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NASA NTRSDate not supplied

What Is the OPP Approach to the Next Generation of Laboratory Requirements

Planetary Protection Quality Management System NASA’s Office of Planetary Protection uses a quality-based process evaluation approach to verify planetary protection bioburden compliance over a project’s life cycle. The verification strategy shifts from a comparative direct assay approach validating hardware bioburden at a “moment in time,” to one that implements a continual quality assurance demonstration of the analytical process via established data requirements, laboratory operational, and management parameters throughout the mission’s entire assembly process. Laboratory quality-based systems and management strategies are standardized and implemented across government and industry. A standardized laboratory quality system approach increases transparency throughout the project’s life cycle and aligns planetary protection analytical approaches with government and industry practices to support both NASA and commercial endeavors. Strategies include the implementation of a laboratory quality management structure that documents and routinely validates parameters critical to experimental design and data collection, provides data quality assessment parameters, and ultimately validates that the collected data is of sufficient quality and quantity to meet the specified technical goals. Planetary protection can draw on these practices to provide a systematic process-based quality approach to support planetary protection validation and compliance requirements. Quality approaches including data quality objectives, method performance, data acceptance criteria and the associated laboratory quality management system are presented to provide an overview and framework for a planetary protection laboratory quality management system.

Amy Baker↗

NASA NTRS1973

Investigation of water quality parameters at selected points on the Tennessee River

Physical, chemical, and biological water quality parameters have been investigated at the Widow's Creek steam plant. The water quality parameters and field site locations have been selected so as to be compatible with the interests and needs of the Environmental Application Office at Marshall Space Flight Center. All sampling and testing was conducted as directed in the 13th Edition of Standard Methods of Analysis for Water and Waste Water or as suggested by NASA'S Technical Officer. Data is presented in a form compatible with that presently being collected by other agencies.

Manger, M. C.↗

NASA NTRSDate not supplied

Mission Overview and Status IGARSS Conference

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission is a strategic climate continuity mission that was defined in the 2010 document Responding to the Challenge of Climate and Environmental Change: NASA’s Plan for Climate-Centric Architecture for Earth Observations and Applications from Space (referred to as the “Climate Initiative”). Scheduled for launch in January 2024, the PACE mission represents NASA’s next investment in ocean biology, clouds, and aerosol data records [1]. PACE will extend the high quality ocean ecological, ocean biogeochemical, cloud, and aerosol particle data records begun by NASA in the 1990s, building on the exceptional heritages of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), and the Visible Infrared Imaging Radiometer Suite (VIIRS). The PACE project office at NASA’s GSFC is responsible for the satellite development, launch and operations. The NASA Headquarters PACE Program Science office is responsible for supporting the science data processing system and assembling competed community science teams, which will include field-based vicarious calibration and data product validation efforts to support the PACE Project Science team. The mission is planned for launch into a Sun synchronous polar orbit at 676.5 km with an inclination of 98 degrees and a 1 pm local ascending node crossing time. The PACE observatory is comprised of three instruments, an Ocean Color Instrument (OCI) and two polarimeters, the Hyper-Angular Rainbow Polarimeter 2 (HARP2) and the Spectro-Polarimeter for Exploration (SPEXone). The OCI is the primary instrument on the observatory and is being developed at Goddard Space Flight Center (GSFC). The OCI is a hyperspectral scanning radiometer designed to measure spectral radiances from the ultraviolet to shortwave infrared (SWIR) to enable advanced ocean color and heritage cloud and atmospheric aerosol science [2]. The HARP2 and SPEXone are complimentary instruments on the PACE observatory, acquired outside of GSFC. The HARP2 is multi-spectral, wide swath (supporting atmospheric correction of OCI), and hyper-angular, with capabilities for cloud science that exceed what is capable from OCI alone. The SPEXone is narrow swath, multi-angular, and hyperspectral, with capabilities for atmospheric aerosol science that exceed what can be accomplished with OCI. NASA Headquarters directed the mission development to be guided by a Design-to-Cost (DTC) process. All elements of the mission, other than the cost, are in the DTC trade space. At the heart of the DTC process are the mission studies, performed across all the mission elements. The mission studies were used to define appropriate approaches within and across elements while maximizing science capabilities at a high cost confidence. Mission baseline requirements development is also embedded within the DTC process, as these requirements were not established at the onset of the mission concept development. Baseline mission requirements are a product of the mission studies and are defined by the project office as part of the DTC process. At the time of this writing, OCI, HARP2 and SPEXone are integrated onto the PACE spacecraft, creating the PACE observatory. The instruments and spacecraft subsystems are undergoing combined functional performance testing in preparation for observatory-level environmental testing prior to shipment to the launch site. Here, we will provide an update on science capabilities and observatory readiness for its early 2024 launch.

