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Geochemical background is the basis of anomaly evaluation in mineral exploration and environmental investigation. However, the background value obtained from secondary media is inevitably affected by weathering, transportation, and sorting, which leads to secondary depletion or enrichment of chemical elements. This problem can be avoided by the geochemical mapping of the exposed crust. In this paper, more than 38,000 samples of rocks data were collected, and an original method was presented for the first time to produce the cobalt geochemical map of exposed crust across the whole of China. Using a geological map grid of 1:200,000 as the basic calculation unit, the area and content of rock types in each unit were calculated, and then the grid was assigned using the area-weighted average method. Based on this, an geochemical contour map was drawn in ArcGIS. The results show that the median of the exposed crust is 9.74 mg/kg, which is lower than that of the whole crust and soils. This value is explained by the vertical heterogeneity of the crust and the secondary enrichment, respectively. Co anomalies and anomalous centers of exposed crust are distributed in southwest, north, and northwest China, where they are primarily related to Permian Emeishan Large Igneous Province (ELIP), Pacific plate, and the big mantle wedge (BMW), Archean metamorphic basic rocks, and ophiolite belts.

Rapid detection of pathogenic bacteria within a few minutes is the key to control infectious disease. However, rapid detection of pathogenic bacteria in clinical samples is quite a challenging task due to the complex matrix, as well as the low abundance of bacteria in real samples. Herein, we employ a label-free single-particle imaging approach to address this challenge. By tracking the scattering intensity variation of single particles in free solution, the morphological heterogeneity can be well identified with particle size smaller than the diffraction limit, facilitating the morphological identification of single bacteria from a complex matrix in a label-free manner. Furthermore, the manipulation of convection in free solution enables the rapid screening of low-abundance bacteria in a small field of view, which significantly improves the sensitivity of single-particle detection. As a proof of concept demonstration, we are able to differentiate the group B streptococci (GBS)–positive samples within 10 min from vaginal swabs without using any biological reagents. This is the most rapid and low-cost method to the best of our knowledge. We believe that such a single-particle imaging approach will find wider applications in clinical diagnosis and disease control due to its high sensitivity, rapidity, simplicity, and low cost.

Semantic segmentation is a popular research topic in computer vision, and many efforts have been made on it with impressive results. In this paper, we intend to search an optimal network structure that can run in real-time for this problem. Towards this goal, we jointly search the depth, channel, dilation rate and feature spatial resolution, which results in a search space consisting of about 2.78*10^324 possible choices. To handle such a large search space, we leverage differential architecture search methods. However, the architecture parameters searched using existing differential methods need to be discretized, which causes the discretization gap between the architecture parameters found by the differential methods and their discretized version as the final solution for the architecture search. Hence, we relieve the problem of discretization gap from the innovative perspective of solution space regularization. Specifically, a novel Solution Space Regularization (SSR) loss is first proposed to effectively encourage the supernet to converge to its discrete one. Then, a new Hierarchical and Progressive Solution Space Shrinking method is presented to further achieve high efficiency of searching. In addition, we theoretically show that the optimization of SSR loss is equivalent to the L_0-norm regularization, which accounts for the improved search-evaluation gap. Comprehensive experiments show that the proposed search scheme can efficiently find an optimal network structure that yields an extremely fast speed (175 FPS) of segmentation with a small model size (1 M) while maintaining comparable accuracy.

It is anticipated that future networks support network functions, such as firewalls, load balancers and intrusion prevention systems in a fully automated, flexible, and efficient manner. In cloud computing environments, network functions virtualization (NFV) aims to reduce cost and simplify operations of such network services through the virtualization technologies. To enforce network policies in NFV-based cloud environments, network services are composed of virtualized network functions (VNFs) that are chained together as service function chains (SFCs). All network traffic matching a policy must traverse network functions in the chain in a sequence to comply with it. While SFC has drawn considerable attention, relatively little has been given to dynamic auto-scaling of VNF resources in the service chain. Moreover, most of the existing approaches focus only on allocating computing and network resources to VNFs without considering the quality of service requirements of the service chain such as end-to-end latency. Therefore, in this paper, we define a unified framework for building elastic service chains. We propose a dynamic auto-scaling algorithm called ElasticSFC to minimize the cost while meeting the end-to-end latency of the service chain. The experimental results show that our proposed algorithm can reduce the cost of SFC deployment and SLA violation significantly.

