The study evaluated the environmental impact of COVID-19 on neglected diseases in the State of Alagoas. Data on neglected diseases (dengue, tuberculosis, leishmaniasis and leprosy) and COVID-19 were obtained from the DATASUS system between 03/2020 and 01/2023. Both sets of health data along with census data (IBGE - 2010) and climate data (TerraClimate) were subjected to descriptive and multivariate statistical techniques (cluster analysis - AA). In the study, the maps were created using Quantum GIS (QGIS) version 3.16. The results of the AA technique applied to municipalities in the mesoregions of the State showed that the East formed two homogeneous groups (G1 and G2). The Hinterland and Arid Zone mesoregions formed three homogeneous groups (G1, G2 and G3) of COVID-19 cases. The disease most impacted in the scenario before and during COVID-19 in Alagoas was dengue. In the East, dengue impacted Maceió, Penedo and Maragogi. In Agreste, only Arapiraca. Tuberculosis in the east impacted Maceió, followed by Arapiraca in the Hinterland and Arid Zone, only Santana do Ipanema and Delmiro Gouveia. Leishmaniasis only impacted Maceió. Leprosy impacted Maceió and União dos Palmares (East), followed by Arapiraca in the Hinterland and Arid Zone, Delmiro Gouveia and Santana do Ipanema. The Pearson correlation (r) showed that between the Total COVID-19 and the sociodemographic variables were positive and with a high correlation (r > 0.90), the exception was the Gini index (r = 0.61). The correlations between Total COVID-19 by mesoregion and sociodemographic variables were positive, except for the Gini index (r = 0.47). The correlation between COVID-19 cases in the 1st wave and precipitation (r = -0.7), humidity (r = -0.63) and temperature (r = -0.57) were negative, except for atmospheric pressure (r = 0.64). The correlation between COVID-19 cases in the 2nd wave, again atmospheric pressure, was positive (r = 0.84). The results obtained from MRLM showed that Maceió had a significant correlation between cases and wind speed (1st wave) and water vapor pressure (2nd wave), followed by Mata Grande between precipitation and cases, only in 1st wave and Palestine, with a significant correlation between precipitation, vapor pressure and cases in the 2nd wave. The exceptions were Penedo, Arapiraca and Palmeira dos Índios, with no correlation. In short, COVID-19 had an impact on Alagoas and actions are necessary post-pandemic to protect the population, especially the most socially vulnerable.

The growing demand for accurate seasonal climate forecasts in the areas of energy,
agriculture, transportation, leisure, among others, can save lives, support emergency
management and improve response planning to the effects of climate, avoiding
economic losses with a high impact on society . In view of this, the main objective of
the present study is to calibrate raw predictions of direct model outputs from the
international project North-Amerian MultiModel Ensemble (NMME) for the Northeast
of Brazil (NEB), reducing systematic biases and increasing assertiveness for prognostic
purposes. monthly/quarterly precipitation data. The justification for this study is due to
the questioning of the predictive capacity of climate forecasting by models in specific
areas, as is the case of the NEB, as it presents climate response patterns associated with
modes of variability such as ENSO (El Niño Southern Oscillation) . Precipitation data
from stations of the National Institute of Meteorology (INMET) and state meteorology
centers in the Northeast will be used. Data from gridded analyzes made available by
XAVIER, CHIRPS and ERA5Land were also used. To correct systematic errors in the
NMME model, the Canonical Correlation Analysis (ACC) method will be used, which
consists of associating indices to each of the 2 sets of data in order to maximize the

correlation between the two indices, retaining as much information as possible.
contained in the original variables. Due to the non-linear aspects of the climate,
representation by simple statistical models, that is, based on linear relationships, with
those using multiple linear regressions, is called canonical correlation analysis and
therefore its use in this work is the combination of statistical knowledge with dynamic
modeling, resulting in the term called: hybrid coupled models.

