed spectra and raw spectra is seen in the SO2 ν3 band around ∼ 1371 cm-1 and ∼ 1377 cm-1, which is in agreement with results of Clarisse et al. (2008, 2012) showing the sensitivity of the ν3 band. Indeed, the peak found at 1371.50 cm-1 is associated with the presence of the SO2 plume in the upper troposphere/lower stratosphere (∼ 14 km; 150 hPa) between 0.5 and 200 DU (saturation; Clarisse et al., 2011). Such detection is expected in this case due to the high quantity of SO2 emitted. It is worth noting that other peaks in the GMI pseudo-residual also show strong absorptions, which were initially associated with HNO3. Even if this constituent has previously been reported in volcanic plumes in some active degassing volcanoes (Mather et al., 2004), peaking in the GMI at ∼ 763, ∼ 879 and ∼ 897, and ∼ 1326 cm-1 associated with ν8, ν5, 2ν9, ν3, and ν2 nitric acid absorption bands, respectively, it has never been observed by remote sensing before. As the analysis of the IASI HNO3 L2 products shows no HNO3 enhancement, further investigations were performed to identify where the signature comes from.

The HNO3 detection by the IASI-PCA-GE method was further investigated by applying the whitening method proposed by De Longueville et al. (2021). The use of a covariance matrix, calculated from a set of IASI spectra, shows similar results to those found with the IASI-PCA-GE method. However, using a covariance matrix excluding the SO2 absorption band, no HNO3 spectral feature was found. This suggests that no nitric acid is present in the plume. The features found in the HNO3 absorption band by the IASI-PCA-GE method is likely related to SO2 features, given that the SO2 ν3 absorption band superimposes with the HNO3 ν3 band.

Furthermore, other spectral signatures remain difficult to characterize in the 1200–1300 cm-1 spectral domain. This spectral range corresponds to the absorption of different volcanic compounds such as ash, aerosols, and other possible volcanic molecules such as H2S or H2SO4 (Karagulian et al., 2010) but is also sensitive to strong H2O absorptions.

After applying the threshold filters defined in Sect. 4.2 to the GMI pseudo-residual, the spatial distribution of the pixels associated with outliers can be mapped. Figure 5 shows a plume of SO2 (left) in southeastern South America, with large signal intensity values reaching around -150 in the center of the plume. The spatial distribution of the retrieved IASI SO2 L2 operational products (right) also shows the plume located in southeastern South America and is in excellent agreement with the SO2 plume detected from the IASI-PCA-GE method.

The time series of the SO2 detections derived from the IASI-PCA-GE method is applied to the IASI/Metop-B global dataset over the 2013–2022 period. Figure 6 shows the comparison of the SO2 IASI-PCA-GE signal intensity with the SO2 hyperspectral range indexes (HRIs) product at 5 km (Bauduin et al., 2016). HRIs at 5 km are chosen because of a good sensitivity around this altitude (Clarisse et al., 2014), compared to the L2 SO2 concentration data that show concentrations above 5 km (likely high-intensity volcanism). Only daily SO2 extrema of both the IASI-PCA-GE method and HRI product are compared. They are spatially co-located and associated with documented volcanic events from the Global Volcanism Program, Smithsonian Institution (https://volcano.si.edu/, last access: 19 April 2023). It is observed that both methods are able to detect not only intense eruptions but also moderate or degassing volcanic events. The largest volcanic eruptions detected during this period for both methods are Calbuco on 22 April 2015, Raikoke on 22 June 2019, and Ubinas on 19 July 2019 (Sennert, 2015, 2019a, b). Furthermore, for all major events (corresponding to 2810 d over 3373 d in total), an excellent correlation between HRIs and IASI-PCA-GE signal intensity (R2 = 0.96) is found between the two datasets.

In order to analyze and understand the differences between the two records, the correlation between the latitudes of both datasets shown in Fig. 6 are plotted (see Fig. 7). An excellent location correlation between both HRI and IASI-PCA-GE methods is observed for high-intensity detections. However, some discrepancies are found in the case of low-intensity events, corresponding to commonly active degassing volcanoes.

