Seasonal dynamics and geographic influences on the total ozone column in the Maceió region

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Takemura, Deniz Özonur, Elias Silva de Medeiros, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4897879/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study analyzes the Total Ozone Column (TCO) over six cities in the state of Alagoas, Brazil, with the objective of evaluating their spatial and temporal homogeneity and identifying seasonal and annual patterns over the period from 2008 to 2016. OCT is an important indicator for monitoring the ozone layer and its implications for public health, due to the role of ozone in filtering ultraviolet radiation. For the analysis, OCT data provided by satellite measurements were used, and the homogeneity of the variances was verified by means of the Bartlett test with a significance level of 95%. In addition, descriptive statistical analyses were performed to characterize the distribution of TCO values over time, and probability density functions (PDFs) were applied to identify the distribution that best fits the data. The results showed a significant homogeneity in the annual and seasonal concentrations of TCO, with an annual average of 263.24 ± 9.91 DU. The results indicated that the seasonal cycle of TCO is dominated by a biannual cycle, with two maximums and two minimums throughout the year, reflecting the influence of the Earth's orbit around the Sun and the photochemistry of ozone in the stratosphere. The highest seasonal average TCO was observed during the spring, while the lowest values occurred in the fall. The Normal distribution was identified as the one that best represents the data over the analyzed period. These patterns reflect the influence of the Brewer-Dobson Circulation, which contributes to the uniform distribution of ozone in the stratosphere, minimizing the impacts of atmospheric phenomena such as the Antarctic Polar Vortex. In conclusion, this study provides a comprehensive overview of TCO variability in six cities in Alagoas, highlighting the importance of continuous monitoring to understand atmospheric dynamics and their implications for health and the environment. The limitations of the work, such as the sensitivity of the statistical tests and the restricted geographic coverage, indicate the need for future studies to deepen the understanding of the factors that affect the distribution of ozone in the region. Total Ozone Column Brazil Seasonal variability Anthropogenic Factors Air quality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Atmospheric ozone is essential for the Earth's radiative balance and for protection against UV-B radiation harmful to living things (Gilford and Solomon, 2016; Ming et al., 2017; Kim et al., 2013). Changes in ozone concentration affect atmospheric dynamics, influencing cloudiness, cyclone frequency, Hadley cell expansion, and precipitation patterns (Grise et al., 2013; Grise et al., 2014). Ozone-depleting substances are primarily responsible for long-term variations (Steinbrecht et al., 2018; Weber et al., 2018), but dynamic factors such as El Niño-Southern Oscillation (ENSO) and the Quasi-Biennial Oscillation (OQB) also influence ozone variability at different time scales (Peres et al., 2017; Toihir et al., 2018). The Brewer-Dobson Circulation, which transports ozone from the tropical stratosphere to the poles, is affected by these interannual variabilities (Albers et al., 2017; Diallo et al., 2018). Studies indicate that phenomena such as ENSO play a crucial role in modulating ozone concentrations, and it is essential to understand how these dynamics affect the Total Ozone Column (CTO), especially in vulnerable regions such as the Northeast of Brazil, where sun exposure is intense (Xie et al., 2014; Zhang et al., 2015). Recent research investigates the variability of OCT at different geographic and temporal scales, examining the influence of natural and anthropogenic factors. Coldewey-Egbers et al. (2022) analyzed the global TCO from 1995 to 2020, highlighting regional variations and the influence of ENSO. They noted that while ozone recovery in the upper stratosphere is ongoing, trends in the lower stratosphere are still uncertain. Another study, conducted by Wang et al. (2024), explored the variability of TCO in southwestern Europe between 2010 and 2021, revealing that the distribution of ozone is affected by both natural factors and human activities, such as industrial emissions and urban traffic. Long-term monitoring of ozone is crucial to assess the effectiveness of the Montreal Protocol. Despite the significant reduction of ozone-depleting substances (ODS), positive ozone recovery trends are not yet statistically significant on a global scale (Braesicke et al., 2018). Interannual ozone variability, especially at mid- and high-latitudes, may mask these trends, and feedback mechanisms with climate change may introduce additional variations (Coldewey-Egbers et al., 2022; Wang et al., 2024). The evolution of ozone in the troposphere, particularly in the tropics, complicates the determination of the overall trend in TCO, with regional increases observed due to factors such as ENSO (Coldewey-Egbers et al., 2022; Wang et al., 2024). Initiatives such as ESA's Climate Change Initiative (CCI) and the Copernicus Climate Change Service (EU-C3S) are key to generating climate data records, enabling comprehensive analysis of total ozone columns and ensuring a better understanding of ozone dynamics at different temporal and spatial scales. This study uses TCO data collected between 2008 and 2016 by the OMI sensor aboard the Aura satellite. The objective is to evaluate TCO trends and spatial distribution in Alagoas, contributing to a deeper understanding of regional air quality dynamics and broader atmospheric implications. Materials and methods 2.1 Field of study and data The study area covers latitudes 09º 19' 06" S to 10º 07' 32" S and longitudes 35º 33' 40" W to 37°26'12"W, with an average altitude of about 100 meters above sea level, as shown in Figure 1. Data from the OMI sensor on the Aura satellite from January 2008 to December 2016 were used. Table 1 provides geographic coordinates (latitude and longitude), altitude, and statistical summaries of total ozone concentrations (TCO) in all municipalities. OCT measurements are performed using solar radiation spectrometry and advanced algorithms, which are essential for interpreting the data and formulating effective air pollution control strategies (Levelt et al., 2006a; Levelt et al., 2006b; Lyra et al., 2014; Souza et al., 2021). These methods not only help in the identification of sources of pollution, such as O3 and NO2, but are also fundamental for the development of mitigation strategies adapted to different climate zones. Data processing and statistical analysis The Environmental Information System Integrated into Environmental Health (SISAM), originally initiated in 2008, is the result of a collaboration between the Oswaldo Cruz Foundation (Fiocruz), the National Institute for Space Research (INPE), the Ministry of Health (MS) and the Pan American Health Organization (PAHO/WHO). This online platform makes it possible to assess exposure to air pollution and its impacts on human health by providing not only recent data on estimated air pollution concentrations, urban and industrial pollution, but also the monitoring of fire outbreaks and retroactive weather data between 2000 and 2019 for all municipalities in Brazil (Fiocruz, 2019). In SISAM, all ten satellites equipped with orbital sensors in the 4μM thermal band are used. This data is processed operationally, in the Imaging Division (DGI) and in the Satellite and Environmental Systems Division, imagery from polar satellites, including AVHRR/3 from NOAA-18 and 19, Metop-B and C, as NASA Earth and aqua modes, and as viirs from NPP-suomi and NOAA-20. Geostationary satellite images are also used, such as Aurea from the IMO sensor (INPE, 2019). Despite its scope and technological sophistication, Sisam faces an important limitation: the lack of surface stations for data validation. The use of satellite data is valuable for providing a broad and continuous view of the environment, but this data has its inherent limitations and uncertainties. The main problems associated with the lack of surface stations include: satellite data can be influenced by various atmospheric and environmental factors, such as cloud cover, aerosols, and temporal and spatial variability. Without validation through surface measurements, it is difficult to guarantee the accuracy and accuracy of the estimates provided by satellites. Although satellites provide data with wide spatial coverage, spatial resolution can be limited. This means that fine details, such as local variations in air pollution, cannot be captured accurately. Surface stations can provide detailed, localized measurements that complement satellite observations. Satellite data needs to be calibrated and validated with actual measurements to ensure that the observations are correct. Surface stations are essential to this validation process, providing a reliable point of comparison. Surface stations can provide a continuous history of data at a fixed point, which is crucial for long-term studies and trend detection. Satellites, while comprehensive, may have gaps in data due to technical or orbital issues. Some satellite technologies may have difficulty distinguishing between different types of pollutants or their specific concentrations. Surface stations, equipped with specific instruments, can provide more detailed data on the composition of pollutants. Therefore, to maximize the effectiveness of SISAM and ensure that it can provide highly reliable and usable data for public health and environmental policy, it is critical to invest in the installation and maintenance of a comprehensive network of surface monitoring stations. These stations would not only validate satellite data, but also provide crucial supplemental information for an accurate and detailed assessment of air quality and its impacts on human health. Table 1 : Geographic coordinates, population, area and statistics of average daily TCO concentrations in municipalities during the period (2008-2016), derived from measurements by the OMI sensor on board the Aura satellite. City Latitude (S) Longitude (W) Altitude (m) Population Area (km2|) Alverage (DU) SD CV (%) Min (DU) Max (DU) N 3. Arapiraca 09º 45' 09" 36º 39' 40" 264 233,047 345.655 263.04 9.88 3.76 236 304 2710 24.Coruripe 10º 07' 32" 36º 10' 32" 16 57,294 897.800 263.32 9.87 3.75 236.8 310.6 2717 68. Sugar Loaf 9°44'53" 37°26'12" 85 24,307 688.870 262.74 9.94 3.78 235 310 2744 67. Indian Palm Trees 9°24'58'' 36°37' 52'' 296 73,337 450.990 263.04 9.88 3.76 236 304 2710 91.São Luis do Quitunde 09º 19' 06" 35º 33' 40" 4 34,825 397.257 263.66 9.93 3.77 236 311 2686 47.Maceió 9° 39′ 59″ 35° 44′ 6″ 4 1025,000 509.600 263.66 9.93 3.77 236 311 2686 Applied Statistics The data from the ozone series were submitted to statistical analysis using descriptive statistics (mean, maximum - Max, minimum - Min, coefficient of variation - CV% and standard deviation - SD), exploratory statistics (boxplot), homogeneity test and trend analysis by the Mann-Kendall Contextual method (CMK) (See Supplementary Material 1).element. All statistical procedures were performed in the R environment version 3.4.3 (R Core Team, 2020). For the analysis of the distribution functions, the following distributions were used: Normal (or Gaussian) Distribution, Lognormal Distribution, Logistic Distribution and Weibull Distribution, and to know which is the best function that fits the data, the Kolmogorov-Smirnov test was