Some meteorological phenomena in South America develop quickly and take on large dimensions. These phenomena cause disasters for aviation, such as incidents and accidents. Mesoscale convective complexes (MCCs) forced a commercial airplane into an emergency landing at Ezeiza International Airport in Buenos Aires (Argentina) in October 2018. The airplane took off from São Paulo (Brazil) to Santiago (Chile) and had to alternate to Ezeiza after encountering unanticipated agglomerations of MCCs along the flight route; its structure was seriously damaged, which affected the safety of the flight. A synoptic and thermodynamic analysis of the atmosphere, prior to the event, was made based on GOES16 infrared satellite data, radiosonde data, maps of several variables such as stream lines, temperature advection, surface synoptic maps and layer thickness from CPTEC/INPE and NCEP reanalysis data. The main observed processes that influenced the formation and development of conglomerates of MCCs were the following: (1) the cyclogenesis of a baroclinic cyclone on the cold front; (2) the coupling of subtropical and polar jet streams; (3) the advection of warm and humid air along a low-level jet stream. Recommendations for meteorologists in weather forecasting and for aviators in flight safety were prepared.

The present research refers to the investigation of the influence of climate changes on the coast of Maceió and how they can be associated with the vulnerability of the studied area. Over the years, several of these changes have been monitored, and discovering which variables tend to trigger negative impacts is currently essential. Data from the climatic variables of: Total monthly precipitation, Average monthly compensated temperature, Average wind speed, and Average tidal level were used. The Mann-Kendall test will be used to analyze and identify trends, which in turn is indicated by theWorld Meteorological Organization (WMO) for studies that work with trends with time series. The T-Student significance test will be applied to indicate whether the trend values obtained by the Mann-Kendall test have statistical significance. The temporal series of the meteorological variables of Maceió will be submitted to Wavelet Analysis (WA) with the objective of identifying the temporal scales present in the series and this will lead to the identification of the systems or phenomena that occur or influence the local variables. In this way, it becomes clearer which climatic phenomena act on the variables and thus induces climatic interference on the site. For the determination of the positional dynamics of the coastline of the coast of the city of Maceió, a free open source tool called Coastal Analyst System from Space Imagery Engine - CASSIE was adopted. After this stage, the quantitative data referring to the occurrence of erosion processes were refined for a better organization of the results in tables. The elaboration of the layouts was carried out in the free software Qgis version 3.22 Białowieża to focus on the study area. The results showed erosive processes found on the coast of Maceió, with increased areas, critically eroded, as well as stable. In several sectors of the coast, a visual comparison of satellite images from 2002 and another from 2021 was carried out, and sector 3, the region of Pajuçara beach with the most eroded stretch, was identified, which was identified by the LRR method with -59.6100 m/year, and the same section showed in the trend graphs that the climate variability on the coast had an influence on local meteorological variables and, consequently, on its beaches. With the use of images from the on-site visit, it was noted that in this sector, the already calculated trends of precipitation of 0.5546 mm, Temperature 0.9891 °C, Wind -0.0123 m/s, and Tide Levels 1, 2677 cm, indicated the potential that these variables exercised to modify the beaches on the coast of Maceió, in some casesthey showed a situation of vulnerability and risks associated with the coastal zone. When listing the impacts found through the occupation of the beaches, mitigating measures were suggested in order to mitigate and/or reduce the risks caused by erosion and additions to the beaches. The research aimed to stimulate the socio-environmental importance of coastal zones and their preservation through anthropic use and in the face of climate variability, and/or climate change, and developing scientific support for decision-making by public authorities due to the environmental impacts on the coast of Maceio.

This study aimed to analyze extreme flow events in the Mundaú River and identify the synoptic scale systems that directly influenced these cases, between the years 1990 to 2019. For this analysis, daily flow data from the Agência Nacional de Águas (ANA) for four fluviometric stations located in the cities of Murici; Rio Largo; Santana do Mundaú and União dos Palmares, and with these data the percentile technique was applied, and then a value equal to or greater than the 95th percentile was considered an extreme flow event. With these values, 19 cases were identified where the flow was considered extreme in at least one fluviometric station. Soon after, the systems that acted in the region of the River Mundaú Watershed (RMW) were identified, through the data of the ECMWF model in 10 levels of the atmosphere and satellite images. The year 2000 was the year with the highest number of occurrences of extreme flow (03), which represents 16% of the total. Dividing the three decades, from 1990 to 1999 it concentrated 31.5% of the events, from 2000 to 2009, 52.5%, and from 2010 to 2019, 16%. Extreme flow events in the Mundaú River had a concentrated distribution between the months of autumn and winter, with the months of June (9) and August (4) showing the highest number of cases within the series. As for the systems that were identified, 15 cases with Wave Disturbance of Trades Winds (WDTW); 2 with Upper Tropospheric Cyclonic Vortices (UTCV) and 2 with Tropical Waves (TW).

