Chlorinated herbicide (triallate) dehalogenation by iron powder

The reductive degradation of a chlorinated herbicide by iron powder was investigated at lab scale. The studied substrate was triallate (S-2,3,3-trichloroallyl di-isopropyl thiocarbamate) which contains a trichloroethylene moiety potentially reducible by zero-valent iron. Degradation reactions were carried out in batch, at 25 degrees C, in the absence of oxygen, by contacting electrolytic iron powder (size range: 20-50 microm) with a triallate aqueous solution (2.5 mgl(-1)). Herbicide decay, corresponding evolutions of TOC, TOX and chloride ion release were regularly monitored throughout the reactions. Furthermore, the main degradation by-products were identified by HPLC/MS. The results showed that, after 5 days, herbicide degradation extent was about 97% and that the reaction proceeded through the formation of a dechlorinated alkyne by-product (S-2-propinyl di-isopropyl thiocarbamate) resulting from the complete dechlorination of triallate. The subsequent reduction of such an alkyne intermediate gave S-allyl di-isopropyl thiocarbamate as main end by-product. The identified by-products suggested that dechlorination took place mainly via reductive beta-elimination. However, as traces of dichloroallyl di-isopropyl thiocarbamate were also detected, a role, although minor, was assigned even to hydrogenolysis in the overall dechlorination process.     

Handling of dioxin measurement data in the presence of non-detectable values: overview of available methods and their application in the Seveso chloracne study

Exposure measurements of concentrations that are non-detectable or near the detection limit (DL) are common in environmental research. Proper statistical treatment of non-detects is critical to avoid bias and unnecessary loss of information. In the present work, we present an overview of possible statistical strategies for handling non-detectable values, including deletion, simple substitution, distributional methods, and distribution-based imputation. Simple substitution methods (e.g., substituting 0, DL/2, DL/ radical2, or DL for the non-detects) are the most commonly applied, even though the EPA Guidance for Data Quality Assessment discouraged their use when the percentage of non-detects is >15%. Distribution-based multiple imputation methods, also known as robust or "fill-in" procedures, may produce dependable results even when 50-70% of the observations are non-detects and can be performed using commonly available statistical software. Any statistical analysis can be conducted on the imputed datasets. Results properly reflect the presence of non-detectable values and produce valid statistical inference. We describe the use of distribution-based multiple imputation in a recent investigation conducted on subjects from the Seveso population exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), in which 55.6% of plasma TCDD measurements were non-detects. We suggest that distribution-based multiple imputation be the preferred method to analyze environmental data when substantial proportions of observations are non-detects.     

Evaluating risks associated with transport of the ghost shrimp <Emphasis Type="Italic">Neotrypaea californiensis</Emphasis> as live bait

The ghost shrimp Neotrypaea californiensis is imported into southern California from Oregon and Washington for use as live bait in recreational marine fisheries. We studied the population genetic structure of N. californiensis across much its range to assess the possibility that the transport of ghost shrimp across phylogeographic boundaries poses a risk of homogenizing existing genetic variation in the species. Analyses of two mitochondrial DNA markers showed little phylogeographic structure across the sampled range, suggesting that this risk is low. Unexpectedly, mitochondrial DNA analyses revealed that a second putative species of ghost shrimp frequently coexisted with N. californiensis in southern California intertidal habitats; almost all previous studies of soft-sediment communities in the region report the presence of N. californiensis only. We also assessed the possibility that the import of ghost shrimp might pose a risk of introduction of a parasitic castrator, the bopyrid isopod Ione cornuta, into southern California waters, where it does not appear to be native. Prevalence of living I. cornuta in samples purchased from bait shops was high (5.8%), suggesting that this is a real risk that merits further study.     

Achieving conservation impact by shifting focus from human attitudes to behaviors

Attitudes have been a commonly used psychological measure of program effectiveness in conservation social science research. The major limitation of this approach is that attitudes do not always translate into behavior and therefore may not provide an accurate assessment of program success. Given that achieving conservation goals generally relies on understanding and changing human behavior, we argue for the need to assess behavior rather than attitudes as an indicator of conservation outcomes. Psychological theory shows that attitudes and behavior are distinct, but related, concepts. Measuring conservation behaviors involves identifying the target behavior or behaviors and the optimal time to measure and then selecting the most appropriate method of measurement (i.e., direct observation, objective indicators, self-reported behavior, and behavioral intentions) that considers the strengths and weaknesses of each approach. We call for conservation programs to focus on influencing behavior rather than attitudes alone and encourage conservation practitioners and researchers to collect high-quality behavioral data to more effectively inform policy and programs.     

