VPN技术在煤炭地质档案网络服务中的应用

网络服务是煤炭地质档案业务发展的必由之路,为了解决在公共网络传输过程中的安全问题,本文介绍了一种虚拟专用网络技术,通过分析其应用于煤炭地质档案网络服务中的优势,为行业内外部的信息共享提供了一个新的思路。     

鲁西金岭矽卡岩型铁矿区早白垩世石英闪长岩成因:年代学和地球化学证据

金岭岩体是鲁西地区代表性的中生代侵入杂岩体,与金岭矽卡岩型铁矿的成因密切相关.为了解石英闪长岩的成因及与矽卡岩铁矿成矿关系,本次研究对金岭杂岩体中的石英闪长岩进行了年代学、地球化学及锆石Lu—Hf同位素和角闪石矿物微区成分的系统研究.LA-ICP-MS锆石U-Pb年代学显示石英闪长岩加权平均年龄为(128±1)Ma,侵...     

桩锚支挡体系在广西寨-任二级公路高边坡治理工程中的应用

(六)寨-(水)任二级公路施工后,滑坡、坍塌、崩塌现象甚多,边坡治理迫在眉睫。桩锚支挡体系是一项主动性加固边坡技术,具结构轻巧、用材合理、施工简单、工期短、费用低等特点。论文通过其在(六)寨-(水)任二级公路K56段高边坡治理工程中的应用,对桩锚支挡体系的设计及施工要点作了介绍。     

Implementation of a coupled plastic damage distinct lattice spring model for dynamic crack propagation in geomaterials

This paper studies dynamic crack propagation by employing the distinct lattice spring model (DLSM) and 3‐dimensional (3D) printing technique. A damage‐plasticity model was developed and implemented in a 2D DLSM. Applicability of the damage‐plasticity DLSM was verified against analytical elastic solutions and experimental results for crack propagation. As a physical analogy, dynamic fracturing tests were conducted on 3D printed specimens using the split Hopkinson pressure bar. The dynamic stress intensity factors were recorded, and crack paths were captured by a high‐speed camera. A parametric study was conducted to find the influences of the parameters on cracking behaviors, including initial and peak fracture toughness, crack speed, and crack patterns. Finally, selection of parameters for the damage‐plasticity model was determined through the comparison of numerical predictions and the experimentally observed cracking features.     

北祁连山东部早石炭世早期腕足动物群落的古地理意义

北祁连山东部早石炭世早期经历了一次完整的海水进退旋回。此期该区腕足动物的演化明显受控于相对海平面变化。生存环境的稳定性似乎是群落演替过程能否持久进行的基础。在影响生物群落演替的诸多因素中,底质性质及其稳定性最为重要,其次是食物供应、海水盐度和水动力强度等。各群落在时间上相互取代的主要控制因素是海水深度,与海平面的变化直接相关。而造成这一时期腕足动物群落横向变化的主要因素除了海水深度外,陆源碎屑供应量、海水循环性以及含盐度都具有十分重要的影响。本区早石炭世早期腕足动物群落的生态位介于BA1和BA2之间,缺乏BA3－BA5较深水群落（BA－Benthic Assemblage,底栖组合）,说明此期该区早石炭世早期海水深度一般不超过10米。受沉积古地理环境的制约,本区早石炭世早期BA2生态域的主要分布范围局限于东部的景泰一带,向西水体明显变浅。     

利用全球环流模式进行我国汛期短期气候预测的试验

利用OSU/NCC全球大气环流模式耦合全球混合层海洋与海冰模式,采用集合预报的方法,对中国汛期降水进行了1982~1995年共14年的跨季度综合性回报检验研究。结果表明,该模式对我国汛期降水具有一定的跨季度预报能力,对部分地区（江淮至华东沿海、东北部分地区等）有较强的预报能力。     

次天气尺度及中尺度暴雨系统研究进展

回顾了75年来中国科学院大气物理研究所科研人员在次天气尺度及中尺度暴雨系统领域的研究工作,这些领域主要包括暴雨、中尺度低空急流、低涡、梅雨锋结构及梅雨锋生、对称不稳定和涡层不稳定以及暴雨等灾害天气的天气动力学及数值模拟研究等;总结了在次天气尺度及中尺度暴雨系统研究的不同时期所取得的成就以及这些成就在防止和减轻中尺度暴雨灾害方面所起到的重要作用.     

