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Surface and Tropospheric Column Observations of Combustion Tracers During the 2021 Wildfire Crisis in the Central Mediterranean: Insights from the WMO/Gaw Station of Lamezia Terme in Calabria, Southern Italy

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07 December 2024

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09 December 2024

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Abstract
The central Mediterranean and nearby regions were affected by extreme wildfires during summer 2021. During the crisis, Türkiye, Greece, Italy and other countries faced numerous challenges ranging from the near complete destruction of landscapes to human losses, and high outputs in emissions which compromised local air quality. In the Mediterranean basin, atmospheric monitoring stations perform continuous measurements of chemical and meteorological parameters meant to track and evaluate greenhouse gas and pollutant emissions in the area. In the case of wildfires, CO (carbon monoxide) and formaldehyde (HCHO) are effective tracers and the integration of satellite data on tropospheric column densities with surface measurements can provide additional insights on the transport of air masses originating from wildfires. At the Lamezia Terme (code: LMT) World Meteorological Organization – Global Atmosphere Watch (WMO/GAW) observation site in Calabria, Southern Italy, a new multiparameter approach combining different methodologies has been used to expand the knowledge the summer 2021 crisis. A previous study focused on wildfires affecting specifically the Aspromonte massif area in Calabria itself: in this study, the integration of surface concentrations, tropospheric columns, and backtrajectories has allowed to pinpoint additional contributions from other southern Italian regions, north Africa and Greece. CO data were available for both surface and column assessments, while HCHO data were only available through satellite. In order to correlate the observed peaks with wildfire phenomena, surface BC (black carbon) was also analyzed. The analysis, which focused on July and August 2021 data, pinpointed case studies highlighting distinct sources of combustion processes during the wildfire crisis of that year. Although wildfires have certain local effects in terms of biomass loss and direct damage to both the environment and human infrastructures, their broad impact on air quality also requires monitoring via the integration of multiple methodologies and the cross-analysis of satellite and surface data.
Keywords: 
Subject: Environmental and Earth Sciences  -   Atmospheric Science and Meteorology