The concentrations, variations and distribution of molybdenum (Mo) in catchment outlet sediments from China were studied in this contribution based on the results of recently-finished, continental-scale, China Geochemical Baselines (CGB) project. The data from CGB were combined with other published data-sets with the purpose of understanding the spatial distribution of Mo in China and, in addition, relating the observed geochemical features to natural and/or anthropogenic processes. This geochemical mapping with a density of ca. 3000 km2 per site involves catchment sediments in two horizons at each site: a top sample (TOS) at a depth of 0–25 cm and a bottom sample (BOS) at a depth greater than 100 cm. In total, 3382 TOS and 3380 BOS samples were collected. The analytical results show that the concentrations of Mo in the catchment outlet sediments of China range from 0.07 to 57.86 μg/g for the TOS and 0.09–32.43 μg/g for the BOS. The median and mean values of the TOS data are 0.73 and 1.01 μg/g respectively, and those of the BOS data are 0.71 and 0.94 μg/g. The variations of both the TOS and BOS are high at this continental scale. The Mo concentrations vary greatly from one geological and/or geographic unit to another. For instance, from the Cathaysia Block with the TOS and BOS median values of 1.22 and 1.46 μg/g to the North China craton with those of approximately 0.61 and 0.58 μg/g. Various processes govern the spatial distribution of Mo in the catchment sediments of China but regional-scale natural sources (e.g. lithologies) predominate. Sedimentary sandstones and carbonate rocks, alluvium in plain terrain and aeolian materials (e.g. loess) account for the extensive low concentrations ( 1.2 μg/g) in the Yangtze and Cathaysia blocks. Simultaneously, some local mineralized areas can be also identified at this scale because the sampled sites just happened to fall on sediments derived from mineralized rocks or anthropogenic addition by mining activities. Furthermore, the TOS share the similar patterns with the BOS indicating relatively stable inheritance and no apparent extensive secondary enrichment or depletion in the catchment sediments of China. The CGB demonstrates that a series of baseline levels of Mo are needed at different regions and scales and ultra-low density mapping can still deliver robust geochemical patterns.

The China Geochemical Baseline (CGB) project (2008–2012) obtained comparable, continental-scale, geochemical data including lead (Pb) concentrations in the pedosphere. Some 3382 topsoil samples were collected at depths of 0–25 cm. Lead concentrations in soil were determined by inductively coupled plasma mass spectrometry (ICP-MS) using 4-acid extraction under strict quality control. The median Pb concentration throughout China was 22.1 mg/kg and varied between 15.6 and 30 mg/kg according to the morphological landscape. There were predominantly low Pb concentrations in northwestern China and much higher concentrations in southeastern China. This is mainly due to differences in climate and landscape, geology (parent rocks) and the presence of mineralization. The arid and semi-arid landforms in northern and northwestern China had low Pb concentrations. High Pb concentrations were most often linked to geology (occurrence of granite) and climate. The most important Pb anomalies occurred in the southern and southwestern parts of China in areas with known polymetallic mineralization. Such anomalies may be further enhanced by mining or smelting activities.

Based on the data of China Geochemical Baselines project, geostatistical analysis was used to investigate the spatial variations in soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP) contents across the “Three Rivers” regions of southwest China, and the factors affecting them were analyzed by the redundancy analysis (RDA) and Person's correlation. Results showed that, the average content of SOC, TN and TP in the study area were 7.20 g/kg, 0.84 g/kg and 0.55 g/kg, respectively. The SOC and TN contents showed an obvious enrichment characteristic with great spatial variability, while TP content was stable on regional scale. The SOC, TN and TP contents decreased with elevation increase in the northern frigid highland, but showed an opposite character in the southern tropical & subtropical, which actually reflected the control of temperature on them. Combined with that there were higher SOC, TN and TP contents in subalpine meadow soil and red earth–yellow earth of the middle latitude zone, these suggested that the suitable temperature was conducive to the accumulation of soil nutrients. The weak positive correlation between population density and soil nutrients, together with high level of soil nutrients in the vicinity of large cities, demonstrated that human activities had significantly increased the soil nutrients contents in the study area. The RDA results showed that soil nutrients in the northern frigid highland were mainly controlled by the environmental factors dominated by temperature and soil structural factors dominated by parent materials with the total explanatory power as high as 75.87%, while in the southern tropical & subtropical mainly by the environmental factors dominated by chemical and biological weathering and the biological factors dominated by vegetation with the total explanatory power as high as 88.53%. The above factors superimposing a certain degree of human activities converged to cause that the SOC and TP contents in the south were higher than that in the north while the TN content was lower than that in the north.

