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NASA NTRS1955

Ditching Investigation of a 1/20-Scale Model of the Boeing Stratocruiser Airplane (C-97)

An investigation of a 1/20-scale dynamically similar model of the Boeing Stratocruiser airplane (C-97) was made to determine the ditching characteristics and proper technique for ditching the airplane. Scale-strength bottoms were used to determine probable damage to the fuselage and the effect of damage on behavior. The behavior of the model was determined from visual observations, motion-picture records, and time-history deceleration records. Data are presented in a table, photographs, and curves. It was concluded that the airplane should be ditched at a medium nose-high landing attitude (near 6 deg) with landing flaps full down. The airplane will probably make a smooth run of medium depth with light spray and may even trim up slightly in the water. The fuselage will probably be damaged and the lower compartment filled with water. In calm water, the maximum longitudinal deceleration will be about 4g and the landing run will be about four fuselage lengths.

Fisher, Lloyd J.↗

DOE OSTI2021

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (°C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a “0” if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the “time warp” period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503–2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100–103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278–283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Understanding the relative importance of vertical and horizontal flow in ice-wedge polygons

Abstract. Ice-wedge polygons are common Arctic landforms. The future of these landforms in a warming climate depends on the bidirectional feedback between the rate of ice-wedge degradation and changes in hydrological characteristics. This work aims to better understand the relative roles of vertical and horizontal water fluxes in the subsurface of polygonal landscapes, providing new insights and data to test and calibrate hydrological models. Field-scale investigations were conducted at an intensively instrumented location on the Barrow Environmental Observatory (BEO) near Utqiaġvik, AK, USA. Using a conservative tracer, we examined controls of microtopography and the frost table on subsurface flow and transport within a low-centered and a high-centered polygon. Bromide tracer was applied at both polygons in July 2015 and transport was monitored through two thaw seasons. Sampler arrays placed in polygon centers, rims, and troughs were used to monitor tracer concentrations. In both polygons, the tracer first infiltrated vertically until encountering the frost table and was then transported horizontally. Horizontal flow occurred in more locations and at higher velocities in the low-centered polygon than in the high-centered polygon. Preferential flow, influenced by frost table topography, was significant between polygon centers and troughs. Estimates of horizontal hydraulic conductivity were within the range of previous estimates of vertical conductivity, highlighting the importance of horizontal flow in these systems. This work forms a basis for understanding complexity of flow in polygonal landscapes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Integrated GW Farm ABM

This Data Repository includes data used for the integrated groundwater- farm ABM model, raw model output from scenario ensemble, and processed outputs that isolate the groundwater storage depletion outcomes for the 35,000 farm cells. Model Inputs: Farm ABM Inputs: This folder contains the input data used by the integrated groundwater - farm ABM modelling script (Python file) used for the high performance computing (HPC) experiments. The sub-folder "data inputs" contains all of the farm attribute data, while the three files in the folder have the hydrogeological data lookup table (NLDAS Cost Curve Attributes.csv), a lookup table (Theis well function table.csv) for the groundwater cost curve function, and the farm indexes and corresponding NLDAS ids for all of the cells run in this experiment (nldas farms subset final.csv). NLDAS Cost curve hydrogeological data: Hydrogeological data aggregated to 1/8 degree resolution and aligned with the NLDAS grid. Parameters include: water depth below ground surface [meters], subsurface porosity [unitless], aquifer depth from ground surface to aquifer bottom [meters], annual average recharge (USGS: mm, Doll: meters), and three different hydraulic conductivity (K) values (meters/day). The three K values represent the mean value from Gleeson et al. (2018), one standard deviation above the mean from Gleeson et al. (2018), and the de Graaf et al. 2020 modifications to certain lithologies. Additional information about these datasets and their processing are documented in the supplement to Yoon et al. 2025 (in review). Output: Raw outputs: This folder contains a .zip file that has model outputs for the entire scenario ensemble. There is one csv for each farm id, using the format "farm farmid cases.csv". The relationship between the farm id and NLDAS id is defined by the "nldas farms subset final.csv" located in the Farm ABM Inputs folder. Each csv has 625 rows, corresponding to 625 combinations of different scenario parameter values. Each row (scenario) represents the outcome of a 100 year simulation. Columns define scenario settings and summary statistics for each scenario. The first four columns define the scenario settings: "hydro ratio," "econ ratio," "K scenario," and "gamma scenario." The hydro and econ ratios are values passed to the modeling script that influence multipliers for other model parameters, as documented in the supplement to Yoon et al. 2025 (in review). The gamma multiplier is a coefficient multiplier applied to the baseline gamma values (values below 1 represent lower unobserved costs compared to baseline, values above 1 represent higher costs). The K scenario names represent K values of: "low": 0.5 m/d, "int 1": 2.5 m/d, "int 2": 10 m/d, "high": 50 m/d, and "gleeson": mean Gleeson K value. "Perc vol depleted" is the fraction of groundwater depleted at the end of the 100 simulation. Processed Output: Derived depletion outcomes from raw outputs: All of the individual csv files from the Raw outputs were aggregated into a single file that has the scenario settings and fraction depletion "Perc vol depleted" for every farm cell, for every scenario. The other two files define relationships between the farm id, NLDAS id, and local and major aquifer units, used for aquifer-level depletion analysis.

