Material stock analysis of urban road from nighttime light data based on a bottom-up approach

In recent years, there has been an increasing focus on the dynamics of material stock, that is, the basis of material flow in the entire ecosystem. With the gradual improvement of the global road network encryption project, the uncontrolled extraction, processing, and transportation of raw materials impose serious resource concerns and environmental pressure. Quantifying material stocks enable governments to formulate scientific policies because socio-economic metabolism, including resource allocation, use, and waste recovery, can be systematically assessed. In this study, OpenStreetMap road network data were used to extract the urban road skeleton, and nighttime light images were divided by watershed to construct regression equations based on geographical location attributes. Resultantly, a generic road material stock estimation model was developed and applied to Kunming. The authors concluded that (1) the top three stocks are stone chips, macadam, and grit (total weight is 380 million tons), (2) the proportion of asphalt, mineral powder, lime, and fly ash is correspondingly similar, and (3) the unit area stock decreases as the road grade declines; therefore, the branch road has the lowest unit stock.

• Record URL:
  • http://dx.doi.org/10.1016/j.envres.2023.115902
• Record URL:
  • https://www.sciencedirect.com/science/article/pii/S0013935123006941
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00139351
• Supplemental Notes:
  • Copyright © 2023 Elsevier Inc. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zhao, Fei
  • Wu, Huixia
  • Zhu, Sijin
  • Zeng, Hongyun
  • Zhao, Zhifang
  • Yang, Xutao
  • Zhang, Sujin
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 115902
• Serial:
  • Environmental Research
  • Volume: 228
  • Publisher: Elsevier
  • ISSN: 0013-9351
  • Serial URL: http://www.sciencedirect.com/science/journal/00139351

Subject/Index Terms

• TRT Terms: Inventory; Night visibility; Resource allocation; Street lighting; Surface course (Pavements); Urban highways
• Geographic Terms: Kunming (China)
• Subject Areas: Highways; Operations and Traffic Management; Safety and Human Factors;

Filing Info

• Accession Number: 01884648
• Record Type: Publication
• Files: TRIS
• Created Date: Jun 6 2023 1:31PM