Observations of pyroclast and hydroclast production during subaqueous volcanism at depths >1 km have become common in the literature, despite early predictions that such activity would be pressure-limited. Direct near-vent observations of active submarine eruptions (first at NW Rota-1 and West Mata in the 2000s, e.g., Murch et al., EPSL 2022) revealed multiple volcaniclastic generation modes: in-conduit magmatic fragmentation, external water-induced fragmentation at the vent, and cooling-contraction fragmentation of lava injected molten into the water column. Follow-up studies of stratigraphy and edifice morphology at these and other sites indicate that such processes are widespread and contribute significantly to volcanic structure, hydrology, and ecology. We report here on several substantial primary and secondary pyroclastic deposits from recent eruptions in Tonga’s NE Lau Basin (mainly at West Mata and Tafu volcanoes), as well as undated but likely late Holocene deposits in nearby areas, which display volumetrically significant volcaniclastic output during mafic submarine eruptions as thick vent-proximal and thinner, broad distal deposits with diverse particle morphologies. Explosive fragmentation under these conditions can also produce varied landforms and promote slope instability. Observations from 7 NSF-, NOAA-, and SOI-sponsored cruises (2009–2018), using ROVs and AUVs, reveal both new and older tephra blankets, periodic downslope transport, and distal ripple-structured deposits. Decreasing grain size and thickness generally with distance from the vent location, imply primary pyroclast deposition as well as secondary reworking; in other cases (especially at deep sites), sand-sized and smaller grained-deposits show evidence for transport bedforms (ripples) and post-depositional reworking (sinkholes). At West Mata (2016–17 eruptions; Chadwick et al. 2019) and post-2011 Tafu (Rubin et al., AGU 2020) events, pyroclastic layers up to 1–2 m thick buried lava flows, indicating explosive final phases. Sampling these deposits for grain size/shape characterization is challenging due to coarse fragments near vents and fine particles distally, but can be done using push cores with catchers and scoop bags for basic grain size and shape analysis

Deep learning models have demonstrated success in geospatial applications, yet quantifying the role of geolocation information in enhancing model performance and geographic generalizability remains underexplored. A new generation of self-supervised location encoder foundation models have emerged with the goal of capturing attributes present at any given location for downstream use in predictive modeling. Being a nascent area of research, their evaluation has remained largely limited to static tasks such as species distributions or average temperature mapping. Here, we discuss and quantify the impact of incorporating geolocation into deep learning for a real-world application domain that is characteristically dynamic (with fast temporal change) and spatially heterogeneous at high resolutions: estimating surface-level daily PM2.5 levels using remotely sensed and ground-level data. We build on a recently published deep learning-based PM2.5 estimation model that achieves state-of-the-art performance on data observed in the contiguous United States. We examine three approaches for incorporating geolocation: excluding geolocation as a baseline, using raw geographic coordinates, and leveraging pretrained location encoders. We evaluate each approach under within-region (WR) and out-of-region (OoR) evaluation scenarios. Aggregate performance metrics indicate that while naïve incorporation of raw geographic coordinates improves within-region performance by retaining the interpolative value of geographic location, it can hinder generalizability across regions. In contrast, pretrained location encoders like GeoCLIP enhance predictive performance and geographic generalizability for both WR and OoR scenarios. However, our qualitative analysis reveals artifact patterns caused by high-degree basis functions and sparse upstream samples in certain areas, and our ablation results indicate varying performance among location encoders such as SatCLIP vs. GeoCLIP. To the best of our knowledge, this is a first integration and systematic evaluation of location encoders in a complex, temporally dynamic estimation scenario. In addition to guiding better model development for air pollution estimation and location encoders, this study provides insights for effective incorporation of location into deep learning for geospatial predictive tasks.

