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NASA NTRS2021

Air Pollution Scenario over Pakistan: Characterization and Ranking of Extremely Polluted Cities using Long-Term Concentrations of Aerosols and Trace Gases

Pakistan ranks third in the world in terms of mortality attributable to air pollution, with aerosol mass concentrations (PM2.5) consistently well above WHO (World Health Organization) air quality guidelines (AQG). However, regulation is dependent on a sparse network of air quality monitoring stations and insufficient ground data. This study utilizes long-term observations of aerosols and trace gases to characterize and rank the air pollution scenarios and pollution characteristics of 80 selected cities in Pakistan. Datasets used include (1) the Aqua and Terra (AquaTerra) MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2 Collection 6.1 merged Dark Target and Deep Blue (DTB) aerosol optical depth (AOD) retrieval products; (2) the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis PM1, PM2.5, and PM10 data; (3) the MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM2.5 data, (4) the OMI (Ozone Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO2), and VCD of sulfur dioxide (SO2) in the Planetary Boundary Layer (PBL), (5) the VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights data, (6) MODIS Collection 6 Version 2 global monthly fire location data (MCD14ML), (7) population density, (8) MODIS Level 3 Collection 6 land cover types, (9) AERONET (AErosol RObotic NETwork) Version 3 Level 2.0 data, and (10) ground-based PM2.5 concentrations from air quality monitoring stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with ground-based PM2.5 concentrations and the NOAA (National Oceanic and Atmospheric Administration) HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) air parcel back trajectories to identify potential pollution source areas which are responsible for extreme air pollution in Pakistan. Results show that the ranking of the top polluted cities depends on the type of pollutant considered and the metric used. For example, Jhang, Multan, and Vehari were characterized as the top three polluted cities in Pakistan when considering AquaTerra DTB AOD products; for PM1, PM2.5, and PM10 Lahore, Gujranwala, and Okara were the top three; for tropospheric NO2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO2 VCD Lahore, Mirpur, and Gujranwala. The results demonstrate that Pakistan’s entire population has been exposed to high PM2.5 concentrations for many years, with a mean annual value of 54.7 μg/cu. m, over all Pakistan from 2003 to 2020. This value exceeds Pakistan’s National Environmental Quality Standards (Pak-NEQS, i.e., <15 μg/cu. m annual mean) for ambient air defined by the Pakistan Environmental Protection Agency (Pak-EPA) as well as the WHO Interim Target-1 (i.e., mean annual PM2.5 <35 μg/cu. m). The spatial analyses of the concentrations of aerosols and trace gases in terms of population density, nighttime lights, land cover types, and fire location data, and the PSCF analysis indicate that Pakistan’s air quality is strongly affected by anthropogenic sources inside of Pakistan, with contributions from surrounding countries. Statistically significant positive (increasing) trends in PM1, PM2.5, PM10, tropospheric NO2 VCD, and SO2 VCD were observed in ~89%, ~67%, ~48%, 91%, and ~88% of the Pakistani cities (80 cities), respectively. This comprehensive analysis of aerosol and trace gas levels, their characteristics in spatio-temporal domains, and their trends over Pakistan, is the first of its kind. Results will be helpful to the Ministry of Climate Change (Government of Pakistan), Pak-EPA, SUPARCO (Pakistan Space and Upper Atmosphere Research Commission), policymakers, and the local research community to mitigate air pollution and its effects on human health.

Muhammad Bilal↗

NASA NTRS2002

Monitoring Thermal Status of Ecosystems with MODIS Land-Surface Temperature and Vegetation Index Products

The global land-surface temperature (LST) and normalized difference vegetation index (NDVI) products retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data in 2001 were used in this study. The yearly peak values of NDVI data at 5km grids were used to define six NDVI peak zones from -0.2 to 1 in steps of 0.2, and the monthly NDVI values at each grid were sorted in decreasing order, resulting in 12 layers of NDVI images for each of the NDVI peak zones. The mean and standard deviation of daytime LSTs and day-night LST differences at the grids corresponding to the first layer of NDVI images characterize the thermal status of terrestrial ecosystems in the NDVI peak zones. For the ecosystems in the 0.8-1 NDVI peak zone, daytime LSTs distribute from 0-35 C and day-night LST differences distribute from -2 to 22 C. The daytime LSTs and day-night LST differences corresponding to the remaining layers of NDVI images show that the growth of vegetation is limited at low and high LSTs. LSTs and NDVI may be used to monitor photosynthetic activity and drought, as shown in their applications to a flood-irrigated grassland in California and an unirrigated grassland in Nevada.

