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Marshall Space Flight Center collaborated with Quality Monitoring and Control (QMC) of Humble, Texas, through a Space Act Agreement to design a balanced flow meter for the Space Shuttle Program. QMC founded APlus-QMC LLC to commercialize the technology, which has contributed to 100 new jobs, approximately $250,000 in yearly sales, and saved customers an estimated $10 million.
From June to July 2009 more than thirty different in-situ and remote sensing instruments from all over the world participated in the Cabauw Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI). The campaign took place at KNMI's Cabauw Experimental Site for Atmospheric Research (CESAR) in the Netherlands. Its main objectives were to determine the accuracy of state-ofthe- art ground-based measurement techniques for the detection of atmospheric nitrogen dioxide (both in-situ and remote sensing), and to investigate their usability in satellite data validation. The expected outcomes are recommendations regarding the operation and calibration of such instruments, retrieval settings, and observation strategies for the use in ground-based networks for air quality monitoring and satellite data validation. Twenty-four optical spectrometers participated in the campaign, of which twenty-one had the capability to scan different elevation angles consecutively, the so-called Multi-axis DOAS systems, thereby collecting vertical profile information, in particular for nitrogen dioxide and aerosol. Various in-situ samplers and lidar instruments simultaneously characterized the variability of atmospheric trace gases and the physical properties of aerosol particles. A large data set of continuous measurements of these atmospheric constituents has been collected under various meteorological conditions and air pollution levels. Together with the permanent measurement capability at the CESAR site characterizing the meteorological state of the atmosphere, the CINDI campaign provided a comprehensive observational data set of atmospheric constituents in a highly polluted region of the world during summertime. First detailed comparisons performed with the CINDI data show that slant column measurements of NO2, O4 and HCHO with MAX-DOAS agree within 5 to 15%, vertical profiles of NO2 derived from several independent instruments agree within 25% of one another, and MAX-DOAS aerosol optical thickness agrees within 20-30% with AERONET data. For the in-situ NO2 instrument using a molybdenum converter, a bias was found as large as 5 ppbv during day time, when compared to the other in-situ instruments using photolytic converters.
Surface ozone is a secondary air pollutant formed from reactions between nitrogen oxides (NOx = NO + NO2) and volatile organic compounds in the presence of sunlight. In this work we examine effects of the climate pattern known as the El Niño-Southern Oscillation (ENSO) and NOx variability on surface ozone from 1990 to 2007 over the South African Highveld, a heavily populated region in South Africa with numerous industrial facilities. Over summer and autumn (December-May) on the Highveld, El Niño, as signified by positive sea surface temperature (SST) anomalies over the central Pacific Ocean, is typically associated with drier and warmer than normal conditions favoring ozone formation. Conversely, La Niña, or negative SST anomalies over the central Pacific Ocean, is typically associated with cloudier and above normal rainfall conditions, hindering ozone production. We use a generalized regression model to identify any linear dependence that the Highveld ozone, measured at five air quality monitoring stations, may have on ENSO and NOx. Our results indicate that four out of the five stations exhibit a statistically significant sensitivity to ENSO at some point over the December-May period where El Niño amplifies ozone formation and La Niña reduces ozone formation. Three out of the five stations reveal statistically significant sensitivity to NOx variability, primarily in winter and spring. Accounting for ENSO and NOx effects throughout the study period of 18 years, two stations exhibit statistically significant negative ozone trends in spring, one station displays a statistically significant positive trend in August, and two stations show no statistically significant change in surface ozone.
