TY - JOUR ID - 254253 TI - Municipal solid wastes quantification and model forecasting JO - Global Journal of Environmental Science and Management JA - GJESM LA - en SN - 2383-3572 AU - Teshome, Y. M. AU - Habtu, N. G. AU - Molla, M. B. AU - Ulsido, M. D. AD - Climate Change and Bioenergy Development, Wondo Genet College of Forestry and Natural Resources, Hawassa University, Ethiopia AD - Chemical, Environmental and Process Engineering, Institute of Technology, Bahir Dar University, Ethiopia AD - GIS-Remote Sensing and Environmental Management, Wondo Genet College of Forestry and Natural Resources, Hawassa University, Ethiopia AD - Water Supply and Environmental Engineering, Institute of Technology, Hawassa University, Ethiopia Y1 - 2023 PY - 2023 VL - 9 IS - 2 SP - 227 EP - 240 KW - Income levels KW - model development KW - Socioeconomic factors KW - Solid waste KW - Waste composition DO - 10.22034/GJESM.2023.02.04 N2 - 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. UR - https://www.gjesm.net/article_254253.html L1 - https://www.gjesm.net/article_254253_c7e9eddd4ebdadfdd1a4a1f323f374d9.pdf ER -