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Abstract. China is a country prone to natural disasters, and roads are easily damaged in natural disasters. As the infrastructure of people's production and life and the lifeline of post-disaster rescue, roads play a very important role. There are many previous studies on geological disasters risk assessment, but few special researches on highway geological disasters risk assessment, especially quantitative risk assessment combined with remote sensing images. Therefore, the risk assessment of geological disasters along the road is of great significance for the prevention and control of geological disasters and the protection of life and property safety. Based on GF-6 data and other geographic data, this study used the comprehensive evaluation method to realize the geological disasters risk assessment of the Maoxian-Wenchuan section along the G213 line, and analysed and discussed the causes and spatial distribution of geological disasters along the G213 line. Our study presents several key findings, (1) The order of impact degree of each impact factor is road network density, soil erosion, slope, human activities, vegetation coverage, rainfall, and slope aspect, among them, the road network density factor plays a leading role, and the aspect factor is the least impact factor; (2) The geological disasters risk in the study area is divided into five categories: extremely low risk, low risk, medium risk, high risk, and extremely high risk. In general, the geological disasters risk of the research road section is relatively high, and the risk area division is consistent with the historical disaster point data. The research results can provide reference for the smooth development and implementation of road geological disasters risk assessment in the mountainous areas in southwestern China.

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.