Model framework to quantify the effectiveness of garbage classification in reducing dioxin emissions

Abstract

Although waste incineration is a promising disposal method, it produces unwanted combustion by-products, such as toxic dioxins, that can be unintentionally emitted. Kitchen scraps can result in incomplete combustion of waste, which accelerates the formation of dioxins, especially for the small-sized incinerators without identical operating temperature. Consequently, garbage classification before waste incineration is critical for dioxin control in the small-sized waste incineration industries. To date, the influence of garbage classification on dioxin emissions has not been quantified. In this study, a model framework integrating the grey prediction model and autoregressive prediction model was established and used to predict future dioxin emissions from small-sized waste incineration. If garbage classification is ideally strictly implemented, annual dioxin emissions could be reduced by up to 1697 g TEQ over the next 10 years. Garbage classification reduced emissions by about 30.7% compared with incineration of mixed municipal solid waste without classification (5534 g TEQ over the next 10 years). The established model framework can effectively assess the influence of garbage classification on dioxin emissions from waste incineration, which could facilitate the widespread adoption of garbage classification.

Keywords: Autoregressive prediction model; Dioxin emission; Garbage classification; Grey prediction model; Waste incineration.