# Early Perspectives on the Planned Brazilian Program to Address Ship-Sourced Pollution
## 4 Conclusions  
SisMOM is designed to integrate various technologies, including satellite data, AI algorithms, autonomous sensors, and high-resolution modeling, to detect and respond to oil spills and maritime threats. However, the success of SisMOM heavily relies on the microsatellite constellation to provide continuous, near real-time coverage of BJW. These satellites are critical to provide surveillance and high-quality input data for the entire system. Integration of satellite data with AI algorithms enhances SisMOM's ability to detect anomalies such as illegal dumping or oil bunkering. AI systems are essential to process vast amounts of satellite, RF, and AIS data to identify ships engaged in illegal activities, supporting more effective enforcement. An operational high-resolution coupled ocean-atmosphere modeling system has been a long-standing goal of the Brazilian scientific community, and it is essential for accurately forecasting oil spill trajectories and the areas affected. The real-time integration of the three situation rooms enables immediate, coordinated responses from IBAMA, the Brazilian Navy, and INPE, allowing decision-makers to quickly assess data and deploy resources to mitigate oil spill impacts. However, overlapping capacities raise concerns about conflicting or redundant actions, as all parties will access the same data and modeling tools, potentially leading to differing interpretations and responses during emergencies. To prevent inefficient resource allocation and delays, it is essential to clearly define protocols, coordination, and decision-making hierarchies. Additionally, the integration of these autonomous systems with the broader SisMOM platform presents both technical opportunities and challenges. The real-time coordination between satellite-based alerts (Goals 1 and 2), ocean forecast models (Goal 5), and sensor deployment requires robust data communication protocols and intelligent tasking algorithms. If successful, this system could significantly reduce response times and improve the precision of oil spill confirmation in remote or difficult-to-access maritime areas. Ultimately, this goal reflects an emerging trend in environmental monitoring that combines remote sensing, robotics, and artificial intelligence to deliver faster, more reliable, and cost-effective responses to marine pollution events. SisMOM is expected to face significant scientific and technological challenges in achieving its objectives, particularly due to the complexity of integrating heterogeneous data sources and coordinating operational workflows. Therefore, it is not expected to be operational for at least two years. However, just the process' initiation already represents a shift in the mindset of how Brazil approaches environmental risk governance in its maritime jurisdiction. By fostering cooperation among scientific institutions, federal agencies, and local authorities, SisMOM tries to promote a unified, science-based response to marine pollution threats and contributes to long-term strategic planning through the identification of vulnerable areas, optimization of response logistics, and development of regulatory frameworks for environmental protection. Conceptualization, D.C.Z. and A.T.L. ; writing--original draft preparation, D.C.Z. ; writing--review and editing, A.T.L. All authors have read and agreed to the published version of the manuscript. This article only reflects the authors' perspectives, interpretations, points of view, and opinions regarding the proposed program (SISMOM) led by some federal institutions. This paper does not represent an official communication of the program, nor does it express the views of its coordinators or responsible authorities. This research was supported by the Multi-user System for Detection, Forecasting, and Monitoring of Oil Spills at Sea Project (SisMOM), with institutional funding from the National Fund for Scientific and Technological Development (FNDCT) employed by the Inova Funding Authority for Studies and Projects (FINEP).