Wan, Zhengming↗

NASA NTRS2000

OCTS And Seawifs Bio-Optical Algorithm and Product Vaildattion and Intercomparison in US Coastal Waters

Monitoring the health of U.S. coastal waters is an important goal of the National Oceanic and Atmospheric Administration (NOAA). Satellite sensors are capable of providing daily synoptic data of large expanses of the U.S. coast. Ocean color sensor, in particular, can be used to monitor the water quality of coastal waters on an operational basis. To appraise the validity of satellite-derived measurements, such as chlorophyll concentration, the bio-optical algorithms used to derive them must be evaluated in coastal environments. Towards this purpose, over 21 cruises in diverse U.S. coastal waters have been conducted. Of these 21 cruises, 12 have been performed in conjunction with and under the auspices of the NASA/SIMBIOS Project. The primary goal of these cruises has been to obtain in-situ measurements of downwelling irradiance, upwelling radiance, and chlorophyll concentrations in order to evaluate bio-optical algorithms that estimate chlorophyll concentration. In this Technical Memorandum, we evaluate the ability of five bio-optical algorithms, including the current SeaWiFS algorithm, to estimate chlorophyll concentration in surface waters of the South Atlantic Bight (SAB). The SAB consists of a variety of environments including coastal and continental shelf regimes, Gulf Stream waters, and the Sargasso Sea. The biological and optical characteristics of the region is complicated by temporal and spatial variability in phytoplankton composition, primary productivity, and the concentrations of colored dissolved organic matter (CDOM) and suspended sediment. As such, the SAB is an ideal location to test the robustness of algorithms for coastal use.

Brow, Chirstopher↗

DOE OSTI2024

The Austin Chalk/Eagle Ford Field Laboratory (ACEFFL)

This project, led by Texas A&M University, developed a science-based field laboratory in the Austin Chalk/Eagle Ford Shale Formation to determine the stimulated reservoir volume created by the fracturing of multiple wells. Utilizing newly developed monitoring solutions, the team delivered comprehensive high-quality field data on the extent and morphologies of productive fractures created from these wells. Advanced field monitoring was complemented by laboratory testing on cores and drill cuttings, and coupled modeling for design, prediction, calibration, and code validation. The Austin Chalk/Eagle Ford Field Laboratory (ACEFFL) was hosted by SM Energy, which provided access to the wells of opportunity.

02 PETROLEUM↗

NASA NTRS2001

OCTS and SeaWiFS Bio-Optical Algorithm and Product Validation and Intercomparison in US Coastal Waters

Monitoring the health of US coastal waters is an important goal of the National Oceanic and Atmospheric Administration (NOAA). Satellite sensors are capable of providing daily synoptic data of large expanses of the US coast. Ocean color sensors, in particular, can be used to monitor the water quality of coastal waters on an operational basis. To appraise the validity of satellite-derived measurements, such as chlorophyll concentration, the bio-optical algorithms used to derive them must be evaluated in coastal environments. Towards this purpose, over 21 cruises in diverse US coastal waters have been conducted. Of these 21 cruises, 12 have been performed in conjunction with and under the auspices of the NASA/Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project. The primary goal of these cruises has been to obtain in-situ measurements of downwelling irradiance, upwelling radiance, and chlorophyll concentrations in order to evaluate bio-optical algorithms that estimate chlorophyll concentration. In this Technical Memorandum, we evaluate the ability of five bio-optical algorithms, including the current Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) algorithm, to estimate chlorophyll concentration in surface waters of the South Atlantic Bight (SAB). The SAB consists of a variety of environments including coastal and continental shelf regimes, Gulf Stream waters, and the Sargasso Sea. The biological and optical characteristics of the region is complicated by temporal and spatial variability in phytoplankton composition, primary productivity, and the concentrations of colored dissolved organic matter (CDOM) and suspended sediment. As such, the SAB is an ideal location to test the robustness of algorithms for coastal use.

