AI-Driven Approaches to Plastic and Chemical Pollutant Monitoring in Wastewater Treatment: A Case Study

Plastic particles and chemical pollutants have become a significant contaminant in wastewater system, threatening the wellbeing of human health and aquatic ecosystem. These pollutants can accumulate in waterbodies, in fishes and many other aquatic organisms, and may enter the food chain. Conventional methods for monitoring such pollutants is time consuming, labour-intensive and lack real-time accuracy, making them inappropriate prolonged environment monitoring. This case study explores how AI-driven systems are enhancing the identification and detection of plastic and chemical pollutant and also helps in wastewater treatment processes. Interpretation of the existing results, shows how intelligent monitoring tools effectively help in detecting contaminants, improve accuracy and strength anticipatory analysis for pollution control. The combination of these technologies not only speed up the assessment process but also provide critical insight into the dynamic of pollutant dispersion and impact on ecosystem health.