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Globally, millions of lives are impacted every year by infectious diseases outbreaks. Comprehensive and innovative surveillance strategies aiming at early alert and timely containment of emerging and reemerging pathogens are a pressing priority. Shortcomings and delays in current pathogen surveillance practices further disturbed informing responses, interventions, and mitigation of recent pandemics, including H1N1 influenza and SARS-CoV-2. We present the design principles of the architecture for an early-alert surveillance system that leverages the vast available data landscape, including syndromic data from primary health care, drug sales, and rumors from the lay media and social media to identify areas with an increased number of cases of respiratory disease. In these potentially affected areas, an intensive and fast sample collection and advanced high-throughput genome sequencing analyses would inform on circulating known or novel pathogens by metagenomics-enabled pathogen characterization. Concurrently, the integration of bioclimatic and socioeconomic data, as well as transportation and mobility network data, into a data analytics platform, coupled with advanced mathematical modeling using artificial intelligence or machine learning, will enable more accurate estimation of outbreak spread risk. Such an approach aims to readily identify and characterize regions in the early stages of an outbreak development, as well as model risk and patterns of spread, informing targeted mitigation and control measures. A fully operational system must integrate diverse and robust data streams to translate data into actionable intelligence and actions, ultimately paving the way toward constructing next-generation surveillance systems.

The paper introduces the Oil-Slick Hub (OSH), a computational platform to facilitate the data visualization of a large database of petroleum signatures observed on the surface of the ocean with synthetic aperture radar (SAR) measurements. This Internet platform offers an information search and retrieval system of a database resulting from >20 years of scientific projects that interpreted ~15 thousand offshore mineral oil “slicks”: natural oil “seeps” versus operational oil “spills”. Such a Digital Mega-Collection Database consists of satellite images and oil-slick polygons identified in the Gulf of Mexico (GMex) and the Brazilian Continental Margin (BCM). A series of attributes describing the interpreted slicks are also included, along with technical reports and scientific papers. Two experiments illustrate the use of the OSH to facilitate the selection of data subsets from the mega collection (GMex variables and BCM samples), in which artificial intelligence techniques—machine learning (ML)—classify slicks into seeps or spills. The GMex variable dataset was analyzed with simple linear discriminant analyses (LDAs), and a three-fold accuracy performance pattern was observed: (i) the least accurate subset (~65%) solely used acquisition aspects (e.g., acquisition beam mode, date, and time, satellite name, etc.); (ii) the best results (>90%) were achieved with the inclusion of location attributes (i.e., latitude, longitude, and bathymetry); and (iii) moderate performances (~70%) were reached using only morphological information (e.g., area, perimeter, perimeter to area ratio, etc.). The BCM sample dataset was analyzed with six traditional ML methods, namely naive Bayes (NB), K-nearest neighbors (KNN), decision trees (DT), random forests (RF), support vector machines (SVM), and artificial neural networks (ANN), and the most effective algorithms per sample subsets were: (i) RF (86.8%) for Campos, Santos, and Ceará Basins; (ii) NB (87.2%) for Campos with Santos Basins; (iii) SVM (86.9%) for Campos with Ceará Basins; and (iv) SVM (87.8%) for only Campos Basin. The OSH can assist in different concerns (general public, social, economic, political, ecological, and scientific) related to petroleum exploration and production activities, serving as an important aid in discovering new offshore exploratory frontiers, avoiding legal penalties on oil-seep events, supporting oceanic monitoring systems, and providing valuable information to environmental studies.