Fundamental insights on when social network data are most critical for conservation planning

As declines in biodiversity accelerate, there is an urgent imperative to ensure that every dollar spent on conservation counts toward species protection. Systematic conservation planning is a widely used approach to achieve this, but there is growing concern that it must better integrate the human social dimensions of conservation to be effective. Yet, fundamental insights about when social data are most critical to inform conservation planning decisions are lacking. To address this problem, we derived novel principles to guide strategic investment in social network information for systematic conservation planning. We considered the common conservation problem of identifying which social actors, in a social network, to engage with to incentivize conservation behavior that maximizes the number of species protected. We used simulations of social networks and species distributed across network nodes to identify the optimal state-dependent strategies and the value of social network information. We did this for a range of motif network structures and species distributions and applied the approach to a small-scale fishery in Kenya. The value of social network information depended strongly on both the distribution of species and social network structure. When species distributions were highly nested (i.e., when species-poor sites are subsets of species-rich sites), the value of social network information was almost always low. This suggests that information on how species are distributed across a network is critical for determining whether to invest in collecting social network data. In contrast, the value of social network information was greatest when social networks were highly centralized. Results for the small-scale fishery were consistent with the simulations. Our results suggest that strategic collection of social network data should be prioritized when species distributions are un-nested and when social networks are likely to be centralized.     

Daytime air pollution transport mechanisms in stable atmospheres of narrow versus wide urban valleys

Transport of air pollutants emitted from urban valleys can be strongly restricted by interactions between static and dynamic factors including topographic forcing, low-level atmospheric stability related to temperature inversions, and urban heat island-induced circulations. Interplay between these processes has a complex and dynamic nature, and is determinant for the evolution of different ventilation mechanisms and the associated impacts on air quality. Here we investigate these transport mechanisms through large eddy simulations using EULAG, an established model for multiscale flows, to simulate an idealized atmospheric environment in narrow versus wide urban valleys during critical conditions for air quality (high atmospheric stability). Our results show how the ventilation of valleys depends on a dynamic (variable during the daytime) balance between interacting and sometimes competing processes related to thermally-driven slope flows, urban heat island-induced flows, and the trapping effect of atmospheric stability; and how valley width affects this balance. Particularly important is that the time-space distribution of pollutants (a passive tracer) varies greatly between both valleys despite having the same urban area and emission rates. These variations lead to pollutants being mostly concentrated in different areas of the narrow and wide valleys. We discuss the mechanisms behind these results and their potential implications for real urban valleys. Further understanding of these mechanisms is crucial for explaining the occurrence of severe air pollution episodes and informing related decision-making processes in urban valleys.

     

Are you an ambitious cyclist? Results of the cyclist profile questionnaire in Germany

Abstract

Objective: A number of studies have already grouped cyclists according to different aspects of their mobility behavior. This could be used e.g., to improve the bicycle infrastructure planning, to detect critical spots and, to reduce obstacles for cycling. This wide, preexisting, range of cyclist typologies usually concentrates on one or two influence factors and differs, content-wise, in both factors used, as well as, methodically. Based on existing cyclist typologies we extracted all possible influence factors to integrate them in one single questionnaire. The objective of this study, using an empirical, based approach, is to compare this typology of cyclists with existing ones, integrating all known influence factors of recent studies.

Methods: To address these issues, we conducted a Germany-wide online survey on cycling behavior, covering all relevant aspects we derived from both literature and especially, former cyclist typology studies including: social factors; the impact of environmental, individual; and route factors; as well as motives. The main goal was to identify distinct types of cyclists, and describe them as detailed as possible. The heterogeneous sample included a total of 10,294 responses.

Results: Using factor and cluster analyses, a multidimensional typology with four groups of cyclists was derived which were interpreted as: ambitious, functional, pragmatic, and passionate cyclists. In addition, socio-economic factors, cyclist’s motivation, and crash history were analyzed.

Conclusion: The results produced by grouping different characteristics of cyclists can lead to policy recommendations or communal bicycle traffic planning. Policy planners can estimate reactions of the different types on interventions and adjust their decisions which can serve to support already passionate cyclists or, encourage normally under-represented infrequent cyclists to cycle more. The extent of perceived safety plays here an important role in the classification, e.g., the handling of high-risk areas for crashes.     

Negative affectivity as a moderator of the form and magnitude of the relationship between felt accountability and job tension

The present research examined the influence of negative affectivity (NA) on the form of the felt accountability–job tension relationship. It was hypothesized that the nonlinear relationship between felt accountability and job tension would surface only for individuals low in NA, whereas the association between these constructs would be positive and linear for individuals high in NA. Data from two studies (N = 198; N = 118) provided support for the hypothesis. These results have important implications for science and practice. Conceptually, this study contributes to the stress, accountability, and personality literatures by demonstrating that accountability can positively or negatively predict tension, and that this association is dependent upon affective disposition. Practically, managers need to be cognizant of the fact that increases in accountability are not always associated with favorable attitudes when managing employees. Copyright © 2005 John Wiley & Sons, Ltd.