Effects of the El Niño-Southern Oscillation and seasonal weather conditions on Aedes aegypti infestation in the State of São Paulo (Brazil): A Bayesian spatio-temporal study.

BACKGROUND: Seasonal fluctuations in weather are recognized as factors that affect both Aedes (Ae.) aegypti mosquitoes and the diseases they carry, such as dengue fever. The El Niño-Southern Oscillation (ENSO) is widely regarded as one of the most impactful atmospheric phenomena on Earth, characterized by the interplay of shifting ocean temperatures, trade wind intensity, and atmospheric pressure, resulting in extensive alterations in climate conditions. In this study, we investigate the influence of ENSO and local weather conditions on the spatio-temporal variability of Ae. aegypti infestation index. METHODS: We collected seasonal entomological survey data of immature forms of Ae. aegypti mosquitoes (Breteau index), as well as data on temperature, rainfall and the Oceanic Niño Index (ONI) for the period 2008-2018 over the 645 municipalities of the subtropical State of São Paulo (Brazil). We grounded our analytical approach on a Bayesian framework and we used a hierarchical spatio-temporal model to study the relationship between ENSO tracked by ONI, seasonal weather fluctuations and the larval index, while adjusting for population density and wealth inequalities. RESULTS: Our results showed a relevant positive effect for El Niño on the Ae. aegypti larval index. In particular, we found that the number of positive containers would be expected to increase by 1.30-unit (95% Credible Intervals (CI): 1.23 to 1.37) with El Niño events (i.e., ≥ 1°C, moderate to strong) respect to neutral (and weak) events. We also found that seasonal rainfall exceeding 153.12 mm appears to have a notable impact on vector index, leading potentially to the accumulation of ample water in outdoor discarded receptacles, supporting the aquatic phase of mosquito development. Additionally, seasonal temperature above 23.30°C was found positively associated to the larval index. Although the State of São Paulo as a whole has characteristics favourable to proliferation of the vector, there were specific areas with a greater tendency for mosquito infestation, since the most vulnerable areas are predominantly situated in the central and northern regions of the state, with hot spots of abundance in the south, especially during El Niño events. Our findings also indicate that social disparities present in the municipalities contributes to Ae. aegypti proliferation. CONCLUSIONS: Considering the anticipated rise in both the frequency and intensity of El Niño events in the forthcoming decades as a consequence of climate change, the urgency to enhance our ability to track and diminish arbovirus outbreaks is crucial.

Monitoring anomalies on large-scale energy and water balance components by coupling remote sensing parameters and gridded weather data.

The SAFER (Simple Algorithm for Evapotranspiration Retrieving) algorithm was applied with MODIS images and gridded weather data from 2007 to 2021, to monitor the energy balance components and their anomalies, in the Atlantic Forest (AF) and Caatinga (CT) biomes inside the coastal agricultural growing zone, Northeast Brazil. Considering the long-term data, the Rn values between the biomes are not significantly different, however presenting distinct Rn partitions into latent (λE), sensible (H), and ground (G) heat fluxes between biomes. The Rn values annual averages are 9.40 ± 0.21 and 9.50 ± 0.23 MJ m-2 d-1, for AF and CT, respectively. However, for respectively AF and CT, they are respectively 5.10 ± 1.14 MJ m-2 d-1 and 4.00 ± 0.99 MJ m-2 d-1 for λE; 3.80 ± 1.12 MJ m-2 d-1 and 5.00 ± 1.00 MJ m-2 d-1 for H; 0.50 ± 0.12 MJ m-2 d-1 and 0.40 ± 0.10 MJ m-2 d-1 for G, yielding respective mean evaporative fraction (Ef = λE/(Rn - G) values of 0.60 ± 0.12 and 0.50 ± 0.15. Anomalies on λE, H, and Ef were detected through standardized index for these energy balance components by comparing the results for the years 2018 to 2021 with the long-term values from 2007 to each of these years, showing that the energy fluxes between surfaces and the lower atmosphere, and then the root-zone moisture conditions for both biomes, may strongly vary along seasons and years, with alternate positive and negative anomalies. These assessments are important for water policies as they can picture suitable periods and places for rainfed agriculture as well as the irrigation needs in irrigated agriculture, allowing rational agricultural environmental management while minimizing water competitions among other water users, under climate and land-use changes conditions.

Influence of weather conditions and plant growth on populations of the solenopsis mealybug (Phenacoccus solenopsis Tinsley) infesting maize plants.

