电感耦合等离子体质谱法测定土壤重金属普查样品中铬铜镉铅的关键环节研究

土壤重金属普查样品的基质复杂、数量大、待测元素性质各异,现有土壤中重金属的电感耦合等离子体质谱法(ICP-MS)存在样品分解方式不适宜、上机测定参数需优化等问题。本文采用电热板-酸分解样品,研究了ICP-MS测定土壤中Cr、Cu、Cd、Pb四种主要重金属在分解和上机测定中的关键环节。结果表明采用"硝酸+盐酸+氢氟酸+50%硫酸"体系,样品分解完全且待测元素无损失,并使用工作曲线校准仪器可降低基体效应;优选质谱测定同位素和采用碰撞反应池或数学校正可减少质谱干扰;质谱积分驻留时间和重复次数对痕量元素Cd的精密度有较大影响,宜设为0.4 s和3次。本方法对Cr、Cu、Cd、Pb的检出限分别为1.74、0.89、0.015、0.08μg/g,为普查工作提供了一种可借鉴的分析手段。     

Are cladoceran fossils in lake sediment samples a biased reflection of the communities from which they are derived?

A study on the taphonomy of Cladocera was carried out in a small (9 ha), oligotrophic mountain loch, Loch Coire Fionnaraich (LCFR) in northwest Scotland. Four approaches were used. First, the fossil assemblage of Cladocera in the core-top sample taken from the deepest basin (14 m) of the loch were compared with the fossil assemblages of Cladocera in surface sediments along eight depth transects with samples taken at 2, 5, 8 and 11 m, respectively. The results of the deposition of remains of individual Cladocera and of the PCA ordination showed that littoral Cladocera were dominant in the 2 m-depth samples, while the planktonic Cladocera dominated the deeper water (8, 11 m and core-top) samples. Second, the fossil assemblages of Cladocera in the core-top sample were compared with the assemblages in a sediment trap sample. The core-top sample showed a better representation of the cladoceran taxa present in the loch than the trap sample, but rare taxa were missing in the core sample. Third, the fossil assemblages of Cladocera in the core sample were compared with the contemporary assemblages in the source samples derived from seasonal sampling across all habitats (macrophyte, sand, boulder) over 2 years. Only a small proportion of Cladocera in the source samples was represented by the fossil assemblages in the core sample. Finally, ‘integrated’ approach samples (spatial, trap, source and core together) were compared using PCA. The Cladocera in the core-top sample were closely related to the trap and surface sediment samples, but weakly related to the source samples. The overall results indicate that biases may occur whilst reconstructing the past environmental change based on the fossil assemblages of Cladocera in the core sample taken from the deepest basin of the lake.     

碳酸钠浸取-紫外荧光光谱法测定地质样品中痕量铀

地质样品经HF—HNO3-HCl-HClO4溶解后，用50g／L碳酸钠溶液浸取分离，采用紫外荧光光谱法直接测定上清液的铀含量。浸取时间选择30min，Fe、Zn、Ca、Co、Ni、Cu和Mn等元素留在残渣中不产生干扰。方法精密度（RSD，n=12）为3．37％～7．06％，检出限为0．009μg／g（进样量100μL）。方法参加区域地球化学考核样品的测试，合格率100％，适用于土壤、水系沉积物、岩石及Fe、Zn、Ca、Co、M、Cu和Mn含量较高的样品中痕量铀的测定。     

磁参数测量装置的设计原理及操作

利用高精度磁测仪器,采用高斯第一位置测定磁性标本。使标本架上倾斜板面垂直于地磁场T_0的磁力线,使标本的中心与探头中心等高,根据标本磁性的强弱,可调节标本与探头的距离(不小于15 cm)。根据上述原理设计并制造了磁参数测量装置.与MP—4高精度磁力仪配套使用,大大提高了磁性参数的准确度。     

Elicited Data and Incorporation of Expert Opinion for Statistical Inference in Spatial Studies

Spatial data are often sparse by nature. However, in many instances, information may exist in the form of soft data, such as expert opinion. Scientists in the field often have a good understanding of the phenomenon under study and may be able to provide valuable information on its likely behavior. It is thus useful to have a sensible mechanism that incorporates expert opinion in inference. The Bayesian paradigm suffers from an inherent subjectivity that is unacceptable to many scientists. Aside from this philosophical problem, elicitation of prior distributions is a difficult task. Moreover, an intentionally misleading expert can have substantial influence on Bayesian inference. In our experience, eliciting data is much more natural to the experts than eliciting prior distributions on the parameters of a probability model that is a purely statistical construct. In this paper we elicit data, i.e., guess values for the realization of the process, from the experts. Utilizing a hierarchical modeling framework, we combine elicited data and actual observed data for inferential purposes. A distinguishing feature of this approach is that even an intentionally misleading expert proves to be useful. Theoretical results and simulations illustrate that incorporating expert opinion via elicited data substantially improves the estimation, prediction, and design aspects of statistical inference for spatial data.     

