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The BOREAS TGB-9 team collected data in order to inventory and quantify the anthropogenic and biogenic NMHCs over the BOREAS study areas. This data set contains concentration and mixing ratio values for several NMHCs collected at the BOREAS SSA from 27-May-1994 to 15-Sep-1994. The data are provided in tabular ASCII files.
The author has identified the following significant results. ERTS-1 data is ideally suited for small-scale geologic mapping and structural analysis of remote, inaccessible areas such as the Andes of South America. The synoptic view of large areas, low sun-angle and multispectral nature of the images provide the right ingredients for improving existing geologic and other maps of the regions. In most areas it has been possible to compile geologic, drainage, and cultural interpretive overlays to individual scenes mainly using MSS bands 4, 5, and 7. A test image mosaic using MSS band 6 is being compiled for Test Area 7 (La Paz, Bolivia). It will be at a scale of 1:1,000,000 and cover 4 x 6 degrees of latitude and longitude and will serve as a compilation base on which to join the overlays. Repetitive data shows changes in river channels and sedimentation plumes, changes in lake shorelines, and surface moisture distribution. Vegetation and snow line changes in the Andes have been recognized. A year of seasonal data, however, has not yet been acquired due to tape recorder failure.
The author has identified the following significant results. Evaluation of ERTS-1 imagery for the Sand Hills region of Nebraska has shown that the data can be used to effectively measure several parameters of inventory needs. (1) Vegetative biomass can be estimated with a high degree of confidence using computer compatable tape data. (2) Soils can be mapped to the subgroup level with high altitude aircraft color infrared photography and to the association level with multitemporal ERTS-1 imagery. (3) Water quality in Sand Hills lakes can be estimated utilizing computer compatable tape data. (4) Center pivot irrigation can be inventoried from satellite data and can be monitored regarding site selection and relative success of establishment from high altitude aircraft color infrared photography. (5) ERTS-1 data is of exceptional value in wide-area inventory of natural resource data in the Sand Hills region of Nebraska.
Spectral classes of urban phenomena identified from ERTS multispectral scanner data in Milwaukee included Surburban, Inner City, Industry, Grassy (open area), Road, Wooded Suburb, Water, Cloud, and Shadow. The Milwaukee spectral class statistics were used to classify the Chicago area, within the same ERTS frame, and similar results were achieved. In another ERTS frame, Marion County (Indianapolis) data were classified into similar classes. The Marion County ERTS study was supported by a land use classification of an area near downtown Indianapolis that utilized 12-band MSS data collected by aircraft from 3000 feet. The results of the ERTS analyses suggest that satellite data will be a useful tool for the urban planner for monitoring urban land use.
Comparative statistics were presented on the capability of LANDSAT-1 and three of the Skylab remote sensing systems (S-190A, S-190B, S-192) for the recognition and inventory of analogous natural vegetations and landscape features important in resource allocation and management. Two analogous regions presenting vegetational zonation from salt desert to alpine conditions above the timberline were observed, emphasizing the visual interpretation mode in the investigation. An hierarchical legend system was used as the basic classification of all land surface features. Comparative tests were run on image identifiability with the different sensor systems, and mapping and interpretation tests were made both in monocular and stereo interpretation with all systems except the S-192. Significant advantage was found in the use of stereo from space when image analysis is by visual or visual-machine-aided interactive systems. Some cost factors in mapping from space are identified. The various image types are compared and an operational system is postulated.
There are no author-identified significant results in this report.
The report, Commercial Spent Nuclear Fuel and High-Level Radioactive Waste Inventory Report [FCRDNFST-2013-000263, Vinson/Metzger/Carter 2017], provides information on the inventory of commercial spent nuclear fuel (SNF) as well as Government-owned SNF and High Level Waste (HLW) in the U.S. Department of Energy (DOE) complex. Inventory forecasts for commercial SNF were made for a few selected scenarios of future commercial nuclear power generation involving the existing reactor fleet including one scenario involving reactors under construction. This Dry Storage Cask Inventory Assessment report uses the data contained in FCRD-NFST-2013- 000263 to define the existing inventory of SNF in dry storage. This information is integrated with data on dry storage canisters and casks from the report, Storage and Transport Cask Data for Used Commercial Nuclear Fuel, 2013 U. S. Edition [ATI-TR-13047 2013] in a Microsoft Access database (hereinafter referred to as the Dry Storage Cask/Inventory Database). The Dry Storage Cask/Inventory Database is used to produce queries for assessing the various systems used for the existing inventory of dry storage casks. The database and this report will be revised in the future as the inventory of fuel in dry storage changes and as additional information becomes available. Both bare fuel and canistered dry storage casks are currently used to dry store SNF. Only 4 utilities currently use bare fuel storage systems for dry storage at 5 reactor sites. Seven unique bare fuel storage cask systems are used to store a total of 9,694 spent fuel assemblies in 212 total casks.
