Null models reveal preferential sampling, spatial autocorrelation and overfitting in habitat suitability modelling

Nowadays, species are driven to extinction at a high rate. To reduce this rate it is important to delineate suitable habitats for these species in such a way that these areas can be suggested as conservation areas. The use of habitat suitability models (HSMs) can be of great importance for the delineation of such areas. In this study MaxEnt, a presence-only modelling technique, is used to develop HSMs for 223 nematode species of the Southern Bight of the North Sea. However, it is essential that these models are beyond discussion and they should be checked for potential errors. In this study we focused on two categories (1) errors which can be attributed to the database such as preferential sampling and spatial autocorrelation and (2) errors induced by the modelling technique such as overfitting, In order to quantify these adverse effects thousands of nulls models were created. The effect of preferential sampling (i.e. some areas where visited more frequenty than others) was investigated by comparing model outcomes based from null models sampling the actual sampling stations and null models sampling the entire mapping area (Raes and ter Steege, 2007). Overfitting is exposed by a fivefold cross-validation and the influence of spatial autocorrelation is assessed by separating test and training sets in space. Our results clearly show that all these effects are present: preferential sampling has a strong effect on the selection of non-random species models. Crossvalidation seems to have less influence on the model selection and spatial autocorrelation is also strongly present. It is clear from this study that predefined thresholds are not readily applicable to all datasets and additional tests are needed in model selection.     

Occupational Health Care in Small and Medium-Sized Enterprises—How Many Doctors Do We Need and How Do We Ensure Good Care?

The translation of the framework directive 89/391/EEC (Council Directive 89/391/EEC) into national law aims at supplying occupational protection and health care to all employees of large, small, and medium-sized enterprises (SMEs) likewise, depending on assessed exposure. Prior incomplete protection of the German workforce with bias against the SMEs requires an assessment of quantitative and qualitative adequacy of present occupational health care practice and also of future needs.

Therefore, the Federal Institute for Occupational Safety and Health of Germany initiated a study to evaluate the present state of occupational health care in 4 regions with different geographic and economic structure. Based on these data the future demand for adequately trained occupational physicians will be estimated by employing a statistical method that allows for including a large number of modifying variables (economic development, demographic change, etc.).

Expected result of the applied technique are the estimated minimal and maximal number of occupational physicians that have to be trained to meet future demand. In the same study models of best practice will be identified and evaluated for general application. The project started in October 1997 and will be finished by January 2000.     

基于环境逸度模型的化学物质暴露与风险评估研究进展

工业化学品通过各种迁移转化过程后在环境区间中广泛分布,同时新化学品正被大量的生产并进入环境,使得对于化学品管控的需求和压力不断增加。基于这一背景,多国政府颁布了相应的政策法规对化学品进行管控。除了用相应的政策法规进行预先管控之外,使用基于逸度概念的多介质环境模型来表征化学品的行为与归趋是一个相对简单而有效的方法,不仅适用于环境中存在的现有化学品的暴露评估,也成为各国化学品管理中对新化学品的环境暴露进行预测的有力工具。本文综述逸度概念与方法、相关环境模型、建模过程、应用验证等方面的国内外研究进展,希望能够对我国化学品环境暴露评估与风险评估的模型构建、优化与方法应用提供信息和借鉴。     

有机化合物生物富集因子的计算机预测研究

有机化合物在生物体内的富集,通常用生物富集因子(bioconcentration factor,简称BCF)来表达,这是化合物生态环境毒性评估的重要指标。为合理预测有机化合物是否易于生物富集,首先从美国环保局网站收集了624个具有不同BCF值的化合物,然后采用7种分子指纹结合5种机器学习方法(包括支持向量机、C4.5决策树、k最近邻法、随机森林法和朴素贝叶斯法),构建了化合物BCF的分类预测模型,所有模型均采用独立外部验证集进行验证。其中,使用Chemo Typer分子指纹结合支持向量机方法得到的二分类模型,整体预测准确度最好,达到了85.4%。通过采用信息增益、频率分析等方法,进一步确定了化合物中易于引起生物富集的关键子结构,包括芳基氯、二芳基醚、氯代烷烃等。研究中所用到的方法为有毒化学品的生态风险评价提供了良好可靠的预测工具。     

洁净室亚微米颗粒污染的模型模拟研究

采用基于欧拉框架下的Small Slip Model颗粒拟流体模型和Three Layer Model壁面沉积边界条件,并运用标准的k-ε湍流模型和壁面函数方法对非单向流洁净室内亚微米颗粒污染物的输运和分布结构进行数值模拟。模拟结果和实验结果具有较好的一致性,说明所采用的计算模型和方法具有良好的合理性和适用性。洁净室内颗粒污染物的输运和分布特征受空气流场的制约,尤其是室内存在的回流区和旋涡区,对污染控制和排除效率有着重要的影响。此外,颗粒的湍流扩散作用在对流作用较弱的区域发挥着重要的作用,对其机理的正确认识和分析有助于提高模型研究的准确性。     

垃圾渗滤液与再生水生物毒性的体外检测方法应用

本文总结了近年来在垃圾渗滤液和再生水生物毒性检测方面的主要体外试验模型和检测方法,并整理了这些模型和方法在生物毒性评价中的应用.目前常用的体外试验模型包括人源细胞系、其他哺乳动物细胞以及微生物细胞,相比较而言,人源细胞系在检测结果外推至人体时具有更强的说服力,因而其应用最为广泛.体外检测方法可概括为细胞毒性、遗传毒性和内分泌干扰效应检测三个方面,其中内分泌干扰效应的研究较多集中于雌激素效应.最后,本文提出开发三维体外细胞模型、体外试验与化学分析相结合、全面分析内分泌干扰效应和建立成组体外试验体系是渗滤液和再生水生物毒性检测方法的发展方向.     

