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Rapid assessment of surface water quality using statistical multivariate analysis approach: Oder River system case

Grażyna Balcerowska-Czerniak1, Beata Gorczyca2

  • 1Institute of Mathematics and Physics, Bydgoszcz University of Science and Technology, Poland.

The Science of the Total Environment
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Summary

This study introduces a new TQ_PCA quality index for real-time water quality monitoring. It effectively detects poor water conditions early, even before individual parameter changes are apparent.

Keywords:
Hotelling T(2) chartMultivariate analysisPhysicochemical parametersPrincipal component analysisQuality monitoringWater quality index

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Statistical Process Control

Background:

  • Evaluating surface water quality requires monitoring numerous physicochemical and biological parameters.
  • Rapid assessment of deteriorating water quality is crucial in many scenarios.
  • Existing methods may lack the ability for simultaneous, real-time monitoring of multiple parameters.

Purpose of the Study:

  • To develop a universal methodology for simultaneous monitoring of multiple water quality parameters.
  • To enable early detection of out-of-control water samples in real-time.
  • To introduce a quality index that requires no prior knowledge of control limits.

Main Methods:

  • Utilized multivariate statistical quality control charts.
  • Employed Principal Component Analysis (PCA) for modeling.
  • Incorporated Hotelling's T-squared (T²) statistics and Q-statistic for anomaly detection.
  • Developed a TQ_PCA quality index for on-line assessment.

Main Results:

  • The TQ_PCA index demonstrated excellent performance in analyzing water quality data, including a dataset from the Oder River during a major ecological disaster.
  • The index successfully identified consecutive alarms a month prior to the disaster, despite no evident changes in individual parameters.
  • Validated performance on physicochemical data from Polish stations and physicochemical/biological data from a German station.

Conclusions:

  • The TQ_PCA index provides an effective on-line assessment of water sample quality.
  • The methodology can detect subtle changes in water quality that precede significant environmental events.
  • The TQ_PCA approach is versatile and can be extended to various monitoring applications with large datasets.