# Early Perspectives on the Planned Brazilian Program to Address Ship-Sourced Pollution
## 2 Project Description  
SisMOM is a multidisciplinary and inter-institutional system that integrates operational oceanographic and meteorological forecasting models with remote sensing, data assimilation, and artificial intelligence components. Its architecture allows multiple government agencies, including environmental, defense, and emergency response sectors, to access real-time information and collaborate effectively in managing oil spill incidents. The system combines deterministic and probabilistic modeling tools for oil dispersion and weathering, facilitating scenario analysis and impact assessment. One of its core innovations is the incorporation of high-resolution numerical simulations and adaptive learning algorithms, which enable more accurate predictions of oil drift under complex oceanographic conditions [15; 16; 17]. The system is structured around a modular and scalable framework that integrates various components for real-time data acquisition, atmospheric and oceanic forecasting, oil spill modeling, and user interaction. The system relies heavily on high-performance computing infrastructure to support the execution of atmosphere and oceanic models, such as the ETA model [18] ([[http://etamodel.cptec.inpe.br/](http://etamodel.cptec.inpe.br/)]([http://etamodel.cptec.inpe.br/](http://etamodel.cptec.inpe.br/)), accessed on 15 June 2025), widely used by INPE for atmospheric dynamics, and the Modular Ocean Model (MOM6) [19] and TELEMAC for regional and coastal circulation [20]. SisMOM is structured around nine goals that focus mainly on detecting vessels and oil slicks at sea, as well as predicting oil drift and dispersion. The monitoring system will be equipped to identify suspicious vessel behavior using artificial intelligence algorithms that analyze Automatic Identification System (AIS) and high-frequency radio wave data and detect potential oil slicks through high-resolution satellite imagery. High-resolution atmospheric and oceanographic data will be assimilated to run oil spill dispersion models, estimating the probability of oil slicks and tar balls reaching the shore. This data will provide critical information to response teams (Figure 1). The project also includes a data assimilation layer that integrates satellite remote sensing, AIS (Automatic Identification System) ship tracking data, and in situ observations from buoys and coastal stations. Another key feature is the multi-user decision support interface (Situation Room), designed for interoperability across institutions. The Situation Room integrates live data from satellites, weather stations, and ocean sensors to track environmental changes and support coordinated emergency responses. Thus, SisMOM is structured in a sequence of integrated levels, ranging from the identification of potential polluters to supporting government actions led by the Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renovaveis\({}^{4}\) (IBAMA), Marinha\({}^{5}\) do Brasil (MB), and the Ministerio Publico\({}^{6}\) (MP), following what was established by the National Contingency Plan for Oil Pollution Incidents in Waters under National Jurisdiction (PNC)\({}^{7}\), established by Federal Decree 10.950/2022. Figure 1: Conceptual diagram of SisMOM, including the operational structure and the activation sequence of each stage. The development of the SisMOM is organized around nine distinct goals, and each one is segmented into independent working groups. As a result, not all technical details and methodological choices are fully accessible at this time. Several components of the system involve sensitive information that remains protected for strategic and institutional reasons. Furthermore, many procedures and tools are still under refinement, given the current stage of project implementation. In light of these constraints, some analyses presented in this work are supported primarily by literature reviews and established methodologies available in the scientific domain, rather than by finalized operational data from the system itself.