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.
Highlights
After 1978, the area of urban land in China expanded rapidly and the urbanization rate increased rapidly
It can be seen from the long box length in winter that the dispersion degree of particle concentration is very high, which indicates that extreme pollution events often occur during this period
The results show that the Wald_spatial_lag (9.7389, P>0.001) and Wald_spatial_error (4.0402, P>0.001) tests accept the original hypothesis that the spatial Durbin model (SDM) can be reducible to the Spatial lag model (SLM) or spatial error model (SEM) at the significance level of 0.001
Summary
After 1978, the area of urban land in China expanded rapidly and the urbanization rate increased rapidly. The urbanization rate exceeded 56% in 2015 [1, 2], and it is expected to reach 60% by 2020 [3]. Land is an important carrier of economic activities and a rare and nonrenewable resource. In the Beijing-Tianjin-Hebei area, which has high urbanization levels, land resources available for development are very rare.
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