Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method

doi:10.1021/acs.est.0c01802

	Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method
	Ma, Shijun 1,2; Zhou, Chuanbin 1,2; Chi, Ce 3; Liu, Yijie 1; Yang, Guang 1,2
	2020-08-04
发表期刊	ENVIRONMENTAL SCIENCE & TECHNOLOGY
ISSN	0013-936X
卷号	54 期号:15 页码:9609-9617
摘要	Physical composition of municipal solid waste (PCMSW) is the fundamental parameter in domestic waste management; however, high fidelity, wide coverage, upscaling, and year continuous data sets of PCMSW in China are insufficient. A traceable and predictable methodology for estimating PCMSW in China is established for the first time by analyzing 503 PCMSW data sets of 135 prefecture-level cities in China. A hyperspherical transformation method was used to eliminate the constant sum constraint in statistically analyzing PCMSW data. Moreover, a back-propagation (BP) neural network methodology was applied to establish quantitative models between city-level PCMSW and its socio-economic factors, including city size, per capita gross regional product, geographical location, gas coverage rate, and year. Results show that (1) national-level PCMSW in 2017 was estimated as organic fraction (53.7%), ash and stone (8.3%), paper (16.9%), plastic and rubber (13.6%), textile (2.3%), wood (2.2%), metal (0.6%), glass (1.5%), and others (1.0%); (2) organic fraction, paper, and plastics showed an increasing trend from 1990 to 2017, while ash and stone decreased significantly; (3) organic fractions in East, North, and Central-South China were higher than those in other regions. This enables us to fill the data gap in the practice of municipal solid waste management in China.
DOI	10.1021/acs.est.0c01802
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[41871206] ; National Key R&D Program of China[2018YFC1903601] ; Youth Innovation Promotion Association CAS[2017061]
WOS研究方向	Engineering ; Environmental Sciences & Ecology
WOS类目	Engineering, Environmental ; Environmental Sciences
WOS记录号	WOS:000558753900047
出版者	AMER CHEMICAL SOC
引用统计	被引频次：84[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/15818
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhou, Chuanbin
作者单位	1.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Urban & Reg Ecol, Beijing 100085, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing 100089, Peoples R China
推荐引用方式 GB/T 7714	Ma, Shijun,Zhou, Chuanbin,Chi, Ce,et al. Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method[J]. ENVIRONMENTAL SCIENCE & TECHNOLOGY,2020,54(15):9609-9617.
APA	Ma, Shijun,Zhou, Chuanbin,Chi, Ce,Liu, Yijie,&Yang, Guang.(2020).Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method.ENVIRONMENTAL SCIENCE & TECHNOLOGY,54(15),9609-9617.
MLA	Ma, Shijun,et al."Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method".ENVIRONMENTAL SCIENCE & TECHNOLOGY 54.15(2020):9609-9617.