Search Results (13)

Landfill contaminants pose significant risks to environmental and human health, particularly in rural Alaska. These communities are predominantly Alaska Native and face unique challenges in solid waste management due to geography, climate, and limited infrastructure. This scoping review assessed published research on the impacts of landfill contaminants in the Arctic (Aim 1) and Alaska specifically (Aim 2). Seventy-one studies met the inclusion criteria, all of which were used to develop a conceptual model of contaminant transport pathways. Thirty-nine studies included Alaska-specific research: thirty-three focused on environmental impacts, and six addressed human health (e.g., birth outcomes, cancer). Key topics included waste burning, heat generation, carbon release, leachate characterization, and water or sediment contamination. Evidence specific to Alaska suggested landfill leachate may contaminate surface water and groundwater, and that microbes can migrate beyond the landfill site boundaries in communities using honeybuckets (plastic bag-lined buckets that collect human waste). Landfill contaminants also impacted wildlife through consumption of garbage, which may have human health implications for subsistence-based communities. Major research gaps remain in understanding individual-level exposures, the effects of emerging contaminants, and the mechanisms of contaminant transport pathways. Further research designed for causal inference is needed to support improvements to public and environmental health. Full article

The Arctic is warming three to four times faster than the global average. This is transforming global maritime routes, thereby increasing shipping and resource extraction in Alaska. This surge requires sustainable energy solutions as policy trends towards stricter emissions standards. This article assesses the potential of Geothermal-to-X (GtX) technologies in establishing clean refueling infrastructure across Alaska, using its untapped geothermal resources. GtX uses electrolysis to split water into hydrogen and oxygen, a process powered by geothermal energy. Hydrogen and its X products, such as green methane or green ammonia, can be stored as fuels and are largely recognized as the key to a carbon-free future to address the growing energy demand. This study assesses the technical, economic, strategic, and geological feasibility of GtX refueling hubs in Alaska. Five locations were denoted as potential candidates and beckon future research. This study concludes that Unalaska is the most viable initial GtX hub given the highest Multi Criteria Decision Analysis (MCDA) score from its combination of a high-quality geothermal resource, an existing and accessible deepwater port, and a sizable local energy demand. The goal of this study is to provide an accessible and comprehensive resource for stakeholders and policymakers, outlining an energy future with sustainable maritime development, powered by affordable and secure energy. Full article

Power system models of electric systems are crucial in system planning for operations, economics, and expansion analyses. However, as these models contain critical infrastructure data, they are not publicly available. This poses challenges in future expansion scenarios and evaluating technological advancements in an electric grid. Synthetic models are realistic power system models, both topologically and operationally. However, since the electrical network is typically produced using statistical data and often uses machine learning, it does not contain propriety information. This allows researchers to evaluate system behavior under various operating conditions and as test cases for emerging technologies. These test cases are particularly important in highly evolving electric grids and areas of high renewable energy integration such as Alaska. Currently, no publicly available benchmark power system models of rural Alaskan island microgrids exist. This paper presents a rural Alaskan microgrid synthetic power system model and the methodology adopted to develop the model. The performance of the developed synthetic grid was assessed through steady state and positive-sequence dynamic simulations under various operating conditions. Full article

This study develops a multi-dimensional sustainable tourism optimization framework for Juneau, Alaska, integrating economic, social, and environmental dimensions to balance tourism-driven prosperity with ecological and socio-cultural integrity. Utilizing a hybrid Analytic Hierarchy Process and entropy weighting method, the model assigns robust indicator weights. Optimized via the NSGA-II algorithm, it identifies an optimal tourist threshold, achieved through a strategic tax adjustment. This policy not only sustains economic revenue at USD 325 million but also funds a critical feedback loop: revenue reinvestment into environmental conservation and social infrastructure, which stabilizes cost indices and enhances community well-being. The model’s projections show this approach significantly mitigates environmental degradation, notably glacier retreat, and alleviates social pressures such as infrastructure overload and resident dissatisfaction. A key contribution of this research is the framework’s adaptability, which was validated through its application to Barcelona, Spain. There, the framework was recalibrated with social indicators tailored to address urban overtourism, achieving substantial reductions in housing and congestion costs alongside environmental improvements, while economic recovery was maintained. Sensitivity analyses confirm the model’s stability, though data limitations and subjective weighting suggest future enhancements via real-time analytics and dynamic modeling. Key policy recommendations include dynamic tourist caps, diversified attractions, and community engagement platforms, offering scalable solutions for global tourism destinations. This framework advances sustainable tourism by providing a blueprint to decouple economic growth from ecological and social harm, ensuring the longevity of natural and cultural assets amidst climate challenges. Full article

