锡的地球化学性质与华南晚白垩世锡矿成因

锡在地质过程中表现出亲氧、亲硫和亲铁三重特性。在地幔岩浆过程中,锡是一种中等不相容的金属元素。锡成矿主要与酸性岩浆活动有关,其地球化学性质决定了其成矿主要受源区性质、氧逸度以及挥发分含量、岩浆结晶分异等因素控制。高度结晶分异可使锡在岩浆中进一步富集,是锡成矿相关花岗岩的普遍特征;锡为变价元素,岩浆体系氧逸度影响源区中锡的迁移能力和分离结晶过程中锡的元素行为,还原性岩浆体系有利于锡富集成矿。富含F、Cl和B等挥发组分对锡元素的迁移和富集起积极的作用。全球锡矿床分布与俯冲带关系密切,特提斯和环太平洋构造域是主要蕴藏区。重要的锡成矿事件表现出区域性和阶段性的特征。结合锡的地球化学特性以及锡矿分布特征,我们认为最有利锡成矿的动力学机制是俯冲板片后撤机制。俯冲板片后撤引发深部软流圈地幔上涌,导致强烈的壳幔相互作用,形成低氧逸度、富F、Cl和B等花岗岩,有利于锡成矿。对于华南晚白垩世锡成矿事件,新特提斯洋俯冲板片发生后撤是其成矿地球动力学背景。     

PCA-BP神经网络在SO2浓度预报中的应用

将基于主成分分析(PCA)的BP神经网络预报方法引入大气污染预报,建立SO2浓度预报模型.结果表明:应用主成分分析对数据进行前处理,以原始预报因子的主成分作为BP神经网络的输入,降低了数据维数,消除了样本间存在的相关性,大大加快了BP神经网络的收敛速度.对模型进行预报验证,预报值与实际值之间的绝对误差为0.0098,预报值与实际值的相关系数达到0.885,得到较好的预报效果.并且比一般的BP神经网络模型具有较高的拟合和预报精度.     

Lead isotope evolution of the Central European upper mantle:Constraints from the Bohemian Massif

The Pb isotope composition of the upper mantle beneath Central Europe is heterogeneous due to the subduction of regionally contrasting material during the Variscan and Alpine orogenies.Late Variscan to Cenozoic mantlederived melts allow mapping this heterogeneity on a regional scale for the last ca.340 Myr.Late Cretaceous and Cenozoic anorogenic magmatic rocks of the Bohemian Massif(lamprophyres,volcanic rocks of basanite/tephrite and trachyte/phonolite series) concentrate mostly in the Eger Rift.Cretaceous ultramafic lamprophyres yielded the most radiogenic Pb isotope signatures reflecting a maximum contribution from metasomatised lithospheric mantle,whereas Tertiary alkaline lamprophyres originated from mantle with less radiogenic ~(206)Pb/~(204)b ratios suggesting a more substantial modification of lithospheric source by interaction with asthenosphericderived melts.Cenozoic volcanic rocks of the basanite/tephrite and trachyte/phonolite series define a linear mixing trend between these components,indicating dilution of the initial lithospheric mantle signature by upwelling asthenosphere during rifting.The Pb isotope composition of Late Cretaceous and Cenozoic magmatic rocks of the Bohemian Massif follows the same Pb growth curve as Variscan orogenic lamprophyres and lamproites that formed during the collision between Laurussia,Gondwana,and associated terranes.This implies that the crustal Pb signature in the post-Variscan mantle is repeatedly sampled by younger anorogenic melts.Most Cenozoic mantle-derived rocks of Central Europe show similar Pb isotope ranges as the Bohemian Massif.     

