Growth and survival of Halimione portulacoides stem cuttings in heavy metal contaminated soils

The halophytic shrub Halimione portulacoides demonstrates a high tolerance to heavy metal contamination and a capacity for accumulating metals within its tissues. On the Iberian Peninsula, this species has colonized habitats with high levels of metal pollution. The aim of this study is to analyze the response of H. portulacoides stem cuttings to this pollution. Growth, photosynthesis and metal uptake were examined in H. portulacoides through an experiment in which stem cuttings were replanted in metal-contaminated soil. This condition decreased growth and lowered both photosynthetic rate and stomatal conductance. Reduced photosynthetic performance was largely due to the reduced concentration of photosynthetic pigments. Despite these responses, there was some important evidence suggesting the phytoremediatory potential of Halimione stem cuttings. The results of our study indicate that this salt-marsh shrub may represent a biotool of value in the restoration of polluted areas.     

A stratified optimization method for a multivariate marine environmental monitoring network in the Yangtze River estuary and its adjacent sea

An efficient monitoring network is very important in accessing the marine environmental quality and its protection and management. In an estuary, there are fronts that separate distinctly different water masses and affect material transport, nutrient distribution, pollutant aggregation, and diffusion. This stratified heterogeneous surface neither satisfies the stationary requirements of kriging, nor can be handled adequately by removing a spatially continuous trend. This article presents a stratified optimization method for a multivariate monitoring network. In this method, principal component analysis (PCA) was used to reduce the dimensionality of the correlated targets, and the mean of surface with nonhomogeneity (MSN) method was adopted to produce the best linear unbiased estimator for a spatially stratified heterogeneous surface that failed to satisfy the requirements for a kriging estimate. The existing monitoring network in the Yangtze River estuary and its adjacent sea, which was designed by purposive sampling year ago was optimized as an illustration. The optimization consisted of two steps: reduce the redundant monitoring sites and then optimally add new sites to the remaining sites. After optimization, the inclusion of 51 sites in the monitoring network was found to produce a smaller total estimated error than that of the current network, which has 70 sites; moreover, the use of 55 sites can produce a higher precision of estimation for all three principal components (PCs) than that of the current 70 sites. The results demonstrated that the proposed method is suitable for optimizing environmental monitoring sites that have dominant stratified nonhomogeneity and that involve multiple factors.     

Identification of the controlling factors for hydrological responses by artificial neural networks

Identifying the controlling factors for hydrological responses is of great importance for artificial neural network-based flood forecasting models, which are often hindered by the lack of physical mechanisms. To explore the first-order controlling factors of hydrograph patterns, a hybrid neural network was designed to analyse the impacts of potential driving variables with different temporal and spatial resolutions on hydrograph patterns. The Jinhua River Basin in Southeast China was used as an example in this study. Flood events with different hydrograph patterns and six external factors denoting potential controlling factors were individually classified into specific clusters using self-organizing maps (SOMs). Based on the back-propagation neural network (BPNN) and leave-one-out cross-validation methods, the controlling factors of different flood patterns were identified by comparing the performances of flood simulation models trained with datasets before and after the potential controlling factor classification. The results showed that (i) the classification of controlling factors indicating various runoff regimes significantly improved the performance of data-driven models in flood simulation in terms of correlation coefficient, Nash-Sutcliffe coefficient, and normalized root mean square error; (ii) the spatial distribution of antecedent soil moisture and vegetation conditions as well as the temporal distribution of rainfall dominated different hydrograph patterns; and (iii) the transition of dominant rainfall-runoff processes could be identified in an individual flood event using the hybrid SOM–BPNN model, indicating the varying influence of potential controlling factors on streamflow. Overall, the hybrid neural network models trained with datasets classified by controlling factors provide a general analytical framework to identify the governing dynamics for different flood patterns and improve the accuracy of flood simulations. Additionally, more attention should be devoted to improving the time to peak error of hydrological models, which cannot be settled by data-driven models trained with different data-splitting strategies.     

线性滚动极值处理方法对数值模拟风速的订正研究

为了进一步提高WRF模式对风速预报的准确性,以我国某风电场01#测风塔2007年5月和11月的数据为例,通过线性回归方法并结合滚动和极值处理技术,对WRF模式模拟风速进行了订正。结果表明,直接使用线性回归方法对于模拟风速的订正无明显效果;采用滚动技术的线性回归订正效果与步长有关,与线性回归订正相比总体上有较大改进,其中3 h步长改进更明显;相同步长,线性滚动极值处理订正较线性滚动订正相比有进一步改进,其中1 h步长线性滚动极值处理效果最优,如5月和11月订正前模拟风速的相对均方根误差(rRMSE)分别为29.274%、33.583%,订正后下降为14.714%、14.493%。订正后精度明显提高,更接近实况风速,线性滚动极值处理订正方法能够较好订正模式模拟风速,有效提高风速预报准确率。      

