Category: Waste Analytics • Read Time: 7 min
Introduction
Waste composition analysis (WCA) is fundamental for planning modern waste systems. Reliable data drives decisions in facility sizing, material recovery, collection routing, and policy design.
1. Sampling Design
Effective WCA requires:
- Representative sampling
- Sufficient sample mass
- Seasonal and demographic variability
Common methods:
- Random sampling
- Stratified sampling
- Cluster sampling
2. Statistical Tools
Sample Size Determination
Depends on:
- Variance of waste fractions
- Confidence interval
- Acceptable margin of error
Data Treatment
Analysts often use:
- Regression models
- Monte Carlo simulations
- Confidence interval estimation
3. Why WCA Matters
Accurate composition data influences:
National waste reporting metrics
Recycling system design
Waste-to-energy feedstock quality
Organics diversion strategies