used. (See Supplementary Material 2) Results The homogeneity test of the annual total ozone column (OCT) and the seasonal time series was performed over the state of Alagoas, using the Bartlett test (1937) with 95% significance. The results indicated that the average annual and seasonal TCO values are homogeneous for all the series analyzed. Bartlett's test is a statistical procedure that checks the homogeneity of variances between various groups or samples, testing the null hypothesis that all variances are equal against the alternative hypothesis that at least one of them is different. This test is particularly relevant in analyses such as ANOVA, where the assumption of equal variances (homoscedasticity) is essential for the validity of the results. However, it is important to note that the Bartlett test is sensitive to the normality of the data; If the data does not follow a normal distribution, the test may erroneously indicate that the variances are different. Table 1 shows the position and statistical dispersion of the concentrations of the Total Ozone Column (TCO) in the municipalities analyzed. The annual average of TCO in the region is 263.24 ± 9.91 DU (Dobson Units), with a maximum value of 311 DU observed in the cities of Maceió and São Luís do Quitunde, and a minimum value of 304 DU in Arapiraca. When analyzing the annual averages of OCT, it was considered essential to assess the degree of dispersion of these data. The standard deviation (σ), which measures the uniformity of a data set (Wilks, 2006), was 9.91 DU on average, with a maximum variation of 9.94 DU and a minimum of 9.87 DU between cities. The average coefficient of variation was 3.77%, with a maximum of 3.78% and a minimum of 3.75%, indicating a low variation and suggesting that the regions studied have homogeneous characteristics. This homogeneity in the distribution of TCO can be attributed to region-specific atmospheric factors, such as the Brewer-Dobson Circulation (CBD), which, according to Hauchecorne et al. (2002), Mohanakumar (2008) and Solomon et al. (2016), does not suffer significant interference from the Antarctic Polar Vortex (VPA), whose intensity is weak in this area. This atmospheric dynamic facilitates the uniform distribution of observed ozone concentrations. From an environmental and public health perspective, variations in TCO concentration can have significant impacts. The concentration of ozone in the stratosphere is crucial for protecting life on Earth from harmful ultraviolet (UV) rays. Therefore, the homogeneity in the distribution of ozone suggests a relatively uniform protection against UV radiation in Alagoas, which is positive for public health. On the other hand, any significant variation in TCO may indicate changes in atmospheric conditions that could lead to adverse consequences, such as increased risk of skin cancer and other health problems related to UV radiation exposure. Additionally, TCO analysis can provide important insights into climate change and atmospheric circulation patterns, which are key to understanding large-scale environmental changes. Therefore, ongoing studies on TCO and its variations are essential to monitor environmental impacts and protect public health. Figure 2 illustrates the average monthly TCO in DU in the six cities from 2008 to 2016. Maceió and São Luiz do Quitunde recorded the highest TCO values at 263.66 ± 9.93 DU, with São Luiz do Quitunde reaching a maximum of 311 DU. On the other hand, Pão de Açúcar de Açúcar recorded the lowest TCO at 235 DU, with a variation of 76 DU. Compared to Souza et al. (2022a), ozone concentrations in Mato Grosso do Sul from 2005 to 2020 exhibited significant variability, ranging from 260 DU in the Pantanal to 347 DU in the Cerrado, with a variation of 101 DU. The analysis highlights temporal and seasonal fluctuations in ozone concentrations, reaching the peak on 09/13/2010 (winter) for the cities of São L do Quintude and Maceió (311 DU); 09/13/2010 (winter) for the cities of Arapiraca (304 DU), Coruripe (319) and Palmeira dos Índios (304 DU) and Pao de Açucar on 10/22/2010 (winter) with (310 DU). In the seasons of the year, the highest average TCO occurs in the spring season with an average of 271 DU. This seasonality underscores the profound influence of climatic and seasonal factors on ozone dynamics, emphasizing its critical role in interpreting atmospheric trends. These findings deepen our understanding of ozone distribution and the impact of seasonal and climate variations, for effective environmental management and strategies to mitigate potential impacts on regional air quality. The homogeneous distribution of OCT according to Hauchecorne et al. (2002), Mohanakumar (2008) and Solomon et al. (2016), can be attributed to the Brewer-Dobson Circulation (CBD), which does not suffer significant interference from the Antarctic Polar Vortex (VPA), which, in this region, has a weak intensity. This atmospheric dynamic contributes to the uniform distribution of observed ozone concentrations. In southern Brazil, Crespo et al. (2011) analyzed the average behavior of the total ozone column and observed a decline in minimum values from 1979 to 1996. Peres et al 2024, using total ozone column (TOC) data, studied the mean reduction of 7.0 ± 2.9 DU in 62 events over southern Brazil between 2005 and 2014, using total ozone column (TOC) data. These events were more frequent in October, the positive phase of the ENSO (El Niño-Southern Oscillation) index had a significant influence on most events, while the Quasi-Biennial Oscillation (QBO) showed a balance in its influence. These findings underscore the importance of understanding the interplay between global and regional phenomena on ozone variability In Alagoas, from 2008 to 2017, monthly averages showed a drop in minimum ozone values in May, at a rate of 5% per year (see Supplementary Material 1.1). The time series of the Total Ozone Column (TCO) analyzed by the Mann-Kendall (MK) method showed slightly similar trends, with a slight increase in tropospheric ozone, ranging from 1.75 DU for Cururipe to 2.84 DU for São Luis do Quitunde. However, trend analysis by the Mann-Kendall test revealed a slight decrease in stratospheric ozone for the summer, autumn, and spring seasons, but an increase in the winter ozone column (OCT) (MS 1.1, Table 2). The relatively low increasing trend of ozone in the ozone column (TCO) may be related to an increase in emissions of ozone precursors. It is observed that, for the annual values, the Mann-Kendall (MK) test indicates significant trends at the level of 5% of significance in the cities of Arapiraca, Palmeira dos Índios, São Luís do Quitunde and Maceió. Regarding the summer and spring seasons, the MK test did not identify any significant trend. For the autumn season, significant trends were identified in the cities of Arapiraca, Coruripe, Pão de Açúcar and Palmeira dos Índios. In winter, a significant trend was observed in the cities of Arapiraca, Palmeira dos Índios, São Luís do Quitunde and Maceió (Pohlert, T. (2016)). As reported by Clain et al. (2009), Thompson et al. (2014), and Souza et al. (2022), ozone enhancement (OCT) may be associated with anthropogenic activities such as urbanization, transportation, industrialization, and biomass burning, as well as long-range transport of pollutants. These results agree with the findings of Kim and Newchurch (1998), who investigated the influences of biomass burning on the ozone column (TCO), noting that the annual maximum of ozone during the spring (September to November) is related to increased biomass burning activity. The decrease in the Total Ozone Column (TOC) in the region can be explained by several factors that interact in a complex way in the atmosphere: The Brewer-Dobson Circulation (CBD) is an atmospheric process that transports ozone from the tropical region to the poles. Changes in the intensity and structure of this circulation can influence the amount of ozone that remains in tropical regions, including the region. During certain weather phenomena, such as El Niño, this circulation can be altered, reducing the amount of ozone transported to tropical areas. El Niño and OQB are phenomena that affect the distribution of ozone in the stratosphere and troposphere. During El Niño events, for example, changes in atmospheric circulation can lead to a temporary reduction in ozone concentrations in certain regions. These variations are caused by changes in wind patterns and the temperature of the stratosphere. The seasonal cycle of ozone can vary, with lower concentrations observed at certain times of the year due to natural changes in atmospheric dynamics. In tropical regions, the variability may be more pronounced, with influences from semiannual and semiannual modes of variability. Although the Montreal Protocol has significantly reduced the emission of ozone-depleting substances (such as CFCs), there are still residual effects that can temporarily impact CTO. In addition, local and regional pollutants can chemically interact with ozone, contributing to its depletion on a local scale. These factors, when combined, may result in a temporary reduction of the Total Ozone Column in the study area, thereby increasing the risk of exposure to UV radiation. These findings underscore the dynamic nature of ozone levels over time and emphasize the importance of ongoing monitoring and understanding for effective environmental management and policymaking. The evaluation of the behavior of interannual and seasonal variability of the CTO for the entire region is shown in Fig. 2. The interannual variability for the period 2008-2016 is represented in Fig. 2. It is possible to observe that the CTO values in the analyzed period are typically concentrated between 304 DU and 311 DU, reinforced by Sousa et al. (2020) with some atypical years 2010 where the highest values occur (Fig. 2), in line with studies by Lima (2018) with a study period from 2005 to 2015 for the same region, similar to that observed by Lopo et al. (2013) in the period 2001 to 2009 and Sousa et al. (2020) in the period from 1978 to 2013, Lima et al 2020 in the period from 1997 to 2018. The variation in OCT is not as pronounced as seen in Sahai et al. 2000, Lopo et al. (2013) and Sousa et al. (2020), but it presents a smooth curve with a variation of approximately 24.0 DU, its minimum value is in May (250 DU) and the maximum value in October (274 DU). In the first quarter of the year, January-February-March, the CTO values remain almost stationary between 262 DU and 263 DU, in the following months it decreases until reaching the lowest value, in May, with 250 DU. After this period, there is a constant increase until October, where it reaches the maximum annual value with 274 DU. November and December show a gradual decrease in values, 271 DU and 267 DU, respectively (Fig 5 and 6). This annual cycle is observed over higher latitudes, however with greater variations as shown in Peres et al. (2017) and Dias Nunes (2017; 2020). Normal, Lognormal, Logistic, and Weibull probability density functions (PDFs) were applied to model the TCO time series over a 9-year period (2008-2016). These distributions are widely used in environmental studies to analyze the frequency of variables such as ozone concentrations and temperatures. The probability density [f(x)] functions and cumulative distribution functions [F(x)] of these PDFs were employed to fit the TCO data (Table 2, see Supplementary Material 1.2), as described by Sousa (2020a) and Reis et al. (2022). To identify the most appropriate distribution function to describe OCT concentrations, we performed the Kolmogorov-Smirnov (KS) statistical test (Table 3, see Supplementary Material 1.2)), which evaluates the quality of the fit between the observed data and the theoretical distributions, providing information on the adequacy of each distribution over the study period. The analysis of the asymmetry of the empirical distributions in each city is also presented in Figures 3 (MS1.2). These figures show the distribution of the OCT data over the study period, including the estimated densities of the distributions (DM), allowing a preliminary verification of the proximity between the estimated densities and the empirical distribution of the data. Tables 2 and 3 (SM1.2) present the results of the calculations of the shape and scale parameters, as well as the results of the Kolmogorov-Smirnov (KS) test, used to select the most appropriate model for each city studied. According to the results of the KS test, it was observed that the Normal distribution was the one that came closest to the OCT data in the period evaluated, with a p-value higher than 0.05, indicating a good fit. In the study by Souza et al. (2020b), ozone concentrations in Campo Grande, Mato Grosso do Sul, Brazil, were evaluated for the year 2016. 