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Article

The Influence of Australian Bushfire on the Upper Tropospheric CO and Hydrocarbon Distribution in the South Pacific

by
Donghee Lee
1,
Jin-Soo Kim
2,
Kaley Walker
3,
Patrick Sheese
3,
Sang Seo Park
4,
Taejin Choi
5,
Minju Park
1,
Hwan-Jin Song
6,7 and
Ja-Ho Koo
1,7,*
1
Department of Atmospheric Sciences, Yonsei University, Seoul 03722, Republic of Korea
2
School of Energy and Environment, City University of Hong Kong, Kowloon Tong, Hong Kong
3
Department of Physics, University of Toronto, Toronto, ON M5S1A7, Canada
4
Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea
5
Division of Oceanic and Atmospheric Sciences, Korea Polar Research Institute, Incheon 21990, Republic of Korea
6
BK21 Weather Extremes Education & Research Team, Department of Atmospheric Sciences, Kyungpook National University, Daegu 41566, Republic of Korea
7
Center for Atmospheric Remote Sensing, Kyungpook National University, Daegu 41566, Republic of Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(12), 2092; https://doi.org/10.3390/rs17122092
Submission received: 26 March 2025 / Revised: 31 May 2025 / Accepted: 16 June 2025 / Published: 18 June 2025
(This article belongs to the Special Issue Satellite Observations for Particulate Matter and Gaseous Pollutants Research)
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Abstract

:
To determine the long-term effect of Australian bushfires on the upper tropospheric composition in the South Pacific, we investigated the variation in CO and hydrocarbon species in the South Pacific according to the extent of Australian bushfires (2004–2020). We conducted analyses using satellite data on hydrocarbon and CO from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and on fire (fire count, burned area, and fire radiative power) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, we compared the effects of bushfires between Northern and Southeastern Australia (N_Aus and SE_Aus, respectively). Our analyses show that Australian bushfires in austral spring (September to November) result in the largest increase in CO and hydrocarbon species in the South Pacific and even in the west of South America, indicating the trans-Pacific transport of smoke plumes. In addition to HCN (a well-known wildfire indicator), CO and other hydrocarbon species (C2H2, C2H6, CH3OH, HCOOH) are also considerably increased by Australian bushfires. A unique finding in this study is that the hydrocarbon increase in the South Pacific mostly relates to the bushfires in N_Aus, implying that we need to be more vigilant of bushfires in N_Aus, although the severe Australian bushfire in 2019–2020 occurred in SE_Aus. Due to the surface conditions in springtime, bushfires on grassland in N_Aus during this time account for most Australian bushfires. All results show that satellite data enables us to assess the long-term effect of bushfires on the air composition over remote areas not having surface monitoring platforms.

1. Introduction

Biomass burning represents a major global source of atmospheric aerosols and organic components [1,2,3,4]. Australian wildfire contributes approximately 6 to 8% of global biomass burning carbon emissions [5,6]. Since the anthropogenic emissions in the Southern Hemisphere are relatively lower than those in the Northern Hemisphere [7], the Southern Hemispheric chemical composition can be considerably affected by natural emissions and biomass burning [8]. Thus, we need to examine fire activity to better understand the atmospheric chemistry in the Southern Hemisphere.
In general, Australian bushfire largely occurs in austral winter and spring (April to November) when the ambient conditions are quite dry [9,10]. From December 2019 to January 2020, there was unprecedented bushfire activity in the New South Wales and Victoria regions in Australia [11,12], known as the Australian New Year (ANY) fires [13,14]. The intense heat from these fires contributed to the widespread formation of pyrocumulonimbus (PyroCb) clouds [13,15,16,17]. These clouds can inject large amounts of smoke plumes emitted from ANY fires into the upper troposphere and lower stratosphere (UTLS) [18,19,20]. As a result, air components stay in the UTLS for several months [15,21]. Particularly, the increase in carbon-containing gaseous species is one of the distinctive effects of bushfires [22], and has potential effects on stratospheric chemical reactions such as ozone depletion [23,24,25]. Recently, the damage to human health in the bushfire area was also evaluated [26,27].
Hydrogen cyanide (HCN) is a well-known indicator of biomass burning events. Although HCN is strongly depleted near the surface due to the uptake by ocean water [28], its spatial distribution in the upper troposphere clearly impacts the intensity of and variation in wildfires due to its long lifetime. Tape-recording patterns of HCN can be used to show its relationship to climate variability [29]. Ethane (C2H6) and acetylene (C2H2) are non-methane hydrocarbons (NMHCs) emitted from both biomass burning and other anthropogenic sources associated with the fossil fuel combustion [30]. Some oxidized hydrocarbons, such as methanol (CH3OH) and formic acid (HCOOH), are also known to be related to wildfires [31,32,33]. Carbon monoxide (CO) is a typical species and one of the biomass burning indicators, having a strong correlation with black carbon [34]. Formaldehyde (HCHO) is usually produced by the oxidation of hydrocarbons; therefore, its amount and distribution have also been investigated in terms of its regional impact on fire cases [35].
These gaseous hydrocarbon species, however, cannot yet be widely observed due to technical limitations. Owing to the high radiative absorbing property of hydrocarbon species, Fourier transform infrared spectroscopy (FTIR) has been utilized extensively, enabling the successful monitoring of hydrocarbons. However, the number of monitoring sites using this FTIR measurement is still very scarce. Thus, previous studies have generally been performed based on certain events and cases. While the amount of FTIR measurement is spatially limited, regional patterns can be investigated if long-term data are collected. In this context, measurement data from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) onboard the SCISAT satellite are very useful for determining the mean pattern of trace gases in the UTLS region. Several studies have been conducted to find the climatological mean patterns of multiple trace gases, including hydrocarbon species [36,37].
Therefore, this study aims to extend the usage of long-term satellite measurements to investigate the spatial distribution of upper tropospheric hydrocarbons in the South Pacific in accordance with the activity of Australian bushfires. The South Pacific does not have large surface emission sources, but it is located on the leeward side of bushfires; therefore, it is necessary to determine how much the hydrocarbon pattern in this area can be affected by Australian fire events. Owing to the restriction of ground-based measurement in maritime regions and the difficulty of upper atmospheric in situ measurements, a satellite dataset can be a good research tool for the target region of this study. Satellite measurements also provide some useful parameters about the bushfire activity and surface land cover types, indicating the possibility of assessment for the Australian bushfire influence with the combination of various satellite products. This merit of our study can be a good reference in other bushfire analyses in the future, we believe. This study will also focus on the regional consistency or differences in bushfire effects and hydrocarbon variations, enabling us to conduct an advanced evaluation of the influence and damage of wildfire events on the atmospheric environment on both regional and hemispheric scales.

