The Influence of Linear Elements on Plant Species Diversity of Mediterranean Rural Landscapes: Assessment of Different Indices and Statistical Approaches

This paper mainly aims to study the linear element influence on the estimation of vascular plant species diversity in five Mediterranean landscapes modeled as land cover patch mosaics. These landscapes have several core habitats and a different set of linear elements -habitat edges or ecotones, roads or railways, rivers, streams and hedgerows on farm land- whose plant composition were examined. Secondly, it aims to check plant diversity estimation in Mediterranean landscapes using parametric and non-parametric procedures, with two indices: Species richness and Shannon index.Land cover types and landscape linear elements were identified from aerial photographs. Their spatial information was processed using GIS techniques. Field plots were selected using a stratified sampling design according to relieve and tree density of each habitat type. A 50×20 m² multi-scale sampling plot was designed for the core habitats and across the main landscape linear elements. Richness and diversity of plant species were estimated by comparing the observed field data to ICE (Incidence-based Coverage Estimator) and ACE (Abundance-based Coverage Estimator) non-parametric estimators.The species density, percentage of unique species, and alpha diversity per plot were significantly higher (p < 0.05) in linear elements than in core habitats. ICE estimate of number of species was 32% higher than of ACE estimate, which did not differ significantly from the observed values. Accumulated species richness in core habitats together with linear elements, were significantly higher than those recorded only in the core habitats in all the landscapes. Conversely, Shannon diversity index did not show significant differences.     

Characterisation of Selected Lentic Habitats of Dharwad, Haveri and Uttar Kannada Districts of Karnataka State, India

Water quality survey was undertaken to study the current status of water quality of some lentic habitats of Dharwad, Haveri and Uttar Kannada districts, to provide a baseline data, comprising study of their main physical and chemical characteristics and also to evaluate the recent limnological changes those have occurred over a time period in some lentic habitats. Trophic status was also analysed. Present study reveals that Unkal lake, and B.K. Shigigatti tank of Dharwad district, Timmapur, Hosalli, and Krishnapur tanks of Haveri district are fast progressing towards hypereutrophication. Of the other tanks, Galgi tank of Dharwad district, Gudgur, Hanamapur, Medleri, and Kadkol tanks of Haveri district have reached the eutrophic stage. The anthropogenic activities, sewage and fertilizers used in agricultural fields appear to be the major causes of the eutrophication in these tanks. If the habitats have to be preserved for their intended use, sustainable and holistic management measures for the remediation of the tanks are an immediate necessity.     

Spatial Patterns and Rankings of Contaminant Concentrations in Herring Gull Eggs from 15 Sites in the Great Lakes and Connecting Channels, 1998–2002

Mean values of eight contaminants in Herring Gull (Larus argentatus) eggs were calculated for 15 Great Lakes sites for the 5 year period 1998–2002. The sites were ranked according to the concentrations of each of seven compounds relative to fish flesh criteria for the protection of piscivorous wildlife, and a single overall rank of contamination was calculated for each site. Based on this weighted ranking scheme, we found that sum PCBs, dioxin and DDE contributed the most (60.2, 30.5% and 8.5%, respectively) to the overall rankings. The weighted ranking scheme showed that eggs from Channel-Shelter Island (Saginaw Bay, Lake Huron), Strachan Island (St. Lawrence River) and Gull Island (northern Lake Michigan) ranked as the three most contaminated sites, while Agawa Rocks (eastern Lake Superior), Chantry Island (southern Lake Huron) and Port Colborne (eastern Lake Erie) ranked as the three least contaminated sites. Two of the three most contaminated sites are Areas of Concern; none of the three least contaminated sites are Areas of Concern.     

The Metallic Composition of Aerosols at Three Monitoring Sites in Korea During Winter 2002

In the present study, the distribution patterns of various metals were analyzed and compared using PM samples collected concurrently from three monitoring sites located in Korea (Seoul, Busan, and Jeju island) in December 2002. As these sites can represent metal pollution with different degrees of anthropogenic activities, their concentration levels were distinguished in a systematic manner in the order of Jeju, Busan, and Seoul. By comparing the present data sets with those measured previously from other locations in Korea and around the world, we attempted to diagnose the general status of elemental pollution on the Korean peninsula. Through an application of different statistical approaches, the major processes controlling elemental levels were assessed for each of the three study sites. The results indicated the importance of both crustal and anthropogenic sources in all sites with their relative roles varying significantly from each other. The results of the metal analysis data, when examined in relation to back trajectory analysis, confirmed that their concentration changes are affected quite sensitively with air mass movement patterns. The overall results of this study consistently indicated the contribution of a strong anthropogenic source area (e.g., China) to the observed metal concentration levels in the study area, but the strengths of such signals vary considerably across the Korean peninsula.     