1. Introduction

Recent assessments on extreme wildfire events and their trends indicate that areas such as the Mediterranean could be exposed to more events of this kind, possibly linked to anthropogenic climate change [1,2,3].
Extreme wildfire events generally have a common factor, which is the phenomenon of pyroconvection [4,5]. This phenomenon results in fire convection columns extending to the middle and upper troposphere [6]. The typical framework of pyroconvection are flammagenitus (or fire) clouds that develop above the smoke plumes linked to a fire event [7]. These clouds, known as pyrocumulonimbus and pyrocumulus, as well as their post-development dynamics influence the transport of smoke particles and hot gases, which then contribute to the formation of additional clouds following condensation processes [8]. These dynamics can result into several effects that increase the potential of extreme wildfire spreading, such as lightnings of pyrogenic nature [9,10,11], the injection of aerosols as high as the lower stratosphere [12,13], additional fire growth [13], and significant changes both in surface winds [15,16,17] and synoptic-scale flows [18].
The summer 2021 wildfire crisis in the central Mediterranean area and neighboring regions. EFFIS, the European Forest Fire Information System, issued a comprehensive report on the effects of these extreme events on European ecosystems [19].
Türkiye has a natural vulnerability to wildfire across multiple regions of the country and the 2021 crisis resulted into unprecedented wildfire occurrences [20] that challenged the country’s capacity to detect them in time and counter the hazard efficiently [21]. The alteration of landscapes resulted in increased exposure to flood hazards [22]. The damage to biodiversity was also notable [23,24,25]. In addition to the main hazards and damage linked to wildfire occurrences, air quality was also affected [26,27]. Satellite data from Sentinel-2 and Sentinel-5P allowed an assessment of burn severity and CO emissions linked to Turkish fires [28]. The impact of these wildfires on air quality was also assessed via the integration of satellite data with ground measurements [29].
In Greece, heat waves affected the summer 2021 and triggered a number of wildfires [30]. Fires with similar degrees of intensity were not seen in Greece for 13 years, since the previous summer crisis of 2007 which resulted in the loss of more than 12% of Greece’s forested areas [31,32,33,34,35,36,37], however their damage was considerable, and the country’s early warning systems were challenged [38]. Research research also evidenced the susceptibility of the country’s wildfire hazard to synoptic scale drivers [39]. Overall, both Greece and Türkiye were very heavily affected, and surveys attempted to provide a reliable estimate on the combined impact of wildfire-related emissions in the eastern Mediterranean sector [40].
In Italy, fires affected several regions [41] and the atmospheric outputs of these events have also been observed by WMO/GAW (World Meteorological Organization—Global Atmosphere Watch) observation sites across the country [42,43]. Among the Italian regions affected by the 2021 crisis, Calabria reported extensive wildfire events especially in the southern area of the region, with coincides with the southernmost area of the entire Italian peninsula, in the Aspromonte National Park [43]. The 2021 crisis led to a more detailed analysis on the areas affected by these events in Italy: areas with intense land abandonment have showed to be vulnerable to such events [44]. The effects on air quality were also assessed [45], as well as the increase in soil exposure to erosion [46].
In this research, three among the main products of wildfires will be analyzed using a combination of ground measurements, satellite data on tropospheric column concentrations, and both methods when applicable: carbon monoxide (CO, ground and satellite), formaldehyde (HCHO, satellite), and black carbon (BC, ground). Ground measurements have been performed at the WMO/GAW observation site of Lamezia Terme (code: LMT) in Calabria, Southern Italy.
Carbon monoxide is a primary output of combustion and is attributed to both anthropogenic and natural sources [47,48]; among the carbon compounds present in the atmosphere, carbon monoxide is now characterized by a declining trend [49], likely linked to more sustainable policies [50] and efficient combustion engines introduced ever since the 2000s [51]. Prior to these measures, atmospheric carbon dioxide trends and annual emissions were considerable [52]. However, the annual decline rate has lowered in the past few years also due to notable wildfire-related outputs [53,54]. CO is used as an effective tracer of wildfire emissions [43]
Formaldehyde (FA) is a carcinogen and mutagen [55,56] characterized by multiple sources: wildfires contribute to outdoor HCHO concentrations in the atmosphere [57,58,59], in addition to anthropogenic sources such as the combustion of coal, industrial activities, manufacturing, and others [60,61,62]. Formaldehyde also poses a considerable indoor air quality (IAQ) risk due to releases from wooden furniture, tobacco smoking, and other sources which combine with poor indoor ventilation and further increase exposure-related risks [63,64,65,66,67].
Black carbon, or soot, is a relevant product of combustion processes, like CO [48]. Wildfires are known to result in notable BC emissions into the atmosphere [68]. Black carbon poses health hazards [69,70,71] and is also capable of altering Earth’s climate via perturbations in radiative forcing [72,73,74]. It has a high GWP (global warming potential) [75], which is counterbalanced by its short persistence time in the atmosphere [76,77].
The paper is divided as follows: Section 2 describes the observation site of LMT, its findings and their implications on the diffusion of greenhouse gases and pollutants; Section 3 reports the methods used for analysis and evaluations, as well as those employed for data gathering; Section 4 and Section 5 focus on results and discussion, respectively; Section 6 closes the paper with a summary of this study’s findings and their implications.