Ground-level ozone (O3), nitrogen dioxide (NO2), and nitric oxide (NO) are key components of photochemical smog and well-established threats to public health and ecosystems. Because these gases interconvert within hours through photochemical reactions (e.g., NO + O3 → NO2 + O2), modeling them together lets information from each pollutant inform and improve the prediction of the others. However, regulatory monitoring networks remain sparse and uneven across space and time: the Contiguous United States (CONUS) hosts approximately 1000 O3 sites but only 400 NO2 and fewer NO stations, posing a challenge to the development of data-hungry deep learning models. We present a multi-task learning (MTL) framework that simultaneously estimates daily 1 km-resolution O3, NO2, and NO concentrations for CONUS. The architecture couples a shared spatiotemporal encoder—composed of a convolutional neural network (CNN) followed by a bi-directional long short-term memory (Bi-LSTM) network—with learned location and temporal embeddings; pollutant-specific decoders then produce individual pollution concentrations. Training leverages a comprehensive set of predictors: meteorology from Daymet (temperature, precipitation, vapor pressure, short‑wave radiation) and GridMET (wind speed and direction); chemical precursors from the National Emissions Inventory (VOC and NOx); land‑surface information from MODIS NDVI and a digital elevation model (DEM); reanalysis data from CAMS EAC4 and MERRA‑2; and satellite retrievals from Aura/OMI (total‑column O3, NO2, NO). MTL yields clear benefits over single-task baselines in all three pollutants, demonstrating that knowledge shared across chemically linked pollutants enhances generalization even for the well-monitored pollutant. The resulting 1km, daily O3-NO2-NO dataset from 2005 to 2021 will be made publicly available, providing an unprecedented resource for longitudinal epidemiological studies, exposure disparity assessments, and air quality management.

Rapid estimation of bolide energy from infrasonic signals is essential for hazard assessment, yet most current procedures apply a single period–yield curve that assumes a uniform source behavior. Such generalization neglects the influence of entry geometry, altitude-dependent energy release, and fragmentation history on the observed infrasound signal period, producing large uncertainties for many events. We present a multiparameter empirical framework that embeds those physical factors directly into empirical period–energy scaling. A global catalog of bolides detected by both US Government (USG) sensors and worldwide infrasound stations provides the foundation for this endeavor. Measured dominant signal periods are then paired with USG sensor-derived energies. A bootstrap-based regression strategy partitions the data according to various parameters (e.g., trajectory angle, peak-brightness altitude, fragmentation pattern) delivering a family of period–yield relations tailored to specific source conditions. The resulting relations show that classical single-curve models do not sufficiently represent energy deposition whenever an event departs from median geometry or undergoes complex fragmentation. By capturing the observed variability across the expanding bolide database, the framework supplies uncertainty envelopes that remain realistic even when only infrasound is available. This approach transforms infrasound energy assessment from a coarse heuristic into a physics-aware tool optimized for hazard-monitoring and planetary defense applications. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

CRISPI (Compositional Regolith and Icy Surface analysis via Particle Impact) is a proposed ridealong in-situ mass spectrometer (MS) smallsat mission to provide synergistic astrobiology science at Ariel to the Uranus Orbiter and Probe (UOP) mission. By utilizing a high-resolution impact ionization MS based on the Europa Clipper’s Surface Dust Analyzer on a deployable smallsat platform, CRISPSI will provide in-situ mass data of the surface of Ariel, a moon of Uranus and one of the highest-priority astrobiology targets in the outer solar system. Ariel is specifically called out in the Origins, Worlds, and Life (OWL) Decadal Survey as a high-priority target because it is potentially a liquid-water-bearing icy ocean world that is known to have ammonia, an organic volatile species that indicates recent surface processing (possibly from cryovolcanism or other subsurface sources) and may act as an anti-freeze for water. However, the baseline UOP mission has few flybys of Ariel with limited compositional data provided only by optical spectrometers. CRISPI addresses this gap by enabling the direct and unambiguous detection of organics and salts present in and the quantification of ice-to-rock and salt-to-water ratios of >1000 surface dust grains mapped to visible features on Ariel’s surface. In principle, CRISPI may also be used to study Miranda (another high priority astrobiology target) and the Uranus ring systems, which are poorly understood and are also specifically called out in the OWL Decadal Survey. Here we present results from the Cornell SmallSat Mission Design School (SMDS) that developed the CRISPI mission concept. We propose CRISPI to work in synergy with the UOP’s magnetometer, imagers, and optical spectrometers to extend its science reach, break ambiguities in optical spectrometer data, and provide unique and critical in-situ mass spectral data to assess Ariel’s habitability and search for organic signs of past or extant life.