2. Materials and Methods

2.1. Study Area

Australia is located between the South Pacific and Indian oceans, and its land surface is composed of areas with different conditions. While most of the land area is arid, most of Northern Australia (N_Aus) is covered by grassland and most of Southeastern Australia (SE_Aus) is covered by forest. Thus, wildfires usually occur in these regions, with both FC and BA generally being larger in N_Aus compared to SE_Aus (Figure 1).
Our study mainly targeted the hydrocarbon distribution in the South Pacific, a downwind region from the Australian bushfire area [38,39]. We confirmed that a consistent westerly wind exists typically in Australia and the South Pacific, and this westerly is more enhanced in austral spring (Figures S1 and S2). Hirsch and Koren [40] indicated that the aerosol optical depth (AOD) in the Southern Hemisphere increased by 3 standard deviations compared to the climatological AOD because of smoke plumes from the Australian bushfire in 2019–2020, and Khaykin et al. [21] showed that plume-charged vortices produced from this bushfire ascended up to a 35 km altitude. Considering these previous studies, we aimed to decide the hydrocarbon distribution in the upper troposphere (UT) and lower stratosphere (LS) in the South Pacific. Horizontally, the latitudinal and longitudinal ranges were designated between 20°S and 60°S and 100°E and 140°W, respectively, and divided into two regions according to the latitude: Region A (latitudes from 20 to 40°S) and B (latitudes from 40 to 60°S) (Figure 2a). As mentioned in Section 2.1, we analyzed the data on CO and some hydrocarbon species from the ACE-FTS for Regions A and B from 2004 to 2020.
The N_Aus and SE_Aus regions were also assigned to two rectangular areas (Figure 2b) with high fire occurrences (Figure 1). As will be mentioned in Section 2.3, we used the moderate resolution imaging spectroradiometer (MODIS) FC, BA, and TFRP values across the N_Aus and SE_Aus to examine the relationship between the intensity of Australian bushfire and the quantity of hydrocarbons in the South Pacific.

2.2. ACE-FTS Data

The ACE-FTS is the infrared solar occultation instrument onboard the Canadian satellite SCISAT [41,42]. The SCISAT satellite stays at a 650 km altitude with a 74° inclination angle, which is beneficial for dense monitoring in high-latitude regions. Despite the highest density of measurements over high-latitude regions, ACE-FTS measurements can cover the whole Earth every 3 months; therefore, the regional mean distribution can be derived in every part of the world [37].
For this study, we utilized the ACE-FTS version 4.1 dataset, which is based on measurements performing a maximum of 15 sunrise and 15 sunset observations per day. After that, mixing ratio values of individual molecules are prepared in both Network Common Data Form (NetCDF) and American Standard Code for Information Interchange (ASCII) formats. Compared to the old version, version 4.1 expands the dataset by incorporating seven new molecular species and three additional minor isotopologues. Version 4.1 data also improved the CO2 line saturation issue that influenced pressure, temperature retrievals, and modulated unrealistic peaks associated with the presence of polar stratospheric clouds [43].
These solar occultation ACE-FTS measurements have been used for examining the vertical profiles of trace gases [43], which include 44 gaseous species and 26 isotopologues from spectra measured in the infrared region of 750–4400 cm−1, with a spectral resolution of 0.02 cm−1. By using the sun as a strong infrared light source, ACE-FTS achieves a high signal-to-noise ratio of approximately 100:1 to 400:1, which offers a significant advantage in quantifying trace gases with weak absorption features [41,42]. Consequently, ACE-FTS can provide vertical profiles even in UT, where other limb-viewing satellite measurements used to have difficulty, even for weak signals in the upper atmosphere [43,44]. In the lower troposphere (below ~8 km in height), however, the high density of atmospheric constituents makes high noise levels of remote sensing; therefore, reliable concentrations of air components cannot be retrieved. Namely, the lowest altitude that we investigate in this study is 7–8 km.
To examine the hydrocarbon distribution in terms of the Australian bushfire patterns, we used the vertical profile of CO and several hydrocarbons (C2H2, C2H6, CH3OH, HCOOH, HCHO, and HCN) using the ACE-FTS dataset. We determined their mixing ratio with a 1 km vertical height interval and provided the profile from 8 to 20 km height. Long-term data from February 2004 to December 2020 were investigated, enabling us to examine the climatological mean pattern.
For handling the noise and outliers, we utilized the quality information provided by the ACE-FTS community, the ACE-FTS quality flag proposed by Sheese et al. [45]. In other words, we screened out the unreliable level 2 data using quality flags, which are partitioned into 10 levels from 0 to 9 [45]. ACE-FTS data with flag 0 are validated, the use of data with flags 4 to 9 is not recommended because of technical limitations, and data with flags 1, 2, or 3 are somewhat biased but can be used according to researchers’ decision [45]. Here, we used data only with flag 0 in order to obtain the most qualified information. Quality flag files are available separately in NetCDF format by each molecule via this link (https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP2/BC4ATC (accessed on 15 March 2025)