2. The LMT Observation Site

Located 600 meters from the Tyrrhenian coast of Calabria, Southern Italy, the Lamezia Terme (LMT) observation site (Lat: 38°52.605′ N; Lon: 16°13.946′ E; Alt: 6 m a.s.l.) is a regional WMO/GAW (World Meteorological Organization—Global Atmosphere Watch) performing continuous measurements of greenhouse gases, aerosols, key meteorological data, and other parameters. Located in the westernmost area of the Catanzaro isthmus, which is the narrowest point in the entire Italian peninsula (≈31 kilometers between the Tyrrhenian and Ionian coasts), the site is affected by a local wind circulation pattern that is well oriented on the W/NE axis, as described in two works by Federico et al. (2010a, 2010b) [78,79]. Wind circulation also has an impact on local air traffic, as the runway orientation of the Lamezia Terme International Airport (IATA: SUF; ICAO: LICA) located 3 kilometers north from the WMO station is 10/28 (100/280 °N).
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Figure 1. A: LMT’s location in the central Mediterranean basin. B: Modified EMODnet [80] map showing LMT’s location in the southern Italian region of Calabria.
Figure 1. A: LMT’s location in the central Mediterranean basin. B: Modified EMODnet [80] map showing LMT’s location in the southern Italian region of Calabria.
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The station’s location in the center of the Mediterranean exposes its observations to Saharan dust events [81] and summer open fire emissions [43]. During LMT’s observation history, local wind circulation has been characterized using different methodologies, such as the analysis of vertical wind profiles [82] and PBL variability [83,84,85]. An analysis of solar radiation using several methodologies was also performed [86]. With respect to reactive gases, the study by Cristofanelli et al. (2017) [87] provided a milestone characterization of the site with remarks on a number of local emission sources (e.g., the above-mentioned airport, livestock farming, the A2 highway). Some of these sources could be characterized even further in a study on the first COVID-19 lockdown of 2020 and the exceptional circumstances of reduced anthropic activities that followed [88]. Additional details on specific greenhouse gases became available in cyclic and multi-year studies: in D’Amico et al. (2024a) [89], seven continuous years of methane (CH4) measurements showed that emission peaks were linked to northeastern-continental winds, while western-seaside winds yielded lower values. A seasonal influence was also observed, with winter and summer yielding the highest and lowest concentrations, respectively. Methane concentrations at the site have also shown a strong correlation with wind speeds in a so-defined HBP (Hyperbola Branch Pattern), as the highest concentrations were linked to low speeds and, vice versa, the lowest mole fractions were correlated with high speeds, further demonstrating the influence of local emission sources. A study on surface ozone (O3) however demonstrated that these patterns are not constant among gases, as ozone showed a “reversed” pattern adding additional complexity to the correlations between winds and other parameters on a local scale [90]. The analysis of gases and aerosols at LMT also involved cross-station research with other southern Italian stations [91,92] and the evaluation of weekly patterns in pollutants to discriminate natural and anthropogenic outputs [93]. In D’Amico et al. (2024e) [94] an additional level of complexity was observed on the nature of LMT measurements: the study, which assessed peplospheric influences on a number of parameters at the site, evidenced the presence of four wind regime categories affecting LMT data gathering that need to be taken in consideration.

