Social learning in a policy-mandated collaboration: community wildfire protection planning in the eastern United States

Policies such as the US Healthy Forests Restoration Act (HFRA) mandate collaboration in planning to create benefits such as social learning and shared understanding among partners. However, some question the ability of top-down policy to foster successful local collaboration. Through in-depth interviews and document analysis, this paper investigates social learning and transformative learning in three case studies of Community Wildfire Protection Planning (CWPP), a policy-mandated collaboration under HFRA. Not all CWPP groups engaged in social learning. Those that did learned most about organisational priorities and values through communicative learning. Few participants gained new skills or knowledge through instrumental learning. CWPP groups had to commit to learning, but the design of the collaborative-mandate influenced the type of learning that was most likely to occur. This research suggests a potential role for top-down policy in setting the structural context for learning at the local level, but also confirms the importance of collaborative context and process in fostering social learning.     

Evaluation of airborne particulate matter and metals data in personal,indoor and outdoor environments using ED-XRF and ICP-MS and co-located duplicate samples

Factors and sources affecting measurement uncertainty associated with monitoring metals in airborne particulate matter (PM) were investigated as part of the Windsor, Ontario Exposure Assessment Study (WOEAS). The assessment was made using co-located duplicate samples and a comparison of two analytical approaches: ED-XRF and ICP-MS. Sampling variability was estimated using relative percent difference (RPD) of co-located duplicate samples. The comparison of ICP-MS and ED-XRF results yields very good correlations (R² ≥ 0.7) for elements present at concentrations that pass both ICP-MS and ED-XRF detection limits (e.g. Fe, Mn, Zn, Pb and Cu). PM concentration ranges (median, sample number) of 24-h indoor PM₁₀ and personal PM₁₀ filters, and outdoor PM_2.5 filters were determined to be 2.2–40.7 (11.0, n = 48) μg m^?3, 8.0–48.3 (11.9, n = 48) μg m^?3, and 17.1–42.3 (21.6, n = 18) μg m^?3, respectively. The gravimetric analytical results reveal that the variations in PM mass measurements for same-day sampling are insignificant compared to temporal or spatial variations: 92%, 100% and 96% of indoor, outdoor and personal duplicate samples, respectively, pass the quality criteria (RPD ≤ 20%). Uncertainties associated with ED-XRF elemental measurements of S, Ca, Mn, Fe and Zn for 24-h filter samples are low: 78%–100% of the duplicate samples passed the quality criteria. In the case of 24-h filter samples using ICP-MS, more elements passed the quality criteria due to the lower detection limits. These were: Li, Na, K, Ca, Si, Al, V, Fe, Mn, Co, Cu, Mo, Ag, Zn, Pb, As, Mg, Sb, Sn, Sr, Th, Ti, Tl, and U. Low air concentrations of metals (near or below instrumental detection limits) and/or inadvertent introduction of metal contamination are the main causes for excluding elements based on the pass/fail criteria. Uncertainty associated with elemental measurements must be assessed on an element-by-element basis.     

Herbicide residues in grapes and wine

Abstract

The persistence of several common herbicides from grapes to wine has been studied. Shiraz, Tarrango and Doradillo grapes were separately sprayed with either norflurazon, oxyfluorfen, oxadiazon or trifluralin ‐ persistent herbicides commonly used for weed control in vineyards. The dissipation of the herbicides from the grapes was followed for 28 days following treatment. Results showed that norflurazon was the most persist herbicide although there were detectable residues of all the herbicides on both red and white grapes at the end of the study period. The penetration of herbicides into the flesh of the grapes was found to be significantly greater for white grapes than for red grapes. Small‐lot winemaking experiments showed that norflurazon persisted at levels close to the initial concentration through vinification and into the finished wine. The other herbicides degraded, essentially via first‐order kinetics, within the period of “ first fermentation”; and had largely disappeared after 28 days. The use of charcoal together with filter pads, or with diatomaceous earth was shown to be very effective in removing herbicide residues from the wine. A 5% charcoal filter removed more than 96% of the norflurazon persisting in the treated wine.     

