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| 基于物质流分析的建筑垃圾产生量预测 | |
| 引用本文： | 张敏, 董莉, 刘景洋, 毕莹莹, 张建强, 杜明辉. 基于物质流分析的建筑垃圾产生量预测[J]. 环境工程技术学报, 2021, 11(5): 869-878. doi: 10.12153/j.issn.1674-991X.20210060 |
| 作者姓名： | 张敏 董莉 刘景洋 毕莹莹 张建强 杜明辉 |
| 作者单位： | 1.西南交通大学地球科学与环境工程学院;;2.国家环境保护生态工业重点实验室, 中国环境科学研究院清洁生产与循环经济研究中心;;3.华北理工大学建筑与工程学院 |
| 摘 要： | 针对建筑垃圾产生量激增但其统计数据缺失问题,基于1stOpt拟合平台和Visual Basic编程软件,构建城镇住宅和非住宅建筑垃圾产生量动态预测模型,定量模拟不同建筑寿命情景下建筑垃圾产生量及其组分的变化趋势。结果表明:我国城镇住宅建筑和非住宅建筑存量面积分别在2058年和2064年达到峰值(657.35亿和569.41亿m2)。在短、中、长建筑寿命情景下,住宅和非住宅建筑的新建面积均在21世纪20年代达到峰值,拆除面积峰值将在21世纪下半叶实现。在短、中、长寿命3种情景下,我国城镇住宅建筑垃圾总产量分别于2072年、2081年和2100年达到峰值(28.69亿、21.71亿和16.50亿t);非住宅建筑垃圾总产量分别于2077年、2084年和2100年达到峰值(26.25亿、20.29亿、15.48亿t)。2000年之前,建筑垃圾主要成分为施工垃圾,2000年之后,以拆除垃圾为主,至2100年拆除垃圾占比达98%。其中,混凝土、砖块、钢铁分别占44%~71%、22%~51%、0.50%~2.89%,其他成分占2.76%~4.68%。综合考虑建筑流量发展趋势和建筑垃圾产生特征,政府部门应宏观调控减缓人均建筑面积增长速度,延长建筑使用寿命,提高建筑垃圾循环利用率和二次建材原料的市场消纳量,从而减少建筑垃圾产生量,降低建筑垃圾对生态环境的破坏。 |
| 关 键 词： | 建筑垃圾 物质流分析 存量-流量 产生量预测 组成分析 |
| 收稿时间： | 2021-03-13 |
Prediction of construction and demolition waste production based on material flow analysis |
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| Min ZHANG, Li DONG, Jingyang LIU, Yingying BI, Jianqiang ZHANG, Minghui DU. Prediction of construction and demolition waste production based on material flow analysis[J]. Journal of Environmental Engineering Technology, 2021, 11(5): 869-878. doi: 10.12153/j.issn.1674-991X.20210060 | |
| Authors: | Min ZHANG Li DONG Jingyang LIU Yingying BI Jianqiang ZHANG Minghui DU |
| Affiliation: | 1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University;;2. State Key Laboratory of Environmental Protection and Ecological Industry, Centre of Cleaner Production and Circular Economy Research, Chinese Research Academy of Environmental Sciences;;3. Faculty of Architecture and Engineering, North China University of Science and Technology |
| Abstract: | In response to the rapid increase in the amount of construction and demolition waste (C&DW) and the lack of statistical data on the output of C&DW, based on the 1stOpt fitting platform and Visual Basic programming software, a dynamic prediction model of urban residential and non-residential construction waste was constructed, and the changing trend of C&DW production and its compositions under three scenarios of short lifetime, medium lifetime and long lifetime quantitatively simulated. The results showed that: The stock area of urban residential buildings and non-residential buildings in China would reach their peaks in 2058 and 2064, respectively, with the peaks of 65.74 billion m2 and 56.94 billion m2. Under the short lifetime, medium lifetime and long lifetime scenarios, the construction area of residential and non-residential buildings would reach their peaks in 2020s, and the demolition area would peak in the second half of this century. The total output of China’s urban residential C&DW would reach its peak in 2072, 2081 and 2100 under the three scenarios of short lifetime, medium lifetime and long lifetime, respectively, with peaks of 2.869 billion tons, 2.171 billion tons and 1.65 billion tons. Under the three scenarios, the total output of non-residential C&DW would reach peaks in 2077, 2084 and 2100, with a total output of 2.625 billion tons, 2.029 billion tons and 1.548 billion tons, respectively. Before 2000, C&DW was mainly composed of construction waste. After 2000, demolition waste was the main composition. By 2100, the proportion of demolition waste would reach 98%. Among them, concrete, bricks, and steel accounted for 44%-71%, 22%-51%, 0.50%-2.89%, and other compositions accounted for about 2.76%-4.68%. Taking into account the development trend of construction flow and the characteristics of C&DW generation, the government departments should slow down the growth rate of per capita building area through macro-control, prolong the service life of buildings, improve the recycling rate of C&DW and the market consumption of secondary buildings materials, so as to cut down the output of C&DW and reduce their damage to the ecological environment. |
| Keywords: | construction and demolition waste material flow analysis stock-flow production forecast composition analysis |
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