Evaluation of Aerosol Optical Depth (Aod) Estimated by Copernicus Atmosphere Monitoring Service (Cams) in Brazil

Evaluation of Aerosol Optical Depth (Aod) Estimated by Copernicus Atmosphere Monitoring Service (Cams) in Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluation of Aerosol Optical Depth (Aod) Estimated by Copernicus Atmosphere Monitoring Service (Cams) in Brazil Altemar Lopes Pedreira Júnior, Leone Francisco Amorim Curado, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3942950/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jan, 2025 Read the published version in Theoretical and Applied Climatology → Version 1 posted 11 You are reading this latest preprint version Abstract The optical depth of aerosols plays a crucial role in scientific research and environmental policies, making it possible to understand the distribution and extent of aerosols in various regions of Brazil. The objective of this study is to evaluate the estimates of Aerosol Optical Depth (AOD) from the Copernicus Atmosphere Monitoring Service (CAMS) product in Brazil. The study covered the sites of Alta Floresta, Ji-Paraná, Rio Branco, Manaus, ATTO, São Paulo-EACH, São Paulo, Itajubá, Cuiabá, São Martinho, Petrolina and Campo Grande. Measured and estimated values were evaluated using Pearson correlation index "r", accuracy using Willmott index "d", Mean Squared Error, Mean Absolute Error and Percentage Bias. Results from the CAMS product showed good agreement with AOD measurements from the Aerosol Robotic Network. There was a strong correlation between the data, with Willmott index "d" values close to 1 and relatively low errors. However, significant differences were observed in some sites, such as Ji-Paraná, Rio Branco, Manaus and ATTO, where the CAMS tended to overestimate the AOD, while in Petrolina there was an underestimation. Variations in AOD occurred in various regions of Brazil over the years analyzed, with an increase during the dry season due to fires and human activities, and a reduction during the rainy months. The areas most affected were those close to the arc of deforestation in the Amazon. Aerosol concentrations have also been influenced by climatic factors, agricultural, industrial and urban activities in different regions of the country. This variability highlights the complexity of the natural and anthropogenic factors that affect air quality and emphasizes the importance of control and mitigation strategies for aerosol emissions. Therefore, the CAMS has demonstrated satisfactory performance in estimating the AOD in Brazil, providing valuable information on aerosol concentrations. Atmospheric aerosols South America Statistical analysis Reanalysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION Brazil, with its vast territorial extension and environmental diversity, presents a complex scenario where atmospheric aerosols play a significant role in both climate processes and air quality (Pivello et al., 2021 ). Thus, dense tropical forests, unique ecosystems, urban and industrial areas all contribute to a remarkable diversity of aerosol sources and types in the country (Borma et al. , 2022). These tiny solid or liquid particles, which come from both natural and anthropogenic activities, have a significant impact on the atmospheric balance, affecting solar radiation, cloud formation and the occurrence of rainfall (Bellouin et al. , 2020; De Oliveira et al., 2019 ). In this sense, Aerosol Optical Depth (AOD) assumes a unique relevance in scientific research and environmental policies, allowing scientists and managers to understand the extent and spatial distribution of aerosols in different regions of Brazil (De Oliveira et al., 2019 ). AOD is a crucial parameter for understanding the behavior and distribution of aerosols in the atmosphere (Pendharkar et al., 2021 ). AOD is a measure of the extent of solar radiation caused by the presence of aerosols suspended in the atmosphere (Li et al., 2021 ). Aerosols are microscopic particles, solid or liquid, which can be found naturally in the air or produced by human activities such as the burning of fossil fuels, industrial processes and forest fires (Palácios et al., 2022 ). However, observational measures of AOD are susceptible to challenges and uncertainties that need to be carefully considered. However, in situ measurements can provide accurate data, but are limited in terms of spatial and temporal coverage, as is the case with AERONET (Porfirio et al., 2020 ). AERONET is an extensive global network of monitoring stations that collect aerosol optical depth data (Holben et al., 1998 ). This data provides valuable information on the distribution and concentration of particles suspended in the atmosphere, such as dust, smoke, pollutants, and other particulate matter. The data obtained by AERONET is widely used in scientific research, climate models and to provide important information for decision-makers regarding air quality management and mitigation of the effects of aerosols on the terrestrial environment (Ogunjobi; Awoleye, 2019 ). On the other hand, the use of satellite data and atmospheric reanalysis models provides estimates on a global scale and continuous coverage, including remote and poorly monitored areas (Handschuh; Erbertseder; Baier, 2023). In this context, by combining satellite data, in situ measurements and atmospheric models, the Copernicus Atmosphere Monitoring Service (CAMS) plays a crucial role in providing accurate estimates of AOD on a global scale (Garrigues et al., 2022 ). Developed by the Copernicus Atmosphere Monitoring Service (CAMS), the CAMS reanalysis represents the latest global compilation of information on atmospheric composition. This dataset consists of three-dimensional fields of atmospheric composition parameters, including aerosols, chemical species and greenhouse gases. These fields are obtained by reanalyzing the Global Greenhouse Gas (EGG4), which focuses specifically on greenhouse gases on a global scale. This multidisciplinary approach allows for a more complete view of aerosol distribution patterns. Although CAMS has been widely used in various regions of the world, the evaluation of its reliability in the Brazilian context is still scarce (Asutosh et al., 2022 ; Dahal et al., 2022 ; Fu et al., 2022 ; Garrigues et al., 2022 ; Inness et al., 2019 ; Williams et al., 2022 ; Wu; Li; Bai, 2020 ). Therefore, given the vast territorial extension of Brazil, its ecological and environmental diversity and the sources of aerosol emissions present in the country, it is essential to investigate the accuracy of CAMS estimates in relation to AOD in the Brazilian territory. This evaluation will make it possible to identify discrepancies between CAMS estimates and observational data, as well as providing valuable insights for improving air quality monitoring (Garrigues et al., 2022 ). Therefore, the objective of this study is to evaluate Aerosol Optical Depth (AOD) estimates from the Copernicus Atmosphere Monitoring Service (CAMS) product in Brazil. 2. MATERIAL AND METHODS 2.1. STUDY AREA The study covered several collection points belonging to the Aerosol Robotic Network (AERONET) distributed in different regions of Brazil, each within a specific biome (Fig. 1 ). The following stand out: i) Amazon Biome, represented by the Alta_Floresta (Alta Floresta), Ji-Parana_SE (Ji-Paraná), Rio_Branco (Rio Branco), Manaus_Embrapa (Manaus) and Amazon_ATTO_Tower (ATTO) sites; ii) Atlantic Forest biome with the São Paulo-EACH (SP-EACH), São_Paulo (São Paulo) and Itajuba (Itajubá) sites, located in the southeast of the country; iii) Pantanal Biome with the Cuiabá-Miranda (Cuiabá) site; iv) Pampa Biome with the São_Martinho_Sonda (São Martinho) site; v); Caatinga biome with the Petrolina_Sonda (Petrolina) site and vi) Cerrado Biome with the Campo_Grande_Sonda (Campo Grande) site (Fig. 1 ). 2.2. MEASURED DATA Aerosol Optical Depth (AOD) data was collected by monitoring stations between 2003 and 2021 (18 years) (Fig. 2 ). This reference data belongs to the Aerosol Robotic Network (AERONET) project, which is available through NASA's Earth Observing System (EOS) ( http://aeronet.gsfc.nasa.gov/ ). In this study, priority was given to using level 2.0 data from the AERONET Network, which represents the highest level of quality (Holben et al., 1998 ). These data are subject to a rigorous quality assurance protocol, including corrections for local factors, guaranteeing their reliability (Palácios et al., 2022 ). AOD was analyzed for measurements taken at 500 nm. A direct comparison of the AERONET AOD data with the AOD from the CAMS product was made after converting the AERONET AOD values from 500 to 550 nm, using the Ångström Extinction Exponent (AEE) in the 440–870 nm spectral range (Eq. 1 ). $${\text{A}\text{O}\text{D}}_{550 \text{n}\text{m}} = {\text{A}\text{O}\text{D} }_{500 \text{n}\text{m}} {\left(\frac{550}{500}\right)}^{-\text{A}\text{E}\text{E}}$$ 1 2.3. ESTIMATED DATA Reanalysis data from the Copernicus Atmosphere Monitoring Service (CAMS) product, which provides information on the estimation of Aerosol Optical Depth (AOD), was used. CAMS has a spatial resolution of ~ 80 km and is made up of three-dimensional fields of atmospheric composition parameters, including aerosols ( https://atmosphere.copernicus.eu/ ). 