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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

BEYONDPLANCK XIII. Intensity foreground sampling, degeneracies, and priors

We present the intensity foreground algorithms and model employed within the BEYONDPLANCK analysis framework. The BEYONDPLANCK analysis is aimed at integrating component separation and instrumental parameter sampling within a global framework, leading to complete end-to-end error propagation in the Planck Low Frequency Instrument (LFI) data analysis. Given the scope of the BEYONDPLANCK analysis, a limited set of data is included in the component separation process, leading to foreground parameter degeneracies. In order to properly constrain the Galactic foreground parameters, we improve upon the previous Commander component separation implementation by adding a suite of algorithmic techniques. These algorithms are designed to improve the stability and computational efficiency for weakly constrained posterior distributions. These are: (1) joint foreground spectral parameter and amplitude sampling, building on ideas from MIRAMARE; (2) component-based monopole determination; (3) joint spectral parameter and monopole sampling; and (4) application of informative spatial priors for component amplitude maps. We find that the only spectral parameter with a significant signal-to-noise ratio using the current BEYONDPLANCK data set is the peak frequency of the anomalous microwave emission component, for which we find $ν$ p = 25.3 ± 0.5 GHz; all others must be constrained through external priors. Future works will be aimed at integrating many more data sets into this analysis, both map and time-ordered based, thereby gradually eliminating the currently observed degeneracies in a controlled manner with respect to both instrumental systematic effects and astrophysical degeneracies. When this happens, the simple LFI-oriented data model employed in the current work will need to be generalized to account for both a richer astrophysical model and additional instrumental effects. This work will be organized within the Open Science-based COSMOGLOBE community effort.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Design of smart 3D-digital X-ray microtomographic scanners for non-destructive testing of materials and components of electronic devices with a multilayered structure

The article studies the operating procedures of an X-ray microtomographic scanner and the module of reconstruction and analysis 3D-image of a test sample in particular. An algorithm for 3D-image reconstruction based on image shadow projections and mathematical methods of the processing are described. Chapter 1 describes the basic principles of X-ray tomography and general procedures of the device developed. Chapters 2 and 3 are devoted to the problem of resources saving by the system during the X-ray tomography procedure, which is achieved by preprocessing of the initial shadow projections. Preprocessing includes background noise removing from the images, which reduces the amount of shadow projections in general and increases the efficiency of the group shadow projections compression. Chapter 4 covers general procedures of defect search, which is based on vector analysis principles. In conclusion, the main applications of X-ray tomography are presented.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Sequential lateral solidification of silicon thin films on low-k dielectrics for low temperature integration

We present the excimer laser crystallization of amorphous silicon on a low dielectric constant (low-k) insulator for very large scale integration monolithic 3D integration and demonstrate that low dielectric constant materials are suitable substrates for 3D integration through laser crystallization of silicon thin films. We crystallized 100 nm amorphous silicon on top of SiO{sub 2} and SiCOH (low-k) dielectrics, at different material thicknesses (1 μm, 0.75 μm, and 0.5 μm). The amorphous silicon crystallization on low-k dielectric requires 35% less laser energy than on an SiO{sub 2} dielectric. This difference is related to the thermal conductivity of the two materials, in agreement with one dimensional simulations of the crystallization process. We analyzed the morphology of the material through defect-enhanced microscopy, Raman spectroscopy, and X-ray diffraction analysis. SEM micrographs show that polycrystalline silicon is characterized by micron-long grains with an average width of 543 nm for the SiO{sub 2} sample and 570 nm for the low-k samples. Comparison of the Raman spectra does not show any major difference in film quality for the two different dielectrics, and polycrystalline silicon peaks are closely placed around 517 cm{sup −1}. From X-ray diffraction analysis, the material crystallized on SiO{sub 2} shows a preferential (111) crystal orientation. In the SiCOH case, the 111 peak strength decreases dramatically and samples do not show preferential crystal orientation. A 1D finite element method simulation of the crystallization process on a back end of line structure shows that copper (Cu) damascene interconnects reach a temperature of 70 °C or lower with a 0.5 μm dielectric layer between the Cu and the molten Si layer, a favorable condition for monolithic 3D integration.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Verification of Special Nuclear Material (SNM) using Active Well Coincidence Counter (AWCC) and a High Purity Germanium (HPGe) Detector