Emissions into the Atmosphere Due to the Ceramic Salt Glazing Process

The techniques of Principal Component Analysis (PCA) and subsequent regression analysis were used in an attempt to describe local and upwind chemical and physical factors which affect the variability of SO₄ ^–2 concentrations observed in a rural area of the northeastern U.S. The data used in the analyses included upwind and local O₃ concentrations, temperature, relative humidity and other climatological information, SO₂, and meteorological information associated with backward trajectories. The investigation identified five principal components, three major (eigenvalues >1) and two minor (eigenvalues < one), which accounted for 52% (r = 0.72) of the variability in the SO₄ ^–2 regression model. These components can be described as representing local and upwind photochemistry, droplet growth, SO₂ emissions, and air mass characteristics. The study also indicated that in future studies it will be necessary to a priori select air pollution and meteorological variables for measurement to potentially increase the sensitivity of this type of receptor model.     

Gas and Particle Emissions from Automobile Tires in Laboratory and Field Studies

This paper is directed to air pollution scientists interested in special mobile emission sources. The purpose was to determine the contribution which automobile tires make to air pollution. The gaseous hydrocarbon and sulfur compounds emitted in laboratory tests were identified. Although these hydrocarbons can participate in smog reactions, their mass emission rate is less than 0.1 % of the current exhaust hydrocarbon emission rate. Hydrocarbons from tires are not measurable near a freeway. The particulate emitted from tires ranges in size from 0.01 μm to more than 30 μm, with the larger particles dominating the total mass. Measurements along a California freeway showed that most of the tire debris had settled within 5 m of the pavement edge. Airborne rubber concentrations were less than 0.5 μg/m³, or less than 5% of the total tire wear. These field measurements confirm the indoor emission pattern and verify that tire wear products are not a significant air pollution problem.     

Comparing universal kriging and land-use regression for predicting concentrations of gaseous oxides of nitrogen (NOx) for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

BACKGROUND: Epidemiological studies that assess the health effects of long-term exposure to ambient air pollution are used to inform public policy. These studies rely on exposure models that use data collected from pollution monitoring sites to predict exposures at subject locations. Land use regression (LUR) and universal kriging (UK) have been suggested as potential prediction methods. We evaluate these approaches on a dataset including measurements from three seasons in Los Angeles, CA. METHODS: The measurements of gaseous oxides of nitrogen (NOx) used in this study are from a "snapshot" sampling campaign that is part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). The measurements in Los Angeles were collected during three two-week periods in the summer, autumn, and winter, each with about 150 sites. The design included clusters of monitors on either side of busy roads to capture near-field gradients of traffic-related pollution. LUR and UK prediction models were created using geographic information system (GIS)-based covariates. Selection of covariates was based on 10-fold cross-validated (CV) R(2) and root mean square error (RMSE). Since UK requires specialized software, a computationally simpler two-step procedure was also employed to approximate fitting the UK model using readily available regression and GIS software. RESULTS: UK models consistently performed as well as or better than the analogous LUR models. The best CV R(2) values for season-specific UK models predicting log(NOx) were 0.75, 0.72, and 0.74 (CV RMSE 0.20, 0.17, and 0.15) for summer, autumn, and winter, respectively. The best CV R(2) values for season-specific LUR models predicting log(NOx) were 0.74, 0.60, and 0.67 (CV RMSE 0.20, 0.20, and 0.17). The two-stage approximation to UK also performed better than LUR and nearly as well as the full UK model with CV R(2) values 0.75, 0.70, and 0.70 (CV RMSE 0.20, 0.17, and 0.17) for summer, autumn, and winter, respectively. CONCLUSION: High quality LUR and UK prediction models for NOx in Los Angeles were developed for the three seasons based on data collected for MESA Air. In our study, UK consistently outperformed LUR. Similarly, the 2-step approach was more effective than the LUR models, with performance equal to or slightly worse than UK.     