The uniform sampling strategy is widely used in time series segmentation, but unable to handle time warping problem or preserve the latent patterns in time series. To solve these shortcomings, a brand new data-driven segmentation method is proposed, which could segment time series into subsequences with different lengths adaptively. Then a time series classification method under the bag-of-word framework is proposed. Two kinds of mutually complementary features, i.e., interval feature and normal cloud model feature, are extracted from subsequences. And then time series are encoded into Fisher Vectors. Finally, a linear support vector machine is used as the classifier. Experiments on 43 UCR datasets show that the newly proposed method has promising classification accuracies comparing with state of the art baselines. Moreover, due to the data-driven segmentation and timesaving local feature extraction, the method has low time complexity, which is also demonstrated in the experiments.

This chapter reviews the state‐of‐the‐art literature on network slicing in 5G, edge/fog, and cloud computing. It identifies the spectrum challenges and obstacles that must be addressed to achieve the ultimate realization of the concept. The chapter provides a brief introduction of 5G, edge/fog, and clouds and their interplay. It outlines the 5G vision for network slicing and identifies a generic framework for 5G network slicing. The chapter reviews research and projects related to network slicing in cloud computing context, while focusing on software‐defined networking (SDN) and network function virtualization (NFV) technologies. It explores network slicing advances in emerging fog and edge cloud computing, which leads us to identify the key unresolved challenges of network slicing within these platforms. The chapter discusses the gaps and trends toward the realization of network slicing vision in fog and edge and software‐defined cloud computing.

With the increasing demands for Cr following China's new infrastructure projects, ascertaining the concentration and distribution of Cr is urgent and meaningful for prospecting Cr deposit and relieving Cr shortage in China. The China Geochemical Baselines (CGB) project provides continental-scale Cr data from 3382 top and 3380 deep catchment sediment/alluvial soil samples and 11605 rock samples. Statistical analysis shows that the median Cr value (baseline) of China is 50 μg/g. The ultramafic and mafic rocks respectively have median Cr values of 1609 and 129 μg/g based on 52 and 551 samples, much higher than those in intermediate rocks (47 μg/g on 991 samples) and felsic rocks (7.0 μg/g on 2558 samples). Parent rock types, especially ultramafic–mafic rocks, predominantly governed the distribution of Cr enrichments in the catchment sediment/alluvial soil of China. High Cr concentrations (>75th percentile) are closely associated with lithology and Cr mineralization, presented in regions where ultramafic rocks, basalts and black shales widely outcropped, such as Changbaishan basalts in Northeast China, Emeishan basalts in South China and black shales along the middle-lower Yangtze River. After eliminating the disturbances of regional background values, the chromium geochemical anomalous centers (>97.5th percentile) are well consistent with the distribution of two-types Cr deposits, especially with the widely-distributed ophiolitic podiform-type Cr metallogenic belts in China. The nation-wide Cr concentration and distribution would provide giant basic data for further exploration prospecting, environmental and hydrogeological studies.

In China, selenium (Se) deficiency and exceeded Se in soils has resulted in serious endemic Se-related diseases. A nationwide dataset of Se is critically needed for prevention and response of Se-related diseases. The China Geochemical Baselines (CGB) project applied standardized methods to create a geochemical dataset that recorded the concentration of 76 elements, including selenium, in the pedosphere of mainland China. A total of 3382 samples were collected from the A-horizon of soil at depths of 0–25 cm. The concentration of Se was determined by hydride generation atomic fluorescence spectrometry. Strategies for quality control included field duplicates, standard reference materials, and analytical replicates. Measured Se concentrations ranged from 0.010 to 16.240 mg·kg−1 with a median of 0.171 mg·kg−1. The Se concentrations showed large variations across regions. Low-Se soils in China occurred in the Tibet, the southern Xinjiang, and some areas of Inner Mongolia, Shanxi and Qinghai provinces. High-Se soils were found in contiguous areas of Hunan, Guizhou, Guangxi, Guangdong, and Chongqing, and sporadically distributed in Xinjiang, Gansu, Sichuan, Yunnan and other provinces. The distribution is controlled by a combination of the underlying geology, local factors such as the geomorphology and soil type, and human activity. Selenium is essential to human and other animal health in trace amounts but is harmful in excess. Keshan disease (KSD) and Kashin-Beck disease (KBD), which occur in the low-Se geological background, are attributed to Se deficiencies, and endemic selenosis is observed in areas with high-Se soils. This enhanced understanding of Se occurrence could help to improve future Se-related disease management in China.

