BACKGROUND AND OBJECTIVES: The amount of solid waste produced and its impact on communities and the environment are becoming a global concern. This study aims to assess the amount, composition, and prediction models of solid waste generation in the study area.
METHODS: Solid waste data were collected from both residential and non-residential areas using stratified and systematic sampling approaches. Interviews and field measurements were used to obtain socioeconomic and solid waste data from 90 households and 69 samples from non-residential areas.
FINDINGS: The research area's mean household solid waste generation rate is 0.39kilograms per capita per day. Organic waste accounted for the majority of the waste generated in the study area (71.28 percent), followed by other waste (9.77 percent), paper (6.71 percent), and plastic waste (6.41 percent). The solid waste generation rate demonstrated a positive relationship (p<0.05) with monthly household income and educational level. However, there was a negative association between family size and age (p > 0.05). Based on a high regression coefficient determination value (0.72), low mean absolute error (0.094), sum square error (1.28), and standard error of the estimate (0.908), Model 4 was chosen as the best-fit model among the proposed models.
CONCLUSION: The developed models met multiple linear regression assumptions and could be used to estimate the rate of household solid waste generation. This study generated large amounts of organic waste present in municipal solid waste sources that can contaminate the environment and have an impact on human health while also having a massive energy recovery capability.
- Solid waste generation varied along with variations in socioeconomic income levels and seasons;
- A large amount of organic waste was generated compared to other wastes;
- Monthly households’ income and educational levels were related to solid waste generation positively, while household family size and age were related negatively;
- Solid waste generation prediction models were developed using a combination of monthly income, educational level, household family size, and age.
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