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Article

Analysis of Stratospheric Ozone and Nitrogen Dioxide over Mid-Brazil for a Period from 2005 to 2020

by
Elvira Kovač-Andrić
1,
Vlatka Gvozdić
1,
Brunislav Matasović
1,*,
Nikola Sakač
2,* and
Amaury de Souza
3
1
Department of Chemistry, Josip Juraj Strossmayer University of Osijek, Cara Hadrijana 8A, 31000 Osijek, Croatia
2
Faculty of Geotechnical Engineering, University of Zagreb, Hallerova aleja 7, 42000 Varaždin, Croatia
3
Institute of Physics, University of Mato Grosso do Sul, C.P. 549, Campo Grande 79070-900, MS, Brazil
*
Authors to whom correspondence should be addressed.
Atmosphere 2025, 16(10), 1159; https://doi.org/10.3390/atmos16101159
Submission received: 16 August 2025 / Revised: 30 September 2025 / Accepted: 30 September 2025 / Published: 3 October 2025
(This article belongs to the Section Upper Atmosphere)
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Abstract

This study analyses the stratospheric concentrations of ozone (O3) and nitrogen dioxide (NO2) over a 16-year period (2005 to 2020) over central Brazil using satellite data with the aim of determining the influence of NO2 on ozone distribution and the impact of fires and volcanic eruptions on these gases. The analysis shows that ozone and NO2 follow seasonal patterns, with the highest concentrations occurring in September and October and the lowest from January to June. A positive correlation was found between the concentrations of ozone and NO2, and the results of the Fourier analysis indicate semi-annual and annual cycles in the concentrations of these gases. Although there was an increase in the number of fires in the last 11 years of the study, this increase did not lead to significant changes in ozone or NO2 concentrations, indicating the stability of these parameters in the observed area. It is presumed that the reason for the lack of changes is lower intensity of fires despite their increased number. Regarding wind patterns, it is observed that they do not differ much either which is in accordance with the fact that the monitored area is fairly close to the equator.

