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Study on the relationship between PM2.5 concentration and intensive land use in Hebei Province based on a spatial regression model

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  • Jingjing Shao
  • Jingfeng Ge
  • Xiaomiao Feng
  • Chaoran Zhao

Abstract

Based on 0.01°×0.01° grid data of PM2.5 annual concentration and statistical yearbook data for 11 cities in Hebei Province from 2000 to 2015, the temporal and spatial distribution characteristics of PM2.5 in the study area are analysed, the level of intensive land use in the area is evaluated, and decoupling theory and spatial regression are used to discuss the relationship between PM2.5 concentration and intensive land use and the influence of intensive land use variables on PM2.5 in Hebei Province. The results show that 1. In terms of time, the concentration of PM2.5 in Hebei Province showed an overall upward trend from 2000 to 2015, with the highest in winter and the lowest in summer. The daily variations show double peaks at 8:00–10:00 and 21:00–0:00 and a single valley at 16:00–18:00. 2. In terms of space, the concentration of PM2.5 in Hebei Province is high in the southeast and low in the northwest, and the pollution spillover initially decreases and then increases. 3. In the past 16 years, the level of intensive land use in Hebei Province has increased annually, but blind expansion still exists. 4. Decoupling theory and the spatial lag model show that land use intensity, land input level and land use structure are positively correlated with PM2.5 concentration, land output benefit is negatively correlated with PM2.5 concentration, and PM2.5 concentration and land intensive use level have not yet been decoupled; thus, the relationship is not harmonious. This research can provide a scientific basis for reducing air pollution and promoting the development of urban land resources for intensive and sustainable development.

Suggested Citation

  • Jingjing Shao & Jingfeng Ge & Xiaomiao Feng & Chaoran Zhao, 2020. "Study on the relationship between PM2.5 concentration and intensive land use in Hebei Province based on a spatial regression model," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-21, September.
  • Handle: RePEc:plo:pone00:0238547
    DOI: 10.1371/journal.pone.0238547
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    Cited by:

    1. Zeng, Juying & Škare, Marinko & Lafont, Juan, 2021. "The co-integration identification of green innovation efficiency in Yangtze River Delta region," Journal of Business Research, Elsevier, vol. 134(C), pages 252-262.
    2. Hongjie Bao & Ling Shan & Yufei Wang & Yuehua Jiang & Cheonjae Lee & Xufeng Cui, 2021. "How Does Local Real Estate Investment Influence Neighborhood PM 2.5 Concentrations? A Spatial Econometric Analysis," Land, MDPI, vol. 10(5), pages 1-21, May.
    3. Chuanming Yang & Qingqing Zhuo & Junyu Chen & Zhou Fang & Yisong Xu, 2022. "Analysis of the spatio-temporal network of air pollution in the Yangtze River Delta urban agglomeration, China," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-13, January.

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