Comparison of Numerical Street Dispersion Models with Results from Wind Tunnel and Field Measurements

Numerical dispersion models developed and validated in different European countries were applied to data sets from wind tunnel and field measurements. The comparison includes the Danish Operational Street Pollution Model (OSPM) and the microscale flow and dispersion model MISKAM. The latter is recommended for application in built-up areas in the draft of the new German guideline VDI 3782/8. In a first step the models were applied to simplified street configurations. Different parameters as length and height of adjacent buildings and the angle of the incoming flow were varied. The results were compared to recent wind tunnel measurements. In a second step the models were applied to two extensively investigated field data sets from Jagtvej, Copenhagen and G ttinger Straße, Hannover. Intensified and more transparent and accessible validation procedures would be helpful for the thorough user.     

Demographic Effects of Harvesting Epiphytic Bromeliads and an Alternative Approach to Collection

Abstract: Hundreds of epiphytic bromeliads species are harvested from the wild for trade and for cultural uses, but little is known about the effects of this harvest. We assessed the potential demographic effects of harvesting from the wild on 2 epiphytic bromeliads: Tillandsia macdougallii, an atmospheric bromeliad (adsorbs water and nutrients directly from the atmosphere), and T. violaceae, a tank bromeliad (accumulates water and organic material between its leaves). We also examined an alternative to harvesting bromeliads from trees—the collection of fallen bromeliads from the forest floor. We censused populations of T. macdougallii each year from 2005 to 2010 and of T. violaceae from 2005 to 2008, in Oaxaca, Mexico. We also measured monthly fall rates of bromeliads over 1 year and monitored the survival of fallen bromeliads on the forest floor. The tank bromeliad had significantly higher rates of survival, reproduction, and stochastic population growth rates (λ_s) than the atmospheric bromeliad, but λ_s for both species were <1, which suggests that the populations will decline even without harvest. Elasticity patterns differed between species, but in both, survival of large individuals had high elasticity values. No fallen bromeliads survived more than 1.5 years on the forest floor and the rate of bromeliad fall was comparable to current harvest rates. Low rates of population growth recorded for the species we studied and other epiphytic bromeliads and high elasticity values for the vital rates that were most affected by harvest suggest that commercial harvesting in the wild of these species is not sustainable. We propose the collection of fallen bromeliads as an ecologically and, potentially, economically viable alternative.     

Stochastic gravity models for modeling lake invasions

Freshwater aquatic systems in North America are being invaded by many different species, ranging from fish, mollusks, cladocerans to various bacteria and viruses. These invasions have serious ecological and economic impacts. Human activities such as recreational boating are an important pathway for dispersal. Gravity models are used to quantify the dispersal effect of human activity. Gravity models currently used in ecology are deterministic. This paper proposes the use of stochastic gravity models in ecology, which provides new capabilities both in model building and in potential model applications. These models allow us to use standard statistical inference tools such as maximum likelihood estimation and model selection based on information criteria. To facilitate prediction, we use only those covariates that are easily available from common data sources and can be forecasted in future. This is important for forecasting the spread of invasive species in geographical and temporal domain. The proposed model is portable, that is it can be used for estimating relative boater traffic and hence relative propagule pressure for the lakes not covered by current boater surveys. This makes our results broadly applicable to various invasion prediction and management models.     

Modelling species distributions with penalised logistic regressions: A comparison with maximum entropy models

An important aspect of species distribution modelling is the choice of the modelling method because a suboptimal method may have poor predictive performance. Previous comparisons have found that novel methods, such as Maxent models, outperform well-established modelling methods, such as the standard logistic regression. These comparisons used training samples with small numbers of occurrences per estimated model parameter, and this limited sample size may have caused poorer predictive performance due to overfitting. Our hypothesis is that Maxent models would outperform a standard logistic regression because Maxent models avoid overfitting by using regularisation techniques and a standard logistic regression does not. Regularisation can be applied to logistic regression models using penalised maximum likelihood estimation. This estimation procedure shrinks the regression coefficients towards zero, causing biased predictions if applied to the training sample but improving the accuracy of new predictions. We used Maxent and logistic regression (standard and penalised) to analyse presence/pseudo-absence data for 13 tree species and evaluated the predictive performance (discrimination) using presence-absence data. The penalised logistic regression outperformed standard logistic regression and equalled the performance of Maxent. The penalised logistic regression may be considered one of the best methods to develop species distribution models trained with presence/pseudo-absence data, as it is comparable to Maxent. Our results encourage further use of the penalised logistic regression for species distribution modelling, especially in those cases in which a complex model must be fitted to a sample with a limited size.     