1. Introduction

Ozone is one of the most important constituents of the stratosphere and changes in its concentration can affect climate patterns and atmospheric circulation. Monitoring and understanding the processes that affect stratospheric ozone are the basis for adopting measures to preserve the ozone layer. Ozone was discovered as early as the 19th century, but its importance was only fully expressed in the first half of the 20th century, when the first qualitative measurements of ozone were carried out in Europe [1]. A major contribution to understanding the chemistry of the stratosphere was made by Paul Crutzen, who in 1970, connected nitrogen oxides with the chemistry of stratospheric ozone. This was preceded by research conducted by Harold Johnson, related to the reduction in stratospheric ozone due to the catalytic action of nitrogen oxides emitted from supersonic aeroplanes. Soon after, Mario Molina and F. Sherwood Rowland predicted the effect of chlorine released from chlorofluorocarbon on stratospheric ozone. Unlike the tropospheric or so-called “bad” ozone, stratospheric ozone forms the ozone layer that protects the Earth from harmful ultraviolet UV-B radiation in the wavelength range of 280–315 nm. The concentration of ozone in the stratosphere is the result of a dynamic balance between the chemical process of its formation and the process of its decomposition.
The main components of the overall ozone formation mechanism are ultraviolet solar radiation, oxygen molecules (O2) and oxygen atoms (O), which can be represented by the following equations:
O2 + hν → O + O
O + O2 + M → O3 + M
O3 + hν → O2 + O
At the appropriate altitude and latitude, there is a dynamic equilibrium corresponding to the appropriate stationary ozone concentration. The interaction of UV radiation with oxygen and ozone prevents the penetration of short-wave UV rays to the Earth’s surface. Therefore, the ozone layer acts as a filter for harmful ultraviolet rays and thus ensures life and biological processes on the Earth’s surface. It has been shown that the lack of ozone in the stratosphere is mainly due to the chemistry of chlorine and bromine (CFC, HCFC). Methyl chloride (CH3Cl) is the only natural source of chlorine in the stratosphere and accounts for 16% of chlorine [2]. On the other hand, about 50% of atmospheric bromine refers to natural sources of bromine. Due to their extremely high stability, the mentioned gases remain in the troposphere for a long time, which allows a part of these compounds to diffuse into the stratosphere. In the stratosphere, CFCs are exposed to short-wave UV radiation with wavelengths λ < 210 nm, which leads to photo dissociation and the release of chlorine atoms (Cl), and in the next step, the chlorine atom reacts with ozone. Chlorine atoms enable additional decomposition of ozone, in addition to the decomposition described by expression (3), which leads to a decrease in stationary ozone concentration.
In the stratosphere, NOx is formed mainly by the oxidation of N2O. Nitric oxide and its oxidation product, NO2 then participate in set of reactions which transfer O3 in O2 and which are effective above cca 24 km [3]. Although stratospheric NO2 is considered an ozone-depleting gas through the catalytic NOx cycle, at the same time it works as a buffer against ozone depletion by converting reactive chlorine and hydrogen compounds into unreactive species (ClONO2, BrONO2, HNO3). The oxidation pathways of NOx that form HONO and thus promote the production of OH radicals, offer an additional mechanism by which changes in NO2 can affect ozone chemistry at regional scales [4].
Based on the observed trends in nitrogen suboxide (N2O) emissions, a similar trend in stratospheric nitrogen dioxide (NO2) would be expected, with potential implications for ozone depletion well into the 21st century [5], this is supported by reports of such trends in stratospheric NO2 observed in locations such as New Zealand and northern Russia [6,7]. On the other hand, Hendricks [8] reports that changes in the NOx partitioning in favour of NO may conceal the effect of trends in N2O. While halocarbons have been successfully reduced by the Montreal Protocol, N2O is unregulated and is expected to become the most important ozone-depleting emission substance during the 21st century [5] on the basis of NOx-driven catalytic ozone loss.
Wildfire events have injected substantial loadings of smoke particles directly into the stratosphere. Major volcanic eruptions of the past half century have been shown to enhance mid latitude stratospheric ozone destructions [9]. Sufficiently explosive eruptions inject sulphur dioxide into the atmosphere, resulting in the formation of fine particles of sulfuric acid, i.e., aerosols. Reactions occurring on such particles affects reactive nitrogen species that can accelerate the decomposition of ozone, which can lead to temporary changes in the concentration of ozone in the stratosphere.
The study of the 2020 Australian wildfire particles estimated that reactions involving wildfire enhanced aerosols could reduce mid latitude stratospheric ozone by about 5–10 DU from July to August 2020 [10]. The results indicate that increasing wildfire activity may slow the recovery of the ozone layer.
Although the concentration of stratospheric ozone in equatorial regions is more stable and varies less compared to polar regions, which is due to different air circulation patterns caused by the different temperature gradients, changes in global atmospheric circulation affects the transport of substances that can damage the ozone layer. Therefore, monitoring changes in stratospheric ozone in the region around the equator remains important for the global understanding and preservation of the ozone layer [11].
Stratospheric ozone has a key role in UV protection and the influence on climate patterns, especially in the equatorial region, and there is a constant need for its monitoring. The goal of this research is to analyse stratospheric ozone and NO2 concentrations over Brazil during a 16-year period (2005–2020) by using satellite data, to determine the influence of NO2 on the ozone distribution and to analyse the influence of wildfires and volcano eruptions on the concentration of given gasses. Furthermore, eventual long-term trends and seasonal cycles in those concentrations will be analysed.

2. Materials and Methods

2.1. Study Area and Temporal Scope

This study focuses on the state of Mato Grosso do Sul (MS), located in the central-western region of Brazil between latitudes 17° S and 24° S and longitudes 51° W and 58° W (Figure 1), covering an area of approximately 358,159 km2. The region includes three major Brazilian biomes: Cerrado (61%), Pantanal (25%) and the Atlantic Forest (14%), and has different climatic zones according to the Köppen classification, including tropical savanna (Aw), monsoon (Am), rainforest (Af) and humid subtropics (Cfa), where the average annual temperature ranges between 20 and 26 °C and annual precipitation between 1000 and 1900 mm, with a pronounced dry season from April to September and a rainy season from October to March. It is important to note that studies using airborne and ground-based DOAS detectors show that spatial and meteorological differences (e.g., humidity and cloudiness) can strongly influence NO2 at high spatial resolution, which should be taken into account when interpreting satellite data [12]. The temporal scope of the analysis extends from 2005 to 2020 and enables the investigation of long-term trends and seasonal patterns in air pollution and the occurrence of fires.