Dimethylsilanediol (DMSD) has been identified as a problematic organic contaminant aboard the ISS. This contaminant was initially identified in humidity condensate and in the Water Processor Assembly (WPA) product water in 2010 when routine water quality monitoring an increasing total organic carbon (TOC) trend in the WPA product water. Although DMSD is not a crew health hazard at the levels observed in the product water, it can degrade the WPA catalytic reactor's effectiveness and cause early replacement of Multifiltration Beds. DMSD may also degrade the performance of the Oxygen Generation System (OGS) which uses the WPA product water for electrolysis. An investigation into the source of DMSD has determined that polydimethylsiloxane (PDMS) compounds are likely hydrolyzing in the Condensing Heat Exchangers (CHX) to form DMSD. PDMS compounds are prevalent aboard ISS from a variety of sources, including crew hygiene products, adhesives, caulks, lubricants, and various nonmetallic materials. PDMS compounds are also known to contribute to CHX hydrophilic coating degradation by rendering it hydrophobic and therefore adversely affecting its ability to effectively transmit water to the condensate bus. Eventually this loss in performance results in water droplets in the air flow exiting the CHX, which may lead to microbial growth in the air ducts and may impact the performance of downstream systems. Several options have been evaluated to address these concerns. Modifications to the Water Processor Multifiltration Beds and Catalytic Reactor for removal of DMSD were not considered viable, and did not address the issue with PDMS compound degradation of the CHX coating. Design concepts are now in development for removing PDMS compounds from the air stream before they can reach the CHX coating, thus preventing coating degradation and hydrolysis of the PDMS compounds to DMSD. This paper summarizes the current status of the effort to treat these contaminants on ISS.
Persistent high aerosol loadings together with extremely high population densities have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground-based air quality monitoring is relatively limited in this area. Recently, satellite-retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD observations from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD observations from handheld sunphotometers, 51% of VIIRS Environmental Data Record (EDR) AOD, 37% of GOCI AOD, 33% of VIIRS Intermediate Product (IP) AOD, 26% of Terra MODIS C6 3km AOD, and 16% of Aqua MODIS C6 3km AOD fell within the reference expected error (EE) envelope (+/-0.05/+/- 0.15 AOD). Comparing against AERONET AOD over the JapanSouth Korea region, 64% of EDR, 37% of IP, 61% of GOCI, 39% of Terra MODIS, and 56% of Aqua MODIS C6 3km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3km products had positive biases.
The new-generation polar-orbiting operational environmental sensor, the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite, provides critical daily global aerosol observations. As older satellite sensors age out, the VIIRS aerosol product will become the primary observational source for global assessments of aerosol emission and transport, aerosol meteorological and climatic effects, air quality monitoring, and public health. To prove their validity and to assess their maturity level, the VIIRS aerosol products were compared to the spatiotemporally matched Aerosol Robotic Network (AERONET)measurements. Over land, the VIIRS aerosol optical thickness (AOT) environmental data record (EDR) exhibits an overall global bias against AERONET of 0.0008 with root-mean-square error(RMSE) of the biases as 0.12. Over ocean, the mean bias of VIIRS AOT EDR is 0.02 with RMSE of the biases as 0.06.The mean bias of VIIRS Ocean Angstrom Exponent (AE) EDR is 0.12 with RMSE of the biases as 0.57. The matchups between each product and its AERONET counterpart allow estimates of expected error in each case. Increased uncertainty in the VIIRS AOT and AE products is linked to specific regions, seasons, surface characteristics, and aerosol types, suggesting opportunity for future modifications as understanding of algorithm assumptions improves. Based on the assessment, the VIIRS AOT EDR over land reached Validated maturity beginning 23 January 2013; the AOT EDR and AE EDR over ocean reached Validated maturity beginning 2 May 2012, excluding the processing error period 15 October to 27 November 2012. These findings demonstrate the integrity and usefulness of the VIIRS aerosol products that will transition from S-NPP to future polar-orbiting environmental satellites in the decades to come and become the standard global aerosol data set as the previous generations missions come to an end.