SNM verification can be accomplished via two methods i.e. destructive analysis (DA) or nondestructive analysis (NDA). The DA modes are much more accurate compared to NDA modes; nevertheless, the sample's physical integrity is destroyed in the process. Techniques such as ICP-MS, ID-ICP-MS, TI-MS are few examples of DA, where a bulk material is ground and mixed with a solution (some form of acid) to acquire a homogenous sample. In contrast, NDA modes utilize signals emitted by the sample such as gamma, neutrons, light, or heat to analyze samples under investigation. HPGe detectors are some of the most frequently used gamma detectors to assay SNM. However, in a high activity scenario it provides a large uncertainty and sometimes becomes unfeasible to measure low energy gamma rays. Meanwhile, neutron detectors are not affected by the gamma background, therefore they can be used in addition to the gamma detectors to verify total fissile mass ({sup 235}U + {sup 239}Pu) of SNM. The AWCC is one of the thermal neutron NDA systems developed at the Los Alamos National Laboratory (LANL). It consists of 42 {sup 3}He detectors to measure gross neutrons (singles), coincidence neutrons (doubles), higher order multiplicity counting (triples, quadruples, etc.). It can effectively operate in both active and passive neutron interrogation modes. It can utilize AmLi, AmBe, or {sup 252}Cf active interrogation neutron sources. It is used to verify declared fissile mass ({sup 235}U or {sup 239}Pu) present in SNM. Objectives: Calibrate HPGe and AWCC for lightly irradiated (with enough {sup 137}Cs gamma background) ∼93% enriched HEU fuels; Verify fissile mass ({sup 235}U +{sup 239}Pu) content using AWCC and HPGe; Model and benchmark AWCC in MCNP6. Neutron coincidence and multiplicity counting: Two or more neutrons are coincident if they are detected by the system within the specified gate window; In AWCC: Gate window is 128 μsec; Induced fission in fissile material such as {sup 235}U and {sup 239}Pu - emission of time correlated neutrons - Singles, Doubles, Triples, or Quadruples; No induced fission in non-fissile materials- no correlated neutrons - only singles. SNM can be verified with high accuracy and precision using gamma and neutron NDA instruments such as HPGe and AWCC. Interaction with thermal neutrons. Non-fissile atoms - do not emit correlated neutrons. Fissile atoms - emit correlated neutrons during induced fission events. The time correlated neutrons can be detected by using coincidence or multiplicity counters such as AWCC. Likewise, {sup 235}U produces 186 keV gamma peak that can be measured by a HPGe gamma detector. Counts under 186 keV gamma peak provide information about {sup 235}U content. Combining AWCC and HPGe results can provide better understanding of special nuclear material under investigation.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1981

NASA and Deere to study effects of low-g on iron processing

A technical exchange agreement to study the effects of processing iron alloys in microgravity is described. Alloy solidification experiments are to be performed in the low-g facilities at Marshall Space Center. Deere is to prepare and evaluate the samples, and perform thermal characterization studies of the furnace used for melting and solidifying the samples. Experiment planning and analysis are to be performed jointly and data shared between the two parties. The technical exchange concept, developed by NASA to involve the private sector in low gravity research programs, is described. Other low gravity processing fields in which industry is invited to participate are listed.

Source record↗
DOE OSTI2014

Residual strain mapping of Roman styli from Iulia Concordia, Italy

Iulia Concordia is an important Roman settlement known for the production of iron objects and weapons during the Roman Empire. A huge number of well-preserved styli were found in the past century in the bed of an old channel. In order to shed light about the production processes used by Roman for stylus manufacturing, a neutron diffraction residual strain analysis was performed on the POLDI materials science diffractometer at the Paul Scherrer Institut in Switzerland. Here, we present results from our investigation conducted on 11 samples, allowing to define, in a non-invasive way, the residual strain map related to the ancient Roman working techniques. - Highlights: • We examined 11 Roman styli from the settlement of Iulia Concordia, Italy. • We performed a neutron diffraction residual strain analysis on POLDI at PSI (CH). • We identified the production processes used by Roman for stylus manufacturing. • We clarified the way and direction of working applied for different classes of styli.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Design and implementation of a data analysis pipeline for paper spray ionization mass spectrometry of UO 2 Cl 2