Brown, Christopher W.↗

NASA NTRS2019

Monitoring Volcanic Eruptions by OMI/OMPS, ASTER, and MERRA-2

Volcanic lava flows and/or the gas eruptions are the most common characteristics that can be remotely monitored with satellite technology in the global perspective and on different timescales. Atmospheric Sulfur Dioxide (SO2), one of the most abundant gases from volcanic eruptions apart from atmospheric common gases Carbon Dioxide and water vapor, can be directly detected by space-based sensors on satellites. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of the 12 Distributed Active Archive Centers (DAACs) within NASA's Earth Observing System Data and Information System (EOSDIS), which archives SO2 data sets from the Nimbus-7 Total Ozone Mapping Spectrometer (TOMS) in 1978, till the ongoing Ozone Monitoring Instrument (OMI) on NASA's EOS-Aura satellite, the Ozone Mapping Profiler Suite (OMPS) Nadir Mapper (NM) on both the Suomi National Polar-Orbiting Partnership (Suomi-NPP or SNPP) and the Joint Polar-orbiting Satellite System-1 (JPSS-1) satellites, into the future JPSS missions. In addition to the standard OMI/Aura and OMPS/S-NPP SO2 products, SO2 products created under the charter of the Making Earth System Data Records for Use in Research Environments (MEaSUREs) project, are also archived at GES DISC, through which NASA enacts to expand understanding the Earth system using consistent data records. The Land Processes Distributed Active Archive Center (LP DAAC) is another EOSDIS's DAAC that provides land data products and operates as a partnership with the U.S. Geological Survey (USGS). The LP DAAC has been archiving the satellite imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard NASA's EOS-Terra satellite, a high spatial resolution (15 meters) and 14 band multispectral instrument. The ASTER imagery is one of the land products contributing to the application for monitoring hot spots and land terrain changes caused by volcanic eruption events. The data potential in GES DISC and LP DAAC to monitor volcanic sources of SO2 and the influence of wind fields on the gas plume spread will be demonstrated with the most recent 2018 May-July Kilauea Volcano eruption.

Zeng, Jian↗

NASA NTRS2013

Airborne MAX-DOAS Measurements Over California: Testing the NASA OMI Tropospheric NO2 Product

Airborne Multi-AXis Differential Optical Absorption Spectroscopy (AMAX-DOAS) measurements of NO2 tropospheric vertical columns were performed over California for two months in summer 2010. The observations are compared to the NASA Ozone Monitoring Instrument (OMI) tropospheric vertical columns (data product v2.1) in two ways: (1) Median data were compared for the whole time period for selected boxes, and the agreement was found to be fair (R = 0.97, slope = 1.4 +/- 0.1, N= 10). (2) A comparison was performed on the mean of coincident AMAX-DOAS measurements within the area of the corresponding OMI pixels with the tropospheric NASA OMI NO2 assigned to that pixel. The effects of different data filters were assessed. Excellent agreement and a strong correlation (R = 0.85, slope = 1.05 +/- 0.09, N= 56) was found for (2) when the data were filtered to eliminate large pixels near the edge of the OMI orbit, the cloud radiance fraction was<50%, the OMI overpass occurred within 2 h of the AMAX-DOAS measurements, the flight altitude was>2 km, and a representative sample of the footprint was taken by the AMAX-DOAS instrument. The AMAX-DOAS and OMI data sets both show a reduction of NO2 tropospheric columns on weekends by 38 +/- 24% and 33 +/- 11%, respectively. The assumptions in the tropospheric satellite air mass factor simulations were tested using independent measurements of surface albedo, aerosol extinction, and NO2 profiles for Los Angeles for July 2010 indicating an uncertainty of 12%.

remote sensing↗

DOE OSTI2023

A Machine Learning Model for Predicting Composition of Catalytic Coprocessing Products from Molecular Beam Mass Spectra

Demand for the development of an automated and integrated refining process for biofuels has increased in recent years due to the lack of generalized process inspection tools. In bio-oil upgrading processes, all process variables are maintained based on the offline specification of intermediates and products. A lack of real-time product specifications in batch-wise monitoring can cause process failure and wasted resources. Therefore, there is a need for a fast and accurate intermediates/product specification tool that can be used for real-time specification to reduce waste and mitigate the risk of process failure. Here, to address this gap, we developed a machine learning (ML) model for predicting speciated bio-oil composition, including paraffin, iso-paraffins, olefins, naphthene, and aromatics. The model is trained using the mass spectra from upgraded products collected in the vapor phase before condensation and predicts the composition of the condensed product. Training ML models using raw mass spectra is challenging due to numerous overlapped peaks originating from different parent compounds. With this in mind, we propose a protocol that (i) transforms raw mass spectra to chemistry-inspired predefined features and (ii) trains decision tree-based models using these features. Our results show that the random forest model was robust against overfitting and had the highest accuracy compared to other models. Moreover, a stochastic ablation method determined the eight most significant features while maximizing the accuracy. Our protocol facilitates real-time compositional analysis of upgraded bio-oils and thus real-time process monitoring. Additionally, this protocol enables the rational design of efficient catalysts and the determination of optimal process conditions.