Coastal ocean observing and modeling systems (coastal observatories), connected with regional and global ocean systems, improve the quality of information and forecasts for effective management of safe and sustainable maritime activities. The public availability of systematic and long-term information of the ocean is an engine for the Blue Economy, boosting economic growth, employment, and innovation. An overview of some Brazilian initiatives is presented in this paper, involving universities, private companies, federal and state agencies, covering institutions from south to north of Brazil. Although these initiatives focus mainly on ocean physics, integrated efforts can extend the scope to include biogeochemistry and marine biodiversity, helping to address interdisciplinary problems. Existing initiatives can be connected, and new ones fostered, to fill in the gaps of temporal and spatial coverage of ocean monitoring in the vast oceanic area under Brazilian jurisdiction (nationally referred to as the Blue Amazon, in reference to the similar richness of the Amazon Forest). The alignment among national and regional initiatives, as well as with international programs, can be promoted if coordinated by a national-level organization, maximizing the return of public investment and socioeconomic benefits. In light of international examples, possible future institutional arrangements are discussed, leveraging from existing national public policies and international cooperation that Brazil is taking part. The United Nations Decade of Ocean Science for Sustainable Development is a timely opportunity to encourage an institutional arrangement to support and articulate an integrated network of coastal observatories in Brazil.

Industrial accidents pose risks to the entire environment located in the area of influence of the enterprises, especially when they are affected by the atmospheric dispersion of pollutants emitted, as in the case of a nuclear power plant, where the concern with the evacuation of the population is the main priority in critical situations , which reinforces the implementation of actions to support emergency planning and its management, whose support tools, among them the computational modeling that, based on atmospheric models and geographic information systems, are essential in the decision making process. Thus, based on the Angra dos Reis nuclear complex and the impacts that may affect its surroundings, studies using the integrated use of computational platforms served to evaluate the risks and consequences of a hypothetical accidental event, including the calculation of the thermo- source of nuclear fuel, meteorological characteristics, mechanisms of atmospheric dispersion of pollutants, as well as socio-environmental aspects of the region. Meteorological data (wind regime) characterize the wind field from the WRF model, which subsidized the dispersion of radionuclides calculated in the HYSPLIT, information associated with socioenvironmental data (landslides, population density, etc.), incorporated in a GIS database. The results demonstrate the importance of the integrated use of computational systems as tools that support decision-making, potentializing the application of solutions that contribute to support actions and guidelines of planning and governance in emergency situations.

Sea-surface petroleum pollution is observed as “oil slicks” (i.e., “oil spills” or “oil seeps”) and can be confused with “look-alike slicks” (i.e., environmental phenomena, such as low-wind speed, upwelling conditions, chlorophyll, etc.) in synthetic aperture radar (SAR) measurements, the most proficient satellite sensor to detect mineral oil on the sea surface. Even though machine learning (ML) has become widely used to classify remotely-sensed petroleum signatures, few papers have been published comparing various ML methods to distinguish spills from look-alikes. Our research fills this gap by comparing and evaluating six traditional techniques: simple (naive Bayes (NB), K-nearest neighbor (KNN), decision trees (DT)) and advanced (random forest (RF), support vector machine (SVM), artificial neural network (ANN)) applied to different combinations of satellite-retrieved attributes. 36 ML algorithms were used to discriminate “ocean-slick signatures” (spills versus look-alikes) with ten-times repeated random subsampling cross validation (70-30 train-test partition). Our results found that the best algorithm (ANN: 90%) was >20% more effective than the least accurate one (DT: ~68%). Our empirical ML observations contribute to both scientific ocean remote-sensing research and to oil and gas industry activities, in that: (i) most techniques were superior when morphological information and Meteorological and Oceanographic (MetOc) parameters were included together, and less accurate when these variables were used separately; (ii) the algorithms with the better performance used more variables (without feature selection), while lower accuracy algorithms were those that used fewer variables (with feature selection); (iii) we created algorithms more effective than those of benchmark-past studies that used linear discriminant analysis (LDA: ~85%) on the same dataset; and (iv) accurate algorithms can assist in finding new offshore fossil fuel discoveries (i.e., misclassification reduction).