15 PDFs were used to identify the best-fit distribution in different seasonal periods: the whole year, spring (September to December), summer (December to March, characterized by high solar radiation), autumn (March to June), and winter (June to September, characterized by low solar radiation). The study focused on the analysis of seasonal variations in the statistical behavior of PDFs. The performance of these distributions was evaluated by means of three quality tests: Kolmogorov-Smirnov (KS), Anderson-Darling (AD) and Chi-Square. The comparative analysis of the results indicated that the generalized distribution of extreme values provided a good overall adjustment throughout the year. However, specific stations showed variations in the best-fit distributions. For winter, the 3-parameter Gamma distribution was the most suitable, while the 3-parameter Lognormal distribution adjusted better in the spring, and the Weibull distribution was optimal for the summer. Autumn also showed a good fit with the Gamma distribution of 3 parameters. Interestingly, winter and autumn, characterized by lower O3 concentrations, kurtosis, and asymmetry, coincided in the distribution adjustment. On the other hand, summer and spring, marked by higher O3 concentrations and different kurtosis and asymmetry values, required distinct PDFs (Figure 3, see Supplementary Material 1.2). A noticeable negative slope from 2008 to 2009 suggests a temporary decrease in TCO during this period, followed by a gradual increase in subsequent years. It is important to note that this trend in the Alagoas region may not reflect global or regional trends. Further investigation into the causes of these TCO fluctuations, such as changes in atmospheric circulation patterns, anthropogenic emissions, or natural phenomena, would provide valuable insights into their underlying mechanisms and implications for ozone layer protection. The interannual variability of TCO is predominantly influenced by annual variations in local weather, solar activity, teleconnection patterns, and other weather modes. Solar radiation plays a critical role in modifying ozone concentrations through photochemical reactions in the upper atmosphere. In addition, the absorption of solar radiation by ozone in the stratosphere influences atmospheric thermal dynamics, thus altering atmospheric circulation and ozone distribution (Zhou et al., 2006). These interannual variations also coincide with ENSO (El Niño-Southern Oscillation) events, in which TCO tends to increase during El Niño years: 2009-2010 (+4 DU or 1.54%, 2014-2016 (3 DU or 1.13%); La Niña: 2008-2009 (-5DU or (1.89%), 2010-2011 (zero), 2011-2012 (-1 or 0.38%), 2016-2017 (-2 DU or 0.76%). These events are cyclical and vary in intensity, with some episodes being stronger or more prolonged than others. Several studies, including Zhou et al. (2013), Li et al. (2020), and Zou et al. (2020), have identified complex temporal and spatial variations in TCO. Zhou et al. (2013) reported significant negative trends, particularly in January, indicating a potential decline in ozone during this period over decades. Li et al. (2020) and Zou et al. (2020) provided additional insights, highlighting negative and positive trends in different seasons and latitudes, reflecting different analysis methodologies, data sources, and regional conditions. In addition, Kuttippurath et al. (2023) introduced further complexity by examining the Hindu Kush Himalayan and Tien Shan regions, revealing significant seasonal variations with predominantly negative trends in summer and autumn, contrasting with positive trends in winter. These findings highlight the seasonal variability in ozone behavior influenced by local climatic conditions and interactions with other factors. In conclusion, these studies underscore the complexity of TCO trends and emphasize the importance of integrated, long-term monitoring and research to fully understand regional atmospheric and climate changes. Figure 4 shows the monthly change in TCO averages for the region. The highest average monthly TCO of 275 DU was observed for the month of September in the city of Maceió, when the highest average ozone averages occur, and the lowest average monthly TCO of 250 DU was in May in the city of Coruripe. The analysis of the data presented and the comparison with the studies by Peres et al. (2017) and Sousa et al. (2020) indicate that the annual cycle dominates the seasonal variability of TCO. The annual cycle is associated with seasonal changes in solar radiation and atmospheric temperature, which affect the production and destruction of ozone. The observations of the present study, with higher concentrations of OCT in September and lower in May, corroborate the predominance of the annual cycle over the semiannual one, reinforcing the idea that seasonal variability is mainly driven by annual factors related to the seasons. Figure 5 shows the seasonal variation of the interannual averages of TCO for the region. The highest seasonal average TCO of 283 DU was observed during the spring months (OND - October, November and December) at the Coruripe station, coinciding with the peak ozone averages. In contrast, the lowest average TCO of 249 DU was observed during the fall months (AMJ – April, May, and June) in the same season. The analysis of the monthly averages of TCO highlights the importance of evaluating the dispersion of the data. Standard deviation (SD) is a measure of data uniformity (Wilks, 2006) and helps to understand variability within the data set. Figure 5 shows the seasonal variation of the interannual averages of the Total Ozone Column (TCO) for the region, showing a seasonal pattern characteristic of mid-latitudes. The highest TCO values occur during the spring, while the lowest values are recorded in the fall (Peres et al., 2017; Toihir et al., 2018). The months of April to June and August to September exhibit the lowest indices of TCO variability, indicating minimal variation and greater homogeneity (Peres et al., 2017; Toihir et al., 2018). In contrast, January, February, July, October, November, and December are transition months, with greater variability, with typical oscillations around ±7.6 DU (Xie et al., 2014a; 2014b; Lima et al., 2020; 2021). This seasonal cycle also reveals that, after spring, winter and summer continue with intermediate levels of TCO, while autumn consistently maintains the lowest values (Xie et al., 2014a; 2014b; Lima et al., 2020; 2021). The regularity of this pattern, with a single annual peak and minimum, is a reflection of the patterns observed at mid- and high-latitudes (Xie et al., 2014a; 2014b; Lima et al., 2020; 2021). It is important to highlight that the equatorial regions exhibit a considerably lower TCO amplitude (~24 DU) compared to the variability of 76 DU observed in the region studied (Xie et al., 2014a; 2014b; Lima et al., 2020; 2021). The seasonal variability of ozone in this region is influenced by factors such as atmospheric circulation, local weather conditions, and human activities (Souza et al., 2022a; 2022b). The observations of the maximum TCO values in September/October and the minimum values in May are in line with the typical seasonal behavior observed in mid-latitude regions (Souza et al., 2022a; 2022b). Differences between ozone production rates and concentrations at latitudes are attributed to large-scale atmospheric circulation patterns that redistribute air masses vertically and horizontally (Souza et al., 2022a; 2022b). Understanding these seasonal variations is crucial for assessing local air quality and implementing effective environmental policies aimed at reducing pollutant emissions and promoting the sustainable development goals (SDGs) related to health, clean energy, sustainable cities, and climate action (UN, 2015). Continuous monitoring and research are essential to further elucidate the impacts of human activities and climate change on atmospheric composition and air quality in Brazil and worldwide. The spatial distribution of the seasonal monthly average of the Total Ozone Column (TOC) during the 9-year study period (2008-2016) of the OMI sensor on the Aura satellite is represented in Figures 6a, 6b, and 7. The largest spatial distribution is observed during spring (OND), while autumn (AMJ) exhibits the smallest, consistent with the temporal trends illustrated in Figure 5. The average annual TCO value evaluated from 2008 to 2016 was 263.50 ± 1.51 DU, reaching a maximum of 264.04 ± 1.75 DU and a minimum of 268.89 ± 1.46 DU (Table 1). Specifically, the highest monthly average occurred in 2010 in the municipality of Arapiraca, measuring 266.07 DU, while the lowest was recorded in 2015 in the municipality of Palmeira dos Índios with 261.80 DU (Figure 6a). Over the years, the monthly average fluctuated around 263.50 ± 0.40 DU, as illustrated in Figure 6b, with a maximum value of 274.56 ± 0.45 DU and a minimum of 250.09 ± 0.35 DU. It is noteworthy that October had the highest monthly average of 275.08 DU in Coruripe, while May had the lowest value of 249.66 DU in Palmeira dos Índios (Figures 6a, 6b and 7). The lower value observed in May can be attributed to its proximity to the winter solstice in the southern hemisphere, while the higher values in October/November and December correlate with the zenith position of the Sun and the reduction of cloud cover in the study region. A strong similarity was identified in interannual OCT behaviors among the six study regions. Seasonal analysis of the O3 data reveals distinct variations in concentration and variability at different altitudes and seasons (Figures 5 and 7). The results indicate TCO peaks during the spring (OND - October, November, and December) and lows during the fall (AMJ - April, May, and June). This cyclical behavior is closely related to the Earth's position in its orbit around the Sun, where solar radiation influences the production of ozone in the stratosphere, resulting in two maximums and two minimums throughout the year. (Figure 5). Discussion The analysis of the Total Ozone Column (TCO) in six cities in the Alagoas region revealed significant seasonal patterns, providing new insights into local atmospheric dynamics. This study identified a dominant biannual cycle in the seasonal variability of OCT, a phenomenon that had not been widely discussed in the current literature. The annual ozone cycle is closely linked to the Earth's position in its orbit around the Sun, directly affecting the intensity of solar radiation reaching the atmosphere. This cyclical behavior is mainly due to seasonal variation in the production and destruction