Some specific latitudes associated with degassing volcanoes, such as Sabancaya (Moussallam et al., 2017), the Vanuatu island arc with Ambae (Bani et al., 2012), Colima and Popocatepetl in Mexico (Varley and Taran, 2003), and the long eruptive Kīlauea volcano (Garcia et al., 2021), respectively, at 15.8∘ S, 15.4∘ S, 19.5∘ N, 19.0∘ N, and 19.4∘ N are illustrated by the horizontal and vertical dashed black lines in Fig. 7. Furthermore, some daily maxima are located around 38∘ S, 37.5∘ N, and 25.2∘ N and are, respectively, related to emissions from Copahue (Reath et al., 2019), Etna (Tamburello et al., 2013; Ganci et al., 2012), and several Chilean volcanoes.

The daily maxima located around 56∘ N have been investigated and found to be associated with Kamchatka degassing volcanoes. Disperse latitudes of IASI-PCA-GE daily maxima are not consistent with the co-registered HRI maxima. These differences between the IASI-PCA-GE and HRI methods can also be explained by the relation between the plume altitude/temperature not being represented in the principal components that will also affect the spectral reconstruction. As a result, the location of daily maxima can be different in the case of low-intensity detections because of the PCA overestimation (or underestimation) of atmospheric anomalies. This also results from the nonlinear relationship between retrieved concentrations and PCA intensities.

It is interesting to note that both IASI-PCA-GE and HRI detections observed at around 30 and 65∘ N are associated with anthropogenic emissions in the region of the Sarcheshmeh Copper Complex, one of the largest industrial mining complexes for copper that is emitting about 789.9 t of SO2 per day (Amirtaimoori et al., 2014), and over the Norilsk city, which is also well known for its mining and smelting industries (Bauduin et al., 2016). That finding illustrates the capacity of both methods to detect industrial emissions.

It is found that the relation between the concentration and signal intensity is not linear, and the PCA-based results cannot be used for an accurate quantification of SO2 concentrations. Indeed, IASI-PCA-GE signals will not only be dependent on the molecule concentration but also on thermal contrast and other surface parameters and atmospheric conditions. This is why discrepancies are found at high latitudes between the location of IASI-PCA-GE and HRI maxima, which are associated with eruptions in the Kamchatka region.

In Australia, fire events known as bushfires occur every year. Coupled with global warming and the lack of rainfall in 2019–2020, the fires were particularly intense, with burned areas covering more than 186 000 km2. It was shown that pyroconvection allowed the plume to reach the lower stratosphere at around 15–16 km (Khaykin et al., 2020). Many species were observed by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) during that episode (e.g., Boone et al., 2020), including CO, C2H6, C2H2, HCN, HCOOH, CH3OH, PAN, acetone (CH3COCH3), and CH3CN.

The IASI-PCA-GE method was applied to the IASI/Metop-B L1C data on 1 January 2020. Figure 8 illustrates an example of a normalized GMI pseudo-residual obtained during the Australia fire event. As expected, peaks relative to the CO absorption lines are found in the 2050–2200 cm-1 spectral domain. Other peaks associated with the absorption of molecules are also visible, including HCN, with a peak at 712.50 cm-1, furan (C4H4O) at 744.50 cm-1, C2H2 at 729.50 cm-1, C2H4 at 949.25 cm-1, HCOOH at 1105.00 and 1777.00 cm-1, CH3OH at 1033.50 cm-1, and peaks associated with NH3 at 931.00 and 967.00 cm-1.

Figure 9 (left column) shows the spatial distribution of the residual values associated with the detected species in the GMI pseudo-residual. Despite their different lifetimes, the plumes for the different species are located in the same region (around 180∘ E in the Pacific Ocean).

Carbon monoxide is retrieved in near-real time (George et al., 2009) from IASI L1C and is used for monitoring fires (Turquety et al., 2009). In Fig. 9, CO is observed with both the IASI-PCA-GE and the L2 retrieval methods. However, some discrepancies are found in terms of location and intensity. A few pixels are detected by the IASI-PCA-GE method in the southeast of Australia, which is in agreement with the CO operational L2 product. However, the retrieval method is able to detect a larger plume over Australia compared to the IASI-PCA-GE method. Furthermore, a large plume is also detected over the Pacific Ocean but is missed by the IASI-PCA-GE method. Note that the high-intensity CO peaks are clearly detected in the residuals (see Fig. 10). However, most of the missing pixels in the PCA detection results are located above the sea. That could be due to the combination of the database chosen in the PCA method and the high variability in this spectral domain. Indeed, a higher thermal contrast variability is observed above land (Clerbaux et al., 2009), but the database contains spectra representing the natural variability without differentiating between sea and land pixels. As a result, the spectral reconstruction above the sea with the PCA method will be less sensitive to spectral variations, causing a reduced sensitivity above the sea. Furthermore, the spectral region between 2050 and 2200 cm-1 has shown a large statistical distribution of extrema signals within the 21 500 granules used for threshold calculation in Sect. 4.2, allowing us to set a restrictive threshold for the outlier detection for CO. That restriction will also impact the number of detected pixels. The sensitivity of PCA reconstruction outliers to strong CO concentrations in fires should be more deeply investigated in further studies.