Particulate nitrate (PN) is a critical component of fine particulate matter (PM2.5), especially during wintertime over agricultural regions. In recent years, the contribution of wintertime PN to PM2.5 mass over the Midwestern United States (MWUS), an agriculturally intensive region, has stayed relatively high over time (up to 40%). PN formation is controlled by nitrogen oxides (NOX = NO + NO2), ammonia (NH3), and volatile organic compounds (VOCs). To best control wintertime PM2.5 burden, it is critical to determine PN sensitivity to precursor gases. Despite increases in NH3 concentrations and the flattening of previously decreasing PN trends since 2013 over the MWUS, the factors controlling local PN formation are not well constrained. Satellite tropospheric column NH3/NO2 ratios have been shown to be effective in diagnosing PN sensitivity over East Asia, Europe, and the Eastern United States. Here, we expand this approach to quantify spatially and temporally resolved multidecadal wintertime PN formation sensitivity in the MWUS. Using GEOS-Chem sensitivity simulations and satellite observations from the Ozone Monitoring Instrument (OMI) and the Infrared Atmospheric Sounding Interferometer (IASI), we diagnose wintertime PN sensitivity to NH3, NOx, and VOCs from 2007 to 2023. The NOx-sensitive regime dominates over rural areas, and the NH3-sensitive regime dominates over major cities. VOCs do not control PN formation. Recently, the NOx-sensitive regime is becoming more prominent over the MWUS. NO2 satellite column densities increase from 2007 to 2017 by 0.7 ± 1.1% yr-1 and accelerate from 2018 to 2023 (7.0 ± 1.5% yr-1). NH3 satellite column densities also slowly increase by 0.8 ± 0.6% yr-1 from 2007 to 2017 and accelerate to 2.1 ± 1.2% yr-1 from 2018 to 2023. Despite the recent increasing trends for both precursor gases, PN is still highly NOx-sensitive over the MWUS because of the high abundance of NH3. Interestingly, trends in surface NO2 concentrations oppose satellite observations, decreasing by -1.8 ± 2.0% yr-1 from 2018 to 2023, suggesting the influence of aloft NOX emissions in satellite retrievals. Our work indicates that targeting NOX emissions over rural areas can reduce wintertime PN and PM2.5, while controlling NH3 is more effective over cities.

Sustainable potato production depends on accurate, timely decisions about nitrogen (N) management and yield prediction that guide efficient resource use and reduce environmental impact. However, conventional approaches remain destructive, labor-intensive, and limited in spatial and temporal resolution. To address these limitations, precision agriculture increasingly relies on scalable, data-driven solutions to monitor in-season N status and predict yield across diverse cultivars and environments. Yet many existing models are still constrained by narrow-band indices, limited temporal scope, or incomplete integration of agronomic and environmental variables. This study addresses these gaps by applying artificial intelligence (AI) to high-resolution hyperspectral remote sensing data (400–2500 nm), genotype variations, N management practices, and environmental data collected across multiple growth stages and growing seasons. Using machine learning models (XGBoost and Random Forest), we integrated spectral vegetation indices, genotype, environmental, and management data to predict in-season leaf N concentration and final tuber yield. The models achieved high predictive accuracy for N status (R² = 0.918) and yield (R² = 0.834), with strong generalizability across growing seasons. Differences in spectral responses among N treatment levels enabled early prediction of yield outcomes, offering a powerful tool for improving N use efficiency and informing site-specific management decisions. These findings demonstrate how AI-driven hyperspectral analysis can enhance sustainability in potato cropping systems by reducing fertilizer waste, improving productivity, and mitigating environmental risks such as groundwater contamination.