莫桑比克东部滨海砂矿矿物组成及特征

滨海砂矿是目前全球最大的潜在矿产资源之一。以莫桑比克的滨海砂矿作为研究对象,利用光学显微镜、X射线衍射、扫描电镜、等离子体质谱（ICP-MS）等手段对莫桑比克东部滨海砂矿的矿物组成及特征进行了研究。并用数理统计方法讨论了重砂矿的元素赋存状态及其载体矿物。研究结果表明：（1）砂矿的矿石矿物主要为钛铁矿、金红石和锆石,脉石矿物主要为石英和长石。（2）重砂矿的主要化学组分为Ti和zr;Hf及REE为次要组分;而主要有害元素组分Th和U,其总体含量低,故重砂矿对采选加工和产品利用均无明显环境危害。（3）重砂矿中元素含量与粒度之间呈一定相关性,显示zr、Hf、U、Cr、Ta的载体矿物的粒度相对较细;Ti、Fe、V、Nb的载体矿物的粒度相对较粗。     

PM10 Source Apportionment in Ahvaz,Iran, Using Positive Matrix Factorization

Source apportionment of particulate matter <10 µm in diameter (PM₁₀), having considerable impacts on human health and the environment, is of high priority in air quality management. The present study, therefore, aimed at identifying the potential sources of PM₁₀ in an arid area of Ahvaz located in southwest of Iran. For this purpose, we collected 24‐h PM₁₀ samples by a high volume air sampler. The samples were then analyzed for their elemental (Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, Pb, Se, Si, Sn, Sr, Li, Ti, V, Zn, Mo, and Sb) and ionic (NH, Cl^?, NO, and SO) components using inductively coupled plasma optical emission spectrometry and ion chromatography instruments, respectively. Eight factors were identified by positive matrix factorization: crustal dust (41.5%), road dust (5.5%), motor vehicles (11.5%), marine aerosol (8.0%), secondary aerosol (9.5%), metallurgical plants (6.0%), petrochemical industries and fossil fuel combustion (13.0%), and vegetative burning (5.0%). Result of this study suggested that the natural sources contribute most to PM₁₀ particles in the area, followed closely by the anthropogenic sources.     

俯冲带板内地震竖向位移谱阻尼修正系数模型研究

基于日本K-NET和KiK-net台网中4695条俯冲板内地震记录的竖向分量,建立了位移谱阻尼修正系数(DMF)模型.采用基于场地周期的场地分类方法,并通过固定效应法推导出模型系数.该DMF模型考虑了谱周期、阻尼比和场地条件的影响,可以用来调整与震源和距离无关的设计反应谱.利用阻尼比对数的二次方程式对原数据进行拟合回归...     

Uncertainty analysis of CO_2 flux components in subtropical evergreen coniferous plantation

We present an uncertainty analysis of ecological process parameters and CO2 flux components (Reco, NEE and gross ecosystem exchange (GEE)) derived from 3 years’ continuous eddy covariance meas-urements of CO2 fluxes at subtropical evergreen coniferous plantation, Qianyanzhou of ChinaFlux. Daily-differencing approach was used to analyze the random error of CO2 fluxes measurements and bootstrapping method was used to quantify the uncertainties of three CO2 flux components. In addition, we evaluated different ...     

Quality mapping of the Ryytimaa dolomite in western Finland

The Ryytimaa dolomite formation in western Finland is mined for fertilizing purposes in agriculture. Ordinary kriging, indicator kriging, and indicator principal components simulation were used to map the dolomite quality. The fertilizing properties depend on the relative amounts of MgO and neutralizing CaO in dolomite. Based on the chemical composition the dolomite is divided into six quality classes according to the Finnish legislation. This quality classification is only partially in accordance with the three populations inferred from the distributions of the MgO, CaO, and neutralizing CaO contents. These populations probably represent compositionally different dolomite layers, now forming complicated fold interference structures that are visible on the kriged maps. The mapping of the quality, and thus, selective mining of the dolomite deposit is possible using the quality classification based on the three populations. In contrast, selective mining of the dolomite based on the legislation is difficult—at least with techniques used in this study.     

Testing spatial heterogeneity in geographically weighted principal components analysis

We propose a method to evaluate the existence of spatial variability in the covariance structure in a geographically weighted principal components analysis (GWPCA). The method, that is extensive to locally weighted principal components analysis, is based on performing a statistical hypothesis test using the eigenvectors of the PCA scores covariance matrix. The application of the method to simulated data shows that it has a greater statistical power than the current statistical test that uses the eigenvalues of the raw data covariance matrix. Finally, the method was applied to a real problem whose objective is to find spatial distribution patterns in a set of soil pollutants. The results show the utility of GWPCA versus PCA.