基于LBM的风窗流场数值模拟与PIV实验研究

风窗流场分布规律对于粉尘、瓦斯、有毒有害气体运移规律以及风窗局部阻力计算具有一定的影响,而常用的CFD方法在模拟风窗后阶湍流流场这类问题上存在计算复杂度高的问题,针对此问题,结合格子玻尔兹曼(LBM)方法在流场模拟方面具有求解精度高、计算复杂度小的特点,采用多松弛格式的格子玻尔兹曼方法(MRT-LBM)对巷道风窗流场进行模拟研究。为验证格子玻尔兹曼方法对风窗流场模拟的有效性,利用粒子图像测速仪(PIV)对风窗流场分布规律进行实验验证研究。研究结果表明:LBM模拟风窗流场回流区长度为340 mm,风窗主流风速最高达19.1 m/s;PIV实验测试风窗流场回流区长度为320 mm,风窗主流风速最高达18.4 m/s,在相同模型条件下,LBM模拟结果与PIV实验测试结果基本保持一致。LBM与Fluent模拟进行对比,模拟精度、运行速度和操作便捷性要优于Fluent模拟。     

Analyzing the Effects of Groundwater Pumping on an Urban Stream‐Aquifer System

When the cone of influence of a pumping well reaches a nearby river, the resulting hydraulic gradient can induce enhanced seepage of streamflow into the aquifer. The rate of seepage is often modeled using analytical solutions that are simple to apply but may not reproduce field data due to mathematical assumptions not being met in the field. Furthermore, the appropriateness of such models has not been investigated in detail due to difficulty in measuring streamflow loss in the field. In this study, a field experiment was conducted on a reach of the South Platte River near Denver, Colorado to estimate pumping‐induced streamflow loss. A network of stream gauges, monitoring wells, and in situ measurements was used to observe streamflow rates, groundwater levels, and temperature to assess if pumping wells have a significant impact on streamflow, and to compare observed streamflow depletion against analytical solutions. Data collected suggest that pumping wells have a noticeable impact on streamflow. The analytical solutions proved accurate if streamflow was low and constant but performed poorly if streamflow was high and variable. Therefore, for this reach, the use of analytical solutions to predict streamflow may only be appropriate under low‐flow, constant‐flow conditions. Methods and results can be used to guide other streamflow depletion studies and to inform cases of pumping‐induced streamflow depletion, particularly in regard to water rights.     

种群水平生态风险评价方法概述及其在环境管理中的应用

生态风险评价的目的是保护生态系统功能的完整性、稳定性和持久性,为环境风险管理提供理论依据。然而,目前常见的用于保护生物的化学污染物浓度阈值大多是以个体水平的毒性试验结果为基础,忽略了物种在时间和空间相互作用等因素,不能够完全保护生态环境安全和生态系统功能的延续性。本文从生态风险评价的概念、目的和意义引出种群水平生态风险评价在环境管理应用的重要性,综述了种群水平生态风险评价的科学问题(如密度依赖、遗传变异和空间结构等),归纳了种群水平风险评价主要模型方法及其应用(如Euler-Lotka方程、预测矩阵、个体模型、空间模型和动态能量预算模型等),列举了各国现有法律法规中关于种群水平生态风险评价的规定,以期为种群水平生态风险评价方法研究及在环境管理中的应用提供有益借鉴。     

A study of the atmospheric dispersion of a high release of krypton-85 above a complex coastal terrain, comparison with the predictions of Gaussian models (Briggs, Doury, ADMS4)

Atmospheric releases of krypton-85, from the nuclear fuel reprocessing plant at the AREVA NC facility at La Hague (France), were used to test Gaussian models of dispersion. In 2001-2002, the French Institute for Radiological Protection and Nuclear Safety (IRSN) studied the atmospheric dispersion of 15 releases, using krypton-85 as a tracer for plumes emitted from two 100-m-high stacks. Krypton-85 is a chemically inert radionuclide. Krypton-85 air concentration measurements were performed on the ground in the downwind direction, at distances between 0.36 and 3.3 km from the release, by neutral or slightly unstable atmospheric conditions. The standard deviation for the horizontal dispersion of the plume and the Atmospheric Transfer Coefficient (ATC) were determined from these measurements. The experimental results were compared with calculations using first generation (Doury, Briggs) and second generation (ADMS 4.0) Gaussian models. The ADMS 4.0 model was used in two configurations; one takes account of the effect of the built-up area, and the other the effect of the roughness of the surface on the plume dispersion. Only the Briggs model correctly reproduced the measured values for the width of the plume, whereas the ADMS 4.0 model overestimated it and the Doury model underestimated it. The agreement of the models with measured values of the ATC varied according to distance from the release point. For distances less than 2 km from the release point, the ADMS 4.0 model achieved the best agreement between model and measurement; beyond this distance, the best agreement was achieved by the Briggs and Doury models.