The cotton or solenopsis mealybug, Phenacoccus solenopsis (Tinsley, 1898) (Hemiptera: Pseudococcidae), infests various host plants in Egypt. A study was conducted to observe the incidence of mealybugs and the possible influences of meteorological variables and plant age on the insect population of maize (single-hybrid 168 yellow maize cultivar) plants in Esna district, Luxor governorate, Egypt, in two consecutive seasons (2021 and 2022). P. solenopsis infested maize plants from the 3rd week of June to harvest, and had three peaks of seasonal incidence/season namely; in the 1st week of June in the 3rd/4th week of July, and the 2nd week of August. Similarly, there were three peaks in the percent of infestations per season. In the first season, the average population density of P. solenopsis per sample was 174.04 ± 16.93 individuals, and in the second season, 156.72 ± 14.28 individuals. The most favorable climate for P. solenopsis population increase and infestation occurred in August in the first season and in September in the second season, while June was less suitable in both growing seasons (as estimated by weekly surveys). The combined effects of weather conditions and plant age are significantly related to the estimates of P. solenopsis populations, with an explained variance (E.V.) of 93.18 and 93.86%, respectively, in the two seasons. In addition, their influences explained differences in infestation percentages of 93.30 and 95.54%, respectively, in the two seasons. Maize plant age was the most effective factor in determining changes in P. solenopsis population densities in each season. The mean daily minimum temperature in the first season and mean daily dew point in the second season were the most important factors affecting the percent changes in infestation. However, in both seasons, the mean daily maximum temperature was the least effective variable in population and infestation variation. This study paves the way for monitoring and early detection of mealybugs in maize; as well as the optimal climatic conditions for its development.

Strategic location model for oil spill response vessels (OSRVs) considering oil transportation and weather uncertainties.

In the event of oil spills in offshore oil and gas projects, containment and dispersion equipment must be sent to the affected areas within a critical time by vessels known as oil spill response vessels (OSRVs). Here, we developed an optimization tool, integrated with an oil spill trajectory simulation model, both in deterministic and stochastic alternatives, to support decision-making during the strategic planning of OSRV operations. The tool was constructed in Python using GNOME for oil spill simulations and the GUROBI to solve the optimization model. The tool was applied to a case study in Brazil and afforded relevant recommendations. In terms of research contributions, we proved the viability of the integration between oil spill simulation and mathematical modeling for OSRV strategic operation planning, we explored the stochasticity of the problem with an innovative strategy and we demonstrated flexibility and easy applicability of the framework on real operations.

Factors influencing fumonisin B1 contamination in maize: insights from two production regions in Costa Rica.

Maize (Zea mays L.) is an important cereal crop worldwide. Contaminated maize kernels pose a significant mycotoxin exposure risk for humans in Latin America. Fumonisins, the most prevalent mycotoxin in maize, typically occur during pre-harvest conditions leading to significant economic losses. Various factors, including weather conditions, may influence this contamination. This study aimed to determine the association between fumonisin B1 (FB1) contamination, prevalence of Fusarium verticillioides, weather conditions and kernel quality in the two primary maize production areas in Costa Rica (Brunca and Chorotega). All maize samples (100%) showed FB1 contamination, with higher concentrations in samples from Brunca region, consistent with the presence of F. verticilliodes. Weather conditions appeared to play an important role in this contamination, since Brunca region had the highest mean temperature and relative humidity after maize silking (R1) and the total monthly rainfall in this region was significantly higher during the last two months of maize cultivation (grain-filling and physiological maturity stages R3 to R6). Interestingly, this study found a negative correlation between grain damage and kernel contamination with FB1 and F. verticillioides. The concentration of mineral nutrients in kernels from both regions was largely similar. Most nutrients in kernels exhibited a negative correlation with FB1, particularly nitrogen. Zinc and phosphorus were the only nutrients in kernels showing a positive correlation with FB1 in samples from the Brunca region. The results highlight elevated levels of FB1 contamination in maize and contribute to a better understanding of pre-harvest factors influencing FB1 contamination in tropical conditions.

Artificial neural networks as a tool for seasonal forecast of attack intensity of Spodoptera spp. in Bt soybean.