2.3. MODIS Data

We obtained data on several parameters of bushfire properties from the MODIS satellite products for the same period as the ACE-FTS data used in this study. The MODIS onboard the Terra (10:30 local time for overpassing) and Aqua (13:30 local time for overpassing) satellite detects fire regions in a sun-synchronous orbit, meaning that full global coverage is achieved within a single day [46]. Terra and Aqua MODIS started the missions in 2000 and 2002, allowing them to produce data throughout the full period of ACE-FTS measurements. The MODIS Collection 6 (C6) Active Fire Product level 2 swath products onboard the Terra and Aqua (MOD14 and MYD14, respectively), derived from MODIS 4 and 11 μm radiances [47], provide two parameters pertaining to bushfire properties: fire count (FC) and total fire radiative power (TFRP). These data have a spatial resolution of 1 × 1 km2.
Another fire parameter, burned area (BA), is also obtained from the MODIS C6 monthly level product MCD64A1. MCD64A1 data, which have a 0.5 × 0.5 km2 spatial resolution, are a sinusoidal grid product and calculated by coupling MODIS daily surface reflectance imagery and MODIS 1 × 1 km2 Active Fire Product (MOD14 and MYD14) [48]. All datasets are distributed in Hierarchical Data Format—Earth Observing System (HDF-EOS) and are accessible via the webpage of NASA Earthdata portal (https://www.earthdata.nasa.gov/data (accessed on 15 March 2025)).
To understand bushfire characteristics associated with the type of land surface cover, we also used the MODIS C6 land cover climate modeling grid product (MCD12C1) during the research period (2004–2020). This annually released dataset has been used in many recent studies [49,50,51]. This dataset provides the surface vegetation type on a global scale with a 0.05 by 0.05-degree angle [52]. There are several techniques for classifying the surface vegetation type, but the International Geosphere-Biosphere Programme (IGBP) is the most widely applied [53]. Thus, we used land cover types specified by the IGBP. The land cover types used in this study are summarized in Table 1, and an example distribution in Australia is shown in Figure 1.
To evaluate this land cover type produced from the combination of MCD12C1 and IGBP information, inter-comparison to other land cover types is needed. In this context, we estimated another vegetation type using Interim Biogeographic Regionalisation for Australia, version 7.1 (hereafter, IBRA v7.1), and the suggested way by Olson et al. [55] and Dinnerstein et al. [56]. From this approach, we can achieve seven surface cover types (Figure S3), and we confirmed that both data are well agreed with each other based on the comparison process (Figures S4 and S5). In this study, the main results were provided based on the MCD12C1 dataset for retaining the consistency of satellite data analyses.
To maximize the usage of MODIS fire observations data, MOD14 and MYD14 level 2 data with class 7 (low-confidence), 8 (nominal-confidence), or 9 (high-confidence) are recommended for the analysis, according to Revision C of the MODIS Collection 6 Active Fire Product User’s Guide [57]. Different from FC and TFRP values, BA values cannot be directly obtained from the released data. Based on the reference known, we achieved proper BA values by multiplying the given value in the MCD64A1 dataset by 21.46 ha [58].