A poluição atmosférica é um dos principais problemas ambientais atualmente nas grandes e médias cidades. Os aerossóis atmosféricos são um dos principais responsáveis pela poluição atmosférica por todo mundo. E tem extrema relevância pois afetam fortemente o balanço de radiativo ao interagir com a radiação solar, o clima, a química da atmosfera, a visibilidade, além de serem altamente prejudiciais à saúde humana expostas às altas concentrações de partículas, desde a escala local até a escala regional e global. As partículas de aerossóis possuem características ópticas no qual é possível quantificar os seus efeitos causados na atmosfera, bem como saber qual é o seu tipo, sendo altamente importante essas características ópticas. Uma das formas de se estudar estas características e/ou propriedades dos aerossóis é através da análise da EOA (espessura óptica dos aerossóis) que quantifica a atenuação da radiação em um meio contendo material opticamente ativo. Sendo assim o presente estudo pretende avaliar o comportamento da EOA sobre a Região Nordeste do Brasil, analisando o seu comportamento médio para o período dos anos de 2005 a 2019. Os dados foram coletados pelo sensor OMI (Ozone monitoring Instrument) da NASA (National Aeronautics and Space Administration) a bordo do satélite Aura com resolução espacial de 1° x 1° e resolução temporal diária. A partir destes dados utilizados foram realizadas as médias e percentis (P99) para saber dentro da área de estudo quais foram os locais que apresentaram os máximos valores de EOA e em quais períodos específicos. Após as análises os resultados mostraram que a variável de aerossóis do sensor possui um comportamento sazonal onde observamos altos valores nos 3 primeiros meses (janeiro, fevereiro e março), os valores vão diminuindo nos próximos 3 meses (abril, maio e junho) e voltam a aumentar nos meses de julho, agosto e tem seus maiores valores a partir do mês de setembro com seu pico máximo ficando entre o mês de setembro e outubro em quase todos os anos analisados.

The state of success of the rains has its economic basis for agriculture, and the knowledge of the rainy seasons and the success of agricultural enterprises is fundamental. The objective was to define the rainy and dry periods in the state of Alagoas through multivariate analysis applied to the Standardized Precipitation Index (SPI). Use of rainfall data and filled in (period of 54 stations not filled in) (1900 data filled in by default2016) via multiple imputation method, filled in and valid2, where they will be filled out2; R2=0.65; R2= =54.85 mm; RQEM=54.84 mm), Agreste (r=0.69; R2=0.47; EPE=70.97 mm; RQEM=70.97 mm) and East (r=0.61; R2=0.37 ; EPE=154.87 mm; RQEM=109.48 mm). Cluster analysis was applied to time series (definition of periods1) and SPI-2 (annual determination of droughts and rainfall). The accumulated annual rainfall presented two distinct periods: 1960 to 1990 (P1) and 1990 to 2016 (P2), according to the Pettitt test. Both periods alternate according to the phases of La Niña and Neutral (rainy years) and El Niño (dry years). In terms of monthly fees, only the eastern part of Alagoas has the rainy months in the historical series. Of the connection methods tested, the average connection, also known as C which believes that it proves to be cophenetic 0.8760) represents or characterizes homogeneous regions of groups, one non-coastal1 (G and two others further inland2), and two non-homogeneous groups (NA) in the state. The SPI assigned to the annual El to identify the independent and wet periods, in the Niño-Oscillation (ENSO) categorization. As categories of the annual SPI12 high variability and decadal presented us, except as extremely dry and wet groups. There is greater variability in the dry and rainy periods near the coast than in the interior of the state.