Assessing the trafficability of Arctic sea ice is vital for Arctic communities, researchers, and operational users who depend on over-ice mobility. While synthetic aperture radar (SAR)-based methods have shown promise for evaluating sea ice roughness, they are limited in resolving surface conditions influenced by snow. In this study, we explore the potential of high-resolution optical satellite imagery for characterizing sea ice surface roughness and estimating trafficability through texture analysis.Using the Grey-Level Co-occurrence Matrix (GLCM) method, we calculate texture measures (contrast, homogeneity, and energy) from optical imagery over landfast sea ice near Utqiaġvik, Alaska. Parameter sensitivity tests across multiple window sizes, orientations, and displacements inform optimal settings for extracting meaningful spatial texture metrics. The texture measures are compared against snowmachine travel speed data collected during field surveys near Utqiaġvik, Alaska. Our results show that GLCM-derived contrast metrics strongly correlate with observed travel speeds. Based on these correlations, we generate speed maps and apply an optimal pathfinding algorithm to identify efficient travel corridors across the ice. This remote sensing–driven approach enables fast, scalable assessments of sea ice trafficability that incorporate snow-related surface complexity. By linking texture measures from high-resolution optical satellite imagery to in situ travel velocity data, we demonstrate a transferable methodology for sea ice condition assessment at spatial scales relevant to both local and regional applications. This framework supports community travel planning and remote logistics and is increasingly valuable with the growing availability of high-resolution, high-temporal-frequency optical imagery across polar environments.

The Cascadia Offshore Subduction Zone Observatory (COSZO) is an NSF-funded Mid-scale Research Infrastructure-1 Project that will add seismic and geodetic instrumentation in summer 2026 to the Ocean Observatories Initiative (OOI) Regional Cabled Array (RCA) off Newport, Oregon. In Cascadia, geophysical observations indicate that the megathrust is mostly locked from the coastline to the deformation front, but off central Oregon they are consistent with a locked megathrust near the deformation front that transitions to creeping behavior beneath the shelf where there are two sustained clusters of earthquakes. To enable COSZO, we have updated the design of the RCA science junction boxes to eliminate obsolete components and new junction boxes are being constructed to connect to three primary nodes on the continental slope and shelf that currently do not support seafloor geophysical observations. At each new junction box and fourth site on the shelf, we will install a Nanometrics Atlantis Cabled Observatory ocean bottom seismic package which comprises a buried broadband seismometer and strong motion accelerometer, a low-frequency hydrophone and a differential pressure gauge. We are building two types of calibrated pressure gauges that utilize Paroscientific resonant quartz crystal sensors. The Geodetic and Seismic Sensor Module combines a triaxial accelerometer with two absolute pressure guages that are periodically calibrated against the internal pressure of the housing measured by a barometer. The Cabled Self Calibrating Pressure Recorder also includes two absolute pressure gauges but performs calibrations with a reference pressure close to ambient generated by a piston gauge. COSZO will also include uncalibrated absolute pressure gauges and Nortec Vektor 3-component ocean current meters. Together with sensors already on the OOI RCA at the Slope Base and Hydrate Ridge sites, the infrastructure will enable studies of fault coupling and transient fault slip of the Cascadia megathrust and the overlying accretionary prism and support efforts to prototype offshore earthquake and tsunami early warning. COSZO is also implementing procedures to stream data into Earthscope Data Services and the data storage system used by the RCA for added instruments.

China and many developing countries face dual challenges in achieving carbon neutrality and controlling multi-media Hg pollution. Previous studies have employed computable general equilibrium models to assess China’s atmospheric Hg emissions under various CO2 mitigation and end-of-pipe Hg control scenarios. However, these models rely on highly aggregated sectoral classifications, failing to differentiate between specific technologies with varying Hg emission factors. Moreover, these studies overlook Hg emissions to other environmental media, particularly industrial waste, which can later re-emit Hg into the atmosphere during waste recycling processes. Given that comprehensive utilization of industrial waste is widely recognized as an effective CO2 reduction strategy, identifying pathways that synergistically mitigate both CO2 and multi-media Hg emissions is crucial. To address these gaps, we selected and enhanced the technology-rich integrated assessment model GCAM-China. First, we disaggregated non-ferrous metal smelting sectors (including zinc and lead smelting) from other industry, as these are major sources of both CO2 and multi-media Hg emissions. Next, we incorporated cement production technologies that co-process industrial waste, given the cement sector’s role as a primary sink for such waste. Finally, we integrated multi-media Hg emission factors and industrial waste generation rates into key GCAM-China sectors, enabling the model to simultaneously estimate CO2 emissions, multi-media Hg emissions, and industrial waste generation. Using this improved framework, we developed scenarios aligned with China’s carbon neutrality goals, end-of-pipe Hg controls, and industrial waste utilization. Preliminary results indicate that achieving carbon neutrality will be the primary driver of multi-media Hg mitigation, reducing atmospheric Hg emissions by 52.9%, aquatic Hg releases by 87.1%, and Hg entering industrial waste by 79.8%. However, while end-of-pipe controls and waste utilization can reduce Hg emissions in one environmental medium, they may inadvertently shift pollution across media. These findings highlight the need for whole-process control strategies to effectively address both climate change and multi-media Hg pollution.