09 BIOMASS FUELS↗

DOE OSTI2023

Methane monitoring and detection apparatus and methods

A low cost, low power, passive optical methane monitoring system for fixed-position installation at oil and gas production well pads and gathering centers is disclosed. The optical methane monitoring system disclosed can be a scannable field of view Near Infrared (NIR) filter photometer to detect and quantify methane concentration in a two dimensional or a three dimensional grid above and around a facility. A randomized fiber optic bundle is disclosed that can be used to direct the total optical power from a collection lens to two or more isolated optical channels. Band pass filters isolate a desired wavelength range for transmission measurements for the two or more channels. Also disclosed is an absorption algorithm which accounts for variable background spectral intensity as well as correcting for water vapor and overall scattering effects to measure methane concentration for a given field of view.

Lievois, John↗

NASA NTRS2012

An efficient plant production system for eXploration habitat demonstration module

The National Aeronautics and Space Administration (NASA) annually provides an academic challenge to colleges around the United States. The goal of this XHab project was to design, enhance and implement a monitoring and control network for a plant production system. This project emphasized on hands-on design, research, development, and manufacture of a functional prototype subsystem. This prototype is to be integrated onto an existing NASA built habitat prototype.

Peter Ling↗

NASA NTRS2018

Application of Aura OMI L2G Products Compared with NASA MERRA-2 Assimilation

The Ozone Monitoring Instrument (OMI) is one of the instruments aboard NASA's Aura satellite. It measures ozone total column and vertical profile, aerosols, clouds, and trace gases including NO2, SO2, HCHO, BrO, and OClO using absorption in the ultraviolet electromagnetic spectrum (280 - 400 nm). OMI Level-2G (L2G) products are based on the pixel-level OMI granule satellite measurements stored within global 0.25 deg. X 0.25 deg. grids, therefore they conserve all the Level 2 (L2) spatial and temporal details for 24 hours of scientific data in one file. The second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) is NASA's atmospheric reanalysis, using an upgraded version of Goddard Earth Observing System Model, version 5 (GEOS-5) data assimilation system. MERRA-2 includes aerosol data reanalysis and improved representations of stratospheric ozone, compared with its predecessor MERRA, in both instantaneous and time-averaged collections. It is found that simply comparing satellite Level-3 products might cause biases, due to lack of detailed temporal and original retrieval information. It is therefore preferable to inter-compare or implement satellite derived physical quantities directly with/to model assimilation with as high temporal and spatial resolutions as possible. This study will demonstrate utilization of OMI L2G daily aerosol and ozone products by comparing them with MERRA-2 hourly aerosol/ozone simulations, matched in both space and time aspects. Both OMI and MERRA-2 products are accessible online through NASA Goddard Earth Sciences Data Information Services Center (GES DISC, https://disc.gsfc.nasa.gov/).

OMI L2G products↗

NASA NTRS2007

Method for Texturing Surfaces of Optical Fiber Sensors Used for Blood Glucose Monitoring

Disclosed is a method and the resulting product thereof comprising a solid light-conducting fiber with a point of attachment and having a textured surface site consisting a textured distal end prepared by being placed in a vacuum and then subjected to directed hyperthermal beams comprising oxygen ions or atoms. The textured distal end comprises cones or pillars that are spaced upon from each other by less than 1 micron and are extremely suitable to prevent cellular components of blood from entering the valleys between the cones or pillars so as to effectively separate the cellular components in the blood from interfering with optical sensing of the glucose concentration for diabetic patients.

Banks, Bruce A.↗

NASA NTRS2007

Energetic atomic and ionic oxygen textured optical surfaces for blood glucose monitoring

Banks, Bruce A.↗

NASA NTRS2007

Energetic Atomic and Ionic Oxygen Textured Optical Surfaces for Blood Glucose Monitoring

Disclosed is a method and the resulting product thereof comprising a solid light-conducting fiber with a point of attachment and having a textured surface site consisting of a textured distal end prepared by being placed in a vacuum and then subjected to directed hyperthermal beams comprising oxygen ions or atoms. The textured distal end comprises cones or pillars that are spaced upon from each other by less than 1 micron and are extremely suitable to prevent cellular components of blood from entering the valleys between the cones or pillars so as to effectively separate the cellular components in the blood from interfering with optical sensing of the glucose concentration for diabetic patients.

