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Portable X-ray Fluorescence Spectrometer (PXRF) is often used in the laboratory and the field because it is a portable, high-efficiency and non-destructive piece of equipment. PXRF can be used to obtain semi-quantitative/quantitative results of multiple elements in geological samples within two minutes. However, there are many factors that can affect the analytical results of geological samples by PXRF, including the surface state, sample heterogeneity, and the measurement time. In order to further understand the effect of sample types and analytical methods on the element contents, the study described here compares the analytical results of rocks by PXRF with those acquired by conventional laboratory analysis. Results of rocks and rock powder samples by PXRF were also compared, and the effect of different detection time on the results of rock powder samples by PXRF was also investigated. A method has been proposed which will reduce the exploration cost and improve work efficiency without a significant loss of analytical precision. The results shows that analytical results of most of the detectable elements is not reliable when analyzing rocks directly by PXRF, especially for the frequently used trace elements (Cu, Pb, Zn, As and Ni). However, PXRF can be used to analyze rock powders and the analytical result is acceptable. There is a difference between the correlation of various elements between rock and rock powder samples by PXRF. Therefore, it is possible to determine whether the rock sample needs to be ground for sample preparation according to the target elements of interest in practical work. The detection time has no obvious effect on the elemental content results. Whether a specific element can be obtained in a relatively short time, further detection time is unnecessary.

Surfaces weathering of rocks in which mineral materials may be similar to or quite different from the minerals in the underlying parent rock completely control the reflectance spectra of the terrain. Our study of typical weathered and fresh rock samples from the Xiemisitai metallogenic belt, Western Junggar region, Xinjiang, found that weathering results in the formation of new materials that cause differences in the spectral features of fresh and weathered rock surfaces. Alterations induce variations in spectrum brightness, presence and intensity of characteristic absorption features, and spectral slope. Spectral differences between weathered and fresh rock surfaces are small for rhyolite, granite, and tuffaceous sandstone, but large for andesite, basalt, and diorite. Spectral changes in the 350–1000 nm wavelength region are attributed to alteration of iron oxides by atmospheric processes or secondary alteration of iron-rich minerals. Spectral features between 1000–2500 nm are caused by O–H vibrations, with features at 2200–2500 nm solely attributed to hydroxyl groups. The strongest Al–OH bands appear near 2200 nm, while Mg–OH bands are found near 2300 nm and 2350 nm. Results from this study can be used to better characterize and discriminate lithological units and potential mineral zones using hyperspectral and multispectral remote sensing techniques.

The eco-environmental risk assessment system of highway, based on the Analytic Hierarchy Process and specialists' ideas, is set up. To give prominence to the spatial attribute of each assessment index the weight values were determined by the magnitude of special range that was influenced by assessment index. By translating relative maps using GIS the influenced range of environmental factors, the area occupied, was gotten. The lengths impacted by disaster factors were measured by the route reconnaissance and design. The results of applying the assessment system to evaluate the eco-environmental risk assessment of G315 Yitunbulake-Qiemo section of Xinjiang show that the eco-environmental risk indices of the road region, eco-environmental risk assessment (ERA) were between 1.55-3.23. According to the heterogeneity of the ecoenvironmental vulnerability indices (sigma x(i)w(i)) and disasters indices (sigma y(i)w(j)) included in ERA, 4 risk ranks to assess the eight units of landscape ecology in the Yitunbulake-Qiemo section of G315 highway were carried out, which factually reflects the characteristics of the eco-environmental risk for the highway.