Ambient mass spectrometry is an emerging characterization method for nuclear materials analysis. SRNL has recently acquired a new JEOL AccuTOF™-DART® LP 4G with a Filter Spray™ front end sample injection system, to develop new methods for ambient mass spectrometry of uranium analytes. Preliminary measurements of uranium-containing samples have demonstrated a need to rapidly evaluate large datasets generated with tailored data analysis scripts. To address this gap a data analysis pipeline written in R within the R Studio environment has been developed. The pipeline comprises a series of processing steps where the data are ingested, analyte peaks of interest are extracted, subsequent ratio and normalization calculations are performed, and results are presented in a series of graphical plots. In detail, the data files containing up to several hundred chronological spectra of a single sample in netCDF format were first extracted into RStudio. Then, initial 3D plots of the data were created in respect to the time, intensity, and mass to charge domains. These graphics serve as an initial easily visualized survey of instrument data. Next, a .csv format file containing the names, masses, and abundances of isotopes of interest were ingested into RStudio and were used to automatically collect intensity values at the mass value. Uranium isotope ratios were plotted against gadolinium isotope ratios (gadolinium was used as an internal standard) and assessed for ion beam stability and intensity. The spectra were filtered by applying a threshold intensity for the 158GdO+ mass channel. A Monte Carlo experiment was performed by creating a theoretical distribution of the data in order to determine the ratio of the uranium isotopes. The results show that the developed data analysis pipeline successfully filters high quality data from overall poor quality runs. Our results also support the idea that paper spray ionization mass spectrometry can be used for both detection and quantification of trace amounts of uranium compounds.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Microscale mechanical modeling of deformable geomaterials with dynamic contacts based on the numerical manifold method

Abstract Micromechanical modeling of geomaterials is challenging because of the complex geometry of discontinuities and potentially large number of deformable material bodies that contact each other dynamically. In this study, we have developed a numerical approach for micromechanical analysis of deformable geomaterials with dynamic contacts. In our approach, we detect contacts among multiple blocks with arbitrary shapes, enforce different contact constraints for three different contact states of separated, bonded, and sliding, and iterate within each time step to ensure convergence of contact states. With these features, we are able to simulate the dynamic contact evolution at the microscale for realistic geomaterials having arbitrary shapes of grains and interfaces. We demonstrate the capability with several examples, including a rough fracture with different geometric surface asperity characteristics, settling of clay aggregates, compaction of a loosely packed sand, and failure of an intact marble sample. With our model, we are able to accurately analyze (1) large displacements and/or deformation, (2) the process of high stress accumulated at contact areas, (3) the failure of a mineral cemented rock samples under high stress, and (4) post-failure fragmentation. The analysis highlights the importance of accurately capturing (1) the sequential evolution of geomaterials responding to stress as motion, deformation, and high stress; (2) large geometric features outside the norms (such as large asperities and sharp corners) as such features can dominate the micromechanical behavior; and (3) different mechanical behavior between loosely packed and tightly packed granular systems.

58 GEOSCIENCES↗
NASA NTRS2004

Microbial Characterization and Comparison of Isolates During the Mir and ISS Missions

Spacecraft represent a semi-closed ecosystem that provides a unique model of microbial interaction with other microbes, potential hosts, and their environment. Environmental samples from the Mir Space Station (1995-1998) and the International Space Station (ISS) (2000-Present) were collected and processed to provide insight into the characterization of microbial diversity aboard spacecraft over time and assess any potential health risks to the crew. All microbiota were isolated using standard media-based methodologies. Isolates from Mir and ISS were processed using various methods of analysis, including VITEK biochemical analysis, 16s ribosomal identification, and fingerprinting using rep-PCR analysis. Over the first 41 months of habitation, the diversity of the microbiota from air and surface samples aboard ISS increased from an initial six to 53 different bacterial species. During the same period, fungal diversity increased from 2 to 24 species. Based upon rep-PCR analysis, the majority of isolates were unique suggesting the need for increased sampling frequency and a more thorough analysis of samples to properly characterize the ISS microbiota. This limited fungal and bacterial data from environmental samples acquired during monitoring currently do not indicate a microbial hazard to ISS or any trends suggesting potential health risks.

Fontenot, Sondra L.↗
NASA NTRSDate not supplied

Determining Surface Exposure Ages of Regolith Grains in Lunar Core Sample 73002 By Investigating Solar Particle Irradiation Damage