2.4. Methods

The whole picture of our study can be illustrated as Figure 3. This study is designed based on satellite data analyses. Bushfire properties and surface cover patterns in Australia are obtained from the nadir-viewing satellite measurement (MODIS), and vertical profiles of upper-tropospheric CO and hydrocarbon species from the limb-viewing satellite measurements (ACE-FTS). Using this information, we mainly examined (1) seasonal differences in CO and hydrocarbons, (2) seasonal patterns of bushfire and surface cover characteristics, and (3) the correlation between bushfire activity and the amount of CO and hydrocarbons.
To see the seasonal difference, we first estimated the climatological (February 2004 to December 2020) average of hydrocarbon volumetric mixing ratio (VMR) per 1 km altitude. Subsequently, the absolute difference for chemical species in each grid was calculated using Equation (1). Here, H C a b . d i f f denotes the absolute difference, H C s e a means the seasonal average VMR, and H C c l i indicates the climatological mean VMR.
H C a b . d i f f V M R = H C s e a − H C c l i
For correlation analyses in this study, we first selected all possible ACE-FTS data (CO and hydrocarbon species) in Region A and B, respectively. As mentioned in Section 2.2, we only used the highest qualified data (i.e., flag 0 data). Next, all selected data were sorted according to the measured altitude. This sorting was processed per every 1 km altitude range. For example, CO and hydrocarbon values measured from 9.5 to 10.5 km altitudes were categorized as the 10 km altitude value. Then seasonal mean mixing ratio values of CO and hydrocarbon species were estimated for each altitude. Also, bushfire parameters (FC, BA, and FRP) were averaged in the N_Aus and SE_AUS regions. Finally, we calculate correlations of bushfire parameters with CO and hydrocarbon mean values for every season and every altitude. All calculated correlation coefficients were provided in the format of a vertical profile.
To investigate the seasonal and regional differences in correlations between bushfire activities and hydrocarbon amounts, we also estimated the portion of each land cover type for each season, for each bushfire area (i.e., N_Aus and SE_Aus), or for each bushfire parameter, using MCD12C1 data described in Section 2.3. Again, all these processes show that we can examine the bushfire impact on the atmospheric chemical composition and its relationship to the type of land surface cover only based on the usage of satellite measurement data. We believe that our approach in this study reveals the capability of satellite data analysis at this present moment and hope to be useful in other studies.

3. Results

We first compared the seasonal mean profiles of CO and some hydrocarbon species in terms of anomalies in the climatological mean pattern. For this purpose, we estimated the vertical profile of absolute seasonal differences for CO, C2H2, C2H6, HCN, CH3OH, HCOOH, and HCHO, as shown in Figure 4. Except for HCHO, all other species show the largest increase in September–October–November (SON), which is the austral springtime, which exhibits dry conditions that lead to wildfire events. This increase is consistent in both Regions A and B, meaning that increased hydrocarbon due to Australian bushfires has wide-ranging effects in the South Pacific that can move to higher latitudes (i.e., Antarctica). We also confirmed that the westerly wind is more enhanced in higher latitudes when CO is high in Region B (Figure S2), illustrating that the transport pattern of Australian air mass is well connected to the variation in upper-tropospheric composition in the South Pacific. Since this increase is found at altitudes higher than 10 km, there is a high possibility of the intrusion of emitted CO and hydrocarbon into the stratosphere, consistent with previous reports [59,60].
As shown in Figure 4, the increase in CO and hydrocarbon species is only obvious in SON, implying that a certain special feature of this season is associated with this increase. Since we have already addressed the possible contribution of bushfires in this season, next we will clarify the relationship between bushfire events and the quantity of chemical species. Since HCN is typically considered to be an indicator of wildfire events [61,62], and CO is also frequently considered when wildfire emissions are assessed [62], we first estimated the degree to which CO and HCN are correlated with three bushfire parameters (FC, BA, and TFRP) (Figure 5). For comparison purposes, we also performed the same correlation analysis using HCHO, which does not show a substantial increase in austral spring, as shown in Figure 4.
Figure 5 summarizes all these correlation coefficients in the format of the vertical profiles of the correlation coefficients. The correlation coefficients were calculated separately for the bushfires in N_Aus and SE_Aus, and for the chemical species in Regions A. One of the main findings is that the correlations of CO and HCN with bushfire properties are different between N_Aus and SE_Aus. The FC, BA, and TFRP values in N_Aus show positive correlations with CO and HCN strongly in the upper troposphere (~12 km height), but those in SE_Aus are not strongly correlated with CO and HCN. This means that bushfire events in N_Aus dominantly affect the air composition of the South Pacific troposphere, but those in SE_Aus have relatively weak effects. While widespread fire events occurred during 2019–2020 (ANY fires) in SE_Aus, our analyses revealed a larger contribution of N_Aus bushfire events in a climatological sense. When we repeated the same analyses with other hydrocarbon species (C2H2, C2H6, CH3OH, and HCOOH), a similar difference between N_Aus and SE_Aus was illustrated (Figure 6).
We investigated this difference between N_Aus and SE_Aus in detail. Figure 7 shows the characteristics of land surface cover in relation to FC, BA, and TFRP in each season for both N_Aus and SE_Aus. The FC, BA, and TFRP values in N_Aus are mostly related to bushfires in grassland and shrubland, but those in SE_Aus are related to bushfires in forests and savannas. We have already confirmed that the FC, BA, and TFRP values in N_Aus are generally much larger than those in SE_Aus (Figure 3), and here we confirm that the land cover types where bushfires occur are also different between N_Aus and SE_Aus in a climatological sense. All these features suggest that the regionality of bushfire events should be carefully considered when the whole ‘Australian bushfire’ effect is assessed.
Additionally, we compared the extent of FC, BA, and TFRP in each season in terms of the type of land cover (Figure 8). All FC, BA, and TFRP values are the largest during the austral springtime (SON), and this pattern is mostly attributed to the grassland and shrubland. In contrast, FC, BA, and TFRP in SE_Aus are not substantially increased by bushfires in forests, which represent the dominant cover type in this region (Figure 7). Based on this detailed comparison, we can see that FC and BA in N_Aus are always larger than those in SE_Aus regardless of land cover type.
Also, the TFRP of grassland and shrubland is mostly larger in N_Aus than in SE_Aus, consistent with the mentioned features of FC and BA. However, the TFRP of a forest shows the opposite pattern: higher in SE_Aus, especially during DJF (Figure 8c,f). Woods in forests are not easily ignited compared to light materials (i.e., grass and shrubs). However, once ignited, the combustion persists with high thermal energy [63,64]. This property can explain the large impact of TFRP in SE_Aus, where the portion of forests is dominant (Figure 7). Nonetheless, it does not seem to enhance the correlations between TFRP and hydrocarbons (Figure 5 and Figure 6).
Finally, we examined whether increases in CO and hydrocarbons reach South America, which is why we defined two more target regions: Chile-A and Chile-B, located near the west coast of South America and having the same latitude ranges as Regions A and B (Figure 2). Similar to the analysis in Figure 4, we also compared the seasonal patterns of CO and hydrocarbons for Chile-A and Chile-B in terms of the absolute difference from the climatological mean (Figure 9). The results were confirmed to be nearly the same as those in Figure 4: a considerable increase in CO and hydrocarbons (except HCHO) in austral spring (SON), although the extent of increase in Chile-A and Chile-B is a little smaller than that in Regions A and B. It seems natural because Chile-A and Chile-B regions are located near the bushfire spots in Australia. We also compared the vertical profiles of the correlation coefficients of CO and hydrocarbon species in the region of Chile-A and Chile-B with the bushfire parameters in both N_Aus and SE_Aus (Figure 10) and confirmed that all of the correlation patterns are almost the same as those shown in Figure 5 and Figure 6.