2.4. DATA ANALYSIS Aerosol Optical Depth (AOD) data was analyzed based on averages, along with ± 95% confidence intervals, using the bootstrap resampling technique with 1,000 iterations. AOD estimates (CAMS) were disregarded when there were failures in the measured AOD record (AERONET). The dry and rainy seasons at each site were analyzed according to Table 1 . Table 1 Dry and rainy seasons for each site analyzed to evaluate Aerosol Optical Depth (AOD) estimates from the Copernicus Atmosphere Monitoring Service (CAMS) product in Brazil. Station Season Reference Dry Wet Alta Floresta July/October November/June Pereira et al., 2022 Ji-Paraná April/September October/March De Sales et al. , 2020 Rio Branco June/August September/May Duarte, 2005 São Paulo-EACH September/May Giulio et al., 2019 São Paulo Cuiabá May/September October/April Machado et al., 2016 São Martinho October/April May/September Rodrigues et al., 2023 Petrolina May/October November/April Prado e Coelho, 2017 Campo Grande April/September October/March Souza et al., 2022 Manaus July/September November/June Espínoza et al. , 2023 ATTO Itajubá April/September October/March Alves et al., 2020 The measured (AERONET) and estimated (CAMS) values were related using Pearson correlation index "r" (Eq. 2) (PEARSON et al., 1994 ) and Willmott accuracy index "d" (Eq. 3) (Willmott et al., 1985 ), which compares the distance between the estimated and measured values and ranges from 0 to 1, indicating no correspondence and perfect correspondence, respectively. The Root Mean Square Error (RMSE) (Eq. 4) was also analyzed to indicate flaws in the model when comparing the estimated and measured values, while the Mean Absolute Error (MAE) (Eq. 5) indicates the absolute average of the distances (deviations) between the estimated and measured values. Both errors should be close to zero to indicate greater precision in the estimates. The Percentage Bias (PBIAS) proposed by Gupta et al. ( 2006 ) was used to evaluate the difference between the estimated values and the measured values, indicating whether CAMS overestimates (positive PBIAS) or underestimates (negative PBIAS) the data measured by AERONET. A ratio of 1 to 1 is characterized by a PBIAS of 0.0% (Eq. 6). \(r=\frac{\sum \left({CAMS}_{\left(i\right)}-CAMS\right)\left({AERONET}_{\left(i\right)}-AERONET\right)}{\sqrt{\sum ({CAMS}_{\left(i\right) }}-CAMS)²\sum \left({AERONET}_{\left(i\right)}-AERONET\right)²}\) (2) \(d=1- \left[\frac{\sum {\left({CAMS}_{\left(i\right)}-{AERONET}_{\left(i\right)}\right)}^{2}}{\sum {\left(\left|{CAMS}_{\left(i\right)}-AERONET\right|+\left|{AERONET}_{\left(i\right)}-AERONET\right|\right)}^{2}}\right]\) (3) \(RMSE= \sqrt{\frac{\sum {\left({CAMS}_{\left(i\right)}- {AERONET}_{\left(i\right)}\right)}^{2}}{n}}\) (4) \(MAE= \sum \frac{\left|{CAMS}_{\left(i\right) }- {AERONET}_{\left(i\right)}\right|}{n}\) (5) \(PBIAS=\frac{\sum {({CAMS}_{\left(i\right)}-{AERONET}_{\left(i\right)})}^{2}}{\sum {AERONET}_{\left(i\right)}}\times 100\) (6) Where, CAMS (i) are the estimated values, AERONET (i) are the measured values, n the amount of data, AERONET and CAMS the averages of the measured and estimated values, respectively. 3. RESULTS AND DISCUSSION The highest Aerosol Optical Depth (AOD) values occurred in Alta Floresta and Ji-Paraná (AERONET = 2.02 and CAMS = 2.24), respectively (Fig. 3 ). During the dry season, there is a significant increase in average AOD in several regions of the southern Amazon (ROCHA; YAMASOE, 2013 ), especially in sites that are located in or close to the arc of deforestation, such as Alta Floresta and Ji-Paraná. This substantial increase is mainly attributed to regional emissions resulting from biomass burning, as well as the transport of aerosols from distant areas (Morais et al., 2022 ; Palácios et al., 2022 ). In addition, the central region of the Amazon is influenced by the descending branch of the Hadley cell, which causes dry conditions, favoring the concentration of aerosols in the atmosphere (Rocha; Yamasoe, 2013 ). AOD values were lowest during the rainy season in Manaus and Cuiabá (AERONET and CAMS = 0.02), which is probably only related to the cities urban emissions, such as thermal power plants near the site. The highest AOD values were observed by AERONET and CAMS in 2007. It is important to note that according to Lizundia et al. (2020), one of the largest burnt areas detected in South America was in 2007 in Brazil, Paraguay and Colombia. From 2004 to 2008, there was a consistent upward trend in AOD (Fig. 3 ). In addition, during the period from 2008 to 2016, there was a downward and stable trend in the concentration of aerosols, except for a notable increase in 2011, observed mainly at sites located in the south and southeast. This particular year was drier in these regions, conditioned by the La Nina phenomenon, which favored uncontrolled deforestation of large areas and fires that occurred mainly in pastures (De Andrade et al. , 2020). From 2017 onwards, the data indicates a clear upward trend in the concentration of aerosols again, occurring mainly in the Amazon and Cerrado biomes (Fig. 3 ). These were responsible for 86% of total particulate matter emissions in Brazil from 2003 to 2020 (Pereira et al., 2022 ). Thus, the variability of AOD over the years is evident, which can be influenced by different precipitation rates, large-scale meteorological phenomena such as El Niño and La Niña, and government policies for managing deforestation (De Andrade et al. , 2020). This variability reflects the complexity of the natural and anthropogenic factors that affect atmospheric aerosol concentrations at the sites analyzed and highlights the importance of considering these aspects when interpreting the results and implementing strategies to control and mitigate aerosol emissions (Marengo; Espinoza, 2016). The Alta Floresta, São Paulo, Cuiabá, São Martinho, Petrolina, Campo Grande and Itajubá sites showed no statistically significant differences between the CAMS estimates and the AERONET measurements (Fig. 4 ). However, at the other sites, such as Ji-Paraná, Rio Branco, Manaus, São Paulo (EACH) and ATTO, significant differences were observed with the AERONET measurements in certain months of the year. In March, Rio Branco recorded an overestimate of 66% (Fig. 4 ). In Manaus, overestimates occurred between March and June, with values ranging from 75 to 166%. ATTO was also overestimated during the months of April to June, with a range from 75 to 128% (Fig. 4 ). CAMS AOD estimates are severely limited during the assimilation of terrestrial and satellite data (Gueymard; Yang, 2020). In addition, the high occurrence of cumulus cloud clusters in the Amazon probably affected AOD detections, influencing the overestimation of estimates at these sites (Pereira et al., 2022 ). The results showed a strong correlation (indicated by *** with a significance level of p < 0.001) between the AERONET measurements and the CAMS product estimates at most of the sites assessed. Furthermore, when analyzing the agreement indices, represented by Willmott index "d", it was found that the CAMS product estimates also showed good agreement with the observed AOD values, with values approaching 1. However, the lowest values in the AOD statistical metrics were observed in Petrolina (Table 2 ). The error parameters, such as RMSE and MAE, showed relatively low values at all the sites analyzed, especially in São Paulo-EACH, Petrolina and Itajubá. However, there was variability in the results between the different sites. For example, Ji-Paraná, Rio Branco, São Martinho, Manaus, ATTO and Itajubá had high PBIAS values, indicating that the CAMS product tends to overestimate the AOD values in these locations. On the other hand, in Petrolina the CAMS product showed a tendency to underestimate AOD (Table 2 ). Table 2 Statistical parameters for Aerosol Optical Depth (AOD) measured by AERONET and estimated by the Copernicus Atmosphere Monitoring Service (CAMS) product. The parameters include: Pearson correlation index "r", Willmott index "d", Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Percentage Bias (PBIAS). Correlations with *** indicate a significance level of p < 0.001. Sites R d RMSE MAE PBIAS Alta Floresta 0.86*** 0.92 0.16 0.07 2.71 Ji-Paraná 0.93*** 0.96 0.11 0.06 15.77 Rio Branco 0.94*** 0.96 0.09 0.06 15.86 São Paulo-EACH 0.87*** 0.92 0.04 0.03 9.30 São Paulo 0.78*** 0.88 0.05 0.04 2.93 Cuiabá 0.95*** 0.97 0.07 0.05 -5.79 São Martinho 0.65*** 0.77 0.09 0.05 20.70 Petrolina 0.65*** 0.72 0.03 0.03 -20.13 Campo Grande 0.94*** 0.97 0.05 0.03 3.26 Manaus 0.88*** 0.86 0.08 0.07 27.71 Itajubá 0.85*** 0.90 0.03 0.03 12.19 ATTO 0.86*** 0.79 0.07 0.06 29.19 All the sites showed a linear relationship between the AOD measured by AERONET and the CAMS product estimates (Fig. 5 ). This behavior is evidenced by the significant values of the angular coefficient, interception coefficient, coefficient of determination (R²), as well as extremely low p-values in the linear regressions carried out at each