两种湍流参数化方案针对水体温度演变过程的模拟与比较

依据山东跋山水库的观测数据,采用MY2.5参数化方案和κ-ε参数化方案在不同的表面边界条件下模拟观测的温度剖面演化过程。模拟结果显示,在风应力强迫的表面边界条件下,采用这两种参数化方案的模拟结果差别不大;当考虑Mellor和Blumberg的波浪破碎表面边界条件时,采用MY2.5参数化方案的模拟结果没有明显的改善,但采用κ-ε参数化方案的模拟结果有明显的改善,得到与观测相符的模拟结果。     

合浦珠母贝基质蛋白KRMP-3对二价金属离子选择性的研究

用大肠杆菌表达含有GST标签的基质蛋白KRMP-3。利用圆二色谱(Circular Dichroism, CD)研究不同浓度钙离子和镁离子对基质蛋白 KRMP-3二级结构的影响。结果表明,钙离子对其二级结构的变化远大于镁离子;同时,采用荧光淬灭法研究 KRMP-3对钙,镁,锶,钡等二价金属离子的选择性,结果表明, KRMP-3对钙离子有特异性选择性,钙离子与KRMP-3的结合常数K约为103 L/mol,结合位点数 n 近似为1,表明 KRMP-3与钙离子的结合能力适中,推测基质蛋白 KRMP-3对合浦珠母贝( Pinctada fucata)棱柱层形成起到促进作用。     

遗传算法在甲流SIR模型参数求解中的应用

论文以全国甲型H1N1流行性感冒（下简称甲流）疫情数据为实例,讨论了采用SIR模型对甲流的传播过程进行模拟时相关参数的求解问题。分别通过优化的遗传算法（Genetic Algorithm,GA）和模拟退火算法（Simula-ted Annealing Algorithm,SA）求得该非线性模型中的重要参数阈值（日治愈率与日传染率的比值）,并由该参数阈值计算出各月患病人数。论文比较分析了两种算法在精度和效率上的优劣,发现遗传算法优于模拟退火。同时模拟结果验证了SIR模型适合甲流疫情的分析模拟。     

Quantitative Integration of High-Resolution Hydrogeophysical Data: A Novel Approach to Monte-Carlo-Type Conditional Stochastic Simulations and Implications for Hydrological Predictions

Geophysical techniques can help to bridge the inherent gap that exists with regard to spatial resolution and coverage for classical hydrological methods. This has led to the emergence of a new and rapidly growing research domain generally referred to as hydrogeophysics. Given the differing sensitivities of various geophysical techniques to hydrologically relevant parameters, their inherent trade-off between resolution and range, as well as the notoriously site-specific nature of petrophysical parameter relations, the fundamental usefulness of multi-method surveys for reducing uncertainties in data analysis and interpretation is widely accepted. A major challenge arising from such endeavors is the quantitative Integration of the resulting vast and diverse database into a unified model of the probed subsurface region that is consistent with all available measurements. To this end, we present a novel approach toward hydrogeophysical data integration based on a Monte-Carlo-type conditional stochastic simulation method that we consider to be particularly suitable for high-resolution local-scale studies. Monte Carlo techniques are flexible and versatile, allowing for accounting for a wide variety of data and constraints of differing resolution and hardness, and thus have the potential of providing, in a geostatistical sense, realistic models of the pertinent target parameter distributions. Compared to more conventional approaches, such as co-kriging or cluster analysis, our approach provides significant advancements in the way that larger-scale structural information contained in the hydrogeophysical data can be accounted for. After outlining the methodological background of our algorithm, we present the results of its application to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the detailed local-scale porosity structure. Our procedure is first tested on pertinent synthetic data and then applied to a field dataset collected at the Boise Hydrogeophysical Research Site. Finally, we compare the performance of our data integration approach to that of more conventional methods with regard to the prediction of flow and transport phenomena in highly heterogeneous media and discuss the Implications arising.     

加权复合分位数自回归模型在隧道围岩变形预测中的应用

提出加权复合分位数自回归模型对隧道围岩变形进行预测的新方法,并给出其原理和实现算法。以昆明市阳宗隧道为例,对加权复合分位数自回归预测模型进行计算,并与其他模型进行对比分析。结果表明,新方法预测效果优于非加权复合分位数估值的AR(2)模型、基于最小二乘参数估计的自回归预测、经遗传算法优化的支持向量机等预测方法。     

关于磁异常的模拟试验研究

通过对不同深度、不同形态、不同倾斜状态的已知模型引起的磁异常垂直分量的测量，进行不同侧面的数据处理，从中探索出不同侧面的数据处理在判断磁异常模型各特征参数中所起的作用，最后总结出测量深度、形形倾斜以及磁化方向对磁异常分布的影响，从中得到了一些有益的结论。