The China Geochemical Baseline (CGB) project (2008–2013) obtained harmonious geochemical baseline data covering all of China, and provided reliable data for 76 elements including F from 3382 top and 3380 deep soil samples. This paper described the statistical and spatial distribution of fluorine (F) throughout China. F concentration ranged from 63 to 9364 μg/g in top soil and ˂ 52 to 9346 μg/g in deep soil. The median concentration of F, which is deemed to be the geochemical baseline in top and deep samples are 488 μg/g and 456 μg/g respectively. The mean concentration of F in top and deep soil samples are 506 μg/g and 482 μg/g respectively. Low F concentrations areas are linearly distributed from northeastern to southwestern China, and high F concentrations areas are distributed in northwestern, northern China and karst area. The anomalous centres of F are exactly correlated with F mineralization. This distribution pattern of F in pedosphere is influenced by minealizations, lithology background, geomorphic landscapes, drainage basins and soil types. Generally, fluorine-bearing rocks provide a reliable source of F in pedosphere, and the geochemical behavior of F determines its ability to migrate in different media.

Desert terrains are widespread in northwestern and northern China, and these areas present particular challenges for exploration. In recent years, partial extraction techniques have been proven to be effective in the search for concealed deposits in arid desert terrains in some cases. However, we still lack an understanding of the dispersion patterns of ore-forming elements in regolith. In this study, air reverse circulation drillings were used to create three-dimensional (3D) distribution patterns of elements in regolith over the Jinwozi gold deposit in China, which is covered by tens of metres of regolith, in order to trace the migration of elements and to understand the dispersion mechanisms. The 3D distribution maps of elements show that (1) coherent anomalies occur at different depths of transported cover over the ore body, (2) Au tends to be enriched in the top and bottom horizons and depleted in the middle horizon in the vertical direction, (3) the anomalous distribution of Au at the bottom is restricted to places at the interface of sediments and bedrock, and (4) the anomaly in the bottom sediments is confined to a width of tens of metres, whereas that in top soils is much wider and can extend up to several kilometres. In addition, close positive correlations were found between the As, Hg, and Au distributions.

Support vector machine (SVM) has been gaining popularity in classification, for its structural risk minimization principle and the usage of kernel function. Traditional SVM as well as many improved algorithms treats all features in training data with equal weights, even though different features may have different impacts on the final classification. To overcome this issue, a new method which combined feature weighting with classification was proposed in this paper. Compared with other similar research, the major advantage of this method is that it brings no change to the algorithm structure. To further improve the performance of SVM classifier, a type-2 fuzzy logic based ensemble SVM was proposed. Type-2 fuzzy logic outperforms type-1 fuzzy logic greatly in terms of handling uncertainty in the ensemble process. Experiment results validated the effectiveness of proposed methods.

With the ever-increasing demand for lithium (Li), understanding its concentration and distribution in the pedosphere is essential for alleviating the Li shortage in China. The China Geochemical Baselines (CGB) project, which commenced in 2008, provided comparable global-scale geochemical Li data. A total of 3394 top (0–25 cm) and 3394 deep (below 100 cm) catchment sediment samples were collected. The range of Li concentrations in the top and deep samples were 5.37–400 mg·kg−1 with a median value of 30.0 mg·kg−1, and 5.27–400 mg·kg−1 with a median value of 28.6 mg·kg−1, respectively. The Li concentrations in the top and deep catchment sediments of China showed large variations across regions but only mild variability across profiles. Parent rock, especially granite, predominantly governed the Li distribution in the catchment sediments of China. Landscapes, mineralization and weathering played regional-scale roles. Macroscopically, the low background area ( P75) was mainly distributed in the Altay, South China, Sanjiang and Himalayan orogenic belts, and the Yangtze craton. Based on a cumulative frequency of the 85th percentile, a total of 16 geochemical anomalies were identified, and eight important prospective Li resources were selected where many world-renowned Li deposits were formed.