The Arteni Volcanic Complex lies within the Ararat Plain, within the Aragats Volcanic Province (AVP) of western Armenia, and is noted for its remarkable diversity of obsidian types. K-Ar dates show Early Pleistocene ages of 1.26 to 1.60 Ma, while fission track dating yields slightly younger ages of 1.17 to 1.38 Ma. The Arteni Volcanic Complex was formed through successive eruptions from two adjacent centers: Mets Arteni and Pokr Arteni, both dome-shaped rhyolitic volcanoes. The complex consists of rhyolitic domes, dome-coulees, necks, fissures, extrusions, and dikes—typical of high-viscosity silicic volcanism. The Arteni rhyolitic lava flow extends a remarkable 8 km from the summit area of Mets Arteni. Covering an area of ~15 km², its volume is estimated at 0.6 km³. The unit consists of a basal layer of pyroclastics and crystalline rhyolitic-dacitic rocks, followed by crystal-clear obsidian, and capped by vesicular water-rich perlite. The obsidian typically contains between 0.06 and 0.2 wt% H₂O, while the overlying perlite contains up to 4 wt% H₂O. Although rhyolitic melts are usually highly viscous, the Arteni flow’s excessive length suggests lower viscosity due to temperature, composition and water content during emplacement. Consequently, we modeled viscosity using 850-970 °C obtained from similar rheological calculations for the Dry Fountain obsidian complex located 22 km north of Yerevan, Armenia. We note that the upper perlite layers of the Aragats flow contain 3.5–4 wt% H₂O. If the underlying obsidian formed by rapid degassing under lithostatic pressure applied by the overlying perlite, the original melt likely had a water content of ~3.8 wt%. For this water content and temperature, the viscosity of Arteni rhyolite was 3.86–4.79 log η (Pa·s), much lower than anhydrous obsidian (7.45–9.18 log η Pa·s). We suggest that this low viscosity led to the exceptional length of the rhyolite flow due to depolymerization of melt structure as well as lack of phenocrysts.

Over the past decades far-ultraviolet (FUV) remote sensing has played a key role in advancing heliophysics by providing valuable measurements of space plasmas and neutral species. However, current inversion techniques for retrieval of physical parameters such as neutral composition and temperature and plasma density from FUV emission data do not consider the Poisson nature of the data or incorporate spatial information. This leads to loss of information that can be leveraged to more accurately retrieve fields of physical parameters with spatial structure and quantify uncertainty. We propose here that by modeling the received photon counts as a permanental process and applying reproducing kernel Hilbert space techniques, we are better able to uncover structure in fields and reduce the effects of shot noise, allowing for more reliable estimates and accurate uncertainty quantification. We demonstrate the method by estimating thermospheric neutral temperature from FUV disk emission data from the NASA Global Observation of the Limb and Disk (GOLD) mission. Case studies with both simulated data during a geomagnetic storm in early November 2018 and actual GOLD L1C data products are presented.

