Mineralogy and geochemistry of a Late Permian coal in the Dafang Coalfield, Guizhou, China: influence from siliceous and iron-rich calcic hydrothermal fluids

This paper describes the influence of siliceous and iron-rich calcic low-temperature hydrothermal fluids (LTHF) on the mineralogy and geochemistry of the Late Permian No. 11 Coal (anthracitic, R_r=2.85%) in the Dafang Coalfield in northwestern Guizhou Province, China. The No. 11 Coal has high contents of vein ankerite (10.2 vol.%) and vein quartz (11.4 vol.%), with formation temperatures of 85 and 180 °C, respectively, indicating that vein ankerite and vein quartz were derived from low-temperature calcic and siliceous hydrothermal fluids in two epigenetic episodes. The vein quartz appears to have formed earlier than vein ankerite did, and at least three distinct stages of ankerite formation with different Ca/Sr and Fe/Mn ratios were observed.The two types of mineral veins are sources of different suites of major and trace metals. Scanning electron microscope and sequential extraction studies show that, in addition to Fe, Mg, and Ca, vein ankerite is the dominant source of Mn, Cu, Ni, Pb, and Zn in the coal, and the contents of these five elements are as high as 0.09% and 74.0, 33.6, 185, and 289 μg/g, respectively. In contrast, vein quartz is the main carrier mineral for platinum-group elements (PGEs) Pd, Pt, and Ir in the coal, and the contents of Pd, Pt, and Ir are 1.57, 0.15, and 0.007 μg/g, respectively. Sequential extraction showed a high PGE content in the silicate fraction, up to 10.4 μg/g Pd, 1.23 μg/g Pt, and 0.05 μg/g Ir, respectively. It is concluded that the formation of ankerite and quartz and the anomalous enrichment of trace elements in the No. 11 Coal in the Dafang Coalfield, Guizhou, result from the influx of calcic and siliceous low-temperature hydrothermal fluids.     

Preliminary data on chemical changes in the Aral Sea during low-level periods from the last 9000 years

The withdrawal of the Aral Sea tributaries (Amu and Syr Daria) for cultures has led to significant falls of its level and an important increase in its salinity. During the Holocene, a succession of low and high water inputs occurred. Silty deposits correspond to the high levels and carbonates to the low levels. This study makes a distinction between the Syr Daria and the Amu Daria water inputs during low-level periods by using mineralogical and chemical compositions of the carbonates deposits. Waters from the Syr Daria are more sulphatic and have a low iron content in comparison with that of the Amu Daria. The Syr Daria was the major tributary around 7500, 4956 and 970 yr?BP, whereas around 6200 and 3610 yr?BP, inflow also from the Amu Daria is observed. To cite this article: L. Le Callonnec et al., C. R. Geoscience 337 (2005).     

天津地区表土PAHs含量的多尺度空间相关性

本文应用因子克立格分析方法,分析了天津表土16种PAHs之间多尺度空间结构特征。分析结果显示,天津表土PAHs在空间上可以划分三种尺度:块金尺度(0～5km),小空间尺度(5～10km)和大空间尺度(10～50km)。在不同的空间尺度上,16种PAHs之间表现出不同的空间相关性,具有空间尺度相关性。这种空间尺度相关性,是由于不同的空间污染过程造成的,有助于下一步的多尺度空间污染源的判断和污染物迁移转化规律的识别。     

Plio-Pleistocene mammals from Mille-Logya,Ethiopia, and the post-Hadar faunal change

We describe the non-primate mammalian fauna from the late Pliocene to earliest Pleistocene deposits of Mille-Logya in the Lower Awash Valley, Ethiopia, dated to c. 2.9–2.4 Ma, and divided into three successive units: Gafura, Seraitu, and Uraitele. We identify 41 mammalian taxa (including rodents), the most diverse group being the Bovidae, with 17 taxa. While the Gafura assemblage still resembles those from the earlier Hadar Formation, the younger Seraitu assemblage documents a major turnover. While there is little change in the species present across this interval, the relative abundances of various taxa change dramatically, with suids being largely replaced by open-country bovids (Alcelaphini and Antilopini). We interpret this faunal change as reflective of an environmental shift, contemporaneous with the replacement of Australopithecus afarensis by Homo in the area.     

