A SPATIAL DISTRIBUTION MODEL OF DOMESTIC WASTE BASED ON GIS REMOTE SENSING DATA ANALYSIS

ZHAO Tianrui; CHEN Zhengrui; LIU Yiming; LI Yanliang; GUO Wei; TANG Xiaomi; TIAN Yu; ZHANG Jun; WANG Shutao

doi:10.13205/j.hjgc.202302028

Volume 41 Issue 2

Feb. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(2): 213-218

ZHAO Tianrui, CHEN Zhengrui, LIU Yiming, LI Yanliang, GUO Wei, TANG Xiaomi, TIAN Yu, ZHANG Jun, WANG Shutao. A SPATIAL DISTRIBUTION MODEL OF DOMESTIC WASTE BASED ON GIS REMOTE SENSING DATA ANALYSIS[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(2): 213-218. doi: 10.13205/j.hjgc.202302028

Citation:

ZHAO Tianrui, CHEN Zhengrui, LIU Yiming, LI Yanliang, GUO Wei, TANG Xiaomi, TIAN Yu, ZHANG Jun, WANG Shutao. A SPATIAL DISTRIBUTION MODEL OF DOMESTIC WASTE BASED ON GIS REMOTE SENSING DATA ANALYSIS[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(2): 213-218. doi: 10.13205/j.hjgc.202302028

Citation:

ZHAO Tianrui, CHEN Zhengrui, LIU Yiming, LI Yanliang, GUO Wei, TANG Xiaomi, TIAN Yu, ZHANG Jun, WANG Shutao. A SPATIAL DISTRIBUTION MODEL OF DOMESTIC WASTE BASED ON GIS REMOTE SENSING DATA ANALYSIS[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(2): 213-218. doi: 10.13205/j.hjgc.202302028

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A SPATIAL DISTRIBUTION MODEL OF DOMESTIC WASTE BASED ON GIS REMOTE SENSING DATA ANALYSIS

doi: 10.13205/j.hjgc.202302028

State Key Laboratory of Urban Water Resources and Water Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China

Received Date: 2022-03-28
Available Online: 2023-05-25
Publish Date: 2023-02-01

Abstract

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.
- domestic waste,
- Kriging algorithm,
- night light,
- space-time distribution,
- GIS

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References(13)

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