of ozone in the stratosphere. During spring, the Earth's greater tilt relative to the Sun increases the intensity of ultraviolet (UV) radiation, promoting the dissociation of oxygen (O2) molecules into oxygen (O) atoms, which later combine with other O2 molecules to form ozone (O3). As a result, ozone levels reach their annual maximum during the spring. In autumn, the tilt of the Earth relative to the Sun results in a lower incidence of UV radiation in the stratosphere. In addition, atmospheric circulation and lower temperatures during this season contribute to lower ozone production and greater depletion, leading to the minimum levels observed. This annual cycle is characterized by two maximums and two minimums throughout the year, resulting from the alternation of seasons and the variation in the intensity of solar radiation. In mid-latitude regions, such as the region studied, the first maximum occurs in the spring, while the second can occur on a smaller scale during the fall. Lows are usually observed in autumn and, to a lesser extent, winter. This explanation provides a clear insight into how the interaction between solar radiation and atmospheric dynamics influences the annual ozone cycle. The results indicate TCO peaks during the spring (OND - October, November, and December) and lows during the fall (AMJ - April, May, and June). This cyclical behavior is closely related to the Earth's position in its orbit around the Sun, where solar radiation influences the production of ozone in the stratosphere, resulting in two maximums and two minimums throughout the year. These patterns are consistent with those found by Xie et al. (2014a, 2014b), who observed similar seasonal variations in mid-latitude regions. In regional terms, coastal cities, such as Maceió and Coruripe, had the highest seasonal averages of TCO, especially during the spring. This can be attributed to the higher intensity of solar radiation and the influence of sea currents, which can affect local atmospheric circulation. In contrast, inland cities, such as Palmeira dos Índios, showed less variability in TCO, suggesting more stable atmospheric conditions. Comparing with previous studies, such as those by Lima et al. (2020, 2021), which also observed seasonal patterns in other mid-latitude regions, the findings of this study provide a new perspective by highlighting the dominance of the biannual cycle in the Alagoas region. This finding suggests the need to reevaluate theories about ozone dynamics in tropical regions. In addition to contributing to scientific understanding, seasonal variations in OCT have practical implications for air quality and environmental policymaking in the region. Understanding these patterns can inform strategies for mitigating the effects of climate change and help develop more effective public health policies. Regarding the possible limitations of the study, the TCO data were obtained from satellites, which may represent a limitation due to the restricted temporal and spatial coverage. Insufficiently long observation periods or limited spatial data may affect the representativeness of the results. The accuracy of TCO data can also be influenced by factors such as the resolution of the measuring instruments, calibration errors, or atmospheric interference. The lack of complementary data, such as detailed meteorological information, may limit the interpretation of the mechanisms underlying the observed variations in OCT. In addition, the choice of statistical methods used to analyze the variation in TCO can influence the results, and alternative or more advanced methods could provide different insights. Analyses performed on monthly, annual, and seasonal averages can mask diurnal variability or extreme events that are important for a full understanding of ozone dynamics. It is possible that the results indicate correlations, but without proving direct causality between the variables analyzed and the variations in OCT. Generalizing the results to different contexts or periods can be challenging, especially if the study was conducted under specific environmental or social conditions. Comparing results with the existing literature may be limited if the studies being compared were conducted under significantly different environmental, temporal, or methodological conditions. Conclusion This study investigated the seasonal and interannual variability of the Total Ozone Column (OCT) in six cities in the region of Alagoas, using data obtained by satellites and statistical analyses. The results revealed a clear seasonal pattern, with the highest TCO values occurring during the spring (October to December) and the lowest values during the fall (April to June). This seasonal cycle is influenced by factors such as the Earth's orbit around the Sun and the photochemistry of ozone in the stratosphere. In addition, it was observed that seasonal variability is dominated by a biannual cycle, with two maximums and two minimums throughout the year, reflecting the passage of the Sun through this region twice a year. The analysis revealed that the city of Coruripe had the highest OCT averages during the study period, while Maceió recorded the lowest values, highlighting the spatial heterogeneity within the region. The new findings from this study include the identification of a dominant biannual cycle in the seasonal variability of TCO, something that had not been widely reported in previous studies for this region. In addition, the variation in the interannual averages between the different cities of Alagoas suggests the influence of local factors, such as topography and proximity to the ocean, on the distribution of ozone. This work contributes to the understanding of the dynamics of stratospheric ozone in tropical and subtropical regions, providing valuable information for air quality management and the development of environmental policies in the region of Alagoas. However, it is recognized that future research should explore in greater detail the meteorological and anthropogenic factors that affect OCT, as well as the integration of more advanced forecasting models to improve the accuracy of the analyses. Declarations Data availability All data generated or analyzed during this study are included in this article and are available at http://aura.gsfc.nasa.gov - Packaging None. Ethical Approval This work is not applied to humans or animals. Consent to participate We all have consent to participate in this work. Consent to Publication We all agreed to publish. Conflicting interests The authors declare that there are no conflicting interests. 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05:59:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4897879/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4897879/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66329421,"identity":"d226dbe8-ba29-4891-be0a-e7bb56e64c2c","added_by":"auto","created_at":"2024-10-10 13:14:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2017441,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of automatic weather stations in the state of Alagoas and their respective altitudes (m).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4897879/v1/e16bc340b38ace8e2a32e24b.png"},{"id":66329422,"identity":"81171880-47f3-46df-8ee8-143d37b405df","added_by":"auto","created_at":"2024-10-10 13:14:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":381120,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal evolution of the monthly average TCO over Alagoas for the period (2008-2016) obtained from the OMI sensor on the Aura satellite.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4897879/v1/b22b88aaa84f83c3687347f6.png"},{"id":66327296,"identity":"ff9fba02-e048-4731-9e36-400582da6ae5","added_by":"auto","created_at":"2024-10-10 13:06:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4493,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"fig.png","url":"https://assets-eu.researchsquare.com/files/rs-4897879/v1/6d046d533ce423423b84e296.png"},{"id":66329732,"identity":"fdf1fa2e-840d-4b43-b0c7-89980d678f6b","added_by":"auto","created_at":"2024-10-10 13:22:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":607980,"visible":true,"origin":"","legend":"\u003cp\u003eInterannual monthly average time series of TCO in UD in the region for the period (2008-2016) obtained from the OMI sensor of the Aura satellite.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4897879/v1/e973ebf327b796fa142a9180.png"},{"id":66327303,"identity":"6ad420d9-4cd8-49e1-9d99-13ca769776a6","added_by":"auto","created_at":"2024-10-10 13:06:59","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1456067,"visible":true,"origin":"","legend":"\u003cp\u003eInterannual seasonal time series (year stickers) of TCO in DU in the region for the period (2008-2016) obtained from the OMI sensor on the aura satellite.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4897879/v1/85b56b2939d1528225924c38.png"},{"id":66327302,"identity":"61310cb1-511f-4d46-a446-770c7d1db5c2","added_by":"auto","created_at":"2024-10-10 13:06:59","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":870446,"visible":true,"origin":"","legend":"\u003cp\u003ea) Average change in TCO in the region in the period (2008-2016), b) Average change in TCO in the region in the period (2008-2016) by year.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4897879/v1/7cc5d58654f8fa08c45bd645.png"},{"id":66327299,"identity":"379ba18a-675f-4a30-9599-3862b88bf5ab","added_by":"auto","created_at":"2024-10-10 13:06:59","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1024311,"visible":true,"origin":"","legend":"\u003cp\u003eAverage change in TCO in the region for the period (2008-2016) by months.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4897879/v1/4a6db3586ff66881854aa515.png"},{"id":66331349,"identity":"b7f15591-10c4-43eb-b3d7-112b3ea0e055","added_by":"auto","created_at":"2024-10-10 13:39:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8569235,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4897879/v1/0b468b66-1df9-456a-ae08-2c801d160e2a.pdf"},{"id":66327295,"identity":"c1ad8b70-1ec8-4f53-b643-644b25e9d55c","added_by":"auto","created_at":"2024-10-10 13:06:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":286713,"visible":true,"origin":"","legend":"","description":"","filename":"INGLESSupplementarymaterial2docx.docx","url":"https://assets-eu.researchsquare.com/files/rs-4897879/v1/d690ec56b5382763f7c1ad56.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal dynamics and geographic influences on the total ozone column in the Maceió region","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtmospheric ozone is essential for the Earth\u0026apos;s radiative balance and for protection against UV-B radiation harmful to living things (Gilford and Solomon, 2016; Ming et al., 2017; Kim et al., 2013). Changes in ozone concentration affect atmospheric dynamics, influencing cloudiness, cyclone frequency, Hadley cell expansion, and precipitation patterns (Grise et al., 2013; Grise et al., 2014). Ozone-depleting substances are primarily responsible for long-term variations (Steinbrecht et al., 2018; Weber et al., 2018), but dynamic factors such as El Ni\u0026ntilde;o-Southern Oscillation (ENSO) and the Quasi-Biennial Oscillation (OQB) also influence ozone variability at different time scales (Peres et al., 2017; Toihir et al., 2018).