Long-term exposure to PM 2.5 can lead to great adverse health effect on human health. To better guide public policies that aim to reduce PM 2.5 population exposure, this work combined multi-source data to realize high-resolution PM 2.5 exposure risk assessment in Nanchang urban region. The land use regression (LUR) model was used to simulate the seasonal-spatial variations of PM 2.5 concentrations at 100-m resolution, and building information extracted from IKONOS image was applied to spatialize population at 100-m resolution. An improved piece-wise population exposure approach was introduced to evaluate the exposure risk, and results were compared with two classical approaches. In all seasons, results by the absolute concentration approach are very different from the other two, showing obvious spatial smoothing effect. Results by population-weighted and piece-wise exposure approaches are similar in spring and autumn, and different in summer and winter. In winter, the area and population percentages divided to severity level 7 by population-weighted exposure approach are 5.21% and 2.35% lower than that by piece-wise exposure approach. When in summer, the area and population percentages divided to severity level 7 by population-weighted exposure approach are 6.77% and 24.79% higher than that by piece-wise exposure approach. The absolute concentration approach is disadvantageous for the identification of high-risk areas, the population-weighted exposure approach would underestimate or overestimate the population exposure when air is seriously polluted or remarkably clean, and the proposed piece-wise exposure approach would be more reasonable. The integrated methodology is effective in exposure risk assessment and can be applied to other regions and pollutants.

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Tool safety is an important part of machining and machine tool safety, and machine tool path image detection can effectively obtain the in-machine condition of a tool. To obtain an accurate image edge and improve image processing accuracy, a novel subpixel edge detection method is proposed in this study. The precontour is segmented by binarization, the second derivative in the neighborhood of the demand point is calculated, and the obtained value is sampled according to the specified rules for curve fitting. The point whose curve ordinate is 0 is the subpixel position. The experiment proves that an improved subpixel edge can be obtained. Results show that the proposed method can extract a satisfactory subpixel contour, which is more accurate and reliable than the edge results obtained by several current pixel-level operators, such as the Canny operator, and can be used in edge detection with high-accuracy requirements, such as the contour detection of online tools.