Water Quality Monitoring and Analysis

Wastewater discharged from industrial and manufacturing processes carries a complex mixture of organic compounds, heavy metals, and other contaminants that must be measured accurately before treatment or release. Researchers combine optical techniques like UV-VIS spectroscopy with chemical oxygen demand analysis and sensor networks to characterize water quality in real time, moving beyond slow laboratory methods toward continuous, automated monitoring. Machine learning is increasingly used to interpret the dense, noisy signals these sensors produce, while photocatalytic sensors offer a promising route to faster and more selective detection of specific pollutants. Open questions include how to maintain sensor accuracy as industrial effluent composition shifts unpredictably, and how to integrate heterogeneous data streams into treatment control systems that can respond fast enough to matter.

Works

196,637

Total citations

859,457

Keywords

WastewaterMonitoringUV-VIS SpectroscopyChemical Oxygen DemandSensorsMachine Learning