We present a detailed investigation of Universal Time (UT) variability in the high-latitude topology of the Earth’s magnetosphere, using the Tsyganenko T96 model to trace magnetic field lines and determine the location of the Open/Closed Boundary (OCB). Our analysis spans all 24 UT hours and evaluates the invariant latitude (INVLAT) of the OCB across seasons and geomagnetic conditions, with particular attention to the dawn-dusk asymmetries near the cusp and cleft regions. We identify a persistent MLT sector (~04:00–07:00 MLT) where open/closed classification becomes sensitive to field-line tracing limits (RLIM), leading to spurious INVLAT “spikes” under quiet conditions. Reducing RLIM to 25 RE successfully mitigates this effect and aligns with values supported by prior literature. Our results demonstrate that even under fixed solar wind and geomagnetic inputs, the OCB exhibits systematic UT-dependent variation due to Earth’s rotation and dipole tilt. This variability is most pronounced in cusp-adjacent regions and has implications for space weather modeling, especially in forecasting polar cap access of energetic particles and HF radio absorption. These findings enhance the temporal fidelity of high-latitude boundary models and offer new diagnostic tools for refining SEP cutoff predictions in operational settings.

As meltwater and precipitation drain through glacier ice into subglacial networks, the water generates seismic energy upon interaction with the bed. These signals, known as glaciohydraulic tremor, are detectable by seismometers on or near the ice. Tremor has been shown to act as a proxy for subglacial water flux, revealing patterns in melt variability, sediment transport, and large-scale glacier dynamics. Despite strong empirical correlations, the quantitative relationship between tremor characteristics and discharge remains poorly constrained. Meanwhile, existing theoretical models lack rigorous validation. This research integrates stream gauge records and passive seismic observations with hydrological modelling to develop an empirical model of the tremor-discharge relationship. Preliminary results reaffirm the correlation between tremor amplitude and discharge, while revealing that existing weather-dependent melt models cannot adequately capture discharge patterns observed in Southeast Alaska. Incorporating an inverse model from the observed discharge using multivariate linear regression reduces uncertainty associated with weather variability and topographic controls. Expanding the analysis across multiple glaciers in Alaska and Central Europe allows for evaluation and correction of local factors, such as glacier size, ice geometry, and receiver-conduit distance, on tremor behavior to better compare results with theoretical models. Ultimately, this work aims to advance the capacity to use seismic methods as a reliable proxy for subglacial discharge, offering valuable insights into large-scale glacier processes and enhancing our ability to predict glacier change.

DL-based earthquake detection can detect substantially more induced microseismic events than conventional detection methods, revealing finer-scale patterns of hydraulic fracturing-induced seismicity (HFIS). We implement a novel workflow for the Preston New Road-1z (PNR-1z) shale gas site, UK, that integrates DL phase picking, Oct-Tree-based phase association, and probabilistic non-linear location to generate a high-resolution “deep” seismic catalogue from a single high-frequency (2000 Hz) borehole array. PhaseNet has detected 11,021 previously undetected events (49,472 events total), compared to the previous catalogue, in which 38,452 events were identified by a beamforming method. We find that the enhanced catalogue increases event cluster densities by an average of 42% and lowers the magnitude of completeness (Mc) from -0.2 to -0.5, adding thousands of additional events to Gutenberg-Richter b-value analyses. The spatial patterns suggest that HFIS interacted with a fault or fracture zone north of the PNR wells, contrasting with previous event locations and their interpretations. Combining the largest and best recorded events, using locations from our independently derived catalogue, with 3D seismic reflection data reveals clustering along previously unmapped seismic discontinuities ~300 m north of the injection wells, indicating strong structural controls on HFIS. In addition, we re-estimate moment magnitudes (Mw) using continuous downhole waveforms, finding that magnitudes reported in the previous operational catalogue were systematically underestimated by an average of 0.93 units. The temporal b-values revealed higher b-values during active injection, consistent with tensile HF-dominated microseismicity, and relatively lower b-values during pauses and certain mid-stages, indicating increased shear failure and interaction with a pre-existing fracture zone. The deep catalogue allows for more robust b-value estimation and temporal tracking, potentially reflecting evolving source characteristics and fluid-driven processes during stimulation. This study demonstrates that DL-enhanced catalogues can refine subsurface interpretations, identify re-activated structures efficiently, and contribute to improved hazard assessments in geo-energy systems.