Space weathering causes the surface soils of airless bodies like the Moon to be micro-structurally and chemically altered by exposure to micrometeoroid bombardment and solar wind irradiation. These alterations accumulate in surface grains with continued residence in the uppermost millimeters of regolith. Low energy solar wind ions (largely H+ and He+) produce a layer of radiation damage as they become implanted in grain surfaces. Also present are solar energetic particle (SEP) tracks, which are nanoscale lineations within grain interiors formed by heavy, high-energy ions (mainly Fe group nuclei) penetrating up to millimeters below the surface. Recent work has determined that the width of solar wind-damaged rims on anorthite and olivine, and their respective SEP track densities are correlated with each other and with grain surface exposure age. Core sample 73002, recently released under the Apollo Next Generation Sample Analysis (ANGSA) Program, has provided an opportunity to study material collected from the light mantle formation in the Taurus-Littrow Valley during Apollo 17. The light mantle is thought to have been deposited vial and slide originating from the neighboring South Massif. Spectral analysis of bulk core soils found a plagioclase rich composition, confirming a highlands origin of the formation. Spectral profiles and ferromagnetic resonance (FMR) measurements of bulk soils sampled at cm-intervals from 73002 indicate that regolith up to~9.5 cm in depth have maturity indices consistent with longer durations of surface processing than samples deeper in the core. Thus, this upper ~9 cm layer likely represents the maximum extent of an in-situ reworking zone which may have persisted over 5 to 17million years. Here we present an analysis of grain exposure ages within the in-situ reworking zone of 73002derived from SEP track density measurements via transmission electron microscopy.

J A McFadden↗
DOE OSTI2023

Vertical Gradient of Size-Resolved Aerosol Compositions over the Arctic Reveals Cloud Processed Aerosol in-Cloud and above Cloud

Arctic aerosols play a significant role in the aerosol-radiation and aerosol-cloud interactions, but ground-based measurements are insufficient to explain the interaction of aerosols and clouds in a vertically stratified Arctic atmosphere. This study shows the vertical variability of aerosol particle composition via tethered balloon system at Oliktok Point, Alaska, in August 2019, at different cloud layers for two case studies (background aerosol condition and polluted condition). Multi-modal micro-spectroscopy analysis of samples during the background case reveals a broadening of chemically specific size distribution above cloud top with a high abundance of sulfate and core-shell morphology, suggesting possible cloud processing of aerosols. The pollution case also indicates a broadening of size distribution at the upper cloud layer with the dominance of carbonaceous aerosol, which suggests the potential of carbonaceous aerosols for Arctic cloud formation. Here this study provides insights into the vertical profile of Arctic aerosols on cloud formation.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Curation and Analysis of Samples from Comet Wild-2 Returned by NASA's Stardust Mission

The NASA Stardust mission returned the first direct samples of a cometary coma from comet 81P/Wild‐2 in 2006. Intact capture of samples encountered at 6 km/s was enabled by the use of aerogel, an ultralow dense silica polymer. Approximately 1000 particles were captured, with micron and submicron materials distributed along ~mm scale length tracks. This sample collection method and the fine scale of the samples posed new challenges to the curation and cosmochemistry communities. Sample curation involved extensive, detailed photo‐documentation and delicate micro‐surgery to remove particles without loss from the aerogel tracks. This work had to be performed in highly clean facility to minimize the potential of contamination. JSC Curation provided samples ranging from entire tracks to micrometer‐sized particles to external investigators. From the analysis perspective, distinguishing cometary materials from aerogel and identifying the potential alteration from the capture process were essential. Here, transmission electron microscopy (TEM) proved to be the key technique that would make this possible. Based on TEM work by ourselves and others, a variety of surprising findings were reported, such as the observation of high temperature phases resembling those found in meteorites, rarely intact presolar grains and scarce organic grains and submicrometer silicates. An important lesson from this experience is that curation and analysis teams must work closely together to understand the requirements and challenges of each task. The Stardust Mission also has laid important foundation to future sample returns including OSIRIS‐REx and Hayabusa II and future cometary nucleus sample return missions.

Nakamura-Messenger, Keiko↗
DOE OSTI2025

Field Testing of Safeguards Technologies in the Hot Fuel Examination Facility

Recent developments in nuclear fuel reprocessing techniques have yielded more efficient processes and fuel cycle options that strengthen the nuclear industry and production of clean energy. One such area of interest is pyroprocessing of used oxide fuel. However, with these advances in the back end of the nuclear fuel cycle, advances in safeguards instrumentation, measurements, and approaches are needed to ensure special nuclear material (SNM) is accounted for according to regulatory requirements. As a high-level overview of a nominal pyroprocessing approach, used oxide fuel from commercial light water reactors (LWR) is mechanically removed from the metallic cladding. Then the fuel is crushed and randomized representative samples are taken and sent to an analytical lab for analysis. The analytical results of the feed material are used for input accountancy into the rest of the process. The crushed oxide fuel is then moved to the oxide reduction (OR) furnace where it is reduced from an oxide to metallic form. The OR product is distilled to remove salt and then moved to an electrorefiner (ER), where it is immersed in a eutectic mixture of lithium chloride potassium chloride (LiCl-KCl) that typically ranges between 450-550 ?. Within the ER, the usable uranium is electrochemically transported through the molten salt from the anode to the cathode, and then subsequently removed as a relatively pure U product.. A simplified model of pyroprocessing techniques with added emphasis on the safeguards can be seen below in Fig. 1

