Distribution patterns of plants explained by human movement behaviour

Distribution patterns of plants are affected by human activities such as creation, destruction or modification of habitats. However, another important question is to what extent humans shape plant distributions by acting as dispersal vectors. In order to answer this question we developed a simulation model for the spread of plant species between human settlements. This was done on the basis of extensive sociological and ecological data on a regional scale. With regard to the sociological data, human movement behaviour defined the amount of exchange between the settlements. Gardening types represented the potential habitat in our model. The ecological data was derived from a vegetation survey carried out in 2003, which was a repeat of a survey between 1974 and 1981 along the same transects. From these surveys, we studied the distributions of 13 species in 67 settlements. In our model, the earlier survey provided the data for the initial distribution. The simulated pattern was consequently compared with the distribution pattern in 2003. In the model, dispersal kernels based on patterns of human movement between settlements led to a better match with the distribution patterns than a null model simulating pure distance dependent dispersal for all species. This was statistically significant for seven of the thirteen species. A striking result was that alien species seem to benefit more from human dispersal than native species. We emphasize the importance of the sociological data on human movement behaviour in parameterizing our regional scale model. This study provides quantitative evidence on the impact of human movement behaviour on the distribution of plant species in suburban areas.     

Robust optimization evaluation of reliance on locally produced foods

While local food production may be beneficial in terms of developing the local economy and reducing greenhouse gases from transportation, sustainability strategies focused on local food production may generate their own risks due to yield variability. We have developed a robust optimization (RO) model to determine the minimum amount of land (cropland and pasture) required to grow food items that would satisfy a local population’s (accounting for gender and age) calorie and nutrient needs. This model has been applied to Boone County, Missouri, which has a population of approximately 170,000. Boone County is 1790 km², with 16% of the land defined as cropland and 30% defined as pasture. The model includes 27 nutrients from 17 potential foods that could be produced: six fruits and vegetables, five grains and six animal-sourced foods. Yield estimates are based on the predominate methods of agriculture in the USA. We first run our model assuming no variability, using the midpoint yield estimates. Then, to quantify uncertainty in yield for different food types, we use historical yield data over 10 years to estimate this variability and run our RO model under these variability estimates. We compare the two model results to illustrate the impact of data uncertainty on meeting sustainable local food for communities. Solutions suggest that nutrition needs can be met for the Boone County population within the land area defined.     

Improved Predictions of Plume Perception with a Human Visual System Model

This paper describes incorporation of a human visual system model in the widely used plume visibility model PLUVUE. The results will be of interest to all involved with siting new sources for which visibility of the plume is a concern and to visibility researchers. The human visual system model allows inclusion of size and shape effects on the perceptibility of a plume. Example calculations are given for 2250- and 1600-MW power plants which show that size and shape effects can reduce the predicted perceptibility by up to a factor of three.     

Cross-reactivity of anti-Salmonella egg-yolk antibodies to Salmonella serovars

The cross-reactivity of egg yolk antibodies specific to antigens of Salmonella Enteritidis and Salmonella Typhimurium to killed bacterial cells of common Salmonella serovars were tested using an indirect Enzyme Linked Immunosorbent Assay (ELISA). Egg yolk antibodies were produced against purified fimbriae, flagella and lipopolysaccharide (LPS) of S. Enteritidis strain ATCC13076 and flagella, LPS and outer membrane proteins (OMP) of S. Typhimurium strain ATCC13311. For immunological specificity of egg yolk antibodies against killed bacterial cells, we found that the titers of the anti-S. Enteritidis egg yolk antibodies were higher than those of the anti-S. Typhimurium antibodies. In the evaluation of cross-reactivity of these egg yolk antibodies to various Salmonella serovars, we observed that the anti-S. Enteritidis antibodies exhibited more specific affinity than those of the anti-S. Typhimurium antibodies. All S. Enteritidis strains reacted specifically with the anti-S. Enteritidis fimbrial and flagellar egg yolk antibody whereas anti-S. Enteritidis LPS and anti-S. Typhimurium LPS, OMP and flagellar antibodies displayed non-specific reactivity to all Salmonella serovars used in this study. This finding suggests that it may be possible to design a anti-fimbrial egg yolk antibody of S. Enteritidis as a diagnostic tool and a cocktail of OMP and LPS antigens of S. Enteritidis and S. Typhimurium could be used for administering broad spectrum passive immunity to protect against the colonization of pathogenic Salmonella strains in food animals.     

A Pilot Indoor-Outdoor Study of Organic Particulate Matter and Particulate Mutagenicity

Abstract

Many large metropolitan areas experience elevated concentrations of ground-level ozone pollution during the summertime “smog season”. Local environmental or health agencies often need to make daily air pollution forecasts for public advisories and for input into decisions regarding abatement measures and air quality management. Such forecasts are usually based on statistical relationships between weather conditions and ambient air pollution concentrations. Multivariate linear regression models have been widely used for this purpose, and well-specified regressions can provide reasonable results. However, pollution-weather relationships are typically complex and nonlinear—especially for ozone—properties that might be better captured by neural networks. This study investigates the potential for using neural networks to forecast ozone pollution, as compared to traditional regression models. Multiple regression models and neural networks are examined for a range of cities under different climate and ozone regimes, enabling a comparative study of the two approaches. Model comparison statistics indicate that neural network techniques are somewhat (but not dramatically) better than regression models for daily ozone prediction, and that all types of models are sensitive to different weather-ozone regimes and the role of persistence in aiding predictions.     

A Review of Treatment Alternatives for Wastes Containing Treatment Technologies For Hazardous Wastes: Part V

The Hazardous and Solid Waste Amendments to the Resource Conservation and Recovery Act direct the Environmental Protection Agency to determine the available treatment technologies for a number of hazardous waste streams, including halogenated organics. If it is determined that existing technology and capacity is sufficient for the safe management of the designated halogenated organic wastes, these wastes will be prohibited from land disposal, effective July 8,1987. This article summarizes the general characteristics and treatment alternatives for this waste category.     

Receptor Modeling Applied to Patterns in Space (RMAPS)Part III–Apportionment of Airborne Particulate Sulfur in Western Washington State

Abstract

Data obtained from 24 of the 31 sites of the Pacific Northwest Regional Visibility Experiment Using Natural Tracers (PREVENT) study were analyzed by the Receptor Model Applied to Patterns in Space (RMAPS) multivariate receptor model. Four spatial patterns were found and interpreted as showing the effect of the coal-fired power plant in Centralia, WA; transport from the northwest; the Se-attle-Tacoma urban area; and transport from the southeast. In Mt. Rainier National Park, up to one-third of the sulfate can be attributed to the Centralia power plant. In the North Cascades National Park, 65-82% of the sulfur is accounted for by transport from Canada. The model was applied separately to sites in the northern and southern sections of the study area. The southern sites were affected only by the Centralia, urban, and southeast transport sources; the northern sites were affected only by the northwest transport, urban, and southeast transport sources. This gave two independent estimates of the normalized source contributions of the urban and southeast transport factors, which had a correlation coefficient of more than 0.90.