2.2. Atmospheric Pollution Data

The tropospheric concentrations of ozone (O3) and nitrogen dioxide (NO2) were determined by the Ozone Monitoring Instrument (OMI) on board NASA’s Aura satellite. The OMI measures backscattered solar radiation in the spectral range of 270–500 nm with a spectral resolution of approximately 0.5 nm using whisker-mode hyperspectral imaging [13,14,15]. Tropospheric NO2 columns were identified using Differential Optical Absorption Spectroscopy (DOAS) in the 405–465 nm range [16,17]. Data filtering, DOAS analysis, algorithms and quality control are described in detail in the user manual for NASA’s OMI product [18]. For NO2 determination, air mass factors under clear and cloudy conditions (0–30% cloud cover) are considered in simulated NO2 profiles [19]. To compare techniques and understand the limitations of satellite columns, Meier et al. [12] presented high-resolution airborne imaging–DOAS measurements that revealed sharp horizontal gradients of NO2 and compared these data with mobile ground-based DOAS (correlations up to R = 0.94 and 0.85). To further compare DOAS with other techniques, studies have also been conducted comparing DOAS measurements with atmospheric dispersion models [20]. The OMI data were obtained from the NASA Giovanni portal (https://giovanni.gsfc.nasa.gov/giovanni/) [18], using level 3 grid products with a spatial resolution of 0.25° × 0.25°, and the geographical boundaries used for data extraction corresponded to the boundaries of Mato Grosso do Sul based on the official coordinates of the Brazilian Institute of Geography and Statistics (IBGE). Tropospheric NO2 has an uncertainty of 0.1 × 1015 molecules/cm2 and can be underestimated by 15–30% [21]. Validation studies show good agreement between OMI-derived NO2 columns, on-site NO2 measurements and bottom-up emission inventories, with seasonal variations consistent with the NASA GSFC Global Modelling Initiative (GMI) chemical transport model [22]. Due to the lack of NO2 measurement campaigns in the central-west of Brazil, reference was made to similar studies [15,23,24,25,26].

2.3. Meteorological Data

Meteorological variables such as rain, evaporation, relative humidity, maximum and minimum temperature and wind speed were obtained from the Brazilian National Institute of Meteorology (INMET) through its public database. The stations were selected based on the completeness of the data and the spatial coverage within the study area. A total of 28 conventional meteorological stations were used, distributed across different regions of the state of Mato Grosso do Sul, in order to adequately represent the atmospheric conditions of the three predominant biomes (Cerrado, Pantanal and Atlantic Forest) and the distinct climatic zones (Am, Af, Cfa and Aw). Figure 1 shows the location and spatial distribution of these stations within the state territory. The observation period covers January 2005 to December 2020, resulting in 192 monthly records per station. This corresponds to a dataset of more than 5300 station–month observations over the 16-year period. The inclusion of meteorological parameters is important because they affect air mass factors (AMFs) and the vertical and horizontal distribution of trace gases and therefore may affect satellite NO2 and O3 column data [12,27].

2.4. Fire Occurrence Data

Data on fire outbreaks were retrieved from the BDQueimadas database, managed by the Brazilian National Institute for Space Research (INPE) (https://terrabrasilis.dpi.inpe.br/queimadas/bdqueimadas/, accessed on 1 August 2025). The dataset is based on the detection of active fires by satellites using AVHRR (NOAA-12) and MODIS (Aqua/NASA) sensors. Monthly aggregates from 2005 to 2020 were used to account for the temporal structure of the pollutant datasets.

