Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Pm”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Performance of the Center-Of-Curvature Optical Assembly During Cryogenic Testing of the James Webb Space Telescope

The James Webb Space Telescope (JWST) primary mirror (PM) is 6.6 meters in diameter and consists of 18 hexagonal segments, each 1.5 meters point-to-point. Each segment has a 6 degree-of-freedom hexapod actuation system and a radius-of-curvature (ROC) actuation system. The full telescope was tested at its cryogenic operating temperature at Johnson Space Center (JSC) in 2017. This testing included center-of-curvature measurements of the PM wavefront error using the Center-of-Curvature Optical Assembly (COCOA), along with the Absolute Distance Meter Assembly (ADMA). The COCOA included an interferometer, a reflective null, an interferometer-null calibration system, coarse and fine alignment systems, and two displacement measuring interferometer systems. A multiple-wavelength interferometer was used to enable alignment and phasing of the PM segments. By combining measurements at two laser wavelengths, synthetic wavelengths up to 15 millimeters could be achieved, allowing mirror segments with millimeter-level piston errors to be phased to the nanometer level. The ADMA was used to measure and set the spacing between the PM and the focus of the COCOA null (i.e., the PM center-of-curvature) for determination of the ROC. This paper describes the COCOA, the PM test setup, the testing performed, the test results, and the performance of the COCOA in aligning & phasing the PM segments and measuring the final PM wavefront error.

JWST↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗
NASA NTRS2006

Electrostatic Beneficiation of Lunar Simulant

Electrostatic beneficiation of lunar regolith is a method allowing refinement of specific minerals in the material for processing on the moon. The use of tribocharging the regolith prior to separation was investigated on the lunar simulant MLS-I by passing the dust through static mixers constructed from different materials; aluminum, copper, stainless steel, and polytetrafluoroethylene (PTFE). The amount of charge acquired by the simulant was dependent upon the difference in the work function of the dust and the charging material. XPS and SEM were used to characterize the simulant after it was sieved into five size fractions (> 100 pm, 75-100 pm, 50- 75 pm, 50-25 pm, and < 25 pm), where very little difference in surface composition was observed between the sizes. Samples of the smallest (< 25 pm) and largest (> 100 pm) size fractions were beneficiated through a charge separator using the aluminum (charged the simulant negatively) and PTFE (charged positively) mixers. The mass fractions of the separated simulant revealed that for the larger particle size, significant unipolar charging was observed for both mixers, whereas for the smaller particle sizes, more bipolar charging was observed, probably due to the finer simulant adhering to the inside of the mixers shielding the dust from the charging material. Subsequent XPS analysis of the beneficiated fractions showed the larger particle size fraction having some species differentiation, but very little difference for the smaller.size. Although MLS-1 was made to have similar chemistry to actual lunar dust, its mineralogy is quite different. On-going experiments are using NASA JSC-1 lunar simulant. A vacuum chamber has been constructed, and future experiments are planned in a simulated lunar environment.

Trigwell, Steve↗
NASA NTRS2016

Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites, Models, and Monitors

We estimated global fine particulate matter (PM(sub 2.5)) concentrations using information from satellite-, simulation- and monitor-based sources by applying a Geographically Weighted Regression (GWR) to global geophysically-based satellite-derived PM(sub 2.5) estimates. Aerosol optical depth from multiple satellite products (MISR, MODIS Dark Target, MODIS and SeaWiFS Deep Blue, and MODIS MAIAC) was combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations for 1998−2014. The GWR predictors included simulated aerosol composition and land use information. The resultant PM(sub 2.5) estimates were highly consistent (R(sup 2) equals 0.81) with out-of-sample cross-validated PM(sub 2.5) concentrations from monitors. The global population-weighted annual average PM(sub 2.5) concentrations were 3-fold higher than the 10 micrograms per cubic meter WHO guideline, driven by exposures in Asian and African regions. Estimates in regions with high contributions from mineral dust were associated with higher uncertainty, resulting from both sparse ground-based monitoring, and challenging conditions for retrieval and simulation. This approach demonstrates that the addition of even sparse ground-based measurements to more globally continuous PM(sub 2.5) data sources can yield valuable improvements to PM(sub 2.5) characterization on a global scale.