Combining Logistic Models with Multivariate Methods for the Rapid Biological Assessment of Rivers Using Macroinvertebrates

This work represents an attempt to define a simple method to classify the relative degree of disturbance of sites in lotic systems on the basis of comparison of their faunistic composition with reference sites. Two ecotypes were selected in northern Portugal where benthic invertebrates were sampled in reaches with different levels of contamination. As a first stage, previous Geographic Information System information was used to define reference sites in each ecotype. Afterwards, multivariate techniques and non linear estimation models were combined to assess biological quality. This method allowed us to quantify sites according to increasing levels of contamination, after the probabilities of occurrence of taxa along a gradient of contamination taking into account the reference condition. The results suggest that this method is sensitive to organic pollution, easy to interpret, namely the species tolerance, and could be a good framework to establish regional rankings depending on the ecological impact of river sites.     

面向实时人群动力学分析的深度基本图网络

当前行人疏散实验中基本图计算方法通常是通过对每个行人进行跟踪实现的.但这种跟踪方法难以实现实时人群动力学分析.针对这一问题,提出了深度基本图网络.实验提出的网络框架由两个模块组成,即多尺度递归卷积神经网络(MSR-Net)和光流模块,分别对行人密度和行人速度进行估计.具体来讲,MSR-Net学习了输入图像与行人密度图之...     

侧墙结构纵向多窗口羽流火焰融合的数值模拟

为了给高层建筑外部火蔓延防控提供参考,利用火灾动态仿真模拟软件PyroSim对无侧墙建筑的纵向多窗口羽流火焰与侧墙建筑的纵向多窗口羽流火焰进行了数值模拟,并改变侧墙长度,引入危险温度T=540℃、T1=350℃及T2=250℃,综合分析窗口温度曲线及等温线数据。结果表明:纵向多窗口羽流火焰产生相互融合现象,无侧墙建筑纵向相邻两窗口与三窗口的危险温度高度相似,比单窗口的危险温度高度提升了2.5~3.0 m;侧墙结构引起烟囱效应的作用效果与侧墙的长度呈正比,侧墙长度为3.6 m时,纵向多窗口的危险温度高度与无侧墙建筑相比,对T1和T2,高度提升了2.0~2.5 m,而对T,高度的影响较弱,羽流火焰的形状在纵向被拉长。     

地铁区间隧道烟气层化现象研究

以重庆某一地铁区间隧道为原型,搭建了1∶15小尺寸隧道试验台,通过小尺寸试验与FDS 6.5.2数值模拟开展隧道顶部烟气温度分布及分层规律研究。基于Newman提出的烟气层分区条件,研究了不同纵向风速下的烟气分层现象,提出了烟气稳定层化长度的概念,并分析了火源热释放速率及断面型式对烟气层化长度的影响。试验证明了Newman提出的烟气分层计算方法是可信的。结果表明:纵向风速较小时,火源下游能呈现烟气分层现象;烟气稳定层化长度受火源热释放速率影响较大,随热释放速率增大而增大;隧道高度的变化对烟气稳定层化长度的影响较小。     

Polynomial Surrogates for Open-Channel Flows in Random Steady State

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on the more realistic case of the Garonne River (France) for which a global sensitivity analysis using sparse least-angle regression was performed to reduce the size of the stochastic problem. For both cases, Galerkin projection approximation coupled to Gaussian quadrature that involves a limited number of forward model evaluations is compared with least-square regression for computing the coefficients when the surrogate is parameterized with respect to the local friction coefficient and the upstream discharge. The results showed that a gPC-surrogate with total polynomial degree equal to 6 requiring 49 forward model evaluations is sufficient to represent the water level distribution (in the sense of the \(\ell _2\) norm), the probability density function and the water level covariance matrix for further use in the framework of data assimilation. In locations where the flow dynamics is more complex due to bathymetry, a higher polynomial degree is needed to retrieve the water level distribution. The use of a surrogate is thus a promising strategy for uncertainty quantification studies in open-channel flows and should be extended to unsteady flows. It also paves the way toward cost-effective ensemble-based data assimilation for flood forecasting and water resource management.     

Air Quality Modeling Using the PSO-SVM-Based Approach,MLP Neural Network,and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (MLP) and the M5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the MLP and M5 model tree prediction methods in terms of statistical estimators and testing performances.