Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method
文献类型：期刊论文
| 作者 | Ma, Shijun1,2; Zhou, Chuanbin1,2; Chi, Ce3; Liu, Yijie1; Yang, Guang1,2 |
| 刊名 | ENVIRONMENTAL SCIENCE & TECHNOLOGY
 |
| 出版日期 | 2020-08-04 |
| 卷号 | 54期号:15页码:9609-9617 |
| ISSN号 | 0013-936X |
| DOI | 10.1021/acs.est.0c01802 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000558753900047 |
| 出版者 | AMER CHEMICAL SOC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/15817]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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