3. Data gathering and analysis

Carbon monoxide (CO) mole fractions in ppm (parts per million) have been gathered by a Picarro G2401 (Santa Clara, California, USA) CRDS (Cavity Ring-Down Spectrometry) [95,96] analyzer. The same instrument also gathers data on CO2 (carbon dioxide), CH4 (methane) and H2O (water vapor). The principle of CRD spectroscopy allows to measure, with high degrees of precision, the concentration of trace gases in the atmosphere. The G2401 analyzer at LMT gathered data every 5 seconds with a precision of 1 ppb, and the resulting outputs have been aggregated on hourly or daily basis, depending on the evaluation. More details concerning G2401 measurements at LMT are available in Malacaria et al. (2024) [43] and D’Amico et al. (2024a) [89].
The tropospheric density of CO and HCHO (or FA) in the vertical column have been obtained by Sentinel-5P, an ESA (European Space Agency) satellite aimed at global air pollution monitoring launched as part of the Copernicus mission [97]. 5P is in a low-Earth afternoon polar orbit yielding a swath of 2600 kilometers and carries TROPOMI (Tropospheric Monitoring Instrument), a device capable of advanced atmospheric monitoring spectrometry. TROPOMI scans have a spatial resolution of 3.5x5.5 kilometers and the signal-to-noise ratio is high. Operational Level 2 (L2) products are publicly available for use via the Copernicus platform [98]. Data on daily observed columns are downloaded in netCDF format, and an algorithm set up at CNR ISAC parses through all parameters required for local data analysis at the LMT observatory. The analysis is divided into several steps: coordinates extraction (latitude and longitude); trace gas data analysis and its division into arrays correlated to a map focused on the region of interest; data processing via a filter applying a Qa > 0.5 condition to the entire set; generation of a georeferenced map showing data filtered from the array; a direct comparison between select coordinates and satellite measurements nearby, with the selection of the minimum distance to the selected site with respect to the slightest reported distance in the array. Qa is a data quality indicator whose assigned values range between 0 and 1: applicable manuals recommend a Qa greater than 0.5 to exclude influences from a number of conditions such as ice/snow warnings, Solar Zenith Angle (SZA) ≤ 70°, cloud radiance fraction at 340 nm < 0.5, surface albedo ≤ 0.2, air mass factor > 0.1, and common error flags [99,100].
Ground indoor and outdoor measurements of formaldehyde at the site are limited to a summer 2021 campaign at the local INAIL (Italian National Institute for Insurance against Accidents at Work) department located nearby [101]. Continuous measurements of surface HCHO at LMT are not available.
Black carbon (BC), and specifically equivalent black carbon (eBC) data in micrograms per cubic meter (μg/m3 or μg PCM) have been gathered by a Thermo Scientific 5012 (Franklin, Massachusetts, USA) MAAP (Multi-Angle Absorption Photometer) [102,103,104] performing measurements on a per-minute basis. More details concerning eBC data gathering at LMT via the MAAP are available in Malacaria et al. (2024) [43] and D’Amico et al. (2024b) [93].
Wind speed (m/s) and direction (°) at a near-ground level have been measured by a Vaisala WXT520 (Vantaa, Finland) weather station. The instrument also gathers data on relative humidity, pressure, accumulated rain, and temperature. Additional details on WXT520 measurements are available in D’Amico et al. (2024c) [88].
Surface measurements at LMT between July and August 2021 are characterized by high coverage rates, shown in Table 1 as percentages compared to the hours and days elapsed during the study period. All days are covered by surface measurements, while gaps of only a few hours, due to instrument calibration and maintenance, are limited. CO and eBC data have been further differentiated by wind sector: northeast (0-90 °N); southeast (90-180 °N); west (240-300 °N).
With respect to wildfire analysis on a Mediterranean scale, this research study relied on FIRMS productions to assess the locations of major wildfires prior to the evaluation of backtrajectories from the LMT observation site in Calabria. FIRMS (Fire Information for Resource Management System) provides NRT (Near Real-Time) data on active fires from the MODIS (Moderate Resolution Imaging Spectroradiometer) instrument aboard the Terra Aqua platform, and the VIIRS (Visible Infrared Imaging Radiometer Suite) abord the NOAA 20, NOAA 21, and S-NPP. Generally, gathered data on a global scale are available within three hours from satellite scanning. Thermal anomalies or active fires represent the center of a 1km pixel that is flagged by the MODIS MOD14/MYD14 Fire and Thermal Anomalies algorithm as containing one or more fires within the pixel [105]. This is the most basic fire product in which active fires and other thermal anomalies, such as volcanoes, are identified. MCD14DL outputs are available in a number of formats (KML, TXT, WMS, and SHP), meant to be used by a variety of programs for data processing. Only the TXT and SHP formats, however, contain all the attributes. Collection 61 (C61) data effectively replaced Collection 6 (C6) in April 2021 [106]. It is worth noting that C61 did not feature substantial upgrades in terms of algorithm, as the updates were mostly focused on improving the calibration in Terra and Aqua MODIS Level 1B data products. The analysis of fires and the consequent detection of daily active fires is obtained by FIRMS via sensors mounted on satellites; these sensors generally have swath widths in the 2300-3000 km range which provide two daily observations for most of Earth’s surface.
Backtrajectories aimed specifically at two case studies have been computed in HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) by NOAA’s (National Oceanic and Atmospheric Administration) Air Resources Laboratory [107,108]. The use of backtrajectories in case studies, combined with additional evaluations, can provide relevant information on specific emission sources [109].

4. Results

4.1. Tropospheric and surface observations of CO, HCHO, and eBC

At the LMT observatory in Calabria, Southern Italy, Picarro G2401 and Thermo Scientific 5012 instruments gathered continuous data on carbon monoxide and black carbon, respectively. Data on surface meteorological parameters (wind direction and speed) have been gathered by a Vaisala WXT520. As shown in Table 1, the coverage rates of all instruments between July and August are very high. Figure 2 shows the hourly averages of carbon monoxide and equivalent back carbon during the observation period.
In addition to hourly averages, daily averages have been computed by integrating CO and eBC concentrations with wind data, differentiating these averages per wind sector. Daily tropospheric column densities of CO and HCHO, obtained via satellite data, have also been computed. The results are shown in Figure 3.
Figure 4 shows the daily cycles, differentiated by wind sector, of CO and eBC. The graphs show diurnal hours (11:00-16:00) gaps where the northeastern and southeastern winds are absent during the study period.
Considering that satellite flybys above LMT occur at 14:00 UTC and data obtained during the passage is considered a daily total column density of both CO and HCHO, these values have been compared with surface data of CO gathered at 14:00 UTC by the Picarro G2401 CRDS analyzer at LMT.
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Figure 5. Direct comparison between daily satellite total column densities of CO (green triangles) and HCHO (red circles), and the hourly concentrations of surface CO (blue diamonds) observed at the time satellite passage, 14:00 UTC.
Figure 5. Direct comparison between daily satellite total column densities of CO (green triangles) and HCHO (red circles), and the hourly concentrations of surface CO (blue diamonds) observed at the time satellite passage, 14:00 UTC.
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4.2. Case studies