site. This linear trend suggests that the AOD measured by AERONET and that estimated by CAMS have a well-defined relationship, allowing a regression line to be established that represents the relationship between them (Fig. 5 ). The sites in Ji-Paraná, Rio Branco, Cuiabá and Campo Grande showed relatively high R², around 0.87 to 0.90, which shows that the data fit the regression line well (Fig. 5 ). On the other hand, São Martinho and Petrolina showed lower R² coefficients, around 0.42, which indicates a less precise fit of the data to the regression line. The Campo Grande station stood out with the highest intercept coefficient value, reaching 0.97. In addition, the Manaus, Cuiabá and Ji-Paraná sites obtained considerable intercept coefficients of between 0.70 and 0.95. On the other hand, the Petrolina station had the lowest intercept coefficient value, at 0.40 (Fig. 5 ). Spatially, during certain times of the year, such as the dry season, an increase in aerosol concentrations was recorded (Fig. 6 ). This is due to the fires that occur during this period, releasing fine particles and gaseous pollutants into the atmosphere, resulting in an increase in the concentration of AOD. This is the result of the fires that have affected almost half of Brazil territory, mainly the Amazon and Cerrado biomes, which have been responsible for 86% of particulate matter emissions in Brazil over the last two decades (Pereira et al., 2022 ). On the other hand, during the rainiest months, there was a reduction in these concentrations (Fig. 6 ). Rain has the effect of temporarily clearing the atmosphere, leading to a decrease in aerosol concentrations. The monthly spatial distribution of AOD in Brazil is influenced by various factors, such as climatic conditions, human activities, fires, vegetation and natural processes. Brazil diverse climate and ecosystems also contribute to seasonal variations in aerosol concentrations. It is important to note that the regions with the highest concentrations of aerosols vary according to the period analyzed. Notably, the areas located in the arc of deforestation are the most affected by high concentrations of aerosols, especially during the month of September (Fig. 6 ). This region, located on the southern border of the Brazilian Amazon, is known for suffering from intense deforestation and agricultural activities (Pope et al. , 2020). The increase in aerosol concentrations in this region is directly linked to deforestation and fires (Pope et al. , 2020). These particles can have adverse impacts on air quality, human health and the environment. In the Midwest and Southeast, harvesting and agricultural fires in the dry season contribute to increased concentrations of particles in the atmosphere (Palácios et al. , 2016; Tariq et al., 2023 ). The Northeast faces fires during the dry season, mainly in semi-arid areas (De Oliveira et al., 2019 ; Oliveira et al., 2021 ). In the South, industrial and urban activities generate pollutants that increase aerosols, especially in densely populated cities (Calado et al., 2021 ). In addition, in this region the values do not vary greatly due to the presence of a more homogeneous precipitation regime throughout the year (Reboita et al., 2015 ). 4. CONCLUSION The study evaluated the Aerosol Optical Depth (AOD) estimates produced by the Copernicus Atmosphere Monitoring Service (CAMS) at different sites in Brazil, considering their seasonal variations. Alta Floresta and Ji-Paraná were the sites with the highest AOD values, especially during the dry season. In addition, the presence of the El Niño phenomenon in 2007 may have contributed to the increase in AOD values. AOD values were lower during the rainy season in Manaus and Cuiabá. The dynamic between the AERONET measurements and the CAMS product estimates was generally strong, indicating a good fit of the data at most of the sites analyzed. However, some sites showed greater differences between AERONET measurements and CAMS estimates. Finally, CAMS showed a satisfactory performance in estimating AOD in Brazil, providing valuable information on aerosol concentrations. Their refined analyses made it possible to capture seasonal variations, such as the increase in aerosol concentrations during the dry season. Its ability to identify the most affected areas, such as the arc of deforestation in the Amazon, highlights its effectiveness in monitoring and analyzing the country's atmospheric composition. DECLARATIONS FUNDING The authors declare that no funding, grants or other support was received during the preparation of this manuscript. The research and work reported here were carried out independently and without the influence of any financial support. This independence guaranteed impartial analysis and interpretation of data and conclusions. The authors also confirmed that they adhered to ethical standards in conducting this research, with all necessary data collected and analyzed through their own efforts and resources. COMPETING INTERESTS The authors have no relevant financial or non-financial interests to disclose. AUTHOR CONTRIBUTIONS The manuscript in question represents a significant collaboration among various professionals, each playing a crucial role in its development and refinement. Altemar Lopes Pedreira Júnior emerges as the principal author, bringing his expertise and dedication to the project. Under the guidance of Leone Francisco Amorim Curado and Rafael da Silva Palácios, the work was steered by solid and experienced leadership. Additionally, Luiz Octávio Fabricio dos Santos made essential contributions to the elaboration of the images, adding an important visual dimension to the manuscript. Carlos Alexandre Santos Querino and Juliane Kayse Albuquerque da Silva Querino played fundamental roles in reviewing the methodology, ensuring the robustness and accuracy of the adopted procedures. Meanwhile, Thiago Rangel Rodrigues and João Basso Marques provided valuable contributions during the text revision, ensuring its clarity, coherence, and quality. DATA AVAILABILITY The datasets generated during and/or analysed during the current study are available in the Copernicus Atmosphere Monitoring Service (CAMS) [https://atmosphere.copernicus.eu/] and Aerosol Robotic Network (AERONET) [http://aeronet.gsfc.nasa.gov/]. ACKNOWLEDGMENTS The research was supported by the Federal University of Mato Grosso (UFMT), Postgraduate Program in Environmental Physics (PPGFA/UFMT), Coordination for the Improvement of Higher Education Personnel (CAPES), Federal University of Amazonas (UFAM), Postgraduate Program in Environmental Sciences, Resolution N.002/2023 - POSGRAD - FAPEAM, Biosphere-Atmosphere Interaction Research Group (GPIBA/IEAA). The authors would like to thank the National Aeronautics and Space Administration (NASA) and the European Center for Medium-Range Weather Forecasts (ECMWF) for making the data available. REFERENCES ALVES, A. M. de M. R.; MARTINS, F. B.; REBOITA, M. S. BALANÇO HÍDRICO CLIMATOLÓGICO PARA ITAJUBÁ-MG: CENÁRIO ATUAL E PROJEÇÕES CLIMÁTICAS. Revista Brasileira de Climatologia, [ s. l. ], v. 26, 2020. Disponível em: https://ojs.ufgd.edu.br/index.php/rbclima/article/view/14239. Acesso em: 31 jul. 2023. ASUTOSH, A. et al. 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VARIABILIDADE DA PROFUNDIDADE ÓTICA DE AEROSSÓIS ATMOSFÉRICOS SOBRE O PANTANAL BRASILEIRO (DEPTH OPTICAL VARIABILITY OF ATMOSPHERIC AEROSOLS ON THE BRAZILIAN PANTANAL). Revista Brasileira de Climatologia, [ s. l. ], v. 18, 2016. Disponível em: http://revistas.ufpr.br/revistaabclima/article/view/44340. Acesso em: 22 ago. 2023. PEARSON, K.; FISHER, R. A.; INMAN, H. F. Karl Pearson and R. A. Fisher on Statistical Tests: A 1935 Exchange from Nature. The American Statistician, [ s. l. ], v. 48, n. 1, p. 2, 1994. PENDHARKAR, J. et al. Impact of Aerosol Size Distribution on Aerosol-Cloud Interaction in the Brazilian Atmospheric Global Model: A Case Study over South America. 