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餐厨垃圾的季节弹性收运体系构建研究

赵天瑞 曹旭冰 李俐频 田禹

能源环境保护2026,Vol.40Issue(2):116-125,10.
能源环境保护2026,Vol.40Issue(2):116-125,10.DOI:10.20078/j.eep.20260307

餐厨垃圾的季节弹性收运体系构建研究

Construction of a Seasonally Resilient Collection and Transportation System for Kitchen Waste

赵天瑞 1曹旭冰 2李俐频 3田禹3

作者信息

  • 1. 国家管网集团工程技术创新有限公司 技术创新中心,天津 300450
  • 2. 国家管网集团建设项目管理分公司北方项目管理中心,河北 廊坊 065000
  • 3. 哈尔滨工业大学 环境学院 城乡水资源与水环境全国重点实验室,黑龙江 哈尔滨 150090
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摘要

Abstract

The rapid pace of urbanization has significantly increased challenges in managing municipal solid waste(MSW),especially in the collection and transportation of kitchen waste.As urban populations and consumption rise,the need for effective kitchen waste management becomes more complex.In this study,we propose an efficient kitchen waste collection system with seasonal flexibility to reduce overall collection costs.To analyze the spatiotemporal variations in kitchen waste generation,we integrated monthly MSW generation,spatial distribution,and separation rates to predict the seasonal spatial distribution of kitchen waste at a 500 m×500 m(0.25 km2)resolution.First,the Seasonal Autoregressive Integrated Moving Average(SARIMA)model with seasonal differencing was applied to characterize monthly MSW generation in Beijing over ten years(2010-2019).The results show that MSW generation is lowest in January–February(off-season)and peaks in July–August(peak season).The average daily MSW generation ratios for the off-season,peak season,and normal season are 88:107:100(normal season=100).This seasonal variability underscores the need for adaptive collection systems.Next,we developed a ridge regression model to examine how district-level socioeconomic and demographic factors,as well as point-of-interest(POI)distributions,influence MSW generation.By combining these predictors with post-sorting kitchen waste separation rates,the model estimated the seasonal spatial distribution of kitchen waste in 2021 across 19,953 grid cells(0.25 km2 each).Spatial validation for the off-season,peak season,and normal seasons in 2021 yielded R2 values greater than 0.98,indicating stable spatiotemporal extrapolation capability.Using this approach,we further projected the spatiotemporal distribution of kitchen waste in 2025.Through location-allocation analysis and a multi-route Vehicle Routing Problem(VRP)optimization,we derived cost-optimal daily collection routes for each season.The analysis indicates that the daily total collection costs for the normal season,off-season,and peak season are approximately in the ratio of 1.00:1.08:0.90.These seasonal cost variations highlight the sensitivity of the sanitation system to seasonal dynamics and the necessity of flexible collection strategies.This study provides a feasible method for optimizing kitchen waste collection in Beijing and offers insights for intelligent and sustainable kitchen waste management under source-separation policies in other cities.The findings serve as a reference for urban areas facing similar challenges and demonstrate that flexible,data-driven strategies can improve the efficiency and sustainability of kitchen waste management systems.Finally,we outline directions for future work,including integrating real-time data and advanced machine learning models to further enhance adaptability and sustainability.

关键词

餐厨垃圾/季节性预测/SARIMA/岭回归/多路径优化

Key words

Kitchen waste/Seasonal prediction/SARIMA/Ridge regression/VRP

分类

资源环境

引用本文复制引用

赵天瑞,曹旭冰,李俐频,田禹..餐厨垃圾的季节弹性收运体系构建研究[J].能源环境保护,2026,40(2):116-125,10.

基金项目

国家自然科学基金资助项目(52570154) (52570154)

国家重点研发计划资助项目(2023YFC3902801) (2023YFC3902801)

能源环境保护

2097-4183

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