As described in Section 1, the Mediterranean wildfire crisis of summer 2021 affected several countries and caused enormous environmental damage. Analyzing two continuous months and surface and satellite data, three case studies (CS1, CS2, CS3) have been selected for additional evaluations.
The July 10th-11th peaks (CS1) observed at Lamezia Terme (LMT) could be attributable to vast wildfires occurred in Calabria itself, the Italian regions of Sicily, Apulia and Sardinia, and northern African regions in Tunisia and Algeria, as shown by FIRMS data (Figure 6) and related estimates on the number of days of prolonged fire activity in these areas. Total column densities of CO and HCHO have been therefore analyzed, and the results are shown in Figure 7. Via the HYSPLIT model, backtrajectories have been computed using LMT’s coordinates as starting point (Figure 8), indicating that the sources of CS1 peaks at LMT may be attributable to Sardinian wildfires.
The second case study (CS2) is aimed at the peaks observed between July 29th and August 1st. Algeria, and the southern Italian regions of Calabria and Sicily were affected by a significant number of prolonged wildfires. FIRMS data on the week elapsed between July 28th and August 3rd shows several wildfires affecting these areas (Figure 9). The tropospheric column densities computed on July 29th-31st data, shown in Figure 10, and the HYSPLIT backtrajectory shown in Figure 11 both indicate the northern African country of Algeria as the most probable source of these emissions.
The third case study (CS3) focused on August 4th, which provided evidence of an eastern source such as Greece and Türkiye, which as described in Section 1 were heavily affected by the wildfire crisis at the time. FIRMS data on the entire week between July 31st and August 6th shows vast areas in both countries being affected by wildfires (Figure 12). The Italian regions of Calabria and Apulia were also affected by prolonged wildfire activity. Tropospheric column data, shown in Figure 13, shows relevant Greek peaks in density. One of the HYSPLIT backtrajectories (Figure 14) centered at LMT is compatible with the surge in CO and HCHO emissions reported in Greece at the time.

