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Updated: Jul 4, 2026
20:36
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
Published on: July 4, 2007
Mathematical modeling to predict residential solid waste generation
Sara Ojeda Benítez1, Gabriela Lozano-Olvera, Raúl Adalberto Morelos
1Engineering Institute, UABC, Boulevard Benito Juárez y Calle de la Normal S/N, Col. Insurgentes Este, C.P. 21280, Mexicali, Baja California, Mexico. sojedab@uabc.mx
Waste Management (New York, N.Y.)
|June 28, 2008
Summary
This study developed mathematical models to predict household residential solid waste (RSW) generation based on education, income, and household size. These models aim to improve waste management and billing systems for fairer waste collection charges.
Area of Science:
- Environmental Science
- Urban Planning
- Mathematical Modeling
Background:
- Waste management authorities face challenges in accurately estimating household waste generation for system design and fair billing.
- Accurate quantification of residential solid waste (RSW) is crucial for effective waste management strategies and equitable cost allocation.
Purpose of the Study:
- To establish mathematical models correlating per capita RSW generation with socioeconomic variables.
- To develop predictive models for household waste generation to inform waste management policies.
- To identify key factors influencing RSW generation for improved system planning.
Main Methods:
- Data collection from a three-stage study on RSW generation, quantification, and composition in a Mexican city.
- Development and comparison of multiple mathematical models using variables like education, income, and household size.
- Statistical analysis including normality, multicollinearity, heteroskedasticity, and Durban-Watson tests to validate models.
Main Results:
- Several mathematical models were developed and tested for their linear predictive capability of RSW generation.
- Models exploring variable combinations identified those with higher R(2) values.
- A general mathematical model was proposed, explaining 51% of the total RSW generation variance.
Conclusions:
- Socioeconomic factors like education, income, and household size are significant predictors of residential solid waste generation.
- The developed mathematical models offer a viable approach for predicting RSW, aiding in waste management planning and financial system design.
- The proposed general model provides a foundational tool for authorities to estimate waste generation more accurately.
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