4. Discussion

The most important finding of this study is that the relationship between bushfire properties and hydrocarbon amounts was confirmed based on a long-term dataset. The long-term monitoring of multiple hydrocarbon species is still rare and spatiotemporally limited. Thus, previous studies have mostly investigated the connection between hydrocarbon and bushfires based on certain severe events. Despite this limitation, this study aimed to ascertain a wildfire–hydrocarbon relationship in a climatological sense while sacrificing spatiotemporal resolution. Through this trial, we found that CO and most hydrocarbons (except HCHO) are similarly increased by bushfires. In future studies, we could compare these results to previous findings about the climatological mean patterns of hydrocarbons [28,37], showing the different zonal mean patterns among hydrocarbon species. In other words, this could reveal which regions are more sensitive to biomass burning events or which regions are largely affected by anthropogenic emissions.
We also found that the correlations of fire parameters with CO and hydrocarbon species are generally larger in Region A than in Region B (Figure 5 and Figure 6), showing that the transport and dispersion of smoke plumes from bushfires have a great impact on downwind areas with similar latitudes to Australia bushfires. Vertically, high correlations are limited to the upper troposphere and not extended to the lower stratosphere. This vertical pattern of correlations does not rule out the occasional occurrence of smoke plume transport from the troposphere to the stratosphere [19], but it shows that temporal variation in stratospheric CO and hydrocarbon in the South Pacific is not mainly explained by the Australian bushfires in a climatological sense.
High concentrations of other upper tropospheric hydrocarbon species (C2H6, C2H2, CH3OH, and HCOOH) with bushfire parameters are also obvious (Figure 6), as reported in previous studies. C2H6 and C2H2 were found to be indicators of biomass burning in [28]; there was an increase of CH3OH in the biomass burning events in [65], and there were high correlations of HCOOH with CO, HCN, C2H6, and C2H2 in [33]. Among those, correlations with oxidized species (CH3OH and HCOOH) are slightly smaller than those with pure hydrocarbons (C2H6, C2H2), probably indicating a weaker relationship between the burning effect to the variation in upper tropospheric hydrocarbons under aged plume conditions.
As expected, the correlations between bushfire and HCHO are not significant (Figure 5), which can be explained by its short lifetime (several hours or days) [66,67]. HCHO has also been examined to evaluate the effect of wildfires [35]. Considering HCHO is reasonable because HCHO is usually produced from the photochemical aging of freshly emitted hydrocarbons, and is strongly associated with atmospheric environmental conditions, especially ozone chemistry [68,69]. Another reason is that the quantity of HCHO is the representative hydrocarbon product from satellite missions. Since HCHO can be retrieved based on the fitting window in ultraviolet channels, most shortwave nadir-viewing satellite missions have provided the column density of HCHO [70,71]. In these contexts, satellite HCHO products have been investigated in various biomass burning cases [72,73]. However, our analysis may indicate that more vigilance is required when the HCHO is considered for evaluating the bushfire effects.
The findings presented in Figure 9 and Figure 10 imply that the occurrence of the trans-Pacific transport of Australian bushfire plumes in the Southern Hemisphere can significantly influence the air composition in South America, which is located more than ~10,000 km from Australia. In addition to a previous report about the increase in aerosol optical depth across the whole Southern Hemisphere due to the ANY fires [40], our study also provides an indication that chemical species in the air originated from a widespread biomass burning increase widely on a hemispheric scale. This is why it is important that we assess the impact of wildfires on atmospheric chemistry.
We achieved these interesting features based on the analysis of various satellite data. Among the many merits of this study, we particularly highlighted two innovative points of our study in terms of satellite data analyses. First, ACE-FTS hydrocarbon data were used to examine the regional mean pattern. Owing to the limited spatiotemporal coverage of ACE-FTS (and similar style limb-viewing satellite) measurements, ACE-FTS CO and hydrocarbon data were mostly used for specific cases [32,69] or zonal mean pattern [28,37] analyses. Here, we showed the possibility of using ACE-FTS hydrocarbon data for examining climatological mean patterns associated with regional bushfires. Second, comprehensive analyses using all FC, BA, and FRP values were performed with land cover types. As a result, we can derive different bushfire properties between N_Aus and SE_Aus regions. For example, bushfires in N_Aus predominantly occurred over grassland and shrubland regions, exhibiting consistently high values in FC, BA, and FRP, whereas those in SE_Aus were primarily associated with forest region, resulting in relatively higher FRP values sorely (Figure 8). This shows that using one of FC, BA, or FRP may not be enough to assess the bushfire effect precisely.