23rd Conference on Atmospheric Chemistry -ACMAP: Atmospheric Chemistry Modeling and Analysis Program, [ s. l. ], 2021. Disponível em: https://repositorio.usp.br/item/003066526. Acesso em: 30 jul. 2023. PEREIRA, A. G. da C. et al. Aplicação dos produtos MODIS Coleção 6 na análise da Profundidade Ótica do Aerossol sobre regiões de Floresta e Cerrado na Amazonia Legal. Revista Brasileira de Geografia Física, [ s. l. ], v. 15, n. 2, p. 886–912, 2022. PIVELLO, V. R. et al. Understanding Brazil’s catastrophic fires: Causes, consequences and policy needed to prevent future tragedies. Perspectives in Ecology and Conservation, [ s. l. ], v. 19, n. 3, p. 233–255, 2021. POPE, R. J. et al. Substantial Increases in Eastern Amazon and Cerrado Biomass Burning-Sourced Tropospheric Ozone. Geophysical Research Letters, [ s. l. ], v. 47, n. 3, p. e2019GL084143, 2020. PORFIRIO, A. C. S. et al. Evaluation of Global Solar Irradiance Estimates from GL1.2 Satellite-Based Model over Brazil Using an Extended Radiometric Network. Remote Sensing, [ s. l. ], v. 12, n. 8, p. 1331, 2020. PRADO, N. V.; DA COSTA COELHO, S. M. S. Estudo da Variabilidade Temporal da Profundidade Óptica do Aerossol Utilizando Dados de Sensoriamento Remoto Sobre a Região de Transição entre a Floresta Amazônica e o Cerrado. Revista Brasileira de Meteorologia, [ s. l. ], v. 32, p. 649–658, 2017. REBOITA, M. S. et al. Entendendo o tempo e o clima na América do Sul. Terrae Didatica, [ s. l. ], v. 8, n. 1, p. 34, 2015. ROCHA, V. R. da; YAMASOE, M. A. Estudo da variabilidade espacial e temporal da profundidade óptica do aerossol obtida com o MODIS sobre a região amazônica. Revista Brasileira de Meteorologia, [ s. l. ], v. 28, p. 210–220, 2013. RODRIGUES, A. A. et al. Tendência e variabilidade da chuva no Rio Grande do Sul, Brasil. Revista Brasileira de Climatologia, [ s. l. ], v. 32, p. 177–207, 2023. SOKHI, R. S. et al. Advances in air quality research – current and emerging challenges. Atmospheric Chemistry and Physics, [ s. l. ], v. 22, n. 7, p. 4615–4703, 2022. SOUZA, A. de et al. Climate Regionalization in Mato Grosso do Sul: a Combination of Hierarchical and Non-hierarchical Clustering Analyses Based on Precipitation and Temperature. Brazilian Archives of Biology and Technology, [ s. l. ], v. 65, p. e22210331, 2022. TARIQ, S. et al. Remote sensing of aerosols due to biomass burning over Kanpur, Sao-Paulo, Ilorin and Canberra. Journal of Atmospheric Chemistry, [ s. l. ], v. 80, n. 1, p. 1–52, 2023. WILLIAMS, J. E. et al. Regional evaluation of the performance of the global CAMS chemical modeling system over the United States (IFS cycle 47r1). Geoscientific Model Development, [ s. l. ], v. 15, n. 12, p. 4657–4687, 2022. WILLMOTT, C. J. et al. Statistics for the evaluation and comparison of models. Journal of Geophysical Research: Oceans, [ s. l. ], v. 90, n. C5, p. 8995–9005, 1985. WU, C.; LI, K.; BAI, K. Validation and Calibration of CAMS PM2.5 Forecasts Using In Situ PM2.5 Measurements in China and United States. Remote Sensing, [ s. l. ], v. 12, n. 22, p. 3813, 2020. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Jan, 2025 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 15 Aug, 2024 Reviews received at journal 14 Aug, 2024 Reviews received at journal 10 Aug, 2024 Reviewers agreed at journal 24 Jul, 2024 Reviewers agreed at journal 19 Jul, 2024 Reviewers agreed at journal 19 Jul, 2024 Reviewers agreed at journal 24 Feb, 2024 Reviewers invited by journal 21 Feb, 2024 Editor assigned by journal 15 Feb, 2024 Submission checks completed at journal 15 Feb, 2024 First submitted to journal 09 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3942950","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272968366,"identity":"a474a659-378d-4c2d-b393-a551bed1140c","order_by":0,"name":"Altemar Lopes Pedreira Júnior","email":"data:image/png;base64,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","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":true,"prefix":"","firstName":"Altemar","middleName":"Lopes Pedreira","lastName":"Júnior","suffix":""},{"id":272968367,"identity":"c411914b-9955-42f1-8b6c-4e7636f05f0e","order_by":1,"name":"Leone Francisco Amorim Curado","email":"","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Leone","middleName":"Francisco Amorim","lastName":"Curado","suffix":""},{"id":272968368,"identity":"63dadd60-1ace-49d7-bc55-6d83d9b3dc62","order_by":2,"name":"Rafael da Silva Palácios","email":"","orcid":"","institution":"Federal University of Para","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"da Silva","lastName":"Palácios","suffix":""},{"id":272968369,"identity":"bbe69870-2b99-4aff-a67e-6fedfe5cf904","order_by":3,"name":"Luiz Octávio Fabricio dos Santos","email":"","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"Luiz","middleName":"Octávio Fabricio dos","lastName":"Santos","suffix":""},{"id":272968370,"identity":"5713b123-4cd6-4ed5-9f3d-3d5f399b7677","order_by":4,"name":"Carlos Alexandre Santos Querino","email":"","orcid":"","institution":"Federal University of Amazonas","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"Alexandre Santos","lastName":"Querino","suffix":""},{"id":272968371,"identity":"43f18a58-d3a5-44ab-a8cc-3e48482db87a","order_by":5,"name":"Juliane Kayse Albuquerque da Silva Querino","email":"","orcid":"","institution":"Federal University of Amazonas","correspondingAuthor":false,"prefix":"","firstName":"Juliane","middleName":"Kayse Albuquerque da Silva","lastName":"Querino","suffix":""},{"id":272968372,"identity":"5ad57225-f3e0-47da-b88f-7336ed12706c","order_by":6,"name":"Thiago Rangel Rodrigues","email":"","orcid":"","institution":"Federal University of Mato Grosso do Sul","correspondingAuthor":false,"prefix":"","firstName":"Thiago","middleName":"Rangel","lastName":"Rodrigues","suffix":""},{"id":272968373,"identity":"f99b40f1-39ec-4a43-805f-3d1028be22d3","order_by":7,"name":"João Basso Marques","email":"","orcid":"","institution":"Universidade Federal de Mato Grosso","correspondingAuthor":false,"prefix":"","firstName":"João","middleName":"Basso","lastName":"Marques","suffix":""}],"badges":[],"createdAt":"2024-02-09 12:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3942950/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3942950/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-024-05335-5","type":"published","date":"2025-01-16T15:57:42+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":51234978,"identity":"25e07fed-f1e6-404f-af06-2eb35aa446a4","added_by":"auto","created_at":"2024-02-16 16:06:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":346710,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the experimental sites used to measure the Aerosol Optical Depth (AOD) belonging to AERONET (Aerosol Robotic Network), used as a reference to evaluate the estimates of the Copernicus Atmosphere Monitoring Service (CAMS) data in Brazil.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3942950/v1/be224fd17b0bc03205e6e371.png"},{"id":51234981,"identity":"e9426f83-799d-4906-acf9-0ec797576b23","added_by":"auto","created_at":"2024-02-16 16:06:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":64652,"visible":true,"origin":"","legend":"\u003cp\u003eTotal years available of data from Aerosol Robotic Network (AERONET) stations, used to validate Aerosol Optical Depth (AOD) estimates from the Copernicus Atmosphere Monitoring Service (CAMS) product in Brazil.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3942950/v1/7cce759083e9d0657d2a97bd.png"},{"id":51234977,"identity":"d91714c5-90d6-4205-955d-8b923baa366d","added_by":"auto","created_at":"2024-02-16 16:06:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":877836,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of Aerosol Optical Depth (AOD) measured by Aerosol Robotic Network (AERONET) stations (A) and estimated by the Copernicus Atmosphere Monitoring Service (CAMS) product (B), between 2004 and 2022, in Brazil.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3942950/v1/11ab241688b935bc3c79da39.png"},{"id":51234979,"identity":"1ad1209d-6950-42af-9b35-a976a478a433","added_by":"auto","created_at":"2024-02-16 16:06:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":508391,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly mean values of Aerosol Optical Depth (AOD) ± confidence interval (CI) measured by Aerosol Robotic Network (AERONET) stations and estimated by the Copernicus Atmosphere Monitoring Service (CAMS) product in Brazil. The shaded area indicates the dry period of each site analyzed.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3942950/v1/80d2b24feaa70ef744210d92.png"},{"id":51234982,"identity":"fd204d05-1e20-47ff-b32c-f62a34980d0d","added_by":"auto","created_at":"2024-02-16 16:06:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":743296,"visible":true,"origin":"","legend":"\u003cp\u003eLinear regression between Aerosol Optical Depth (AOD) measured by AERONET and estimated by the Copernicus Atmosphere Monitoring Service (CAMS) product in Brazil. The sites considered were: Alta Floresta (A), Ji-Paraná (B), Rio Branco (C), São Paulo-EACH (D), São Paulo (E), Cuiabá (F), São Martinho (G), Petrolina (H), Campo Grande (I), Manaus (J), Itajubá (K) and ATTO (L).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3942950/v1/59241edef6dcb718ea18c4d9.png"},{"id":51234980,"identity":"c100aec1-da3c-46e6-9bbb-bd0a93ba1b0d","added_by":"auto","created_at":"2024-02-16 16:06:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1354870,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly average distribution of Aerosol Optical Depth (AOD) estimated by the Copernicus Atmosphere Monitoring Service (CAMS) product in Brazil.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3942950/v1/75823e1514e386afdcfdbd11.png"},{"id":74284627,"identity":"696345d4-cb40-49f7-97ce-d03d0d1cd03f","added_by":"auto","created_at":"2025-01-20 16:09:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4212347,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3942950/v1/1fab83da-c43b-4fac-9474-163223271d48.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEvaluation of Aerosol Optical Depth (Aod) Estimated by Copernicus Atmosphere Monitoring Service (Cams) in Brazil\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eBrazil, with its vast territorial extension and environmental diversity, presents a complex scenario where atmospheric aerosols play a significant role in both climate processes and air quality (Pivello et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Thus, dense tropical forests, unique ecosystems, urban and industrial areas all contribute to a remarkable diversity of aerosol sources and types in the country (Borma \u003cem\u003eet al.