This work introduces a novel application of radiochronometry to estimate nuclide inventories in molten salt reactor off-gas systems based on gamma spectroscopic data from the Molten Salt Reactor Experiment. By analyzing isotopic, isobaric, and isomeric activity ratios, key depletion model parameters related to species transport within the reactor system could be inferred. The findings demonstrate the potential of leveraging a limited subset of gamma spectroscopy measurements to accurately estimate nuclide inventories throughout the off-gas system. The approach can be useful in reactor design activities and support analyses relevant to operations, safety, security, and safeguards.
With the rapid and accelerating growth of PV module installation and an increase of PV modules from the nineteen eighties and nineties reaching the end of their 30 year lifespan, their proper end of life treatment gets into focus. This report deals with the approaches, the environmental impacts and the recovered materials of PV module recycling. In this report, the environmental life cycle assessment of the current generation recycling of crystalline silicon (c-Si) and cadmium telluride (CdTe) PV modules is described. Due to the still limited waste stream today, c-Si PV modules are mainly treated in recycling plants designed for treatment of laminated glass, metals or electronic waste. Only the bulk materials glass, aluminium and copper are recovered, while the cells and other materials such as plastics are incinerated. CdTe PV modules have been treated in dedicated recycling plants for many years and life cycle inventories of this process have been published. The semiconductor is recovered in addition to glass and copper. Life cycle inventories of the recycling of current c-Si and CdTe PV modules are compiled following two modelling approaches related to recycling. The cut-off approach uses economic allocation to divide the total efforts of the recycling process between the treatment of the used PV module and the recovered products. The end-of-life approach considers the recycling process separately from the potentially avoided burdens due to recovered materials. The life cycle inventories of c-Si PV module recycling are based on data obtained from four European recyclers surveyed between 2015 and 2016. For CdTe PV module recycling, the life cycle inventories were established based on published data for the First Solar recycling facility in Germany. The life cycle impact assessment is done based on six environmental indicators previously identified as most relevant for PV electricity: particulate matter, freshwater ecotoxicity, human toxicity non-cancer effects, human toxicity cancer effects, mineral, fossil and renewable resource depletion and climate change. The life cycle inventories according to the cut-off approach can be applied to complement existing life cycle inventory data on PV systems. The environmental impacts of the recycling of c-Si PV modules are very small (maximum 1.1 %) compared to the impacts caused by the production of a 3 kWp residential PV system mounted on a slanted roof. In the case of CdTe PV module recycling, the treatment of the PV panels has the highest but still rather minor contribution in the indicator climate change (4.8 %). The life cycle inventories according to the end-of-life approach allow an assessment of the net environmental benefits of recycling. The recovery of glass, metals, and semiconductor material from c-Si and CdTe PV modules causes lower environmental impacts than the extraction, refinement and supply of the respective materials from primary resources. The highest potential benefits are observed in the indicator mineral, fossil and renewable resource depletion. The data quality of the recycling of c-Si PV modules is classified as fair since only limited information is available. In contrast, the data quality of CdTe PV module recycling is considered very good. The study was financed by the Swiss Federal Office of Energy (SFOE) and carried out in the framework of the Task 12 of the Photovoltaic Powers System Programme (PVPS) of the International Energy Agency (IEA).
NASA conducts airborne and field investigations that produce a wealth of valuable research data. Unfortunately, this data is often scattered across individual scientist hard drives or NASA Distributed Active Archive Centers and it can be difficult to locate and retrieve. Although satellite data has been successfully consolidated by tools such as EarthData Search, airborne and field investigation data present unique challenges stemming from the variability of temporal, spatial, platform, and instrument metadata. To address these difficulties with data retrieval and metadata variability, the Interagency Implementation and Concepts Team established an Airborne Data Management Group to improve airborne data search, understanding, access, and use. Surveys have been conducted of end users in order to build query lists that will drive the augmentation and standardization of existing metadata. Detailed metadata was then laboriously compiled from present and historic airborne and field investigations to build a database that will enable intelligent data search and retrieval. The inventory structure and function will be described and demonstrated. The purpose of this presentation is to bring awareness to this effort, to highlight and describe the issues and complications in development, and to increase user interest prior to public release in 2020.
Solar photovoltaic (PV) installations must be properly dismantled and any waste treated and disposed at the end of project life. However, because most of the world's nearly 400 GW of PV systems have been built in the past decade - each expected to operate for between 20 and 30 years - current PV module waste volumes do not yet justify widespread operation of PV recycling facilities. The necessary policies and technologies for recycling PV systems are currently under rapid development. With modifications to the European waste electrical and electronic equipment directive (WEEE 2012/19/EU) in 2012, take back and recycling of PV modules is, in fact, already mandatory in Europe. There, take back and recycling is currently performed in small but annually-increasing quantities. Even though waste treatment is considered part of a module's life cycle, only a few life cycle inventories (LCI) of energy and materials flows are available for the industrial recycling processes that are used today to recycle crystalline silicon-based (c-Si) PV modules. LCI are the data inputs that inform lifecycle assessments which quantify the environmental impacts across the full life cycle of PV modules - from manufacturing and use to end of life. To help progress the industry forward, a survey of European recyclers was performed to characterize existing commercial recycling processes and share associated life cycle inventory data. System Boundaries: The reference unit, or "functional unit" in the vocabulary of life cycle assessment, is defined as the processing of one metric ton of crystalline silicon PV modules in recycling lines for laminated glass, metals, and electronic wastes. Today, all modules are processed in discrete batches, yet not metered at that scale. Thus the process energy for a batch is estimated based on scaled annual production data as well as on input and output streams.