Due to the pluviometric behavior in the city of Maceió, the general objective of this work is to analyze the spatial and temporal variability of precipitation in Maceió, identifying homogeneous areas and, consequently, identifying the climatic cause for the occurrence of its variability and its extremes. . Precipitation data from Maceió were used for 13 meteorological stations, with periods from 2015 to 2020, obtained from the Municipal Civil Defense, with no failures in the study period. Through spatialization, it was observed that the mean annual Standardized Precipitation Index (SPI) values were classified as normal. However, the upper part of the capital, to the north, and the North Coast of Maceió have lower values that are closer to events classified as dry, unlike the lower part and South Coast, which have SPI closer to rainy events. For the SPI mean, two regions (R1 and R2) were identified, separated into groups by cluster analysis, which showed greater Euclidean distance and less similarity. The BoxPlot graph presents similar values in both the upper limit and the median for both regions (R1 and R2), in which the SPI of the upper value for the two regions has a maximum value of approximately 2.99, whereas the lower limit has a SPI close to to 1.1 for R1 and 1 for R2. In the Mann-Kendall trend, trends of decreasing precipitation were identified in region 1, and a slight increase in the trend of precipitation in region 2, both regions showed statistical significance through the T-Student test. Using wavelet analysis, the following scales were identified for region 1: seasonal, semiannual, interannual, ENOS, extended ENSO, Sunspots and Atlantic Dipole, in which ENOS and extended ENSO presented dominant scale, whereas for region 2, they were The following scales were identified: seasonal, semiannual, interannual, ENSO, Sunspots and Atlantic Dipole, with ENSO as the dominant scale. With the use of the cross wavelet, local managers can predict droughts after the occurrence of maximum IOS and that extreme events in Maceió have a significant relationship with ENSO. It is concluded that, through the statistical analysis used, it was possible to identify the temporal and spatial variability of precipitation in the capital of Alagoas.

The number of occurrences of extreme precipitation events has increased over the last few years, occurring, in some cases, with greater intensity and duration, thus favoring natural disasters such as severe droughts. Therefore, the expansion of knowledge about the quantity/frequency of rain that falls in a region is fundamental for the strategic planning of local and regional water resources. In this context, this research aims to analyze the level of the main reservoirs of the four sub-basins that make up the São Francisco River Basin, identifying rainfall events, particularly drought, and their possible climatic causes. The study was carried out with precipitation and quota data from four pluviometric and four fluviometric stations, respectively, representative for each variable, obtained through the National Water Agency (ANA) for the period from 1968 to 2020. Extreme drought events were classified and characterized by the standardized precipitation index (SPI), in addition to identifying years of greater precipitation deficits. The Mann-Kendall test was used to detect climatic trends in rainfall series in the four sub-regions of the BHSF and were statistically analyzed using the Student's T test. Of the four sub-regions, three of them show a downward trend in precipitation and quota, while only one shows an increase trend, highlighting the possible changes in the sub-regions' reservoir levels. Ondaletas analysis was used with the intention of verifying extreme rainfall cycles and their causes through the temporal scales observed in the precipitation series of the BHSF sub-regions. Preliminary results show that all sub-regions have higher occurrences of droughts than rains, however, extreme drought events are infrequent, whereas mild drought events were more recurrent. The years characterized as almost normal, that is, with values close to the average rainfall for the region, were the ones with the highest number in the four sub-regions, however, in all of them, the mild drought episodes had significant percentages. To identify possible climatic causes, Wavelet Analysis was used. It was found that the seasonal and interannual scales dominated, associated with the persistent El Niño Southern Oscillation sign (7 years). With information on the influence of temporal cycles on precipitation in the BHSF sub-regions, it is possible to visualize the impact of climatic phenomena on the water reserve of the Basin. In relation to the annual averages, two major climatic groups were formed that are indirectly similar to each other. 