Comparative performance of convolutional neural network,weighted and conventional support vector machine and random forest for classifying tree species using hyperspectral and photogrammetric data

ABSTRACT

The classification of tree species can significantly benefit from high spatial and spectral information acquired by unmanned aerial vehicles (UAVs) associated with advanced classification methods. This study investigated the following topics concerning the classification of 16 tree species in two subtropical forest fragments of Southern Brazil: i) the potential integration of UAV-borne hyperspectral images with 3D information derived from their photogrammetric point cloud (PPC); ii) the performance of two machine learning methods (support vector machine – SVM and random forest – RF) when employing different datasets at a pixel and individual tree crown (ITC) levels; iii) the potential of two methods for dealing with the imbalanced sample set problem: a new weighted SVM (wSVM) approach, which attributes different weights to each sample and class, and a deep learning classifier (convolutional neural network – CNN), associated with a previous step to balance the sample set; and finally, iv) the potential of this last classifier for tree species classification as compared to the above mentioned machine learning methods. Results showed that the inclusion of the PPC features to the hyperspectral data provided a great accuracy increase in tree species classification results when conventional machine learning methods were applied, between 13 and 17% depending on the classifier and the study area characteristics. When using the PPC features and the canopy height model (CHM), associated with the majority vote (MV) rule, the SVM, wSVM and RF classifiers reached accuracies similar to the CNN, which outperformed these classifiers for both areas when considering the pixel-based classifications (overall accuracy of 84.4% in Area 1, and 74.95% in Area 2). The CNN was between 22% and 26% more accurate than the SVM and RF when only the hyperspectral bands were employed. The wSVM provided a slight increase in accuracy not only for some lesser represented classes, but also some major classes in Area 2. While conventional machine learning methods are faster, they demonstrated to be less stable to changes in datasets, depending on prior segmentation and hand-engineered features to reach similar accuracies to those attained by the CNN. To date, CNNs have been barely explored for the classification of tree species, and CNN-based classifications in the literature have not dealt with hyperspectral data specifically focusing on tropical environments. This paper thus presents innovative strategies for classifying tree species in subtropical forest areas at a refined legend level, integrating UAV-borne 2D hyperspectral and 3D photogrammetric data and relying on both deep and conventional machine learning approaches.     

悬浮液进样石墨炉原子吸收光谱法测定地质样品中痕量镓和铊

对固体悬浮液直接进样及石墨炉原子吸收光谱法测定Ｇａ和Ｔｌ进行了研究，拟定了以琼脂为悬浮剂，Ｎｉ（对Ｇａ）和Ｖ（对Ｔｌ）为基体改进剂，标准溶液进行校准的、灵敏、快速、准确测定Ｇａ和Ｔｌ的方法。在选定条件下，测得Ｇａ和Ｔｌ的特征量（１％吸收）分别为２．８×１０￣（－１１）ｇ和４．４×１０￣（－１１）ｇ，检出限（３σ）分别为２４ｐｇ和２８ｐｇ，相对标准偏差分别为６．３％（含Ｇａ６．４ｇ／ｇ，ｎ＝１１）和４．８％（含Ｔｌ１．０ｇ／ｇ，ｎ＝１３）。方法适用于地质样品中低至０．１ｇ／ｇＧａ和Ｔｌ的测定。     

1992 compilation of analytical data for rare-earth elements, scandium, yttrium, zirconium and hafnium in twenty-six GSJ reference samples

Abstract: Analytical data for fourteen rare-earth elements, scandium, yttrium, zirconium and hafnium, received by May 1992, have been compiled on twenty-six GSJ (Geological Survey of Japan) reference samples. Seventeen of them are 'Igneous rock series' and nine are "Sedimentary rock series". The reported data including personal communication were evaluated under the consideration on analytical methods and geochemical evidences. No significant difference has been observed between the values obtained by the different analytical methods. Based on the selected available data, 1992 compilation values were tabulated.     

用于实验室间比对的铀矿地质样品制备及铀含量均匀性检验

为进行核地质分析实验室间比对,采集了南方硬岩和北方砂岩铀矿石样本,按照标准物质制备方法制备了2种铀矿地质样品(编号分别为Albriug-09、Albriug-10),其粒度为小于60μm,铀含量分别为497mg·kg~(-1)和935mg·kg~(-1)。对用于样品均匀性检验的时间分辨紫外脉冲光诱导荧光分析法(TRLIF)进行了分析性能有效性评价。TRLIF测定铀的检出限为0.09mg·kg~(-1),测定范围为0.27~1250mg·kg~(-1);当铀含量范围在4.6~514mg·kg~(-1)之间时,其精密度(RSD%)在9.6%~3.43%之间;当铀含量范围在1.7~514mg·kg~(-1)之间时,其正确度(RE%)在±16.3%~±0.2%之间。应用TRLIF对制备的样品进行了铀含量均匀性测试并采用单因子方差分析法进行了统计检验,其统计量F分别为0.61和0.76,均小于临界值F0.05(9,10)(3.02),表明制备的样品均匀性好,符合实验室间比对测定要求。     

MASNUM-WAM海浪模式集合Kalman滤波同化研究——Ⅱ.集合样本对同化效果的影响

背景误差相关结构的确定是影响海浪同化效果的关键因素之一。集合Kalman滤波是一种较为成熟的同化方法,其可以对背景误差进行实时更新和动态估计,现已广泛应用于海洋和大气领域的研究。本文基于MASNUM-WAM海浪模式,分别采用静态样本集合Kalman滤波和EAKF方法,针对2014年全球海域开展海浪数据同化实验,同化资料为Jason-2卫星高度计数据,利用Saral卫星高度计资料对同化实验结果进行检验。结果表明,两组同化方案均有效提高了海浪模式的模拟水平,EAKF方案在风场变化较大的西风带区域表现显著优于静态样本集合Kalman滤波方案,但总体上两者相差不大。综合考虑计算成本和同化效果,静态样本集合Kalman滤波方案更适用于海浪业务化预报。