Analysis of a system for remote, dry removal of solidified mixed waste

This paper describes a novel technology for the removal of solidified radioactive waste from underground storage tanks at the DOE Hanford site in southeast Washington. The process involves the use of a unique high pressure system which pulverizes the hardened saltcake with stainless steel pellets to a powder for easy vacuum removal. The steel pellets can be magnetically separated from the waste material for re-use. Specifically this study analyzes the effects of various steel abrasives, pressure of the air stream, and stand-off distance on removal rates and penetration depth on simulated saltcake samples using the high pressure technique. A full-scale test set-up and protocol were implemented to allow for comprehensive testing. To insure reproducibility of the method, tests were then run for the optimum removal parameters. A time-dependent test was also conducted to determine the relationship of removal rates to length of pressure blasts. The results of these tests revealed that stand-off distance and pressure could be positively correlated to removal volumes. Additionally, a statistical analysis confirmed that nozzle angle is independent of removal rate. This study demonstrated that the pellet ‘blaster’ technique is a safe, effective method for removal of radioactive wastes without any increase in either waste mass or volume.     

Human–nature connectedness as a ‘treatment’ for pro-environmental behavior: making the case for spatial considerations

The degree to which an individual feels connected to the natural world can be a positive predictor of pro-environmental behavior (PEB). This has led to calls to ‘reconnect to nature’ as a ‘treatment’ for PEB. What is not clear is the relationship between where one feels connected to nature and where one acts pro-environmentally. We propose that integrating spatial scale into the conceptualization of these constructs will provide insights into how different degrees of connectedness influence pro-environmental behavior. We discuss trends towards a spatial understanding of human–nature connectedness (HNC) and introduce three archetypes that highlight scalar relationships between scale of connectedness and scale of pro-environmental behavior: (1) equal interactions, (2) embedded interactions, and (3) extended interactions. We discuss potential policy and practice implications of taking a spatially explicit approach to HNC–PEB research, and propose a research agenda for investigating these scalar relationships that can inform nature as a ‘treatment’ intervention.     

Risks of sea level rise to disadvantaged communities in the United States

Climate change and sea level rise (SLR) pose risks to coastal communities around the world, but societal understanding of the distributional and equity implications of SLR impacts and adaptation actions remains limited. Here, we apply a new analytic tool to identify geographic areas in the contiguous United States that may be more likely to experience disproportionate impacts of SLR, and to determine if and where socially vulnerable populations would bear disproportionate costs of adaptation. We use the Social Vulnerability Index (SoVI) to identify socially vulnerable coastal communities, and combine this with output from a SLR coastal property model that evaluates threats of inundation and the economic efficiency of adaptation approaches to respond to those threats. Results show that under the mid-SLR scenario (66.9 cm by 2100), approximately 1,630,000 people are potentially affected by SLR. Of these, 332,000 (～20%) are among the most socially vulnerable. The analysis also finds that areas of higher social vulnerability are much more likely to be abandoned than protected in response to SLR. This finding is particularly true in the Gulf region of the United States, where over 99% of the most socially vulnerable people live in areas unlikely to be protected from inundation, in stark contrast to the least socially vulnerable group, where only 8% live in areas unlikely to be protected. Our results demonstrate the importance of considering the equity and environmental justice implications of SLR in climate change policy analysis and coastal adaptation planning.     

Modeling effects of traffic and landscape characteristics on ambient nitrogen dioxide levels in Connecticut

An integrated exposure model was developed that estimates nitrogen dioxide (NO(2)) concentration at residences using geographic information systems (GIS) and variables derived within residential buffers representing traffic volume and landscape characteristics including land use, population density and elevation. Multiple measurements of NO(2) taken outside of 985 residences in Connecticut were used to develop the model. A second set of 120 outdoor NO(2) measurements as well as cross-validation were used to validate the model. The model suggests that approximately 67% of the variation in NO(2) levels can be explained by: traffic and land use primarily within 2 km of a residence; population density; elevation; and time of year. Potential benefits of this model for health effects research include improved spatial estimations of traffic-related pollutant exposure and reduced need for extensive pollutant measurements. The model, which could be calibrated and applied in areas other than Connecticut, has importance as a tool for exposure estimation in epidemiological studies of traffic-related air pollution.