The role of intergenerational influence in waste education programmes: the THAW project

Whilst the education of young people is often seen as a part of the solution to current environmental problems seeking urgent attention, it is often forgotten that their parents and other household members can also be educated/influenced via home-based educational activities. This paper explores the theory of intergenerational influence in relation to school based waste education. Waste Watch, a UK-based environmental charity (www.wastewatch.org.uk), has pioneered a model that uses practical activities and whole school involvement to promote school based action on waste. This methodology has been adopted nationally. This paper outlines and evaluates how effective school based waste education is in promoting action at a household level. The paper outlines Waste Watch's 'Taking Home Action on Waste (THAW)' project carried out for two and half years in Rotherham, a town in South Yorkshire, England. The project worked with 6705 primary age children in 39 schools (44% of primary schools in the project area) to enable them to take the "reduce, reuse and recycle message" home to their families and to engage these (i.e. families) in sustainable waste management practices. As well as substantial increases in students' knowledge and understanding of waste reduction, measurement of the impact of the project in areas around 12 carefully chosen sample schools showed evidence of increased participation in recycling and recycling tonnages as well as declining levels of residual waste. Following delivery of the project in these areas, an average increase of 8.6% was recorded in recycling set out rates which led to a 4.3% increase in paper recycling tonnages and an 8.7% increase in tonnages of cans, glass and textiles collected for recycling. Correspondingly, there was a 4.5% fall in tonnages of residual waste. Waste Watch's THAW project was the first serious attempt to measure the intergenerational influence of an education programme on behaviour at home (i.e. other than schools' own waste). It clearly shows that household recycling behaviour can be positively impacted by intergenerational influence via a practical school-based waste education model. However, although the model could potentially have a big impact if rolled out nationally, it will require seed funding and the long-term durability of the model has not yet been fully quantified.     

Support for maternal manipulation of developmental nutrition in a facultatively eusocial bee, <Emphasis Type="Italic">Megalopta genalis</Emphasis> (Halictidae)

Developmental maternal effects are a potentially important source of phenotypic variation, but they can be difficult to distinguish from other environmental factors. This is an important distinction within the context of social evolution, because if variation in offspring helping behavior is due to maternal manipulation, social selection may act on maternal phenotypes, as well as those of offspring. Factors correlated with social castes have been linked to variation in developmental nutrition, which might provide opportunity for females to manipulate the social behavior of their offspring. Megalopta genalis is a mass-provisioning facultatively eusocial sweat bee for which production of males and females in social and solitary nests is concurrent and asynchronous. Female offspring may become either gynes (reproductive dispersers) or workers (non-reproductive helpers). We predicted that if maternal manipulation plays a role in M. genalis caste determination, investment in daughters should vary more than for sons. The mass and protein content of pollen stores provided to female offspring varied significantly more than those of males, but volume and sugar content did not. Sugar content varied more among female eggs in social nests than in solitary nests. Provisions were larger, with higher nutrient content, for female eggs and in social nests. Adult females and males show different patterns of allometry, and their investment ratio ranged from 1.23 to 1.69. Adult body weight varied more for females than males, possibly reflecting increased variation in maternal investment in female offspring. These differences are consistent with a role for maternal manipulation in the social plasticity observed in M. genalis.     

Adaptive Management: From More Talk to Real Action

The challenges currently facing resource managers are large-scale and complex, and demand new approaches to balance development and conservation goals. One approach that shows considerable promise for addressing these challenges is adaptive management, which by now is broadly seen as a natural, intuitive, and potentially effective way to address decision-making in the face of uncertainties. Yet the concept of adaptive management continues to evolve, and its record of success remains limited. In this article, we present an operational framework for adaptive decision-making, and describe the challenges and opportunities in applying it to real-world problems. We discuss the key elements required for adaptive decision-making, and their integration into an iterative process that highlights and distinguishes technical and social learning. We illustrate the elements and processes of the framework with some successful on-the-ground examples of natural resource management. Finally, we address some of the difficulties in applying learning-based management, and finish with a discussion of future directions and strategic challenges.