Abstract
Abstract. In recent years, air pollution related to PM2.5 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 PM2.5 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 PM2.5 concentrations in the GC regions and investigated the driving factors of PM2.5 concentrations by using the geographically weighted regression (GWR) model. Results show that (1) PM2.5 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 PM2.5 concentrations featured more and more concentrated over time, whereas, after the year 2010, the distributions gradually featured scattered. (3) The concentrations of PM2.5 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 PM2.5 concentrations gradually decreased over time, however, those with rapidly decreasing in PM2.5 concentrations increased. (5) The influences of the natural factors and socio-economic factors on the distributions of PM2.5 concentrations varied spatially. In detail, the elevation was negatively correlated with PM2.5 concentrations, whereas an opposite relationship between industrial structure and PM2.5 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.
Highlights
Air pollution dominated by inhalable particles seriously affected people's physical and mental health
4.1 Time Series Variation Characteristics of PM2.5 Concentrations Figure 2 shows the variation of the average PM2.5 concentrations in the Grand Canal (GC) region from 2000 to 2018
This paper uses three spatial analysis methods: mean center and standard deviation ellipse, spatial autocorrelation, and trend analysis to explore the spatial distribution of PM2.5 in the region and obtain the result that the PM2.5 concentrations center in the GC region from 2000 to 2018 shifted northward and southward
Summary
Air pollution dominated by inhalable particles seriously affected people's physical and mental health. Studying the temporal and spatial distribution of PM2.5 concentrations in the GC is of great significance for the health of one-third of the population in China, understanding the relationship between economic development and air pollution and formulating corresponding policies. Scholars have conducted a series of studies on the spatiotemporal variation law of PM2.5. From the perspective of time span, air pollution studies in china mainly discussed the changing law of PM2.5 in different seasons, months, and days in a certain year, or the changing law of PM2.5 concentrations in some years over time (Li et al, 2017). From the perspective of space, the study areas of most scholars are mainly concentrated in the Beijing-Tianjin-Hebei region, Pearl River Delta and Yangtze River Delta (Wu et al, 2020). There are few studies on the central and eastern plains, so it is urgent to strengthen the research on the spatiotemporal variation of PM2.5 concentrations over a
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