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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Selecting an appropriate multivariate source apportionment model result.

Ronald C Henry1, Erik R Christensen

  • 1Department of Civil and Environmental Engineering, Environmental Engineering Program, University of Southern California, Los Angeles, California 90089-2531, USA. rhenry@usc.edu

Environmental Science & Technology
|February 25, 2010
PubMed
Summary
This summary is machine-generated.

Unmix and positive matrix factorization (PMF) often yield similar source apportionment. However, this study highlights cases where they differ, suggesting applying both models and using provided methods to select the most accurate results.

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Area of Science:

  • Environmental Science
  • Analytical Chemistry
  • Geochemistry

Background:

  • Multivariate source apportionment models like Unmix and positive matrix factorization (PMF) are commonly used for environmental data analysis.
  • These models typically provide comparable results in source identification and contribution estimation.
  • However, discrepancies can arise, necessitating a critical evaluation of their performance under different data conditions.

Purpose of the Study:

  • To investigate scenarios where Unmix and PMF produce divergent source apportionment results.
  • To identify the specific data characteristics that favor one model over the other.
  • To provide guidance on selecting the most reliable model outputs when discrepancies occur.

Main Methods:

  • Application of Unmix and basic/adjustable PMF to a simulated air pollution dataset (8 species, 200 samples).
  • Application of Unmix and PMF to a real-world water quality dataset (32 PCB congeners, 106 sediment core samples from Sheboygan River).
  • Comparative analysis of model-derived source compositions and contributions against known or inferred true values.

Main Results:

  • For simulated data, basic PMF failed due to lack of zero concentrations in source compositions; Unmix performed better.
  • Adjustable PMF showed improved results on simulated data.
  • For PCB sediment data, PMF identified sources consistent with known Aroclor mixtures and dechlorination profiles, while Unmix results were sensitive to only three data points.

Conclusions:

  • Unmix is favored when data edges are well-defined, whereas PMF performs better with zero values in loading and score matrices.
  • Both models have potential weaknesses, underscoring the importance of applying both in parallel for comprehensive source apportionment.
  • A methodology is proposed to select the most accurate model results when Unmix and PMF yield dissimilar outcomes.