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All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

Moisture convergence from a combined mesoscale moisture analysis and wind field for 24 April 1975

Precipitable water values inferred from the Vertical Temperature Profile Radiometer data of the polar orbiting NOAA-4 satellite are used in conjunction with wind-field analyses obtained from Synchronous Meteorological Satellite visible-channel data to study the moisture convergence in the boundary layer immediately preceding a storm. This combination of data simulates the information that will be available from the Visible and Infrared Spin-Scan Radiometer on board the GOES-D satellite, which is scheduled to begin operation in the 1980s. Serviceable representations of boundary layer flow are developed through analysis of the satellite infrared cumulus velocities, although the flow representations are not exactly located in the vertical.

Negri, A. J.↗
NASA NTRS2000

Laboratory Studies of Optical Characteristics and Condensation Processes of Cosmic Dust Particles

Information about the optical characteristics and physical processes involving cosmic dust particles is vital for interpretation of astronomical observations and an understanding of the formation and processing of dust in the evolutionary cycle of matter in the interstellar medium. Cosmic dust particles are formed in a variety of astrophysical environments such as in cool stellar outflows and circumstellar envelopes. Definitive knowledge of the nature, composition, and physical processes of cosmic dust grains, however, can only be inferred from astronomical observations through laboratory experiments on the analogs of hypothesized dust particles and with modeling calculations. Laboratory investigations of the nature, composition, and optical characteristics of cosmic dust particles are being, carried out at many institutions with a variety of experimental techniques. Despite a wealth of available data, however, many basic issues remain unresolved. An experimental facility based on suspension of dust particles in electrodynamic balance in a pressure/temperature controlled environment in a cavity has been operational at the NASA Marshall Space Flight Center, and is currently being employed for studies of dust particle charging mechanisms using electron beams and with UV radiation. In this paper, we discuss two general classes of experiments under planning stages that may be simultaneously carried out on this facility for cosmic dust investigations (i) Infrared optical characteristics (extinction coefficients and scattering phase functions) of the analogs of hypothesized of cosmic dust particles, such as natural and synthetic amorphous silicates with varying compositions, amorphous carbon grains, polycyclic aromatic hydrocarbons (PAHs), and icy core-mantle particles etc. The initial spectral range under consideration is 1-25 micrometers, to be extended to the far infrared region in the future (ii) Condensation of volatile gases on nucleus dust particles to be investigated for planetary and astrophysical environments.

Spann, J. F., Jr.↗
DOE OSTI2025

Real-time neutron multiplicity and source localization for criticality safety during fuel debris removal

Advancing neutron detection and analysis techniques for complex radiation environments is an ongoing focus in nuclear instrumentation and monitoring. This proposal presents research and development of a generalized real-time neutron monitoring and analysis system, applicable to any detector capable of producing time-tagged neutron count data. While the work is demonstrated using the Neutron Multiplication Analysis Detector (NoMAD), a modular 15-tube helium-3 (He-3) array, due to its availability, spatial resolution, and flexible deployment, the methods developed are extensible to other systems, including organic scintillators and fast digital detectors. This research investigates two complementary analytical techniques for real-time characterization of neutron emitting sources: neutron multiplicity estimation based on the Hage-Cifarelli formalism and spatial localization using supervised machine learning applied to spatial count rate patterns. These methods are designed to operate under dynamic, evolving conditions such as fuel debris retrieval or reactor startup, where neutron-emitting material geometries may be partially unknown or changing over time. By integrating statistical neutron emission data with spatial localization, this research aims to develop and evaluate methods for real time neutron monitoring, source characterization, and material verification. Key contributions include implementation of a low-latency data pipeline for continuous neutron multiplicity analysis, development and validation of machine learning models for spatial inference, and experimental evaluation of system performance under variable measurement conditions. The outcomes are intended to support applications in nuclear safeguards, verification, emergency response, and reactor startup.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS2016