2.5. Data Analysis

Statistical analysis of time series of stratospheric ozone (O3) and nitrogen dioxide (NO2) concentrations for the period 2005 to 2020 was performed to assess long-term trends, seasonal variability and potential associations with the occurrence of fires and meteorological parameters. The non-parametric Mann–Kendall trend test was used to determine the presence and direction of monotonic trends, and the strength and direction of the trends were additionally assessed by determining 95% and 99% confidence intervals, thus ensuring the robustness of the analysis even under the conditions of possible non-linearity and the presence of outliers in the data. The strengths of the test are that it does not assume normal distribution and it is resistant to potentially extreme values. To determine statistically significant differences in concentrations between years, the Kruskal–Wallis test was used, which is suitable for analysing several independent groups when the normal distribution of the data is not guaranteed. It is commonly used for a comparison of more than two datasets. It tests if all of the sets from the sample have the same median value.
Fourier spectral analysis of monthly concentration means was performed to assess the periodicity of the data, and the unreliability of the periodic components was assessed using the advanced method for calculating errors in Fourier transformation is used [28], which provided precise confidence intervals for the identified periods. Fourier analysis decomposes complex signal or time sequences to the set of sinusoidal components of different frequencies. Further insight into the potential impact of meteorological factors was obtained using Pearson correlation analysis. It is commonly used to measure the linearity between two quantitative variables with result values between –1 and 1. The result of 1 means that there is a certain proportionality between variables, while the result of –1 means that there is a certain inverse proportionality. A result of 0 means that there is no proportionality between variables. Values between extremes show the level of certainty that there is a proportionality present. To investigate the possible effects of horizontal and vertical air mass transport on pollutant levels, an analysis of air mass return paths was carried out using METEX software (https://db.cger.nies.go.jp/ged/metex/en/index.html, accessed on 18 July 2025) and a kinematic model based on the methodology of Zenga et al. [29].

