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At least 343 records · Page 19

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NASA NTRSDate not supplied

Tribology Induced Water Pump Bearing Failure

NASA’s In-Space Propulsion facility experienced two infant mortality water pump bearing failures during a recent refurbishment campaign, the cause or causes of which were unclear. A forensic failure study was undertaken that included site visits, collection and examination of physical evidence and a bearing and pump design review. The pumps are large (2000 hp) vertical turbine design machines that utilize metallic sleeve bearings cooled and lubricated by the pumped fluid (water). Though initial possible root cause for the failures were attributed to improper fabrication and debris contamination, detailed examination of failure surfaces combined with bearing design revealed that the failure mode was thermal seizure brought on by inadequate cooling flow. An unusual contributing factor was that the use of grease as an assembly lubricant, as opposed to a fluid like oil, appears to have exacerbated the rather than alleviated the thermal runaway by blocking water flow. Resolution of the problem was achieved through the design and installation of dedicated bearing lubrication water feed system.

bearings↗

NASA NTRS2021

Climate drives modeled forest carbon cycling resistance and resilience in the Upper Great Lakes Region, USA

Forests dominate the global terrestrial carbon budget, but their ability to continue doing so in the face of a changing climate is uncertain. A key uncertainty is how forests will respond to (resistance) and recover from (resilience) rising levels of disturbance of varying intensities. This knowledge gap can optimally be addressed by integrating manipulative field experiments with ecophysiological modeling. We used the Ecosystem Demography-2.2 (ED-2.2)model to project carbon fluxes for a northern temperate deciduous forest subjected to a real-world disturbance severity manipulation experiment. ED-2.2 was run for 150 years, starting from near bare ground in 1900(approximating the clear-cut conditions at the time),and subjected to three disturbance treatments under an ensemble of climate conditions. Both disturbance severity and climate strongly affected carbon fluxes such as gross primary production (GPP),and interacted with one another. We then calculated resistance and resilience, two dimensions of ecosystem stability. Modeled GPP exhibited a two-fold decrease in mean resistance across disturbance severities of 45%, 65%, and 85% mortality; conversely, resilience increased by a factor of two with increasing disturbance severity. This pattern held for net primary production and net ecosystem production, indicating a trade-off in which greater initial declines were followed by faster recovery. Notably, however, heterotrophic respiration responded more slowly to disturbance, and its highly variable response was affected by different drivers. This work provides insight into how future conditions might affect the functional stability of mature forests in this region under ongoing climate change and changing disturbance regimes.

Kalyn Dorheim↗

NASA NTRS2022

Premature Deaths in Africa Due to Particulate Matter Under High and Low Warming Scenarios

Sustainable development and climate change mitigation can provide enormous public health benefits via improved air quality, especially in polluted areas. We use the latest state-of-the-art composition-climate model simulations to contrast human exposure to fine particulate matter in Africa under a “baseline” scenario with high material consumption, population growth, and warming to that projected under a sustainability scenario with lower consumption, population growth, and warming. Evaluating the mortality impacts of these exposures, we find that under the low warming scenario annual premature deaths due to PM2.5 are reduced by roughly 515,000 by 2050 relative to the high warming scenario (100,000, 175,000, 55,000, 140,000, and 45,000 in Northern, West, Central, East, and Southern Africa, respectively). This reduction rises to ∼800,000 by the 2090s, though by that time much of the difference is attributable to the projected differences in population. By contrast, during the first half of the century benefits are driven predominantly by emissions changes. Depending on the region, we find large intermodel spreads of ∼25%–50% in projected future exposures owing to different physics across the ensemble of 6 global models. The spread of projected deaths attributable to exposure to fine particulate matter, including uncertainty in the exposure-response function, are reduced in every region to ∼20%–35% by the non-linear exposure-response function. Differences between the scenarios have an even narrower spread of ∼5%–25% and are highly statistically significant in all regions for all models. These results provide valuable information for policy-makers to consider when working toward climate change mitigation and sustainable development goals.