A Synthesis of the Basal Thermal State of the Greenland Ice Sheet

Greenland's thick ice sheet insulates the bedrock below from the cold temperatures at the surface, so the bottom of the ice is often tens of degrees warmer than at the top, because the ice bottom is slowly warmed by heat coming from the Earth's depths. Knowing whether Greenland's ice lies on wet, slippery ground or is anchored to dry, frozen bedrock is essential for predicting how this ice will flow in the future. But scientists have very few direct observations of the thermal conditions beneath the ice sheet, obtained through fewer than two dozen boreholes that have reached the bottom. Our study synthesizes several independent methods to infer the Greenland Ice Sheet's basal thermal state -whether the bottom of the ice is melted or not-leading to the first map that identifies frozen and thawed areas across the whole ice sheet. This map will guide targets for future investigations of the Greenland Ice Sheet toward the most vulnerable and poorly understood regions, ultimately improving our understanding of its dynamics and contribution to future sea-level rise. It is of particular relevance to ongoing Operation IceBridge activities and future large-scale airborne missions over Greenland.

IceBridge↗
NASA NTRS2007

Automated Target Planning for FUSE Using the SOVA Algorithm

The SOVA algorithm was originally developed under the Resilient Systems and Operations Project of the Engineering for Complex Systems Program from NASA s Aerospace Technology Enterprise as a conceptual framework to support real-time autonomous system mission and contingency management. The algorithm and its software implementation were formulated for generic application to autonomous flight vehicle systems, and its efficacy was demonstrated by simulation within the problem domain of Unmanned Aerial Vehicle autonomous flight management. The approach itself is based upon the precept that autonomous decision making for a very complex system can be made tractable by distillation of the system state to a manageable set of strategic objectives (e.g. maintain power margin, maintain mission timeline, and et cetera), which if attended to, will result in a favorable outcome. From any given starting point, the attainability of the end-states resulting from a set of candidate decisions is assessed by propagating a system model forward in time while qualitatively mapping simulated states into margins on strategic objectives using fuzzy inference systems. The expected return value of each candidate decision is evaluated as the product of the assigned value of the end-state with the assessed attainability of the end-state. The candidate decision yielding the highest expected return value is selected for implementation; thus, the approach provides a software framework for intelligent autonomous risk management. The name adopted for the technique incorporates its essential elements: Strategic Objective Valuation and Attainability (SOVA). Maximum value of the approach is realized for systems where human intervention is unavailable in the timeframe within which critical control decisions must be made. The Far Ultraviolet Spectroscopic Explorer (FUSE) satellite, launched in 1999, has been collecting science data for eight years.[1] At its beginning of life, FUSE had six gyros in two IRUs and four reaction wheels. Over time through various failures, the satellite has been left with one reaction wheel on the vehicle skew axis and two gyros. To remain operational, a control scheme has been implemented using the magnetic torque rods and the remaining momentum wheel.[2] As a consequence, there are attitude regions where there is insufficient torque authority to overcome environmental disturbances (e.g. gravity gradient torques). The situation is further complicated by the fact that these attitude regions shift inertially with time as the spacecraft moves through earth s magnetic field during the course of its orbit. Under these conditions, the burden of planning targets and target-to-target slew maneuvers has increased significantly since the beginning of the mission.[3] Individual targets must be selected so that the magnetic field remains roughly aligned with the skew wheel axis to provide enough control authority to the other two orthogonal axes. If the field moves too far away from the skew axis, the lack of control authority allows environmental torques to pull the satellite away from the target and can potentially cause it to tumble. Slew maneuver planning must factor the stability of targets at the beginning and end, and the torque authority at all points along the slew. Due to the time varying magnetic field geometry relative to any two inertial targets, small modifications in slew maneuver timing can make large differences in the achievability of a maneuver.