Soybean (Glycine max) is the world's most cultivated legume; currently, most of its varieties are Bt. Spodoptera spp. (Lepidoptera: Noctuidae) are important pests of soybean. An artificial neural network (ANN) is an artificial intelligence tool that can be used in the study of spatiotemporal dynamics of pest populations. Thus, this work aims to determine ANN to identify population regulation factors of Spodoptera spp. and predict its density in Bt soybean. For two years, the density of Spodoptera spp. caterpillars, predators, and parasitoids, climate data, and plant age was evaluated in commercial soybean fields. The selected ANN was the one with the weather data from 25 days before the pest's density evaluation. ANN forecasting and pest densities in soybean fields presented a correlation of 0.863. It was found that higher densities of the pest occurred in dry seasons, with less wind, higher atmospheric pressure and with increasing plant age. Pest density increased with the increase in temperature until this curve reached its maximum value. ANN forecasting and pest densities in soybean fields in different years, seasons, and stages of plant development were similar. Therefore, this ANN is promising to be implemented into integrated pest management programs in soybean fields.

Causal association between environmental variables and the excess cases of cutaneous leishmaniasis in Colombia: are we looking to the wrong side?

Our main aim was to estimate and compare the effects of six environmental variables (air temperature, soil temperature, rainfall, runoff, soil moisture, and the enhanced vegetation index) on excess cases of cutaneous leishmaniasis in Colombia. We used epidemiological data from the Colombian Public Health Surveillance System (January 2007 to December 2019). Environmental data were obtained from remote sensing sources including the National Oceanic and Atmospheric Administration, the Global Land Data Assimilation System (GLDAS), and the Moderate Resolution Imaging Spectroradiometer. Data on population were obtained from the TerriData dataset. We implemented a causal inference approach using a machine learning algorithm to estimate the causal association of the environmental variables on the monthly occurrence of excess cases of cutaneous leishmaniasis. The results showed that the largest causal association corresponded to soil moisture with a lag of 3 months, with an average increase of 8.0% (95% confidence interval [CI] 7.7-8.3%) in the occurrence of excess cases. The temperature-related variables (air temperature and soil temperature) had a positive causal effect on the excess cases of cutaneous leishmaniasis. It is noteworthy that rainfall did not have a statistically significant causal effect. This information could potentially help to monitor and control cutaneous leishmaniasis in Colombia, providing estimates of causal effects using remote sensor variables.

Metabarcode insights into the airborne fungal diversity in the indoor and outdoor environments in archives from Yunnan, Southwestern China.

Monitoring dynamics of airborne fungal species and controlling of harmful ones are of vital importance to conservation of cultural relics. However, the evaluation of air quality and the community structure characteristics of microorganisms, especially fungi, in the atmosphere of archives is in a stage of continuous exploration though more than 4,000 archives were constructed in China. Seventy-two air samples were collected in this study under different spatial and weather conditions from the archives of Kunming Medical University, located in the Kunming metropolitan area, Yunnan province, southwestern China. A total of 22 airborne fungal classes, 160 genera and 699 ASVs were identified, the species diversity is on the rise with the strengthening of air circulation with the outside space, and thus the intensive energy metabolism and activity were found in the spaces with window and sunny weather, except for the higher lipid synthesis of indoor samples than that of outdoor ones. Furthermore, there were significant differences in fungal community composition and abundance between sunny and rainy weathers. A considerable number of species have been identified as indicator in various environmental and weather conditions of the archives, and temperature and humidity were thought to have significant correlations with the abundance of these species. Meanwhile, Cladosporium and Alternaria were the dominant genera here, which may pose a threat to the health of archive professionals. Therefore, monitoring and controlling the growth of these fungal species is crucial for both conservation of paper records and health of archive professionals.

Parallel prediction of dengue cases with different risks in Mexico using an artificial neural network model considering meteorological data.