BioSCape (Biodiversity Survey of the Cape) is NASA’s first biodiversity-focused airborne and field campaign, designed to advance understanding of ecosystem structure, function, and composition, and how these are changing over time and space in South Africa’s Greater Cape Floristic Region. This international collaboration involving ~160 US and South African scientists across 19 sub-projects produced an unprecedented, open-access dataset spanning 45,000 km², integrating imaging spectroscopy (AVIRIS-NG, PRISM, HyTES), LiDAR (LVIS, ELMAP-V), and extensive ground data from ~2,600 plots. Data are publicly archived at ORNL DAAC and accessible via a cloud computing environment, fostering open science and reproducibility. In this presentation, we will summarize the results to date across the BioSCape project, highlighting key scientific outcomes from both terrestrial and aquatic landscapes. We will also discuss some of the broader implications of the project, including laying the groundwork for achieving ethical and equitable benefit-sharing via compliance with the Nagoya Protocol. A core principle of BioSCape is ”use-inspired science,” which involves co-developing research questions with local partners to directly address pressing conservation and resource management challenges. This ensures that our scientific findings translate into actionable data products for decision-makers in South Africa and globally. For example, BioSCape’s data and products are already informing conservation and resource management efforts, including kelp range maps for ecosystem classification and woody invasive alien plant maps that feed into national ecosystem condition assessments. The project’s contributions have already been recognized in the 2024 State of Environment Outlook Report for the Western Cape Province.

The Technology Innovation Thermoradiative Uranus Smallsat (TITUS) is a proposed smallsat technology demonstration concept mission to showcase an innovative power source utilizing thermal power conversion from a thermoradiative cell (TRC). The proposed TRC has 3 times higher thermal to electrical power conversion efficiency than heritage MMRTG systems with a lower volume and higher mass specific power density. This makes a TRC particularly attractive for small-scale missions to the outer planets or other locations where low-light environments render solar power impractical. The mission would primarily serve as a proof-of-concept for TRC technology through a low-risk Smallsat companion to NASA’s nominal Uranus Orbiter & Probe (UOP) Mission. The primary objective for TITUS is to demonstrate TRC powering of a functional Smallsat in communication with the UOP while conducting supporting science measurements for a duration of 5 years or more. The conceptual design for the TITUS science package would complement UOP magnetospheric science during 15 additional orbits. By separating from the UOP, TITUS would achieve distinct spatial and temporal samplings of the complex magnetic field, radiation environment, and magnetopause of Uranus. The magnetosphere of Uranus is not well explored because Voyager 2, the only spacecraft to visit Uranus, spent only hours passing through the magnetosphere. Subsequent modeling also suggests that the lone flyby may have observed Uranus during an unusual period of solar activity. The TITUS observations would fill in the three-dimensional profiles of the interaction of the magnetic field with the solar wind. Likewise the TITUS spacecraft will fill in details on the radiation belts, with multiple passes through both Uranus’ electron and proton belts. The design was developed by the NASA Glenn Compass systems engineering team in coordination with Phase II of NASA’s Innovative Advanced Concepts (NIAC) program.

As sea ice melts, there is expected to be increased vessel traffic across the Arctic. In addition, higher numbers of tourists are expected to visit the Arctic. With more vessel traffic, this allows for increased access to services and infrastructure that can further enable tourism markets, particularly cruise ship tourism, to thrive in the Arctic. While tourism can have many potential benefits to communities, the challenges must also be considered. Therefore, it is important for us to consider how tourism may impact a region that is already stressed, such as the introduction of invasive species. To determine whether tourists traveling to the Arctic could serve as a vector for invasive species spread, we created an intercept survey. For our study, survey questions form the basis of understanding terrestrial risks of invasive species introductions. Over two field seasons, we have surveyed over 800 visitors to Alaska across four different locations. We then combined those survey results with wastewater discharge reports and other monitoring efforts to understand the risk of marine introductions. Preliminary results on the risk of invasive species spread from tourism will be shared. This research will help to provide a baseline for understanding tourism in the Arctic before climate change enables a longer season for vessel traffic, as well as improving our understanding of the system as climate change warms the area and changes the amount of suitable habitat for non-native species.