Heatwole, Scott↗
NASA NTRS1969

Testing the Fraunhofer line discriminator by sensing fluorescent dye

The experimental Fraunhofer Line Discriminator (FLD) has detected increments of Rhodamine WT dye as small as 1 ppb in 1/2 meter depths. It can be inferred that increments considerably smaller than 1 ppb will be detectable in depths considerably greater than 1/2 meter. Turbidity of the water drastically reduces luminescence or even completely blocks the transmission of detectable luminescence to the FLD. Attenuation of light within the water by turbidity and by the dye itself are the major factors to be considered in interpreting FLD records and in relating luminescence coefficient to dye concentration. An airborne test in an H-19 helicopter established feasibility of operating the FLD from the aircraft power supply, and established that the rotor blades do not visibly affect the monitoring of incident solar radiation.

Stoertz, G. E.↗
NASA NTRS2018

Remote Sensing of Antarctic Sea Ice with Coordinated Aircraft and Satellite Data Acquisitions

Remote sensing of Antarctic sea ice is required to characterize properties of the vast sea ice cover to understand its long-term increase in contrast to the decrease of Arctic sea ice. For this objective, the OIB/TanDEM-X Coordinated Science Campaign (OTASC) was successfully conducted in 2017 to obtain contemporaneous and collocated remote sensing data from NASA's Operation IceBridge (OIB) and the German Aerospace Center (DLR) TanDEM-X Synthetic Aperture Radar (SAR) system at X-band together with Sentinel-1 and RADARSAT-2 SARs at C-band in conjunction with WorldView satellite spectral sensors, surface measurements, and field observations. The Weddell Sea and the Ross Sea were two primary regions while SAR data were also collected over six other regions in the Southern Ocean. Satellite SAR data included both polarimetric and interferometric capabilities to infer snow and sea ice information in three dimensions (3D), while OIB/P-3 aircraft data include snow radar together with altimeter data for snow and sea ice observations in 3D over the Weddell Sea. Across the Ross Sea, IcePOD and AntNZ/York-University flights were carried out together with satellite SAR data acquisitions.

OIB↗
DOE OSTI2023

Compute in‐Memory with Non‐Volatile Elements for Neural Networks: A Review from a Co‐Design Perspective

Abstract Deep learning has become ubiquitous, touching daily lives across the globe. Today, traditional computer architectures are stressed to their limits in efficiently executing the growing complexity of data and models. Compute‐in‐memory (CIM) can potentially play an important role in developing efficient hardware solutions that reduce data movement from compute‐unit to memory, known as the von Neumann bottleneck. At its heart is a cross‐bar architecture with nodal non‐volatile‐memory elements that performs an analog multiply‐and‐accumulate operation, enabling the matrix‐vector‐multiplications repeatedly used in all neural network workloads. The memory materials can significantly influence final system‐level characteristics and chip performance, including speed, power, and classification accuracy. With an over‐arching co‐design viewpoint, this review assesses the use of cross‐bar based CIM for neural networks, connecting the material properties and the associated design constraints and demands to application, architecture, and performance. Both digital and analog memory are considered, assessing the status for training and inference, and providing metrics for the collective set of properties non‐volatile memory materials will need to demonstrate for a successful CIM technology.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Cinematic reflectometry using QIKR, the quite intense kinetics reflectometer

The Quite Intense Kinetics Reflectometer (QIKR) will be a general-purpose, horizontal-sample-surface neutron reflectometer. Reflectometers measure the proportion of an incident probe beam reflected from a surface as a function of wavevector (momentum) transfer to infer the distribution and composition of matter near an interface. The unique scattering properties of neutrons make this technique especially useful in the study of soft matter, biomaterials, and materials used in energy storage. Exploiting the increased brilliance of the Spallation Neutron Source Second Target Station, QIKR will collect specular and off-specular reflectivity data faster than the best existing such machines. It will often be possible to collect complete specular reflectivity curves using a single instrument setting, enabling “cinematic” operation, wherein the user turns on the instrument and “films” the sample. Samples in time-dependent environments (e.g., temperature, electrochemical, or undergoing chemical alteration) will be observed in real time, in favorable cases with frame rates as fast as 1 Hz. Cinematic data acquisition promises to make time-dependent measurements routine, with time resolution specified during post-experiment data analysis. This capability will be deployed to observe such processes as in situ polymer diffusion, battery electrode charge–discharge cycles, hysteresis loops, and membrane protein insertion into lipid layers.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Using onset times from frequent seismic surveys to understand fluid flow at the Peace River Field, Canada