In 2022, Mexico registered an increase in dengue cases compared to the previous year. On the other hand, the amount of precipitation reported annually was slightly less than the previous year. Similarly, the minimum-mean-maximum temperatures recorded annually were below the previous year. In the literature, it is possible to find studies focused on the spread of dengue only for some specific regions of Mexico. However, given the increase in the number of cases during 2022 in regions not considered by previously published works, this study covers cases reported in all states of the country. On the other hand, determining a relationship between the dynamics of dengue cases and climatic factors through a computational model can provide relevant information on the transmission of the virus. A multiple-learning computational approach was developed to simulate the number of the different risks of dengue cases according to the classification reported per epidemiological week by considering climatic factors in Mexico. For the development of the model, the data were obtained from the reports published in the Epidemiological Panorama of Dengue in Mexico and in the National Meteorological Service. The classification of non-severe dengue, dengue with warning signs, and severe dengue were modeled in parallel through an artificial neural network model. Five variables were considered to train the model: the monthly average of the minimum, mean, and maximum temperatures, the precipitation, and the number of the epidemiological week. The selection of variables in this work is focused on the spread of the different risks of dengue once the mosquito begins transmitting the virus. Therefore, temperature and precipitation were chosen as climatic factors due to the close relationship between the density of adult mosquitoes and the incidence of the disease. The Levenberg-Marquardt algorithm was applied to fit the coefficients during the learning process. In the results, the ANN model simulated the classification of the different risks of dengue with the following precisions (R2): 0.9684, 0.9721, and 0.8001 for non-severe dengue, with alarm signs and severe, respectively. Applying a correlation matrix and a sensitivity analysis of the ANN model coefficients, both the average minimum temperature and precipitation were relevant to predict the number of dengue cases. Finally, the information discovered in this work can support the decision-making of the Ministry of Health to avoid a syndemic between the increase in dengue cases and other seasonal diseases.

Weather-Based Logistic Regression Models for Predicting Wheat Head Blast Epidemics.

Wheat head blast is a major disease of wheat in the Brazilian Cerrado. Empirical models for predicting epidemics were developed using data from field trials conducted in Patos de Minas (2013 to 2019) and trials conducted across 10 other sites (2012 to 2020) in Brazil, resulting in 143 epidemics, with each being classified as either outbreak (≥20% head blast incidence) or nonoutbreak. Daily weather variables were collected from the National Aeronautics and Space Administration (NASA) Prediction of Worldwide Energy Resources (POWER) website and summarized for each epidemic. Wheat heading date (WHD) served to define four time windows, with each comprising two 7-day intervals (before and after WHD), which combined with weather-based variables resulted in 36 predictors (nine weather variables × four windows). Logistic regression models were fitted to binary data, with variable selection using least absolute shrinkage and selection operator (LASSO) and sequentially best subset analyses. The models were validated using the leave-one-out cross-validation (LOOCV) technique, and their statistical performance was compared. One model was selected, implemented in a 24-year series, and assessed by experts and literature. Models with two to five predictors showed accuracies between 0.80 and 0.85, sensitivities from 0.80 to 0.91, specificities from 0.72 to 0.86, and area under the curve (AUC) from 0.89 to 0.91. The accuracy of LOOCV ranged from 0.76 to 0.81. The model applied to a historical series included temperature and relative humidity in preheading date, as well as postheading precipitation. The model accurately predicted the occurrence of outbreaks, aligning closely with real-world observations, specifically tailored for locations with tropical and subtropical climates.

Fatal interactions of albatrosses with weather radiosondes/balloons on the Southern and Southeastern coasts of Brazil.

Weather radiosondes play a crucial role in gathering atmospheric data for weather modeling and forecasting. However, their impact on marine wildlife, particularly seabirds, has raised concerns regarding the potential threats posed by these instruments. This study aims to assess the adverse effects of weather balloons on albatrosses, with a focus on the Southwest Atlantic region. The research reveals seven cases of entanglement of radiosonde equipment, leading to severe injuries and mortality along the Southern and Southeastern coasts of Brazil. Recommendations for mitigating the environmental impact of weather balloons include the adoption of biodegradable materials in their design and the implementation of improved retrieval protocols. Furthermore, the study stresses the importance of continued monitoring and research to address the interaction of weather radiosondes with marine animals. This approach is vital for ensuring the sustainable collection of scientific data while minimizing harm to marine life and ecosystems.

Ecotoxic effect in Allium cepa due to sphalerite weathering arising in calcareous conditions.

