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