aerosols↗
NASA NTRS2016

The Effect of Future Ambient Air Pollution on Human Premature Mortality to 2100 Using Output from the ACCMIP Model Ensemble

Ambient air pollution from ground-level ozone and fine particulate matter (PM(sub 2.5)) is associated with premature mortality. Future concentrations of these air pollutants will be driven by natural and anthropogenic emissions and by climate change. Using anthropogenic and biomass burning emissions projected in the four Representative Concentration Pathway scenarios (RCPs), the ACCMIP ensemble of chemistry climate models simulated future concentrations of ozone and PM(sub 2.5) at selected decades between 2000 and 2100. We use output from the ACCMIP ensemble, together with projections of future population and baseline mortality rates, to quantify the human premature mortality impacts of future ambient air pollution. Future air-pollution-related premature mortality in 2030, 2050 and 2100 is estimated for each scenario and for each model using a health impact function based on changes in concentrations of ozone and PM(sub 2.5) relative to 2000 and projected future population and baseline mortality rates. Additionally, the global mortality burden of ozone and PM(sub 2.5) in 2000 and each future period is estimated relative to 1850 concentrations, using present-day and future population and baseline mortality rates. The change in future ozone concentrations relative to 2000 is associated with excess global premature mortality in some scenarios/periods, particularly in RCP8.5 in 2100 (316 thousand deaths per year), likely driven by the large increase in methane emissions and by the net effect of climate change projected in this scenario, but it leads to considerable avoided premature mortality for the three other RCPs. However, the global mortality burden of ozone markedly increases from 382000 (121000 to 728000) deaths per year in 2000 to between 1.09 and 2.36 million deaths per year in 2100, across RCPs, mostly due to the effect of increases in population and baseline mortality rates. PM(sub 2.5) concentrations decrease relative to 2000 in all scenarios, due to projected reductions in emissions, and are associated with avoided premature mortality, particularly in 2100: between 2.39 and 1.31 million deaths per year for the four RCPs. The global mortality burden of PM(sub 2.5) is estimated to decrease from 1.70 (1.30 to 2.10) million deaths per year in 2000 to between 0.95 and 1.55 million deaths per year in 2100 for the four RCPs due to the combined effect of decreases in PM(sub 2.5) concentrations and changes in population and baseline mortality rates. Trends in future air-pollution-related mortality vary regionally across scenarios, reflecting assumptions for economic growth and air pollution control specific to each RCP and region. Mortality estimates differ among chemistry climate models due to differences in simulated pollutant concentrations, which is the greatest contributor to overall mortality uncertainty for most cases assessed here, supporting the use of model ensembles to characterize uncertainty. Increases in exposed population and baseline mortality rates of respiratory diseases magnify the impact on premature mortality of changes in future air pollutant concentrations and explain why the future global mortality burden of air pollution can exceed the current burden, even where air pollutant concentrations decrease.

Silva, Raquel A.↗
NASA NTRS2016

Characterization of Particulate Matter Profiling and Alveolar Deposition from Biomass Burning in Northern Thailand: The 7-SEAS Study

Biomass burning (BB) frequently occurs in SouthEast Asia (SEA), which significantly affects the air quality and could consequently lead to adverse health effects. The aim of this study was to characterize particulate matter (PM) and black carbon (BC) emitted from BB source regions in SEA and their potential of deposition in the alveolar region of human lungs. A 31-day characterization of PM profiling was conducted at the Doi Ang Khang (DAK) meteorology station in northern Thailand in March 2013. Substantial numbers of PM (10147 +/- 5800 # per cubic centimeter) with a geometric mean diameter (GMD) of 114.4 +/- 9.2 nm were found at the study site. The PM of less than 2.5 micron in aerodynamic diameter (PM sub 2.5) hourly-average mass concentration was 78.0 +/- 34.5 per cubic microgram whereas the black carbon (BC) mass concentration was 4.4 +/- 2.6 micrograms per cubic meter. Notably, high concentrations of nanoparticle surface area (100.5 +/- 54.6 square micrometers per cubic centimeter) emitted from biomass burning can be inhaled into the human alveolar region. Significant correlations with fire counts within different ranges around DAK were found for particle number, the surface area concentration of alveolar deposition, and BC. In conclusion, biomass burning is an important PM source in SEA, particularly nanoparticles, which has high potency to be inhaled into the lung environment and interact with alveolar cells, leading to adverse respiratory effects. The fire counts within 100 to 150 km shows the highest Pearson's r for particle number and surface area concentration. It suggests 12 to 24 hr could be a fair time scale for initial aging process of BB aerosols. Importantly, the people lives in this region could have higher risk for PM exposure.