The ecotoxic effect of Zn species arising from the weathering of the marmatite-like sphalerite ((Fe, Zn)S) in Allium cepa systems was herein evaluated in calcareous soils and connected with its sulfide oxidation mechanism to determine the chemical speciation responsible of this outcome. Mineralogical analyses (X-ray diffraction patterns, Raman spectroscopy, scanning electron microscopy and atomic force microscopy), chemical study of leachates (total Fe, Zn, Cd, oxidation-reduction potential, pH, sulfates and total alkalinity) and electrochemical assessments (chronoamperometry, chronopotentiometry, cyclic voltammetry, and electrochemical impedance spectroscopy) were carried out using (Fe, Zn)S samples to elucidate interfacial mechanisms simulating calcareous soil conditions. Results indicate the formation of polysulfides (Sn2-), elemental sulfur (S0), siderite (FeCO3)-like, hematite (Fe2O3)-like with sorbed CO32- species, gunningite (ZnSO4·H2O)-like phase and smithsonite (ZnCO3)-like compounds in altered surface under calcareous conditions. However, the generation of gunningite (ZnSO4·H2O)-like phase was predominant bulk-solution system. Quantification of damage rates ranges from 75 to 90% of bulb cells under non-carbonated conditions after 15-30 days, while 50-75% of damage level is determined under neutral-alkaline carbonated conditions. Damage ratios are 70.08 and 30.26 at the highest level, respectively. These findings revealed lower ecotoxic damage due to ZnCO3-like precipitation, indicating the effect of carbonates on Zn compounds during vegetable up-taking (exposure). Other environmental suggestions of the (Fe, Zn)S weathering and ecotoxic effects under calcareous soil conditions are discussed.

Climate Change, Migration, and Health Disparities at and Beyond the US-Mexico Border.

This JAMA Insights in the Climate Change and Health series discusses the increase in extreme weather events caused by climate change and how these events bring about increased migration due to effects on water availability, food access, and rates of endemic diseases.

Combined use of SAR images and numerical simulations to identify the source and trajectories of oil spills in coastal environments.

Remote sensing data and numerical simulation are important tools to rebuild any oil spill accident letting to identify its source and trajectory. Through these tools was identified an oil spill that affected Oaxacan coast in October 2022. The SAR images were processed with a standard method included in SNAP software, and the numerical simulation was made using Lagrangian transport model included in GNOME software. With the combining of these tools was possible to discriminate the look-alikes from true oil slicks; which are the main issue when satellite images are used. Obtained results showed that 4.3m3 of crude oil were released into the ocean from a punctual point of oil pollution. This oil spill was classified such as a small oil spill. The marine currents and weathering processes were the main drivers that controlled the crude oil displacement and its dispersion. It was estimated in GNOME that 1.6 m3 of crude oil was floating on the sea (37.2 %), 2.4 m3 was evaporated into the atmosphere (55.8 %) and 0.3 m3 reached the coast of Oaxaca (7 %). This event affected 82 km of coastline, but the most important touristic areas as well as turtle nesting zones were not affected by this small crude oil spill. Results indicated that the marine-gas-pump number 3 in Salina Cruz, Oaxaca, is a punctual point of oil pollution in the Southern Mexican Pacific Ocean. Further work is needed to assess the economic and ecological damage to Oaxacan coast caused by this small oil spill.

Spatiotemporal variability in human thermal comfort perception in open-air spaces: application to the state of Minas Gerais, Brazil.

The objective of this study was to propose bioclimatic zoning to classify human thermal comfort and discomfort in the state of Minas Gerais, Brazil; both historical and future scenarios are considered. Thus, historical series (1961 to 2017) of the effective temperature index as a function of the wind (ETW) were obtained as a function of the monthly average values of the minimum, mean, and maximum dry-bulb air temperatures (tdb,min, tdb,mean, and tdb,max, respectively), in addition to the mean relative humidity ([Formula: see text], %) and mean wind speed ([Formula: see text], m s -1). The data were obtained from 34 weather stations and subjected to trend analysis by using the nonparametric Mann-Kendall test, thus enabling the simulation of future scenarios (for 2028 and 2038). Then, to define the thermal ranges of the bioclimatic zoning, maps of ETWmin, ETWmean, and ETWmax were created from geostatistical analysis. Overall, the results show warming trends for the upcoming years in Minas Gerais municipalities. All climatic seasons showed an increase in the frequency of new classifications in the upper adjacent classes, which indicates climate warming. Therefore, when considering future scenarios for the autumn and winter seasons, attention should be given to changes in predicted thermal sensation, especially in the Central Minas Gerais, Belo Horizonte Metropolitan, South/Southwest Minas, Campo das Vertentes, and Zona da Mata.

Analysis of thermal discomfort associated with synoptic conditions in the city of Pelotas, southernmost region of Brazil.