Agent based modeling↗

NASA NTRS1982

LANDSAT-D investigations in snow hydrology

Snow reflectance in all 6 TM reflective bands, i.e., 1, 2, 3, 4, 5, and 7 was simulated using a delta-Eddington model. Snow reflectance in bands 4, 5, and 7 appear sensitive to grain size. It appears that the TM filters resemble a ""square-wave'' closely enough that a square-wave can be assumed in calculations. Integrated band reflectance over the actual response functions was calculated using sensor data supplied by Santa Barbara Research Center. Differences between integrating over the actual response functions and the equivalent square wave were negligible. Tables are given which show (1) sensor saturation radiance as a percentage of the solar constant, integrated through the band response function; (2) comparisons of integrations through the sensor response function with integrations over the equivalent square wave; and (3) calculations of integrated reflectance for snow over all reflective TM bands, and water and ice clouds with thickness of 1 mm water equivalent over TM bands 5 and 7. These calculations look encouraging for snow/cloud discrimination with TM bands 5 and 7.

Dozier, J.↗

NASA NTRS1993

Ice in the northern plains: Relic of a frozen ocean?

Viking images revealed many features in the northern plains and along their boundary that early investigators believed to be formed by ice-related processes. The features are possible pingos, pseudocraters, table mountains and moberg ridges, thermokarst depressions, moraines, patterned ground, and lobate aprons that suggest viscous flow such as that of ice or rock glaciers. More recently, many of these features were reinterpreted as related to sedimentation in hypothetical former polar lakes, oceans, or alluvial plains or as shoreline features of associated water bodies. Some evidence that points toward the existence of former bodies of standing water in the northern plains, but is also consistent with the idea that these bodies were ice covered or completely frozen is reviewed.

Lucchitta, B. K.↗

NASA NTRS1989

The fragmentation of 670A MeV neon-20 as a function of depth in water. I. Experiment

We present the final analysis of an experiment to study the interaction of a beam of 670A MeV neon ions incident on a water column set to different thicknesses. The atomic number Z (and, in some cases, the isotopic mass A) of primary beam particles and of the products of nuclear interactions emerging from the water column close to the central axis of the beam was obtained for nuclei between Be (Z = 4) and Ne (Z = 10) using a time-of-flight telescope to measure the velocity and a set of silicon detectors to measure the energy loss of each particle. The fluence of particles of a given charge was obtained and normalized to the incident beam intensity. Corrections were made for accidental coincidences between multiple particles triggering the TOF telescope and for interactions in the detector. The background due to beam particles interacting in beam line elements upstream of the detector was calculated. Sources of experimental artifacts and background in particle identification experiments designed to characterize heavy ion beams for radiobiological research are summarized, and some of the difficulties inherent in this work are discussed. Complete tables of absolutely normalized fluence spectra as a function of LET are included for reference purposes.