The Angra dos Reis nuclear power complex, on the southern coast of the state of Rio de Janeiro, has in its geographic space different aspects that integrate the environmental dynamics of its area of influence, where geological, meteorological and demographic factors characterize the region as an environment complex, especially with the projection of emergency actions from an accidental event, especially from the point of view of the evacuation of the population in the impacted areas, presenting particularities that contribute to act as a negative impact in emergency situations, mainly in the phase response to a possible accident. With the support of geographic information systems (GIS), analyzes were made between the susceptibility to landslides and their occurrences (inventory of years 2007-2011), associating the rainfall regime and population density, aspects that demonstrate the vulnerability of the region, especially along the BR-101 highway, with the potential to render escape routes unfeasible in critical situations. The integrated analysis of these factors pointed out that, together, such elements are important bottlenecks for emergency situations in the region, and should be included as critical factors to be analyzed in order to contribute to subsidize actions and guidelines that should be applied in local emergency planning

Ilha Grande Bay is located in Angra dos Reis, Rio de Janeiro State, Brazil. The area is characterized by different land cover, complex topography and proximity to the Atlantic Ocean. These aspects make it susceptible to thermally and dynamically induced atmospheric circulations such as those associated with valley/mountain and land/sea breeze systems, among others. The Almirante Álvaro Alberto Nuclear Complex (CNAAA) is located in this region, with a total of two nuclear power plants (NPPs) in operation in the Brazilian territory, Angra I and Angra II. Therefore, knowledge of local atmospheric circulation has become a matter of national and international security. Considering the importance of the meteorological security tool as a support for licensing, installation, routine operation and nuclear accident mitigation, the main aim of this study is the development of combined strategies of environmental statistical modeling in the analysis of thermally and dynamically driven atmospheric circulations over mountainous and coastal environments. We identified and hierarchized the influence of the thermally and mechanically driven forcing on the wind regime and stability conditions in the coastal atmospheric boundary layer over the complex topography region. A meteorological network of ground-based instruments was used along with physiographic information for the observational characterization of the atmospheric patterns in the spatial and time–frequency domain. The predominant wind directions and intensity are attributed to the combined action of multiscale weather systems, notably, the valley/mountain and continent/ocean breeze circulations, the forced channeling due to valley axis orientation, the influence of the synoptic scale systems and atmospheric thermal tide. The observational investigation of the combined influence of terrain effects and meteorological systems aimed to understand the local atmospheric circulation serves as support for safety protocols of the NPPs, contemplating operation and environmental management. The importance of the study for the adequacy and skill evaluation of computational modeling systems for atmospheric dispersion of pollutants such as radionuclide and conventional contaminants can be also highlighted, in order that such systems are used as tools for environmental planning and managing nuclear operations, particularly those located in regions over mountainous and coastal environments with a heterogeneous atmospheric boundary layer.

Linear discriminant analysis (LDA) is a mathematically robust multivariate data analysis approach that is sometimes used for surface oil slick signature classification. Our goal is to rank the effectiveness of LDAs to differentiate oil spills from look-alike slicks. We explored multiple combinations of (i) variables (size information, Meteorological-Oceanographic (metoc), geo-location parameters) and (ii) data transformations (non-transformed, cube root, log10). Active and passive satellite-based measurements of RADARSAT, QuikSCAT, AVHRR, SeaWiFS, and MODIS were used. Results from two experiments are reported and discussed: (i) an investigation of 60 combinations of several attributes subjected to the same data transformation and (ii) a survey of 54 other data combinations of three selected variables subjected to different data transformations. In Experiment 1, the best discrimination was reached using ten cube-transformed attributes: ~85% overall accuracy using six pieces of size information, three metoc variables, and one geo-location parameter. In Experiment 2, two combinations of three variables tied as the most effective: ~81% of overall accuracy using area (log transformed), length-to-width ratio (log- or cube-transformed), and number of feature parts (non-transformed). After verifying the classification accuracy of 114 algorithms by comparing with expert interpretations, we concluded that applying different data transformations and accounting for metoc and geo-location attributes optimizes the accuracies of binary classifiers (oil spill vs. look-alike slicks) using the simple LDA technique.

