Project description
Our world's coastal zones, brimming with vital ecosystems, face a pressing threat: the unrelenting transport of pollutants from land to sea. Dive into the cutting-edge realm of our PhD project, where we are at the forefront of change, marrying Artificial Intelligence (AI), remote sensing technologies, and the power of citizen science to revolutionise the way we observe and monitor the movement of pollutants. By harnessing these ground-breaking forces, we're on a mission to safeguard these delicate ecosystems, empower informed decision-making, and shape a more sustainable future.
Aims and objectives
Our project's core aim is clear: to enhance the observation and monitoring of land-to-coastal pollutant transport using a triad of cutting-edge tools. With this foundation, we set forth the following objectives:
AI Empowerment: Forge an AI-driven framework that predicts pollutant movement with precision. By harnessing historical data and incorporating diverse datasets, we aim to create robust models capable of real-time prediction.
Remote Sensing Mastery: Unveil pollution's intricate tapestry using remote sensing. Satellite imagery will spotlight pollution sources and patterns across vast coastal landscapes. In tandem, drone technology will provide detailed, high-resolution insights, enriching our understanding of pollutant dynamics.
Citizen Science Collaboration: Unite communities through citizen science, transforming individuals into environmental advocates. Through mobile apps and community-driven initiatives, we'll gather real-time data to validate and refine our AI and remote sensing models, all while fostering a sense of shared responsibility.
Training
Prepare for an immersive learning journey that melds scientific expertise with technological prowess. With guidance from interdisciplinary experts, you'll navigate the realms of AI algorithms, remote sensing methodologies, and community engagement strategies. Gain hands-on experience with state-of-the-art tools, equipping you to spearhead real-world change and drive advancements in environmental preservation.
Project structure
1. AI-Driven Insights: Begin by diving deep into AI's capabilities. Analyze historical data, develop predictive models, and fuse climate, hydrological, and land-use information to create an AI framework that can anticipate real-time pollutant transport. This foundational step sets the stage for the entire project's success.
2. Remote Sensing Exploration: Embark on a journey through remote sensing. Decode satellite imagery to reveal pollution hotspots and sources on a macro scale. Transition to drones, capturing intricate spatial details that enhance your pollution map's richness. By integrating various sensor data, you'll create a comprehensive narrative of pollution's journey.
3. Citizen Science Engagement: Foster a sense of belonging and empowerment through citizen science. Mobilize the public through mobile apps (including Earthtrack), encouraging data collection that bolsters AI and remote sensing models. As you engage communities, you'll not only refine your scientific understanding but also sow the seeds of environmental advocacy.
4. Integration and Impact: The finale marries AI predictions, remote sensing revelations, and citizen science participation into an integrated monitoring system. Rigorous validation cements accuracy, while iterative improvements fine-tune its effectiveness. Ultimately, this multifaceted approach empowers decision-makers, informs policy, and safeguards the coastal environments we hold dear.
The project will be supervised by Dr Iestyn Woolway at Bangor University and Dr Stefan Simis at Plymouth Marine Laboratory.