biomass burning↗
NASA NTRS2022

Secondary Inorganic Aerosol Chemistry and Its Impact on Atmospheric Visibility Over an Ammonia-Rich Urban Area in Central Taiwan

This study investigated the hourly inorganic aerosol chemistry and its impact on atmospheric visibility over an urban area in Central Taiwan, by relying on measurements of aerosol light extinction, inorganic gases, and PM 2.5 water-soluble ions (WSIs), and simulations from a thermodynamic equilibrium model. On average, the sulfate (SO 4 2− ), nitrate (NO 3 − ), and ammonium (NH 4 + ) components (SNA) contributed ∼90% of WSI concentrations, which in turn made up about 50% of the PM 2.5 mass. During the entire observation period, PM 2.5 and SNA concentrations, aerosol pH, aerosol liquid water content (ALWC), and sulfur and nitrogen conversion ratios all increased with decreasing visibility. In particular, the NO 3 − contribution to PM 2.5 increased, whereas the SO 4 2− contribution decreased, with decreasing visibility. The diurnal variations of the above parameters indicate that the interaction and likely mutual promotion between NO 3 − and ALWC enhanced the hygroscopicity and aqueous-phase reactions conducive for NO 3 − formation, thus led to severely impaired visibility. The high relative humidity (RH) at the study area (average 70.7%) was a necessary but not sole factor leading to enhanced NO 3 − formation, which was more directly associated with elevated ALWC and aerosol pH. Simulations from the thermodynamic model depict that the inorganic aerosol system in the study area was characterized by fully neutralized SO 4 2− (i.e. a saturated factor in visibility reduction) and excess NH 4 + amidst a NH 3 -rich environment. As a result, PM 2.5 composition was most sensitive to gas-phase HNO 3 , and hence NOx, and relatively insensitive to NH 3 . Consequently, a reduction of NOx would result in instantaneous cuts of NO 3 − , PM 2.5 , and ALWC, and hence improved visibility. On the other hand, a substantial amount of NH 3 reduction (>70%) would be required to lower the aerosol pH, driving more than 50% of the particulate phase NO 3 − to the gas phase, thereby making NH 3 a limiting factor in shifting PM 2.5 composition.

aerosol chemistry↗
NASA NTRS2023

Automated Machine Learning to Evaluate the Information Content of Tropospheric Trace Gas Columns for Fine Particle Estimates Over India: A Modeling Testbed

India is largely devoid of high-quality and reliable on-the-ground measurements of fine particulate matter (PM 2.5 ). Ground-level PM 2.5 concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using satellite retrievals of tropospheric trace gas columns. We evaluate the information content of tropospheric trace gas columns for PM 2.5 estimates over India within a modeling testbed using an Automated Machine Learning (AutoML) approach, which selects from a menu of different machine learning tools based on the data set. We then quantify the relative information content of tropospheric trace gas columns, AOD, meteorological fields, and emissions for estimating PM 2.5 over four Indian sub-regions on daily and monthly time scales. Our findings suggest that, regardless of the specific machine learning model assumptions, incorporating trace gas modeled columns improves PM 2.5 estimates. We use the ranking scores produced from the AutoML algorithm and Spearman’s rank correlation to infer or link the possible relative importance of primary versus secondary sources of PM 2.5 as a first step toward estimating particle composition. Our comparison of AutoML-derived models to selected baseline machine learning models demonstrates that AutoML is at least as good as user-chosen models. The idealized pseudo-observations (chemical-transport model simulations) used in this work lay the groundwork for applying satellite retrievals of tropospheric trace gases to estimate fine particle concentrations in India and serve to illustrate the promise of AutoML applications in atmospheric and environmental research.