Analyzing the fog formation processes in Manaus-AM and creating an operational
forecasting model are the main purposes of this work. The frequency of fog occurrence
in Manaus and the paucity of publications on the phenomenon in the northern region of
Brazil motivated the study of the causes and local influences in fog formation process.
From the 10 years of data, 30 most intense events were selected for the dry and rainy
seasons. The analysis consisted of: (1) Analysis of surface variables (Air temperature at
2m (T), Dew Point Temperature (Td), Relative Humidity (RH), wind direction and
speed, and precipitation), (2) Thermodynamic analysis (Vertical temperature and
humidity profiles constructed by reanalysis data) and (3) Synoptic Analysis (Streamline,
divergence and omega fields constructed by the reanalysis data). The PAFOG model
was also used to evaluate its applicability for the Northern region. The observed results
did not show significant differences between the dry and rainy seasons. In both seasons
the average temperature was 23°C; T-TD between 0 and 2°C, wind direction between
220 and 50° and wind speed between 00 and 03KT. There was precipitation preceding
the event in most cases. In Ponta Pelada, the surface variables analyzed individually, are
consistent with a scenario less favorable to formations, but the average conditions
showed high relative humidity (between 94 and 100$\%$) and T-TD between 1 and
2°C. Wind direction was the variable that most diverged between airports. Although the
speed was weak (<3 knots) for all cases analyzed, the predominant direction in Eduardo
Gomes, in all stations, was north-northwest, while in Ponta Pelada it was not possible to
determine the predominance, due to variations observed. The thermodynamic profiles
showed that fogs in Manaus are formed under the next conditions: 1- conditionally
unstable atmosphere; 2 - weak or no inversion; 3 - wet layer up to 900hPa. The active
synoptic systems that influenced fog formations during the rainy season were Upper
Tropospheric Cyclonic Vortex, Bolivian High and Trough at the high level in the
northeast region. During the dry season, there was influence of anticyclones and ridges
in the North, Northeast and Midwest, in addition to the systems of the northern
hemisphere. The predictability of the PAFOG model was considered satisfactory for all
cases analyzed and the forecast was classified as good/excellent. The results allowed the
construction of an operational model with favorable conditions for fog formation in
Manaus in observational, synoptic, and thermodynamic aspects. This model is important
to help forecast and mitigate the impact of fog on the operational sector and can be used
as a starting point for improvement and creation of new forecasting methods for the
northern region.

The assessment of the potential for environmental
degradation of the soil is a study of fundamental
importance in helping to conserve the soil and the
environment, as well as helping in the preparation of
management plans for degraded areas and Conservation
Units, as well as in territorial planning in urban and rural
areas. Conducting field studies to assess the potential for
soil degradation is expensive and time-consuming, which
means that mathematical models has been adopted by
many researchers and specialists on the subject. The
present work has the general objective of estimating the
temporal evolution between the years 2000 and 2019 of the
potential for environmental degradation of the soil in the
state of Sergipe, by the fuzzy logic in an environment of the
Geographic Information System. The proposed model
explores causeand-effect relationships of the fundamental
principle of specific relationships between variables that
influence the elevation of the potential for environmental
degradation of the soil from equations of pertinence and
from the Operator Fuzzy Gamma to treat data related to:
geology, geomorphology, slope, hypsometry, pedology,
ease, erodibility, vegetation index and land use and
occupation. It was observed that the factors that contribute
to the increase in the potential for degradation were
geology, geomorphology, pedology and precipitation.
Regarding the change in the potential for environmental
degradation of the soil, it was possible to observe that
between the years 2000 and 2019, 17.66% of the area 

changed to a higher class of potential, 13.87% to a smaller
class and 68.48% there was no change of class. However,
the area where there was no change had a very high
potential in the two years evaluated. The final product of the
model indicates the regions that bring together more or
less factors to promote processes that contribute to the
increase in the potential for environmental degradation of
the soil and, therefore, referring to the input data used,
being, therefore, a valuable tool for decision making. in the
preparation of prevention and control plans and programs.