5. Discussion

Data on the summer 2021 wildfire crisis in the Mediterranean basin have been subject to new analyses aimed at an enhanced characterization of carbon monoxide (CO) and formaldehyde (HCHO) observations, with the addition of equivalent black carbon (eBC) as an additional tracer of combustion processes. At the WMO/GAW (World Meteorological Organization—Global Atmosphere Watch) regional site of Lamezia Terme (code: LMT) in Calabria, Southern Italy, surface observations of CO and eBC have been integrated by tropospheric total column data on CO and HCHO to assess the influence of wildfires over air quality in the central Mediterranean during the peak of that crisis, in July and August 2021. This evaluation considers a longer observation period compared to previous research on local Calabrian wildfires [43].
LMT’s location in the Mediterranean (Figure 1) exposes the observation site to Saharan dust events [81] and wildfire inputs from multiple sources: in the previous study, releases from wildfires affecting the Calabrian Aspromonte massif in August 2021 were observed via a multiparameter approach [43].
In this study, a longer observation period has been selected between July 1st and August 31st of the same year: at the LMT station, this period is characterized by an excellent coverage rate of surface CO and eBC, with instruments nearing 100% in terms of coverage, thus allowing a detailed analysis of all data gathered during the period (Table 1).
Using hourly averages as a reference (Figure 2), the entire observation period has been assessed with respect to surface CO (ppm) and eBC (μg/m3 or μg PCM) which both provided new insights on possible wildfire outputs other than those already described in Malacaria et al. (2024) [43], which accounted for the August 10th–12th peaks linked to wildfires located in the Aspromonte massif in Calabria. The evaluation of hourly averages pinpointed a number of circumstances with high peaks.
Using daily averages and integrating the tropospheric total column densities of CO and HCHO, a cross-analysis of surface and column data was possible (Figure 3). The cross analysis allowed to determine the difference between high altitude air mass transport from remote sources, and nearby surface/near-surface outputs. The LMT coastal site is affected by local wind circulation patterns (Section 2) which have a direct impact on atmospheric measurements. In Figure 3B,C, data have been differentiated by wind sector, which could provide more precise information on the source of observed peaks. The differentiation was also applied to daily cycle analyses (Figure 4) and highlighted the presence of gaps, i.e. hours that were not covered by specific combinations of wind direction and the surface concentration of CO and eBC.
A comparison between daily averages alone however cannot be deemed sufficient to compare surface and tropospheric column data. In the case of CO, which is the only evaluated parameter measured from both surface analyzers and satellite sensors, the daily average calculated from surface data is not directly comparable to total tropospheric column density observed by satellite scans. Considering that these scans occur at 14:00 UTC, the surface hourly averages of CO at that time have been considered and plotted with column data in Figure 5. Although divergences are present, each indicating a circumstance of near-surface peaks which were not distributed on the tropospheric column and, vice versa, high altitude air mass transport which did not result into a surface concentration peak, data shown in Figure 5 indicate an increase of CO between July and August 2021. Formaldehyde, which is not subject to continuous surface measurements, also shows a trend compatible with carbon monoxide variability. It is worth noting that tropospheric column densities are not equally susceptible at all altitudes, as the employed instruments are more sensitive to surface and near-surface concentrations: in the case of CO, this feature enhances the correlation between tropospheric column and surface data.
CO and HCHO are both tracers of combustion (Section 1), however formaldehyde is characterized by a lower persistence time in the atmosphere due to chemical and photochemical processes [110], thus making HCHO a proximity indicator. Air mass transport from remote wildfires would therefore be significantly depleted in HCHO.
The variability of CO and HCHO during the study period (July-August 2021) and related peaks have allowed to indicate three case studies (CS); via FIRMS, daily tropospheric column data, surface measurements, and HYSPLIT backtrajectories, each CS has been constrained to a probable source of emission in the Mediterranean basin.
In the process of evaluating CS1, FIRMS data showed a significant number of wildfires active for 2+ days in Calabria itself, and the Italian regions of Sardinia, Sicily, and Apulia (Figure 6). TVC data on CO and HCHO between July 9th and 12th, subject to availability limitations (CO data not available on July 12th, and HCHO data not available on the 9th and 11th), highlight the presence of Calabrian and Sardinian wildfires in particular (Figure 7). At the LMT observatory, this period was already known to be characterized by CO and eBC concentrations above seasonal and yearly averages, thus indicating wildfires as possible sources [43]. Using the computed HYSPLIT backtrajectory shown in Figure 8, the peaks of CS1 are attributable to Sardinian wildfires: at the site, these trajectories are linked to western wind corridors, which are generally depleted in GHGs and pollutants [89]. However, the perturbation of background atmospheric levels caused by wildfires and the wind inversion processes linked to the daily cycle at LMT [88,93] are both compatible with westerly winds for CS1.
A similar circumstance is observed in CS2, where FIRMS data indicate wildfire activity in north Africa, specifically in Algeria (Figure 9) which is known to be susceptible to wildfires [111,112]. The peaks in CO and HCHO from Algerian wildfires are confirmed by TVC data (Figure 10) and the computed HYSPLIT backtrajectory also results into a western air mass transport observed at LMT (Figure 11), which culminated with the precipitation to surface levels of pollutants at high altitudes, linked to daily cycle wind circulation inversions at LMT [78,79].
In CS3’s case, FIRMS data confirm very heavy wildfire activity in central Greece and in several regions across Türkiye in early August (Figure 12). Tropospheric column data of CO and HCHO highlight, especially for August 4th (Figure 13), significant peaks in column density in areas that were significantly affected by wildfires in central Greece at the time (Section 1). However, the analysis of backtrajectories via HYSPLIT (Figure 14) has shown once again a westerly wind corridor linked to surface LMT observations via a clockwise pattern instead of a northeastern wind corridor. Surface LMT detections are, in CS3, also correlated with inversions in wind circulation caused by the local daily cycle and the precipitation of pollutants from higher altitudes.
Overall, the findings shown during the evaluation of all case studies demonstrate the complexity of wildfire detections from surface observations in the central Mediterranean, as air mass transport combines with local phenomena and influences the impact of wildfire emissions to the surface. In fact, without the local wind inversion cycle at LMT, the peaks linked to CS1 through CS3 would have likely lacked a surface counterpart of tropospheric density peaks.

