...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Environmental Science & Technology >Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method
【24h】

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

机译：应用人工神经网络方法估算中国城市固体废物的物理成分

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

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) organi 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.
机译：市政固体废物（PCMSW）的物理构成是国内废物管理中的基本参数;但是，中国的高保真度，广泛的覆盖范围，升级和年度连续数据集不足。通过分析中国135个县级城市的503个PCMSW数据集，首次建立了中国估算中国PCMSW的可追踪和可预测的方法。使用超球形变换方法来消除统计分析PCMSW数据的恒定约束。此外，应用了反向传播（BP）神经网络方法，以建立城市级PCMSW和其社会经济因素之间的定量模型，包括城市规模，人均区域产品，地理位置，天然气覆盖率和年份。结果表明，（1）2017年国家一级PCMSW估计为有机级分（53.7％），灰和石材（8.3％），纸张（16.9％），塑料和橡胶（13.6％），纺织品（2.3％），木（2.2％），金属（0.6％），玻璃（1.5％），其他（1.0％）; （2）有机体分数，纸张和塑料从1990年到2017年表现出越来越大的趋势，而灰和石头明显减少; （3）东部，北部和中南部的有机分数高于其他地区的组成部分。这使我们能够在中国市政固体废物管理实践中填补数据差距。

著录项

  • 来源
    《Environmental Science & Technology》 |2020年第15期|9609-9617|共9页
  • 作者

    Shijun Ma; Chuanbin Zhou; Ce Chi; Yijie Liu; Guang Yang;

  • 作者单位

    State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing 100085 China University of Chinese Academy of Sciences Beijing 100049 China;

    State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmentat Sciences Chinese Academy of Sciences Beijing 100085 China University of Chinese Academy of Sciences Beijing 100049 China;

    High Performance Computer Research Center Institute of Computing Technology Chineie Academy of Sciences Beijing 100089 China;

    State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing 100085 China;

    State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing 100085 China University of Chinese Academy of Sciences Beijing 100049 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号