\u003c/em\u003e, 2022). These tiny solid or liquid particles, which come from both natural and anthropogenic activities, have a significant impact on the atmospheric balance, affecting solar radiation, cloud formation and the occurrence of rainfall (Bellouin \u003cem\u003eet al.\u003c/em\u003e, 2020; De Oliveira et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this sense, Aerosol Optical Depth (AOD) assumes a unique relevance in scientific research and environmental policies, allowing scientists and managers to understand the extent and spatial distribution of aerosols in different regions of Brazil (De Oliveira et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). AOD is a crucial parameter for understanding the behavior and distribution of aerosols in the atmosphere (Pendharkar et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). AOD is a measure of the extent of solar radiation caused by the presence of aerosols suspended in the atmosphere (Li et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Aerosols are microscopic particles, solid or liquid, which can be found naturally in the air or produced by human activities such as the burning of fossil fuels, industrial processes and forest fires (Pal\u0026aacute;cios et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, observational measures of AOD are susceptible to challenges and uncertainties that need to be carefully considered. However, \u003cem\u003ein situ\u003c/em\u003e measurements can provide accurate data, but are limited in terms of spatial and temporal coverage, as is the case with AERONET (Porfirio et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AERONET is an extensive global network of monitoring stations that collect aerosol optical depth data (Holben et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This data provides valuable information on the distribution and concentration of particles suspended in the atmosphere, such as dust, smoke, pollutants, and other particulate matter. The data obtained by AERONET is widely used in scientific research, climate models and to provide important information for decision-makers regarding air quality management and mitigation of the effects of aerosols on the terrestrial environment (Ogunjobi; Awoleye, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, the use of satellite data and atmospheric reanalysis models provides estimates on a global scale and continuous coverage, including remote and poorly monitored areas (Handschuh; Erbertseder; Baier, 2023). In this context, by combining satellite data, in situ measurements and atmospheric models, the Copernicus Atmosphere Monitoring Service (CAMS) plays a crucial role in providing accurate estimates of AOD on a global scale (Garrigues et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Developed by the Copernicus Atmosphere Monitoring Service (CAMS), the CAMS reanalysis represents the latest global compilation of information on atmospheric composition.\u003c/p\u003e \u003cp\u003eThis dataset consists of three-dimensional fields of atmospheric composition parameters, including aerosols, chemical species and greenhouse gases. These fields are obtained by reanalyzing the Global Greenhouse Gas (EGG4), which focuses specifically on greenhouse gases on a global scale. This multidisciplinary approach allows for a more complete view of aerosol distribution patterns. Although CAMS has been widely used in various regions of the world, the evaluation of its reliability in the Brazilian context is still scarce (Asutosh et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dahal et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Garrigues et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Inness et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Williams et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu; Li; Bai, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, given the vast territorial extension of Brazil, its ecological and environmental diversity and the sources of aerosol emissions present in the country, it is essential to investigate the accuracy of CAMS estimates in relation to AOD in the Brazilian territory. This evaluation will make it possible to identify discrepancies between CAMS estimates and observational data, as well as providing valuable insights for improving air quality monitoring (Garrigues et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, the objective of this study is to evaluate Aerosol Optical Depth (AOD) estimates from the Copernicus Atmosphere Monitoring Service (CAMS) product in Brazil.\u003c/p\u003e"},{"header":"2. MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003e2.1. STUDY AREA\u003c/h2\u003e\n\u003cp\u003eThe study covered several collection points belonging to the Aerosol Robotic Network (AERONET) distributed in different regions of Brazil, each within a specific biome (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The following stand out: i) Amazon Biome, represented by the Alta_Floresta (Alta Floresta), Ji-Parana_SE (Ji-Paran\u0026aacute;), Rio_Branco (Rio Branco), Manaus_Embrapa (Manaus) and Amazon_ATTO_Tower (ATTO) sites; ii) Atlantic Forest biome with the S\u0026atilde;o Paulo-EACH (SP-EACH), S\u0026atilde;o_Paulo (S\u0026atilde;o Paulo) and Itajuba (Itajub\u0026aacute;) sites, located in the southeast of the country; iii) Pantanal Biome with the Cuiab\u0026aacute;-Miranda (Cuiab\u0026aacute;) site; iv) Pampa Biome with the S\u0026atilde;o_Martinho_Sonda (S\u0026atilde;o Martinho) site; v); Caatinga biome with the Petrolina_Sonda (Petrolina) site and vi) Cerrado Biome with the Campo_Grande_Sonda (Campo Grande) site (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003e2.2. MEASURED DATA\u003c/h2\u003e\n\u003cp\u003eAerosol Optical Depth (AOD) data was collected by monitoring stations between 2003 and 2021 (18 years) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). This reference data belongs to the Aerosol Robotic Network (AERONET) project, which is available through NASA's Earth Observing System (EOS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://aeronet.gsfc.nasa.gov/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn this study, priority was given to using level 2.0 data from the AERONET Network, which represents the highest level of quality (Holben et al., \u003cspan class=\"CitationRef\"\u003e1998\u003c/span\u003e). These data are subject to a rigorous quality assurance protocol, including corrections for local factors, guaranteeing their reliability (Pal\u0026aacute;cios et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). AOD was analyzed for measurements taken at 500 nm. A direct comparison of the AERONET AOD data with the AOD from the CAMS product was made after converting the AERONET AOD values from 500 to 550 nm, using the \u0026Aring;ngstr\u0026ouml;m Extinction Exponent (AEE) in the 440\u0026ndash;870 nm spectral range (Eq.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n\u003cdiv id=\"FileID_Equ1\" class=\"mathdisplay\"\u003e$${\\text{A}\\text{O}\\text{D}}_{550 \\text{n}\\text{m}} = {\\text{A}\\text{O}\\text{D} }_{500 \\text{n}\\text{m}} {\\left(\\frac{550}{500}\\right)}^{-\\text{A}\\text{E}\\text{E}}$$\u003c/div\u003e\n\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003e2.3. ESTIMATED DATA\u003c/h2\u003e\n\u003cp\u003eReanalysis data from the Copernicus Atmosphere Monitoring Service (CAMS) product, which provides information on the estimation of Aerosol Optical Depth (AOD), was used. CAMS has a spatial resolution of ~\u0026thinsp;80 km and is made up of three-dimensional fields of atmospheric composition parameters, including aerosols (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://atmosphere.copernicus.eu/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003e2.4. DATA ANALYSIS\u003c/h2\u003e\n\u003cp\u003eAerosol Optical Depth (AOD) data was analyzed based on averages, along with \u0026plusmn;\u0026thinsp;95% confidence intervals, using the bootstrap resampling technique with 1,000 iterations. AOD estimates (CAMS) were disregarded when there were failures in the measured AOD record (AERONET). The dry and rainy seasons at each site were analyzed according to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDry and rainy seasons for each site analyzed to evaluate Aerosol Optical Depth (AOD) estimates from the Copernicus Atmosphere Monitoring Service (CAMS) product in Brazil.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eStation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSeason\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eReference\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDry\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eWet\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlta Floresta\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJuly/October\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNovember/June\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePereira et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJi-Paran\u0026aacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eApril/September\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOctober/March\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDe Sales \u003cem\u003eet al.