NASA Discipline Radiation Health↗

NASA NTRS1980

A clear-sky spectral solar radiation model for snow-covered mountainous terrain

A clear-sky spectral solar radiation model for direct and diffuse fluxes, combined with topographic calculations from digital terrain data, computes either incident, net, or reflected solar radiation at any point on a snow surface in mountainous terrain. The radiation may be integrated over any wavelength range from 250 to 5000 nm, or over any time step. Atmospheric attenuation parameters are ozone, water vapor, the Angstrom turbidity coefficient and exponent, and the absorptance to reflectance ratio of the atmospheric aerosols. The model derives these, from measurements which may contain both systematic and random errors, by finding the least squares solution to an overdetermined set of nonlinear equations. For calculations over a specified area, it employs table look-up procedures, so that computation speed for the spectral model approaches that for a lumped model. Thus it may be useful as part of a snow surface energy budget calculation over a drainage basin.

Dozier, J.↗

NASA NTRS1993

Inference of cirrus cloud properties using satellite-observed visible and infrared radiances. I - Parameterization of radiance fields

The impact of using phase functions for spherical droplets and hexagonal ice crystals to analyze radiances from cirrus is examined. Adding-doubling radiative transfer calculations are employed to compute radiances for different cloud thicknesses and heights over various backgrounds. These radiances are used to develop parameterizations of top-of-the-atmosphere visible reflectance and IR emittance using tables of reflectances as a function of cloud optical depth, viewing and illumination angles, and microphysics. This parameterization, which includes Rayleigh scattering, ozone absorption, variable cloud height, and an anisotropic surface reflectance, reproduces the computed top-of-the-atmosphere reflectances with an accruacy of +/- 6 percent for four microphysical models: 10-micron water droplet, small symmetric crystal, cirrostratus, and cirrus uncinus. The accuracy is twice that of previous models.

Minnis, Patrick↗

NASA NTRS2007

Microbial Extremophiles in Aspect of Limits of Life

During Earth's evolution accompanied by geophysical and climatic changes a number of ecosystems have been formed. These ecosystems differ by the broad variety of physicochemical and biological factors composing our environment. Traditionally, pH and salinity are considered as geochemical extremes, as opposed to the temperature, pressure and radiation that are referred to as physical extremes (Van den Burg, 2003). Life inhabits all possible places on Earth interacting with the environment and within itself (cross species relations). In nature it is very rare when an ecotope is inhabited by a single species. As a rule, most ecosystems contain the functionally related and evolutionarily adjusted communities (consortia and populations). In contrast to the multicellular structure of eukaryotes (tissues, organs, systems of organs, whole organism), the highest organized form of prokaryotic life in nature is the benthic colonization in biofilms and microbial mats. In these complex structures all microbial cells of different species are distributed in space and time according to their functions and to physicochemical gradients that allow more effective system support, self-protection, and energy distribution. In vitro, of course, the most primitive organized structure for bacterial and archaeal cultures is the colony, the size, shape, color, consistency, and other characteristics of which could carry varies specifics on species or subspecies levels. In table 1 all known types of microbial communities are shown (Pikuta et a]., 2005). In deep underground (lithospheric) and deep-sea ecosystems an additional factor - pressure, and irradiation - could also be included in the list of microbial communities. Currently the beststudied ecosystems are: human body (due to the medical importance), and fresh water and marine ecosystems (due to the reason of an environmental safety). For a long time, extremophiles were terra incognita, since the environments with aggressive parameters (compared to the human body temperature, pH, mineralization, and pressure) were considered a priori as a dead zone.

Pikuta, Elena V.↗

DOE OSTI2020

Y-12 Groundwater Protection Program Groundwater and Surface Water Sampling and Analysis Plan (CY 2021)

