Abstract. Air pollution, especially fine particulate matter (PM2.5), has attracted extensive attention due to its adverse impacts on public health. Although PM2.5 pollution was significantly reduced in China over time, while little is known how the spatial disparity of PM2.5 exposure has evolved, especially from both absolute and relative perspectives. Here, we estimate the long-term PM2.5 exposures in China based on satellite observations and convolutional neural network, and characterize the spatial disparity of PM2.5 exposure using Theil index and rank-rank relationship. The result shows that both PM2.5 exposure and absolute spatial disparity were substantially reduced between 2010 and 2019. The nation-wide concentrations (Theil index) declined from 48.0µg/m3 (0.13) to 35.5µg/m3 (0.054). The inter-provincial disparities dominate the overall disparity in 2010, while the intra-provincial disparity contributed the most in 2019. However, while absolute disparities have diminished, relative disparities persist. PM2.5 exposures in the least 20th percentile polluted cities have increased over time, while exposures in other regions declined. On average, the more (less) polluted cities in 2010 were still the more (less) polluted cities in 2019 (except for the very most 2 percentile polluted cities), indicating that the population in more polluted cities still experiences more air pollution than others. Spatial pattern of relative disparity changes was also observed. Overall, understanding not only absolute spatial disparity but also relative disparity is required to help formulate targeted policies for an equitable environment, leaving nobody behind.

Faults, folds and other tectonics regions belong to the weak areas of geology, will form linear geomorphology as a result of erosion, which appears as lineaments on the earth surface. Lineaments control the distribution of regional formation, groundwater, and geothermal, etc., so it is an important indicator for the evaluation of the strength and stability of the geological structure. The current algorithms mostly are artificial visual interpretation and computer semi-automatic extraction, not only time-consuming, but labour-intensive. It is difficult to guarantee the accuracy due to the dependence on the expert’s knowledge, experience, and the computer hardware and software. Therefore, an integrated algorithm is proposed based on the GF-1 satellite image data, taking the loess area in the northern part of Jinlinghe basin as an example. Firstly, the best bands with 4-3-2 composition is chosen using optimum index factor (OIF). Secondly, line edge is highlighted by Gaussian high-pass filter and tensor voting. Finally, the Hough Transform is used to detect the geologic lineaments. Thematic maps of geological structure in this area are mapped through the extraction of lineaments. The experimental results show that, influenced by the northern margin of Qinling Mountains and the declined Weihe Basin, the lineaments are mostly distributed over the terrain lines, and mainly in the NW, NE, NNE, and ENE directions. It provided a reliable basis for analysing tectonic stress trend because of the agreement with the existing regional geological survey. The algorithm is more practical and has higher robustness, less disturbed by human factors.