Environmental Indicators of Biofuel Sustainability: What About Context?

Indicators of the environmental sustainability of biofuel production, distribution, and use should be selected, measured, and interpreted with respect to the context in which they are used. The context of a sustainability assessment includes the purpose, the particular biofuel production and distribution system, policy conditions, stakeholder values, location, temporal influences, spatial scale, baselines, and reference scenarios. We recommend that biofuel sustainability questions be formulated with respect to the context, that appropriate indicators of environmental sustainability be developed or selected from more generic suites, and that decision makers consider context in ascribing meaning to indicators. In addition, considerations such as technical objectives, varying values and perspectives of stakeholder groups, indicator cost, and availability and reliability of data need to be understood and considered. Sustainability indicators for biofuels are most useful if adequate historical data are available, information can be collected at appropriate spatial and temporal scales, organizations are committed to use indicator information in the decision-making process, and indicators can effectively guide behavior toward more sustainable practices.     

Perspectives on Disconnects Between Scientific Information and Management Decisions on Post-fire Recovery in Western US

Environmental regulations frequently mandate the use of “best available” science, but ensuring that it is used in decisions around the use and protection of natural resources is often challenging. In the Western US, this relationship between science and management is at the forefront of post-fire land management decisions. Recent fires, post-fire threats (e.g. flooding, erosion), and the role of fire in ecosystem health combine to make post-fire management highly visible and often controversial. This paper uses post-fire management to present a framework for understanding why disconnects between science and management decisions may occur. We argue that attributes of agencies, such as their political or financial incentives, can limit how effectively science is incorporated into decision-making. At the other end of the spectrum, the lack of synthesis or limited data in science can result in disconnects between science-based analysis of post-fire effects and agency policy and decisions. Disconnects also occur because of the interaction between the attributes of agencies and the attributes of science, such as their different spatial and temporal scales of interest. After offering examples of these disconnects in post-fire treatment, the paper concludes with recommendations to reduce disconnects by improving monitoring, increasing synthesis of scientific findings, and directing social-science research toward identifying and deepening understanding of these disconnects.     

Assessing Linkages in Stream Habitat,Geomorphic Condition,and Biological Integrity Using a Generalized Regression Neural Network

Watershed managers often use physical geomorphic and habitat assessments in making decisions about the biological integrity of a stream, and to reduce the cost and time for identifying stream stressors and developing mitigation strategies. Such analysis is difficult since the complex linkages between reach‐scale geomorphic and habitat conditions, and biological integrity are not fully understood. We evaluate the effectiveness of a generalized regression neural network (GRNN) to predict biological integrity using physical (i.e., geomorphic and habitat) stream‐reach assessment data. The method is first tested using geomorphic assessments to predict habitat condition for 1,292 stream reaches from the Vermont Agency of Natural Resources. The GRNN methodology outperforms linear regression (69% vs. 40% classified correctly) and improves slightly (70% correct) with additional data on channel evolution. Analysis of a subset of the reaches where physical assessments are used to predict biological integrity shows no significant linear correlation, however the GRNN predicted 48% of the fish health data and 23% of macroinvertebrate health. Although the GRNN is superior to linear regression, these results show linking physical and biological health remains challenging. Reasons for lack of agreement, including spatial and temporal scale differences, are discussed. We show the GRNN to be a data‐driven tool that can assist watershed managers with large quantities of complex, nonlinear data.     

Access for all? Barriers to accessing woodlands and forests in Britain

This paper presents results from research that identified and analysed barriers to accessing British woodlands and forests. This paper aims to contribute to an understanding of access and accessibility and to inform the design of policy and management interventions to encourage increased access by under-represented social groups. A brief review of policy and academic literature places the issue of inclusive woodland and forest access in the context of contemporary debates surrounding public health, well-being, diversity and the perceived role of public green space. There follows an analysis of quantitative and qualitative research findings, informing the presentation of a working typology of barriers. The typology is structured around the access needs of various social groups, allowing an analysis of the social distribution of barriers. The findings indicate the deep-seated psychological, emotional and socio-cultural nature of some barriers and highlight the need for carefully designed interventions that may lie outside the conventional remit of woodland management. This paper will be of particular interest to decision-makers and practitioners and to those involved in the design and delivery of policies, programmes and projects aimed at encouraging inclusive use of woodlands, forests and other types of green space.     

