山东省威海市区域地质背景下土壤Cd的地球化学特征

为获取威海市区域地质背景下土壤重金属元素Cd的地球化学特征,以区域表层和深层土壤Cd为研究对象进行讨论。统计分析得出Cd区域土壤背景值为0.090×10-6,基准值为0.056×10-6。区域Cd的土壤环境质量较好,全区仅3.42%的表层土壤、1.17%的深层土壤超出国家土壤质量一级标准限值,其中仅1.67%的表层土壤出现污染,超出国家土壤二级标准。研究区土壤Cd的物源主要表现为自然源和人为源两大类。自然源主导区占54.68%,自然属性特征明显,表层和深层土壤Cd的相关性较好,且Cd与其他重金属元素间具有较好的地球化学组合特征;表层土壤Cd的分布主要受母质影响,在丘陵区土壤Cd含量受中生代中酸性侵入岩的分布影响较大,而平原区受第四系沉积物影响较大,其中陆源为主的平原区土壤Cd相对含量较高,与海水有关的平原区Cd含量普遍偏低。人为源主导区占45.32%,自然属性较差,受人为源影响较大,与工矿点等人为活动密集区分布较吻合。     

http://www.sciencedirect.com/science/article/pii/S1674987113000832

The Sinus Iridum region, the first choice for China＇s＂Lunar Exploration Project＂is located at the center of the lunar LQ-4 area and is the site of Chang＇e-3 （CE-3）＇s soft landing. To make the scientific exploration of Chang＇e-3 more targeted and scientific, and to obtain a better macro-level understanding of the geotectonic environment of the Sinus Iridum region, the tectonic elements in LQ-4 region have been studied and the typical structures were analyzed statistically using data from CE-1, Clementine, LRO and Lunar Prospector missions. Also, the mineral components and periods of mare basalt activities in the study area have been ascertained. The present study divides the tectonic units and establishes the major tectonic events and sequence of evolution in the study area based on morphology, mineral constituents, and tectonic element distribution.     

基于混合单元的三峡库区港口建设场地适宜性评价

长江是我国最大的内陆航运交通要道,三峡大坝建成后,三峡库区长约360 km的河道水位大幅抬升,原航道两岸港口码头均被淹没。在三峡库区现状岸线环境及水库水位动态调蓄条件下,开发拓展新的岸线港口码头的适宜建设区域,具有重要实际意义。基于混合评价单元,选取了地形地貌、区域稳定性、工程地质条件、交通区位条件和生态敏感性5大类因素共14个评价因子,采用改进的综合指数评价模型,对三峡库区宜昌至万州段干流岸线港口建设场地进行了适宜性评价研究。评价结果显示: (1)适宜区占13.81%,较适宜区占25.80%,较不适宜区占31.38%,不适宜区占29.01%; (2)北岸适宜区主要分布在夷陵区乐天溪镇和太平溪镇,云阳县双江镇和万州城区上、下游一带; 南岸适宜区主要分布在秭归县茅坪镇,奉节县城南岸和万州城区南岸上、下游一带。研究评价结果对今后长江宜昌至万州段干流岸线港口资源开发选址具有一定的科技指导意义。     

Different units of measurement of carotenoids estimation in cotton using hyperspectral indices and partial least square regression

This paper examines the use of canopy reflectance for different units of measurements of carotenoids estimation. Field spectral measurements were collected over cotton in different intensive field campaigns organized during the growing seasons of 2010 and 2011. Three units of measurement were evaluated carotenoids expressed as a mass per unit soil surface area (g/m²), a mass per unit leaf area (μg/cm²), and a mass per unit fresh leaf weight (mg/g), respectively. Four methods were compared to retrieve amount of carotenoids: stepwise multiple linear regression (SMLR), published spectral indices, band combination indices, and partial least square regression (PLSR). Results show that maximum sensitivity of reflectance to variation in different units of measurement of carotenoids was found in the green region at 515–550 nm, and at 715 nm and 750 nm regions in the far-red wavelengths. The predictive accuracies of Car (g/m²), Car (μg/cm²) and Car (mg/g) were tested on a validation data set and the results show that the highest R² values between estimations and observations were 0.468 for Car (g/m²), 0.563 for Car (μg/cm²), and 0.456 for Car (mg/g), with relative root mean square error (RMSE%, RMSE/mean) of 48.72%, 22.07% and 21.07%, respectively. Compared to Car (g/m²) and Car (mg/g), the model performance indices for Car (μg/cm²) show a high degree of consistency among the R² values and RMSE% and MAE% values. Further comparison were performed among the estimation accuracies of different unit carotenoids and among the different approaches used in the study by a paired-t-test. The results indicate that although the best estimation results for Car (μg/cm²) and Car (mg/g) were both obtained based on PLSR, they can be estimated by all four adopted methods without significant differences (P > 0.1). Whereas for Car (g/m²), the best estimation results were obtained based on published vegetation indices CIred-edge, which were significantly better than the estimation results based on SMLR (P < 0.000). In summary, the results of this study show that even the carotenoids expressed on concentration (mg/g) or content (μg/cm²) basis at leaf level can be estimated with the same prediction accuracies to the carotenoids expressed as a mass per unit surface area (g/m²) at canopy level using reflectance measurement at canopy level.