6. Conclusions

By expanding the findings of a previous study on the summer 2021 wildfire crisis in the Mediterranean via the implementation of additional methodologies and a longer study period (July-August), the effects of wildfires on atmospheric measurements performed at the WMO/GAW (World Meteorological Organization—Global Atmosphere Watch) regional site of Lamezia Terme (LMT) could be characterized.
Surface measurements at the site of carbon monoxide (CO) are characterized by a very high coverage rate, with nearly 100% of the study period covered by a measurement. Surface equivalent black carbon (eBC) concentrations, although not classified as gaseous, have also been used as an effective tracer of wildfire emissions and also have a very high coverage rate in terms of measurements. Surface findings have been correlated with tropospheric total column densities of CO and formaldehyde (HCHO) to observe the air mass transport of wildfire byproducts in the central Mediterranean. Surface measurements at LMT have pinpointed peaks that resulted into the evaluation of three distinct case studies (CS). The analysis of these CS, which integrated a previous research study on Calabrian wildfires that struck the Aspromonte massif in the southernmost area of the Italian peninsula, has revealed a number of possible sources of the observed surface concentration peaks of CO and eBC. Specifically, the Italian region of Sardinia, northern Algeria, and central Greece have been pinpointed as the wildfire emission sources of the evaluated case studies via the integration of surface measurements with TVC and HYSPLIT backtrajectories. Although they were also affected by heavy wildfire activity, the Italian regions of Sicily and Apulia, as well as Türkiye, are apparently not linked to any of the peaks observed by LMT between July and August 2021. Surprisingly, central Greek outputs have been detected at LMT from the west instead of the east, thus demonstrating the importance of air mass transport in the diffusion of wildfire emissions. In this case, as well as in the other two case studies, surface peaks have been influenced by local wind inversion patterns and the consequent precipitation of pollutants. Without the described local wind regime mechanisms, would have likely missed these peaks.
These findings provide a tangible example of several factors that interplay with each other and add more degrees of complexity to the diffusion of wildfire outputs in the Mediterranean basin. Although the primary focus with respect to wildfires is the effective containment of the damage caused to the environment, enhanced analyses of wind circulation and the broader impacts of wildfires to air quality can also be assessed to issue ad hoc warnings and mitigate the risks associated with them.

Author Contributions

Conceptualization, F.D. and T.L.F.; methodology, F.D. and T.L.F.; software, F.D., G.D.B., L.M., S.S., D.G., and T.L.F.; validation, F.D., G.D.B., L.M., S.S., C.R.C., D.G., I.A. and T.L.F.; formal analysis, F.D., G.D.B., L.M., S.S., C.R.C., D.G., I.A. and T.L.F.; investigation, F.D., G.D.B., L.M., S.S., C.R.C., D.G., I.A. and T.L.F.; resources, C.R.C. and T.L.F.; data curation, F.D., G.D.B., L.M., S.S., C.R.C., D.G., I.A. and T.L.F.; writing—original draft preparation, F.D., G.D.B. and T.L.F.; writing—review and editing, F.D., G.D.B., L.M., S.S., C.R.C., D.G., I.A. and T.L.F.; visualization, F.D., G.D.B. and T.L.F.; supervision, F.D., C.R.C. and T.L.F.; project administration, C.R.C.; funding acquisition, C.R.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by AIR0000032—ITINERIS, the Italian Integrated Environmental Research Infrastructures System (D.D. n. 130/2022—CUP B53C22002150006) under the EU—Next Generation EU PNRR—Mission 4 “Education and Research”—Component 2: “From research to business”—Investment 3.1: “Fund for the realization of an integrated system of research and in-novation infrastructures.”

Data Availability Statement

Data are currently not available as they are subject to other research.