3. Results and Discussion

Figure 2 illustrates the relationship between stratospheric ozone and NO2 for the station considered in this paper within period of the study. The results show a significant positive correlation between stratospheric ozone and NO2 (rs = 0.51; p < 0.01). This implies that the ozone concentrations increase as the NO2 concentrations increase, and they follow the same trends. It was observed that the maximum monthly mean ozone concentrations were mostly recorded in September and October and the minimum was recorded during January through June. Similar behaviour was recorded for nitrogen oxide concentrations, with the difference in the occurrence of the maximum recorded a circa month earlier (during July through September).
Figure 3 and Figure 4 show the statistic graphs of O3 and NO2 distribution obtained from satellite measurements during the 16-year period. A non-parametric Kruskal–Wallis test was conducted to determine the existence of statistically significant differences between ozone concentrations measured during the long-term monitoring of its concentration [30]. The same calculation was made for NO2 concentrations. The results of the Kruskal–Wallis test indicated the existence of statistically insignificant differences between all observed years in ozone concentrations (p = 0.126). Also, no statistically significant differences were found between the NO2 concentrations (p = 0.927).
The analysis of the distribution of ozone and NO2 over 16 years shows low variability and a negligible trend in their concentrations, with maximum ozone values of 288.2 DU and NO2 of 4.26 DU in 2018 and 2020. The NO2 maximum occurs in September, whereas the O3 maximum in October.
The images show low variability and a negligible trend in the stratosphere for both ozone and NO2 average concentrations as well as some indication of annual variations or cycles which we shall further discuss later (Figure 5 and Figure 6). The ozone increase in the concentration obtained by linear regression is only 0.043 DU/month. The change for the NO2 is even less observable with a value of barely 4.06 × 1011 molecules/(cm2 month), which is the change on the fourth decimal of the original data. Such small changes indicate stability in the stratospheric conditions, which we wanted to further confirm by using a stronger Mann–Kendall’s test [31]. That test further confirmed our assumption regarding the very low trends, if any. As for ozone, if we consider the 95% confidence interval, the linear coefficient lies between –0.047 and 0.893 DU/year. Also, a far stricter confidence interval of 99% results in coefficient values between –0.216 and 1.187 DU/year. Even the highest estimates given by Mann–Kendall’s test shows the increase in less than half a percent annually, while the lowest value provides even the possibility of negative trend, although less probable. As for the NO2, Mann–Kendall’s test gives us the linear coefficient range between –1.2 and 1.4 × 1013 molecules/(cm2 year) for the confidence interval of 95%. A much stricter confidence interval of 99% results in the range of the linear coefficient between –1.4 and 2.0 × 1013 molecule/(cm2 year). The maximum and minimum limits being fairly close to each other, especially with the less strict 95% interval, provides the strong indication that the real change can be neglected. However, the additional strength of Mann–Kendall’s test, also shown here, is that according to it, the change for NO2 is in reality close to the changes in ozone levels, while the somewhat less-precise linear regression does not show that trend very clearly. If we take into account the obtained p-values, they indicate absence of a trend since they are higher than 0.05.
Figure 7 and Figure 8 show the connection between stratospheric ozone, NO2 and hot spots.
In our case, the correlation coefficient (from 2005 to 2020) between the number of wildfires, NO2, and O3 was 0.44 (p < 0.01) and 0.34 (p < 0.01), respectively, which falls into the category of moderate correlation coefficients. Calculating the correlation coefficients for each year separately showed certain “irregularities”. For example, the correlation with NO2 had its highest value in 2012 (0.87), when the total number of fires was 331, and a relatively low value in 2020 (0.60), when the number of fires was 1547. A similar pattern was observed for O3: the correlation coefficient in 2012 was 0.82, whereas in 2020 it was 0.37 (and not statistically significant). Some years do not exhibit a statistically significant correlation coefficient, despite a non-negligible number of fires. When regression analysis is conducted separately for each year, the resulting regression coefficients generally fall within the range of 0.4 to 0.6, which also corresponds to moderate values.
However, it was not possible to determine the exact reason why the situation differs from that observed in, for example, Australia (as mentioned later). One plausible hypothesis is that the fires may have occurred more frequently but were of lower intensity compared to those in the Australian context. However, as we lack data on fire intensity and possess only information on fire counts, it is only possible to speculate that this may be a contributing factor.
A similar pattern in the trends of NO2 concentrations and number of wildfires is observed in most of the analysed years, albeit to varying degrees. Despite the increased number of wildfires in the final year (2020), NO2 concentrations were similar to those at the beginning of the monitoring period (2005), when the number of wildfires was significantly lower. Thus, it can be stated that while a comparable pattern exists between the concentrations and wildfire numbers, it is important to emphasise that no substantial changes in O3 and NO2 concentrations were observed.
If, however, further and thorough Pearson’s analysis of acquired data is performed (Table 1), we can draw some further conclusions [32,33]. The strongest correlation is, as expected, between the maximum and minimum temperatures, but other strong correlations can be observed between relative humidity and both minimum and maximum temperatures and evaporation and maximum temperatures. High temperatures can indeed cause higher evaporation, but the relative humidity is not as obvious per se, since the relative humidity drops with air temperature. In order to increase the relative humidity in higher temperature conditions, further and stronger evaporation must also occur. Usually, a value of 0.7 of Pearson’s coefficient is considered to indicate the most probable real correlation between parameters, but in the real systems, it is permitted to include some other data which have a coefficient slightly lower than that. According to analysis, it is probable that humidity is connected to the amount of rain, which is fairly logical. There is some indication that evaporation might be connected to the concentrations of both ozone and nitrogen oxide and, in connection to that, also wind speed. The connection between concentrations of ozone, nitrogen oxide and wind speed may indicate that some horizontal transfer can cause changes in the concentrations of said air components. To comment further on that, the analysis of the trajectories of air movement should also be considered.
