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An Evaluation of Municipal Solid Waste Composition Bias Sources
Hala Sfeir1, Debra R Reinhart2, Pamela R McCauley-Bell3
1a Civil and Environmental Engineering Department , University of Central Florida , Orlando , Florida , USA.
Journal of the Air & Waste Management Association (1995)
|October 28, 2017
Summary
This study developed a statistically valid methodology for municipal solid waste composition studies, improving waste stream analysis. Key findings include adjusting for contamination and increasing sample weight to reduce bias in waste characterization.
Area of Science:
- Environmental Science
- Waste Management Engineering
- Statistical Analysis
Background:
- Municipal solid waste (MSW) composition studies are crucial for effective waste management planning.
- Existing methodologies may contain biases affecting the accuracy of waste stream characterization.
- Standardization is needed for reliable and comparable waste composition data.
Purpose of the Study:
- To develop a statistically valid, economical, and practical methodology for conducting municipal solid waste composition studies.
- To identify and address potential sources of bias in waste composition analysis.
- To provide guidance for future local waste stream composition analysis.
Main Methods:
- A comprehensive waste composition study was designed and implemented in Marion County, Florida.
- Investigated potential sources of bias, focusing on sample weight and contamination.
- Developed a methodology incorporating statistical validity and practical considerations for waste sorting.
Main Results:
- The developed methodology provides a statistically valid approach to waste stream representation.
- Contamination is identified as a significant contributor to error in waste stream analysis.
- Increasing sample weight to at least 200 kg and reducing sorting categories can mitigate bias, especially for commercial waste.
Conclusions:
- The proposed methodology enhances the accuracy and reliability of municipal solid waste composition studies.
- Accounting for contamination and optimizing sample weight are critical for reducing bias.
- Widespread implementation of this methodology will improve waste management strategies and data quality.
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