Search NASA⌕ Search

SEARCH · Search NASA

Results for “air quality”

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 289 records · Page 16

Remote Sensing of Urban Thermal Landscape Characteristics and Their Affects on Local and Regional Meteorology and Air Quality: An Overview of NASA EOS-IDS Project Atlanta

As an entity, the city is a manifestation of human "management" of the land. The act of city-building, however, drastically alters the biophysical environment, which ultimately, impacts local and regional land-atmosphere energy exchange processes. Because of the complexity of both the urban landscape and the attendant energy fluxes that result from urbanization, remote sensing offers the only real way to synoptically quantify these processes. One of the more important land-atmosphere fluxes that occurs over cities relates to the way that thermal energy is partitioned across the heterogeneous urban landscape. The individual land cover and surface material types that comprise the city, such as pavements and buildings, each have their own thermal energy regimes. As the collective urban landscape, the individual thermal energy responses from specific surfaces come together to form the urban heat island phenomena, which prevails as a dome of elevated air temperatures over cities. Although the urban heat island has been known to exist for well over 150 years, it is not understood how differences in thermal energy responses for land covers across the city interact to produce this phenomenon, or how the variability in thermal energy responses from different surface types drive its development. Additionally, it can be hypothesized that as cities grow in size through time, so do their urban heat islands. The interrelationships between urban sprawl and the respective growth of the urban heat island, however, have not been investigated. Moreover, little is known of the consequential effects of urban growth, land cover change, and the urban heat island as they impact local and regional meteorology and air quality.

Quattrochi, Dale A.↗

The Application of TOMS Ozone, Aerosol and UV-B Data to Madagascar Air Quality Determination

Total Ozone Mapping Spectrometer (TOMS) data products for the area of Madagascar are presented. In addition to total ozone, aerosols and UV-B tropospheric ozone results are shown from 1979 to the present. Tropospheric ozone over Africa and Madagascar is enhanced by 10 to 15 DU in October. This maximum coincides with the time of maximum biomass area burning in Africa and Madagascar. Ozone observations were made from 1979 to 1999 using the TOMS tropospheric ozone convective cloud differential method. As a result of easterly trade winds, ozone originating on Madagascar is transported to the west over the Mozambique Channel. In El Nino years higher level westerly winds descend to transport low level ozone easterly. This results in African continental ozone being transported east of Madagascar. Long range transport of African ozone is observed during El Nino periods. The potential of TOMS and other space data for use in public education and research on Madagascar air quality is demonstrated.

Aikin, A.C.↗

The NASA Merra-2 Reanalysis Products: Data and Tools Used for Aerosol and Air Quality Studies

The NASA Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is atmospheric reanalysis data spanning 1980 to present. It has been produced by the NASA Global Modeling and Assimilation Office (GMAO) and is distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). MERRA-2 data includes 100 collections of Earth system variables, mainly from the atmospheric model, such as aerosol fields and meteorological fields, radiation fields, and aerosol fields, guided by the assimilation of as many as six million observations every six hours. MERRA-2 has been one of the most popular datasets from NASA and is widely used in interdisciplinary research and applications, with increasing numbers of new users. For example, at least 7000 users accessed MERRA-2 data at GES DISC in the year 2021, ~1000 more users than in the year 2020. In this presentation, we will introduce the MERRA-2 datasets associated with aerosol and air quality studies and use a wildfire case study to demonstrate the data tools developed at GES DISC to analyze and visualize MERRA-2 data, such as Giovanni and the level 3 and level 4 subsetter, and Jupyter Python notebook. We will also update the status of cloud migration of the MERRA-2 data to Amazon Web Services (AWS).

Xiaohua Pan↗

Explore Air Quality from NASA Satellite Data

Space-borne earth observation has been important to monitor the earth condition and played acritical role in validating other instruments or modeling's outputs. However, the data from satellite earth observation are usually very complex in terms of science contents, formats, and spatiotemporal granularities, making them difficult to use from many aspects. NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), one of the 12 official NASA data centers, archives and distributes rich collections of data from multiple satellite missions and model results. The GES DISC is also the official archive center for data from the Ozone Monitoring Instrument (OMI) aboard NASA's Aura mission since 2004. Recently, the GES DISChas been evolving and improving its data management and services in order to promote NASA data to be easily discovered and accessed, as well as to facilitate interoperability. We'll show in this presentation how to explore and analyze NASA earth observation data for air quality through a suite of user-friendly tools - from Giovanni to ArcGIS, demonstrating in using this set of tools prepares us to serve the Sentinel 5P TROPOMI to the community.