PM2.5↗

NASA NTRS2023

Impact of Pacific Ocean Heatwaves on Phytoplankton Community Composition

Since 2013, marine heatwaves have become recurrent throughout the equatorial and northeastern Pacific Ocean and are expected to increase in intensity relative to historic norms. Among the ecological ramifications associated with these high temperature anomalies are increased mortality of higher trophic organisms such as marine mammals and seabirds, which are likely triggered by changes in the composition of phytoplankton, the base of the marine trophic food web. Here, we assimilated satellite ocean color data into an ocean biogeochemical model to describe changes in the abundance of phytoplankton functional types (PFTs) during the last decade's (2010s) warm anomalies in the equatorial and northeastern Pacific Ocean. We find important changes associated with the "Blob" warm anomaly in the Gulf of Alaska, where reduced silica supply led to a switch in community composition from diatoms to dinoagellates, resulting in an increase in surface ocean chlorophyll during the Summer-Fall of 2014. A more dramatic change was observed in the equatorial Pacific, where the extreme warm conditions of the 2016 El Niño resulted in a major decline of about 40 % in surface chlorophyll, which was associated with a nearly total collapse in diatoms.

Pacific Ocean↗

NASA NTRSDate not supplied

New York Ecological Forecasting: Utilizing NASA Earth Observations to Map Ash Distribution and Inform Emerald Ash Borer Control

Since their first sightings in the U.S. in 2002, emerald ash borer beetles (Agrilus planipennis; EAB) have killed millions of native ash (Fraxinus spp.) trees across 35 states. Infected ash stands frequently exhibit complete mortality, with the predicted result being the functional extinction of native ash in U.S. forests. In August of 2020, EAB was discovered in the 6.1-million-acre Adirondack Park. The team’s partners at the Adirondack Park Invasive Plant Program (APIPP) desired ash tree distribution and EAB susceptibility information to help improve EAB bio-control efficiency and apply the methodology to future invasive programs. To assist, the team mapped ash tree distribution using NASA Earth observations from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Shuttle Radar Topography Mission (SRTM), along with hyperspectral imagery from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Field data from the Monitoring and Managing Ash (MaMA) project, iMapInvasives and iNaturalist databases, and the New York State Department of Environmental Conservation (NYSDEC) provided ground truthing for mapping and modeling. Results indicate that for ash detection, the team’s Spectral Angle Mapping (SAM) hyperspectral classification is slightly more sensitive but less accurate than multispectral Random Forest (RF) classification, though neither method was above a ~20% detection rate. End products include maps of ash extent derived from both imagery types, a model forecasting future spread scenarios based on current EAB presence, and outreach materials. These products inform APIPP’s management decisions and facilitate public awareness of EAB’s threat to communities within the region.

Liam Megraw↗

NASA NTRSDate not supplied

Derivation of the Most Influential Medical Conditions for An Extended Duration Artemis Mission

BACKGROUND: The risk of loss of mission due to medical conditions may be influenced by loss of crew life (LOCL), need for evacuation (RTDC; return to definitive care), and crew task time lost. Predicting what medical conditions are most likely to lead to crew morbidity and mortality may influence medical system design, clinical capability prioritization, and research strategies. NASA’s Informing Mission Planning via Analysis of Complex Tradespaces tool (IMPACT) applies Probabilistic Risk Assessment (PRA) methodology to assess these risks. OVERVIEW: A team of subject matter experts (SME) from a variety of medical disciplines developed a consensus-based process to determine 120 of the most clinically relevant medical conditions for long-duration exploration missions (LDEMs) . This IMPACT Condition List (ICL) expanded upon previous work done for Integrated Medical Model (IMM). For each condition a best-case and worst-case definition were derived. These definitions were used to identify probability of occurrence, proportion of cases that are best case vs. worst case, clinical phase duration, and risk of outcomes (task time loss [TTL], RTDC, and LOCL) for both treated and untreated states. These data were sources from existing spaceflight databases (e.g. Longitudinal Survey of Astronaut Health), relevant models (e.g. the ISS fire model), and/or terrestrial literature. Each condition was then tied to diagnostic and therapeutic resources and capabilities. IMPACT was then run for the LDLOLS DRM (see Abstract #2 for this panel). DISCUSSION: This abstract will present the process for generating the IMPACT condition list, the relevant data for each clinical condition, and present results for the ten most influential conditions impacting LOCL, RTDC, and TTL for a representative extended duration Artemis mission.