This plan provides a description of the groundwater and surface water quality monitoring activities planned for calendar year (CY) 2021 at the U.S. Department of Energy Y-12 National Security Complex (Y-12) that will be managed by the Y-12 Groundwater Protection Program (GWPP). Groundwater and surface water monitoring is performed by the GWPP. Groundwater and surface water monitoring will be performed in three hydrogeologic regimes at Y-12: the Bear Creek Hydrogeologic Regime (Bear Creek Regime), the Upper East Fork Poplar Creek Hydrogeologic Regime (East Fork Regime), and the Chestnut Ridge Hydrogeologic Regime (Chestnut Ridge Regime). The Bear Creek and East Fork regimes are located in Bear Creek Valley and the Chestnut Ridge Regime is located south of Y-12. Additional surface water monitoring will be performed north of Pine Ridge along the boundary of the Oak Ridge Reservation. The following sections of this report provide details regarding the CY 2021 groundwater and surface water monitoring activities. Section 2 describes the monitoring locations in each regime and the processes used to select the sampling locations. A description of the field measurements and laboratory analytes is provided in Section 3. Sample collection methods and procedures are described in Section 4, and Section 5 lists the documents cited for more detailed operational and technical information. The narrative sections of the report reference several appendices. Figures (maps and diagrams) and tables (excluding a data summary table presented in Section 4) are in Appendix A and Appendix B, respectively. Groundwater Monitoring Schedules (when issued throughout CY 2021) will be inserted in Appendix C, and addenda to this plan (if issued) will be inserted in Appendix D. Laboratory requirements (bottle lists, holding times, etc.) are provided in Appendix E, and an approved Waste Management Plan is provided in Appendix F. Modifications to the CY 2021 monitoring program may be necessary during implementation. Changes in programmatic requirements may alter the analytes specified for selected monitoring wells or may add or remove wells from the planned monitoring network. Each modification to the monitoring program will be approved by the Y-12 GWPP manager and documented as an addendum to this sampling and analysis plan.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Morphotype-resolved characterization of microalgal communities in a nutrient recovery process with ARTiMiS flow imaging microscopy

Microalgae-driven nutrient recovery represents a promising technology for phosphorus removal from wastewater while simultaneously generating biomass that can be valorized to offset treatment costs. As full-scale processes come online, system parameters including biomass composition must be carefully monitored to optimize performance and prevent culture crashes. In this study, flow imaging microscopy (FIM) was leveraged to characterize microalgal community composition in near real-time at a full-scale municipal wastewater treatment plant (WWTP) in Wisconsin, USA, and population and morphotype dynamics were examined to identify relationships between water chemistry, biomass composition, and system performance. Two FIM technologies, FlowCam and ARTiMiS, were evaluated as monitoring tools. ARTiMiS provided a more accurate estimate of total system biomass, and estimates derived from particle area as a proxy for biovolume yielded better approximations than particle counts. Deep learning classification models trained on annotated image libraries demonstrated equivalent performance between FlowCam and ARTiMiS, and convolutional neural network (CNN) classifiers proved significantly more accurate when compared to feature table-based dense neural network (DNN) models. Across a two-year study period, Scenedesmus spp. appeared most important for phosphorus removal, and were negatively impacted by elevated temperatures and increase in nitrite/nitrate concentrations. Chlorella and Monoraphidium also played an important role in phosphorus removal. For both Scenedesmus and Chlorella, smaller morphological types were more often associated with better system performance, whereas larger morphotypes likely associated with stress response(s) correlated with poor phosphorus recovery rates. Furthermore, these results demonstrate the potential of FIM as a critical technology for high-resolution characterization of industrial microalgal processes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Metal–Oxo Cluster Formation Using Ammonium and Sulfate to Differentiate M IV (Th, U, Ce) Chemistries

Isolating isostructural compounds of tetravalent metals M IV (Zr, Hf, Ce, Th, U, Pu, Np) improves our understanding of metal hydrolysis and coordination behavior across the periodic table. These metals form polynuclear clusters typified by the hexamer [M IV 6 O 4 (OH) 4 ] 12+ . Exploiting the ammonium M IV -sulfate (Ce IV , Th IV , and U IV ) phase space targeting rapid crystallization, we isolate the common hexamer [M IV 6 (OH) 4 (O) 4 ] 12+ but with different numbers of capping sulfates and water molecules for Ce IV , Th IV , and U IV . Furthermore, these phases allowed a direct comparison of bonding trends across the series. Upon cocrystallization with the hexamers, higher complex structures can be identified. Thorium features assemblies with monomer-linked hexamer chains. Uranium features assemblies with sulfate-bridged hexamers and the supramolecular assembly of 14 hexamers into the U 84 , [U 6 (OH) 4 (O) 4 ) 14 (SO 4 ) 120 (H 2 O) 42 ] 72– . Last, cerium showcases the isolation from monomers to the Ce 62 , [Ce 62 (OH) 30 (O) 58 (SO 4 ) 71 (H 2 O) 33.25 ] 41– . Furthermore, small-angle X-ray scattering (room temperature) shows ammonium-induced cluster assembly for Ce IV but minimal reactivity for U IV and Th IV . In this study, because the phases crystallized at elevated temperature demonstrates favorable cluster assembly, these solution phase results were surprising and suggest some other characteristics such as Ce’s facile redox behavior, contributes to its solution-phase speciation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1985