Endemic fluorosis is a worldwide health concern, which may be related to enhanced fluorine (F) in soil via food-to-mouth or respiratory system activity exposure to humans. Until now, continental scale data on F in the pedosphere is still deficient in China. In this paper, the distribution of enhanced F and the factors influencing this distribution are investigated using data of the China Geochemical Baseline project. The median values of F were 488 and 456 ppm in top and deep soil, respectively. In terms of spatial distribution, F in topsoil of China presents coastal and mountain low-value zones, central-south plain and southwest Karst high-value zones, and a northwest arid basin high-value zone. Thirty-three F geochemical provinces were delineated with F values of >700 ppm, which were predominately associated with geological background, intensive evaporation, and human activities. The formation of a high F anomaly area from the central–southern part to the southwestern part of China was related to the calcium-enriched soil and the superimposed coupling of multiple factors including the high F geological background, such as F-enriched organic matter strata, F-enriched phosphorus-bearing strata, F-enriched gypsum salt strata and mineralization processes. The high F anomaly area in northwestern China was not only related to the high geological background, but also associated with the secondary enrichment caused by evaporation.

The downlink coordinated multiple points transmission (CoMP) has addressed a greet number of attentions in recent years. However, an open problem is still remaining that the QoS requirements from the edge users are not guaranteed to be fully achieved due to the low data rate and the high frequency handoff between cells. In this paper, we discuss the spectrum allocation in the downlink CoMP with incomplete QoS requirements. Based on a real world dataset, China Family Panel Studies (CFPS) dataset, we use a number of learning algorithms to anticipate the users’ QoS requirements based on their profiles before the CoMP implementation. Based on the learning results, we further propose a learning embedded three-side matching to do the spectrum allocation. Simulation results show that the human behavior based learning framework can achieve a larger coverage ratio than other learning approaches. Simulation results also show that the learning embedded three-side matching can obtain a larger coverage ratio than the naive matching and random allocation.

Linear model predictive control (MPC) is a widely-used control strategy in chemical processes. Its extension to nonlinear MPC (NMPC) has drawn increasing attention since many process systems are inherently nonlinear. When implementing the NMPC based on a nonlinear predictive model, a nonlinear dynamic optimization problem must be calculated. For the sake of solving this optimization problem efficiently, a latent-variable dynamic optimization approach is proposed. Two kinds of constraint formulations, original variable constraint and Hotelling T2 statistic constraint, are also discussed. The proposed method is illustrated in a pH neutralization process. The results demonstrate that the latent-variable dynamic optimization based the NMPC strategy is efficient and has good control performance.

For chemical processes with a wide range of operating conditions, a switched multiple model predictive control (MMPC) strategy in the partial least squares (PLS) framework is proposed. Interactive MIMO systems can be automatically decoupled with inputs and outputs paired in their dynamic PLS models. Based on the identified PLS models, companion controllers are designed to form the MMPC strategy. A novel switching criterion based on output statistics is proposed to assure each model/control pair works in its operating region spanned by the identification data sets. The control results of disturbance rejection and setpoint tracking in a two-phase chemical reactor process are presented to demonstrate the capability and effectiveness of the proposed MMPC strategy.

One measurement-based dynamic optimization scheme can achieve optimality under uncertainties by tracking the necessary condition of optimality (NCO-tracking), with a basic assumption that the solution model remains invariant in the presence of all kinds of uncertainties. This assumption is not satisfied in some cases and the standard NCO-tracking scheme is infeasible. In this paper, a novel two-level NCO-tracking scheme is proposed to deal with this problem. A heuristic criterion is given for triggering outer level compensation procedure to update the solution model once any change is detected via online measurement and estimation. The standard NCO-tracking process is carried out at the inner level based on the updated solution model. The proposed approach is illustrated via a bioreactor in penicillin fermentation process.