remote sensing↗

NASA NTRSDate not supplied

Artificial Neural Network (ANN) Surface Longwave and Shortwave Fluxes Trained on CERES Observations

The Clouds and Earth’s Radiant Energy System (CERES) project provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed to provide key data for the applied sciences and educational users within a week of observation. FLASHFlux achieves this by using simplified calibration, an operational meteorological product from Global Modeling and Assimilation Office (GMAO), and its own surface parameterizations model. The CERES FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Terra and NOAA-20 observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1o x 1o gridded data that combines Terra and NOAA-20 observations. Currently, FLASHFlux uses the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) to derive its surface fluxes (Kratz et al., 2010; Gupta et al, 2001). A new Machine Learning (ML) based approach using Artificial Neural Networks to derive Surface Longwave (LW) & Shortwave (SW) fluxes based on training data from the CERES Clouds Radiative Swath (CRS) product is being investigated to replace LPSA and LPLA in the SSF surface flux products. One of the biggest hurdles in training ML model is model fitting. To overcome the problem of overfitting we use feature engineering that helps in finding the important feature and remove features that are irrelevant to the model. In our training we employed the Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training. We intercompare ANN fluxes against surface fluxes produced from the Fu-Liou model in CRS and the LPSA/LPLA in FLASHFlux SSF. Furthermore, we validated ANN derived fluxes to the Baseline Surface Radiation Network (BSRN).

P C Sawaengphokhai↗

NASA NTRS1994

Preliminary results from the flight of the Solar Array Module Plasma Interactions Experiment (SAMPIE)

SAMPIE, the Solar Array Module Plasma Interactions Experiment, flew in the Space Shuttle Columbia payload bay as part of the Office of Aeronautics and Space Technology-2 (OAST-2) mission on STS-62, March, 1994. SAMPIE biased samples of solar arrays and space power materials to varying potentials with respect to the surrounding space plasma, and recorded the plasma currents collected and the arcs which occurred, along with a set of plasma diagnostics data. A large set of high quality data was obtained on the behavior of solar arrays and space power materials in the space environment. This paper is the first report on the data SAMPIE telemetered to the ground during the mission. It will be seen that the flight data promise to help determine arcing thresholds, snapover potentials, and floating potentials for arrays and spacecraft in LEO.

Ferguson, Dale C.↗

DOE OSTI2025

Deep reinforcement learning control for co-optimizing energy consumption, thermal comfort, and indoor air quality in an office building

With the recent demand for decarbonization and energy efficiency, advanced HVAC control using Deep Reinforcement Learning (DRL) becomes a promising solution. Due to its flexible structures, DRL has been successful in energy reduction for many HVAC systems. However, only a few researches applied DRL agents to manage the entire central HVAC system and control multiple components in both the water loop and the air loop, owing to its complex system structures. Moreover, those researches have not extended their applications by incorporating the indoor air quality, especially both CO2 and PM2.5concentrations, on top of energy saving and thermal comfort, as achieving those objectives simultaneously can cause multiple control conflicts. What's more, DRL agents are usually trained on the simulation environment before deployment, so another challenge is to develop an accurate but relatively simple simulator. Therefore, we propose a DRL algorithm for a central HVAC system to co-optimize energy consumption, thermal comfort, indoor CO2 level, and indoor PM2.5 level in an office building. To train the controller, we also developed a hybrid simulator that decoupled the complex system into multiple simulation models, which are calibrated separately using laboratory test data. The hybrid simulator combined the dynamics of the HVAC system, the building envelope, as well as moisture, CO2, and particulate matter transfer. Three control algorithms (rule-based, MPC, and DRL) are developed, and their performances are evaluated on the hybrid simulator environment with a realistic scenario (i.e., with stochastic noises). The test results showed that, the DRL controller can save 21.4 % of energy compared to a rule-based controller, and has improved thermal comfort, reduced indoor CO2 concentration. The MPC controller showed an 18.6 % energy saving compared to the DRL controller, mainly due to savings from comfort and indoor air quality boundary violations caused by unmeasured disturbances, and it also highlights computational challenges in real-time control due to non-linear optimization. Finally, we provide the practical considerations for designing and implementing the DRL and MPC controllers based on their respective pros and cons.

