“棕地”现象及其治理对策

“棕地”的概念至今在国内外没有统一的标准,它只是刚刚引入我国,没有被研究者所重视,但在我国,“棕地”问题已开始出现,必须引起我们的重视。本文通过探讨了“棕地”的概念和“棕地”分类,来描述“棕地”现象,通过介绍国外“棕地”治理的先进管理经验,探讨我国“棕地”治理的对策。     

A new procedure to optimize the selection of groups in a classification tree: Applications for ecological data

Agglomerative cluster analyses encompass many techniques, which have been widely used in various fields of science. In biology, and specifically ecology, datasets are generally highly variable and may contain outliers, which increase the difficulty to identify the number of clusters. Here we present a new criterion to determine statistically the optimal level of partition in a classification tree. The criterion robustness is tested against perturbated data (outliers) using an observation or variable with values randomly generated. The technique, called Random Simulation Test (RST), is tested on (1) the well-known Iris dataset [Fisher, R.A., 1936. The use of multiple measurements in taxonomic problems. Ann. Eugenic. 7, 179–188], (2) simulated data with predetermined numbers of clusters following Milligan and Cooper [Milligan, G.W., Cooper, M.C., 1985. An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179] and finally (3) is applied on real copepod communities data previously analyzed in Beaugrand et al. [Beaugrand, G., Ibanez, F., Lindley, J.A., Reid, P.C., 2002. Diversity of calanoid copepods in the North Atlantic and adjacent seas: species associations and biogeography. Mar. Ecol. Prog. Ser. 232, 179–195]. The technique is compared to several standard techniques. RST performed generally better than existing algorithms on simulated data and proved to be especially efficient with highly variable datasets.     

湖库水生植被光学遥感监测研究进展和展望

水生植被在湖库生态系统中发挥稳定沉积物、净化水质、平衡水生生态系统等作用,监测水生植被变化对湖库生态环境的监测具有重要意义。梳理了国内外利用高光谱、多光谱光学卫星遥感数据提取湖库水生植被的方法,尤其是针对其中涉及的阈值确定问题进行总结分析,介绍了典型研究区水生植被时空分布和变化以及与水质的关系,最后给出一些水生植被遥感监测的展望。     

气相色谱法测定废水中N，N─二甲基甲酰胺

用气相色谱仪氢焰检测器，ＰＥＧ２０Ｍ毛细管柱测定废水中的Ｎ，Ｎ二甲基甲酰胺（ＤＭＦ），结果表明与分光光度法基本一致     

精确三维光谱数据活性关系方法应用于ToxCast数据库中雌激素受体数据分类、验证和特征提取的建模策略

本文利用三维光谱数据活性关系方法(3D-SDAR)对ToxCast数据库中1 528种化合物的雌激素潜能(表示为18分复合高通量筛选生物测定)进行建模。由于缺少170核磁共振(NMR)仿真软件,信息量最大的碳-碳3D-SDAR指纹随着一些指标变量增强,这些指标变量包括来自羰基和酰胺的氧原子、酯基、磺酰、硝基、脂肪族羟基和酚羟基官能团。为了评价本文作者所开发模型的预测性能的真实性,美国环境保护署为研究人员提供了2 008种化合物组成一套盲测。其中,543种化合物有文献可查数据—他们的结合力有助于评估开发模型的外部分类精度：得到预测精度0.62,敏感度0.71,和特异度0.53的结果。与其他建模技术相比,本文作者开发的模型几乎没有简化建模集和预测集之间的性能。3D-SDAR映射技术可以识别拟雌激素活性所必需的结构特征：1) 存在一个酚的OH官能团或环己烯酮,2)与第一苯酚环上的氧距离6 ?到8 ?的第二芳香或酚环,3) 存在一个与苯酚环的几何中心距离大约6 ?的甲基官能团,4)一个靠近(距离几何中心～4?)某个苯酚环的羰基官能团。
精选自Svetoslav H. Slavov, Richard D. Beger. Rigorous 3-dimensional spectral data activity relationship approach modeling strategy for toxcast estrogen receptor data classification, validation, and feature extraction. Environmental Toxicology and Chemistry: Volume 36, Issue 3, pages 823–830, July 2017. DOI: 10.1002/etc.3578
详情请见http://onlinelibrary.wiley.com/wol1/doi/10.1002/etc.3578/full
     

Self healing databases for predictive risk analytics in safety-critical systems

Assuring the quality, consistency and accuracy of safety data repositories is essential in safety-critical systems. In many systems, however, significant effort is required to identify, address, clean and repair data errors and inconsistencies, and to integrate safety data sets and repositories, particularly for risk analyses. Although some self healing and self repairing capabilities leveraging machine learning and predictive analyses have been employed to identify anomalies and monitor quality in structured safety-critical data sets, little attention has been focused on addressing shortcomings in heterogeneous—structured and unstructured—safety data sets, the focus of this work. The text mining and classification analysis employed in this research indicates that machine learning techniques can be employed to improve the accuracy and robustness of large-scale structured and unstructured database repositories, and to improve the effectiveness and efficiency of safety data repository maintenance. Hybrid machine learning approaches, leveraging machine learning, text mining and natural language processing, offer additional promise in future work.     

