SPATIAL PATTERN EVOLUTION AND DRIVING FACTORS OF PM_2.5 CONCENTRATIONS IN THE GRAND CANAL REGION FROM 2000 TO 2018

• Main Authors: X. Wang, M. Hou, S. Cao, B. Li
• Format: Article
• Language: English
• Published: Copernicus Publications 2021-08-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLVI-M-1-2021/829/2021/isprs-archives-XLVI-M-1-2021-829-2021.pdf

In recent years, air pollution related to PM_2.5 has caused a significant impact on human health. The Grand Canal (GC) is not only a great Cultural heritage created in ancient China but also the longest and largest canal in the world. Based on remotely sensed PM_2.5 gridded data in the GC region covering 2000 to 2018, we used the holistic methods of standard deviation ellipse, local moran index, slope trend analysis to reveal the spatiotemporal evolutions of PM_2.5 concentrations in the GC regions and investigated the driving factors of PM_2.5 concentrations by using the geographically weighted regression (GWR) model. Results show that (1) PM_2.5 concentrations in the GC region exhibited an increasing trend and followed by a decreasing trend from 2000 to 2018 (the turning point emerged in 2010). (2) The standard deviation ellipse analyses show that the spatial distributions of PM_2.5 concentrations featured more and more concentrated over time, whereas, after the year 2010, the distributions gradually featured scattered. (3) The concentrations of PM_2.5 exhibited the strong effects of local spatial autocorrelation and areas with "high-high" agglomeration were mainly located in the central and west regions of the GC region and gradually expanded to the north over time. (4) The areas of regions with rapidly increasing in PM_2.5 concentrations gradually decreased over time, however, those with rapidly decreasing in PM_2.5 concentrations increased. (5) The influences of the natural factors and socio-economic factors on the distributions of PM_2.5 concentrations varied spatially. In detail, the elevation was negatively correlated with PM_2.5 concentrations, whereas an opposite relationship between industrial structure and PM_2.5 concentrations was observed. The coefficients of rainfall, population density, GDP per capita and foreign investment show different results in positive and negative correlations depending on the position.