In this paper, a generalized predictive control (GPC) scheme under a dynamic partial least squares (PLS) framework is proposed. At the modeling stage, a model predictive control relevant identification (MRI) approach is used to improve the identification of the model. Within PLS framework, the MIMO system can be automatically decomposed into several SISO subsystems in the latent space. For each subsystem, MRI is implemented and GPC is designed independently. With the advantage of MRI and PLS framework, fewer parameters are needed to be estimated in the identification stage, nonsquare and ill-conditioned system can be handled naturally, control parameters tuning is easier and better control performance can be obtained. Furthermore, the computing time of control action which is very crucial for GPC on-line application decreases since each GPC is running in SISO subsystem in parallel. The results of two simulation examples and a laboratory experiment demonstrate the merit of the proposed method.

In this paper, a dynamic fuzzy partial least squares (DFPLS) modeling method is proposed. Under such framework, the multiple input multiple output (MIMO) nonlinear system can be automatically decomposed into several univariate subsystems in PLS latent space. Within each latent space, a dynamic fuzzy method is introduced to model the inherent dynamic and nonlinear feature of the physical system. The new modeling method combines the decoupling characteristic of PLS framework and the ability of dynamic nonlinear modeling in the fuzzy method. Based on the DFPLS model, a multi-loop nonlinear internal model control (IMC) strategy is proposed. A pH neutralization process and a methylcyclohexane (MCH) distillation column from Aspen Dynamic Module are presented to demonstrate the effectiveness of the proposed modeling method and control strategy.

Eighteen geochemical blocks have been delineated by a wide-spaced deep-penetrating geochemical survey in area of 150,000 km2 in the Eastern Tianshan metallogenic belts, Xinjiang, China. The area has 5 Cu-Pb-Zn-Ag, 3 Cu, 1 Cu-Ni, 4 Au, 3 U, 1 Pt-Pd, and 1 W geochemical anomalies or blocks. Our results indicate that six geochemical blocks match the clusters of known large ore deposits clusters or districts. Newly discovered 11 blocks may have potential for copper, gold, uranium and tungsten ore districts. The geochemical blocks with high metal concentrations are favorable for the formation of ore deposit cluster. Some geochemical blocks have no ore clusters yet. New deposits have recently been discovered in 3 of the 11 newly delineated geochemical anomalies (blocks). They may have potential for future discovery of ore deposits. Ore-forming processes commonly affect large areas of the earth crust, resulting in large scale geochemical anomalies. These large scale geochemical anomalies can only be delineated by geochemical survey in large area, or promising ore districts might even not be noticed if geochemical mapping was carried out in a small area.

Desert terrains are widely distributed in northwestern and northern China, but remain largely unexplored or under-explored. In the past, lag materials (coarse fraction of regolith) have been sampled for regional geochemical surveys. This sampling method was successful in mountainous and hilly regions, but it failed in areas of lower relief, such as slopes, plains, alluvial valleys and basins. Studies conducted since 1999 were aimed at understanding the environment and the nature of geochemical dispersion in order to find a cost-effective, wide-spaced sampling method for delineation of geochemical provinces. The results show that many elements related to mineral deposits are concentrated in the fine fraction ( c. 150 000 km 2 at a density of one sample per 100 km 2 . Samples were collected from the weakly cemented sandy clay-rich horizon at a depth of 15–30 cm and sieved to Geochemical provinces of Au, Cu and U have been recognized. Five Au provinces, with threshold values of more than 2.0 ppb Au, have been delineated. Three of these are consistent with known large Au deposits, and preliminary follow-up surveys indicate that the other two are prospective for undiscovered Au deposits. Seven Cu provinces have been delineated, of which three are endowed with hydrothermal Cu deposits, porphyry Cu deposits and Cu-Ni deposits, respectively. Two provinces of U associated with Mo were delineated in the basin. One of them contains a large sandstone-hosted U deposit and in the other, a large new U deposit was discovered by follow-up drilling.

Certified or reference values for sixty six to sixty seven elements and element oxides are given for geochemical reference materials recently prepared by the Institute of Geophysical and Geochemical Exploration (IGGE), including limestone (GSR-13), granitic gneiss (GSR-14), amphibolite (GSR-15), lake sediment (GSS-9) and stream sediments (GSD-1a, GSD-13, GSD-14). These samples supplement the GSR 1-6, GSD1-8 and GSD 9-12 certified reference materials already prepared and characterized by the IGGE. Cet article donne les concentrations certifiees ou de reference de soixante six a soixante sept elements et oxydes pour des materiaux geochimiques de reference prepares recemment par l'Institut d'Exploration Geophysique et Geochimique de Chine (IGGE), dont un calcaire GSR-13, un gneiss granitique GSR-14, une amphibolite GSR-15, un sediment lacustre GSS-9 et des sediments de riviere GSD-1a, GSD-13 et GSD-14. Ces echantillons completent l'ensemble des materiaux geochimiques de reference GSR 1-6, GSD1-8 and GSD 9-12 deja prepares et caracterises par l'IGGE.