NH3 is also retrieved in near-real time (Van Damme et al., 2017) and observed with a low concentration and occurrence above Australia on 1 January 2020 in the L2 retrievals and with a low signal and occurrence in the IASI-PCA-GE method. Some pixels are detected by the IASI-PCA-GE method but are not spatially correlated with the NH3 total column L2 data. A less frequent detection of NH3 is expected since only low-intensity peaks of NH3 are found in the GMI pseudo-residual, but two plumes are observed above both land and sea, while L2 retrievals only show many isolated pixels.

However, for other indicators, the size of the plume differs; large plumes are found for C2H2, C2H4, and HCOOH, while smaller plumes are found for HCN, C4H4O, and CH3OH. Those differences can be explained by the difference between both methods. Indeed, the column maps include the effects of radiative transfer (thermal contrast in particular), and the presence of clouds can also induce differences between both products, as the retrievals are highly sensitive to clouds. For the IASI-PCA-GE method, the sensitivity to molecule detection highly depends on the selection of spectra to construct the database and the thresholds chosen for the detection.

Figure 10 illustrates the time series of the ethylene detections from the IASI-PCA-GE method, based on the IASI/Metop-B L1C data for the 2013–2022 period. C2H4 is a weak absorber often detected at 949.25 cm-1 in the case of high-intensity fires and is able to show many high-intensity peaks attributed to fire events. In the figure, the most intense fires are characterized by their location (names indicated in black in Fig. 10). The presence of fires was validated by comparing C2H4 detection to the IASI L2 CO that is shown to be a good fire tracker (Logan et al., 1981). The seasonality of fires clearly appears during summer in the Northern Hemisphere and is mainly related to fires in Canada and Russia and during summer in the Southern Hemisphere with annual Australian and Indonesian fires. One of the largest detections of the 2013–2022 period is associated with the 2019–2020 Australian bushfires discussed in Sect. 5.2.1. Note that the highest C2H4 intensity, observed on 29 July 2021 with a signal of 56, could not be associated with biomass burning, as no other indicators are present in the PCA residuals. The source of this C2H4 enhancement is likely linked to anthropogenic activities, in addition to some other maxima, which are all located in Iran near the Iraqi border. This will be further discussed in Sect. 5.3.3.

Boynard et al. (2014) investigated a severe pollution episode that occurred in the North China Plain in January 2013. The episode was caused by the presence of anthropogenic emissions combined with low wind speed and low-altitude boundary layer, leading to the weak mixing and dispersion of pollutants. The ability of IASI to detect high concentrations of trace gases such as CO, SO2, NH3, and ammonium sulfate aerosol ((NH4)2SO4) during the nighttime was demonstrated in the case of large negative thermal contrasts related to the winter season and coal burning in China for domestic heating. The IASI-PCA-GE method was applied on 13 January 2013 during the nighttime. The normalized GMA pseudo-residual obtained during China's anthropogenic pollution is illustrated in Fig. 11. In order to optimize the sensitivity of the method for a low-intensity event, the F2 thresholds were defined as F2=5 for both day- and nighttime conditions for the three species of interest (CO, NH3, and SO2). We clearly see a signal associated with CO, NH3, and SO2 spectral emissions, with the largest signal for SO2 (value reaching ∼ 18). The detection of SO2 around ∼ 1345 cm-1 is less frequent compared to a similar detection of SO2 during volcanic eruptions. This result suggests that the SO2 absorption features around ∼ 1345 cm-1 also allows the detection of SO2 during anthropogenic pollution episodes, which is in agreement with the finding of Bauduin et al. (2014, 2016). Finally, the spectral features around 1180–1200 cm-1 showing a low signal intensity are likely due to the IASI detector band 1–band 2 inter-band domain that is well captured in the IASI-PCA-GE method and should not be associated with an anomalous atmospheric constituent.

