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A statistically coherent robust multidimensional classification scheme for water.

Surendra P Verma1, Oscar Alejandro Uscanga-Junco2, Lorena Díaz-González3

  • 1Instituto de Energías Renovables, Universidad Nacional Autónoma de México, Priv. Xochicalco s/no., Col. Centro, Apartado Postal 34, Temixco, Mor. 62580, Mexico.

The Science of the Total Environment
|November 13, 2020
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Summary

A new water classification system using hybrid log-ratios (hlr) and linear discriminant analysis (LDA) offers a robust method for identifying 256 water types. This statistically coherent nomenclature improves upon existing schemes for diverse water sources.

Keywords:
Log-ratio molar concentrationsMolar concentrationsNew water nomenclatureTernary diagrams

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Area of Science:

  • Hydrogeochemistry and Water Quality Analysis
  • Statistical Modeling and Data Analysis
  • Environmental Science and Water Resource Management

Background:

  • Existing water classification schemes possess significant limitations, necessitating a more statistically robust approach.
  • Accurate water classification is crucial for understanding hydrogeochemical processes and managing water resources effectively.

Purpose of the Study:

  • To develop a novel, statistically coherent water nomenclature system based on ionic concentrations.
  • To establish a multidimensional classification system capable of identifying a comprehensive range of water types, including basic and hybrid categories.
  • To create a user-friendly computational tool for implementing the new classification scheme.

Main Methods:

  • Utilized a large database of 46,292 simulated, outlier-free water samples with charge-balanced ionic concentrations of eight key elements.
  • Employed linear discriminant analysis (LDA) and canonical analysis on seven hybrid log-ratios (hlr) derived from elemental concentrations.
  • Compared the proposed 7 hlr model with alternative methods (Greater molar conc model and 7 M conc model) for robustness against analytical uncertainty and mineral-water interactions.

Main Results:

  • The 7 hlr model demonstrated superior robustness and was selected as the optimal method for water classification.
  • The classification scheme can identify 16 primary water types and, with a probability concept, up to 256 basic and hybrid water types.
  • A new online program, WaterMClaSys_LDA, was developed to facilitate the application of this complex classification system.

Conclusions:

  • The proposed 7 hlr-based water classification system offers a statistically coherent and robust nomenclature.
  • This advanced system significantly expands the capacity to classify diverse water types, aiding in detailed hydrogeochemical studies.
  • The developed online tool ensures accessibility and practical application of the new water classification scheme for researchers and practitioners.