Wei, J.↗

The SPARC-Reanalysis Intercomparison Project: Summary of Phase 1 and Plans for Phase 2 (S-RIP2): Chemical Reanalyses & Air Quality, Tropospheric Circulation, Extreme Events, and More

Reanalysis datasets are widely used to understand numerous atmospheric processes. Different reanalyses may give very different results for the same diagnostics. The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP, https://s-rip.github.io/) is a coordinated activity to compare key diagnostics among available reanalyses, identify differences among reanalyses and their underlying causes, provide guidance on appropriate usage of reanalyses in scientific studies, and contribute to future improvements in the reanalysis products via collaborations with reanalysis centers and data users. S-RIP Phase 1 (completed in early 2022) focused primarily on the upper troposphere through the middle atmosphere and processes linking these regions to the troposphere and surface. We look forward to broadening our efforts in Phase 2 (S-RIP2), with new foci including studies of tropospheric circulation, extreme weather events, and their links to the stratosphere, and focusing on evaluation of chemical reanalyses, both those with a stratosphere / upper troposphere focus and those that focus on air quality applications. This presentation will provide a summary of Phase 1 results and discussion of future directions for S-RIP2, emphasizing applications to composition and chemistry studies as well as fostering capacity building for Early Career Scientists.

Jonathan S Wright↗

Broadening Systematic Reanalysis lntercomparisons in the SPARC-Reanalysis lntercomparison Project Phase 2 (S-RIP2): Chemical Reanalyses & Air Quality, Tropospheric Circulation, Extreme Events, and More

Reanalysis datasets are widely used to understand numerous atmospheric processes. Different reanalyses may give very different results for the same diagnostics. The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP, https://s-rip.github.io/) is a coordinated activity to compare key diagnostics among available reanalyses, identify differences among reanalyses and their underlying causes, provide guidance on appropriate usage of reanalyses in scientific studies, and contribute to future improvements in the reanalysis products via collaborations with reanalysis centers and data users. S-RIP Phase 1 (completed in early 2022) focused primarily on the upper troposphere through the middle atmosphere and processes linking these regions to the troposphere and surface. We look forward to broadening our efforts in Phase 2 (S-RIP2), with new foci including studies of tropospheric circulation, extreme weather events, and their links to the stratosphere, and focusing on evaluation of chemical reanalyses, both those with a stratosphere / upper troposphere focus and those that focus on air quality applications. This presentation will provide a summary of Phase 1 results and discussion of future directions for S-RIP2, emphasizing applications to composition and chemistry studies as well as fostering capacity building for Early Career Scientists.

Sean M Davis↗

Broadening Systematic Reanalysis Intercomparisons in the SPARC-Reanalysis Intercomparison Project Phase 2 (S-RIP2): Chemical Reanalyses & Air Quality, Tropospheric Circulation, Extreme Events, and More

Reanalysis datasets are widely used to understand numerous atmospheric processes. Different reanalyses may give very different results for the same diagnostics. The Stratosphere-troposphere Processes And their Role in Climate (SPARC) Reanalysis Intercomparison Project (S-RIP, https://s-rip.github.io/) is a coordinated activity to compare key diagnostics among available reanalyses, identify differences among reanalyses and their underlying causes, provide guidance on appropriate usage of reanalyses in scientific studies, and contribute to future improvements in the reanalysis products via collaborations with reanalysis centers and data users. S-RIP Phase 1 (completed in early 2022) focused primarily on the upper troposphere through the middle atmosphere and processes linking these regions to the troposphere and surface. We look forward to broadening our efforts in Phase 2 (S-RIP2), with new foci including studies of tropospheric circulation, extreme weather events, and their links to the stratosphere, and focusing on evaluation of chemical reanalyses, both those with a stratosphere / upper troposphere focus and those that focus on air quality applications. This presentation will provide a summary of Phase 1 results and discussion of future directions for S-RIP2, emphasizing applications to composition and chemistry studies as well as fostering capacity building for Early Career Scientists.