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
DOE OSTI2013

Particle beam experiments for the analysis of reactive sputtering processes in metals and polymer surfaces

A beam experiment is presented to study heterogeneous reactions relevant to plasma-surface interactions in reactive sputtering applications. Atom and ion sources are focused onto the sample to expose it to quantified beams of oxygen, nitrogen, hydrogen, noble gas ions, and metal vapor. The heterogeneous surface processes are monitored in situ by means of a quartz crystal microbalance and Fourier transform infrared spectroscopy. Two examples illustrate the capabilities of the particle beam setup: oxidation and nitriding of aluminum as a model of target poisoning during reactive magnetron sputtering, and plasma pre-treatment of polymers (PET, PP)

36 MATERIALS SCIENCE↗
DOE OSTI2025

Neural refinement of sample weights

Monte Carlo simulations are an essential tool in particle physics data analysis. Events are typically generated alongside weights that redistribute the cross section of the simulated process across the phase space. These weights can be negative, and several post hoc methods have been developed to eliminate or mitigate the negative values. All of these methods share the common strategy of approximating the average weight as a function of phase space. We introduce an alternative approach, which, instead of reweighting to the average, refines the initial weights with a scaling transformation, utilizing a phase space-dependent factor. Since this new refinement method does not need to model the full weight distribution, it can be more accurate. High-dimensional and unbinned phase space is processed using neural networks for the refinement method. In addition to the refinement method, we introduce a new resampling protocol, which can be used in conjunction with any weight transformation to not only preserve the average weight but also the statistical uncertainties of the initial distribution. Using both realistic and synthetic examples, we show that the new neural refinement method is able to match or exceed the accuracy of similar weight transformations and that the new resampling protocol is simpler in implementation than previous methods while exhibiting equivalent statistical properties.

Artificial neural networks↗
DOE OSTI2021

Characterizing the Sample Selection for Supernova Cosmology

Type Ia supernovae (SNe Ia) are used as distance indicators to infer the cosmological parameters that specify the expansion history of the universe. Parameter inference depends on the criteria by which the analysis SN sample is selected. Only for the simplest selection criteria and population models can the likelihood be calculated analytically, otherwise it needs to be determined numerically, a process that inherently has error. Numerical errors in the likelihood lead to errors in parameter inference. This article presents toy examples where the distance modulus is inferred given a set of SNe at a single redshift. Parameter estimators and their uncertainties are calculated using Monte Carlo techniques. The relationship between the number of Monte Carlo realizations and numerical errors is presented. The procedure can be applied to more realistic models and used to determine the computational and data management requirements of the transient analysis pipeline.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Riverine Plastic Pollution: Sampling and Analysis Methods

Riverine plastic pollution has been found in all major U.S. rivers, but the exact amount of plastic being released to the oceans has not been quantified. Field studies conducted in U.S. rivers have used a range of sampling and analysis techniques and rarely measured the mass of the plastic collected. Measurements of riverine plastic pollution are needed to calibrate and validate models used to estimate the U.S. riverine plastic emissions to the oceans. This report surveys measurement methods used to quantify riverine pollution and current estimates of U.S. riverine plastic pollution from measurements and models. Measurement methods include field sampling and laboratory analysis. Field sampling methods are described for large (macro) and small (micro) plastic particles. Laboratory analysis methods are described for macro and microplastic with an emphasis on the detailed characterization processes of microplastics. Waterborne leachate analysis is also briefly described. Three models are described that estimate plastic pollution based on mismanaged plastic waste in the river catchment basins. The models were validated and calibrated with global data sources. The data sources were predominantly outside of the U.S., where the magnitude and composition of plastic pollution is different than what is found in U.S. rivers. Comprehensive measurements of riverine plastics are needed not only to characterize the riverine plastic pollution, but also parameterize and validate models of plastic fate and transport. This report also describes five key U.S. rivers that span a range of sizes and environmental conditions that could be sampled to obtain data to support characterization and model development of plastic pollution from rivers to oceans. Sampling and analysis protocol recommendations are made to ensure the highest quality of data are collected in the five rivers.

54 ENVIRONMENTAL SCIENCES↗