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使用预测工具和回归分析预测城市固体废物的产生量。

Forecasting municipal solid waste generation using prognostic tools and regression analysis.

作者信息

Ghinea Cristina, Drăgoi Elena Niculina, Comăniţă Elena-Diana, Gavrilescu Marius, Câmpean Teofil, Curteanu Silvia, Gavrilescu Maria

机构信息

"Stefan cel Mare" University of Suceava, Faculty of Food Engineering, 13 Universitatii Street, 720229, Suceava, Romania; "Gheorghe Asachi" Technical University of Iasi, Faculty of Chemical Engineering and Environmental Protection, Department of Environmental Engineering and Management, 73 Prof.Dr.Docent D. Mangeron Str., 700050, Iasi, Romania.

"Gheorghe Asachi" Technical University of Iasi, Faculty of Chemical Engineering and Environmental Protection, Department of Chemical Engineering, 73 Prof.Dr.Docent D. Mangeron Str., 700050, Iasi, Romania.

出版信息

J Environ Manage. 2016 Nov 1;182:80-93. doi: 10.1016/j.jenvman.2016.07.026. Epub 2016 Jul 22.

Abstract

For an adequate planning of waste management systems the accurate forecast of waste generation is an essential step, since various factors can affect waste trends. The application of predictive and prognosis models are useful tools, as reliable support for decision making processes. In this paper some indicators such as: number of residents, population age, urban life expectancy, total municipal solid waste were used as input variables in prognostic models in order to predict the amount of solid waste fractions. We applied Waste Prognostic Tool, regression analysis and time series analysis to forecast municipal solid waste generation and composition by considering the Iasi Romania case study. Regression equations were determined for six solid waste fractions (paper, plastic, metal, glass, biodegradable and other waste). Accuracy Measures were calculated and the results showed that S-curve trend model is the most suitable for municipal solid waste (MSW) prediction.

摘要

为了对废物管理系统进行充分规划,准确预测废物产生量是至关重要的一步,因为各种因素会影响废物趋势。预测和预后模型的应用是有用的工具,可为决策过程提供可靠支持。本文将一些指标,如居民数量、人口年龄、城市预期寿命、城市固体废弃物总量,用作预后模型的输入变量,以预测固体废弃物组分的数量。我们应用废物预后工具、回归分析和时间序列分析,通过考虑罗马尼亚雅西市的案例研究来预测城市固体废弃物的产生量和组成。确定了六种固体废弃物组分(纸张、塑料、金属、玻璃、可生物降解物和其他废物)的回归方程。计算了准确度指标,结果表明S曲线趋势模型最适合城市固体废弃物(MSW)预测。

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