5. Summary and Conclusions

This study investigated how Australian bushfires affect the quantity of and variation in upper-tropospheric CO and several hydrocarbon species in the South Pacific. Since previous works used to deal with the property of specific events and cases (e.g., ANY fires), those performed from the perspective of climatological patterns are rare. In addition, hydrocarbon species have not been extensively examined in relation to Australian bushfires due to the limitation of usable data. Considering that some recent studies have actively used satellite data over areas with poor infrastructure of ground-based monitoring systems [28,37,74], this study also aimed to utilize the satellite dataset as far as possible.
As a result, we found that CO and hydrocarbon in the South Pacific are considerably higher in austral spring, the period of highest bushfire occurrence in Australia. Correlation analyses showed a large contribution of Australian bushfires to increases in CO and hydrocarbon, except for HCHO due to its short lifetime. One of the unique findings of this study is the different impacts of bushfires between N_Aus and SE_Aus. At least in a climatological sense, bushfire events in N_Aus, mostly associated with the biomass burning of grass and shrubs, lead to increases in predominantly CO and hydrocarbons in the South Pacific. However, bushfire events in SE_Aus, which are mostly associated with biomass burning in forests, have a relatively weaker impact. Another interesting finding of this study is the confirmation of a considerable increase in CO and hydrocarbons, even in the west of South America, which indicates a large-scale influence on the trans-Pacific transport of smoke plumes from Australia.
While we found some interesting relationships between Australian bushfires and South Pacific hydrocarbons, the limitations of this research cannot be ignored. Our study was based on a small number of ACE-FTS hydrocarbon data and should be evaluated further in the future. Once higher-resolution data from the satellite measurements are prepared, we can extend this research further and confirm the reliability of our results. Geostationary satellite missions would provide higher spatiotemporal resolution, but these are currently only available in the Northern Hemisphere such as the Geostationary Environment Monitoring Spectrometer (GEMS) in East Asia [75] and the Tropospheric Emissions: Monitoring of Pollution (TEMPO) in North America [76].
Considering that hydrocarbon species are important for ozone chemistry even in the upper troposphere [77], our work will be useful for conducting deeper analyses of the UTLS ozone in the Southern Hemisphere, and even in the Antarctic region. For example, combining this study with previous studies that investigate the different meteorological effects on the Antarctic ozone between the UT and LS [78], or different intrusions of mid-latitude air masses according to the location of the Antarctic region [8], would provide us with a better understanding of the distribution of and variation in UT and LS ozone in the Antarctic region. This kind of approach would improve our perception of the climatological and environmental effects of wildfires on a global scale.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs17122092/s1. Reference [79] is cited in the Supplementary Materials.

Author Contributions

Conceptualization, D.L. and J.-H.K.; methodology, D.L. and J.-H.K.; software, D.L.; validation, D.L., P.S. and K.W.; formal analysis, D.L., J.-S.K., S.S.P., T.C., M.P., H.-J.S. and J.-H.K.; investigation, D.L., J.-S.K., S.S.P., T.C., M.P., H.-J.S. and J.-H.K.; resources, P.S. and K.W.; data curation, D.L., P.S. and M.P.; writing—original draft preparation, D.L. and J.-H.K.; writing—review and editing, S.S.P., T.C., H.-J.S. and J.-H.K.; visualization, D.L.; supervision, J.-H.K.; project administration, T.C. and J.-H.K.; funding acquisition, J.-H.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Korea Polar Research Institute (KOPRI) grant funded by the Ministry of Oceans and Fisheries (KOPRI PE24030), and also supported by a grant from the National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (ex: NIER-2024-01-02-037).

Data Availability Statement

ACE-FTS volumetric mixing ratio (VMR) dataset can be accessed at https://databace.scisat.ca/level2/ (last access 24 March 2025, but registration required). The MODIS Collection 6 active fire product (MOD14/MYD14), burned area product (MCD64A1), and land cover product (MCD12C1) can be accessed at https://earthdata.nasa.gov/ (last access: 15 March 2025).

Acknowledgments

The authors acknowledge the support of the Korea Polar Research Institute (KOPRI) grant, which is funded by the Ministry of Oceans and Fisheries (KOPRI PE24030), and the support of a grant from the National Institute of Environment Research (NIER), which is funded by the Ministry of Environment (MOE) of the Republic of Korea (ex: NIER-2024-01-02-037).

