Application of derived models for the prediction of municipal solid waste generation rates in developing countries

Abstract

Municipal Solid Waste Management (MSWM) is a complex process that requires a lot of information from various sources such as factors on waste generation and waste quantity forecasts. Waste generation rates are affected by socioeconomic development, degree of industrialisation, and climate. Generally, greater the economic prosperity and higher the percentage of urban population, greater is the amount of solid waste produced. It is essential to know the quantity of waste generated to plan a Municipal Solid Waste (MSW) management strategy for a given region. Various researchers have attempted to construct models to predict the MSW generation rates. In developed countries there are even models available to predict MSW generation rates, however very few researchers have developed models to predict MSW generation rates in developing countries. Therefore, it is a necessity to use MSW generation prediction models for urban and suburban municipalities in developing countries such as Sri Lanka. This study reviews previously tested models related to municipal solid waste generation and identifies possible factors which will help in identifying crucial design options. There are two different ways to classify models when it comes to analysis of MSW generation rates. They are: factor models that use factors describing the processes of waste generation (Consumption or Utilisation) and Input�output models based on the flow of material to or from waste generators (Removal). There are a number of independent and dependent variables that have been used to explain the overall quantity of partial or entire MSW streams. The study identifies that there is a need for an overall forecast model which identifies future growth in waste generated per capita. The major deficiencies of this study were the selection of few number of models and the use of qualitative approach rather than using statistical tools. Therefore, it is recommended to use a statistical analysis to facilitate better approach for the current models.