Machine learning↗
NASA NTRS1992

Characterization of GTP binding and hydrolysis in plasma membranes of zucchini

We have investigated the possibility that G-protein-like entities may be present in the plasma membrane (PM) of zucchini (Cucurbita pepo L.) hypocotyls by examining a number of criteria common to animal and yeast G-proteins. The GTP binding and hydrolysis characteristics of purified zucchini PM are similar to the characteristics of a number of known G-proteins. Our results demonstrate GTP binding to a single PM site having a Kd value between 16-31 nM. This binding has a high specificity for guanine nucleotides, and is stimulated by Mg2+, detergents, and fluoride or aluminium ions. The GTPase activity (Km = 0.49 micromole) of zucchini PM shows a sensitivity to NaF similar to that seen for other G-proteins. Localization of GTP mu 35S binding to nitrocellulose blots of proteins separated by SDS-PAGE indicates a 30-kDa protein as the predominant GTP-binding species in zucchini PM. Taken together, these data indicate that plant PM contains proteins which are biochemically similar to previously characterized G-proteins.

NASA Discipline Plant Biology↗
NASA NTRS2007

Toxicity of Carbon Nanotubes and Its Implications for Occupational and Environmental Health

Carbon nanotubes (CNTs), which possess desirable electrical and mechanical properties, potentially have wide industrial applications. CNTs exist in two forms, single-wall (SW) and multi-wall (MW). There has been great concern that if CNTs enter the work environment as suspended respirable particulate matter (PM), they could pose an inhalation hazard. The results of recent rodent studies have collectively shown that CNTs can produce inflammation, epithelioid granulomas, fibrosis, and biochemical changes in the lungs. Studies in mice given equal amounts of test dusts showed that CNTs were more toxic than quartz and produced lesions that became progressively more pronounced. These results have led us to recommend that respirable CNT dust be considered a serious occupational health hazard, and that exposure limits be established in the expectation of expanded industrial applications. CNTs, which are totally insoluble and fibrous, would be expected to be more biopersistent than mineral fibers. Biopersistence is the key factor determining the long-term toxicity of mineral fibers and certainly of CNTs too. We have postulated that the electrical and fibrous properties of CNTs also play important roles in the toxicity of CNTs in the lungs. Recently, MWCNTs have been found in ultrafine PM aggregates in combustion streams of methane, propane, and natural-gas flames of typical stoves; indoor and outdoor fine (< 2.5 micron) PM samples were reported to contain significant fractions of MWCNTs. Environmental fine PM is mainly formed from combustion of fuels, and fine PM has been reported to be a major contributor to the induction of cardiopulmonary diseases by pollutants. Given that manufactured SWCNTs and/or MWCNTs have elicited pathological changes in the lungs and heart, we have postulated that exposure to combustion-generated MWCNTs in fine PM in the air may play a significant role in air pollution-related cardiopulmonary diseases. Therefore, CNTs from manufacturing and combustion sources in the environment could have adverse effects on human health.

Lam, Chiu-wing↗
NASA NTRS2006

Utility of MODIS Aerosol Optical Depth for Estimating PM2.5 Exposure in Environmental Public Health Surveillance

