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Application of Laser-Induced, Deep UV Raman Spectroscopy and Artificial Intelligence in Real-Time Environmental
Claudia Post1, Simon Brülisauer2, Kryss Waldschläger3
1Department of Engineering Geology and Hydrogeology, RWTH Aachen University, Lochnerstr. 4-20, 52064 Aachen, Germany.
Sensors (Basel, Switzerland)
|July 2, 2021
Summary
This study explores deep UV Raman spectroscopy for real-time water quality monitoring. While effective for nitrates and microplastics, detecting pharmaceuticals at low concentrations remains challenging.
Area of Science:
- Environmental Science
- Analytical Chemistry
- Spectroscopy
Background:
- Effective environmental monitoring of aquatic systems is crucial for sustainable protection and safe drinking water.
- Water quality is impacted by chemical pollutants like nitrates, pharmaceuticals, and microplastics, necessitating real-time monitoring.
- Rapid changes in water quality require immediate action, highlighting the need for advanced detection methods.
Purpose of the Study:
- To assess the feasibility of deep UV Raman spectroscopy for detecting nitrate/nitrite, pharmaceuticals, and microplastics in aquatic environments.
- To develop and train artificial intelligence (AI) software, including convolutional neural networks, for recognizing spectral patterns of pollutants.
- To evaluate the performance of the developed system in real-time water quality monitoring.
Main Methods:
- Utilizing a deep UV Raman spectrometer for spectral data acquisition.
- Employing mathematical filters and machine learning algorithms for data processing.
- Training AI software, specifically convolutional neural networks, to identify pollutant-specific spectral signatures.
Main Results:
- Nitrates and nitrites were successfully detected and quantified, though nitrate detection faced challenges with signal-to-noise ratio and background interference.
- Selected pharmaceutical substances were detectable by Raman spectroscopy, but not at the target low concentrations (µg/l or ng/l).
- Microplastic particles were detected and identified, but their heterogeneous distribution in flow experiments impacted measurement accuracy.
Conclusions:
- Deep UV Raman spectroscopy shows promise for monitoring nitrates and microplastics in water.
- Further research is needed to improve the detection limits for pharmaceuticals and address challenges in microplastic analysis.
- AI-powered spectral analysis is a viable approach for enhancing the capabilities of Raman spectroscopy in environmental monitoring.
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