The lack of rainfall data in the state of Alagoas, similar in many regions of the country, makes use of the regression equations obtained in other regions of Brazil to calculate the R factor of the Universal Soil Loss Equation (USLE). To date, Alagoas does not have a study on rain erosivity. Therefore, the study aims to: i) define an equation to estimate rainfall erosivity based on the index EI30 and the rain coefficient ( R c ) and ii) characterize erosivity in the state of Alagoas in historical rainfall data. Rainfall data from 54 stations in the 56-year period (1960 - 2016) were used in the estimate, filling in the gaps via the imputation method (mtdsi package) in the R environment. The equation used showed consistency and significant correlation between the observed data and the estimated ones, based on the coefficients R2 (86%) and r (93%) and the lowest RMSE (775.2 MJ.mm.ha-1 .h-1 ), which in turn indicated Ordinary Kriging (KO) as better spatial interpolator. Based on the cluster analysis (AA), three homogeneous groups (G1, G2 and G3) of erosivity were identified in the State, the Complete method being defined as the best from the Cophenetic Correlation Coefficient (CCC)> 0.7. The G1 group is the smallest group, located in eastern Alagoas. Groups G2 and G3 are the largest in extension and are located in the climatic mesoregions of Agreste and Sertão. The monthly isoerosivity showed that the highest rates occurred between April and July, with variations between 325.4 and 2.215.9 MJ.mm.ha-1 .h-1 . In annual erosivity, the most erosive locations were in Eastern Alagoas, close to the coast. Highlight for the stations Satuba (11,469.8 MJ.mm.ha-1 .h-1 .year-1 ), Maceió (9,945.4 MJ.mm.ha-1 .h-1 .year-1 ), São Luiz Quitunde (9,806.2 MJ.mm.ha-1 .h-1 .year-1 ) and Flexeiras (9,723.6 MJ.mm.ha-1.h-1.year1), being categorized between moderate and strong. Therefore, the erosivity equation can be used to estimate the USLE R factor in the state of Alagoas. Regarding the use of precipitation products CHIRPS and CHELSA to estimate erosivity, both proved to be an alternative based on the statistical data found. Regarding the trends of rain and erosivity, the first showed a growth trend in the central region of the State, mainly in the Sertão Alagoano mesoregion, while the erosivity trend showed a greater growth close to the coast in the eastern Alagoan mesoregion. The results obtained in this study support the planning of conservationist practices, especially in vulnerable areas in Alagoas

Most of the problems related to environmental degradation in the semi-arid region are
due to the region's own characteristics and the wrong way in which the land is used,
disrespecting its favorable limits for biomass production, and thus making it vulnerable.
Thus, due to the importance of performing a verification of the semi-arid biome of
Alagoas in the municipalities of Ouro Branco and Senador Rui Palmeira, to get a sense
of the magnitude of Caatinga degradation due to the environment itself and in
conjunction with anthropogenic actions. In this work, remote sensing techniques were
used for the identification, evaluation and classification of degraded areas susceptible
to desertification. The present work aims to identify areas susceptible to desertification
in the municipalities Ouro Branco - AL and Senador Rui Palmeira - AL, in the period
from 2007 to 2019 by the availability of data from the main satellite used in this work,
in order to evaluate and classify degradation levels through remote sensing. The

methodology used the products of FVC-MSG, precipitation-CHIRPS, soil moisture-
SMOS use and soil cover CCI-LC. The products of precipitation, FVC and soil moisture

were the interannual averages, generated through monthly averages, and the product
of use and land cover was the data for the reference year 2018. Each product was
used in one or more stages, to identify, assess and classify degraded areas susceptible
to desertification in the analyzed municipalities. The results obtained during the study
show that the regions with degraded areas have FVC values between 0.0 and 0.4 and
average annual precipitation values between 30 mm and 40 mm. It was also possible
to see that the response of the product of soil moisture can have different behaviors,
according to the precipitation regime or the soil characteristics. The product of use and
land cover, made it possible to see that the soils of the studied region underwent
changes in their soil types, where the main changes were: from natural vegetation to
cultivated soil or agricultural soil. However, I conclude that the methodology adopted
in the work was valid, because through the method used, it was possible to identify,
evaluate and classify the areas, with different levels of degradation.

