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Related Concept Videos

Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

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Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
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Clean Sampling and Analysis of River and Estuarine Waters for Trace Metal Studies
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Comparing catchment sediment fingerprinting procedures using an auto-evaluation approach with virtual sample

Leticia Palazón1, Borja Latorre1, Leticia Gaspar2

  • 1Department of Soil and Water, Estación Experimental de Aula Dei (EEAD-CSIC), Avda. Montañana 1005, Zaragoza, 50059, Spain.

The Science of the Total Environment
|June 24, 2015
PubMed
Summary

This study introduces a novel method using virtual sample mixtures to evaluate sediment fingerprinting procedures for accurate sediment source apportionment. The findings highlight the importance of specific statistical tests and source characterizations for reliable results in river catchment management.

Keywords:
Mixing modelRiver catchmentsSediment contributionSediment fingerprintingSediment source ascription

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Area of Science:

  • Environmental Science
  • Hydrology
  • Geomorphology

Background:

  • Effective sediment management requires understanding sediment sources in river catchments for erosion control and pollutant transport.
  • Sediment fingerprinting is a crucial tool for quantifying sediment source contributions, but its application is variable and locally dependent.

Purpose of the Study:

  • To propose an auto-evaluation approach for different sediment fingerprinting procedures using virtual sample mixtures.
  • To support the selection of fingerprinting procedures with the best capacity for source discrimination and apportionment in river catchments.

Main Methods:

  • Generated virtual sample mixtures from surface samples of four land uses in a Central Spanish Pyrenean catchment.
  • Compared 24 fingerprinting procedures using statistical tests, source characterizations (mean, median, corrected mean), and mixing models solved by Monte Carlo simulations.
  • Assessed procedures using root mean squared error (RMSE) between known and assessed source contributions.

Main Results:

  • The highest accuracy in source apportionment was achieved using corrected mean source characterizations with composite fingerprints selected by Kruskal Wallis H-test and principal components analysis.
  • High goodness of fit (GOF) values did not consistently indicate accurate source apportionment, necessitating caution when interpreting GOF for mixing model performance.
  • The auto-evaluation approach using virtual samples provides a robust method for selecting optimal fingerprinting procedures.

Conclusions:

  • The proposed auto-evaluation method enhances the selection of sediment fingerprinting procedures for improved source discrimination and apportionment.
  • Accurate sediment source attribution is critical for developing effective soil erosion and sediment management plans.
  • Careful consideration of statistical methods and source characterization is vital for reliable sediment fingerprinting results.