\u003c/p\u003e\n\u003cp\u003eThe Brewer-Dobson Circulation, which transports ozone from the tropical stratosphere to the poles, is affected by these interannual variabilities (Albers et al., 2017; Diallo et al., 2018). Studies indicate that phenomena such as ENSO play a crucial role in modulating ozone concentrations, and it is essential to understand how these dynamics affect the Total Ozone Column (CTO), especially in vulnerable regions such as the Northeast of Brazil, where sun exposure is intense (Xie et al., 2014; Zhang et al., 2015). Recent research investigates the variability of OCT at different geographic and temporal scales, examining the influence of natural and anthropogenic factors. Coldewey-Egbers et al. (2022) analyzed the global TCO from 1995 to 2020, highlighting regional variations and the influence of ENSO. They noted that while ozone recovery in the upper stratosphere is ongoing, trends in the lower stratosphere are still uncertain. Another study, conducted by Wang et al. (2024), explored the variability of TCO in southwestern Europe between 2010 and 2021, revealing that the distribution of ozone is affected by both natural factors and human activities, such as industrial emissions and urban traffic.\u003c/p\u003e\n\u003cp\u003eLong-term monitoring of ozone is crucial to assess the effectiveness of the Montreal Protocol. Despite the significant reduction of ozone-depleting substances (ODS), positive ozone recovery trends are not yet statistically significant on a global scale (Braesicke et al., 2018). Interannual ozone variability, especially at mid- and high-latitudes, may mask these trends, and feedback mechanisms with climate change may introduce additional variations (Coldewey-Egbers et al., 2022; Wang et al., 2024).\u003c/p\u003e\n\u003cp\u003eThe evolution of ozone in the troposphere, particularly in the tropics, complicates the determination of the overall trend in TCO, with regional increases observed due to factors such as ENSO (Coldewey-Egbers et al., 2022; Wang et al., 2024). Initiatives such as ESA\u0026apos;s Climate Change Initiative (CCI) and the Copernicus Climate Change Service (EU-C3S) are key to generating climate data records, enabling comprehensive analysis of total ozone columns and ensuring a better understanding of ozone dynamics at different temporal and spatial scales. This study uses TCO data collected between 2008 and 2016 by the OMI sensor aboard the Aura satellite. The objective is to evaluate TCO trends and spatial distribution in Alagoas, contributing to a deeper understanding of regional air quality dynamics and broader atmospheric implications.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Field of study and data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study area covers latitudes 09\u0026ordm; 19\u0026apos; 06\u0026quot;\u0026nbsp;S to 10\u0026ordm; 07\u0026apos; 32\u0026quot;\u0026nbsp;S and longitudes 35\u0026ordm; 33\u0026apos; 40\u0026quot;\u0026nbsp; W to 37\u0026deg;26\u0026apos;12\u0026quot;W, with an average altitude of about 100 meters above sea level, as shown in Figure 1. Data from the OMI sensor on the Aura satellite from January 2008 to December 2016 were used.\u003c/p\u003e\n\u003cp\u003eTable 1 provides geographic coordinates (latitude and longitude), altitude, and statistical summaries of total ozone concentrations (TCO) in all municipalities. OCT measurements are performed using solar radiation spectrometry and advanced algorithms, which are essential for interpreting the data and formulating effective air pollution control strategies (Levelt et al., 2006a; Levelt et al., 2006b; Lyra et al., 2014; Souza et al., 2021). These methods not only help in the identification of sources of pollution, such as O3 and NO2, but are also fundamental for the development of mitigation strategies adapted to different climate zones.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData processing and statistical analysis\u003c/p\u003e\n\u003cp\u003eThe Environmental Information System Integrated into Environmental Health (SISAM), originally initiated in 2008, is the result of a collaboration between the Oswaldo Cruz Foundation (Fiocruz), the National Institute for Space Research (INPE), the Ministry of Health (MS) and the Pan American Health Organization (PAHO/WHO). This online platform makes it possible to assess exposure to air pollution and its impacts on human health by providing not only recent data on estimated air pollution concentrations, urban and industrial pollution, but also the monitoring of fire outbreaks and retroactive weather data between 2000 and 2019 for all municipalities in Brazil (Fiocruz, \u0026nbsp;2019). In SISAM, all ten satellites equipped with orbital sensors in the 4\u0026mu;M thermal band are used. This data is processed operationally, in the Imaging Division (DGI) and in the Satellite and Environmental Systems Division, imagery from polar satellites, including AVHRR/3 from NOAA-18 and 19, Metop-B and C, as NASA Earth and aqua modes, and as viirs from NPP-suomi and NOAA-20. Geostationary satellite images are also used, such as Aurea from the IMO sensor (INPE, 2019).\u003c/p\u003e\n\u003cp\u003eDespite its scope and technological sophistication, Sisam faces an important limitation: the lack of surface stations for data validation. The use of satellite data is valuable for providing a broad and continuous view of the environment, but this data has its inherent limitations and uncertainties. The main problems associated with the lack of surface stations include: satellite data can be influenced by various atmospheric and environmental factors, such as cloud cover, aerosols, and temporal and spatial variability. Without validation through surface measurements, it is difficult to guarantee the accuracy and accuracy of the estimates provided by satellites.\u003c/p\u003e\n\u003cp\u003eAlthough satellites provide data with wide spatial coverage, spatial resolution can be limited. This means that fine details, such as local variations in air pollution, cannot be captured accurately. Surface stations can provide detailed, localized measurements that complement satellite observations.\u003c/p\u003e\n\u003cp\u003eSatellite data needs to be calibrated and validated with actual measurements to ensure that the observations are correct. Surface stations are essential to this validation process, providing a reliable point of comparison.\u003c/p\u003e\n\u003cp\u003eSurface stations can provide a continuous history of data at a fixed point, which is crucial for long-term studies and trend detection. Satellites, while comprehensive, may have gaps in data due to technical or orbital issues.\u003c/p\u003e\n\u003cp\u003eSome satellite technologies may have difficulty distinguishing between different types of pollutants or their specific concentrations. Surface stations, equipped with specific instruments, can provide more detailed data on the composition of pollutants.\u003c/p\u003e\n\u003cp\u003eTherefore, to maximize the effectiveness of SISAM and ensure that it can provide highly reliable and usable data for public health and environmental policy, it is critical to invest in the installation and maintenance of a comprehensive network of surface monitoring stations. These stations would not only validate satellite data, but also provide crucial supplemental information for an accurate and detailed assessment of air quality and its impacts on human health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Geographic coordinates, population, area and statistics of average daily TCO concentrations in municipalities during the period (2008-2016), derived from measurements by the OMI sensor on board the Aura satellite.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.54180602006689%\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.698996655518394%\"\u003e\n \u003cp\u003eLatitude\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(S)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.698996655518394%\"\u003e\n \u003cp\u003eLongitude (W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003eAltitude (m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.033444816053512%\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003eArea\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(km2|)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003eAlverage (DU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.518394648829432%\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003eCV\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003cp\u003e(DU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.354515050167224%\"\u003e\n \u003cp\u003eMax (DU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.54180602006689%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3. 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Indian Palm Trees\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.698996655518394%\"\u003e\n \u003cp\u003e9\u0026deg;24\u0026apos;58\u0026apos;\u0026apos;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.698996655518394%\"\u003e\n \u003cp\u003e36\u0026deg;37\u0026apos; 52\u0026apos;\u0026apos;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003e296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.033444816053512%\"\u003e\n \u003cp\u003e73,337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003e450.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003e263.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.518394648829432%\"\u003e\n \u003cp\u003e9.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.354515050167224%\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003e2710\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.54180602006689%\"\u003e\n \u003cp\u003e\u003cstrong\u003e91.S\u0026atilde;o Luis do Quitunde\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.698996655518394%\"\u003e\n \u003cp\u003e09\u0026ordm; 19\u0026apos; 06\u0026quot;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.698996655518394%\"\u003e\n \u003cp\u003e35\u0026ordm; 33\u0026apos; 40\u0026quot;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.033444816053512%\"\u003e\n \u003cp\u003e34,825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003e397.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003e263.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.518394648829432%\"\u003e\n \u003cp\u003e9.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.354515050167224%\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003e2686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.54180602006689%\"\u003e\n \u003cp\u003e\u003cstrong\u003e47.Macei\u0026oacute;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.698996655518394%\"\u003e\n \u003cp\u003e9\u0026deg; 39\u0026prime; 59\u0026Prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.698996655518394%\"\u003e\n \u003cp\u003e35\u0026deg; 44\u0026prime; 6\u0026Prime;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.033444816053512%\"\u003e\n \u003cp\u003e1025,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003e509.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.361204013377927%\"\u003e\n \u003cp\u003e263.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.518394648829432%\"\u003e\n \u003cp\u003e9.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.354515050167224%\"\u003e\n \u003cp\u003e311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.023411371237458%\"\u003e\n \u003cp\u003e2686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eApplied Statistics\u003c/p\u003e\n\u003cp\u003eThe data from the ozone series were submitted to statistical analysis using descriptive statistics (mean, maximum - Max, minimum - Min, coefficient of variation - CV% and standard deviation - SD), exploratory statistics (boxplot), homogeneity test and trend analysis by the Mann-Kendall Contextual method (CMK) (See Supplementary Material 1).element. All statistical procedures were performed in the R environment version 3.4.3 (R Core Team, 2020).