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A SPATIAL DISTRIBUTION MODEL OF DOMESTIC WASTE BASED ON GIS REMOTE SENSING DATA ANALYSIS
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摘要: 面向我国无废城市建设的源头减量指标要求,开展生活垃圾的时空产生特征进行科学预测,是生活垃圾管理全局规划的重要基础工作。研究目标区域在2020年生活垃圾年产量达到12.60万t,近年来增幅显著,亟待对生活垃圾的时空产生特征进行精细分析,以服务当地无废城市的建设。在综合分析研究区域历史数据的背景下,建立了研究区域夜间灯光与生活垃圾年产量的线性关联关系,形成了R2达到0.92的回归模型;基于ArcGIS,以2.25 hm2为空间单位,结合克里金算法,为生活垃圾空间分布建立基于夜间灯光的网格化模型;利用泰森多边形算法,对1个生活垃圾收集点进行插值,获取各个收集点的生活垃圾年负荷状况,揭示了某无废城市试点地区中城乡垃圾产量的空间不均衡性。Abstract: Facing the source reduction requirements of the construction of Zero-Waste Cities in China, scientific prediction of the spatio-temporal characteristics of domestic waste is an essential fundamental work for the overall planning of domestic waste management. The annual output of domestic waste in the study area reaches 126,000 tons in 2020, and the increase is becoming significant in recent years. It is urgent to conduct a detailed analysis of domestic waste's temporal and spatial characteristics to serve the construction of local Zero-Waste Cities' construction. Based on the comprehensive analysis of the historical domestic waste data of the study area, a linear relationship between the night light in the study area and the annual output of domestic waste was established to form a high-fit regression model with an R2 of 0.92; based on the Kriging algorithm of ArcGIS, using 2.25 hectares as the spatial unit, a domestic waste spatial distribution grid model based on night lights was established; the Thiessen polygon algorithm was used to interpolate each domestic waste collection point to obtain the annual domestic waste load status of each collection point, revealed the spatial imbalance of urban and rural waste production.
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Key words:
- domestic waste /
- Kriging algorithm /
- night light /
- space-time distribution /
- GIS
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