Version 1
: Received: 1 November 2021 / Approved: 4 November 2021 / Online: 4 November 2021 (08:57:00 CET)
How to cite:
Song, L.F.; Feng, Y.F. Temporal and Spatial Impacts of Land Transfer on PM2.5 Concentration in 285 Cities of China. Preprints2021, 2021110095. https://doi.org/10.20944/preprints202111.0095.v1
Song, L.F.; Feng, Y.F. Temporal and Spatial Impacts of Land Transfer on PM2.5 Concentration in 285 Cities of China. Preprints 2021, 2021110095. https://doi.org/10.20944/preprints202111.0095.v1
Song, L.F.; Feng, Y.F. Temporal and Spatial Impacts of Land Transfer on PM2.5 Concentration in 285 Cities of China. Preprints2021, 2021110095. https://doi.org/10.20944/preprints202111.0095.v1
APA Style
Song, L.F., & Feng, Y.F. (2021). Temporal and Spatial Impacts of Land Transfer on PM2.5 Concentration in 285 Cities of China. Preprints. https://doi.org/10.20944/preprints202111.0095.v1
Chicago/Turabian Style
Song, L.F. and Yan Fei Feng. 2021 "Temporal and Spatial Impacts of Land Transfer on PM2.5 Concentration in 285 Cities of China" Preprints. https://doi.org/10.20944/preprints202111.0095.v1
Abstract
The threat of fine particulate matter concentration (PM2.5) is increasing globally, Tackling this issue requires an accurate understanding of its trends and drivers. The article investigates the PM2.5 characteristics of 285 prefecture-level cities in China from 2000-2018 based on multiscale geographically weighted regression(MGWR), and the results show that（1）previous studies based on classical MGWR models may be somewhat unstable, while MGWR can reflect the scale of influence of different variables on the dependent variable, and its regression results are more reliable.（2）PM2.5 is very sensitive to carbon emission(CE) factors, and there is a high degree of spatial heterogeneity, and the influence scale of location is the smallest among all variables, close to the municipal scale.（3）In 2000, the constant term all, IS, OFT, CE, and LT positively affect PM2.5, while GDP (jurisdiction) and UR negatively affect PM2.5; in 2010, the constant term all, GDP (jurisdiction), IS, OFT and LT positively affect PM2.5, while UR and CE negatively affect PM2.5; in 2018 the constant term all, IS, OFT and CE factors positively affect PM2.5, and GDP (jurisdiction), UR and LT negatively affect PM2.5.
Keywords
PM2.5; GDP; MGWR; land transfer; industrial structure
Subject
Social Sciences, Geography, Planning and Development
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
