Smoke free restaurant ordinance, compliance with the regulation to reduce exposure to environmental tobacco smoke while dining at local restaurants in San Antonio, Texas

This study is a secondary analysis of a survey developed by Dr. Jimmy Perkins and administered by San Antonio/Bexar County Metropolitan Health District. The survey was developed subsequent to the implementation of the city smoking ordinance effective January 1, 2004. The survey had a multi-purpose plan to establish the number of restaurants having smoke free status prior to and following the ordinance, determine compliance as it relates to a necessary smoking section and proper signage, and expose the rationale for restaurants to become smoke free. The data resulting from the survey was presented to the San Antonio/Bexar County Metropolitan Health District. The summary presented the types of establishments surveyed, smoking status of the establishment, reasons for the establishment becoming smoke free, compliance with smoking sections, compliance with signage requirements, awareness of ordinance, and chain status of the establishment. ^ The results of this study display the relationships among the variables previously mentioned. The following relationships have been examined and the outcomes have determined whether each is significant. After careful analysis, knowledge translates into compliance with signage regulations, which then translate into ordinance compliance. Size does matter as it relates to an establishment's number of employees and seating capacity. The smaller the establishment the more likely the establishment is to have become smoke free before the ordinance went into effect. Restaurants, rather than fast food establishments most commonly cited their reason for becoming smoke free was to comply with the ordinance and only ten percent of restaurants gave policy as the main reason for becoming smoke free. ^ This study is important for public health because the negative health effects of environmental tobacco smoke (ETS) are still an overwhelming problem in the United States (3). ETS is a Known Human Group A Carcinogen (5). The Environmental Protection Agency (EPA) has estimated that around 3,000 non-smoking Americans die every year from lung cancer caused by ETS (6). This information illustrates the importance of providing smoke free establishments, especially to non-smoking patrons. ^

ANN and Fuzzy c-Means applied to environmental pollution prediction

Salamanca, situated in center of Mexico is among the cities which suffer most from the air pollution in Mexico. The vehicular park and the industry, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Sulphur Dioxide (SO2). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables and air pollutant concentrations of SO2. Before the prediction, Fuzzy c-Means and K-means clustering algorithms have been implemented in order to find relationship among pollutant and meteorological variables. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of SO2 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours.

A trait-based approach for predicting species responses to environmental change from sparse data : how well might terrestrial mammals track climate change?

Funded by COST (European Cooperation in Science and Technology) CEH projects. Grant Numbers: NEC05264, NEC05100 Natural Environment Research Council UK. Grant Number: NE/J008001/1 © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Plant ionomics : From elemental profiling to environmental adaptation

FUNDING UK Biotechnology and Biological Sciences Research Council grant BB/L027739/1 and BB/L000113/1 (to D.E.S.), the US National Institutes of Health grant 2R01GM078536 (to D.E.S.), and the US National Science Foundation grant IOB 0419695 (to D.E.S.) ACKNOWLEDGMENTS We wish to thank our collaborators Mary Lou Guerinot, Niko Geldner, and Christian Hermans for kindly allowing us to incorporate in this update unpublished data on BRUTUS, SGN1, and SGN3, respectively. We also thank Mary Lou Guerinot, Niko Geldner, Takehiro Kamiya, and the ERACAPS Root Barrier project for productive discussions relating to ionomics and the plant ionome. No conflict of interest declared.

Remodeling of Yeast Genome Expression in Response to Environmental Changes

We used genome-wide expression analysis to explore how gene expression in Saccharomyces cerevisiae is remodeled in response to various changes in extracellular environment, including changes in temperature, oxidation, nutrients, pH, and osmolarity. The results demonstrate that more than half of the genome is involved in various responses to environmental change and identify the global set of genes induced and repressed by each condition. These data implicate a substantial number of previously uncharacterized genes in these responses and reveal a signature common to environmental responses that involves ∼10% of yeast genes. The results of expression analysis with MSN2/MSN4 mutants support the model that the Msn2/Msn4 activators induce the common response to environmental change. These results provide a global description of the transcriptional response to environmental change and extend our understanding of the role of activators in effecting this response.