Guo, Fangzhou↗

NASA NTRS2005

California Space Grant Consortium

The organizational and administrative structure of the CaSGC has the Consortium Headquarters Office (Principal Investigator - Dr. John Kosmatka, California Statewide Director - Dr. Michael Wiskerchen) at UC San Diego. Each affiliate member institution has a campus director and an scholarship/fellowship selection committee. Each affiliate campus director also serves on the CaSGC Advisory Council and coordinates CMIS data collection and submission. The CaSGC strives to maintain a balance between expanded affiliate membership and continued high quality in targeted program areas of aerospace research, education, workforce development, and public outreach. Associate members are encouraged to participate on a project-by-project basis that meets the needs of California and the goals and objectives of the CaSGC. Associate members have responsibilities relating only to the CaSGC projects they are directly engaged in. Each year, as part of the CaSGC Improvement Plan, the CaSGC Advisory Council evaluates the performance of the affiliate and associate membership in terms of contributions to the CaSGC Strategic Plan, These CaSGC membership evaluations provide a constructive means for elevating productive members and removing non-performing members. This Program Improvement and Results (PIR) report will document CaSGC program improvement results and impacts that directly respond to the specific needs of California in the area of aerospace-related education and human capital development and the Congressional mandate to "increase the understanding, assessment, development and utilization of space resources by promoting a strong education base, responsive research and training activities, and broad and prompt dissemination of knowledge and technology".

Kosmatka, John↗

NASA NTRS2014

NASA's Optical Measurement Program 2014

The Optical Measurements Group (OMG) within the NASA Orbital Debris Program Office (ODPO) addresses U.S. National Space Policy goals by monitoring and characterizing debris. Since 2001, the OMG has used the Michigan Orbital Debris Survey Telescope (MODEST) at Cerro Tololo Inter-American Observatory (CTIO) in Chile for general orbital debris survey. The 0.6-m Schmidt MODEST provides calibrated astronomical data of GEO targets, both catalogued and uncatalogued debris, with excellent image quality. The data are utilized by the ODPO modeling group and are included in the Orbital Debris Engineering Model (ORDEM) v. 3.0. MODEST and the CTIO/SMARTS (Small and Moderate Aperture Research Telescope System) 0.9 m both acquire filter photometric data, as well as synchronously observing targets in selected optical filters. This information provides data used in material composition studies as well as longer orbital arc data on the same target, without time delay or bias from a rotating, tumbling, or spinning target. NASA, in collaboration with the University of Michigan, began using the twin 6.5-m Magellan telescopes at Las Campanas Observatory in Chile for deep imaging (Baade) and spectroscopic data (Clay) in 2011. Through the data acquired on Baade, debris have been detected that are ~3 magnitudes fainter than detections with MODEST, while the data from Clay provide better resolved information used in material characterization analyses via selected bandpasses. To better characterize and model optical data, the Optical Measurements Center (OMC) at NASA/JSC has been in operation since 2005, resulting in a database of comparison laboratory data. The OMC is designed to emulate illumination conditions in space using equipment and techniques that parallel telescopic observations and source-target-sensor orientations. Lastly, the OMG is building the Meter Class Autonomous Telescope (MCAT) at Ascension Island. The 1.3-m telescope is designed to observe GEO and LEO targets, using a modified Ritchey-Chrétien configuration on a double horseshoe equatorial mount to allow tracking objects at LEO rates through the dome's keyhole at zenith. Through the data collection techniques employed at these unique facilities, NASA's ODPO has developed a multi-faceted approach to characterize the orbital debris risk to satellites in various altitudes and provide material characterization of debris via photometric and spectroscopic measurements. Ultimately, the data are used in conjunction with in-situ and radar measurements to provide accurate data for models of our space environment and service spacecraft risk assessment.

