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Reducing greenhouse gas emissions is an international impetus to transition from vehicles with internal combustion engines (ICE) to electric vehicles (EV). While this transition is happening rapidly in some regions of the world that are mainly urbanized, other predominantly rural and less developed regions are slower to adopt this technology. Rural Alaska serves as an example with its not-road-connected communities, high cost of electricity, extreme environmental conditions, and isolated power grids often powered by diesel. This study used co-production and mixed methods to identify barriers and perceived benefits towards EV adoption and explore EV adoption rates across the Arctic. We conducted community workshops in Bethel, Galena, and Kotzebue, Alaska, and 25 interviews with businesses and local governments. The top five impediments to EV adoption are the inability to maintain vehicles locally, cold weather performance, higher purchase prices compared to ICE vehicles, and the cost of electricity. The successful adoption of EVs in isolated microgrid communities in the Arctic requires investments in appropriate financial incentives, especially for low-income households, expansion of renewable power generation, and climate and culture-relevant proof-of-concept vehicles. Residents acknowledged that EVs generally operate much cleaner than vehicles with ICE, can have lower fuel and maintenance costs, and cause less air and noise pollution. We propose a framework to develop policies to facilitate the adoption of EVs in rural areas. Policy implications for overcoming the challenges related to the transition to EVs in remote rural parts of the globe are discussed. 

While wildfires can be beneficial and part of a natural process, there have been numerous instances around the world, particularly in recent years, where wildfires have had devastating consequences for society. Weather conditions have created extreme wildfire behavior, resulting in speeds and intensities that can overpower suppression resources. It is ever more critical that communities and agencies take actions to mitigate and prevent wildfire disasters. We have developed a tool that enables wildfire practitioners to assess the risk of wildfire to structures in a straightforward, rapid, and affordable manner. The approach leverages information often collected by communities (e.g., building footprints, zoning) and available vegetation datasets. In conjunction with local wildfire management regulations, our project also used wildfire exposure to help identify wildland-urban interface boundaries. We used this approach on three communities in the Arctic (Anchorage and Fairbanks, Alaska, and Whitehorse, Yukon) to assess wildfire risk. We determined that there is considerable wildfire risk in urban Arctic communities, with a greater percentage of structures at high or very high risk in Fairbanks (26 percent (%)) and Whitehorse (22%) compared to Anchorage (14%). Combining local wildfire management practices with wildfire exposure is a successful way to identify meaningful Wildland Urban Interface (WUI) boundaries, which are essential for obtaining mitigation funds and planning. The key to producing updatable wildfire risk and vulnerability maps is accurate, up-to-date information on vegetation, building footprints, and zoning. With this information and the tool outlined here, communities and agencies have a way to inform community wildfire protection plans and identify impactful mitigation actions. 

A map of degraded ice wede polygon terrain for the lidar mapped portion of the Fairbanks North Star Borough. The purpose of this mapping effort is to identify areas with massive ice present. Mapping of high ground-ice content areas was conducted by manually digitizing ice wedge polygon terrain at a scale of 1:2000 using lidar data. Lidar data covering 2,208 square kilometers (km²) of the Fairbanks North Star Borough (FNSB) were acquired in 2018 from the United States Geological Survey (USGS). Elevation tiles with 1 meter (m) spatial resolution were downloaded from the Alaska Division of Geological and Geophysical Surveys (https://elevation.alaska.gov/). A hillshade mosaic was generated in ArcGIS Pro and used to identify areas of ice wedge polygon degradation, which appear as surface grids of ~10 m wide polygons. A limitation of this mapping approach is that massive ground ice may occur without a surface expression, and thus our mapped extent should be considered conservative. The dataset is provided in the Alaska Albers NAD83 coordinate system. 

Qualitative studies have suggested that forest changes following a wildfire can challenge a hunter's ability to harvest big game, such as moose (Alces alces). Quantitative effects have not been estimated. Given the increasing prevalence of wildfires, the strong linkages between wildfire and moose habitat, and the importance of moose to the people of the boreal region of North America, our goal was to assess if and how moose harvest patterns changed immediately following a wildfire. To address that goal, we used 36 years (1984-2019) of spatially-explicit wildfire and moose harvest data in Alaska to compare moose harvest variables the year before and year after a wildfire occurred. With a few exceptions, the number of hunters, kills, and success rates were similar (p > 0.05, Effect size < 0.3) between pre- and post-wildfire years. We estimated a weak to moderate effect on change in moose hunter numbers, kills, and success rate in only a small percentage (1.5%) of wildfires that burned a very large proportion (>38%) of a moose harvest reporting unit. Our findings suggest that wildfire has not caused a clear and functional quantitative effect on hunters’ ability to harvest moose in Alaska. 

We modified an existing approach to assess decadal wildfire hazards based primarily on ember dispersal and wildfire proximity, referencing landscape changes from 1984 through 2014. Our modifications created a categorial flammability hazard scheme, rather than dichotomous, and the integration of wildfire exposure results across spatial scales. We used remote sensed land cover from four decadal points to create flammability hazard and wildfire exposure maps for three arctic communities (Anchorage and Fairbanks, Alaska and Whitehorse, Yukon). Within the Fairbanks study area, we compared 2014 flammability hazard, wildfire exposure, and FlamMap burn probabilities among burned (2014-2021) and unburned areas. Exposure values were greater in burned than unburned, unlike burn probabilities. These datasets reflect the hazardous fuels or flammability hazard layers used in the process. 