Sean M Davis↗

International Space Station Air Quality Assessed According to Toxicologically-Grouped Compounds

Scores of compounds are found in the International Space Station (ISS) atmospheric samples that are returned to the Johnson Space Center Toxicology Laboratory for analysis. Spacecraft Maximum Allowable Concentrations (SMACs) are set with the view that each compound is present as if there were no other compounds present. In order to apply SMACs to the interpretation of the analytical data, the toxicologist must employ some method of combining the potential effects of the aggregate of compounds found in the atmospheric samples. The simplest approach is to assume that each quantifiable compound has the potential for some effect in proportion to the applicable SMAC, and then add all the proportions. This simple paradigm disregards the fact that most compounds have potential to adversely affect only a few physiological systems, and their effects would be independent rather than additive. An improved approach to dealing with exposure to mixtures is to add the proportions only for compounds that adversely affect the same physiological system. For example, toxicants that cause respiratory irritation are separated from those that cause neurotoxicity or cardio-toxicity. Herein we analyze ISS air quality data according to toxicological groups with a view that this could be used for understanding any crew symptoms occurring at the time of the sample. In addition, this approach could be useful in post-flight longitudinal surveys where the flight surgeon may need to identify post-flight, follow-up medical studies because of on-orbit exposures that target specific physiological systems.

James, John T.↗

International Space Station Air Quality Assessed According to Toxicologically-Grouped Compounds

Scores of compounds are found in the International Space Station (ISS) atmospheric samples that are returned to the Johnson Space Center Toxicology Laboratory for analysis. Spacecraft Maximum Allowable Concentrations (SMACs) are set with the view that each compound is present as if there were no other compounds present. In order to apply SMACs to the interpretation of the analytical data, the toxicologist must employ some method of combining the potential effects of the aggregate of compounds found in the atmospheric samples. The simplest approach is to assume that each quantifiable compound has the potential for some effect in proportion to the applicable SMAC, and then add all the proportions. This simple paradigm disregards the fact that most compounds have potential to adversely affect only a few physiological systems, and their effects would be independent rather than additive. An improved approach to dealing with exposure to mixtures is to add the proportions only for compounds that adversely affect the same physiological system. For example, toxicants that cause respiratory irritation are separated from those that cause neurotoxicity or cardio-toxicity. Herein we analyze ISS air quality data according to toxicological groups with a view that this could be used for understanding any crew symptoms occurring at the time of the sample acquisition. In addition, this approach could be useful in post-flight longitudinal surveys where the flight surgeon may need to identify post-flight, follow-up medical studies because of on-orbit exposures that target specific physiological systems.

James, John T.↗

Air Quality Satellite Monitoring by TROPOMI on Sentinel-5P

The recently launched Sentinel satellite mission, the Sentinel-5 Precursor (Sentinel-5P), is one of the European Space Agency's (ESA) new mission family – Sentinels. The sole payload on Sentinel-5P is the TROPOspheric Monitoring Instrument (TROPOMI), a nadir-viewing 108⁰ field-of-view push-broom grating hyperspectral spectrometer, covering the wavelengths of ultraviolet-visible (270 nm - 495 nm), near infrared (675 nm - 775 nm), and shortwave infrared (2305 nm - 2385 nm). Sentinel-5P is the first of the Atmospheric Composition Sentinels, and is providing measurements of atmospheric chemistry, aerosols, and clouds at high spatial, temporal, and spectral resolution. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) supports over a thousand data collections in the focus areas of Atmospheric Composition, Water & Energy Cycles, and Climate Variability. Sentinel-5P TROPOMI Level-1B (L1B) and Level-2 (L2) products are curated at the GES DISC. Sentinel-5P data are provided by the European Union and the European Space Agency (ESA) through an agreement between ESA and NASA. Through its convenient and enhanced tools/services, such as OPeNDAP and L2 Subsetting, GES DISC offers the air quality remote sensing user community facile solutions for using complex Earth science data and applications. This presentation will demonstrate up-to-date TROPOMI products including EarthView (EV) radiance, solar irradiance, Aerosol Index, Carbon Monoxide, Total column Ozone, Nitrogen Dioxide, and cloud, as well as easy ways to access, visualize and subset TROPOMI data.