The importance of stakeholder engagement in ocean observation and in particular the realization of economic and societal benefits is discussed, introducing a number of overarching principles such as the convergence on common goals, effective communication, co-production of information and knowledge and the need for innovation. A series of case studies examine the role of coordinating frameworks such as the United States’ Interagency Ocean Observing System (IOOS®), and the European Ocean Observing System (EOOS), public–private partnerships such as Project Azul and the Coastal Data Information Program (CDIP) and finally the role of the “third” or voluntary sector. The paper explores the value that stakeholder engagement can bring as well as making recommendations for the future.

We classify low-backscatter regions observed in Synthetic Aperture Radar (SAR) measurements of the surface of the ocean as either oil slicks or look-alike slicks (radar false targets). Our proposed classification algorithm is based on Linear Discriminant Analyses (LDAs) of RADARSAT-1 measurements (402 scenes off the southeast coast of Brazil from July 2001 to June 2003) and Meteorological-Oceanographic (MetOc) data from other earth observation sensors: Advanced Very High Resolution Radiometer (AVHRR), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Quick Scatterometer (QuikSCAT). Oil slicks are sea-surface expressions of exploration and production oil, ship- and orphan-spills. False targets are associated with environmental phenomena, such as biogenic films, algal blooms, upwelling, low wind, or rain cells. Both categories have been interpreted by domain-experts: mineral oil (n = 350; 45.5%) and petroleum free (n = 419; 54.5%). We explore nine size variables (area, perimeter, etc.) and three types of MetOc information (sea surface temperature, chlorophyll-a, and wind speed) that describe the 769 samples analyzed. Seven attribute–domain combinations are tested with three non-linear transformations (none, cube root, log10), with and without MetOc, adding to 39 attribute subdivisions. Classification accuracies are independent of data transformation and improve when selected size attributes are combined with MetOc, leading to overall accuracies of ~80% and sound levels of sensitivity (~90%), specificity (~80%), positive (~80%) and negative (~90%) predictive values. The effectiveness of this data-driven attempt supports further commercial or academic implementation of our LDA algorithm.

A pesquisa propõe uma metodologia para a avaliação da suscetibilidade bacias hidrográficas ao desenvolvimento de inundações por integração de dados fisiográficos e de sensoriamento remoto por radar. Foram utilizadas imagens dos satélites RADARSAT- 2, dados de altimetria, hidrografia e pluviometria. A área de estudo abrange o entorno da cidade de Villahermosa, México, onde ocorrem inundações periódicas. A classificação textural foi utilizada para individualizar as classes de cobertura representativas de corpos d'água, floresta seca e vegetação inundada. Os dados de altimetria e hidrografia permitiram a construção de um MDT específico. A partir de tal produto, foi definida a bacia hidrográfica na qual a cidade está inserida e foi feita a análise geomorfológica da bacia e de suas sub-bacias. Os dados de precipitação provenientes de estações climatológicas foram associados à evolução da distribuição em superfície das áreas cobertas por corpos d'agua e também foram analisados de acordo com seus valores extremos. A integração da análise do meio físico da bacia hidrográfica com aquela dos dados de precipitação permitiu a construção de uma matriz, a partir da mesma foi possível gerar um índice com diferentes níveis de susceptibilidade ao fenômeno de inundação.

The aim of the present work is a preliminary set of groups that make up the Crust Lateritico Niqueliferas Cobaltiferas of Punta Gorda Reservoir in Cuba by neural networks applied to a sef of radioactive well logs according to a predetermined geolical classification . It also undertakes assesment of nickel content in each of the defined groups also implementing neural networks.

This study, firstly, provides an up-to-date global review of the potential, technologies, prototypes, installed capacities, and projects related to ocean renewable energy including wave, tidal, and thermal, and salinity gradient sources. Secondly, as a case study, we present a preliminary assessment of the wave, ocean current, and thermal gradient sources along the Brazilian coastline. The global status of the technological maturity of the projects, their different stages of development, and the current global installed capacity for different sources indicate the most promising technologies considering the trend of global interest. In Brazil, despite the extensive coastline and the fact that almost 82% of the Brazilian electricity matrix is renewable, ocean renewable energy resources are still unexplored. The results, using oceanographic fields produced by numerical models, show the significant potential of ocean thermal and wave energy sources in the northern and southern regions of the Brazilian coast, which could contribute as complementary supply sources in the national electricity matrix.