Trajectories are analysed using METEX software and a kinematic model [29]. Air mass trajectories for the 1st of July 2006, 2010, 2014 and 2018 are shown in Figure 9. Mostly, the relevant wind patterns show that the air masses come from the north in days that immediately precede the days of observation. Furthermore, apart from the earliest year, air flow direction is from the north-east and, in all cases, no major movements are observed while the air masses are above the continent. Significant difference occurs when the air masses are over the ocean, as can be seen in the year 2010 when the air masses travelled further, which is mainly caused by differences in friction. Related to Pearson’s coefficient calculation, wind speed might be correlated with the concentration of ozone and nitrogen oxide, which adds to the trajectory calculations to further prove that no major changes can be observed.
The aim of conducting research in this extremely important part of Brazil during a period (2005 through 2020) is to identify, by means of Fourier analysis, significant regularities (cycles) in the obtained measurement data, as well as in the less noticeable ones in the t-domain.
In a Fourier transformation, these plots are also called the power spectrums and, in our case, they represent the squared magnitudes of the Fourier coefficients: Ck2 = Ak2 + Bk2 as a function of the frequencies (or periods). Here, A and B are the real and imaginary parts of the complex Fourier coefficients. In both cases (Figure 10 and Figure 11) the results indicate pronounced semi-annual and annual variations, both for ozone and NO2. In addition to the usual calculation of periodicity, it is also very important to give some perspective on how precise those calculations are. In order to do that, the new method for calculating margins of errors in Fourier transformation is used [28]. This method gives a semi-annual period for ozone as 6.0 ± 0.5 months and an annual period as 12 ± 4 months, while the periods for NO2 can be given as 6 ± 1 months and 12 ± 3 months, respectively. The relatively wide margins of error can be attributed to the fact that these calculations have been based on the monthly average concentrations of the gases in question. Error calculations also showed its advantage in that the suspected periodicity of three years for ozone can be safely discarded since it would be otherwise given as 40 ± 30 months, which clearly has no real significance.
In general, these studies show seasonal patterns of stratospheric ozone and NO2 concentrations over the central part of Brazil, with the highest concentrations of both gases occurring in September and October. The reason for this could be related to increased emissions due to seasonal changes and natural sources such as fires but also shows the stability of concentrations during the observed period despite the increasing number of fires [34]. The observed trends and the results of the Fourier analysis support the notion of semi-annual and annual cycles in the distribution of these gases, which may be a consequence of the circulation of atmospheric masses and photochemical processes. The lack of statistically significant changes in ozone and NO2 concentrations between years can be explained by the stable atmospheric conditions in this equatorial region, where seasonal fluctuations have less of an impact on stratospheric ozone than in the polar regions. This finding can relate to the finding that even record wildfires in Australia have influences on the ozone layer over Antarctica, reducing it from 20 to 25% while such occurrence is not noted in the place of the wildfires, i.e., in Australia itself [35]. Previous episodes of such wildfires in Australia, like those in 2019, caused warming of the stratosphere over Antarctica in September of the same year, although the wildfires were only one of the contributors to that exceptional effect, with others being vast changes in air circulation, disturbances in the ionosphere and, at the time, recovery of an ozone holes [36]. The fact that the ozone hole was, at the time at its minimum size, was also confirmed by Klekociuk et al. [37] who concluded that ozone holes are inherently connected to climate changes in the Southern hemisphere, albeit not referring to wildfires as the possible causes of a disturbance in the dynamic equilibrium in the atmosphere at the moment. Another possible explanation of a relatively stable at-site concentration of ozone and lack of its depletion can be attributed to the possibility of further vertical movement of air masses, which can cause increased production of ozone which is shown to be able to compensate for as much as 70% of original depletion. But if the depletion is low, then it can be suspected that an even higher percentage of ozone recovery may be obtained [38]. It is well known, however, that fluids are prone to horizontal transfers much more than on vertical ones. It is shown that vertical transfers of particles from wildfires can reach the tropopause. However, together with the conclusions of Ma et al. [38], another study in Northern hemisphere shows that even massive wildfires like those in Canada in 2023 can have a negligible impact on the stratosphere, or even the tropopause which is directly below it [39]. Therefore, the same explanation can be given in our case. This is even more plausible given the fact that Brazil is located right at the equator while Canada is at the mid-latitudes of the Northern hemisphere. It is also worth mentioning that even earlier studies on the issue, show that very far wildfires or volcano eruptions may influence ozone layer depletion over poles. It is even shown that volcano eruptions on the Northern hemisphere may cause an increase in ozone holes over Antarctica. On the other hand, it is shown that new ozone holes can cause hemispheric-scale surface climate change [40]. All of those studies can give us an explanation as to why no significant depletion in the ozone layer was not observed above Brazil, which is confirmed with these measurements and statistical analyses given in this article. It is, however, also worth mentioning here that such vertical transfers of material to the stratosphere from the wildfires can cause another phenomenon that is seldom observed—a stratospheric intrusion. Of course, for an intrusion to occur, some other atmospheric conditions must be fulfilled but this observation is very significant because tropospheric ozone levels are always significantly increased during such events. This then may cause various problems because of persistence (for hours if not days) above the permitted levels of tropospheric ozone, which can cause significant health problems and also have a negative effect on agriculture and vegetation in general [41].