TROPOMI↗

Gulf of Mexico Health & Air Quality Ii: Mapping Methane Emission Plumes Using Sunglint-Configured Imagery for Monitoring Offshore Oil and Gas Activity

Offshore oil and gas production in the United States is a major source of anthropogenic greenhouse gas emissions and accounts for nearly 30% of global oil and gas production. Methane venting and flaring are primary contributors to offshore emissions, and monitoring these activities is crucial for mitigating greenhouse gas emissions. Limited ground truthing and intermittent offshore satellite revisits make monitoring venting and flaring challenging. The Bureau of Ocean Energy Management (BOEM) and the Bureau of Safety and Environmental Enforcement (BSEE) oversee offshore oil and gas activity but rely primarily on operator-reported data. The non-profit organization SkyTruth monitors natural resources like methane and identifies sources of fugitive emissions. By combining BOEM and BSEE’s operational data along with observations from Sentinel-2 Multispectral Instrument (MSI), Landsat 8 Operational Land Imager (OLI) and Landsat 9 OLI-2, and PRecursore IperSpettrale della Missione Applicativa (PRISMA), the team further identified ultra-emitter point sources in the Gulf of Mexico using sunglint-configured imagery. We quantified these plume emission rates using the methodology from Varon et al. (2020). The team found three plumes in the Gulf of Mexico occurring between 2020 and 2022 using Sentinel-2 MSI and Landsat 9 OLI-2 imagery, in addition to the single plume identified by the Gulf of Mexico Health & Air Quality I team, and successfully quantified three plumes. Our statistical retrieval of three PRISMA images tasked over areas of interest yielded no methane plumes, despite a successful test of a known plume in Assam, India. These analyses serve as a proof of concept for the utility of remote sensing for methane emission monitoring offshore, which can complement regulator emission inventories and validate self-reported operator records.

sunglint↗

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗

Assessment of Air Quality in the Shuttle and International Space Station (ISS) Based on Samples Returned by STS-102 at the Conclusion of 5A.1

The toxicological assessment of air samples returned at the end of the STS-102 (5A.1) flight to the ISS is reported. ISS air samples were taken in late February 2001 from the Service Module, FGB, and U.S. Laboratory using grab sample canisters (GSCs) and/or formaldehyde badges . A "first-entry" sample of the multipurpose logistics module (MPLM) atmosphere was taken with a GSC, and preflight and end-of-mission samples were obtained from Discovery using GSCs. Analytical methods have not changed from earlier reports, and all quality control measures were met for the data presented herein. The two general criteria used to assess air quality are the total-non-methane-volatile organic hydrocarbons (NMVOCs) and the total T-value (minus the CO2 contribution). Control of atmospheric alcohols is important to the water recovery system engineers, hence total alcohols were also assessed in each sample. Formaldehyde is quantified separately.

James, John T.↗

Assessment of Air Quality in the Shuttle and International Space Station (ISS) Based on Samples Returned by STS-100 at the Conclusion of 6A

The toxicological assessment of air samples returned at the end of the STS-100 (6A) flight to the ISS is reported. ISS air samples were taken in March and April 2001 from the Service Module, FGB, and U.S. Laboratory using grab sample canisters (GSCs) and/or formaldehyde badges. An unplanned "first-entry" sample of the MPLM2 (multipurpose logistics module) atmosphere was taken with a GSC, and preflight and end-of-mission samples were obtained from Endeavour using GSCs. Analytical methods have not changed from earlier reports, and all quality control measures were met for the data presented herein. The two general criteria used to assess air quality are the total-non-methane-volatile organic hydrocarbons (NMVOCs) and the total T-value (minus the CO2 and formaldehyde contribution). Because of the Freon 218 (octafluoropropane, OFP) leak, its contribution to the NMVOC is indicated in brackets. When comparing the NMVOC values with the 25 mg/cubic m guideline, the OFP contributions should be subtracted. Control of atmospheric alcohols is important to the water recovery system engineers, hence total alcohols were also assessed in each sample.

James, John T.↗