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Continuous Instream Monitoring of Nutrients and Sediment in Agricultural Watersheds
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Published on: September 26, 2017

Temporal aspects of surface water quality variation using robust statistical tools.

Adamu Mustapha1, Ahmad Zaharin Aris, Mohammad Firuz Ramli

  • 1Centre of Excellence for Environmental Forensics, Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia.

Thescientificworldjournal
|August 25, 2012
PubMed
Summary

Statistical analysis of surface water quality revealed significant seasonal variations. Discriminant analysis identified key parameters influencing water quality, with strong correlations found between biochemical oxygen demand and chemical oxygen demand.

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Area of Science:

  • Environmental Science
  • Water Quality Monitoring
  • Statistical Analysis

Background:

  • Surface water quality is crucial for ecosystem health and human use.
  • Understanding temporal variations in water quality is essential for effective management.
  • Physicochemical parameters are key indicators of surface water health.

Purpose of the Study:

  • To identify significant water quality parameters influencing temporal variations.
  • To determine the contribution of these parameters to water quality changes between seasons.
  • To apply robust statistical tools for comprehensive water quality assessment.

Main Methods:

  • Collection of surface water samples from four locations during dry and wet seasons.
  • Analysis of physicochemical constituents of the water samples.
  • Application of Discriminant Analysis (DA), Partial Correlation, Multiple Linear Regression, and Repeated Measure t-test.

Main Results:

  • Discriminant analysis achieved high correct assignation rates (>96% for dry season, up to 82% for wet season).
  • Partial correlation revealed strong relationships between BOD(5) and COD (dry season), and TS and DS (wet season).
  • Multiple linear regression indicated significant contributions of variables to water quality (R² up to 0.976). Repeated measure t-test confirmed significant seasonal variations (P < 0.05).

Conclusions:

  • Surface water quality exhibits significant seasonal variability.
  • Specific physicochemical parameters, including BOD(5), COD, TS, DS, ammonia, and conductivity, are critical determinants of water quality.
  • The applied statistical methods effectively identified key water quality indicators and their seasonal impacts.