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Updated: Jul 2, 2026

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Describing variability of MSW composition data with the log-logistic distribution.

Mark W Milke1, Vincent Wong, Edward A McBean

  • 1Department of Civil Engineering, University of Canterbury, Christchurch, New Zealand. mark.milke@canterbury.ac.nz

Waste Management & Research : the Journal of the International Solid Wastes and Public Cleansing Association, ISWA
|August 30, 2008
PubMed
Summary
This summary is machine-generated.

Accurate solid waste planning requires understanding waste composition variability. The log-logistic distribution effectively models diverse solid waste types, improving data reliability for planning.

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Area of Science:

  • Environmental Science
  • Data Analysis
  • Waste Management

Background:

  • Solid waste planning relies on accurate composition data.
  • Uncertainty exists in selecting appropriate probability distributions for waste variability.
  • British Columbia, Canada, provided detailed solid waste analyses.

Purpose of the Study:

  • To identify generally valuable probability distributions for solid waste composition variability.
  • To assess the suitability of various distributions for diverse waste types.
  • To inform more robust solid waste management strategies.

Main Methods:

  • Twenty-two solid waste composition analyses were fitted to probability distributions using BestFit software.
  • Distributions were ranked using three goodness-of-fit parameters across twelve waste fractions.
  • Sensitivity analysis was performed regarding the number of waste components and distribution parameters.

Main Results:

  • The log-logistic distribution demonstrated the best overall fit for a wide range of solid waste composition types.
  • Results were insensitive to the number of waste components or the choice of two- vs. three-parameter distributions.
  • While other distributions fit individual waste types better, the log-logistic provided superior general applicability.

Conclusions:

  • The log-logistic distribution is a highly suitable and versatile model for representing solid waste composition variability.
  • This finding enhances the reliability of data inputs for solid waste planning and management.
  • The study provides a robust statistical approach for waste composition analysis.