The Arctic presents various challenges for a transition to electric vehicles compared to other regions of the world, including environmental conditions such as colder temperatures, differences in infrastructure, and cultural and economic factors. For this study, academic researchers partnered with three rural communities: Kotzebue, Galena, and Bethel, Alaska, USA. The study followed a co-production process that actively involved community partners to identify 21 typical vehicle use cases that were then empirically modeled to determine changes in fueling costs and greenhouse gas emissions related to a switch from an internal combustion engine to an electric vehicle. While most use cases showed decreases in fueling costs and climate emissions from a transition to electric versions of the vehicles, some common use profiles did not. Specifically, the short distances of typical commutes, when combined with low idling and engine block heater use, led to an increase in both fueling costs and emissions. Arctic communities likely need public investment and additional innovation in incentives, vehicle types, and power systems to fully and equitably participate in the transition to electrified transportation. More research on electric vehicle integration, user behavior, and energy demand at the community level is needed. 

We used crowdsourced data in Alaska and the literature to develop a light-duty electric vehicle model to help policymakers, researchers, and consumers understand the trade-offs between internal combustion and electric vehicles. This model forms the engine of a calculator, which was developed in partnership with residents from three partner Alaskan communities. This calculator uses a typical hourly temperature profile for any chosen community in Alaska along with a relationship of energy use vs. temperature while driving or while parked to determine the annual cost and emissions for an electric vehicle. Other user inputs include miles driven per day, electricity rate, and whether the vehicle is parked in a heated space. A database of community power plant emissions per unit of electricity is used to determine emissions based on electricity consumption. This tool was updated according to community input on ease of use, relevance, and usefulness. It could easily be adapted to other regions of the world. The incorporation of climate, social, and economic inputs allow us to holistically capture real world situations and adjust as the physical and social environment changes. 

Abstract Increased wildfire activity has raised concerns among communities about how to assess and prepare for this threat. There is a need for wildfire hazard assessment approaches that capture local variability to inform decisions, produce results understood by the public, and are updatable in a timely manner. We modified an existing approach to assess decadal wildfire hazards based primarily on ember dispersal and wildfire proximity, referencing landscape changes from 1984 through 2014. Our modifications created a categorical flammability hazard scheme, rather than dichotomous, and integrated wildfire exposure results across spatial scales. We used remote sensed land cover from four historical decadal points to create flammability hazard and wildfire exposure maps for three arctic communities (Anchorage and Fairbanks, Alaska and Whitehorse, Yukon). Within the Fairbanks study area, we compared 2014 flammability hazard, wildfire exposure, and FlamMap burn probabilities among burned (2014–2023) and unburned areas. Unlike burn probabilities, there were significantly higher in exposure values among burned and unburned locations (Wilcoxon;p < 0.001) and exposure rose as flammability hazard classes increased (Kruskal–Wallis;p < 0.001). Very high flammability hazard class supported 75% of burned areas and burns tended to occur in areas with 60% exposure or greater. Areas with high exposure values are more prone to burn and thus desirable for mitigation actions. By working with wildfire practitioners and communities, we created a tool that rapidly assesses wildfire hazards and is easily modified to help identify and prioritize mitigation activities. 

Abstract Unalaska Island, Alaska, served as a US military base during World War II. The military installed bases on Unalaska and nearby islands, many of which were built adjacent to Unangan communities. The military used toxic compounds in its operations and left a legacy of pollution that may pose health risks to residents and local wildlife. The goals of this study were to identify hotspots of contamination remaining at Unalaska formerly used defense (FUD) sites, evaluate the risk posed by arsenic, and examine “no US Department of Defense action indicated” (NDAI) status determinations for FUD sites near communities. We compiled soil chemistry data from remediation reports prepared by the US Army Corps of Engineers at 18 FUD sites on and near Unalaska. Nine had past and/or active remediation projects and on‐site sampling data. Eight sites did not have sampling data and were characterized as NDAI. One site was listed as closed. For the nine sites with sampling data, we compiled data for 22 contaminants of concern (COC) and compared concentrations to soil cleanup levels for human health (18 AAC 75.341). We mapped contaminant concentrations exceeding these levels to identify hotspots of contamination. We found that concentrations of some of the 22 COC exceeded Alaska cleanup levels despite remediation efforts, including diesel range organics, arsenic, and lead. The highest COC concentrations were at the FUD site adjacent to the City of Unalaska. A quantitative risk assessment for arsenic found that the risk of exposure through drinking water is low. We highlight concerns with NDAI designations and current remedial practices at remote FUD sites located adjacent to communities. Our data suggest the need for further remediation and monitoring efforts on Unalaska for certain contaminants and research to examine potential threats to human and animal health associated with these sites.Integr Environ Assess Manag2024;00:1–12. © 2024 SETAC 

Abstract BackgroundSituational engagement in science is often described as context-sensitive and varying over time due to the impact of situational factors. But this type of engagement is often studied using data that are collected and analyzed in ways that do not readily permit an understanding of the situational nature of engagement. The purpose of this study is to understand—and quantify—the sources of variability for learners’ situational engagement in science, to better set the stage for future work that measures situational factors and accounts for these factors in models. ResultsWe examined how learners' situational cognitive, behavioral, and affective engagement varies at the situational, individual learner, and classroom levels in three science learning environments (classrooms and an out-of-school program). Through the analysis of 12,244 self-reports of engagement collected using intensive longitudinal methods from 1173 youths, we found that the greatest source of variation in situational engagement was attributable to individual learners, with less being attributable to—in order—situational and classroom sources. Cognitive engagement varied relatively more between individuals, and affective engagement varied more between situations. ConclusionsGiven the observed variability of situational engagement across learners and contexts, it is vital for studies targeting dynamic psychological and social constructs in science learning settings to appropriately account for situational fluctuations when collecting and analyzing data.