Pattern recognition and classification of sediments according to their metal content using chemometric tools. A case study: the estuary of Nerbioi-Ibaizabal River (Bilbao, Basque Country)

Chemometrics are increasingly used in environmental monitoring studies, but are still far from being accepted as routine tools by field specialists. The multivariate character of usually highly correlated environmental data recommends the use of advanced chemometrics as part of the analytical methodology in order to get information on the basic structure of data. In this work, we have applied a battery of non-supervised (Principal Component Analysis (PCA)) and supervised (k-Nearest Neighbour (k-NN), Soft Independent Modelling of Class Analogies (SIMCA), Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs)) multivariate techniques on a specific environmental dataset. The dataset consists on the concentration of 14 elements (Al, As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, V and Zn) in 95 sediments collected at eight different locations of the estuary of the Nerbioi-Ibaizabal River (Bilbao, Basque Country) during 12 sampling campaigns conducted every 3 months between 2005 and 2008. The study aims to present a simple methodology of general applicability which may result in a flexible and practical tool to assess chemical pollution in sediments of a given specific site.     

基于模糊综合识别技术的粉尘危害评价应用研究

针对目前粉尘危害评价存在的不足,采用最优权法确定各评价指标的权系数,利用模糊识别技术对粉尘危害程度进行综合评价,初步建立了粉尘危害评价指标体系和评价分级标准.结合某矿山实例,对粉尘危害进行模糊综合评价.评价结果表明,模糊综合识别技术能够较好反应实际情况,是一种有效的评价方法,对粉尘防治工作具有一定的指导意义.     

Microsatellites and artificial neural networks: tools for the discrimination between natural and hatchery brown trout (Salmo trutta, L.) in Atlantic populations

Artificial Neural Networks (ANN) were applied to microsatellite data (highly variable genetic markers) to separate genetically differentiated forms of brown trout (Salmo trutta) in south-western France. A classic feed-forward network with one hidden layer was used. Training was performed using a back-propagation algorithm and reference samples representing the different genetic types. The hold-out and the leave-one-out procedures were used to test the validity of the network. They were chosen according to the populations and the questions analysed. The informative content of the different variables used for the distinction (the alleles of the different loci) was also evaluated using the Garson–Goh algorithm. The results of learning gave high percentages of well-classified individuals (up to 95% for the test with the hold-out analysis). This confirms that ANNs are suitable for such genetic analyses of populations. From a biological point of view, the study enabled evaluation of the genetic composition and differentiation of different river populations and of the impact of stocking.     

eChemPortal及其在我国重点环境管理危险化学品环境风险评估中的应用

危害性识别是化学品环境风险评估工作的重要基础,危害性数据的质量直接影响化学品环境风险评估工作的结果与结论。通过介绍经济合作与发展组织(OECD)eChemPortal近几年来的主要变化,以上海地区涉及的30种重点环境管理危险化学品为例,比较分析我国《危险化学品分类信息表》及eChemPortal引用的按照《欧盟物质和混合物的分类、标签和包装法规》的公开分类和标签目录(ECHA CL inventory)、日本政府全球化学品统一分类和标签分类结果(GHS-J)的差异。结果表明:eChemPortal作为OECD建立的全球化学物质信息门户,加入的数据库多数经过政府机构或同行评审,准确性和可靠性相对较高,目前已有32个数据库加入到该平台,其中32个数据库支持化学物质检索,4个数据库支持化学性质检索,2个数据库支持GHS检索,并且数据库的数量和功能还在持续增加;相对于国家安全监管总局的《危险化学品分类信息表》和ECHA CL inventory,GHS-J的危害性分类结果较为完善,而且多数分类结果提供了具体的分类数据基础信息,这对于分类结果的评估十分有用。上述研究表明我国政府监管部门有必要结合eChemPortal等权威的数据信息平台发布我国重点环境管理危险化学品环境风险评估推荐数据库,从而规范重点环境管理危险化学品环境风险评估过程并推动对化学品的有效环境监管。