Acknowledgments

TBFIL.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 2. Hourly averages of surface CO (A) and eBC (B) at LMT, between July and August 2021.
Figure 2. Hourly averages of surface CO (A) and eBC (B) at LMT, between July and August 2021.
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Figure 3. Daily averaged tropospheric total column densities of CO (green triangle) and HCHO (red circle) (A); surface concentration of CO (B) and eBC (C) at Lamezia Terme station, both differentiated by wind corridor.
Figure 3. Daily averaged tropospheric total column densities of CO (green triangle) and HCHO (red circle) (A); surface concentration of CO (B) and eBC (C) at Lamezia Terme station, both differentiated by wind corridor.
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Figure 4. Daily cycles of surface CO (A) and eBC (B), differentiated by wind sector.
Figure 4. Daily cycles of surface CO (A) and eBC (B), differentiated by wind sector.
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Figure 6. FIRMS data on fires affecting the central Mediterranean area between July 8th and July 14th 2021. Light colors indicate fires lasting for 5+ days, thus contributing to prolonged emissions. Italian regions are marked in italics, while other countries are marked in bold. Malta, Spain, and France, as well as several Italian regions, have been omitted to improve visualization.
Figure 6. FIRMS data on fires affecting the central Mediterranean area between July 8th and July 14th 2021. Light colors indicate fires lasting for 5+ days, thus contributing to prolonged emissions. Italian regions are marked in italics, while other countries are marked in bold. Malta, Spain, and France, as well as several Italian regions, have been omitted to improve visualization.
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Figure 7. CO and HCHO vertical column data referring to the July 10th–12th, which is the first case study assessed in this research (CS1). CO column data on July 12th, and HCHO column data on July 9th and 11th were not available.
Figure 7. CO and HCHO vertical column data referring to the July 10th–12th, which is the first case study assessed in this research (CS1). CO column data on July 12th, and HCHO column data on July 9th and 11th were not available.
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Figure 8. HYSPLIT backtrajectory computed from LMT’s coordinates, showing well defined paths leading to the Italian region of Sardinia.
Figure 8. HYSPLIT backtrajectory computed from LMT’s coordinates, showing well defined paths leading to the Italian region of Sardinia.
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Figure 9. FIRMS data on fires affecting the central Mediterranean area between July 28th and August 3rd 2021. Light colors indicate fires lasting for 5+ days, thus contributing to prolonged emissions. Country names are in bold. Malta, Spain, and France have been omitted to improve visualization.
Figure 9. FIRMS data on fires affecting the central Mediterranean area between July 28th and August 3rd 2021. Light colors indicate fires lasting for 5+ days, thus contributing to prolonged emissions. Country names are in bold. Malta, Spain, and France have been omitted to improve visualization.
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Figure 10. Tropospheric columns of CO and HCHO referred to CS2 (specifically, July 29th-31st), showing a northern African source of the peaks observed at LMT.
Figure 10. Tropospheric columns of CO and HCHO referred to CS2 (specifically, July 29th-31st), showing a northern African source of the peaks observed at LMT.
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Figure 11. HYSPLIT backtrajectory of CS2, indicating Algeria as a probable source.
Figure 11. HYSPLIT backtrajectory of CS2, indicating Algeria as a probable source.
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Figure 12. FIRMS map showing areas affected by wildfires between July 31st and August 6th, with lighter colors indicating wildfires lasting for 5+ days. Balkan countries other than Greece, and Cyprus, have been omitted from labeling to improve visualization.
Figure 12. FIRMS map showing areas affected by wildfires between July 31st and August 6th, with lighter colors indicating wildfires lasting for 5+ days. Balkan countries other than Greece, and Cyprus, have been omitted from labeling to improve visualization.
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Figure 13. Vertical columns of the Case Study 3 (CS3), referring to the period between August 2nd and Aug. 4th. Column density data show surges in emissions from Greece.
Figure 13. Vertical columns of the Case Study 3 (CS3), referring to the period between August 2nd and Aug. 4th. Column density data show surges in emissions from Greece.
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Figure 14. HYSPLIT computed backtrajectories, set at LMT’s coordinates, for CS3.
Figure 14. HYSPLIT computed backtrajectories, set at LMT’s coordinates, for CS3.
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Table 1. Coverage rate per instrument/dataset compared to the total number of hours (1488) and days (62) elapsed between July 1st and August 31st, 2021.
Table 1. Coverage rate per instrument/dataset compared to the total number of hours (1488) and days (62) elapsed between July 1st and August 31st, 2021.
Type G2401 MAAP WXT520 Sat. CO Sat. FA
Hours 99.66% 98.18% 100% - -
Days 100% 100% 100% 87.09% 82.25%
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