A Nelson↗

NASA NTRSDate not supplied

Introduction to the IMPACT Probabilistic Risk and Tradespace Analysis Tool for Medical System Design

Background: Probabilistic risk analysis (PRA) is a method for estimating risk in complex engineered systems that, at a basic level, focuses on what can go wrong and the likelihood and consequences of those occurrences. NASA has used PRA as an integral component of medical system risk estimation and design for spaceflight. IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) is a novel tool to meet these goals for exploration missions. Overview: IMPACT performs hundreds of thousands of Monte Carlo simulations of missions to build aggregate pictures of medical risk. These simulations are based on 120 possible medical conditions (the IMPACT Condition List) selected in a consensus-based process because they are of highest likelihood and/or consequence for exploration spaceflight. The conditions are then tied to clinical capabilities which can be used for management (e.g., inserting an IV) and then to over 600 specific resources needed to deliver a capability (e.g., an angiocath or an ultrasound). Different mission profiles can be simulated with user-specified inputs such as mission duration, destination, number of crew and pre-existing medical conditions, and EVA frequency. While the IMPACT evidence base is designed for exploration environments, these user inputs allow the tool to be used across a broad range of missions. IMPACT’s primary outcome metrics include loss of crew life (LOCL, a measure of in-flight mortality due to medical conditions), need for evacuation (RTDC, return to definitive care), and crew disability (TTL, task time lost based on how medical conditions impact the ability to perform over 1000 specific exploration mission crew tasks). In addition to modeling medical risk, IMPACT also accepts user-specified constraints, such as limitations of mass or volume, and will output a recommended clinical capability set and specific medical resources that meet the mission constraints. Discussion: This abstract will provide an introduction to IMPACT and describe the nature of the underlying medical evidence. It will also detail potential use cases for how this tool can be utilized by NASA or commercial spaceflight providers.

Ben Easter↗

NASA NTRSDate not supplied

Wichita Climate II: Quantifying and Mapping Urban Heat to Inform Equitable and Sustainable Urban Planning Initiatives in Wichita, Kansas

Extreme heat is the deadliest weather-related event in the United States, as well as one of the least discussed. Climate change has raised the frequency, intensity, and duration of severe heat events. The built, urban environment is often hotter than neighboring rural areas due to a denser concentration of pavement, metal and other building materials absorbing and retaining heat, as well as a low amount of vegetation – creating a phenomenon commonly known as the urban heat island effect. This effect contributes to a wide range of public health issues, associated with heat strokes, dehydration, loss of work productivity, decreased learning, respiratory difficulties, and heat-related mortality. The average summer temperature in Wichita has increased 1.3 degrees (F) since 1970, and the number of days over 100 degrees (F) has historically increased, from 40 days in 1934 to 53 days in 2011.

Ritisha Ghosh↗

NASA NTRS2022

Modeling Demographic-Driven Vegetation Dynamics and Ecosystem Biogeochemical Cycling in NASA GISS’s Earth System Model (ModelE-BiomeE v.1.0)

We developed a demographic vegetation model, BiomeE, to improve the modeling of vegetation dynamics and ecosystem biogeochemical cycles in the NASA Goddard Institute of Space Studies' ModelE Earth system model. This model includes the processes of plant growth, mortality, reproduction, vegetation structural dynamics, and soil carbon and nitrogen storage and transformations. The model combines the plant physiological processes of ModelE's original vegetation model, Ent, with the plant demographic and ecosystem nitrogen processes that have been represented in the Geophysical Fluid Dynamics Laboratory's LM3-PPA. We used nine plant functional types to represent global natural vegetation functional diversity, including trees, shrubs, and grasses, and a new phenology model to simulate vegetation seasonal changes with temperature and precipitation fluctuations. Competition for light and soil resources is individual based, which makes the modeling of transient compositional dynamics and vegetation succession possible. Overall, the BiomeE model simulates, with fidelity comparable to other models, the dynamics of vegetation and soil biogeochemistry, including leaf area index, vegetation structure (e.g., height, tree density, size distribution, and crown organization), and ecosystem carbon and nitrogen storage and fluxes. This model allows ModelE to simulate transient and long-term biogeophysical and biogeochemical feedbacks between the climate system and land ecosystems. Furthermore, BiomeE also allows for the eco-evolutionary modeling of community assemblage in response to past and future climate changes with its individual-based competition and demographic processes.