Chemical transport models together with emission inventories are widely used to simulate NO2 concentrations over China, but validation of the simulations with in situ measurements has been extremely limited. Here we use ground measurements obtained from the air quality monitoring network recently developed by the Ministry of Environmental Protection of China to validate modeling surface NO2 concentrations from the CHIMERE regional chemical transport model driven by the satellite-derived DECSO and the bottom-up MIX emission inventories. We applied a correction factor to the observations to account for the interferences of other oxidized nitrogen compounds (NOz), based on the modeled ratio of NO2 to NOz. The model accurately reproduces the spatial variability in NO2 from in situ measurements, with a spatial correlation coefficient of over 0.7 for simulations based on both inventories. A negative and positive bias is found for the simulation with the DECSO (slopeD0.74 and 0.64 for the daily mean and daytime only) and the MIX (slopeD1.3 and 1.1) inventories, respectively, suggesting an underestimation and overestimation of NOx emissions from corresponding inventories. The bias between observed and modeled concentrations is reduced, with the slope dropping from 1.3 to 1.0 when the spatial distribution of NOx emissions in the DECSO inventory is applied as the spatial proxy for the MIX inventory, which suggests an improvement of the distribution of emissions between urban and suburban or rural areas in the DECSO inventory compared to that used in the bottom-up inventory. A rough estimate indicates that the observed concentrations, from sites predominantly placed in the populated urban areas, may be 10-40% higher than the corresponding model grid cell mean. This reduces the estimate of the negative bias of the DECSO-based simulation to the range of -30 to 0% on average and more firmly establishes that the MIX inventory is biased high over major cities. The performance of the model is comparable over seasons, with a slightly worse spatial correlation in summer due to the difficulties in resolving the more active NOx photochemistry and larger concentration gradients in summer by the model. In addition, the model well captures the daytime diurnal cycle but shows more significant disagreement between simulations and measurements during nighttime, which likely produces a positive model bias of about 15% in the daily mean concentrations. This is most likely related to the uncertainty in vertical mixing in the model at night.
Science missions such as Earth-exploration and remote sensing thatuse frequencies allocated for Earth Exploration-Satellite Service(EESS) provide valuable information about, for example: (1) Air quality monitoring (2) Soil moisture mapping (3) Sensing sea salinity (4) Weather forecasting and climate models (winds, storms, clouds).Another set of science missions use Space Research Service (SRS)allocations. These space research missions can support: (1) On orbit research that for example can help in the creation of new drugsor the impact of the space environment on satellites (2) Scientific information about the universe – stars, planetary science, solarsystem, and the future evolution of the universe.
With the advancement of space hardware technologies such as smaller spacecraft, component and instrument miniaturization and high performance space processors, and with the advancement of software technologies in artificial intelligence, big data analysis and autonomous decision making, Earth Science is looking at novel ways to observe phenomena that previously could not have been studied or would have been too expensive to study with traditional missions. In particular, the New Observing Strategies (NOS) component of the NASA Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST) Program aims at leveraging these novel technologies as well as low cost and easy access to space to acquire multi-temporal or simultaneous multi-angular, multi-locations, multi-resolution and multi-spectral observations that will provide better multi-source measurements and will build a more dynamic and comprehensive picture of Earth Science phenomena that need to be studied and analyzed. For applications such as water resources management, air quality monitoring, biodiversity studies or disaster management, NOS will integrate the use of small instruments, small spacecraft, constellations of spacecraft and networks of sensors to design new missions that will provide the necessary measurements to improve future forecast and science modeling systems.Measurement acquisition will therefore be approached as a system of systems rather than on a mission basis, and a system of this complexity should not be expected to work without full integration and experimental characterization. Although most of the individual technologies enabling to link and coordinate multi-source observations are more or less mature, a few technologies need to be developed and all of them need to be integrated and tested as a system. In order for this validation to occur, the AIST Program is developing the NOS Testbed that includes 3 main goals:1.Validate novel NOS technologies, independently and as a system2.Demonstrate novel distributed operations concepts3.Socialize new Distributed Spacecraft Mission (DSM) and SensorWeb (SW) technologies and concepts to the science community by significantly retiring the risk of integrating these new technologies.The NOS Testbed will consist of multiple sensing nodes, simulated or actual, representing space, air and/or ground measurements, that are interconnected by a communications fabric (infrastructure that permits nodes to transmit and receive data between one another and interact with each other). Each node will be supported by hardware capabilities required to perform nodes monitoring and command & control, as well as intelligent "onboard" computing. The nodes will work together in a collaborative manner to demonstrate optimal science capabilities. The testbed will enable to validate technologies such as inter-node communication models, techniques and protocols; inter-node coordination; real-time data fusion and understanding; planning; sensor re-targeting; etc. Additionally, the testbed will have the capability to interact with various mission design tools, OSSEs and one or several forecast models. More details about the NOS Testbed will be presented at the confererence.
