Developing and validating low-cost, AI-enhanced frameworks for nitrogen monitoring in surface waters

This thesis develops low-cost tools to monitor nitrogen in rivers and creeks. Excessive nitrogen causes algal blooms and poor water quality, which harm people and aquatic life. The study designs affordable sensors and adaptable computer models that can track nitrogen levels across different sites and times. These approaches make large-scale monitoring possible at a fraction of current costs. The work provides practical solutions for governments, water managers, and communities to better protect waterways and support sustainable water use.