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At least 73 records · Page 4

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↗

Acoustic emission used as weld quality monitor

Acoustic emission technique is described for use as quality control tool in nondestructive inspection of welds. Stress mounts around weld defect until it exceeds yield strength of material. Pressure wave relieving the stress is emitted and followed by oscillations caused by multiple reflections. Acoustic emissions are then detected by sensor similar to ultrasonic sensor.

Jolly, W. O.↗

Hyper-Spectral Infrared Remote Sensing for Wildfire-Related Research and Air Quality Monitoring

The National Airborne Sounder Testbed-Interferometer (NAST-I) is an airborne FTS remote sensor that nominally flies on NASA high-altitude aircraft to serve as a spaceborne instrument simulator. NAST-I continues to serve as a pathfinder for future satellite FTS systems and the next generation advanced atmospheric sounders in general. NAST-I provides high resolution spectrally resolved infrared radiances. NAST-I retrieval products characterizing the surface (i.e., skin temperature and spectral emissivity), atmosphere (i.e., profiles of temperature, moisture, ozone, carbon monoxide, and other trace species), and clouds (e.g., optical depth, particle size, temperature, and height) can be used to support fire-related monitoring and research. NAST-I provided 3-d characterizations of wildfire-induced plumes of CO during the FIREX-AQ field campaign (conducted during 2019) which showed the intensity and size evolution of wildfire plumes at high spatial and temporal resolutions. Wildfire-induced CO plumes, in conjunction with their evolution, transport, and age have been identified and recently published in scientific journals. Other research applications, such as surface emissivity changes due to fire burning of ground landscape, have been under investigation. These products could be used in support of wildfire management to better inform decision making and operations for pre-, active, and post-fire environments.

remote sensing↗

Space Station Environmental Health System water quality monitoring

One of the unique aspects of the Space Station is that it will be a totally encapsulated environment and the air and water supplies will be reclaimed for reuse. The Environmental Health System, a subsystem of CHeCS (Crew Health Care System), must monitor the air and water on board the Space Station Freedom to verify that the quality is adequate for crew safety. Specifically, the Water Quality Subsystem will analyze the potable and hygiene water supplies regularly for organic, inorganic, particulate, and microbial contamination. The equipment selected to perform these analyses will be commercially available instruments which will be converted for use on board the Space Station Freedom. Therefore, the commercial hardware will be analyzed to identify the gravity dependent functions and modified to eliminate them. The selection, analysis, and conversion of the off-the-shelf equipment for monitoring the Space Station reclaimed water creates a challenging project for the Water Quality engineers and scientists.

Vincze, Johanna E.↗

Water Quality Monitor

In the photo above, the cylindrical container being lowered into the water is a water quality probe developed by NASA's Langley Research Center for the Environmental Protection Agency (EPA) in an applications engineering project. It is part of a system- which also includes recording equipment in the helicopter-for on-the-spot analysis of water samples. It gives EPA immediate and more accurate information than the earlier method, in which samples are transported to a lab for analysis. Designed primarily for rapid assessment of hazardous spills in coastal and inland waters, the system provides a wide range of biological and chemical information relative to water pollution.

Source record↗

Chesapeake Bay Water Resources: Characterization of Sediment Dynamics for Enhanced Water Quality Monitoring in the Chesapeake Bay

An increase in total suspended sediment (TSS) concentrations and turbidity have contributed to poor water quality in the Chesapeake Bay since the 1970s. Although turbidity and TSS have been moderately improving over the past few decades, poor water quality is detrimental to the Chesapeake Bay’s ecosystems and the surrounding watersheds. The Summer 2022 Chesapeake Bay Water Resources project observed sediment dynamics and turbidity in the York River watershed using remote sensing tools, models, and Earth observations including NASA and United States Geological Survey’s (USGS) Landsat satellite series, NASA DEVELOP’s Optical Reef Coastal Area Assessment Tool 2.0 (ORCAA), and Soil and Water Assessment Tool (SWAT). The collaborators on this project were the Chesapeake Bay National Estuarine Research Reserve (CBNERR), Group on Earth Observations (GEO) AquaWatch, the Committee on Earth Observation Satellites Coastal Observations, Applications, Services and Tools (CEOS COAST), and the Virginia Department of Environmental Quality (VA DEQ). The team concluded that the upstream sections of the York River watershed increased in TSS from 2009–2019. However, the seasonal TSS patterns often correlated with higher precipitation levels, although not necessarily major storm events. This may be due to factors like wind and waves contributing to the sedimentation trends observed. Although projects have been conducted to improve water quality, additional efforts like planting riparian buffers along areas with high TSS are needed to reduce the intensity of runoff. The team’s results will allow the end user, the VA DEQ, to inform their policymaking regarding future Bay conservation efforts.