\u003c/em\u003e, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRio Branco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eJune/August\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeptember/May\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDuarte, \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS\u0026atilde;o Paulo-EACH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eSeptember/May\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eGiulio et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS\u0026atilde;o Paulo\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCuiab\u0026aacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMay/September\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOctober/April\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMachado et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS\u0026atilde;o Martinho\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOctober/April\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMay/September\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRodrigues et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePetrolina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMay/October\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNovember/April\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrado e Coelho, 2017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCampo Grande\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eApril/September\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOctober/March\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSouza et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eManaus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eJuly/September\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eNovember/June\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eEsp\u0026iacute;noza \u003cem\u003eet al.\u003c/em\u003e, 2023\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eATTO\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItajub\u0026aacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eApril/September\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOctober/March\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlves et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe measured (AERONET) and estimated (CAMS) values were related using Pearson correlation index \"r\" (Eq.\u0026nbsp;2) (PEARSON et al., \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e) and Willmott accuracy index \"d\" (Eq.\u0026nbsp;3) (Willmott et al., \u003cspan class=\"CitationRef\"\u003e1985\u003c/span\u003e), which compares the distance between the estimated and measured values and ranges from 0 to 1, indicating no correspondence and perfect correspondence, respectively.\u003c/p\u003e\n\u003cp\u003eThe Root Mean Square Error (RMSE) (Eq.\u0026nbsp;4) was also analyzed to indicate flaws in the model when comparing the estimated and measured values, while the Mean Absolute Error (MAE) (Eq.\u0026nbsp;5) indicates the absolute average of the distances (deviations) between the estimated and measured values. Both errors should be close to zero to indicate greater precision in the estimates. The Percentage Bias (PBIAS) proposed by Gupta et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) was used to evaluate the difference between the estimated values and the measured values, indicating whether CAMS overestimates (positive PBIAS) or underestimates (negative PBIAS) the data measured by AERONET. A ratio of 1 to 1 is characterized by a PBIAS of 0.0% (Eq.\u0026nbsp;6).\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(r=\\frac{\\sum \\left({CAMS}_{\\left(i\\right)}-CAMS\\right)\\left({AERONET}_{\\left(i\\right)}-AERONET\\right)}{\\sqrt{\\sum ({CAMS}_{\\left(i\\right) }}-CAMS)\u0026sup2;\\sum \\left({AERONET}_{\\left(i\\right)}-AERONET\\right)\u0026sup2;}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e(2)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(d=1- \\left[\\frac{\\sum {\\left({CAMS}_{\\left(i\\right)}-{AERONET}_{\\left(i\\right)}\\right)}^{2}}{\\sum {\\left(\\left|{CAMS}_{\\left(i\\right)}-AERONET\\right|+\\left|{AERONET}_{\\left(i\\right)}-AERONET\\right|\\right)}^{2}}\\right]\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e(3)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(RMSE= \\sqrt{\\frac{\\sum {\\left({CAMS}_{\\left(i\\right)}- {AERONET}_{\\left(i\\right)}\\right)}^{2}}{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e(4)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(MAE= \\sum \\frac{\\left|{CAMS}_{\\left(i\\right) }- {AERONET}_{\\left(i\\right)}\\right|}{n}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e(5)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(PBIAS=\\frac{\\sum {({CAMS}_{\\left(i\\right)}-{AERONET}_{\\left(i\\right)})}^{2}}{\\sum {AERONET}_{\\left(i\\right)}}\\times 100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e(6)\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere, CAMS\u003csub\u003e(i)\u003c/sub\u003e are the estimated values, AERONET\u003csub\u003e(i)\u003c/sub\u003e are the measured values, n the amount of data, AERONET and CAMS the averages of the measured and estimated values, respectively.\u003c/p\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cp\u003eThe highest Aerosol Optical Depth (AOD) values occurred in Alta Floresta and Ji-Paran\u0026aacute; (AERONET\u0026thinsp;=\u0026thinsp;2.02 and CAMS\u0026thinsp;=\u0026thinsp;2.24), respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). During the dry season, there is a significant increase in average AOD in several regions of the southern Amazon (ROCHA; YAMASOE, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e), especially in sites that are located in or close to the arc of deforestation, such as Alta Floresta and Ji-Paran\u0026aacute;. This substantial increase is mainly attributed to regional emissions resulting from biomass burning, as well as the transport of aerosols from distant areas (Morais et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pal\u0026aacute;cios et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, the central region of the Amazon is influenced by the descending branch of the Hadley cell, which causes dry conditions, favoring the concentration of aerosols in the atmosphere (Rocha; Yamasoe, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAOD values were lowest during the rainy season in Manaus and Cuiab\u0026aacute; (AERONET and CAMS\u0026thinsp;=\u0026thinsp;0.02), which is probably only related to the cities urban emissions, such as thermal power plants near the site.\u003c/p\u003e\n\u003cp\u003eThe highest AOD values were observed by AERONET and CAMS in 2007. It is important to note that according to Lizundia \u003cem\u003eet al.\u003c/em\u003e (2020), one of the largest burnt areas detected in South America was in 2007 in Brazil, Paraguay and Colombia. From 2004 to 2008, there was a consistent upward trend in AOD (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). In addition, during the period from 2008 to 2016, there was a downward and stable trend in the concentration of aerosols, except for a notable increase in 2011, observed mainly at sites located in the south and southeast. This particular year was drier in these regions, conditioned by the La Nina phenomenon, which favored uncontrolled deforestation of large areas and fires that occurred mainly in pastures (De Andrade \u003cem\u003eet al.\u003c/em\u003e, 2020). From 2017 onwards, the data indicates a clear upward trend in the concentration of aerosols again, occurring mainly in the Amazon and Cerrado biomes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). These were responsible for 86% of total particulate matter emissions in Brazil from 2003 to 2020 (Pereira et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThus, the variability of AOD over the years is evident, which can be influenced by different precipitation rates, large-scale meteorological phenomena such as El Ni\u0026ntilde;o and La Ni\u0026ntilde;a, and government policies for managing deforestation (De Andrade \u003cem\u003eet al.\u003c/em\u003e, 2020). This variability reflects the complexity of the natural and anthropogenic factors that affect atmospheric aerosol concentrations at the sites analyzed and highlights the importance of considering these aspects when interpreting the results and implementing strategies to control and mitigate aerosol emissions (Marengo; Espinoza, 2016).