P.O.B EBUNILO

The marine ecosystem in Delta State, Nigeria, plays a crucial role in supporting biodiversity, fisheries, and the livelihoods of coastal communities. However, this ecosystem is increasingly threatened by pollution from various human activities. This study explores the major pollutants affecting the marine
environment in Delta State, aiming to identify their sources, types, and impacts. This study will adopt a single method approach. This design is chosen because it allows for a
comprehensive understanding of the problem by quantitative data on the physical presence of pollutants. Quantitative Component That involves the systematic collection and statistical analysis of water and sediment samples to quantify the concentration of specific pollutants. Measurement of pollutant levels in water and sediment samples. Key pollutants identified include crude oil and petroleum products, heavy metals such as lead and mercury, plastic waste, agricultural runoff containing fertilizers and pesticides, and untreated sewage. These pollutants orignate mainly from oil exploration, industrial discharge, poor waste disposal practices, and agricultural activities.The research highlights how these pollutants harm aquatic life, reduce water quality, and disrupt the ecological balance. Fish kills, habitat destruction, and the accumulation of toxins in marine organisms are among the observed effects. The study also considers the social and economic consequences for communities that rely on fishing and marine resources. To address these challenges, the study recommends stronger environmental regulations, improved waste management systems, and community involvement in conservation efforts. Protecting the marine ecosystem in Delta State is essential for sustainable development and the well-being of future generations

Rapid urbanization, particularly in places like Nigeria, intensifies waste management challenges due to the inefficiency and high cost of traditional collection. While current smart waste monitoring and management systems offer improvements through IoT monitoring, they often lack advanced sorting and comprehensive management features. This project
directly addresses these gaps by designing an innovative smart waste monitoring and management system that critically incorporates the automated segregation of metals and plastics, alongside enhanced automation and real-time data capabilities. Our prototype is a sophisticated solution that integrates various sensors—including ultrasonic and load sensors for fill-level monitoring and compaction, plus specialized inductive (for metals) and optical/capacitive (for plastics) detectors for sorting—with linear actuators for automated processes and a GSM/GPS module for wireless communication. This setup allows the system to not only monitor bin levels, automatically compact waste, and control the lid, but most importantly, to accurately sort plastic and metal materials at the source. This ensures more efficient resource recovery and recycling. Through rigorous testing of both hardware and software, we will verify the reliability of sensor performance, the effectiveness of automation, the accuracy of communication, and the precision of the waste segregation mechanism. The anticipated outcome is a fully integrated system that successfully demonstrates its capacity to accurately detect fill levels, initiate automated compaction, send prompt location-based alerts, and effectively sort waste. This proven dependability under real-world conditions highlights the system's potential to ignificantly alleviate urban waste management issues by reducing overflow, boosting collection efficiency, increasing recycling rates, and providing a scalable, sustainable solution for residential, commercial, and municipal settings. settings.

The increasing need for effective and cheap surveillance solutions across various sectors in Nigeria, including security, agriculture, environmental protection, and disaster management and development of homemade drones. Although quite popular and ubiquitous in technologically advanced nations, drones are currently not being produced in Nigeria that can be used for both surveillance and monitoring. Modifications were done on a locally adapted drone assembled from parts sourced abroad. The
drone was enhanced with advanced technical capabilities optimizing it for smooth surveillance operations. Major modification upgrades include the integration of pivotal components including microchips ranging from Raspberry Pi 5, Arduino Atmel Amega 2560, STM32H7 controller, along with the Pixhawk 2.4.8 flight controller, a high-resolution Raspberry Pi Camera Rev 1.3, ultrasonic sensors, PWM to PPM converter, and GPS navigation system into the F450 drone frame. These enhancements allow for functionalities such as object tracking, obstacle avoidance, and an automatic return-home capability, enhancing the drone's adaptability for various surveillance applications