Banks, Bruce A.↗

NASA NTRS2010

Seagrass Health Modeling and Prediction with NASA Science Data

Previous research has demonstrated that MODIS data products can be used as inputs into the seagrass productivity model developed by Fong and Harwell (1994). To further explore this use to predict seagrass productivity, Moderate Resolution Imaging Spectroradiometer (MODIS) custom data products, including Sea Surface Temperature, Light Attenuation, and Chlorophyll-a have been created for use as model parameter inputs. Coastal researchers can use these MODIS data products and model results in conjunction with historical and daily assessment of seagrass conditions to assess variables that affect the productivity of the seagrass beds. Current monitoring practices involve manual data collection (typically on a quarterly basis) and the data is often insufficient for evaluating the dynamic events that influence seagrass beds. As part of a NASA-funded research grant, the University of Mississippi, is working with researchers at NASA and Radiance Technologies to develop methods to deliver MODIS derived model output for the northern Gulf of Mexico (GOM) to coastal and environmental managers. The result of the project will be a data portal that provides access to MODIS data products and model results from the past 5 years, that includes an automated process to incorporate new data as it becomes available. All model parameters and final output will be available through the use National Oceanic and Atmospheric Administration?s (NOAA) Environmental Research Divisions Data Access Program (ERDDAP) tools as well as viewable using Thematic Realtime Environmental Distributed Data Services (THREDDS) and the Integrated Data Viewer (IDV). These tools provide the ability to create raster-based time sequences of model output and parameters as well as create graphs of model parameters versus time. This tool will provide researchers and coastal managers the ability to analyze the model inputs so that the factors influencing a change in seagrass productivity can be determined over time.

Robinson, Harold D.↗

NASA NTRS2019

Using GES DISC Data to Study Kilauea Volcano of 2018

Kilauea volcano in Hawaii which erupted in early May 2018 injected massive amount of SO2 and ash into the atmosphere. The lava flow during the eruption destroyed many home and neighborhoods. The SO2 plume during the eruption of Kilauea volcano is analyzed from May to August 2018 using multiple satellite products such as Level 2 TROPspheric Monitoring Instrument (TROPOMI) and Level 3 Ozone Monitoring Instrument (OMI) from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). GES DISC hosts multi-disciplinary Earth science data sets that can be used to analyze natural disasters, such as the Kilauea volcano. Additionally, GES DISC's Giovanni tool can be used to visualize these data. We acquired OMI through the subsetting function, which is processed by the GES DISC in-house developed backend software Level3/4 Regrider and Subsetter (L34RS) and TROPOMI using OPeNDAP.Data from the OMI OMSO2e product showed elevated levels of SO2 amounts during the eruption between May to August 2018. Similarly, ground-based stations at Hawaii Volcanoes National Park recorded higher SO2 concentrations during the same time period. This study uses wind direction from Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) to analyze the transport and dispersion of SO2 plume and map lava flows from the volcano using thermal images from Visible Infrared Imaging Radiometer Suite (VIIRS). Furthermore, satellite observations combined with socioeconomic and public health data are used to analyze its impact in public health.

KC, Binita↗

NASA NTRS1990

Computer graphics aid mission operations

The application of computer graphics techniques in NASA space missions is reviewed. Telemetric monitoring of the Space Shuttle and its components is discussed, noting the use of computer graphics for real-time visualization problems in the retrieval and repair of the Solar Maximum Mission. The use of the world map display for determining a spacecraft's location above the earth and the problem of verifying the relative position and orientation of spacecraft to celestial bodies are examined. The Flight Dynamics/STS Three-dimensional Monitoring System and the Trajectroy Computations and Orbital Products System world map display are described, emphasizing Space Shuttle applications. Also, consideration is given to the development of monitoring systems such as the Shuttle Payloads Mission Monitoring System and the Attitude Heads-Up Display and the use of the NASA-Goddard Two-dimensional Graphics Monitoring System during Shuttle missions and to support the Hubble Space Telescope.

Jeletic, James F.↗

NASA NTRS2008

MODIS Land Data Products: Generation, Quality Assurance and Validation

The Moderate Resolution Imaging Spectrometer (MODIS) on-board NASA's Earth Observing System (EOS) Terra and Aqua Satellites are key instruments for providing data on global land, atmosphere, and ocean dynamics. Derived MODIS land, atmosphere and ocean products are central to NASA's mission to monitor and understand the Earth system. NASA has developed and generated on a systematic basis a suite of MODIS products starting with the first Terra MODIS data sensed February 22, 2000 and continuing with the first MODIS-Aqua data sensed July 2, 2002. The MODIS Land products are divided into three product suites: radiation budget products, ecosystem products, and land cover characterization products. The production and distribution of the MODIS Land products are described, from initial software delivery by the MODIS Land Science Team, to operational product generation and quality assurance, delivery to EOS archival and distribution centers, and product accuracy assessment and validation. Progress and lessons learned since the first MODIS data were in early 2000 are described.

Masuoka, Edward↗