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations are used in this manuscript:
ACE-FTSAtmospheric Chemistry Experiment—Fourier Transform Spectrometer
ANYAustralian New Year
AODAerosol Optical Depth
ASCIIAmerican Standard Code for Information Interchange
BABurned Area
C2H2Acetylene
C2H6Ethane
C6Collection 6
CH3OHMethanol
COCarbon Monoxide
DJFDecember–January–February (austral summer)
FCFire Count
FTIRFourier-Transform infrared spectroscopy
GEMSGeostationary Environment Monitoring Spectrometer
HCabsAbsolute Difference volumetric mixing ratio
HCcliClimatological mean volumetric mixing
HCHOFormaldehyde
HCNHydrogen Cyanide
HCOOHFormic Acid
HCseaSeasonal average volumetric mixing ratio
IBRA v7.1Interim Biogeographic Regionalisation for Australia, version 7.1
IGBPInternational Geosphere-Biosphere Programme
JJAJune–July–August
LSThe Lower Stratosphere
MAMMarch–April–May
MODISModerate Resolution Imaging Spectroradiometer
N_Aus Northern Australia
NetCDFNetwork Common Data Form
NMHCsNon-methane Hydrocarbons
PyroCbPyrocumulonimbus
SCISATScientific Satellite Atmospheric Chemistry Experiment
SE_AusSoutheastern Australia
SONSeptember–October–November (austral spring)
TEMPOTropospheric emissions: Monitoring of pollution
TFRPTotal Fire Radiative Power
UTThe Upper Troposphere
UTLSThe Upper Troposphere and Lower Stratosphere
VMRVolumetric Mixing ratio

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Figure 1. The spatial distribution of (a) fire count (FC), (b) burned area (BA), and (c) total fire radiative power (TFRP) from the MODIS dataset across Australia from 2004 to 2020. (d) The land surface cover types in 2020 are also compared as an example: forest (green), savanna (dark green), cropland (yellow), shrubland (apricot), grassland (orange), and others (gray).
Figure 1. The spatial distribution of (a) fire count (FC), (b) burned area (BA), and (c) total fire radiative power (TFRP) from the MODIS dataset across Australia from 2004 to 2020. (d) The land surface cover types in 2020 are also compared as an example: forest (green), savanna (dark green), cropland (yellow), shrubland (apricot), grassland (orange), and others (gray).
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Figure 2. Study domains: (a) hydrocarbons research area from ACE-FTS. Region A has a low latitude (20°S < latitude < 40°S; 140°E < longitude < 100°W), and Region B has a high latitude (40°S < latitude < 60°S; 140°E < longitude < 100°W). The following research regions are also shown: Chile-A (20°S < latitude < 40°S; 95°S < longitude < 75°W) and Chile-B (40°S < latitude < 60°S; 95°S < longitude < 75°W). There are 1597 data points for Region A, 3901 for Region B, 265 for Chile-A, and 678 for Chile-B from 2004 to 2020. (b) The target areas for the investigation of Australian bushfires are North Australia (N_Aus) and Southeast Australia (SE_Aus). Dots indicate the locations of the highest fire counts from 2004 to 2020 (three dots in each of N_Aus and SE_Aus).
Figure 2. Study domains: (a) hydrocarbons research area from ACE-FTS. Region A has a low latitude (20°S < latitude < 40°S; 140°E < longitude < 100°W), and Region B has a high latitude (40°S < latitude < 60°S; 140°E < longitude < 100°W). The following research regions are also shown: Chile-A (20°S < latitude < 40°S; 95°S < longitude < 75°W) and Chile-B (40°S < latitude < 60°S; 95°S < longitude < 75°W). There are 1597 data points for Region A, 3901 for Region B, 265 for Chile-A, and 678 for Chile-B from 2004 to 2020. (b) The target areas for the investigation of Australian bushfires are North Australia (N_Aus) and Southeast Australia (SE_Aus). Dots indicate the locations of the highest fire counts from 2004 to 2020 (three dots in each of N_Aus and SE_Aus).
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Figure 3. Description of the usage of satellite data to investigate the influence of Australian bushfires on the upper tropospheric hydrocarbon in the South Pacific. Regions A (red) and B (blue) are defined in Figure 2.
Figure 3. Description of the usage of satellite data to investigate the influence of Australian bushfires on the upper tropospheric hydrocarbon in the South Pacific. Regions A (red) and B (blue) are defined in Figure 2.
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Figure 4. Vertical profiles of absolute differences in the climatological mean of (a) C2H2, (b) C2H6, (c) CO, (d) HCN, (e) CH3OH, (f) HCOOH, and (g) HCHO obtained from the ACE-FTS from February 2004 to December 2020 for various seasons: December–January–February (DJF) in green, March–April–May (MAM) in navy blue, June–July–August (JJA) in purple, and September–October–November (SON) in red. Values are shown as the unit of volumetric mixing ratios (VMRs). The top row in this figure represents Region A, while the bottom row represents Region B.
Figure 4. Vertical profiles of absolute differences in the climatological mean of (a) C2H2, (b) C2H6, (c) CO, (d) HCN, (e) CH3OH, (f) HCOOH, and (g) HCHO obtained from the ACE-FTS from February 2004 to December 2020 for various seasons: December–January–February (DJF) in green, March–April–May (MAM) in navy blue, June–July–August (JJA) in purple, and September–October–November (SON) in red. Values are shown as the unit of volumetric mixing ratios (VMRs). The top row in this figure represents Region A, while the bottom row represents Region B.
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Figure 5. The vertical distributions of the correlation coefficients (R) of the bushfire parameters—FC (left), BA (middle), and TFRP (right)—with CO (a), HCN (b), and HCHO (c) in both N_Aus (red) and SE_Aus (blue). Regions A (star) and B (circle) are also considered separately.
Figure 5. The vertical distributions of the correlation coefficients (R) of the bushfire parameters—FC (left), BA (middle), and TFRP (right)—with CO (a), HCN (b), and HCHO (c) in both N_Aus (red) and SE_Aus (blue). Regions A (star) and B (circle) are also considered separately.
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Figure 6. Same as Figure 5 but for C2H2, C2H6, CH3OH, and HCOOH.
Figure 6. Same as Figure 5 but for C2H2, C2H6, CH3OH, and HCOOH.
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Figure 7. The seasonal ratio of (a) FC, (b) BA, and (c) TFRP according to land cover types in N_Aus, and those of (d) FC, (e) BA, and (f) TFRP in SE_Aus. The land cover types considered are forest (green), savanna (kaki), cropland (yellow), shrubland (apricot), grassland (orange), and others (gray).
Figure 7. The seasonal ratio of (a) FC, (b) BA, and (c) TFRP according to land cover types in N_Aus, and those of (d) FC, (e) BA, and (f) TFRP in SE_Aus. The land cover types considered are forest (green), savanna (kaki), cropland (yellow), shrubland (apricot), grassland (orange), and others (gray).
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Figure 8. Seasonal mean values (using all data from 2004 to 2020) of (a) FC, (b) BA, and (c) TFRP in N_Aus, and those of (d) FC, (e) BA, and (f) TFRP in SE_Aus according to land cover type.
Figure 8. Seasonal mean values (using all data from 2004 to 2020) of (a) FC, (b) BA, and (c) TFRP in N_Aus, and those of (d) FC, (e) BA, and (f) TFRP in SE_Aus according to land cover type.
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Figure 9. Same as Figure 4 but for the region Chile-A and Chile-B.
Figure 9. Same as Figure 4 but for the region Chile-A and Chile-B.
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Figure 10. Vertical distributions of the correlation coefficients (R) between bushfire parameters across N_Aus and SE_Aus and hydrocarbons across Chile-A and Chile-B (as shown in Figure 2): Vertical profiles of correlation coefficients of FC (top), BA (middle), and TFRP (bottom) in N_Aus (red) and SE_Aus (blue) with CO and six hydrocarbons (C2H2, C2H6, HCN, CH3OH, HCOOH, and HCHO).
Figure 10. Vertical distributions of the correlation coefficients (R) between bushfire parameters across N_Aus and SE_Aus and hydrocarbons across Chile-A and Chile-B (as shown in Figure 2): Vertical profiles of correlation coefficients of FC (top), BA (middle), and TFRP (bottom) in N_Aus (red) and SE_Aus (blue) with CO and six hydrocarbons (C2H2, C2H6, HCN, CH3OH, HCOOH, and HCHO).
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Table 1. Classified land cover type in this study from Feng and Bai. (2019) [54].
Table 1. Classified land cover type in this study from Feng and Bai. (2019) [54].
IGBP Land Cover SchemeThis Study
Evergreen Needleleaf ForestForest
Evergreen Broadleaf Forest
Deciduous Needleleaf Forest
Deciduous Broadleaf Forest
Mixed Forest
Woody Savannas
SavannasSavannas
GrasslandGrassland
CroplandCropland
Cropland/Natural Vegetation Mosaic
Closed ShrublandsShrubland
Open Shrubland
Permanent wetlandsOthers
Water
Urban
Barren or Sparsely Vegetated
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MDPI and ACS Style