The spatial distribution of the residual values associated with the detected species in the GMA pseudo-residual (see Fig. 11) is presented in Fig. 12 (left). The IASI-PCA-GE method allows the spectral detection of NH3, SO2, and CO. However only a few pixels are detected for NH3, which is due to the very low (< 5) signal intensity found for that species. We see the same behavior for CO. However, a clear SO2 plume characterized by a signal reaching ∼ 18 (at 1345.00 cm-1; see Fig. 11) is found by the IASI-PCA-GE method.

Figure 12 (right) illustrates the spatial distribution of NH3 and CO total column and SO2 plume altitude L2 data retrieved from the IASI/Metop-A L1C data (Clarisse et al., 2012). The retrieval and IASI-PCA-GE methods shows different patterns. We clearly see two plumes for SO2 plume altitude and CO concentrations, but only a few pixels of detection are found for NH3.

During the period extending from 20 to 27 October 2016, a sulfur mine burned in Al-Mishraq near Mosul, Iraq. This fire on the sulfur plant, which was set by members of the Islamic State in Iraq and the Levant (ISIL), caused a large emission of SO2 and other sulfured species in the atmosphere, which was observed from several satellite instruments (Björnham et al., 2017). Similar plant fires occurred in June 2003 during 4 weeks, with approximately 600 kt of SO2 emitted (Carn et al., 2004). This was a major health hazard (Baird et al., 2012). Nearly 1000 people were intoxicated due to toxic fire plumes, and two Iraqis died.

Figure 13 illustrates the normalized GMI pseudo-residual obtained during the Iraqi industrial disaster on 24 October 2016 PM. The GMI pseudo-residual is characterized by an absorption peak at ∼ 1326.00 cm-1 that could be assigned to HNO3 and two absorption peaks associated with SO2 at 1345.00 and 1371.00 cm-1. The signal intensity is about -14 for SO2, which suggests that the event is of low to medium intensity. However, the SO2 peaks found around ∼ 1371 and ∼ 1377 cm-1 are mostly seen in the case of intense volcanic eruptions, suggesting that the SO2 concentrations are larger than the concentrations found above most of degassing volcanoes. This suggestion for an industrial origin is well supported by Fig. 14, showing SO2 total columns up to 5 DU.

The detection at ∼ 1326 cm-1 is not associated with HNO3 and is due to the contribution of SO2 and aerosols, as already discussed in the case of the Ubinas eruption (see Sect. 5.1.1).

The spatial distribution of the residual values associated with SO2 detections is illustrated in Fig. 15. The IASI-PCA-GE method allows the spectral detection of this molecule in the region of interest 4 d after the fire started, thus showing the transport of the plume in the eastern part of the country. Fewer pixels are detected by the IASI-PCA-GE method than by the L2 retrieval method. This can be explained by the fact that SO2 thresholds associated with the IASI-PCA-GE method were empirically chosen to minimize false positive detections, and thus, the detections of low-intensity residuals can be missed.

In Sect. 5.2.2, we reported that the IASI-PCA-GE method is well suited to the detection of biomass burning by using the C2H4 indicator found in conjunction with other signatures of molecules usually associated with fire activity. Among the events that we detected, on a few occasions, we found intense signatures in the Iran/Iraq region with no absorption other than C2H4, which suggests that sources other than biomass burning – likely due to anthropogenic activities – are at play. The main event occurred in July 2021, and some other weaker ones are also identified in Fig. 10. By averaging IASI data over time and using a super-sampling technique, Franco et al. (2022) uncovered and identified over 300 worldwide emitters of C2H4 emanating from petrochemical clusters, steel plants, coal-related industries, and megacities. However, no C2H4 point source was formally identified in this Iran/Iraq region. But the method described in this paper is also well suited to the detection of sporadic events, which contrasts with the continuous emissions identified by Franco et al. (2022). Indeed, oversampling methods are well suited for the detection of regular, even weak, anthropogenic sources but typically miss transient sources lasting less than 24 h. A new analysis was therefore performed on the events spotted by the IASI-PCA-GE method, which led to the identification of plumes lasting for only a few hours (see Fig. 15) and for specific days, as identified in Fig. 10. Although visible satellite imagery and independent online information indicate the presence of oil and gas activities in that area, no firm identification was possible, and further investigation is needed to identify the potential sources of these sporadic emissions.