\u003c/p\u003e\n\u003cp\u003eFor the analysis of the distribution functions, the following distributions were used: Normal (or Gaussian) Distribution, Lognormal Distribution, Logistic Distribution and Weibull Distribution, and to know which is the best function that fits the data, the Kolmogorov-Smirnov test was used. (See Supplementary Material 2)\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe homogeneity test of the annual total ozone column (OCT) and the seasonal time series was performed over the state of Alagoas, using the Bartlett test (1937) with 95% significance. The results indicated that the average annual and seasonal TCO values are homogeneous for all the series analyzed. Bartlett\u0026apos;s test is a statistical procedure that checks the homogeneity of variances between various groups or samples, testing the null hypothesis that all variances are equal against the alternative hypothesis that at least one of them is different. This test is particularly relevant in analyses such as ANOVA, where the assumption of equal variances (homoscedasticity) is essential for the validity of the results. However, it is important to note that the Bartlett test is sensitive to the normality of the data; If the data does not follow a normal distribution, the test may erroneously indicate that the variances are different.\u003c/p\u003e\n\u003cp\u003eTable 1 shows the position and statistical dispersion of the concentrations of the Total Ozone Column (TCO) in the municipalities analyzed. The annual average of TCO in the region is 263.24 \u0026plusmn; 9.91 DU (Dobson Units), with a maximum value of 311 DU observed in the cities of Macei\u0026oacute; and S\u0026atilde;o Lu\u0026iacute;s do Quitunde, and a minimum value of 304 DU in Arapiraca. When analyzing the annual averages of OCT, it was considered essential to assess the degree of dispersion of these data. The standard deviation (\u0026sigma;), which measures the uniformity of a data set (Wilks, 2006), was 9.91 DU on average, with a maximum variation of 9.94 DU and a minimum of 9.87 DU between cities. The average coefficient of variation was 3.77%, with a maximum of 3.78% and a minimum of 3.75%, indicating a low variation and suggesting that the regions studied have homogeneous characteristics.\u003c/p\u003e\n\u003cp\u003eThis homogeneity in the distribution of TCO can be attributed to region-specific atmospheric factors, such as the Brewer-Dobson Circulation (CBD), which, according to Hauchecorne et al. (2002), Mohanakumar (2008) and Solomon et al. (2016), does not suffer significant interference from the Antarctic Polar Vortex (VPA), whose intensity is weak in this area. This atmospheric dynamic facilitates the uniform distribution of observed ozone concentrations.\u003c/p\u003e\n\u003cp\u003eFrom an environmental and public health perspective, variations in TCO concentration can have significant impacts. The concentration of ozone in the stratosphere is crucial for protecting life on Earth from harmful ultraviolet (UV) rays. Therefore, the homogeneity in the distribution of ozone suggests a relatively uniform protection against UV radiation in Alagoas, which is positive for public health. On the other hand, any significant variation in TCO may indicate changes in atmospheric conditions that could lead to adverse consequences, such as increased risk of skin cancer and other health problems related to UV radiation exposure.\u003c/p\u003e\n\u003cp\u003eAdditionally, TCO analysis can provide important insights into climate change and atmospheric circulation patterns, which are key to understanding large-scale environmental changes. Therefore, ongoing studies on TCO and its variations are essential to monitor environmental impacts and protect public health.\u003c/p\u003e\n\u003cp\u003eFigure 2 illustrates the average monthly TCO in DU in the six cities from 2008 to 2016. Macei\u0026oacute; and S\u0026atilde;o Luiz do Quitunde recorded the highest TCO values at 263.66 \u0026plusmn; 9.93 DU, with S\u0026atilde;o Luiz do Quitunde reaching a maximum of 311 DU. On the other hand, P\u0026atilde;o de A\u0026ccedil;\u0026uacute;car de A\u0026ccedil;\u0026uacute;car recorded the lowest TCO at 235 DU, with a variation of 76 DU. Compared to Souza et al. (2022a), ozone concentrations in Mato Grosso do Sul from 2005 to 2020 exhibited significant variability, ranging from 260 DU in the Pantanal to 347 DU in the Cerrado, with a variation of 101 DU.\u003c/p\u003e\n\u003cp\u003eThe analysis highlights temporal and seasonal fluctuations in ozone concentrations, reaching the peak\u0026nbsp;on 09/13/2010 (winter) for the cities of S\u0026atilde;o L do Quintude and Macei\u0026oacute; (311 DU); 09/13/2010 (winter) for the cities of Arapiraca (304 DU),\u0026nbsp;Coruripe\u0026nbsp;(319) and Palmeira dos \u0026Iacute;ndios (304 DU) and Pao de A\u0026ccedil;ucar on 10/22/2010 (winter) with (310 DU). In the seasons of the year, the highest average TCO occurs in the spring season with an average of 271 DU.\u0026nbsp;This seasonality underscores the profound influence of climatic and seasonal factors on ozone dynamics, emphasizing its critical role in interpreting atmospheric trends. These findings deepen our understanding of ozone distribution and the impact of seasonal and climate variations, for effective environmental management and strategies to mitigate potential impacts on regional air quality. The homogeneous distribution of OCT according to Hauchecorne et al. (2002), Mohanakumar (2008) and Solomon et al. (2016), can be attributed to the Brewer-Dobson Circulation (CBD), which does not suffer significant interference from the Antarctic Polar Vortex (VPA), which, in this region, has a weak intensity. This atmospheric dynamic contributes to the uniform distribution of observed ozone concentrations.\u003c/p\u003e\n\u003cp\u003eIn southern Brazil, Crespo et al. (2011) analyzed the average behavior of the total ozone column and observed a decline in minimum values from 1979 to 1996.\u0026nbsp;Peres et al 2024,\u0026nbsp;using total ozone column (TOC) data, studied the mean reduction of 7.0 \u0026plusmn; 2.9 DU in 62 events over southern Brazil between 2005 and 2014, using total ozone column (TOC) data. These events were more frequent in October, the positive phase of the ENSO (El Ni\u0026ntilde;o-Southern Oscillation) index had a significant influence on most events, while the Quasi-Biennial Oscillation (QBO) showed a balance in its influence. These findings underscore the importance of understanding the interplay between global and regional phenomena on ozone variability\u003c/p\u003e\n\u003cp\u003eIn Alagoas, from 2008 to 2017, monthly averages showed a drop in minimum ozone values in May, at a rate of 5% per year (see Supplementary Material 1.1). The time series of the Total Ozone Column (TCO) analyzed by the Mann-Kendall (MK) method showed slightly similar trends, with a slight increase in tropospheric ozone, ranging from 1.75 DU for Cururipe to 2.84 DU for S\u0026atilde;o Luis do Quitunde. However, trend analysis by the Mann-Kendall test revealed a slight decrease in stratospheric ozone for the summer, autumn, and spring seasons, but an increase in the winter ozone column (OCT) (MS 1.1, Table 2). The relatively low increasing trend of ozone in the ozone column (TCO) may be related to an increase in emissions of ozone precursors. It is observed that, for the annual values, the Mann-Kendall (MK) test indicates significant trends at the level of 5% of significance in the cities of Arapiraca, Palmeira dos \u0026Iacute;ndios, S\u0026atilde;o Lu\u0026iacute;s do Quitunde and Macei\u0026oacute;. Regarding the summer and spring seasons, the MK test did not identify any significant trend. For the autumn season, significant trends were identified in the cities of Arapiraca, Coruripe, P\u0026atilde;o de A\u0026ccedil;\u0026uacute;car and Palmeira dos \u0026Iacute;ndios. In winter, a significant trend was observed in the cities of Arapiraca, Palmeira dos \u0026Iacute;ndios, S\u0026atilde;o Lu\u0026iacute;s do Quitunde and Macei\u0026oacute; (Pohlert, T. (2016)).\u003c/p\u003e\n\u003cp\u003eAs reported by Clain et al. (2009), Thompson et al. (2014), and Souza et al. (2022), ozone enhancement (OCT) may be associated with anthropogenic activities such as urbanization, transportation, industrialization, and biomass burning, as well as long-range transport of pollutants. These results agree with the findings of Kim and Newchurch (1998), who investigated the influences of biomass burning on the ozone column (TCO), noting that the annual maximum of ozone during the spring (September to November) is related to increased biomass burning activity.\u003c/p\u003e\n\u003cp\u003eThe decrease in the Total Ozone Column (TOC) in the region can be explained by several factors that interact in a complex way in the atmosphere: The Brewer-Dobson Circulation (CBD) is an atmospheric process that transports ozone from the tropical region to the poles. Changes in the intensity and structure of this circulation can influence the amount of ozone that remains in tropical regions, including the region. During certain weather phenomena, such as El Ni\u0026ntilde;o, this circulation can be altered, reducing the amount of ozone transported to tropical areas. El Ni\u0026ntilde;o and OQB are phenomena that affect the distribution of ozone in the stratosphere and troposphere. During El Ni\u0026ntilde;o events, for example, changes in atmospheric circulation can lead to a temporary reduction in ozone concentrations in certain regions. These variations are caused by changes in wind patterns and the temperature of the stratosphere. The seasonal cycle of ozone can vary, with lower concentrations observed at certain times of the year due to natural changes in atmospheric dynamics. In tropical regions, the variability may be more pronounced, with influences from semiannual and semiannual modes of variability. Although the Montreal Protocol has significantly reduced the emission of ozone-depleting substances (such as CFCs), there are still residual effects that can temporarily impact CTO. In addition, local and regional pollutants can chemically interact with ozone, contributing to its depletion on a local scale.\u003c/p\u003e\n\u003cp\u003eThese factors, when combined, may result in a temporary reduction of the Total Ozone Column in the study area, thereby increasing the risk of exposure to UV radiation.\u003c/p\u003e\n\u003cp\u003eThese findings underscore the dynamic nature of ozone levels over time and emphasize the importance of ongoing monitoring and understanding for effective environmental management and policymaking.\u003c/p\u003e\n\u003cp\u003eThe evaluation of the behavior of interannual and seasonal variability of the CTO for the entire region is shown in Fig. 2. The interannual variability for the period 2008-2016 is represented in Fig. 2. It is possible to observe that the CTO values in the analyzed period are typically concentrated between 304 DU and 311 DU, reinforced by Sousa et al. (2020) with some atypical years 2010 where the highest values occur (Fig. 2), in line with studies by Lima (2018) with a study period from 2005 to 2015 for the same region, \u0026nbsp;similar to that observed by Lopo et al. (2013) in the period 2001 to 2009 and Sousa et al. (2020) in the period from 1978 to 2013, Lima et al 2020 in the period from 1997 to 2018.\u003c/p\u003e\n\u003cp\u003eThe variation in OCT is not as pronounced as seen in Sahai et al. 2000, Lopo et al. (2013) and Sousa et al. (2020), but it presents a smooth curve with a variation of approximately 24.0 DU, its minimum value is in May (250 DU) and the maximum value in October (274 DU). In the first quarter of the year, January-February-March, the CTO values remain almost stationary between 262 DU and 263 DU, in the following months it decreases until reaching the lowest value, in May, with 250 DU. After this period, there is a constant increase until October, where it reaches the maximum annual value with 274 DU. November and December show a gradual decrease in values, 271 DU and 267 DU, respectively (Fig 5 and 6). This annual cycle is observed over higher latitudes, however with greater variations as shown in Peres et al. (2017) and Dias Nunes (2017; 2020).