Cowardin, H.↗

NASA NTRS2014

NASA's Optical Measurement Program 2014

The Optical Measurements Group (OMG) within the NASA Orbital Debris Program Office (ODPO) addresses U.S. National Space Policy goals by monitoring and characterizing debris. Since 2001, the OMG has used the Michigan Orbital Debris Survey Telescope (MODEST) at Cerro Tololo Inter-American Observatory (CTIO) in Chile for general orbital debris surveys. The 0.6-m Schmidt MODEST provides calibrated astronomical data of GEO targets, both catalogued and uncatalogued debris, with excellent image quality. The data are utilized by the ODPO modeling group and are included in the Orbital Debris Engineering Model (ORDEM) v. 3.0. MODEST and the CTIO/SMARTS (Small and Moderate Aperture Research Telescope System) 0.9 m are both employed to acquire filter photometry data as well as synchronously observe targets in selected optical filters. Obtaining data synchronously yields data for material composition studies as well as longer orbital arc data on the same target without time delay or bias from a rotating, tumbling, or spinning target. Observations of GEO orbital debris using the twin 6.5-m Magellan telescopes at Las Campanas Observatory in Chile for deep imaging (Baade) and spectroscopic data (Clay) began in 2011. Through the data acquired on Baade, debris has been detected that reaches approx. 3 magnitudes fainter than detections with MODEST, while the spectral data from Clay provide better resolved information used in material characterization analyses. To better characterize and model optical data, the Optical Measurements Center (OMC) at NASA/JSC has been in operation since 2005, resulting in a database of comparison laboratory data. The OMC is designed to emulate illumination conditions in space using equipment and techniques that parallel telescopic observations and sourcetarget- sensor orientations. Lastly, the OMG is building the Meter Class Autonomous Telescope (MCAT) at Ascension Island. The 1.3-m telescope is designed to observe GEO and LEO targets, using a modified Ritchey-Chrétien configuration on a double horseshoe equatorial mount to allow tracking objects at LEO rates through the dome's keyhole at zenith. Through the data collection techniques employed at these unique facilities, NASA's ODPO has developed a multifaceted approach to characterize the orbital debris risk to satellites in various altitudes and provide insight leading toward material characterization of debris via photometric and spectroscopic measurements. Ultimately, the data are used in conjunction with in-situ and radar measurements to provide accurate data for models of our space environment and for facilitating spacecraft risk assessment.

Cowardin, H.↗

NASA NTRS1998

Incorporating Parallel Computing into the Goddard Earth Observing System Data Assimilation System (GEOS DAS)

Atmospheric data assimilation is a method of combining actual observations with model forecasts to produce a more accurate description of the earth system than the observations or forecast alone can provide. The output of data assimilation, sometimes called the analysis, are regular, gridded datasets of observed and unobserved variables. Analysis plays a key role in numerical weather prediction and is becoming increasingly important for climate research. These applications, and the need for timely validation of scientific enhancements to the data assimilation system pose computational demands that are best met by distributed parallel software. The mission of the NASA Data Assimilation Office (DAO) is to provide datasets for climate research and to support NASA satellite and aircraft missions. The system used to create these datasets is the Goddard Earth Observing System Data Assimilation System (GEOS DAS). The core components of the the GEOS DAS are: the GEOS General Circulation Model (GCM), the Physical-space Statistical Analysis System (PSAS), the Observer, the on-line Quality Control (QC) system, the Coupler (which feeds analysis increments back to the GCM), and an I/O package for processing the large amounts of data the system produces (which will be described in another presentation in this session). The discussion will center on the following issues: the computational complexity for the whole GEOS DAS, assessment of the performance of the individual elements of GEOS DAS, and parallelization strategy for some of the components of the system.

Larson, Jay W.↗

NASA NTRS2000

Using TRMM Field Campaign Data for Assessing GEOS Forecast and Assimilation Products

The wealth of in-situ measurements gathered during Tropical Rain Measuring Mission (TRMM) field campaigns over a wide range of tropical conditions constitute an important data source for evaluating the quality of global model forecasts and assimilated datasets. In this study we use selected observations of cloud microphysics and atmospheric sounding from TEFLUN-1998, SCEMEX-1998, and TRMMLBA-1999 to examine the assimilation and forecast fields produced by the operational GEOS-3 (Goddard Earth Observing System - version 3) global data assimilation system (DAS) and a new finite-volume DAS under development at the Data Assimilation Office. Additionally, TRMM field campaign measurements are used to verify the impact of assimilating rainfall and moisture data derived from TRMM Microwave Imager and Special Sensor Microwave/Imager instruments on the GEOS analysis. We will also explore issues concerning the 'error of representativeness' in using in-situ observations of quantities with large spatial and temporal variability such as precipitation for validating gridded global data products.

Hou, Arthur Y.↗

NASA NTRS2005

Real-time Enhanced Vision System

Flying in poor visibility conditions, such as rain, snow, fog or haze, is inherently dangerous. However these conditions can occur at nearly any location, so inevitably pilots must successfully navigate through them. At NASA Langley Research Center (LaRC), under support of the Aviation Safety and Security Program Office and the Systems Engineering Directorate, we are developing an Enhanced Vision System (EVS) that combines image enhancement and synthetic vision elements to assist pilots flying through adverse weather conditions. This system uses a combination of forward-looking infrared and visible sensors for data acquisition. A core function of the system is to enhance and fuse the sensor data in order to increase the information content and quality of the captured imagery. These operations must be performed in real-time for the pilot to use while flying. For image enhancement, we are using the LaRC patented Retinex algorithm since it performs exceptionally well for improving low-contrast range imagery typically seen during poor visibility conditions. In general, real-time operation of the Retinex requires specialized hardware. To date, we have successfully implemented a single-sensor real-time version of the Retinex on several different Digital Signal Processor (DSP) platforms. In this paper we give an overview of the EVS and its performance requirements for real-time enhancement and fusion and we discuss our current real-time Retinex implementations on DSPs.