\u003c/p\u003e\n\u003cp\u003eThe Alta Floresta, S\u0026atilde;o Paulo, Cuiab\u0026aacute;, S\u0026atilde;o Martinho, Petrolina, Campo Grande and Itajub\u0026aacute; sites showed no statistically significant differences between the CAMS estimates and the AERONET measurements (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). However, at the other sites, such as Ji-Paran\u0026aacute;, Rio Branco, Manaus, S\u0026atilde;o Paulo (EACH) and ATTO, significant differences were observed with the AERONET measurements in certain months of the year. In March, Rio Branco recorded an overestimate of 66% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). In Manaus, overestimates occurred between March and June, with values ranging from 75 to 166%. ATTO was also overestimated during the months of April to June, with a range from 75 to 128% (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). CAMS AOD estimates are severely limited during the assimilation of terrestrial and satellite data (Gueymard; Yang, 2020). In addition, the high occurrence of cumulus cloud clusters in the Amazon probably affected AOD detections, influencing the overestimation of estimates at these sites (Pereira et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe results showed a strong correlation (indicated by *** with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the AERONET measurements and the CAMS product estimates at most of the sites assessed. Furthermore, when analyzing the agreement indices, represented by Willmott index \"d\", it was found that the CAMS product estimates also showed good agreement with the observed AOD values, with values approaching 1. However, the lowest values in the AOD statistical metrics were observed in Petrolina (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe error parameters, such as RMSE and MAE, showed relatively low values at all the sites analyzed, especially in S\u0026atilde;o Paulo-EACH, Petrolina and Itajub\u0026aacute;. However, there was variability in the results between the different sites. For example, Ji-Paran\u0026aacute;, Rio Branco, S\u0026atilde;o Martinho, Manaus, ATTO and Itajub\u0026aacute; had high PBIAS values, indicating that the CAMS product tends to overestimate the AOD values in these locations. On the other hand, in Petrolina the CAMS product showed a tendency to underestimate AOD (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eStatistical parameters for Aerosol Optical Depth (AOD) measured by AERONET and estimated by the Copernicus Atmosphere Monitoring Service (CAMS) product. The parameters include: Pearson correlation index \"r\", Willmott index \"d\", Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Percentage Bias (PBIAS). Correlations with *** indicate a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSites\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ed\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eRMSE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMAE\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePBIAS\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlta Floresta\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJi-Paran\u0026aacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.93***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRio Branco\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.94***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS\u0026atilde;o Paulo-EACH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.87***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.30\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS\u0026atilde;o Paulo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.78***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.93\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCuiab\u0026aacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-5.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eS\u0026atilde;o Martinho\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e20.70\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePetrolina\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e-20.13\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCampo Grande\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.94***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.26\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eManaus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.88***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e27.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eItajub\u0026aacute;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.85***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eATTO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e29.19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAll the sites showed a linear relationship between the AOD measured by AERONET and the CAMS product estimates (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). This behavior is evidenced by the significant values of the angular coefficient, interception coefficient, coefficient of determination (R\u0026sup2;), as well as extremely low p-values in the linear regressions carried out at each site. This linear trend suggests that the AOD measured by AERONET and that estimated by CAMS have a well-defined relationship, allowing a regression line to be established that represents the relationship between them (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe sites in Ji-Paran\u0026aacute;, Rio Branco, Cuiab\u0026aacute; and Campo Grande showed relatively high R\u0026sup2;, around 0.87 to 0.90, which shows that the data fit the regression line well (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). On the other hand, S\u0026atilde;o Martinho and Petrolina showed lower R\u0026sup2; coefficients, around 0.42, which indicates a less precise fit of the data to the regression line. The Campo Grande station stood out with the highest intercept coefficient value, reaching 0.97. In addition, the Manaus, Cuiab\u0026aacute; and Ji-Paran\u0026aacute; sites obtained considerable intercept coefficients of between 0.70 and 0.95. On the other hand, the Petrolina station had the lowest intercept coefficient value, at 0.40 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eSpatially, during certain times of the year, such as the dry season, an increase in aerosol concentrations was recorded (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). This is due to the fires that occur during this period, releasing fine particles and gaseous pollutants into the atmosphere, resulting in an increase in the concentration of AOD. This is the result of the fires that have affected almost half of Brazil territory, mainly the Amazon and Cerrado biomes, which have been responsible for 86% of particulate matter emissions in Brazil over the last two decades (Pereira et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eOn the other hand, during the rainiest months, there was a reduction in these concentrations (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Rain has the effect of temporarily clearing the atmosphere, leading to a decrease in aerosol concentrations. The monthly spatial distribution of AOD in Brazil is influenced by various factors, such as climatic conditions, human activities, fires, vegetation and natural processes. Brazil diverse climate and ecosystems also contribute to seasonal variations in aerosol concentrations.\u003c/p\u003e\n\u003cp\u003eIt is important to note that the regions with the highest concentrations of aerosols vary according to the period analyzed. Notably, the areas located in the arc of deforestation are the most affected by high concentrations of aerosols, especially during the month of September (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). This region, located on the southern border of the Brazilian Amazon, is known for suffering from intense deforestation and agricultural activities (Pope \u003cem\u003eet al.\u003c/em\u003e, 2020). The increase in aerosol concentrations in this region is directly linked to deforestation and fires (Pope \u003cem\u003eet al.\u003c/em\u003e, 2020). These particles can have adverse impacts on air quality, human health and the environment.\u003c/p\u003e\n\u003cp\u003eIn the Midwest and Southeast, harvesting and agricultural fires in the dry season contribute to increased concentrations of particles in the atmosphere (Pal\u0026aacute;cios \u003cem\u003eet al.\u003c/em\u003e, 2016; Tariq et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Northeast faces fires during the dry season, mainly in semi-arid areas (De Oliveira et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Oliveira et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In the South, industrial and urban activities generate pollutants that increase aerosols, especially in densely populated cities (Calado et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, in this region the values do not vary greatly due to the presence of a more homogeneous precipitation regime throughout the year (Reboita et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e"},{"header":"4. CONCLUSION","content":"\u003cp\u003eThe study evaluated the Aerosol Optical Depth (AOD) estimates produced by the Copernicus Atmosphere Monitoring Service (CAMS) at different sites in Brazil, considering their seasonal variations. Alta Floresta and Ji-Paran\u0026aacute; were the sites with the highest AOD values, especially during the dry season. In addition, the presence of the El Ni\u0026ntilde;o phenomenon in 2007 may have contributed to the increase in AOD values.