Checking the concurrence among macrobenthic organism distribution patterns at different taxonomic scales in relation to environmental factors

The interaction of both natural conditions and anthropogenic environmental impacts can lead to different soft-bottom macrobenthic distribution patterns. Soft-bottom macrobenthic community was analysed at different taxonomic scales in order to evaluate whether diverse subset of organisms respond to the variability of the environmental pressures (natural and human induced) showing or not similar distribution patterns. Therefore, this long-term survey had been focused on a heterogeneous area, where both anthropogenic and natural stress may affect the community. Three perpendicular transects to the coast were established and stations at 4, 10 and 15 m depths were sampled at each transect twice a year (summer- winter) from 2004 to 2009. Non-parametric multivariate techniques were used to analyse soft-bottom macrobenthic community distribution and its relation to the environmental factors. Similar distribution patterns between investigated taxonomic levels were detected and they were mainly related to depth.

Access to environmental information in an Open European Society - Directive 2003/4. Research Paper in Law 5/2003

[From the Introduction]. Information gives knowledge and knowledge gives power. Though in all EC Member States, the task to protect the environment is given to the administration, it is obvious that the administration is not the owner of the environment. The environment is everybody's. It is for this reason that administrative decisions which affect the environment must be transparent, open and must strike a balance between the general interest to preserve, protect and improve the quality of the environment on the one hand, the satisfying of specific private or public interests on the other hand. In order to allow at least a certain control of whether the administration strikes the right balance between the need to protect the environment and other legitimate or less legitimate needs, it appears normal and self-evident that information on the environment which is in the hands of public authorities, be also made available to the public and to citizens.

Long-term Responses of Mountain Ecosystems to Environmental Changes: Resilience, Adjustment, and Vulnerability

The steep environmental gradients of mountain ecosystems over short distances reflect large gradients of several climatic parameters and hence provide excellent possibilities for ecological research on the effects of environmental change. To gain a better understanding of the dynamics of abiotic and biotic parameters of mountain ecosystems, long-term records are required since permanent plots in mountain regions cover in the best case about 50 - 70 years. In order to extend investigations of ecological dynamics beyond these temporal limitations of permanent plots, paleoecological approaches can be used if the sampling resolution can be adapted to ecological research questions, e.g. a sample every 10 years. Paleoecological studies in mountain ecosystems can provide new ecological insights through the combination of different spatial and temporal scales. [f we thus improve our understanding of processes across both steep environmental gradients and different time scales, we may be able to better estimate ecosystem responses to current and future environmental change (Ammann et al. 1993; Lotter et al. 1997). The complexity of ecological interactions in mountain regions forces us to concentrate on a number of sub-systems - without losing sight of the wider context. Here, we summarize a few case studies on the effects of Holocene climate change and disturbance on the vegetation of the Western Alps. To categorize the main response modes of vegetation to climatic change and disturbance in the Alps we use three classes of ecological behaviour: "resilience", "adjustment", and "vulnerability", We assume a resilient (or elastic) behaviour if vegetation is able to recover to its former state, regaining important ecosystem characteristics, such as floristic composition, biodiversity, species abundances, and biomass (e.g. Küttel 1990; Aber and Melillo 199 1). Conversely, vegetation displacements may occur in response to climatic change and/or disturbance. In some cases, this may culminate in irreversible large-scale processes such as species and/or community extinctions. Such drastic developments indicate high ecosystem vulnerability (or inelasticity or instability, for detailed definitions see Küttel 1990; Aber and Melillo 199 1) to climatic change and/or disturbance. In this sense, the "vulnerability" (or instability) of an ecosystem is expressed by the degree of failure to recover to the original state before disturbance and/or climatic change. Between these two extremes (resilience vs. vulnerability), ecosystem adjustments to climatic change and/or disturbance may occur, including the appearance of new and/or the disappearance of old species. The term "adjustment" is hence used to indicate the response of vegetational communities, which adapted to new environmental conditions without losing their main character. For forest ecosystems, we assume vegetational adjustments (rather than vulnerability) if the dominant (or co-dominant) tree species are not outnumbered or replaced by formerly unimportant plant species or new invaders. Adaptation as a genetic process is not discussed here and will require additional pbylogeographical studies (that incorporate the analysis of ancient DNA) in order to fully understand the distributions of ecotypes.

A gas-exchange system for assessing plant performance in response to environmental stress /

Mode of access: Internet.

Highest return farming systems for Drummer-Flanagan soils : an application of linear programming to farm planning /

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Your time to shine: planning guide.

Mode of access: Internet.

Environmental assessment using remotely sensed data : application of remote sensing data to environmental impact studies for transmission line routes /

Mode of access: Internet.

Junior flower shows; a complete guide to the planning, staging, and judging of children's flower shows,

Mode of access: Internet.