Biogeochemical cycles↗

NASA NTRS2022

Wichita Climate II: Quantifying and Mapping Urban Heat to Inform Equitable and Sustainable Urban Planning Initiatives in Wichita, Kansas

Wichita, Kansas is experiencing a host of climate threats, particularly extreme heat manifested through Urban Heat Islands (UHI). Heat is unevenly distributed within cities due to factors such as income inequality, historical discriminatory practices like redlining, and divestment in neighborhoods of color. This leads to less vegetation and more heat-absorbing infrastructure in specific communities. Moreover, adverse effects of heat, including heat-related morbidity and mortality, disproportionately impact populations that experience vulnerability through social inequities and structural discrimination. Heat vulnerability is a combination of the factors of heat exposure, sensitivity, and adaptive capacity, and can be harnessed to guide urban heat interventions. This DEVELOP project partnered with the City of Wichita to understand the spatial distribution and drivers of UHIs and heat vulnerability indicators. The team modeled outcomes of tree cover interventions using Landsat 8’s Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI), Landsat 9 TIRS-2 and OLI-2, and the International Space Station’s Ecosystem Spaceborne Thermal Radiometer Experiment on the International Space Station (ECOSTRESS) sensor, along with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Urban Cooling model. The team also leveraged statistical analysis by implementing principal component analysis to develop a heat vulnerability index (HVI) specific to Wichita. Ultimately, the project’s outputs will inform the City of Wichita’s Climate Adaptation and Mitigation Plan, identify priority areas for heat mitigation initiatives, and be used in public-facing communications to educate communities on the impacts of urban heat.

Environmental Justice↗

NASA NTRS2023

Assessment of the Impact of Discontinuity in Satellite Instruments and Retrievals on Global PM2.5

Exposure to fine particulate matter (PM2.5) is the leading environmental risk factor for mortality globally. Satellite-derived estimates of surface PM2.5 developed from a combination of satellites, simulations, and ground monitor data are relied upon for health impact studies. The ability to develop satellite-derived PM2.5 estimates requires the continued availability of aerosol optical depth (AOD) sources. This work examines the impact of the addition or loss of satellite AOD data sources on global PM2.5 estimation and the impact of continuing the longterm record with AOD from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar orbiting Partnership (S-NPP) satellite after the loss of the MODIS (MODerate resolution Imaging Spectroradiometer) and MISR (Multi-angle Imaging Spectroradiometer) instruments on board the Terra and Aqua satellites. We find that the addition of VIIRS S-NPP AOD products to geophysical PM2.5 estimates from satellites and simulations causes regional differences that correspond to differences in the VIIRS and MODIS Deep Blue AOD algorithms and sampling. Changes in long-term trends and timeseries due to the addition or loss of AOD data sources are generally within their uncertainties. Statistical fusion with ground monitor data partially corrects for changes due to sampling differences when introducing the VIIRS AOD products, but uncertainty remains over desert regions where ground monitor coverage is sparse. This work provides promise for the sustained development of global satellite-derived PM2.5 estimates, despite discontinuities in instruments and retrieval methods.

Remote sensing↗

NASA NTRSDate not supplied

Impact of Pacific Ocean Heatwaves on Phytoplankton Community Composition

Since 2013, marine heatwaves have become recurrent throughout the equatorial and northeastern Pacific Ocean and are expected to increase in intensity relative to historic norms. Among the ecological ramifications associated with these high temperature anomalies are increased mortality of higher trophic organisms such as marine mammals and seabirds, which are likely triggered by changes in the composition of phytoplankton, the base of the marine trophic food web. Here, we assimilated satellite ocean color data into an ocean biogeochemical model to describe changes in the abundance of phytoplankton functional types (PFTs) during the last decade’s (2010s) warm anomalies in the equatorial and northeastern Pacific Ocean. We find important changes associated with the “Blob” warm anomaly in the Gulf of Alaska, where reduced silica supply led to a switch in community composition from diatoms to dinoflagellates, resulting in an increase in surface ocean chlorophyll during the Summer–Fall of 2014. A more dramatic change was observed in the equatorial Pacific, where the extreme warm conditions of the 2016 El Niño resulted in a major decline of about 40% in surface chlorophyll, which was associated with a nearly total collapse in diatoms.