A novel empirical approach to categorize oil slicks’ sea surface expressions in synthetic aperture radar (SAR) measurements into oil seeps or oil spills is investigated, contributing both to academic remote sensing research and to practical applications for the petroleum industry. We use linear discriminant analysis (LDA) to try accuracy improvements from our previously published methods of discriminating seeps from spills that achieved ~70% of overall accuracy. Analyzing 244 RADARSAT-2 scenes containing 4562 slicks observed in Campeche Bay (Gulf of Mexico), our exploratory data analysis evaluates the impact of 61 combinations of SAR backscatter coefficients (σ°, β°, γ°), SAR calibrated products (received radar beam given in amplitude or decibel, with or without a despeckle filter), and data transformations (none, cube root, log10). The LDA ability to discriminate the oil-slick category is rather independent of backscatter coefficients and calibrated products, but influenced by data transformations. The combination of attributes plays a role in the discrimination; combining oil-slicks’ size and SAR information is more effective. We have simplified our analyses using fewer attributes to reach accuracies comparable to those of our earlier studies, and we suggest using other multivariate data analyses—cubist or random forest—to attempt to further improve oil-slick category discrimination.

Our research focuses on refining the ability to discriminate two petrogenic oil-slick categories: the sea surface expression of naturally-occurring oil seeps and man-made oil spills. For that, a long-term RADARSAT-2 dataset (244 scenes imaged between 2008 and 2012) is analyzed to investigate oil slicks (4562) observed in the Gulf of Mexico (Campeche Bay, Mexico). As the scientific literature on the use of satellite-derived measurements to discriminate the oil-slick category is sparse, our research addresses this gap by extending our previous investigations aimed at discriminating seeps from spills. To reveal hidden traits of the available satellite information and to evaluate an existing Oil-Slick Discrimination Algorithm, distinct processing segments methodically inspect the data at several levels: input data repository, data transformation, attribute selection, and multivariate data analysis. Different attribute selection strategies similarly excel at the seep-spill differentiation. The combination of different Oil-Slick Information Descriptors presents comparable discrimination accuracies. Among 8 non-linear transformations, the Logarithm and Cube Root normalizations disclose the most effective discrimination power of almost 70%. Our refined analysis corroborates and consolidates our earlier findings, providing a firmer basis and useful accuracies of the seep-spill discrimination practice using information acquired with space-borne surveillance systems based on Synthetic Aperture Radars.

Aromatic compounds are still poorly evaluated in geochemical studies of Brazilian oil samples. For this reason, the objective of this work was to analyze and evaluate 26 oil samples from four Brazilian sedimentary basins. Aromatic hydrocarbon fractions have been analyzed by gas chromatography coupled to mass spectrometry. The results were also compared with previously evaluated saturated hydrocarbon data. Maturation parameters obtained using the 4- and 1-methyl-dibenzothiophenes and trimethyl-dibenzothiophenes led to the best results because a better grouping was observed. For this correlation, the samples were grouped by maturity degree enabling the routine use of these parameters to evaluate oil maturity levels.