On the potential of iPhone significant location data to characterize individual mobility for air pollution health studies

● We evaluated the accuracy of iPhone data in capturing time-activity patterns. ● iPhone data captured the most important microenvironments and time spent in them. ● iPhone data also accurately captured daily exposure to ambient PM pollution. ● A considerable fraction of the population in the USA may have iPhone data available. ● iPhone data has great potential in air pollution health studies. In many air pollution health studies, the time-activity pattern of individuals is often ignored largely due to lack of data. However, a better understanding of this location-based information is expected to decrease uncertainties in exposure estimation. Here, we showcase the potential of iPhone’s Significant Location (iSL) data in capturing the user’s historical time-activity patterns in order to estimate exposure to ambient air pollutants. In this study, one subject carried an iPhone in tandem with a reference GPS tracking device for one month. The GPS device recorded locations in 10 second intervals while the iSL recorded the time spent in locations the subject visited frequently. Using GPS data as a reference, we then evaluated the accuracy of iSL data in capturing the subject’s time-activity patterns and time-weighted air pollution concentration within the study time period. We found the iSL data accurately captured the time the subject spent in 16 microenvironments (i.e. locations the subject visited more than once), which was 93% of the time during the study period. The average error of time-weighted aerosol optical depth value, a surrogate of particle pollution, is only 0.012%. To explore the availability of iSL data among iPhone users, an online survey was conducted. Among the 349 surveyed participants, 72% of them have iSL data available. Considering the popularity of iPhones, iSL data may be available for a significant portion of the general population. Our results suggest iSL data have great potential for characterizing historical time-activity patterns to improve air pollution exposure estimation.     

Inferring pollution records in sediment cores from transitional environments of Marquelia coast,Guerrero, Mexico

Environmental Science and Pollution Research - The vertical distribution pattern and concentrations of elements (Fe, Al, Ca, Mg, Mn, Cr, Cu, Ni, Co, Pb, Zn, and As) in the estuarine and lagoon...     

The impact of invasive grasses on the population growth of Anemone patens, a long-lived native forb

Negative impacts of invasive plants on natives have been well documented, but much less is known about whether invasive plants can cause population level declines. We used demographic models to investigate the effects of two invasive grasses on the demography and population growth of Anemone patens, a long-lived native perennial of North American grasslands. Demographic data of A. patens growing in patches characterized by Bromus inermis, Poa pratensis, or native grasses were used to parameterize integral projection models. Models based on both average conditions and those allowing for environmental stochasticity indicate that A. patens is slowly increasing in patches of native grass (lambda = 1.02) and declining in patches of invasive grasses, particularly those dominated by B. inermis (lambda = 0.93). Extinction probabilities indicate that A. patens should persist in native grass patches, but has a much higher probability of extinction in Bromus patches compared to Poa patches. While sensitivity analyses showed that survival had the biggest effect on population growth rates in all habitats, results of a Life Table Response Experiment (LTRE) revealed that slower individual growth rates in patches of invasive grasses contributed the most to the observed reduction in population growth. These results suggest that invasive grasses may cause slow declines in A. patens, despite short-term coexistence, and that controlling B. inermis only would not be sufficient to ensure A. patens persistence.     

The Brazilian National Intercomparison Program (PNI/IRD/CNEN): evaluation of 15 years of data

The Instituto de Radioprote??o e Dosimetria has been coordinating the National Intercomparison Program (PNI) for 15 years, from 1991 to 2005. The objective is to evaluate the analytical performance of the laboratories in low-level activity concentration radionuclide assays in environmental samples. This work presents an evaluation of PNI data from 42 intercomparison runs, which distributed to 22 Brazilian laboratories 2511 samples evaluating 5768 radionuclide assays involving 32 radionuclides in the period from 1991 to 2004. The laboratory performance was evaluated using the Normalized Standard Deviation used by the U.S. EPA. For comparison aims, the Normalized Deviation, used by BIPM was also applied. Laboratory performance were grouped and evaluated in three periods of 5 years each. The first period shows, an average value of good performance of 71.2%, the second shows an average of 78.6% and the last an average of 82.3%. Performance for each kind of radionuclide is also presented.