As part of the National Environmental Public Health Tracking Network (EPHTN) the National Center for Environmental Health (NCEH) at the Centers for Disease Control and Prevention (CDC) is leading a project called Health and Environment Linked for Information Exchange (HELIX-Atlanta). The goal of developing the National Environmental Public Health Tracking Network is to improve the health of communities. Currently, few systems exist at the state or national level to concurrently track many of the exposures and health effects that might be associated with environmental hazards. An additional challenge is estimating exposure to environmental hazards such as particulate matter whose aerodynamic diameter is less than or equal to 2.5 micrometers (PM(2.5)) HELIX-Atlanta's goal is to examine the feasibility of building an integrated electronic health and environmental data network in five counties of Metropolitan Atlanta, GA (Clayton, Cobb, DeKalb, Fulton, and Gwinnett counties). Under HELIX-Atlanta, pilot projects are being conducted to develop methods to characterize exposure; link health and environmental data; analyze the relationship between health and environmental factors; and communicate findings. NASA Marshall Space Flight Center (NASA/MSFC) is collaborating with CDC to combine NASA earth science satellite observations related to air quality and environmental monitoring data to model surface estimates of PM(2.5) concentrations that can be linked with clinic visits for asthma. From 1999-2000 there were over 9,400 hospitalizations per year in Georgia with asthma as the primary diagnosis. The majority of these hospitalizations occurred in medical facilities in the five most populous Metro-Atlanta counties. Hospital charges resulting from asthma in Georgia are approximately $59 million dollars annually. There is evidence in the research literature that asthmatic persons are at increased risk of developing asthma exacerbations with exposure to environmental factors, including PM(2.5). Thus, HELIX-Atlanta is focusing on methods for characterizing population exposure to PM(2.5) for the Atlanta metropolitan area that could be used in on-going surveillance. While use of the Air Quality System, (AQS) PM(2.5) data alone could meet HELIX Atlanta, specifications, there are only five AQS sites in the Atlanta area, thus the spatial coverage is not ideal. Also, the AQS ground observations are made at time intervals ranging from one hour to six days leaving some temporal gaps. NASA Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Aerosol Optical Depth (AOD) data have the potential for estimating daily ground level PM(2.5) at 10 km resolution over the metropolitan Atlanta area supplementing the AQS ground observations and filling their spatial and temporal gaps.

Al-Hamdan, Mohammad↗
NASA NTRS2011

A Study on the Potential Applications of Satellite Data in Air Quality Monitoring and Forecasting

In this study we explore the potential applications of MODIS (Moderate Resolution Imaging Spectroradiometer) -like satellite sensors in air quality research for some Asian regions. The MODIS aerosol optical thickness (AOT), NCEP global reanalysis meteorological data, and daily surface PM(sub 10) concentrations over China and Thailand from 2001 to 2009 were analyzed using simple and multiple regression models. The AOT-PM(sub 10) correlation demonstrates substantial seasonal and regional difference, likely reflecting variations in aerosol composition and atmospheric conditions, Meteorological factors, particularly relative humidity, were found to influence the AOT-PM(sub 10) relationship. Their inclusion in regression models leads to more accurate assessment of PM(sub 10) from space borne observations. We further introduced a simple method for employing the satellite data to empirically forecast surface particulate pollution, In general, AOT from the previous day (day 0) is used as a predicator variable, along with the forecasted meteorology for the following day (day 1), to predict the PM(sub 10) level for day 1. The contribution of regional transport is represented by backward trajectories combined with AOT. This method was evaluated through PM(sub 10) hindcasts for 2008-2009, using ohservations from 2005 to 2007 as a training data set to obtain model coefficients. For five big Chinese cities, over 50% of the hindcasts have percentage error less than or equal to 30%. Similar performance was achieved for cities in northern Thailand. The MODIS AOT data are responsible for at least part of the demonstrated forecasting skill. This method can be easily adapted for other regions, but is probably most useful for those having sparse ground monitoring networks or no access to sophisticated deterministic models. We also highlight several existing issues, including some inherent to a regression-based approach as exemplified by a case study for Beijing, Further studies will be necessa1Y before satellite data can see more extensive applications in the operational air quality monitoring and forecasting.