Realizaram-se avaliações climatológicas, sazonais e diárias, da estabilidade atmosférica na região da Central Nuclear Almirante Álvaro Alberto (CNAAA), Angra dos Reis - RJ. A climatológica foi baseada no critério de Pasquill-Gifford (P-G) para um período de 26 anos (1980-2006) e a sazonal-diária pelos números de Richardson Global (RiB) e de Froude (Fr) - estudo de caso (2002-05). O Fr foi usado na caracterização do escoamento da região. O critério de P-G mostrou que as classes predominantes foram D, E e F (no período noturno e diurno). Avaliaram-se as classes predominantes com a direção e velocidade do vento, os setores mais freqüentes foram S, SSW, SSE no período diurno e N, NNE, NNW e E no noturno. Quanto à velocidade verificou-se que a classe D foi mais veloz, e as classes E e F menos velozes, em qualquer período. As maiores velocidades foram coincidentes com a brisa marítima. Baseado no RiB, a condição estaticamente estável prevaleceu em comparação às demais, sendo de 79%, seguida da instável (17%) e neutra (4%). O Fr indicou que o escoamento na CNAAA foi caracterizado por regime de vento fraco, com forte estabilidade e ar estagnado. A maior parte das ocorrências (63%) foi para Fr inferior a 0,1, seguida de poucas ocorrências nas categorias Fr igual a 1,0 (8%) e superior a 1,7 (12%). Os critérios utilizados na caracterização da estabilidade na região e na análise do regime de escoamento indicaram a baixa capacidade da atmosfera para a dispersão de poluentes, devido à predominância da condição estável e do regime de bloqueio.The atmospheric stability over the nuclear power plant Almirante Álvaro Alberto (CNAAA), Angra dos Reis - RJ, Brazil, was analyzed at climatological, seasonal, and daily scales. The climatologic analysis was based on the Pasquill-Gifford (P-G) for a long-term series of 26 years (1980 - 2006), while the seasonal/daily was performed using the Global Richardson number, RiB, and the Froude number, Fr, (case study 2002-2005). The Froude number was used for the regional flow characterization. According to the P-G criteria, the dominant classes were D, E and F (for diurnal and nocturnal periods). The most frequent sectors of wind direction were S, SSW, SSE in diurnal period and N, NNE, NNW and E in nocturnal. The highest wind velocities were observed for class D while the lowest values were observed for classes E and F, for both periods. The highest wind velocities were correlated to the occurrence of sea breeze. Based on RiB, the dominant stratification regime was stable (for 79% of the time), followed by unstable (17%) and neutral (4%). According to Fr, the flow around CNAAA was characterized by low wind velocities, strong stability and stagnant air. The Fr was lesser than 0.1 during 63% of the time, equal to 1 for 8% of the time and greater than 1.7 during 12% of the time. The criteria used in the determination of the stability and flow characteristics of this region indicated the lower dispersion capacity of the local atmosphere due to the prevalence of stable stratification regimes and blocking conditions.

An Exploratory Data Analysis (EDA) aims to use Synthetic Aperture Radar (SAR) measurements for discriminating between two oil slick types observed on the sea surface: naturally-occurring oil seeps versus human-related oil spills—the use of satellite sensors for this task is poorly documented in scientific literature. A long-term RADARSAT dataset (2008–2012) is exploited to investigate oil slicks in Campeche Bay (Gulf of Mexico). Simple Classification Algorithms to distinguish the oil slick type are designed based on standard multivariate data analysis techniques. Various attributes of geometry, shape, and dimension that describe the oil slick Size Information are combined with SAR-derived backscatter coefficients—sigma-(σo), beta-(βo), and gamma-(γo) naught. The combination of several of these characteristics is capable of distinguishing the oil slick type with ~70% of overall accuracy, however, the sole and simple use of two specific oil slick’s Size Information (i.e., area and perimeter) is equally capable of distinguishing seeps from spills. The data mining exercise of our EDA promotes a novel idea bridging petroleum pollution and remote sensing research, thus paving the way to further investigate the satellite synoptic view to express geophysical differences between seeped and spilled oil observed on the sea surface for systematic use.

This work presents the implementation of a low cost computing systemfor the forecast of the atmospheric pollutant dispersion generated in petroleum platforms in Campos Basin - RJ. A mesoscale meteorological model was used to complement the necessary meteorological database for the use of the dispersion model. To this meteorological model, a series of statistical tests were applied seeking to compare the behavior of the model face to the monitored data available. The results indicated that the meteorological model represented satisfactorily the wind field on the area and that the values foreseen for the concentration of pollutant SO2 were always below the effective patterns however, they indicated that there is transport of pollutant from producing units to the continent.