Katherine Hahn↗

Toward the Next Generation of Air Quality Monitoring Indicators

This paper introduces an initiative to bridge the state of scientific knowledge on air pollution with the needs of policymakers and stakeholders to design the "next generation" of air quality indicators. As a first step this initiative assesses current monitoring and modeling associated with a number of important pollutants with an eye toward identifying knowledge gaps and scientific needs that are a barrier to reducing air pollution impacts on human and ecosystem health across the globe. Four outdoor air pollutants were considered e particulate matter, ozone, mercury, and Persistent Organic Pollutants (POPs) e because of their clear adverse impacts on human and ecosystem health and because of the availability of baseline data for assessment for each. While other papers appearing in this issue will address each pollutant separately, this paper serves as a summary of the initiative and presents recommendations for needed investments to provide improved measurement, monitoring, and modeling data for policyrelevant indicators. The ultimate goal of this effort is to enable enhanced public policy responses to air pollution by linking improved data and measurement methods to decision-making through the development of indicators that can allow policymakers to better understand the impacts of air pollution and, along with source attribution based on modeling and measurements, facilitate improved policies to solve it. The development of indicators represents a crucial next step in this process.

Particulate matter↗

Multidate Landsat lake quality monitoring program

A unified package of files and programs has been developed to automate the multidate Landsat-derived analyses of water quality for about 3000 inland lakes throughout Wisconsin. A master lakes file which stores geographic information on the lakes, a file giving the latitudes and longitudes of control points for scene navigation, and a program to estimate control point locations and produce microfiche character maps for scene navigation are among the files and programs of the system. The use of ground coordinate systems to isolate irregular shaped areas which can be accessed at will appears to provide an economical means of restricting the size of the data set.

Fisher, L. T.↗

Landsat change detection can aid in water quality monitoring

Comparison between Landsat-1 and -2 imagery of Arkansas provided evidence of significant land use changes during the 1972-75 time period. Analysis of Arkansas historical water quality information has shown conclusively that whereas point source pollution generally can be detected by use of water quality data collected by state and federal agencies, sampling methodologies for nonpoint source contamination attributable to surface runoff are totally inadequate. The expensive undertaking of monitoring all nonpoint sources for numerous watersheds can be lessened by implementing Landsat change detection analyses.

Macdonald, H. C.↗

A quality monitor and monitoring technique employing optically stimulated electron emission

A light source directs ultraviolet light onto a test surface and a detector detects a current of photoelectrons generated by the light. The detector includes a collector which is positively biased with respect to the test surface. Quality is indicated based on the photoelectron current. The collector is then negatively biased to replace charges removed by the measurement of a nonconducting substrate to permit subsequent measurements. Also, the intensity of the ultraviolet light at a particular wavelength is monitored and the voltage of the light source varied to maintain the light a constant desired intensity. The light source is also cooled via a gas circulation system. If the test surface is an insulator, the surface is bombarded with ultraviolet light in the presence of an electron field to remove the majority of negative charges from the surface. The test surface is then exposed to an ion field until it possesses no net charge. The technique described above is then performed to assess quality.

Yost, William T.↗

Expanding SPoRT RGBs and Machine Learning Techniques to Enhance Air Quality Monitoring in Southern Asia

Air pollution poses significant environmental, public health, and societal concerns in the Hindu Kush Himalaya (HKH) region of south-central Asia, notably during the dry monsoon months (~November to May). Key contributors to poor air quality include dust from the Middle East and western India, persistent nocturnal fog/smog, and biomass burning. To address this issue, we established a robust air quality and chemistry observation and modeling product suite utilizing multi-spectral red-green-blue (RGB) composite satellite products from Korea’s GEO-KOMPSAT-2A satellite, the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for dust transport forecasts, and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) model to predict aerosols and chemical species concentrations. Our team employed similar RGB recipes transitioned by the NASA Short-term Prediction Research and Transition (SPoRT) Center for the GOES-R era products over the Western Hemisphere, with significant success in depicting dust and nocturnal fog / low clouds, and to a lesser extent smoke and fire hot spots. We will extend these capabilities for the HKH region by applying an artificial intelligence (AI) model that objectively identifies dust from multi-spectral satellite data over the Southwestern United States. The AI model will be calibrated for automated dust detection over HKH from GEO-KOMPSAT-2A satellite data, with a goal of developing a similar AI model for objectively identifying smoke as well. The ultimate goal of this effort is to enhance dust and smoke predictions in the region by establishing improved emission initializations in HYSPLIT and/or WRF-Chem through the automated AI detection of dust and smoke.

Jonathan L. Case↗

Multidimensional Data Aggregation in the Cloud with Application to Geostationary Satellite-based Air Quality Monitoring

Scientists use satellite data for studying Earth's systems, and the remote sensing data that these satellites collect are typically separated into files of a size small enough for efficient network transfer and storage. However, researchers usually prefer to analyze the data based on real-world dimensions like time, space, or elevation. To help with this, NASA's Atmospheric Science Data Center (ASDC) developed a new cloud-based tool that combines these smaller data chunks into larger, more useful datasets. The tool works on Network Common Data Form (netCDF4) and some HDF5 formatted files, and it is available as a service in NASA's Earthdata Cloud. In this presentation, we showcase this service using data from the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument. By combining TEMPO's continuous observations over time, we create longer and more informative analysis-ready time series to facilitate the study of air quality patterns. Insights gained will provide a more comprehensive understanding of pollution sources, transport patterns, and their effects on the environment and human health.