Li, Can↗
NASA NTRS2014

Fine Particulate Matter Predictions Using High Resolution Aerosol Optical Depth (AOD) Retrievals

To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examined using models. On the other hand, satellites extend spatial coverage but their spatial resolution is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1 km resolution AOD product of Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM(sub 2.5) concentrations within the New England area of the United States. To improve the accuracy of our model, land use and meteorological variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM(sub 2.5) relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance among others. Out-of-sample "ten-fold" cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM(sub 2.5) mass concentrations are highly correlated with the actual observations, with out-of- sample R(sub 2) of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examining exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM(sub 2.5) levels.

aerosol optical depth↗
NASA NTRS2016

Performance of the Primary Mirror Center-of-curvature Optical Metrology System During Cryogenic Testing of the JWST Pathfinder Telescope

The James Webb Space Telescope (JWST) primary mirror (PM) is 6.6 m in diameter and consists of 18 hexagonal segments, each 1.5 m point-to-point. Each segment has a six degree-of-freedom hexapod actuation system and a radius-of-curvature (RoC) actuation system. The full telescope will be tested at its cryogenic operating temperature at Johnson Space Center. This testing will include center-of-curvature measurements of the PM, using the Center-of-Curvature Optical Assembly (COCOA) and the Absolute Distance Meter Assembly (ADMA). The COCOA includes an interferometer, a reflective null, an interferometer-null calibration system, coarse & fine alignment systems, and two displacement measuring interferometer systems. A multiple-wavelength interferometer (MWIF) is used for alignment & phasing of the PM segments. The ADMA is used to measure, and set, the spacing between the PM and the focus of the COCOA null (i.e. the PM center-of-curvature) for determination of the ROC. The performance of these metrology systems was assessed during two cryogenic tests at JSC. This testing was performed using the JWST Pathfinder telescope, consisting mostly of engineering development & spare hardware. The Pathfinder PM consists of two spare segments. These tests provided the opportunity to assess how well the center-of-curvature optical metrology hardware, along with the software & procedures, performed using real JWST telescope hardware. This paper will describe the test setup, the testing performed, and the resulting metrology system performance. The knowledge gained and the lessons learned during this testing will be of great benefit to the accurate & efficient cryogenic testing of the JWST flight telescope.

optical testing↗
NASA NTRS2017

Daily Ambient Air Pollution Metrics for Five Cities: Evaluation of Data Fusion-Based Estimates and Uncertainties

Spatiotemporal characterization of ambient air pollutant concentrations is increasingly relying on the combination of observations and air quality models to provide well-constrained, spatially and temporally complete pollutant concentration fields. Air quality models, in particular, are attractive, as they characterize the emissions, meteorological, and physiochemical process linkages explicitly while providing continuous spatial structure. However, such modeling is computationally intensive and has biases. The limitations of spatially sparse and temporally incomplete observations can be overcome by blending the data with estimates from a physically and chemically coherent model, driven by emissions and meteorological inputs. We recently developed a data fusion method that blends ambient ground observations and chemical transport-modeled (CTM) data to estimate daily, spatially resolved pollutant concentrations and associatedcorrelations. In this study, we assess the ability of the data fusion method to produce daily metrics (i.e., 1-hr max, 8-hr max, and 24-hr average) of ambient air pollution that capture spatiotemporal air pollution trends for 12 pollutants (CO, NO2, NOx, O3, SO2, PM (sub10), PM (sub 2.5), and five PM (sub 2.5) components) across five metropolitan areas (Atlanta, Birmingham, Dallas, Pittsburgh, and St. Louis), from 2002 to 2008. Three sets of comparisons are performed: (1) the CTM concentrations are evaluated for each pollutant and metropolitan domain, (2) the data fusion concentrations are compared with the monitor data, (3) a comprehensive cross-validation analysis against observed data evaluates the quality of the data fusion model simulations across multiple metropolitan domains. The resulting daily spatial field estimates of air pollutant concentrations and uncertainties are not only consistent with observations, emissions, andmeteorology, but substantially improve CTM-derived results for nearly all pollutants and all cities, with the exception of NO2 for Birmingham. The greatest improvements occur for O3 and PM (sub 2.5). Squared spatiotemporal correlation coefficients range between simulations and observations determined using cross-validation across all cities for air pollutants of secondary and mixed origins are R-squared equal to 0.88-0.93 (O3), 0.81-0.89 (SO4), 0.67-0.83 (PM (sub 2.5)), 0.52-0.72 (NO3), 0.43-0.80 (NH4), 0.32-0.51 (OC), and 0.14-0.71 (PM (sub 10)). Results for relatively homogeneous pollutants of secondary origin, tend to be better than those for more spatially heterogeneous (larger spatial gradients) pollutants of primary origin (NOx, CO, SO2 and EC). Generally, background concentrations and spatial concentration gradients reflect interurban airshed complexity and the effects of regional transport, whereas daily spatial pattern variability shows intra urban consistency in the fused data. With sufficiently high CTM spatial resolution, traffic-related pollutants exhibit gradual concentration gradients that peak toward the urban centers. Ambient pollutant concentration uncertainty estimates for the fused data are both more accurate and smaller than those for either the observations or the model simulations alone.