Hines, Glenn D.↗

NASA NTRSDate not supplied

Florida Water Resources: Assessing Coastal Resiliency Across Florida's Aquatic Preserves Response To Hurricane Forces

Intensifying weather events, sea level rise, and extensive coastal development in Southwestern Florida are escalating the need for Florida’s mangrove conservation. These mangroves are imperative for coastline stabilization, habitat provision for native species, and water quality management. Our partner, the Florida Department of Environmental Protection (FDEP), Office of Resilience and Coastal Protection is tasked with monitoring and conserving the Charlotte Harbor, Estero Bay, Rookery Bay, and Pinellas County Aquatic Preserves. We developed the Growth, Resilience, and Optical Vegetation Evaluator (GROVE) Google Earth Engine toolset for partners to determine mangrove forest extent through time, analyze mangrove forest health, and collect several water quality parameters within the preserves from January 2002–August 2022. The toolset provides easily accessible data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Landsat 9 Operational Land Imager 2 (OLI-2), and the Shuttle Radar Topography Mission (SRTM). Using training datasets of known mangrove forest locations, we also established a machine learning approach to create mangrove extent maps. Maps from all four preserves indicated migration of mangrove forests inland as the greatest areas of change were transitional zones. Additionally, normalized difference vegetation index (NDVI), normalized difference turbidity index (NDTI), and chlorophyll-a maps were generated for the partners. This project provides decision makers with a useful tool for understanding temporal changes in Florida’s aquatic preserves, identifying areas of ecological stress, and providing actionable data to make informed plans for mangrove preservation.

Samuel Perrello↗

NASA NTRS2022

Florida Water Resources: Assessing Coastal Resiliency Across Florida's Aquatic Preserves in Response to Hurricane Forces

Remote sensing↗

DOE OSTI2025

Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (June to October 2020)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken June to October 2020 at two locations (OBJ1 and OBJ2) near the confluence of the Oh-Be-Joyful Creek and Slate River. The site is one of the field sites in focus for the SLAC National Accelerator Laboratory Groundwater Quality Science Focus Area (SFA) program. Sediment samples from a deep soil pit were collected from 30 cm depth below surface to just above the cobble layer (~190-250 cm depth) at discrete depths every 40 cm for microbial analyses. A total of 35 metagenomes were sequenced through the Joint Genome Institute (JGI) and can be found under Genomes Online Database (GOLD) sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 2848 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (September 2019)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken September 2019 at one locations (OBJ1) near the confluence of the Oh-Be-Joyful Creek and Slate River. The site is one of the field sites in focus for the SLAC National Accelerator Laboratory Groundwater Quality Science Focus Area (SFA) program. Sediment samples from a deep soil pit were collected from 50 to 150 cm depth below surface at discrete depths every 20 cm for microbial analyses. A total of 6 metagenomes were sequenced through the Joint Genome Institute (JGI) and can be found under Genomes Online Database (GOLD) sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 2562 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (June 2018)

Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken June 2018 at two locations (OBJ1 and OBJ2) near the confluence of the Oh-Be-Joyful Creek and Slate River. The site is one of the field sites in focus for the SLAC National Accelerator Laboratory Groundwater Quality Science Focus Area (SFA) program. Sediment samples from a deep soil pit were collected from 50 to 150 cm depth below surface at discrete depths every 20 cm for microbial analyses. A total of 12 metagenomes were sequenced through the Joint Genome Institute (JGI) and can be found under Genomes Online Database (GOLD) sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 1233 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2020

TPSAS-NF1676L-36113-DND

Concerns about the effects of extreme heat and poor air quality are increasing in North America’s largest urban centers. In Philadelphia, environmental and public health groups are concerned about how these phenomena disproportionality affect marginalized communities and populations, which often have extensive impervious surfaces and little access to green space. In order to address these concerns, the Philadelphia Department of Public Health and the Office of Sustainability seek to effectively prioritize cooling initiatives to reduce urban heat and decrease air pollutants. We evaluated land surface temperature (LST) and the Normalized Difference Vegetation Index (NDVI), as a measure of overall greenness, obtained from NASA Earth observations Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). These analyses we recombined with local tree inventory, air quality, and socioeconomic data through a multivariate analysis to identify areas where new trees or cooling adaptations are most needed. The results and data of this project can be used by our partners to inform both short-term heat relief planning and a long-term, multi-agency heat response.

Spencer Nelson↗