The SERVIR program, a joint development initiative of National Aeronautics and Space Administration (NASA) and United States Agency for International Development (USAID), works in partnership with leading regional organizations world-wide to help developing countries use information provided by Earth observing satellites and geospatial technologies for managing climate risks and land use. SERVIR empowers decision-makers with tools, products, and services to act locally on critical issues related to four key areas: Food Security and Agriculture, Water and Water-related Disasters, Land Use/Land Cover and Ecosystems, Weather and Climate. SERVIR is improving awareness, increasing access to information, and supporting analysis to help people in West Africa, Eastern and Southern Africa, Hindu Kush-Himalaya (HKH), the Lower Mekong, South America and Mesoamerica. NASA funds an Applied Sciences Team (AST), composed of experts from universities and research institutions around the United States. Through AST, SERVIR adopted the Tethys platform introduced by Brigham Young University (BYU) for the implementation of tools to deliver scientific data to stakeholders in the Hindu Kush-Himalaya region. Several Tethys applications developed within SERVIR have raised interest amongst decision makers in the countries served by our regional hubs. They see these new apps as key vehicles to disseminate complex information related to weather models, hydrological models, and air quality monitoring, and as such the scope of our Tethys efforts continue to expand to other regions. As of July 2019, each one of the SERVIR hubs (with the exception of Amazonia, which has only recently started), have stood up Tethys portals and have at least one individual trained in the implementation and maintenance of a Tethys portal and in the creation of basic applications. HKH, with BYU's valuable support, has implemented very sophisticated applications that showcase the possibilities and the benefits that the Tethys platform brings.
The first active nano-electronic device flown in space was launched on a flight demonstration of a nano-chemical sensor aboard a US Navy satellite in 2007. The same sensor was flown again to the International Space Station (ISS) in 2009 as part of JPL electronic nose for crew cabin air quality monitoring.
A new approach to the study of stratospheric intrusions (SIs)? the introduction of ozone-rich lower stratospheric air into the troposphere ? uses NASA's Goddard Earth Observing System Model (GEOS) model and assimilation products with an objective feature tracking algorithm to investigate the atmospheric dynamics in the UTLS region that generate SIs and the different mechanisms through which SIs may influence tropospheric chemistry and surface air quality. In particular, SIs have been linked with surface ozone air quality exceedances, especially at the high elevations in the western USA in springtime. However, the impact of SIs in the remaining seasons and over the rest of the USA is less clear. Improved understanding of the connections between large-scale climate variability and local-scale dynamically-driven air quality events may support improved seasonal prediction of SI events. While the spring over the western USA does exhibit the largest number of SIs affecting the lower troposphere, we demonstrate that the number of intrusions in the remaining seasons and over the eastern USA is sizable. Using a multitude of observational datasets, including lidar data, ozonesondes and air quality monitoring surface sites, in combination with GEOS forecast and reanalysis products, the transport of ozone from the stratosphere toward the surface is assessed. By focusing on recent reports of high ozone at monitoring station sites likely associated with SIs, we aim to provide the public with tools which are available in near-real time to enhance their capability to identify the impact of stratospheric air on surface ozone concentrations separate from anthropogenic sources.
AVIRIS operations at the Jet Propulsion Laboratory consist primarily of a sensor task and a data facility task. The sensor task is responsible for AVIRIS sensor meaintenance, laboratory calibration, and field operations. The AVIRIS data facility is responsible for data archiving, data calibration, quality monitoring and distribution. In this paper we describe recent improvements in these two primary AVIRIS tasks.
