title: Mathematical modeling to predict residential solid waste generation.
abstract: One of the challenges faced by waste management authorities is determining the amount of waste generated by households in order to establish waste management systems, as well as trying to charge rates compatible with the principle applied worldwide, and design a fair payment system for households according to the amount of residential solid waste (RSW) they generate. The goal of this research work was to establish mathematical models that correlate the generation of RSW per capita to the following variables: education, income per household, and number of residents. This work was based on data from a study on generation, quantification and composition of residential waste in a Mexican city in three stages. In order to define prediction models, five variables were identified and included in the model. For each waste sampling stage a different mathematical model was developed, in order to find the model that showed the best linear relation to predict residential solid waste generation. Later on, models to explore the combination of included variables and select those which showed a higher R(2) were established. The tests applied were normality, multicolinearity and heteroskedasticity. Another model, formulated with four variables, was generated and the Durban-Watson test was applied to it. Finally, a general mathematical model is proposed to predict residential waste generation, which accounts for 51% of the total.
authors.affiliation: Engineering 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
authors.forename: Sara Ojeda
authors.initials: SO
authors.lastname: Benítez
authors.forename: Gabriela
authors.initials: G
authors.lastname: Lozano-Olvera
authors.forename: Raúl Adalberto
authors.initials: RA
authors.lastname: Morelos
authors.forename: Carolina Armijo de
authors.initials: CA
authors.lastname: Vega
mesh_terms: D004493:Education; D004787:Environmental Pollution / Q000517:prevention & control; D005191:Family Characteristics; D007182:Income; D008962:Models, Theoretical; D012037:Refuse Disposal / Q000706:statistics & numerical data*; D013997:Time Factors