Lionel A Quintero↗

NASA NTRS2023

IMPACTing Medical System Design with a Risk Analysis Tool [“IMPACT” sur la Conception du Système Médical avec un Outil d'Analyse des Risques]

Background: Following the success of Artemis I, NASA is preparing for human extended duration missions. Ongoing efforts are focused on mitigating mission-related risks, including those affecting crew health and performance. Communication latency, logistics of resupply and time frame of medical evacuation are barriers to provision of healthcare for these missions, especially with respect to constraints in mass, volume, and crew training. An in-depth assessment of medical risks, capabilities and resources for a specific mission design is necessary to determine an optimal balance that maximizes likelihood of mission success. Overview: IMPACT (Informed Mission Planning via Analysis of Complex Tradespaces) is a dynamic tool designed to estimate medical risk and outcomes for a specific mission design. In its current iteration, a list of medical conditions selected based on likelihood of occurrence and/or consequence was linked to a set of clinical capabilities and resources necessary for diagnosis and management. A probabilistic risk analysis tool was then used to identify and estimate the likelihood and consequence of risks through the following outcome metrics: loss of crew life (inflight mortality due to medical conditions), need for medical evacuation (return to definitive care), and crew disability (task time affected based on how medical conditions influence the ability to perform specific exploration mission crew tasks). Finally, the model’s optimization algorithm provides recommendations for medical capabilities that maximize risk mitigation relative to mass and volume constraints. In the Spring of 2023, IMPACT was utilized to estimate outcome metrics for a design reference mission that would be representative of an extended duration Artemis mission. Notional data generated were then used to determine a recommended set of medical capabilities and resources relative to user-defined mass and volume constraints. A multidisciplinary team has also been updating IMPACT to strengthen the model’s fidelity. Figure 1 shows how updates to outcome metric inputs for the conditions resulted in different capability and resource allocation recommendations. Discussion: This presentation will discuss the IMPACT tool and share the latest data generated for a representative extended duration Artemis mission. Efforts to improve the fidelity of data generated by the model’s algorithm will also be discussed.

K A Shair↗

NASA NTRS2022

A Network of Field-Calibrated Low-Cost Sensor Measurements of PM2.5 in Lomé, Togo, Over One to Two Years

Air pollution is a leading cause of global premature mortality and is especially prevalent in many low- and middle-income countries (LMICs). In sub-Saharan Africa, preliminary monitoring networks, satellite retrievals of air-quality-relevant species, and air quality models show ambient fine particulate matter (PM 2.5 ) concentrations that far exceed the World Health Organization guidelines, yet many areas remain largely unmonitored and understudied. Deploying a network of five low-cost PurpleAir PM 2.5 monitors over 2 years (2019–2021), we present the first multiyear ambient air pollution monitoring data results from Lomé, Togo, a major West African coastal city with a population of about 1.4 million people. The full-study time period network-wide mean measured daily PM 2.5 concentration is 23.5 μ g m –3 m –3 . The strong regional influence of the dry and dusty Harmattan wind increases the local average PM 2.5 concentration by up to 58% during December through February, but the diurnal and weekly trends in PM 2.5 are largely controlled by local influences. At all sites, more than 87% of measured days exceeded the new WHO Daily PM 2.5 guidelines; these first measurements highlight the need for air quality improvement in a rapidly growing urban metropolis.

air pollution↗

NASA NTRSDate not supplied

Can We Model Forest Demography Globally? Benchmarking of State-of-the-Art Demographic DGVMs

Forests in Dynamic Global Vegetation Models (DGVMs) have historically been simulated as area-averaged plant functional types in each gridcell instead of representing communities of trees of different sizes and ages (demography). Just as the behaviour of a tree differs according to its ontogeny, so the behaviour of forests is known to differ depending on their demography. Accurately simulating demography is therefore key in order to address questions on afforestation and management strategies, as well as assessments of resilience of forests to disturbances such as drought and fire or diversity changes after a disturbance. Ultimately, demography determines the overall forest biomass in natural forests and is a key arbiter of growth and mortality rates. DGVMs are now able to simulate size and age structure of the trees in forests. However, these models have so far not been benchmarked alongside each other. We evaluate 6 DGVMs (BiomeE, CABLE-POP, FATES, LPJ-GUESS, JULES-RED, ORCHIDEE) against observations on regrowth dynamics as well as natural forests at boreal, temperate and tropical sites. We examine whether the models capture observed regrowth dynamics after disturbance, well-known stand size structure and well-established processes such as self-thinning. We outline the planned route forward towards a standardised international benchmarking framework for demographic DGVMs.