With our limited knowledge of the relationship between changes in the state of an aquifer or reservoir and the corresponding changes in the elastic moduli, that is the rock physics model, hampers the effective use of time-lapse seismic observations for estimating flow properties within the Earth. A central problem is the complicated dependence of the magnitude of time-lapse changes on the saturation, pressure, and temperature changes within an aquifer or reservoir. We describe an inversion methodology for reservoir characterization that uses onset times, the calendar time of the change in seismic attributes, rather than the magnitude of the changes. We find that onset times are much less sensitive than magnitudes to the rock physics model used to relate time-lapse observations to changes in saturation, temperature and fluid pressure. We apply the inversion scheme to observations from daily monitoring of enhanced oil recovery at the Peace River field in Canada. An array of 1492 buried hydrophones record seismic signals from 49 buried sources. Time-shifts for elastic waves traversing the reservoir are extracted from the daily time-lapse cubes. In our analysis 175 images of time-shifts are transformed into a single map of onset times, leading to a substantial reduction in the volume of data. These observations are used in conjunction with bottom hole pressure data to infer the initial conditions prior to the injection, and to update the reservoir permeability model. The combination of a global and local inversion scheme produces a collection of reservoir models that are best described by three clusters. The updated model leads to a nearly 70 percent reduction in seismic data misfit. The final set of solutions successfully predict the observed normalized pressure history during the soak and flow-back into the wells between 82 and 175 days into the cyclic steaming operation.

58 GEOSCIENCES↗
NASA NTRS1976

Contributions of experimental protobiogenesis to the theory of evolution

Inferences from experiments in protobiogenesis are examined as a forward extension of the theory of evolutionary biology. A nondiscontinuous, intraconsistent theory of general evolution embracing both protobiology and biology is outlined. This overview emphasizes Darwinian selection in the later stages of evolution, and stereochemical molecular selection in some of its earlier stages. It incorporates the concept of limitation of the scope of evolution by internal constraints on variation, based on the argument that internally limiting constraints observed in experiments with molecules are operative in organisms, if chemical processes occur within biological processes and biological processes are assumed to be exponentializations of chemical processes. Major evolutionary events might have occurred by rapid self-assembly processes analogous to those observed in the formation of phase-separated microspheres from amorphous powder or supersaturated solutions.

Fox, S. W.↗
NASA NTRS1982

Design and calibration of a coherent lidar for measurement of atmospheric backscatter

This paper discusses the background requirements for, and the design and calibration of a coherent Doppler lidar which is oriented specifically toward the measurement of low values of atmospheric backscatter at a wavelength of 10.6 microns. The lidar is a compact, continuous wave system which can operate in two modes, multiple particle scattering and single particle scattering. In the multiple scattering mode, the lidar employs an extended focal volume and utilizes a technique similar to Dicke switching to achieve measurement of the volume backscatter. In the single scattering mode, the focal volume is reduced to ensure a high probability of single particle scattering. Measurement of the single particle backscatter as a function of time leads to the formation of signal histogram from which the volume backscatter can be inferred. In addition to providing the atmospheric backscatter value, appropriate data processing algorithms and focal volume calibration allow the single particle mode to yield information on the atmospheric aerosol scattering cross-section distribution.