spatiotemporal fusion↗
NASA NTRS2021

Spatial Variation of Fine Particulate Matter Levels in Nairobi Before and During the COVID-19 Curfew: Implications for Environmental Justice

The temporary decrease of fine particulate matter (PM(sub 2.5)) concentrations in many parts of the world due to the COVID-19 lockdown spurred discussions on urban air pollution and health. However there has been little focus on sub-Saharan Africa, as few African cities have air quality monitors and if they do, these data are often not publicly available. Spatial differentials of changes in PM(sub 2.5) concentrations as a result of COVID also remain largely unstudied. To address this gap, we use a serendipitous mobile air quality monitoring deployment of eight Sensirion SPS 30 sensors on motorbikes in the city of Nairobi starting on 16 March 2020, before a COVID-19 curfew was imposed on 25 March and continuing until 5 May 2020. We developed a random-forest model to estimate PM(sub 2.5) surfaces for the entire city of Nairobi before and during the COVID-19 curfew. The highest PM(sub 2.5) concentrations during both periods were observed in the poor neighborhoods of Kariobangi, Mathare, Umoja, and Dandora, located to the east of the city center. Changes in PM(sub 2.5) were heterogeneous over space. PM(sub 2.5) concentrations increased during the curfew in rapidly urbanizing, the lower-middle-class neighborhoods of Kahawa, Kasarani, and Ruaraka, likely because residents switched from LPG to biomass fuels due to loss of income. Our results indicate that COVID-19 and policies to address it may have exacerbated existing air pollution inequalities in the city of Nairobi. The quantitative results are preliminary, due to sampling limitations and measurement uncertainties, as the available data came exclusively from low-cost sensors. This research serves to highlight that spatial data that is essential for understanding structural inequalities reflected in uneven air pollution burdens and differential impacts of events like the COVID pandemic. With the help of carefully deployed low cost sensors with improved spatial sampling and at least one reference-quality monitor for calibration, we can collect data that is critical for developing targeted interventions that address environmental injustice in the African context.

Environmental justice↗
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 NTRS1986

An evaluation of the benefits of utilizing rapid solidification for development of 2XXX (Al-Cu-Mg) alloys

The advantages of rapid solidification processing over ingot metallurgy processing in the development of 2XXX aluminum alloy compositions were evaluated using a similarly processed ingot metallurgy (IM) control alloy. The powder metallurgy (PM) alloy extrusions showed a reduced age-hardening response in comparison with similar IM compositions, with higher tensile properties for naturally aged extrusions but lower properties for artificially aged ones. However, the tensile properties of naturally and artificially aged PM alloy extrusions based on a version of IM 2034 alloy, but containing 0.6 weight percent zirconium, were comparable to those of the IM control extrusions and had significantly superior combinations of strength and toughness. The tensile properties of this PM alloy showed even greater advantage in 6.4-mm (0.25-in.) and 1.8-mm (0.070-in.) plate and sheet, the yield strength being about 68 MPa (10 ksi) greater than reported values for the IM 2034 alloy sheet. An artificially aged PM alloy based on 2219 alloy also showed a strength and strength-toughness combination comparable to those of the PM Al-Cu-Mg-Zr alloy, substantially outperforming the IM 2219 alloy. These results show that rapid solidification offers the flexibility needed to modify conventional IM compositions to produce new alloy compositions with superior mechanical properties.

Paris, H. G.↗