Influence of grain size on species-habitat models

High resolution remote sensing data facilitate the use of small-scale habitat features such as trees or hedges in the analysis of species-habitat relationships. Such data potentially enable more accurate species-habitat mapping than lower resolution data. Here, for the first time, we systematically investigated this hypothesis by altering the spatial resolution from 1 m up to 1000 m grain size in species-habitat models of 13 bird species. The study area covered the Nidda river catchment in central Germany, a large heterogeneous landscape of 1620 km². A high resolution habitat map of the area was converted to coarser spatial and thematic resolutions in seven steps. We investigated how model performance responded to grain size, and we compared the differential effects of spatial resolution and thematic resolution on model performance. Explained deviance (D²) of the bird models generally decreased with coarser spatial resolution of the data, although it did not decrease monotonically in all species. On average across all species, model D² decreased from 41.5 at 1 m grain size to 15.9 at 1000 m grain size. Ten species were best modelled at 1 m, two species at 3 m and one species at 32 m grain size. Model performance degraded continuously with increasing grain size, both in habitat generalist and habitat specialist bird species, and was systematically lower in habitat generalists. The higher model performance observed at finer grain sizes was most likely caused by the combination of three factors: (1) high spatial accuracy of bird records and (2) a more precise location and delineation of habitat features and, (3) to a lesser degree, by more habitat types differentiated in maps of finer resolution. We conclude that higher spatial and thematic resolution data can be essential for deriving accurate predictions on bird distribution patterns from species-habitat models. Especially for bird species that are sensitive to specific land-use types or to small-scaled habitat features, a grain size of 1-3 m seems most promising.     

Parallel agent-based modeling of spatial opinion diffusion accelerated using graphics processing units

In this article, we describe a parallel agent-based model of spatial opinion diffusion that is driven by graphics processing units (GPUs). Modeling opinion exchange and diffusion across landscapes often involves the simulation of large numbers of geographically located individual decision-makers and a massive number of individual-level interactions. This simulation requires substantial computational power. GPU-enabled computing resources provide a massively parallel processing platform based on a fine-grained shared memory paradigm. This massively parallel processing platform holds considerable promise for meeting the computing requirement of agent-based models of spatial problems. In this article, we focus on the parallelization of an agent-based spatial opinion model using GPU technologies. We discussed key algorithms designed for parallel agent-based opinion modeling: including domain decomposition and mutual exclusion. Experiments conducted to examine computing performance show that GPUs provide a computationally efficient alternative to traditional parallel computing architectures and substantially accelerate agent-based models of large-scale opinion exchange among individual decision makers.     

Design of an Air-Quality Surveillance System for Buenos Aires City Integrated by a NO x Monitoring Network and Atmospheric Dispersion Models

This paper presents an air-quality surveillance system designed to detect the occurrence of air pollutant concentrations greater than a reference level in an urban area. The system is integrated by an air-quality monitoring network and atmospheric dispersion models simulations. An objective methodology to design an urban air-quality monitoring network is proposed. This methodology is based on the analysis of air-quality modelling results. The procedure is applied to design an air-quality monitoring network to control NO _x concentration levels in Buenos Aires City. Results indicate that six monitors will detect the occurrence of concentration greater than the air-quality guidelines with an efficiency of about 67%. Once a violation is detected, results of atmospheric dispersion models will help in the determination of affected areas. Four possible examples are included to illustrate the assistance that the results of atmospheric dispersion models can bring to a better estimation of possible affected areas in the city. Combining these results with the last census data, an estimation of the inhabitants possibly exposed is obtained.     

A Stream Network Model for Integrated Watershed Modeling

We describe a flexible, computationally efficient stream network model, which forms the core of a simulation framework that spatially integrates the contributions from point and nonpoint sources in a watershed. The model uses the map and stream topology information in the US Environmental Protection Agency’s Reach File 3 to generate a spatially explicit network of stream reaches. Water and materials are routed through the stream network to the watershed outlet, and the routing process accounts for transit times and for possible nutrient losses in streams. This model can be applied wherever Reach File maps or maps from the newer National Hydrography Dataset are available, and it can be combined with models of other watershed processes to create a complete watershed simulation system. We present an application of the stream network model to two watersheds of different sizes in the Patuxent River watershed of Maryland, USA. Simulated predictions of streamflow and nitrate concentrations are either very good or good according to standards developed for evaluating the widely used Hydrologic Simulation Program – Fortran (HSPF) watershed model.     

花垣河锰污染及其成因分析

对花垣河河水中8个监测断面（S1-S8）水样和堆积在河岸附近MnSO4废渣及其浸出液中Mn、Cd、Pb、Cu和Zn等5种重金属元素的总量进行了分析。结果表明,花垣河河水中Mn和Cd质量浓度超过《地表水环境质量标准》（GB3838—2002）V类水体标准;MnSO4废渣浸出液中Cd超过《危险废物鉴别标准》（GB5058．3—1996）规定值。将花垣河河水中Mn、Cd、Pb、Cu和Zn的含量与MnSO4废渣浸出液中相应的元素含量进行相关性分析,r=0．957（P〈0．01）,表明堆积在花垣河河岸附近MnSO4废渣是可能造成花垣河污染的主要来源。