The future of marine sustainability depends on public understanding and trust in the policy recommendations that emerge from scientific research. For common pool marine resource decisions made by the people who depend on these resources for their food, employment, and economic future, understanding the current status of these marine systems and change is essential to ensure these resources will persist into the future. As such, the informal learning infrastructure is essential to increasing marine science literacy in a changing world. This mixed-methods research study analyzed the distribution and accessibility of marine science education and research across Alaska’s five geographic regions. Using the PRISMA framework, we synthesized data from 198 institutions and analyzed peer-reviewed literature on marine ecosystems to identify geographic and thematic gaps in access to informal science learning and research focus. In parallel, we undertook geospatial analysis and resource availability to describe the distribution of resources, types of informal learning infrastructure present across the state, regional presence, and resources to support informal marine science learning opportunities. Findings from this multifactor research revealed a concentration of resources in urban hubs and a lack of consistent access to learning resources for rural and Indigenous communities. The configurative literature review of 9549 publications identified topical underrepresentation of the Bering Sea and Aleutian Islands, as well as a lack of research on seabirds across all regions. Considered together, these results recommend targeted investments in rural engagement with marine science programming, culturally grounded partnerships, and research diversification. This review concludes that disparities in learning resource support and government-funded priorities in marine wildlife research have created conditions that undermine the local people’s participation in the sustainability of sensitive resources and are likely exacerbating declines driven by rapid change in Arctic and sub-Arctic waters. Full article

Arctic Alaska is warming at twice the rate of the rest of the nation, severely impacting infrastructure built on permafrost. As winters warm, the effectiveness of thermosyphons used to stabilize foundations diminishes, increasing the risk of infrastructure failure. Because thermosyphons operate with the highest efficiency during winter cold snaps, studying the variation trends and patterns of winter cold snaps in Alaska is particularly important. To address this issue, this study analyzes the historical temperature data of four selected locations in Subarctic and Arctic Alaska, including Bethel, Fairbanks, Nome, and Utqiagvik. The winter cold snap is defined as a period when the average daily temperature drops below a specific site’s mean winter air temperature. The frequency, duration, and intensity of the winter cold snaps are computed to reveal their trends. The results indicate that the mean annual air temperature (MAAT) shows a warming trend, accompanied by sudden warming after 1975 for all study sites. The long-term average monthly air temperature also indicates that the most significant warming occurs in the winter months from December to March. While the frequencies of winter cold snaps remain relatively unchanged, the mean intensity and duration of cold snaps show a declining trend. Most importantly, the most intense cold snap during which the thermosyphons are the most effective is becoming much milder over time for all study sites. This study focuses specifically on the impact of changes in winter cold spells on thermosyphon effectiveness while acknowledging the complexity of other influencing factors, such as temperature differences, design features, coolant properties, and additional climatic parameters (e.g., wind speed, precipitation, and humidity). The data for this study were obtained from the NOAA NCEI website. The findings of this study can serve as a valuable reference for the retrofit or design of foundations and for decision making in selecting appropriate foundation stabilizing measures to ensure the long-term stability and resilience of infrastructure in permafrost regions. Moreover, the insights gained from this research on freeze–thaw dynamics, which are also relevant to black soils, align with the journal’s focus on sustainable soil utilization and infrastructure resilience. Full article

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. Full article

Cold exposure remains a significant public health concern, particularly in the Arctic regions prone to extremely cold weather. While the physical health impacts of cold exposure are well documented, understanding the social vulnerability aspects is crucial for effective mitigation and policy development. This study investigates the multifaceted dimensions of social vulnerability in the face of cold temperatures across various communities in Alaska. Alaska, renowned for its extreme cold temperatures and harsh environmental conditions, poses unique challenges to its residents, particularly in the context of social vulnerability. Drawing on a combination of quantitative data analysis and qualitative insights, we examine the factors contributing to social vulnerability, including demographic, economic, geographic, and infrastructural elements, in terms of the Extremely Cold Social Vulnerability Index, for seven Public Health Regions in Alaska. The Universal Thermal Climate Index in two very cold categories (<−27 °C) was used to identify cold exposure. Factors such as income, housing quality, health status, and resilience of the population play crucial roles in determining an individual or community’s sensitivity to, and ability to cope with, cold temperatures. Our analysis reveals that social vulnerability in Alaska is not uniform but varies significantly among regions. The research findings highlight the importance of considering factors of both sensitivity and adaptivity in understanding and addressing social vulnerability, thereby informing the development of targeted strategies and policies to enhance the resilience of Alaskan communities. As cold temperatures are projected to continue to challenge the region, addressing social vulnerability is essential for ensuring the well-being and safety of Alaska’s diverse populations. Full article

Rockfall presents a significant risk to the safety and economy of communities and infrastructure in mountainous regions. The recently-developed Rockfall Activity Index (RAI) utilizes high-resolution terrestrial lidar-derived digital elevation models (DEMs) of rock slopes to categorize a slope face into seven distinct morphological units, or “RAI classes”. This paper focuses on a comprehensive study conducted at four sites in Alaska, USA, where a robust lidar-based five-year inventory of 4381 rockfall events was analyzed. The primary objective was to investigate variations in failure characteristics, such as cumulative magnitude–frequency distributions, non-cumulative power–law parameters, average annual failure rates, and average failure depths, among the different RAI classes. The findings demonstrate that the seven RAI classes effectively differentiate the rock slope based on unique mass-wasting characteristics. Furthermore, the research explores spatial and temporal variations in these failure characteristics. Based on the study’s outcomes, recommendations are provided for modifying the RAI parameters for each RAI class, specifically the annual failure rate and average failure depth. These modifications aim to enhance the accuracy and effectiveness of rockfall hazard assessments. Full article