Characteristics and cause analysis of heavy haze in Changchun City in Northeast China

Northeast China has been reported as having serious air pollution in China with increasing occurrences of severe haze episodes. Changchun City, as the center of Northeast China, has longstanding industry and is an important agricultural base. Additionally, Changchun City has a long winter requiring heating of buildings emitting pollution into the air. These factors contribute to the complexity of haze pollution in this area. In order to analyze the causes of heavy haze, surface air quality has been monitored from 2013 to 2015. By using satellite and meteorological data, atmospheric pollution status, spatio-temporal variations and formation have been analyzed. Results indicated that the air quality in 88.9% of days exceeding air quality index(AQI) level-1 standard(AQI 50) according to the National Ambient Air Quality Standard(NAAQS) of China. Conversely, 33.7% of the days showed a higher level with AQI 100. Extreme haze events(AQI 300) occurred frequently during agricultural harvesting period(from October 10 to November 10), intensive winter heating period(from Late-December to February) and period of spring windblown dust(April and May). Most daily concentrations of gaseous pollutants, i.e., NO_2(43.8 μg/m~3), CO(0.9 mg/m~3), SO_2(37.9 μg/m~3), and O_3(74.9 μg/m~3) were evaluated within level-1 concentration limits of NAAQS standards. However, particulate matter(PM_(2.5) and PM10) concentrations(67.3 μg/m~3 and 115.2 μg/m~3, respectively) were significantly higher than their level-1 limits. Severe haze in spring was caused by offsite transported dust and windblown surface soil. Heavy haze periods during fall and winter were mainly formed by intensive emissions of atmospheric pollutants and steady weather conditions(i.e., low wind speed and inversion layer). The overlay emissions of widespread straw burning and coal combustion for heating were the dominant factors contributing to haze in autumn, while intensive coal burning during the coldest time was the primary component of total emissions. In addition, general emissions including automobile exhaust, road and construction dust, residential and industrial activities, have significantly increased in recent years, making heavy haze a more frequent occurrence. Therefore, both improved technological strategies and optimized pollution management on a regional scale are necessary to minimize emissions in specified seasons in Changchun City, as well as comprehensive control measures in Northeast China.     

Impacts of the Kuwait Oil Fires on the Mount Qomolangma Region

Mt. Qomolangma (also known as Mt. Everest), the world’s highest mountain, is situated over the world’s highest plateau, the Tibetan Plateau. Because of its height and because of its distance from industrialized areas, the environmental state of the Mt. Qomolangma region can normally be considered “undisturbed”. It is interesting to investigate how this “undisturbed” state has been changing with time and whether it has been influenced by large environmentally disruptive events such as the Kuwait oil fires of 1990 and 1991 (Small, 1991). In order to do this, riv-er water samples were collected from the Rongpu River at Rongpu Temple Station in the summers of 1992 and 1993, as was done in 1975, and aerosol samples were collected in the summer of 1992 at the same station as was done in 1980. River water samples were analyzed using atomic absorption spectroscopy (AAS) at the Chinese Academy of Sciences. Aerosol samples were analyzed using proton-induced x-ray emission (PIXE) at the University of Fudan in Shanghai. The results show that the concentrations of chemical species in the river water at Rongpu Temple Station were much higher in the summer of 1992 than they were in 1975 and 1993, and the concentrations of atmospheric chemical species were much higher in 1992 than they were in 1980. The environment of the north slope of Mt. Qomolangma was therefore heavily polluted before and/or during the summer of 1992, possibly due to the Kuwait oil fires in 1990 and 1991.     

The aboveground biomass of desert steppe and its spatiotemporal variation in western Inner Mongolia

A precise understanding of the aboveground biomass of desert steppe and its spatio-temporal variation is important to understand how arid ecosystems respond to climate change and to ensure that scarce grassland resources are used rationally. On the basis of 756 ground survey quadrats sampled in western Inner Mongolia steppe in 2005–2011 and remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS)—the normalized difference vegetation index (NDVI) dataset for the period of 2001–2011—we developed a statistical model to estimate the aboveground biomass of the desert steppe and further explored the relationships between aboveground biomass and climate factors. The conclusions are as follows: (1) the aboveground biomass of the steppe in the research area was 5.27 Tg (1 Tg=10¹² g) on average over 11 years; between 2001 and 2011, the aboveground biomass of the western Inner Mongolia steppe exhibited fluctuations, with no significant trend over time; (2) the aboveground biomass of the steppe in the research area exhibits distinct spatial variation and generally decreases gradually from southeast to northwest; and (3) the important factor causing interannual variations in aboveground biomass is precipitation during the period from January to July, but we did not find a significant relationship between the aboveground biomass and the corresponding temperature changes. The precipitation in this period is also an important factor influencing the spatial distribution of aboveground biomass (R²=0.39, P<0.001), while the temperature might be a minor factor (R²=0.12, P<0.01). The uncertainties in our estimate are primarily due to uncertainty in converting the fresh grass yield estimates to dry weight, underestimates of the biomass of shrubs, and error in remote sensing dataset.