Jones, W. D.↗
NASA NTRS1997

Structure and Rotation of the Solar Interior: Initial Results from the MDI Medium-L Program

The medium-l program of the Michelson Doppler Imager instrument on board SOHO provides continuous observations of oscillation modes of angular degree, l, from 0 to approximately 300. The data for the program are partly processed on board because only about 3% of MDI observations can be transmitted continuously to the ground. The on-board data processing, the main component of which is Gaussian-weighted binning, has been optimized to reduce the negative influence of spatial aliasing of the high-degree oscillation modes. The data processing is completed in a data analysis pipeline at the SOI Stanford Support Center to determine the mean multiplet frequencies and splitting coefficients. The initial results show that the noise in the medium-l oscillation power spectrum is substantially lower than in ground-based measurements. This enables us to detect lower amplitude modes and, thus, to extend the range of measured mode frequencies. This is important for inferring the Sun's internal structure and rotation. The MDI observations also reveal the asymmetry of oscillation spectral lines. The line asymmetries agree with the theory of mode excitation by acoustic sources localized in the upper convective boundary layer. The sound-speed profile inferred from the mean frequencies gives evidence for a sharp variation at the edge of the energy-generating core. The results also confirm the previous finding by the GONG (Gough et al., 1996) that, in a thin layer just beneath the convection zone, helium appears to be less abundant than predicted by theory. Inverting the multiplet frequency splittings from MDI, we detect significant rotational shear in this thin layer. This layer is likely to be the place where the solar dynamo operates. In order to understand how the Sun works, it is extremely important to observe the evolution of this transition layer throughout the 11-year activity cycle.

Kosovichev, A. G.↗
NASA NTRS2018

Remote Sensing, Uncertainty Quantification, and a Theory of Data Systems; Workshop Report

The purpose of the workshop was to invite statisticians, applied mathematicians, computer scientists, data system architects, experts in remote sensing technology, and Climate and Earth System scientists to review, discuss, and plan research on issues related to large-scale, efficient analysis of distributed data using spatial statistical methods. Our motivation in organizing this event was to catalyze interchange among experts on the fast-emerging problem of analysis of distributed data. As part of SAMSI's 2017-2018 Program on Mathematical and Statistical Methods for Climate and the Earth System, a Working Group on Remote Sensing was established to address statistical and mathematical research problems in the analysis of remote sensing data. The Working Group has five subgroups: 1) Spatial Retrieval Methodology (the so-called \Spatial-X" subgroup); 2) Spatial Analysis for Hyperspectral Data (the so-called \Spatial-Y" subgroup); 3) Emulators for Complex Forward Models; 4) Optimization for Remote Sensing Retrievals; and 5) Theory of Data Systems (ToDS). The ToDS subgroup spent the first half of this academic year formulating a framework in which to consider the joint problem of a) optimizing statistical methods for environments where data are distributed and too large to move to a central location, and b) the design of data system infrastructures within which to implement those statistical methods. To x ideas, the Workshop focused on spatial statistical methods. To date there are many new spatial statistical methods designed with massive data sets in mind, in the literature. However, very few have been implemented for remote sensing data, and none have been implemented in operational settings like those used by NASA and NOAA. A major impediment to their use in these cases is that the data are not only massive, but are stored in different physical locations. These data must be brought together in some way in order to estimate spatial covariance functions, but moving data to a central location for analysis is tedious at best and impossible at worst. Some remote data reduction is almost certainly necessary, but how much? What are the consequences for inference? The fundamental issue underlying these questions is how to navigate the trade-space between costs and uncertainty in the estimates or inferences that are ultimately produced.