\u003c/p\u003e\n\u003cp\u003eNormal, Lognormal, Logistic, and Weibull probability density functions (PDFs) were applied to model the TCO time series over a 9-year period (2008-2016). These distributions are widely used in environmental studies to analyze the frequency of variables such as ozone concentrations and temperatures. The probability density [f(x)] functions and cumulative distribution functions [F(x)] of these PDFs were employed to fit the TCO data (Table 2, see Supplementary Material 1.2), as described by Sousa (2020a) and Reis et al. (2022). To identify the most appropriate distribution function to describe OCT concentrations, we performed the Kolmogorov-Smirnov (KS) statistical test (Table 3, see Supplementary Material 1.2)), which evaluates the quality of the fit between the observed data and the theoretical distributions, providing information on the adequacy of each distribution over the study period.\u003c/p\u003e\n\u003cp\u003eThe analysis of the asymmetry of the empirical distributions in each city is also presented in Figures 3 (MS1.2). These figures show the distribution of the OCT data over the study period, including the estimated densities of the distributions (DM), allowing a preliminary verification of the proximity between the estimated densities and the empirical distribution of the data.\u003c/p\u003e\n\u003cp\u003eTables 2 and 3 (SM1.2) present the results of the calculations of the shape and scale parameters, as well as the results of the Kolmogorov-Smirnov (KS) test, used to select the most appropriate model for each city studied. According to the results of the KS test, it was observed that the Normal distribution was the one that came closest to the OCT data in the period evaluated, with a p-value higher than 0.05, indicating a good fit.\u003c/p\u003e\n\u003cp\u003eIn the study by Souza et al. (2020b), ozone concentrations in Campo Grande, Mato Grosso do Sul, Brazil, were evaluated for the year 2016. 15 PDFs were used to identify the best-fit distribution in different seasonal periods: the whole year, spring (September to December), summer (December to March, characterized by high solar radiation), autumn (March to June), and winter (June to September, characterized by low solar radiation). The study focused on the analysis of seasonal variations in the statistical behavior of PDFs.\u003c/p\u003e\n\u003cp\u003eThe performance of these distributions was evaluated by means of three quality tests: Kolmogorov-Smirnov (KS), Anderson-Darling (AD) and Chi-Square. The comparative analysis of the results indicated that the generalized distribution of extreme values provided a good overall adjustment throughout the year. However, specific stations showed variations in the best-fit distributions. For winter, the 3-parameter Gamma distribution was the most suitable, while the 3-parameter Lognormal distribution adjusted better in the spring, and the Weibull distribution was optimal for the summer. Autumn also showed a good fit with the Gamma distribution of 3 parameters. Interestingly, winter and autumn, characterized by lower O3 concentrations, kurtosis, and asymmetry, coincided in the distribution adjustment. On the other hand, summer and spring, marked by higher O3 concentrations and different kurtosis and asymmetry values, required distinct PDFs (Figure 3, see Supplementary Material 1.2).\u003c/p\u003e\n\u003cp\u003eA noticeable negative slope from 2008 to 2009 suggests a temporary decrease in TCO during this period, followed by a gradual increase in subsequent years. It is important to note that this trend in the Alagoas region may not reflect global or regional trends. Further investigation into the causes of these TCO fluctuations, such as changes in atmospheric circulation patterns, anthropogenic emissions, or natural phenomena, would provide valuable insights into their underlying mechanisms and implications for ozone layer protection. The interannual variability of TCO is predominantly influenced by annual variations in local weather, solar activity, teleconnection patterns, and other weather modes. Solar radiation plays a critical role in modifying ozone concentrations through photochemical reactions in the upper atmosphere. In addition, the absorption of solar radiation by ozone in the stratosphere influences atmospheric thermal dynamics, thus altering atmospheric circulation and ozone distribution (Zhou et al., 2006). These interannual variations also coincide with ENSO (El Ni\u0026ntilde;o-Southern Oscillation) events, in which TCO tends to increase during El Ni\u0026ntilde;o years:\u0026nbsp;2009-2010 (+4 DU or 1.54%, 2014-2016\u0026nbsp;(3 DU or 1.13%);\u0026nbsp;La Ni\u0026ntilde;a: 2008-2009 (-5DU or (1.89%), 2010-2011 (zero), 2011-2012 (-1 or 0.38%), 2016-2017 (-2 DU or 0.76%).\u003c/p\u003e\n\u003cp\u003eThese events are cyclical and vary in intensity, with some episodes being stronger or more prolonged than others.\u003c/p\u003e\n\u003cp\u003eSeveral studies, including Zhou et al. (2013), Li et al. (2020), and Zou et al. (2020), have identified complex temporal and spatial variations in TCO. Zhou et al. (2013) reported significant negative trends, particularly in January, indicating a potential decline in ozone during this period over decades. Li et al. (2020) and Zou et al. (2020) provided additional insights, highlighting negative and positive trends in different seasons and latitudes, reflecting different analysis methodologies, data sources, and regional conditions.\u003c/p\u003e\n\u003cp\u003eIn addition, Kuttippurath et al. (2023) introduced further complexity by examining the Hindu Kush Himalayan and Tien Shan regions, revealing significant seasonal variations with predominantly negative trends in summer and autumn, contrasting with positive trends in winter. These findings highlight the seasonal variability in ozone behavior influenced by local climatic conditions and interactions with other factors. In conclusion, these studies underscore the complexity of TCO trends and emphasize the importance of integrated, long-term monitoring and research to fully understand regional atmospheric and climate changes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 4 shows the monthly change in TCO averages for the region. The highest average monthly TCO of 275 DU was observed for the month of September in the city of Macei\u0026oacute;, when the highest average ozone averages occur, and the lowest average monthly TCO of 250 DU was in May in the city of Coruripe. The analysis of the data presented and the comparison with the studies by Peres et al. (2017) and Sousa et al. (2020) indicate that the annual cycle dominates the seasonal variability of TCO. The annual cycle is associated with seasonal changes in solar radiation and atmospheric temperature, which affect the production and destruction of ozone. The observations of the present study, with higher concentrations of OCT in September and lower in May, corroborate the predominance of the annual cycle over the semiannual one, reinforcing the idea that seasonal variability is mainly driven by annual factors related to the seasons.\u003c/p\u003e\n\u003cp\u003eFigure 5 shows the seasonal variation of the interannual averages of TCO for the region. The highest seasonal average TCO of 283 DU was observed during the spring months (OND - October, November and December) at the Coruripe station, coinciding with the peak ozone averages. In contrast, the lowest average TCO of 249 DU was observed during the fall months (AMJ \u0026ndash; April, May, and June) in the same season. The analysis of the monthly averages of TCO highlights the importance of evaluating the dispersion of the data. Standard deviation (SD) is a measure of data uniformity (Wilks, 2006) and helps to understand variability within the data set.\u003c/p\u003e\n\u003cp\u003eFigure 5 shows the seasonal variation of the interannual averages of the Total Ozone Column (TCO) for the region, showing a seasonal pattern characteristic of mid-latitudes. The highest TCO values occur during the spring, while the lowest values are recorded in the fall (Peres et al., 2017; Toihir et al., 2018). The months of April to June and August to September exhibit the lowest indices of TCO variability, indicating minimal variation and greater homogeneity (Peres et al., 2017; Toihir et al., 2018). In contrast, January, February, July, October, November, and December are transition months, with greater variability, with typical oscillations around \u0026plusmn;7.6 DU (Xie et al., 2014a; 2014b; Lima et al., 2020; 2021).\u003c/p\u003e\n\u003cp\u003eThis seasonal cycle also reveals that, after spring, winter and summer continue with intermediate levels of TCO, while autumn consistently maintains the lowest values (Xie et al., 2014a; 2014b; Lima et al., 2020; 2021). The regularity of this pattern, with a single annual peak and minimum, is a reflection of the patterns observed at mid- and high-latitudes (Xie et al., 2014a; 2014b; Lima et al., 2020; 2021). It is important to highlight that the equatorial regions exhibit a considerably lower TCO amplitude (~24 DU) compared to the variability of 76 DU observed in the region studied (Xie et al., 2014a; 2014b; Lima et al., 2020; 2021).\u003c/p\u003e\n\u003cp\u003eThe seasonal variability of ozone in this region is influenced by factors such as atmospheric circulation, local weather conditions, and human activities (Souza et al., 2022a; 2022b). The observations of the maximum TCO values in September/October and the minimum values in May are in line with the typical seasonal behavior observed in mid-latitude regions (Souza et al., 2022a; 2022b). Differences between ozone production rates and concentrations at latitudes are attributed to large-scale atmospheric circulation patterns that redistribute air masses vertically and horizontally (Souza et al., 2022a; 2022b). Understanding these seasonal variations is crucial for assessing local air quality and implementing effective environmental policies aimed at reducing pollutant emissions and promoting the sustainable development goals (SDGs) related to health, clean energy, sustainable cities, and climate action (UN, 2015). Continuous monitoring and research are essential to further elucidate the impacts of human activities and climate change on atmospheric composition and air quality in Brazil and worldwide.\u003c/p\u003e\n\u003cp\u003eThe spatial distribution of the seasonal monthly average of the Total Ozone Column (TOC) during the 9-year study period (2008-2016) of the OMI sensor on the Aura satellite is represented in Figures 6a, 6b, and 7. The largest spatial distribution is observed during spring (OND), while autumn (AMJ) exhibits the smallest, consistent with the temporal trends illustrated in Figure 5.