The southeastern U.S. produces the most industrial roundwood in the U.S. each year, largely from commercial pine plantations. The extent of plantation forests and management dynamics can be difficult to ascertain from periodic forest inventories, yet short-rotation tree plantations also present challenges for remote sensing. Here, we integrated spectral, temporal, and structural information from airborne and satellite platforms to distinguish pine plantations from natural forests and evaluate the contribution from planted forests to regional forest coverin the southeastern U.S. Within flight lines from NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, lidar metrics of forest structure had the highest overall accuracy for pine plantations among single-source classifications (90%), but the combination of spectral and temporal metrics from Landsat generated comparable accuracy (91%). Combined structural, temporal, and spectral information from G-LiHT and Landsat had the highest accuracy for plantations (92%) and natural forests (88%). At a regional scale, classifications using Landsat spectral and temporal metrics had between 74 and 82% mean class accuracy for plantations.Regionally, plantations accounted for 28% of forest cover in the southeastern U.S., a result similar to plot-based estimates, albeit with greater spatial detail. Regional maps of plantation forests differed from existing map products, including the National Land Cover Database. Combining plantation extent in 2011 with Landsat based forest change data identified strong regional gradients in plantation dynamics since 1985, with distinct spatial patterns of rotation age (east-west) and plantation expansion (interior). Our analysis demonstrates the potential to improve the characterization of dynamic land cover classes, including economically important timber plantations, by integrating diverse remote sensing datasets. Critically, multi-source remote sensing provides an approach to leverage periodic forest inventory data for annual monitoring of managed forest landscapes.
This work discusses challenges and approaches to uncertainty analyses associated with the development of a nuclide inventory benchmark for fuel irradiated in a boiling water reactor. The benchmark under consideration is being developed based on experimental data from the SFCOMPO international database. The focus herein is on how to address missing data in fuel design and operating conditions that are important for adequately simulating the time-dependent changes in fuel during irradiation in the reactor. The effects of modeling assumptions and uncertainties in modeling parameters on the calculated nuclide inventory were analyzed and quantified through computational models developed using capabilities in the SCALE code system. Particular attention was given to the impact of the power history and water coolant density on the calculated nuclide inventory, as well as to the effect of geometry modeling considerations not usually addressed in a nuclide inventory benchmark. These considerations include gap closure, channel bow, and channel corner radius, which do not usually apply to regular reactor operation but are relevant for assessing impacts of potential anomalous operating scenarios.
The Electricity Baseline (2022) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2022" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569193. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).
The Electricity Baseline (2020) is a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data and was created using the ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0). The Python package used the "ELCI_2020" model configuration to set the facility and generation data sources and years that were used to create this life cycle inventory, which were taken from publicly accessible datasets and automatically curated into a local data store. An archive of the data stores used in this model is available online: https://doi.org/10.18141/2569605. This model is presented in GreenDelta's openLCA schema v2 JSON-LD format (https://greendelta.github.io/olca-schema/).
With the continental United States divided into ten forest and grassland ecosystems, the Ten Ecosystem Study (TES) is designed to investigate the feasibility and applicability of state-of-the-art automatic data processing remote sensing technology to inventory forest, grassland, and water resources by using Land Satellite data. The study will serve as a prelude to a possible future nationwide remote sensing application to inventory forest and rangeland renewable resources. This plan describes project design and phases, the ten ecosystem, data utilization and output, personnel organization, resource requirements, and schedules and milestones.
Data acquired by synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) were processed and analyzed to derive forest-related resources inventory information. The SAR data were acquired by using the NASA aircraft X-band SAR with linear (HH, VV) and cross (HV, VH) polarizations and the SEASAT L-band SAR. After data processing and data quality examination, the three polarization (HH, HV, and VV) data from the aircraft X-band SAR were used in conjunction with LANDSAT MSS for multisensor data classification. The results of accuracy evaluation for the SAR, MSS and SAR/MSS data using supervised classification show that the SAR-only data set contains low classification accuracy for several land cover classes. However, the SAR/MSS data show that significant improvement in classification accuracy is obtained for all eight land cover classes. These results suggest the usefulness of using combined SAR/MSS data for forest-related cover mapping. The SAR data also detect several small special surface features that are not detectable by MSS data.
Low altitude, oblique and vertical color photography taken from EPA's Enviro-Pod Ka 85 camera system has provided the data for taking an inventory of building surface materials in a test area of downtown Baltimore. Photography was acquired from a gridded flight plan to provide views of all sides of buildings. Color, texture, and linear detail are employed in the photo interpretation aided by contextual reference to a classification of building construction type developed in an earlier study. The work could potentially support a materials inventory initiated by the National Acid Precipitation Assessment Program (NAPAP) by scientists from EPA, Geological Survey, and the Department of Energy. Initial results show the method to be viable. Discrete surface materials such as brick, both bare and painted, stone, and metal are identified.