Unlike the situation for other elements, few reference materials certified for the platinum-group elements are currently available. Therefore, the GPt1-7 series of PGE geochemical CRMs, prepared by the IGGE, represent an important addition and comprise a range of matrix types, including a soil, stream sediment, Mg-rich ultramafic rock, Fe-rich ultramafic rock, platinoid ore depleted in Cu, Ni and Fe, and a chromitite. The concentration of PGE in these samples ranges over 4-5 orders of magnitude and the samples were shown to have good homogeneity as assessed by a variance test. Analytical methods based on different principles were used in sample certification, including several fire assay and wet chemical procedures used for decomposition and preconcentration. Certified values for the elements Pt, Pd, Os, Ru, Ir, Rh and Au were determined by AAS, catalytic colourimetry (COL), catalytic polarography (POL), atomic emission spectrometry (AES), ICP-MS and NAA. Recently, the GPt1-7 samples were approved as national primary CRMs by the State Bureau of Technical Supervision of China. A la difference d'autres elements, bien peu de materiaux de reference a valeurs certifies existent dans le cas des elements du groupe du platine. En consequence, l'ensemble de materiaux de reference certifies GPt1-7 prepares par l'IGGE est un apport important, car il comprend des materiaux a matrices variees: un sol, un sediment de riviere, des peridotites riches en magnesium ou en fer, un minerai de platinoides appauvri en Cu, Ni et Fe et une chromitite. Les concentrations en PGE de ces materiaux setalent sur cinq ordres de grandeur et les echantillons sont bien homogenes, comme le demontre un test de variance. Differentes methodes analytiques ont ete utilisees pour la certification, incluant plusieurs preconcentrations par fusion et des procedures de chimie humide classiques pour la mise en solution et la preconcentration. Les valeurs certifiees pour les elements Pt, Pd, Os, Ru, Ir, Rh, Au ont ete determinees par AAS, colorimetrie catalytique (COL), polarographie catalytique (POL), spectrometrie d'emission (AES), ICP-MS et activation neutronique (NAA). Recemment, les echantillons GPt1-7 ont ete reconnus comme materiaux primaires de reference nationale par le Bureau d'Etat de la Supervision Technique de Chine.

In an area of 3.3×106 km2 within eastern China, 28 253 rock samples were collected systematically and combined into 2718 composite samples which were analyzed by 15 reliable methods using national preliminary certified reference materials (CRMs) for data quality monitoring. The average chemical compositions of the exposed crust, the sedimentary cover and the exposed basement as well as the upper crust for 76 chemical elements in eastern China are given.

Eleven gold geochemical reference samples, GAu 8–18, have been prepared by the Institute of Geophysical and Geochemical Exploration, People's Republic of China. The concentration range of gold in these samples is from 0.5 ng/g to 10 μg/g. Fifteen Chinese Institutes, experienced in gold analysis of geological samples, have participated in the certification procedure using seven reliable analytical methods.

Actual granitoid analytical data of 767 composited samples are presented here. The data source is 6080 samples collected mainly from 750 large- to middle-sized granitoid bodies across China. Data from the composited samples, which includes that of 70 elements, is analyzed according to geological age — Archeozoic (Ar), Proterozoic (Pt), Eopaleozoic (Pz 1 ), Neopaleozoic (Pz 2 ), Mesozoic (Mz), and Cenozoic (Cz) — and three major compositional varieties, e.g. alkali-feldspar granite, syenogranite and adamellite. Petrochemical parameters, trace-element content and rare-earth element (REE) distributions of the different rock types and geological ages are characterized, and change tendencies through Archean to Cenozoic time are recorded. The comprehensive analytical data presented here has not been previously published. This significant data set can be used as fundamental information in studies of basic China geology, magma petrogenesis, ore exploration and geochemistry.