\u003c/p\u003e\n\u003cp\u003eThe average annual TCO value evaluated from 2008 to 2016 was 263.50 \u0026plusmn; 1.51 DU, reaching a maximum of 264.04 \u0026plusmn; 1.75 DU and a minimum of 268.89 \u0026plusmn; 1.46 DU (Table 1). Specifically, the highest monthly average occurred in 2010 in the municipality of Arapiraca, measuring 266.07 DU, while the lowest was recorded in 2015 in the municipality of Palmeira dos \u0026Iacute;ndios with 261.80 DU (Figure 6a). Over the years, the monthly average fluctuated around 263.50 \u0026plusmn; 0.40 DU, as illustrated in Figure 6b, with a maximum value of 274.56 \u0026plusmn; 0.45 DU and a minimum of 250.09 \u0026plusmn; 0.35 DU. It is noteworthy that October had the highest monthly average of 275.08 DU in Coruripe, while May had the lowest value of 249.66 DU in Palmeira dos \u0026Iacute;ndios (Figures 6a, 6b and 7). The lower value observed in May can be attributed to its proximity to the winter solstice in the southern hemisphere, while the higher values in October/November and December correlate with the zenith position of the Sun and the reduction of cloud cover in the study region. A strong similarity was identified in interannual OCT behaviors among the six study regions.\u003c/p\u003e\n\u003cp\u003eSeasonal analysis of the O3 data reveals distinct variations in concentration and variability at different altitudes and seasons (Figures 5 and 7). The results indicate TCO peaks during the spring (OND - October, November, and December) and lows during the fall (AMJ - April, May, and June). This cyclical behavior is closely related to the Earth\u0026apos;s position in its orbit around the Sun, where solar radiation influences the production of ozone in the stratosphere, resulting in two maximums and two minimums throughout the year. (Figure 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe analysis of the Total Ozone Column (TCO) in six cities in the Alagoas region revealed significant seasonal patterns, providing new insights into local atmospheric dynamics. This study identified a dominant biannual cycle in the seasonal variability of OCT, a phenomenon that had not been widely discussed in the current literature.\u003c/p\u003e\n\u003cp\u003eThe annual ozone cycle is closely linked to the Earth\u0026apos;s position in its orbit around the Sun, directly affecting the intensity of solar radiation reaching the atmosphere. This cyclical behavior is mainly due to seasonal variation in the production and destruction of ozone in the stratosphere. During spring, the Earth\u0026apos;s greater tilt relative to the Sun increases the intensity of ultraviolet (UV) radiation, promoting the dissociation of oxygen (O2) molecules into oxygen (O) atoms, which later combine with other O2 molecules to form ozone (O3). As a result, ozone levels reach their annual maximum during the spring.\u003c/p\u003e\n\u003cp\u003eIn autumn, the tilt of the Earth relative to the Sun results in a lower incidence of UV radiation in the stratosphere. In addition, atmospheric circulation and lower temperatures during this season contribute to lower ozone production and greater depletion, leading to the minimum levels observed.\u003c/p\u003e\n\u003cp\u003eThis annual cycle is characterized by two maximums and two minimums throughout the year, resulting from the alternation of seasons and the variation in the intensity of solar radiation. In mid-latitude regions, such as the region studied, the first maximum occurs in the spring, while the second can occur on a smaller scale during the fall. Lows are usually observed in autumn and, to a lesser extent, winter. This explanation provides a clear insight into how the interaction between solar radiation and atmospheric dynamics influences the annual ozone cycle.\u003c/p\u003e\n\u003cp\u003eThe results indicate TCO peaks during the spring (OND - October, November, and December) and lows during the fall (AMJ - April, May, and June). This cyclical behavior is closely related to the Earth\u0026apos;s position in its orbit around the Sun, where solar radiation influences the production of ozone in the stratosphere, resulting in two maximums and two minimums throughout the year. These patterns are consistent with those found by Xie et al. (2014a, 2014b), who observed similar seasonal variations in mid-latitude regions.\u003c/p\u003e\n\u003cp\u003eIn regional terms, coastal cities, such as Macei\u0026oacute; and Coruripe, had the highest seasonal averages of TCO, especially during the spring. This can be attributed to the higher intensity of solar radiation and the influence of sea currents, which can affect local atmospheric circulation. In contrast, inland cities, such as Palmeira dos \u0026Iacute;ndios, showed less variability in TCO, suggesting more stable atmospheric conditions.\u003c/p\u003e\n\u003cp\u003eComparing with previous studies, such as those by Lima et al. (2020, 2021), which also observed seasonal patterns in other mid-latitude regions, the findings of this study provide a new perspective by highlighting the dominance of the biannual cycle in the Alagoas region. This finding suggests the need to reevaluate theories about ozone dynamics in tropical regions.\u003c/p\u003e\n\u003cp\u003eIn addition to contributing to scientific understanding, seasonal variations in OCT have practical implications for air quality and environmental policymaking in the region. Understanding these patterns can inform strategies for mitigating the effects of climate change and help develop more effective public health policies.\u003c/p\u003e\n\u003cp\u003eRegarding the possible limitations of the study, the TCO data were obtained from satellites, which may represent a limitation due to the restricted temporal and spatial coverage. Insufficiently long observation periods or limited spatial data may affect the representativeness of the results. The accuracy of TCO data can also be influenced by factors such as the resolution of the measuring instruments, calibration errors, or atmospheric interference. The lack of complementary data, such as detailed meteorological information, may limit the interpretation of the mechanisms underlying the observed variations in OCT. In addition, the choice of statistical methods used to analyze the variation in TCO can influence the results, and alternative or more advanced methods could provide different insights. Analyses performed on monthly, annual, and seasonal averages can mask diurnal variability or extreme events that are important for a full understanding of ozone dynamics. It is possible that the results indicate correlations, but without proving direct causality between the variables analyzed and the variations in OCT. Generalizing the results to different contexts or periods can be challenging, especially if the study was conducted under specific environmental or social conditions. Comparing results with the existing literature may be limited if the studies being compared were conducted under significantly different environmental, temporal, or methodological conditions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study investigated the seasonal and interannual variability of the Total Ozone Column (OCT) in six cities in the region of Alagoas, using data obtained by satellites and statistical analyses. The results revealed a clear seasonal pattern, with the highest TCO values occurring during the spring (October to December) and the lowest values during the fall (April to June). This seasonal cycle is influenced by factors such as the Earth\u0026apos;s orbit around the Sun and the photochemistry of ozone in the stratosphere.\u003c/p\u003e\n\u003cp\u003eIn addition, it was observed that seasonal variability is dominated by a biannual cycle, with two maximums and two minimums throughout the year, reflecting the passage of the Sun through this region twice a year. The analysis revealed that the city of Coruripe had the highest OCT averages during the study period, while Macei\u0026oacute; recorded the lowest values, highlighting the spatial heterogeneity within the region.\u003c/p\u003e\n\u003cp\u003eThe new findings from this study include the identification of a dominant biannual cycle in the seasonal variability of TCO, something that had not been widely reported in previous studies for this region. In addition, the variation in the interannual averages between the different cities of Alagoas suggests the influence of local factors, such as topography and proximity to the ocean, on the distribution of ozone.\u003c/p\u003e\n\u003cp\u003eThis work contributes to the understanding of the dynamics of stratospheric ozone in tropical and subtropical regions, providing valuable information for air quality management and the development of environmental policies in the region of Alagoas. However, it is recognized that future research should explore in greater detail the meteorological and anthropogenic factors that affect OCT, as well as the integration of more advanced forecasting models to improve the accuracy of the analyses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article and are available at http://aura.gsfc.nasa.gov\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e- Packaging\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is not applied to humans or animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe all have consent to participate in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe all agreed to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicting interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbers, J.R.; Perlwitz, J.; Butler, A.H.; Birner, T.; Kiladis, G.N.; Lawrence, Z.D.; Manney, G.L.; Langford, A.O.; Dias, J. 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OCT is an important indicator for monitoring the ozone layer and its implications for public health, due to the role of ozone in filtering ultraviolet radiation. For the analysis, OCT data provided by satellite measurements were used, and the homogeneity of the variances was verified by means of the Bartlett test with a significance level of 95%. In addition, descriptive statistical analyses were performed to characterize the distribution of TCO values over time, and probability density functions (PDFs) were applied to identify the distribution that best fits the data. The results showed a significant homogeneity in the annual and seasonal concentrations of TCO, with an annual average of 263.24 ± 9.91 DU. The results indicated that the seasonal cycle of TCO is dominated by a biannual cycle, with two maximums and two minimums throughout the year, reflecting the influence of the Earth's orbit around the Sun and the photochemistry of ozone in the stratosphere. The highest seasonal average TCO was observed during the spring, while the lowest values occurred in the fall. The Normal distribution was identified as the one that best represents the data over the analyzed period. These patterns reflect the influence of the Brewer-Dobson Circulation, which contributes to the uniform distribution of ozone in the stratosphere, minimizing the impacts of atmospheric phenomena such as the Antarctic Polar Vortex. In conclusion, this study provides a comprehensive overview of TCO variability in six cities in Alagoas, highlighting the importance of continuous monitoring to understand atmospheric dynamics and their implications for health and the environment. The limitations of the work, such as the sensitivity of the statistical tests and the restricted geographic coverage, indicate the need for future studies to deepen the understanding of the factors that affect the distribution of ozone in the region.","manuscriptTitle":"Seasonal dynamics and geographic influences on the total ozone column in the Maceió region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-10 13:06:54","doi":"10.21203/rs.3.rs-4897879/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d34f1edd-b777-45ee-bfa0-cb67a0c1485f","owner":[],"postedDate":"October 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-10T13:06:56+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-10 13:06:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4897879","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4897879","identity":"rs-4897879","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}