Braverman, Amy↗
DOE OSTI2020

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Discovering causal structure with reproducing-kernel Hilbert space ε -machines

We merge computational mechanics’ definition of causal states (predictively equivalent histories) with reproducing-kernel Hilbert space (RKHS) representation inference. The result is a widely applicable method that infers causal structure directly from observations of a system’s behaviors whether they are over discrete or continuous events or time. A structural representation—a finite- or infinite-state kernel ϵ-machine—is extracted by a reduced-dimension transform that gives an efficient representation of causal states and their topology. In this way, the system dynamics are represented by a stochastic (ordinary or partial) differential equation that acts on causal states. We introduce an algorithm to estimate the associated evolution operator. Paralleling the Fokker–Planck equation, it efficiently evolves causal-state distributions and makes predictions in the original data space via an RKHS functional mapping. We demonstrate these techniques, together with their predictive abilities, on discrete-time, discrete-value infinite Markov-order processes generated by finite-state hidden Markov models with (i) finite or (ii) uncountably infinite causal states and (iii) continuous-time, continuous-value processes generated by thermally driven chaotic flows. The method robustly estimates causal structure in the presence of varying external and measurement noise levels and for very high-dimensional data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

Deployed quantum link characterization via Bayesian ancilla-assisted process tomography

The development of large-scale quantum networks requires reliable quantum channels, the quality of which can be quantified by the framework of quantum process tomography. Here, in this work, we leverage ancilla-assisted process tomography (AAPT) and Bayesian inference to probe a 1.6 km deployed fiber-optic link. We send one of the two polarization-entangled photons at Alice in one building to Bob in another, exploiting the local qubit as an ancilla system to characterize the corresponding quantum channel. Monitoring over a 24 h period returns a steady process fidelity of 97.6(1)%, while controllable spectral filtering with passbands from 0.025 to 4.38 THz finds fidelities that first increase, and then level off with bandwidth, suggesting both stable operation with time and minimal polarization mode dispersion. To our knowledge, these results represent the first AAPT of a deployed quantum link, revealing a valuable tool for in situ analysis of entanglement-based quantum networks.

Rahman, Arefur [Arizona State Univ., Tempe, AZ (Un↗
NASA NTRS2006

Application of Aerosol Hygroscopicity Measured at the Atmospheric Radiation Measurement Program's Southern Great Plains Site to Examine Composition and Evolution

A Differential Mobility Analyzer/Tandem Differential Mobility Analyzer (DMA/TDMA) was used to measure submicron aerosol size distributions, hygroscopicity, and occasionally volatility during the May 2003 Aerosol Intensive Operational Period (IOP) at the Central Facility of the Atmospheric Radiation Measurement Program's Southern Great Plains (ARM SGP) site. Hygroscopic growth factor distributions for particles at eight dry diameters ranging from 0.012 micrometers to 0.600 micrometers were measured throughout the study. For a subset of particle sizes, more detailed measurements were occasionally made in which the relative humidity or temperature to which the aerosol was exposed was varied over a wide range. These measurements, in conjunction with backtrajectory clustering, were used to infer aerosol composition and to gain insight into the processes responsible for evolution. The hygroscopic growth of both the smallest and largest particles analyzed was typically less than that of particles with dry diameters of about 0.100 micrometers. It is speculated that condensation of secondary organic aerosol on nucleation mode particles is largely responsible for the minimal hygroscopic growth observed at the smallest sizes considered. Growth factor distributions of the largest particles characterized typically contained a nonhygroscopic mode believed to be composed primarily of dust. A model was developed to characterize the hygroscopic properties of particles within a size distribution mode through analysis of the fixed size hygroscopic growth measurements. The performance of this model was quantified through comparison of the measured fixed size hygroscopic growth factor distributions with those simulated through convolution of the size-resolved concentration contributed by each of the size modes and the mode-resolved hygroscopicity. This transformation from sizeresolved hygroscopicity to mode-resolved hygroscopicity facilitated examination of changes in the hygroscopic properties of particles within a size distribution mode that accompanied changes in the sizes of those particles. This model was used to examine three specific cases in which the sampled aerosol evolved slowly over a period of hours or days.

Gasparini, Roberto↗