There has been a growth in the number of composite indicator tools used to assess community risk, vulnerability, and resilience, to assist study and policy planning. However, existing research shows that these composite indicators vary extensively in method, selected variables, aggregation methods, and sample size. The result is a plethora of qualitative and quantitative composite indices to choose from. Despite each providing valuable location-based information about specific communities and their qualities, the results of studies, each using disparate methods, cannot easily be integrated for use in decision making, given the different index attributes and study locations. Like many regions in the world, the Arctic is experiencing increased variability in temperatures as a direct consequence of a changing planetary climate. Cascading effects of changes in permafrost are poorly characterized, thus limiting response at multiple scales. We offer that by considering the spatial interaction between the effects of permafrost, infrastructure, and diverse patterns of community characteristics, existing research using different composite indices and frameworks can be augmented. We used a system-science and place-based knowledge approach that accounts for sub-system and cascade impacts through a proximity model of spatial interaction. An estimated ‘permafrost vulnerability surface’ was calculated across Alaska using two existing indices: relevant infrastructure and permafrost extent. The value of this surface in 186 communities and 30 military facilities was extracted and ordered to match the numerical rankings of the Denali Commission in their assessment of permafrost threat, allowing accurate comparison between the permafrost threat ranks and the PVI rankings. The methods behind the PVI provide a tool that can incorporate multiple risk, resilience, and vulnerability indices to aid adaptation planning, especially where large-scale studies with good geographic sample distribution using the same criteria and methods do not exist. Full article

Cost-effective monitoring of forest carbon resources is critical to the development of national policies and enforcement of international agreements aimed at reducing carbon emissions and mitigating the impacts of climate change. While carbon monitoring systems are often based on national forest inventories (NFI) utilizing a large sample of field plots, in remote regions the lack of transportation infrastructure often requires heavier reliance on remote sensing technologies, such as airborne lidar. The challenge motivating our research is that the efficacy of estimating carbon with lidar varies across the various carbon pools within forest ecosystems. Lidar measurements are typically highly correlated with aboveground tree carbon but are less strongly correlated with other carbon pools, such as down woody materials (DWM) and soil. Field measurements are essential to both (1) estimate soil and DWM carbon directly and (2) develop regression models to estimate tree carbon indirectly using lidar. With limited budgets and time, however, decision makers must find an optimal way to combine field measurements with lidar to minimize standard errors in carbon estimates for the various pools. We introduce a multi-objective binary programming formulation that quantifies the tradeoffs behind the competing objectives of minimizing standard errors for tree carbon, DWM carbon, and soil carbon. Using NFI and airborne lidar data from a remote boreal forest region of interior Alaska, we demonstrate the operational feasibility of the method and suggest that it is generalizable to other carbon sampling projects because of its generic mathematical structure. Full article

Differencing multi-temporal topographic data (radar, lidar, or photogrammetrically derived point clouds or digital elevation models—DEMs) measures landscape change, with broad applications for scientific research, hazard management, industry, and urban planning. The United States Geological Survey’s 3D Elevation Program (3DEP) is an ambitious effort to collect light detection and ranging (lidar) topography over the United States’ lower 48 and Interferometric Synthetic Aperture Radar (IfSAR) in Alaska by 2023. The datasets collected through this program present an important opportunity to characterize topography and topographic change at regional and national scales. We present Indiana statewide topographic differencing results produced from the 2011–2013 and 2016–2020 lidar collections. We discuss the insights, challenges, and lessons learned from conducting large-scale differencing. Challenges include: (1) designing and implementing an automated differencing workflow over 94,000 km² of high-resolution topography data, (2) ensuring sufficient computing resources, and (3) managing the analysis and visualization of the multiple terabytes of data. We highlight observations including infrastructure development, vegetation growth, and landscape change driven by agricultural practices, fluvial processes, and natural resource extraction. With 3DEP and the U.S. Interagency Elevation Inventory data, at least 37% of the Contiguous 48 U.S. states are already covered by repeat, openly available, high-resolution topography datasets, making topographic differencing possible. Full article

The Collaborative Research Center for American Indian Health (CRCAIH) was established in September 2012 as a unifying structure to bring together tribal communities and health researchers across South Dakota, North Dakota and Minnesota to address American Indian/Alaska Native (AI/AN) health disparities. CRCAIH is based on the core values of transdisciplinary research, sustainability and tribal sovereignty. All CRCAIH resources and activities revolve around the central aim of assisting tribes with establishing and advancing their own research infrastructures and agendas, as well as increasing AI/AN health research. CRCAIH is comprised of three divisions (administrative; community engagement and innovation; research projects), three technical cores (culture, science and bioethics; regulatory knowledge; and methodology), six tribal partners and supports numerous multi-year and one-year pilot research projects. Under the ultimate goal of improving health for AI/AN, this paper describes the overarching vision and structure of CRCAIH, highlighting lessons learned in the first three years. Full article