4. Conclusions

The research results show the persistence of stratospheric ozone and NO2 over central Brazil over a period of 16 years (2005 to 2020), with seasonal cycles related to natural phenomena and emissions. Although more fires were recorded, they did not lead to significant changes in ozone and NO2 concentrations. This research contributes to a better understanding of the dynamics of stratospheric ozone in equatorial regions, where the stability of ozone concentrations can have positive effects on health and the environment, but also emphasises the importance of further monitoring to better understand the long-term effects of climate and anthropogenic factors on stratospheric ozone. There is also significant importance in addressing the anthropogenic influences and regional emissions on NO2 and O3 concentrations. This research was focused on stratospheric ozone and NO2 analysis over Brazil, with special emphasis on wildfires and volcano eruptions as a primary natural and partly anthropogenic (in the case of wildfires) cause that may influence O3 and NO2 concentrations. Although global atmospheric circulations have an effect on the various compounds transfer that can damage the ozone layer, stratospheric ozone over Brazil has a higher stability and lower variability in comparison with the polar regions. These results again prove that stability, showing low variability and trends in ozone and NO2 concentrations during the 16-year period despite the increase in the number of wildfires, which may be explained in the way that local, stable, atmospheric conditions, can lower the influence of certain regional emissions in stratosphere. Using satellite data for stratospheric concentrations gives great spatial and time coverage but not information on the influence of certain ground anthropogenic sources, like agriculture, which are beyond the scope of this paper.