Lee, D.; Kim, J.-S.; Walker, K.; Sheese, P.; Park, S.S.; Choi, T.; Park, M.; Song, H.-J.; Koo, J.-H. The Influence of Australian Bushfire on the Upper Tropospheric CO and Hydrocarbon Distribution in the South Pacific. Remote Sens. 2025, 17, 2092. https://doi.org/10.3390/rs17122092

AMA Style

Lee D, Kim J-S, Walker K, Sheese P, Park SS, Choi T, Park M, Song H-J, Koo J-H. The Influence of Australian Bushfire on the Upper Tropospheric CO and Hydrocarbon Distribution in the South Pacific. Remote Sensing. 2025; 17(12):2092. https://doi.org/10.3390/rs17122092

Chicago/Turabian Style

Lee, Donghee, Jin-Soo Kim, Kaley Walker, Patrick Sheese, Sang Seo Park, Taejin Choi, Minju Park, Hwan-Jin Song, and Ja-Ho Koo. 2025. "The Influence of Australian Bushfire on the Upper Tropospheric CO and Hydrocarbon Distribution in the South Pacific" Remote Sensing 17, no. 12: 2092. https://doi.org/10.3390/rs17122092

APA Style

Lee, D., Kim, J.-S., Walker, K., Sheese, P., Park, S. S., Choi, T., Park, M., Song, H.-J., & Koo, J.-H. (2025). The Influence of Australian Bushfire on the Upper Tropospheric CO and Hydrocarbon Distribution in the South Pacific. Remote Sensing, 17(12), 2092. https://doi.org/10.3390/rs17122092

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