Dynamic Global Vegetation Models↗

NASA NTRSDate not supplied

Improving the Fidelity of Capability & Resource Weighting in A Probalistic Risk Assessment Model for Spaceflight

INTRODUCTION NASA’s Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool uses Probabilistic Risk Assessment (PRA) to provide an evidence-based, data-driven estimate of how medical system capabilities affect mission outcomes. IMPACT maps condition incidence to available resources thereby facilitating the calculation of outcome metrics that allow the estimation of mission medical risk. Conditions can be nominally categorized as either treated or untreated depending on the availability of necessary diagnostic and therapeutic capabilities. This categorization enables IMPACT to estimate the effect of various medical system configurations on mission outcomes such as crew mortality, disability, crew member down-time, return to duty/recovery, and need for evacuation. IMPACT currently employs an equal weighting, “partial credit” approach to define treatment in which each of the capabilities associated with a given condition contributes an equal amount to management of the condition. This feature enables IMPACT to report values in between “fully untreated” and “fully treated” based on the proportion of capabilities available within the model. However, as is normal in medical/clinical practice, not all individual capabilities contribute equally to medical care. For example, the ability to provide intramuscular epinephrine during an anaphylactic episode contributes more likelihood of overall management success than does the administration of oral diphenhydramine. We hypothesize that weighting the relative contribution of each capability to each specific condition will improve outcome prediction and therefore will better provide mission planners with more nuanced and accurate options when designing space medical systems. METHODS Using a five-point Fibonacci scaling sequence (1, 2, 3, 5, 8) subject matter experts from NASA’s Exploration Medical Capabilities (ExMC) element assigned relative contribution weighting values to each identified capability within IMPACT. Since the relative importance of each capability varies depending on the specific condition, the resulting “partial” weighting was completed for more than 1,600 individual weighting assignments for 666 capabilities across 121 conditions. Each assignment required three-physician concurrence based on the overall importance of the capability to the diagnosis and management of the condition being considered and the difficulty with which it could be improvised by the crew. Once complete, 100,000 IMPACT simulations were run for a 6-month Lunar mission with a 30-day surface stay to evaluate the effect of this modification of the model. RESULTS Partial weighting significantly decreased predicted task time loss (TTL), evacuation, and loss of crew life without causing significant changes to the recommended medical system design. CONCLUSIONS The paucity of real-world referent data to support long-duration space missions of this type limits the ability to judge one predictive analytics method as superior to another. However, since the proposed method significantly reduces and optimizes outcome risks—without changing the medical system design—incorporating a partial weighting methodology is likely to provide a more accurate and operationally-relevant representation of medical risk without compromising IMPACTs ability to inform overarching medical system requirements.

Steller JG↗

NASA NTRSDate not supplied

IMPACTing Medical System Design with a Risk Analysis Tool

K A Shair↗

NASA NTRSDate not supplied

Assessing Long-Term Health (LTH) Outcomes in Astronauts

NASA has linked potential Long-Term Health (LTH) risks of astronauts to their spaceflight experience, including the effects of space radiation, microgravity, and stressors such as isolation, sleep deprivation, and stress. Therefore, assessing the LTH outcomes for astronauts exposed to these hazards becomes a critical investigation in ensuring any deleterious effects are minimal. Prior research conducted into LTH outcomes of astronauts has not revealed increases in non-accidental mortality rates when compared to the U.S. general population, analog healthy cohorts, and professional athletes. One study did report an increased incidence of non-fatal cardiovascular disease events (Hazard Ratio=2.41, 95% Confidence Interval 1.26 to 4.63). Additional evidence indicates that astronauts may have increases in incidences of melanoma, prostate, and hematologic cancers, though the former is consistent with rates observed among aircraft pilots and the latter two are likely caused by detection bias due to increased screening on former astronauts.

long term health↗