A Statistical Regression Method for Characterization of Household Solid Waste: A Case Study of Awka Municipality in Nigeria

This work contributes to waste management from two major perspectives. Firstly, waste generation in a previously not-studied location—Awka municipality—was sampled and characterized. Secondly, the characterization was done with improved accuracy using a new method called zero-intercept first-order polynomial regression. The proposed method arrives at composition values and per capita values through polynomial regression that considers sampled waste generation data and household size as regressors, respectively. There are no constituents when no waste is generated and there is no per capita waste when household size is zero, therefore, zero-intercept was imposed on the proposed linear regression approach. An 820 by 11 data matrix from a ten-day sampling in Awka Municipality was used to illustrate the proposed approach; eighty percent of the data was used for training, while twenty percent was used for testing. The results from the proposed method proved more accurate when compared with traditional averaging techniques. The results established for the study area are equally in consonance with known results for similar Nigerian locations, such as organic (73.2%), plastic (8.0%), and recyclable (20.3%). The calculated specific loose volume, specific compact volume, the loose bulk density, and compact bulk density are 2.0 × 10−3 m3/kg, 9.9 × 10−4 m3/kg, 500.0 kg∙m−3, and 1010.2 kg∙m−3, respectively. The waste generation rate is 416.9 g/capita/day, the organic waste generation rate is 307.1 g/capita/day, the recyclable waste generation rate is 83.0 g/capita/day, paper and textile waste generation rate is 25.2 g/capita/day, loose waste volume rate is 9.02 × 10−1 dm3/capita/day, and compact waste volume rate is 4.51 × 10−1 dm3/capita/day. The solid waste characters were compared among the three income groups of low, middle, and high income earners and the observed trends are literature compliant with city-specific coloration. City-wide estimations were made based on demography, literature, and the established results that would aid waste management planning.