Author Contributions

Conceptualization, E.K.-A. and V.G.; methodology, V.G. and B.M.; software, V.G. and B.M.; validation, E.K.-A., N.S. and A.d.S.; formal Analysis, V.G., B.M. and N.S.; investigation, E.K.-A. and A.d.S.; resources, A.d.S.; data curation, E.K.-A., V.G. and A.d.S.; writing—original draft preparation, E.K.-A., V.G. and A.d.S.; writing—review and editing, V.G., B.M. and N.S.; visualisation, V.G., B.M. and N.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Atmosphere 16 01159 g001
Figure 1. Locations of the Mato Grosso do Sul weather stations. (a) depicts location of Mato Grosso do Sul province within Brazil. The location of weather stations are shown on other subfigures; in addition (b) shows altitude distribution in metres above sea level, (c) shows climate zones, and (d) shows biogeographic regions.
Figure 1. Locations of the Mato Grosso do Sul weather stations. (a) depicts location of Mato Grosso do Sul province within Brazil. The location of weather stations are shown on other subfigures; in addition (b) shows altitude distribution in metres above sea level, (c) shows climate zones, and (d) shows biogeographic regions.
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Atmosphere 16 01159 g002
Figure 2. Relationship between O3 (blue) and NO2 (red) concentrations during the 16-year period.
Figure 2. Relationship between O3 (blue) and NO2 (red) concentrations during the 16-year period.
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Atmosphere 16 01159 g003
Figure 3. Box and whiskers plot of yearly ozone concentrations.
Figure 3. Box and whiskers plot of yearly ozone concentrations.
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Figure 4. Box and whiskers plot of yearly NO2 concentrations.
Figure 4. Box and whiskers plot of yearly NO2 concentrations.
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Figure 5. Monthly average ozone concentrations in DU during the observed period. Dotted line represents a trendline of changes in the same period from 2005 to 2020.
Figure 5. Monthly average ozone concentrations in DU during the observed period. Dotted line represents a trendline of changes in the same period from 2005 to 2020.
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Figure 6. Monthly average NO2 concentrations in 1015 cm−2 during the observed period. Dotted line represents a trend line of changes in the same period from 2005 to 2020.
Figure 6. Monthly average NO2 concentrations in 1015 cm−2 during the observed period. Dotted line represents a trend line of changes in the same period from 2005 to 2020.
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Atmosphere 16 01159 g007
Figure 7. Dependence of ozone concentrations on the number of hot spots in the period from 2005 to 2020.
Figure 7. Dependence of ozone concentrations on the number of hot spots in the period from 2005 to 2020.
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Figure 8. Dependence of NO2 concentrations on the number of hot spots in the period from 2005 to 2020.
Figure 8. Dependence of NO2 concentrations on the number of hot spots in the period from 2005 to 2020.
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Figure 9. Air trajectory calculated using METEX software and a kinematic model on the 1st of July of the respective years (from the left to right and from the top to bottom: 2006, 2010, 2014 and 2018). Trajectory is calculated for 10 days backwards from the date mentioned above. Blue lines depict the trajectory, red dots show the middle point of the observation area while the triangles show positions of the air masses with a one day difference.
Figure 9. Air trajectory calculated using METEX software and a kinematic model on the 1st of July of the respective years (from the left to right and from the top to bottom: 2006, 2010, 2014 and 2018). Trajectory is calculated for 10 days backwards from the date mentioned above. Blue lines depict the trajectory, red dots show the middle point of the observation area while the triangles show positions of the air masses with a one day difference.
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Figure 10. Periodogram of average ozone values derived from the original monthly averages for the period from 2005 to 2020 with periods shown in months. The main peaks are shown and described in the figure with the values of 6.0 ± 0.5 months, 12 ± 4 months and 3 years, representing the main periodicities of ozone. Periods shown on the figure are given in months.
Figure 10. Periodogram of average ozone values derived from the original monthly averages for the period from 2005 to 2020 with periods shown in months. The main peaks are shown and described in the figure with the values of 6.0 ± 0.5 months, 12 ± 4 months and 3 years, representing the main periodicities of ozone. Periods shown on the figure are given in months.
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Figure 11. Periodogram of average NO2 values derived from the original monthly averages for the period from 2005 to 2020 with periods shown in months. The main peaks are shown and described in the figure with the values of 6 ± 1 months and 12 ± 3, representing the main periodicities of NO2. Periods shown on the figure are given in months.
Figure 11. Periodogram of average NO2 values derived from the original monthly averages for the period from 2005 to 2020 with periods shown in months. The main peaks are shown and described in the figure with the values of 6 ± 1 months and 12 ± 3, representing the main periodicities of NO2. Periods shown on the figure are given in months.
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Table 1. Pearson’s coefficient analysis of monthly average data of various air components during the observed period from 2005 to 2020. The data includes number of hotspots, rain, evaporation, relative humidity, maximum and minimum temperature, wind speed, ozone and nitrogen oxide concentrations. Calculated values are based on all of the monthly average data available.
Table 1. Pearson’s coefficient analysis of monthly average data of various air components during the observed period from 2005 to 2020. The data includes number of hotspots, rain, evaporation, relative humidity, maximum and minimum temperature, wind speed, ozone and nitrogen oxide concentrations. Calculated values are based on all of the monthly average data available.
HotspotsRainEvaporationHumidityTmaxTminSpeedO3NO2
Hotspots1
Rain–0.381491
Evaporation0.335563–0.085211
Humidity–0.365320.6197480.241130271
Tmax0.0344890.2682330.7064317930.7617157051
Tmin–0.039690.4224850.6393047750.8316334640.9381481
Speed0.375435–0.330150.400937185–0.47241823–0.07233–0.118221
O30.340537–0.155810.615287145–0.096750640.2912030.2374020.481981
NO20.442866–0.240780.558262827–0.244941660.1936050.1171990.5886260.5035151
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Kovač-Andrić, E.; Gvozdić, V.; Matasović, B.; Sakač, N.; de Souza, A. Analysis of Stratospheric Ozone and Nitrogen Dioxide over Mid-Brazil for a Period from 2005 to 2020. Atmosphere 2025, 16, 1159. https://doi.org/10.3390/atmos16101159

AMA Style

Kovač-Andrić E, Gvozdić V, Matasović B, Sakač N, de Souza A. Analysis of Stratospheric Ozone and Nitrogen Dioxide over Mid-Brazil for a Period from 2005 to 2020. Atmosphere. 2025; 16(10):1159. https://doi.org/10.3390/atmos16101159

Chicago/Turabian Style

Kovač-Andrić, Elvira, Vlatka Gvozdić, Brunislav Matasović, Nikola Sakač, and Amaury de Souza. 2025. "Analysis of Stratospheric Ozone and Nitrogen Dioxide over Mid-Brazil for a Period from 2005 to 2020" Atmosphere 16, no. 10: 1159. https://doi.org/10.3390/atmos16101159

APA Style

Kovač-Andrić, E., Gvozdić, V., Matasović, B., Sakač, N., & de Souza, A. (2025). Analysis of Stratospheric Ozone and Nitrogen Dioxide over Mid-Brazil for a Period from 2005 to 2020. Atmosphere, 16(10), 1159. https://doi.org/10.3390/atmos16101159

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