Beans are a demanding legume in climatic conditions, rainfall and temperatures, in
general, are the main meteorological variables that affect its development and
productivity. In the Northeast Region of Brazil (NEB), the bean crop is one of the most
practiced and important, whether from a social or economic point of view, and is based
on family farming, which has few technological resources. The NEB is inserted, most of
its territory, in the semi-arid climate, characteristic of droughts and prolonged droughts,
factors that directly influence the productivity of agricultural crops and which,
according to the Intergovernmental Panel on Climate Change (IPCC) tends towards an
increase in temperatures . From this perspective, this research aimed to analyze the
impact of climate change on bean yield, based on the climate change scenario RCP4.5,
through an agrometeorological model of penalty for water deficit. Three homogeneous
groups of productivity were identified in the NEB, based on IBGE data between 1974
and 2018 of 1817 municipalities. Of these municipalities, it was possible to calibrate
and validate the agrometeorological model in 73, as they present reliable meteorological
and productivity data, classified into three groups (low, medium and high productivity).
These productivity patterns reflect important parameters, such as the maximum
technological yield and the water deficit sensitivity factor. To generate regionalized
future scenarios and assess the impacts of climate change on edible beans productivity,
the statistical downscaling technique was used for the RCP4.5 scenario. Six global
climate models that made up the fifth IPCC report were used: the Canadian CANESM2,
the French CNRM-CM5 and IPSL-CM5A-MR, the American GFDL-ESM2M, the
German MPI-ESMMR, and the Norwegian model NORESM1-MS. Through the
models, the NEB areas that will have an impact on productivity were observed, based
on the climatological period of simulations, for the years 2011-2040, 2041-2070 and
2071-2100. The simulations indicate that most NEB producing municipalities will have
their productivity reduced, as a direct consequence of the tendency of significant
reduction in accumulated precipitation, and of an increase in temperatures, which will
therefore increase potential evapotranspiration.

Atmospheric pollution is a widely discussed topic, as it is responsible for several chemical processes, occurring in the absorption of different substances, whether in solid, liquid or gaseous state, from natural or anthropogenic sources. Chemical compounds can disperse, react with each other or with other substances present in the air. Among the biggest environmental issues in Brazil, air pollution in urban areas stands out, where the burning of fuels by motor vehicles is the main component of pollution and due to population and fleet growth. Pollution has a direct influence on air quality, which can be compromised if the substances emitted must meet the standards established by CONAMA, which has as reference the guide values recommended by the World Health Organization. as its dispersion in the medium is directly associated, meteorological variables are responsible for this dispersion or concentration of substances. Therefore, the main objective of this study is to identify the main pollutants and evaluate the air quality of the capitals of the Northeast of Brazil, for the year 2015. To verify the influence of meteorological variables on the concentration of pollutants, modeling was used computational (WRF/CHEM Model) to simulate the state of the atmosphere, as it allows quantifying the chemical species present in the air, as well as observing their dispersion and concentration process.

 O Estado de Alagoas tem uma participação na produção agrícola brasileira nas culturas de cana de açúcar, coco, mandioca e milho, entretanto, alguns municípios são bastante afetados por variações climáticas, logo, a produção local pode sofrer quedas significativas em anos de secas ou de extremas precipitações. Desse modo, objetivou-se analisar os efeitos da variabilidade climática sobre as principais culturas agrícolas do estado de Alagoas, identificando as causas climáticas dos principais eventos pluviométricos e seus impactos sobre as produções de cana de açúcar, coco, mandioca e milho. Foram utilizados dados de precipitação do período de 1987 a 2017,  na análise de Ondeletas. Foram também utilizados dados de precipitação com os dados de produtividade dos anos de 1987 a 2017, para as correlações e foram gerados também mapas de distribuição espacial dos dados de produção dos principais cultivos em anos de eventos extremos climáticos. Desse modo uns dos principais resultados que podem ser citados: foram através das análises de Ondeletas, para a Região Metropolitana na qual as escalas dominantes foram ENOS estendido e o Dipolo do Atlântico, nas demais macrorregiões a escala dominante foi a Oscilação Decadal do Pacifico para as Regiões Agreste, Baixo São Francisco, Médio Sertão e Planalto de Borborema e ENOS estendido para as Regiões Alto Sertão, Norte e Tabuleiro do Sul e ENOS para a Região Serrana dos Quilombos. Outros resultados obtidos que merecem destaque foram obtidos pelos gráficos de produções, na qual a cana de açúcar mostrou que a maior produção na Região Metropolitana foi no município de Rio Largo durante La Niña Moderada, nas demais macrorregiões a maior produção foi na Região Agreste durante La Niña Fraca, a maior produção de coco na Região Metropolitana foi em Rio Largo durante El Niño Fraco, nas outras macrorregiões a maior produção foi na Região Serrana dos Quilombos durante El Niño Fraco, para a cultura da mandioca na Região Metropolitana, Marechal Deodoro foi a que apresentou maior produção de mandioca durante El Niño Forte, nas demais macrorregiões a maior produção foi na Região Médio Sertão durante La Niña Fraca, na Região Metropolitana a maior produção de milho foi em Rio Largo nos anos de La Niña Moderada, já as maiores produções de milho foram observadas em diversas Regiões nas diversas categorias de ENOS. Diante disto essa pesquisa não só tem cunho cientifico pelo fato de ser um trabalho inédito como contribuiu para o aumento na produtividade da agricultura do Brasil, principalmente no estado de Alagoas, ofertando assim o entendimento da relação entre os eventos extremos e as principais produções agrícolas de Alagoas, permitindo assim a diminuição das percas agrícolas e favorecendo o aumento na produtividade das grandes culturas de Alagoas.