Practical applications of Landsat data

Some practical applications of Landsat MSS and TM data are discussed, including renewable resource inventory and monitoring; water resources inventory and assessment; and urban land use analysis. Consideration is also given to the geological applications of Landsat MSS and TM data. These applications include: mapping major geological units; rock-type recognition; mapping volcanic surface deposits; and mapping linears. The TM and MSS spectral bands corresponding to the data applications are listed in a table.

Conner, P. K.↗

DOE OSTI2020

Soil Water Content (m3 m-3) from Australia, June 2011 - December 2016

Hourly data processed to monthly. Data collected from Cosmic Neutron Probes at variable depth averaging 38 cm that was interpolated to near-surface (0-10 cm). Site located at latitude -14.16 deg N, longitude 131.39 deg E. Data was downloaded from the The COsmic-ray Soil Moisture Observing System (COSMOS) soil moisture data network and is publicly available. Data from this site located at, referenced as Australia (2) in Table 1 and Figure 4 of Solander et al., 2020, The pan-tropical soil moisture response to El Nino, Hydrol. Earth Syst. Sci., in press.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Soft X‐Ray Absorption Spectroscopy With a Flat Liquid Jet in Vacuum Using a Table‐Top Laser‐Induced Plasma Source

ABSTRACT In this work, we demonstrate the integration of a flat liquid jet sample delivery system into a compact soft x‐ray absorption spectrometer using a table‐top laser‐induced plasma source. A high‐speed flat liquid sheet is formed by the collision of two cylindrical jets. This micrometer‐thin lamella can ideally be utilized for transmission‐mode soft x‐ray absorption spectroscopy using krypton plasma emission. Detailed analysis of the jet's thickness profile is achieved applying Lambert–Beer's law. Measurements on water, focusing on the oxygen K‐edge, reveal a lamella thickness profile ranging from 500 nm to 1 μm over a length of 3.8 mm. Additionally, we have investigated aqueous solutions of iron salts, capturing near edge x‐ray absorption fine structure spectra over a broad spectral range from the nitrogen K‐edge to the iron L‐edge. Focused analysis on iron species in aqueous solutions enabled us to distinguish quantitatively between the oxidation states of Fe 2+ and Fe 3+ at the iron L‐edge. Our results are compared with measurements obtained under similar conditions at a synchrotron.

Holburg, Jonathan [Department Optics/Short Wavelen↗

DOE OSTI2020

Storm Water Samples

In the California lndustrial General Permit (IGP) 2014-0057-DWQ for storm water monitoring, effective July 1, 2015, there are 21 contaminants that have been assigned NAL (Numeric Action Level) values, both annual and instantaneous. For annual NALs, an exceedance occurs when the average of all analytical results from all samples taken at a facility during a reporting year for a given parameter exceeds an annual NAL value listed in Table 2 of the General Permit. For instantaneous maximum NALs, an exceedance occurs when two or more analytical results from samples taken for any parameter within a reporting year exceed the instantaneous maximum NAL value (for TSS and O&G), or are outside of the instantaneous maximum NAL range (for pH) listed in Table 2.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

QSE 04-21-2022 ST-11 Alpha Data Report

In the California Industrial General Permit (IGP) 2014-0057-DWQ for storm water monitoring, effective July 1, 2015, there are 21 contaminants that have been assigned NAL (Numeric Action Level) values, both annual and instantaneous. For annual NALs, an exceedance occurs when the average of all analytical results from all samples taken at a facility during a reporting year for a given parameter exceeds an annual NAL value listed in Table 2 of the General Permit. For instantaneous maximum NALs, an exceedance occurs when two or more analytical results from samples taken for any parameter within a reporting year exceed the instantaneous maximum NAL value (for TSS and O&G), or are outside of the instantaneous maximum NAL range (for pH) listed in Table 2.

54 ENVIRONMENTAL SCIENCES↗