This work serves as the second of a two-part study to improve surface PM2.5 forecasts in the continental U.S. through the integrated use of multi satellite aerosol optical depth (AOD) products (MODIS Terra/Aqua and VIIRS DT/DB), multichemical transport model (CTM) (GEOS-Chem, WRF-Chem, and CMAQ) outputs, and ground observations. In Part I of the study, an ensemble Kalman filter (KF) technique using three CTM outputs and ground observations was developed to correct forecast bias and generate a single best forecast of PM2.5 for next day over non rural areas that have surface PM2.5 measurements in the proximity of 125 km. Here, with AOD data, we extended the bias correction into rural areas where the closest air quality monitoring station is at least 125–300 km away. First, we ensembled all of satellite AOD products to yield the single best AOD. Second, we corrected daily PM2.5 in rural areas from multiple models through the AOD spatial pattern between these areas and non rural areas, referred to as “extended ground truth” or EGT, for the present day. Lastly, we applied the KF technique to reduce the forecast bias for next day using the EGT. Our results find that the ensemble of bias-corrected daily PM2.5 from three CTMs for both today and next day show the best performance. Together, the two-part study develops a multimodel and multi-AOD bias-correction technique that has the potential to improve PM2.5 forecasts in both rural and non rural areas in near real time, and be readily implemented at state levels.
The NASA Student Airborne Science Activation (SaSa) for Minority Serving Institutions (MSIs) held its inaugural summer research program for early career undergraduates interested in the Geosciences. SaSa is a NASA Science Activation funded 8-week summer internship program. Twenty-four first- and second-year undergraduates from MSIs across the U.S. participated in the summer program - June 6 to July 29, 2022. Students had the opportunity to gain hands-on research experience in all components of an airborne science campaign including flying on-board a NASA research aircraft to collect atmospheric measurements. Students conducted independent research projects related to the atmosphere, ocean, and geosciences that feed into NASA’s broader Earth Science Division’s and Decadal Survey goals using air quality, meteorological, and oceanic measurements from surface, airborne, and satellite-based observations. The program split its time between partner institution, University of Maryland Baltimore County and NASA’s Wallops Flight Facility in Wallops Island, Virginia. Students also made site visits at partner institutions, including: Hampton University, University of Maryland Eastern Shore, Morgan State University, Howard University, and Coppin State University and attended lectures from visiting faculty and NASA Subject Matter Experts. Students were guided on their research projects by near-peer graduate mentors, SaSa program leadership, co-Is at partner institutions, and NASA scientists to address two major research themes: 1) how human-caused air pollution has human and environmental implications, and 2) how large-scale meteorological factors influence local weather conditions. Students sorted into research groups, based on sub-discipline areas in the Geosciences, including: “Clouds, Aerosols and Radiation”, “Meteorology and Planetary Boundary Layer”, “Air Quality: Particle Pollution and Trace Gases”, and “Air-Water-Land Interface”. Their research was presented as 3-minute lightning talks and in-person poster presentations at a close-out event at NASA Goddard Space Flight Center in Greenbelt, Maryland. The students’ inter- and trans-disciplinary research experiences centered in the use of multiple ground, airborne, and satellite remote sensing NASA Earth Science Division assets. Providing a unique experience aligned to recognize the societal benefits that NASA contributes in the areas of resource management, air quality monitoring and policy decisions, energy and weather predictions, and research on the Earth’s climate. The SaSa program aims to increase the number of students from MSIs that identify as underrepresented or underserved individuals in the Geosciences discipline, Earth System Science graduate programs, and the NASA workforce. A summary of the summer research program, educational, scientific, and programmatic outcomes, as well as lessons learned will be presented.
The Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE) cruise in the Gulf of Mexico was conducted in May 2019 by NASA and the Bureau of Ocean Energy Management to determine the feasibility of using satellite data to measure air quality in a region of concentrated oil and natural gas (ONG) operations. SCOAPE addressed both technological and scientific issues related to measuring NO2 columns over the Outer Continental Shelf. Featured were nitrogen dioxide (NO 2 ) instruments (Pandora, Teledyne API analyzer) at Cocodrie, LA (29.26°, -90.66°), and on the Research Vessel Point Sur operating off the Louisiana coast with measurements of ozone, carbon monoxide and volatile organic compounds (VOC). The findings: (1) All NO 2 observations revealed two atmospheric regimes over the Gulf, the first influenced by tropical air in 10-14 May, the second influenced by flow from urban areas on 15-17 May; (2) Comparisons of OMI v4 and TROPOMI v1.3 TC (total column) NO 2 data with shipboard Pandora NO 2 column observations averaged 13% agreement with the largest difference during 15-17 May (~20%). At Cocodrie, the satellite-Pandora agreement was ~5%. (3) Three new-model Pandora instruments displayed a TC NO 2 precision of 0.01 Dobson Units (~5%); (4) Regions of smaller, older natural gas operations showed high methane readings from leakage; elevated VOC were also detected. Neither satellite nor spectrometer captured the magnitude of ambient NO 2 variability near ONG platforms. Given an absence of regular air quality monitoring over the Gulf of Mexico, SCOAPE data constitute a baseline against which future observations can be compared.
Foundation Models (FM) are AI models that are designed to replace a task or an application specific model. These FM can be applied to many different downstream applications. These FM are trained using self supervised techniques and can be built on any type of sequence data. The use of self supervised learning removes the hurdle for developing a large labeled dataset for training. Most FM use transformer architecture utilizes the notion of self attention which allows the network to model the influence of distant data points to each other both in space and time. The FM models exhibit emergent properties that are induced from the data. FM can be an important tool for science. The scale of these models results in better performance for different downstream applications and these applications show better accuracy over models built from scratch. FM drastically reduces the cost of entry to build different downstream applications both in time and effort. FM for selected science datasets such as optical satellite data, can accelerate applications ranging from data quality monitoring, feature detection and prediction. FM can make it easier to infuse AI into scientific research by removing the training data bottleneck and increasing the use of science data.
NASA’s Multi-Angle Imager for Aerosols (MAIA) mission, under development at the Jet Propulsion Laboratory, is designed to study the adverse health effects of different types of particulate air pollution. Planned for launch in 2023 for a 3-year mission, the MAIA satellite instrument will focus on a selected set of metropolitan target areas, where air quality monitors and health data are available. Aerosol concentration and speciation are inferred from multi-angle measurements of backscattered sunlight in 14 spectral bands from 350-2200 nm, with bands near 442, 645 and 1040 nm measuring the degree (DoLP) and angle of linear polarization (AoLP) in addition to radiance. The pushbroom camera has a ~240-km cross-track field of view with a nadir resolution of ~200 m, and is mounted onto a biaxial gimbal to provide along-track view angles within ±60°, to extend the field of regard to ±48°, and to view the instrument’s onboard calibrator (OBC) and dark target. The OBC consists of a sunlit transmissive diffuser, followed by 12 polarizers at different orientations. MAIA’s polarimetry is implemented using miniature wire grid polarizers on the focal plane array, and dual photoelastic modulators (PEMs) and achromatic quarter-wave plates to rapidly rotate the polarization. The resulting ~26-Hz intensity modulation encodes the linearly polarized and total radiance in each pixel, leaving the DoLP and AoLP insensitive to gain calibration. We report on the polarimetric calibration of the MAIA camera using a vacuum-compatible polarization state generator, consisting of a 1600W Xenon lamp, 12-inch integrating sphere, and rotating high-extinction polarizer. Mueller-matrix-based calibration coefficients for each detector pixel are derived from measurements at multiple polarizer angles, and are used to correct the measurements for instrumental polarization aberrations. Prior to flight, the calibrated MAIA camera is panned across the OBC to characterize its output, using uniform illumination with an irradiance similar to the Sun.