Air pollution is a problem faced daily in regions where there is an intensification of pollutant emissions and is directly related to the reduction in the quality of human life, due to the increase in cases of respiratory diseases. Among the sources of the main gases emitted, those of anthropogenic origin are those that most impact in metropolitan areas through industrial activities and vehicle traffic where sulfur dioxide (SO2) and nitrogen (NO2), methane (CH4) and monoxide are highlighted. carbon (CO). The metropolitan regions of the present study have different characteristics (topography, vegetation and climate) and the pollution problems are attributed to anthropogenic concentrations close to the Planetary Boundary Layer as well as in some regions it is inserted in locations close to mountain ranges (RMLP and RMS). The main objective of this work is to carry out analysis of the monthly concentrations of the total pollutant column under five metropolitan South American regions through sensors coupled to satellites. The methodology consists in the use of interquantial analysis methods, which will investigate monthly trends in three quantiles, and Mann Kendall, for annual and monthly trends, from 2005 to 2019. The data used in this study were obtained directly from AIRS sensors and OMI with different spatial and temporal resolutions (CH4 and CO - 1 ° x1 ° and 12h, NO2 - 0.25 °x0.25 ° and daily, SO2 - 0.125 ° x0.125 and daily). The results showed that for the interquartile analysis there was a greater amount of trends (negative and positive) under maximum (Q90) and minimum (Q10) values in the monthly concentrations of CH4, CO and SO2, finally in NO2 growth seasonality was detected (March to June) and decrease (July to February) in RMBA, RMS and RMSP. For the Mann Kendall test, the results indicated that there was a reduction in the concentrations of the four pollutants in the five metropolitan regions (annual and monthly) and an increase in growth trends, in 90% of the data, some regions can be attributed can be influenced by a presence of negative trends under extreme values

Of the adverse natural phenomena, drought is the one that most affects society because it acts over large territorial extensions and lasts for long periods of time. Drought is a phenomenon characterized by a water deficit long enough to impact on edaphic, meteorological, water and social factors. The prediction of drought has contributed worldwide to actions to mitigate the impacts of the phenomenon and increase resilience. Brazil has the largest semi-arid inhabited territory in the world: the Northeast. With each drought that occurs in the Northeast of Brazil (NEB), water rationing, loss of crops, death of animals and hunger of part of the population are recorded. The impacts of drought provoke a great rural exodus to the coastal cities of NEB and even to large urban centers in the country. Several studies have been conducted to understand the dynamics of drought in NEB. It is known that the occurrence of the phenomenon is partly modeled by the South-El Niño Oscillation (OSEN) and partly by the Temperature of the Tropical Atlantic Sea Surface (TSM). Such oceanic phenomena of slow variability make it possible to anticipate the accumulated rainfall and the average temperature of the following months with certain quality. This work analyzed the predictability of meteorological drought in the Northeast of Brazil in order to consolidate a methodology that contributes to coping with drought using robust methods and operational products of the North American Multi Model Ensamble that consist of state-of-the-art general circulation models joined to make the better forecast. In general, the predictability of NEB drought is quite considerable. The northern sector of NEB showed the best results between the periods analyzed. In this sector, drought predictability extends beyond its rainy season. Followed by the northern sector comes the eastern and southern sectors with considerable predictability in their respective rainy periods. The central region, in turn, represented by the Northeastern semi-arid region, showed the worst results, where the predictability of drought in this sector is particularly difficult. Thus, the meteorological drought in the NEB has good predictability and the model can be used operationally to assist actions to combat drought.