A comparison study of bottom‐up and top‐down methods for analyzing the physical composition of municipal solid waste. (1st April 2021)

• Record Type: Journal Article
• Authors: Zhou, Chuanbin; Ma, Shijun; Yu, Xiao; Chen, Zhuqi; Liu, Jingru; Yan, Li
• Other Names: Majeau‐Bettez Guillaume guestEditor.; Frayret Jean‐Marc guestEditor.; Ramaswami Anu guestEditor.; Li Yang guestEditor.; Heeren Niko guestEditor.
• Is Part Of: Journal of industrial ecology. Volume 26:Number 1(2022)
• Page Start: 240
• Page End: 251
• Publication Date: 2021-04-01
• Subjects: artificial neural network -- composition -- data -- inversion algorithm -- material flow analysis (MFA) -- municipal solid waste; Industrial ecology -- Periodicals; Product life cycle -- Environmental aspects -- Periodicals; Industrial management -- Environmental aspects -- Periodicals; Écologie industrielle -- Périodiques; 658.56
• DOI: 10.1111/jiec.13128
• Languages: English
• ISSNs: 1088-1980

Abstract

Abstract: Municipal solid waste (MSW) management is a crucial issue in socioeconomic metabolism and requires multicategory and high‐resolution data, and data on the physical composition of municipal solid waste (PCMSW) are fundamental in MSW research. Extensive financial resources have been invested in the research on field investigations of PCMSW; however, it is time‐consuming and sometimes not truly representative of the studied case. In this work, two bottom‐up and two top‐down approaches were applied for analyzing the PCMSW, namely, field investigation (FI), BP neural network (BPNN), material flow analysis (MFA), and inversion algorithm based on electricity generation of waste incinerator (IAEI). Wuhan City, China, was chosen as the studied case for analyzing and comparing the PCMSW results. The PCMSW values obtained by applying those four methods showed acceptable differences, and the standard deviations of organic fraction, ash and stone, paper, plastic and rubber, textile, wood, metal, glass, and others were 3.94%, 2.77%, 6.57%, 2.22%, 2.49%, 1.36%, 0.53%, 1.19%, and 0.28%, respectively. Use of the MFA, BPNN, and IAEI methods could reduce time and labor spent on manual sampling, sorting, and weighing of MSW, compared to the FI method. BPNN algorithm advances in providing PCMSW data in history and by trajectory throughout the year, whereas IAEI contributes PCMSW data with much higher temporal resolution. Data quality and applicability of different methods were discussed, considering the availability of time, labor, and reference data.