Daniel Kaufman↗

Real Time Nitrogen Monitoring and Quality Control System for NASA Astromaterials Collection

The Nitrogen system is a vital utility supporting the curation office within NASA's Astromaterials Re-search and Exploration Science (ARES) Division at the Johnson Space center (JSC). Since the Apollo samples were returned in 1969. It has played an essential role in preserving NASA’s astromaterial collections (now nine collections) by maintaining contamination-free, controlled environments required for long term curation and advanced scientific research. To meet the stringent purity requirements for contamination control, high purity modified Grade C Liquid Nitrogen (LN2) is delivered weekly by an industrial gas vendor to a 15,000-gallon tank located outside at NASA JSC near the ARES facility. This nitrogen is vital for creating an inert atmosphere to store the collection in, ensuring they are continuously shielded from contaminants and alterations through a constant N2purge. For most of the past 65 years deliveries of LN2 have required a CoA (Certificate of Analysis) prior to delivery. If those were not available, then LN2 deliver were sent to third-party laboratories for verification, creating an operational bottleneck and quality gap. To address this challenge and maintain rigorous quality control, NASA JSC Infrastructure and Astromaterials Acquisition & Curation Office initiated the development of an on-site Analytical Gas Sampling (AGS) Laboratory. Designed to streamline quality verification if a CoA unavailable, the AGS lab was constructed by a subcontractor in Newton, NJ. After a successful factory acceptance test conducted by the NASA ARES Infrastructure team, the lab was trans-ported to NASA JSC, where it was anchored and integrated into the site’s nitrogen pressure system using stainless steel, oxygen-cleaned tubing. The ARES nitrogen system was modified to provide parallel flows to the AGS lab for quality control and to the astromaterials collection labs for maintaining an inert environment. The AGS lab features two operational modes: (1)Primary: Gaseous Nitrogen (GN2) Quality Monitoring –The AGS system continuously samples the site’s GN2 supply every 15 minutes to ensure compliance with required purity levels. (2) Secondary: LN2 Quality Verification when a comprehensive CoA is not available, a cryogenic vaporizer converts LN2 to GN2, which is then analyzed for impurities: H₂, Ar, O₂, CO, CO₂, H₂O, and total hydrocarbons (THC). By significantly enhancing on-site analytical capabilities, the AGS lab eliminates reliance on external testing, ensures uninterrupted quality monitoring of the nitrogen pressure system, and reinforces NASA’s commitment to preserving pristine astromaterials under the highest standards of curation and research integrity.

glove box↗

Shoshone River Water Resources II​: Quantifying Sediment Input in the Shoshone River in Wyoming using the Soil and Water Assessment Tool for Enhanced Water Quality Monitoring

The Willwood Dam, an irrigation diversion dam located on the Shoshone River (Wyoming, USA), has faced ongoing issues with sediment accumulation and needs frequent sediment flushing to remain operable. However, high suspended sediment levels during flushing events have negatively impacted downstream aquatic ecology and recreational opportunities. To address these problems, DEVELOP partnered with the Wyoming Department of Environmental Quality, Shoshone River Partners, and United States Geologic Survey. During term one, the team developed a workflow to map turbidity using PlanetScope imagery, analyze time series precipitation data, and create landcover maps. For this term, we focused on three methods to gain a better understanding of sediment sources: 1) improving remote sensing of turbidity, 2) modeling sediment transport within the watershed using the Soil and Water Assessment Tool (SWAT), and 3) conducting a snow cover time series analysis using Suomi NPP VIIRS imagery. Through remote sensing, we found Dry Creek/Homesteader Creek and Penney Gulch/Rough Gulch had the highest concentration sediment plumes. The SWAT+ model created a high-resolution grid model of the watershed, identifying high sedimentation in the western and southern subbasins but displayed low-correlated calibration and validation results due to limited observed data. We analyzed the snow cover extent alongside other hydrologic variables and found that snow melt events correlated with increases in suspended sediment concentration and turbidity values. Coupling remote sensing with hydrological modeling will give watershed managers a new perspective on high-priority regions for implementation of remediation to reduce the sediment build-up at the Willwood Dam.

Suspended Sediment↗

Monitoring Air Quality from Space using AURA Data

Measurements from the Earth Observing System (EOS) AURA mission will provide a unique perspective on air quality monitoring. Ozone, nitrogen dioxide, formaldehyde and aerosols from the Ozone Monitoring Instrument (OMI) and carbon monoxide from the Tropospheric Emission Spectrometer (TES) will be simultaneously measured with the spatial resolution and coverage needed for improving our understanding of air quality. AURA data products useful for air quality monitoring will be given.

Gleason, James F.↗