Modelling and experimental study of coupled exchange of p,p′‐DDE and colloids in a flume simulated stream–streambed system

Stream–subsurface exchange plays a significant role in the fate and transport of contaminants in streams. It has been modelled explicitly by considering fundamental processes such as hydraulic exchange, colloid filtration, and contaminant interactions with streambed sediments and colloids. The models have been successfully applied to simulate the transport of inorganic metals and nutrients. In this study, laboratory experiments were conducted in a recirculating flume to investigate the exchange of a hydrophobic organic contaminant, p,p′‐dichloro‐diphenyl‐dichloroethane (DDE), between a stream and a quartz sand bed. A previously developed process‐based multiphase exchange model was modified by accounting for the p,p′‐DDE kinetic adsorption to and desorption from the bed sediments/colloids and was applied to interpret the experimental results. Model input parameters were obtained by conducting independent small‐scale batch experiments. Results indicate that the immobilization of p,p′‐DDE in the quartz sand bed can occur under representative natural stream conditions. The observed p,p′‐DDE exchange was successfully simulated by the process‐based model. The model sensitivity analysis results show that the exchange of p,p′‐DDE can be sensitive to either the sediment sorption/desorption parameters or colloidal parameters depending on the experimental conditions tested. For the experimental conditions employed here, the effect of colloids on contaminant transport is expected to be minimal, and the stream–subsurface exchange of p,p′‐DDE is dominated by the interaction of p,p′‐DDE with bed sediment. The work presented here contributes to a better mechanistic understanding of the complex transport process that hydrophobic organic contaminants undergo in natural streams and to the development of reliable, predictive models for the assessment of impacted streams. Copyright © 2015 John Wiley & Sons, Ltd.     

A new standardized Palmer drought index for hydro‐meteorological use

Accepting the concept of standardization introduced by the standardized precipitation index, similar methodologies have been developed to construct some other standardized drought indices such as the standardized precipitation evapotranspiration index (SPEI). In this study, the authors provided deep insight into the SPEI and recognized potential deficiencies/limitations in relating to the climatic water balance it used. By coupling another well‐known Palmer drought severity index (PDSI), we proposed a new standardized Palmer drought index (SPDI) through a moisture departure probabilistic approach, which allows multi‐scalar calculation for accurate temporal and spatial comparison of the hydro‐meteorological conditions of different locations. Using datasets of monthly precipitation, temperature and soil available water capacity, the moisture deficit/surplus was calculated at multiple temporal scales, and a couple of techniques were adopted to adjust corresponding time series to a generalized extreme value distribution out of several candidates. Results of the historical records (1900–2012) for diverse climates by multiple indices showed that the SPDI was highly consistent and correlated with the SPEI and self‐calibrated PDSI at most analysed time scales. Furthermore, a simple experiment of hypothetical temperature and/or precipitation change scenarios also verified the effectiveness of this newly derived SPDI in response to climate change impacts. Being more robust and preferable in spatial consistency and comparability as well as combining the simplicity of calculation with sufficient accounting of the physical nature of water supply and demand relating to droughts, the SPDI is promising to serve as a competent reference and an alternative for drought assessment and monitoring. Copyright © 2013 John Wiley & Sons, Ltd.     

Operational climate prediction in the era of big data in China: Reviews and prospects

Big data has emerged as the next technological revolution in IT industry after cloud computing and the Internet of Things. With the development of climate observing systems, particularly satellite meteorological observation and high-resolution climate models, and the rapid growth in the volume of climate data, climate prediction is now entering the era of big data. The application of big data will provide new ideas and methods for the continuous development of climate prediction. The rapid integration, cloud storage, cloud computing, and full-sample analysis of massive climate data makes it possible to understand climate states and their evolution more objectively, thus predicting the future climate more accurately. This paper describes the application status of big data in operational climate prediction in China; it analyzes the key big data technologies, discusses the future development of climate prediction operations from the perspective of big data, speculates on the prospects for applying climatic big data in cloud computing and data assimilation, and puts forward the notion of big data-based super-ensemble climate prediction methods and computerbased deep learning climate prediction methods.