贵州织金新华含稀土磷矿床稀土元素地球化学及生物成矿基本特征

对贵州织金新华含稀土磷矿床进行的稀土元素及微量元素分析结果表明,磷块岩中普遍富集稀土元素,稀土总量∑REE较高,并富集Y元素及La,Nd等轻稀土元素。LREE／HREE比值较高。含稀土磷块岩普遍具Ce负异常,显示其对源区的继承性。指示成磷环境处于氧化程度相对较高状态。稀土元素球粒陨石标准化模式曲线、北美页岩标准化模式曲线、Ce元素异常、LREE／HREE比值、微量元素特征及岩石矿物特征表明,织金新华含稀土磷矿床具以正常海相生物-化学沉积等为主伴有海相热水沉积混合成因的特征。在磷矿成矿过程中沉积物沉积时,活体生物及死亡残骸不同程度地摄取和富集了La,Nd,Ce等稀土元素、Y元素及其他微量元素,沉积在含磷层位,构成大量的含稀土、含生物碎屑白云质磷块岩。     

Occurrence and fate of polycyclic aromatic hydrocarbons in the coastal surface microlayer

Polycyclic aromatic hydrocarbons (PAHs) were measured in the coastal sea surface microlayer (SML), the sub-surface waters (SSW) and the overlying atmosphere in order to investigate the influence of the SML on contaminant enrichment and air-sea exchange. Samples were collected at two contrasting locations of the NW Mediterranean, one urban influenced (off Barcelona, Spain) and another comparatively clean (off Banyuls-sur-Mer, France). Statistical data analysis confirmed the accumulation of PAHs in the SML with respect to the SSW (20.3+/-9.1 vs. 13.1+/-10.0 ng L(-1) in the dissolved phase; 709+/-207 vs. 158+/-111 ng g(-1) in the particulate phase). This accumulation was higher at the contaminated location (Barcelona station) compared with the more pristine one, with PAH enrichments 1.5 and 4.5 times higher for the dissolved and particulate phases, respectively, indicating that the enrichment of PAHs in the SML is dominated by particle transport processes.     

热力强迫对越赤道气流产生和维持的作用——定性分析

从原始方程出发,应用大气运动的对称反对称性理论,就赤道地区一呈偶极子分布的反对称冷热源对越赤道气流的形成和维持作用进行了定性分析。结果表明,非绝热加热的反对称分布对越赤道气流的影响是通过诱发和维持反对称的气压场分量来实现的。即在热源上空低层形成低压,高层形成高压;在热汇上空形成相反的气压场。这样,在赤道上空高、低层就将形成与相应气压梯度方向一致的越赤道气流     

邯邢煤田岩浆侵入及对煤层煤质的影响

根据邯邢煤田岩浆侵入特点,分析岩浆侵入煤系层位与分布规律,岩浆侵入体的产状及岩性特征。邯邢煤田岩浆岩的侵入,不仅破坏了该区煤层的连续性和完整性,减少了煤炭的可采储量,并使煤层的物理性质、化学成分和工艺性能也发生了显著变化,致使煤质变差,煤炭工业价值降低。     

交会图和Carbon软件在火山碎屑岩识别中的应用

针对火山岩储层的特殊性(复杂性、离散性和随机性),应用交会图及Carbon软件对火山岩测井解释中岩性识别问题进行了研究。该方法的技术关键是交会图的选取,以交会图和Carbon软件为基础实现对岩性的识别。将研究方法应用在海拉尔地区火山碎屑岩岩性识别问题中,取得了很好的效果,岩性解释符合率80%。     

Hybrid denoising-jittering data pre-processing approach to enhance multi-step-ahead rainfall–runoff modeling

Successful modeling of stochastic hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. On the other hand in training phase of any Artificial Intelligence based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole population. Accordingly in the present article first, wavelet-based denoising method was used in order to smooth hydrological time series and then small normally distributed noises with the mean of zero and various standard deviations were generated and added to the smoothed time series to form different denoised-jittered training data sets, for Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling of daily and multi-step-ahead rainfall–runoff process of the Milledgeville station of the Oconee River and the Pole Saheb station of the Jighatu River watersheds, respectively located in USA and Iran. The proposed hybrid data pre-processing approach in the present study is used for the first time in modeling of time series and especially in modeling of hydrological processes. Furthermore, the impacts of denoising (smoothing) and noise injection (jittering) have been simultaneously investigated neither in hydrology nor in any other engineering fields. To evaluate the modeling performance, the outcomes were compared with the results of multi linear regression and Auto Regressive Integrated Moving Average models. Comparing the achieved results via the trained ANN and ANFIS models using denoised-jittered data showed that the proposed data pre-processing approach which serves both denoising and jittering techniques could improve performance of the ANN and ANFIS based single-step-ahead rainfall–runoff modeling of the Milledgeville station up to 14 and 12% and of the Pole Saheb station up to 22 and 16% in the verification phase. Also the results of multi-step-ahead modeling using the proposed data pre-processing approach showed improvement of modeling for both watersheds.     

Climate and climate change in the Austrian–Swiss region of the European Alps during the twentieth century according to Feddema

We present a 1-km² gridded German dataset of hourly surface climate variables covering the period 1995 to 2012. The dataset comprises 12 variables including temperature, dew point, cloud cover, wind speed and direction, global and direct shortwave radiation, down- and up-welling longwave radiation, sea level pressure, relative humidity and vapour pressure. This dataset was constructed statistically from station data, satellite observations and model data. It is outstanding in terms of spatial and temporal resolution and in the number of climate variables. For each variable, we employed the most suitable gridding method and combined the best of several information sources, including station records, satellite-derived data and data from a regional climate model. A module to estimate urban heat island intensity was integrated for air and dew point temperature. Owing to the low density of available synop stations, the gridded dataset does not capture all variations that may occur at a resolution of 1 km². This applies to areas of complex terrain (all the variables), and in particular to wind speed and the radiation parameters. To achieve maximum precision, we used all observational information when it was available. This, however, leads to inhomogeneities in station network density and affects the long-term consistency of the dataset. A first climate analysis for Germany was conducted. The Rhine River Valley, for example, exhibited more than 100 summer days in 2003, whereas in 1996, the number was low everywhere in Germany. The dataset is useful for applications in various climate-related studies, hazard management and for solar or wind energy applications and it is available via doi: 10.5676/DWD_CDC/TRY_Basis_v001.