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Results for “single-particle detection”

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.

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5 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Single-Particle Measurements of Midlatitude Black Carbon and Light-Scattering Aerosols from the Boundary Layer to the Lower Stratosphere

A single-particle soot photometer (SP2) was flown on a NASA WB-57F high-altitude research aircraft in November 2004 from Houston, Texas. The SP2 uses laser-induced incandescence to detect individual black carbon (BC) particles in an air sample in the mass range of approx.3-300 fg (approx.0.15-0.7 microns volume equivalent diameter). Scattered light is used to size the remaining non-BC aerosols in the range of approx.0.17-0.7 microns diameter. We present profiles of both aerosol types from the boundary layer to the lower stratosphere from two midlatitude flights. Results for total aerosol amounts in the size range detected by the SP2 are in good agreement with typical particle spectrometer measurements in the same region. All ambient incandescing particles were identified as BC because their incandescence properties matched those of laboratory-generated BC aerosol. Approximately 40% of these BC particles showed evidence of internal mixing (e.g., coating). Throughout profiles between 5 and 18.7 km, BC particles were less than a few percent of total aerosol number, and black carbon aerosol (BCA) mass mixing ratio showed a constant gradient with altitude above 5 km. SP2 data was compared to results from the ECHAM4/MADE and LmDzT-INCA global aerosol models. The comparison will help resolve the important systematic differences in model aerosol processes that determine BCA loadings. Further intercomparisons of models and measurements as presented here will improve the accuracy of the radiative forcing contribution from BCA.

Schwartz, J. P.↗
NASA NTRS1979

Analysis of suspended solids by single-particle scattering

Light scattering by individual particulates is used in a multiple-detector system to categorize the composition of suspended solids in terms of broad particulate categories. The scattering signatures of red clay and taconite tailings, the two primary particulate contaminants in western Lake Superior, along with two types of asbestiform fibers, amphibole and chrysolite, were studied in detail. A method was developed to predict the concentration of asbestiform fibers in filtration plant samples for which electron microscope analysis was done concurrently. Fiber levels as low as 50,000 fibers/liter were optically detectable. The method has application in optical categorization of samples for remote sensing purposes and offers a fast, inexpensive means for analyzing water samples from filtration plants for specific particulate contaminants.

Diehl, S. R.↗
NASA NTRS1986

Argon ions injected parallel and perpendicular to the magnetic field

Preliminary results are reported of measurements obtained with the 0-10-kHz electric-field plasma-wave receivers and single-particle ion detectors of the ARCS-3 sounding-rocket payload over the auroral oval on February 10, 1985, after downward/field-parallel or transverse-spiral injection of Ar ions from the accelerator subpayload (separating itself at 2.3 m/s). The data are presented graphically and briefly characterized. The waves induced by the parallel injections are found to be much weaker than those induced by the perpendicular injections. In addition to the expected response to the Ar beam, an ion flux at pitch angle 90 deg was detected during the second parallel injection.

Erlandson, R. E.↗
NASA NTRSDate not supplied

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↗
NASA NTRSDate not supplied

Enhanced Monitoring and Forecasting of South Asian Air Quality Episodes with Multi-Sensor Satellite Products and Dispersion Modeling

Air pollution and particulates represent a serious environmental, public health, and overarching societal concern in the Hindu Kush Himalaya (HKH) region of south-central Asia, especially during the boreal winter and spring months. Frequent contributors to poor air quality of the region include dust and particulate matter transport from the Middle East region and western India, persistent nocturnal fog and smog during the stable dry monsoon months, and biomass burning during the pre-monsoon months of boreal spring. Nocturnal and multi-day persistent stable fog and smog events can occur during the stable conditions of the dry monsoon months that lead to hazardous visibility reductions (e.g., at major airports such as Delhi, India)and poor air quality, particularly in urban corridors.Our project goal is therefore to design and implement a robust air quality and chemistry observation and modeling system using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) and the Weather Research and Forecasting coupled with Chemistry (WRF-Chem) models. This system effectively assimilates aerosol and trace gas retrievals from geostationary and polar-orbiting satellites to advance the monitoring and prediction capabilities of air quality and visibility reductions throughout the HKH region. For this presentation, we highlight several[multi-spectral] geostationary satellite products and dispersion modeling capabilities being developed and implemented for improved situational awareness and forecasting of air quality episodes.Launched in December 2018, the Advanced Meteorological Imager (AMI) aboard the Geostationary-Korean Multi-Purpose Satellite-2A (GEO-KOMPSAT-2A, or GK2A) provides 16 channels of multi-spectral information similar to the U.S. GOES-16/17 satellites. Proven community recipes for generating multi-spectral Red-Green-Blue (RGB) composite products to detect specific meteorological phenomena are applied to the AMI data for the HKH region. We implemented four such RGB products consisting of truecolor with Rayleigh correction to aid in smoke detection; dust RGB for monitoring larger particulate matter; nighttime microphysics for fog/smog detection;and natural color fire RGB and the shortwave 3.8 micron channel for fire hot-spot detection.We additionally configured HYSPLIT for dust/sand dispersion and concentration forecasts over HKH. The HYSPLIT dispersion simulations are initially being conducted with two different methodologies: (1) dust/sand lofting based on initializing plume releases from visual inspection of the dust RGB imagery; and (2) dust/sand release and dispersion using the internal HYSPLIT algorithm that determines lofting from input wind speeds and prescribed land use. The goal is to develop a near real-time HYSPLIT modeling solution for routinely simulating the transport and dispersion of dust plumes across the HKH region. This presentation illustrates these products and capabilities for various use cases from the 2019 to 2021 period, highlighting a particularly unhealthy episode during late March 2021 across Nepal.

Remote Sensing↗