\u003c/p\u003e \u003cp\u003eAOD values were lower during the rainy season in Manaus and Cuiab\u0026aacute;. The dynamic between the AERONET measurements and the CAMS product estimates was generally strong, indicating a good fit of the data at most of the sites analyzed. However, some sites showed greater differences between AERONET measurements and CAMS estimates.\u003c/p\u003e \u003cp\u003eFinally, CAMS showed a satisfactory performance in estimating AOD in Brazil, providing valuable information on aerosol concentrations. Their refined analyses made it possible to capture seasonal variations, such as the increase in aerosol concentrations during the dry season. Its ability to identify the most affected areas, such as the arc of deforestation in the Amazon, highlights its effectiveness in monitoring and analyzing the country's atmospheric composition.\u003c/p\u003e"},{"header":"DECLARATIONS","content":"\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funding, grants or other support was received during the preparation of this manuscript. The research and work reported here were carried out independently and without the influence of any financial support. This independence guaranteed impartial analysis and interpretation of data and conclusions. The authors also confirmed that they adhered to ethical standards in conducting this research, with all necessary data collected and analyzed through their own efforts and resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe manuscript in question represents a significant collaboration among various professionals, each playing a crucial role in its development and refinement. Altemar Lopes Pedreira J\u0026uacute;nior emerges as the principal author, bringing his expertise and dedication to the project. Under the guidance of Leone Francisco Amorim Curado and Rafael da Silva Pal\u0026aacute;cios, the work was steered by solid and experienced leadership. Additionally, Luiz Oct\u0026aacute;vio Fabricio dos Santos made essential contributions to the elaboration of the images, adding an important visual dimension to the manuscript. Carlos Alexandre Santos Querino and Juliane Kayse Albuquerque da Silva Querino played fundamental roles in reviewing the methodology, ensuring the robustness and accuracy of the adopted procedures. Meanwhile, Thiago Rangel Rodrigues and Jo\u0026atilde;o Basso Marques provided valuable contributions during the text revision, ensuring its clarity, coherence, and quality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available in the Copernicus Atmosphere Monitoring Service (CAMS) [https://atmosphere.copernicus.eu/] and Aerosol Robotic Network (AERONET) [http://aeronet.gsfc.nasa.gov/].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research was supported by the Federal University of Mato Grosso (UFMT), Postgraduate Program in Environmental Physics (PPGFA/UFMT), Coordination for the Improvement of Higher Education Personnel (CAPES), Federal University of Amazonas (UFAM), Postgraduate Program in Environmental Sciences, Resolution N.002/2023 - POSGRAD - FAPEAM, Biosphere-Atmosphere Interaction Research Group (GPIBA/IEAA).\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the National Aeronautics and Space Administration (NASA) and the European Center for Medium-Range Weather Forecasts (ECMWF) for making the data available.\u003c/p\u003e"},{"header":"REFERENCES","content":"\u003col\u003e\n\u003cli\u003eALVES, A. M. de M. R.; MARTINS, F. B.; REBOITA, M. S. BALAN\u0026Ccedil;O H\u0026Iacute;DRICO CLIMATOL\u0026Oacute;GICO PARA ITAJUB\u0026Aacute;-MG: CEN\u0026Aacute;RIO ATUAL E PROJE\u0026Ccedil;\u0026Otilde;ES CLIM\u0026Aacute;TICAS. Revista Brasileira de Climatologia, [\u003cem\u003es. l.\u003c/em\u003e], v. 26, 2020. Dispon\u0026iacute;vel em: https://ojs.ufgd.edu.br/index.php/rbclima/article/view/14239. Acesso em: 31 jul. 2023.\u003c/li\u003e\n\u003cli\u003eASUTOSH, A. \u003cem\u003eet al.\u003c/em\u003e Investigation of June 2020 giant Saharan dust storm using remote sensing observations and model reanalysis. Scientific Reports, [\u003cem\u003es. l.\u003c/em\u003e], v. 12, n. 1, p. 6114, 2022. \u003c/li\u003e\n\u003cli\u003eBELLOUIN, N. \u003cem\u003eet al.\u003c/em\u003e Bounding Global Aerosol Radiative Forcing of Climate Change. Reviews of Geophysics, [\u003cem\u003es. l.\u003c/em\u003e], v. 58, n. 1, p. e2019RG000660, 2020.\u003c/li\u003e\n\u003cli\u003eBORMA, L. 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C5, p. 8995\u0026ndash;9005, 1985. \u003c/li\u003e\n\u003cli\u003eWU, C.; LI, K.; BAI, K. Validation and Calibration of CAMS PM2.5 Forecasts Using In Situ PM2.5 Measurements in China and United States. Remote Sensing, [\u003cem\u003es. l.\u003c/em\u003e], v. 12, n. 22, p. 3813, 2020. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Atmospheric aerosols, South America, Statistical analysis, Reanalysis","lastPublishedDoi":"10.21203/rs.3.rs-3942950/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3942950/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe optical depth of aerosols plays a crucial role in scientific research and environmental policies, making it possible to understand the distribution and extent of aerosols in various regions of Brazil. The objective of this study is to evaluate the estimates of Aerosol Optical Depth (AOD) from the Copernicus Atmosphere Monitoring Service (CAMS) product in Brazil. The study covered the sites of Alta Floresta, Ji-Paran\u0026aacute;, Rio Branco, Manaus, ATTO, S\u0026atilde;o Paulo-EACH, S\u0026atilde;o Paulo, Itajub\u0026aacute;, Cuiab\u0026aacute;, S\u0026atilde;o Martinho, Petrolina and Campo Grande. Measured and estimated values were evaluated using Pearson correlation index \"r\", accuracy using Willmott index \"d\", Mean Squared Error, Mean Absolute Error and Percentage Bias. Results from the CAMS product showed good agreement with AOD measurements from the Aerosol Robotic Network. There was a strong correlation between the data, with Willmott index \"d\" values close to 1 and relatively low errors. However, significant differences were observed in some sites, such as Ji-Paran\u0026aacute;, Rio Branco, Manaus and ATTO, where the CAMS tended to overestimate the AOD, while in Petrolina there was an underestimation. Variations in AOD occurred in various regions of Brazil over the years analyzed, with an increase during the dry season due to fires and human activities, and a reduction during the rainy months. The areas most affected were those close to the arc of deforestation in the Amazon. Aerosol concentrations have also been influenced by climatic factors, agricultural, industrial and urban activities in different regions of the country. This variability highlights the complexity of the natural and anthropogenic factors that affect air quality and emphasizes the importance of control and mitigation strategies for aerosol emissions. Therefore, the CAMS has demonstrated satisfactory performance in estimating the AOD in Brazil, providing valuable information on aerosol concentrations.\u003c/p\u003e","manuscriptTitle":"Evaluation of Aerosol Optical Depth (Aod) Estimated by Copernicus Atmosphere Monitoring Service (Cams) in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-16 16:06:48","doi":"10.21203/rs.3.rs-3942950/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-15T14:00:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-14T21:53:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-10T20:46:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"222301921458427812730473647126054796177","date":"2024-07-24T07:30:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216316810138079506752708241148929197536","date":"2024-07-19T11:56:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68970563855454236858143462617573467540","date":"2024-07-19T11:55:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a8020ea6-cbf7-4299-b09a-0db510da004f","date":"2024-02-24T15:17:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-21T20:01:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-15T07:04:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-15T07:04:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2024-02-09T12:08:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"a5976a97-fce8-4930-95eb-e57bc5b48795","owner":[],"postedDate":"February 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:02:56+00:00","versionOfRecord":{"articleIdentity":"rs-3942950","link":"https://doi.org/10.1007/s00704-024-05335-5","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2025-01-16 15:57:42","publishedOnDateReadable":"January 16th, 2025"},"versionCreatedAt":"2024-02-16 16:06:48","video":"","vorDoi":"10.1007/s00704-024-05335-5","vorDoiUrl":"https://doi.org/10.1007/s00704-024-05335-5","workflowStages":[]},"version":"v1","identity":"rs-3942950","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3942950","identity":"rs-3942950","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}