Here, we evaluated the influence of outdoor environmental conditions (synoptic weather conditions) on human thermal discomfort in the five macro-regions of Pelotas city, located in the southernmost region of Brazil. To do this, meteorological sensors (HOBO MX2301A) were installed outside the residences to measure the air temperature, dew point temperature, and relative humidity between 18 January and 20 August 2019. Two well-established simplified biometeorological indices were examined seasonally: (i) humidex for the summer months and (ii) effective temperature as a function of wind for the autumn and winter months. Our findings showed seasonal differences related to human thermal discomfort and outdoor environmental conditions. The thermal discomfort was highest in the afternoons during the summer months and at night during the winter months. The seasonal variation in human thermal discomfort was highly associated with the meteorological conditions. In summer, the presence of the South Atlantic Subtropical Anticyclone (SASA) contributed to heat stress. The SASA combined with the continent's low humidity contributed to the perceived sensation of thermal discomfort. In the winter, thermal discomfort was associated with the decrease in air humidity caused by high atmospheric pressure systems, which led to a decrease in both air temperature and air moisture content. Our findings suggest that a better understanding of the complex interplay between outdoor environmental factors and human thermal comfort is needed in order to mitigate the negative effects of thermal discomfort.

Environmental drivers of long-term variations in the abundance of the red hocicudo mouse (Oxymycterus rufus) in Pampas agroecosystems.

During the 20th century, there has been an ongoing agricultural expansion and global warming, two of the main determinants influencing biodiversity changes in Argentina. The red hocicudo mouse (Oxymycterus rufus) inhabits subtropical grasslands and riparian habitats and has increased its abundance in recent years in central Argentina agroecosystems. This paper describes the long-term temporal changes in O. rufus abundance in Exaltación de la Cruz department, Buenos Aires province, Argentina, in relation to weather fluctuations and landscape features, as well as analyzes the spatio-temporal structure of captures of animals. We used generalized liner models, semivariograms, the Mantel test, and autocorrelation functions for the analysis of rodent data obtained from trappings conducted between 1984 and 2014. O. rufus showed an increase in abundance across the years of study, with its distribution depending on landscape features, such as habitat types and the distance to floodplains. Capture rates showed a spatio-temporal aggregation, suggesting expansion from previously occupied sites. O. rufus was more abundant at lower minimum temperatures in summer, higher precipitation in spring and summer, and lower precipitations in winter. Weather conditions affected O. rufus abundance, but there was local variation that differed from global patterns of climate change.

Optimizing evapotranspiration and crop irrigation requirements of tropical forages cropping systems in Southern Brazil.

Crop irrigation requirements are usually estimated based on crop evapotranspiration (ETc) as determined by the reference evapotranspiration (ETo) and crop coefficient (Kc). There is a lack of knowledge on the irrigation requirements of tropical forage crops in Brazil, contrasting with the increasing use of irrigation in pastures. The effort of this study was to investigate what would be the water needs of tropical forages in Southern Brazil, based on a robust experimental database. The study was carried out in São Paulo State-Brazil using different forages species and their combinations [Guinea grass (GG); Guinea grass + black oat + ryegrass (GOR); Bermuda grass (BG), and Bermuda + black oat + ryegrass (BOR)]. The experimental fields were fully irrigated, and the Kc values were derived from ETc measurements on lysimeters; ETo was estimated using daily data from a nearby weather station and the standard FAO56 parameterization. Mean daily ETc values for GG, GOR, BG and BOR were 4.1, 2.9, 3.6, and 3.4 mm, respectively, and respective mean Kc values were 0.99, 0.90, 1.0, and 0.94. Average Kc values for all plots decreased as ETo increased, producing a negative Kc-ETo relationship, mainly when ETo reached values greater than 5 mm d-1. This was most likely due to internal plant stomatal resistance to vapor release from the leaves diffusing to the atmosphere at high ETo. So, the time-based Kc curves described by FAO 56 manual should be adjusted for the analyzed crops considering different ranges of ETo to improve the required irrigation depth.

Effects of meteo-oceanographic conditions on the weathering processes of oil spills in northeastern Brazil.

This paper presents the graphical results of the Lagrangian-model and the weathering processes associated with oil spills in the tropical South Atlantic, taking into account the meteorological and oceanographic conditions of the study region. The scenarios were created in the Brazilian-NE waters adjacent, with simulation times of 670 h, and densities of 35, 25, and 15API with volume of 1590 m3 were considered. The main results showed that the meteo-oceanographic characteristics of the study region influence the trajectories and weathering processes in the oil spill. The trajectories varied for each launch point and reached the continent severely in January and October. The associated weathering processes showed higher rates in September and lower rates in April, indicative of the influence of phenomena such as Intertropical Tropical Convergence Zone and warm pool in the South Atlantic region. Sea surface temperature and wind speed are key factors that correlate positively with these months.