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At least 253 records · Page 14

Stakeholder analysis for designing an urban air quality data governance ecosystem in smart cities

Cities, the world over, are fuelling economic growth. At the same time, rapid urbanization is a root cause of serious environmental damage. Recent WHO global air pollution guidelines highlight air pollution as a critical environmental threat along with climate change. To address these threats, smart cities and clean air programs are on a rise. In smart cities, data and Information and Communication Technologies (ICT) are major drivers of city transformations. The 4th Industrial Revolution (4IR) technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), and cloud computing have the potential to accelerate these transformations toward urban resilience. However, the success of smart cities and clean air programs depends on cohesive multi-sector stakeholder contributions. This study conducted interdisciplinary participative stakeholder analysis to understand the data, and sectorial challenges, to outline the technological opportunities to facilitate clean air programs in Indian smart cities. The research highlights gaps due to siloed stakeholder operations, lack of data calibration, non-alignment of smart city and air quality management services, non-availability of health exposure data, and difficulty in translating scientific data into implementable actions. Stakeholders expressed potential ‘fit for the purpose’ use of IoT devices, satellites, smartphones, and mobility data augmented by AI methods in bridging these gaps. In conclusion, the analysis points toward a need to develop an easily accessible and ubiquitous urban data governance ecosystem enabling seamless cross-sector data exchanges to build trusting relationships among the stakeholders across the air quality management value chain.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Time to Use Dendrohydrological Data in Water Resources Management?

Despite a wide consensus on the potential importance of streamflow reconstructions to water resources planning and management activities, applications of dendrohydrology have been piecemeal. This review article describes barriers to widespread use of dendrohydrological data and suggests research needs for operationalizing streamflow records reconstructed from these proxy data.

42 ENGINEERING↗

Towards scalable dataframe systems

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in R and Python, dataframes face performance issues even on moderately large datasets. Moreover, there is significant ambiguity regarding dataframe semantics. In this paper we lay out a vision and roadmap for scalable dataframe systems. To demonstrate the potential in this area, we report on our experience building Modin, a scaled-up implementation of the most widely-used and complex dataframe API today, Python's pandas. With pandas as a reference, we propose a simple data model and algebra for dataframes to ground discussion in the field. Given this foundation, we lay out an agenda of open research opportunities where the distinct features of dataframes will require extending the state of the art in many dimensions of data management. We discuss the implications of signature dataframe features including flexible schemas, ordering, row/column equivalence, and data/metadata fluidity, as well as the piecemeal, trial-and-error-based approach to interacting with dataframes.

97 MATHEMATICS AND COMPUTING↗

Annual Status Report (FY 2020): Performance Assessment for the Environmental Restoration Disposal Facility

DOE O 435.1 and DOE M 435.1-1 require that a determination of continued adequacy of the performance assessment (PA) (CP-60089), composite analysis, and disposal authorization statement (DAS) be made annually, and these guidelines must be used to consider the results of data collection and analysis from research, field studies, and monitoring as well as provide the need to update any radioactive waste management basis documents. Beginning in 1996, the Environmental Restoration Disposal Facility (ERDF) started accepting low-level radioactive, hazardous, and mixed wastes generated during cleanup activities at the Hanford Site. ERDF is composed of a series of cells or disposal areas and can accommodate future design expansions as needed. Currently, there are 10 cells. During this reporting period (fiscal year 2020, which extended from October 1, 2019, through September 30, 2020), approximately 3.39E+04 U.S. tons (3.07E+04 metric tons) of waste was disposed at ERDF. From ERDF inception through September 30, 2020, approximately 18.5 million U.S. tons of waste has been disposed at ERDF, which equates to the consumption of approximately 88% of the disposal volume. As a condition of the DAS, disposal operations within ERDF must be in accordance with the waste acceptance criteria (ERDF-00011) that provide specific radionuclide disposal limits, waste form restrictions, and descriptions of acceptable waste packages in compliance with DOE M 435.1-1 requirements. The ERDF waste acceptance criteria stipulate that waste destined for disposal at ERDF be controlled based on source, physical form, and contaminant concentration and activity levels. There have been no changes to the physical configuration of ERDF or to the waste forms (source, physical form, etc.). No new Unreviewed Disposal Question Screenings or Evaluations have been generated during this reporting period. Therefore, there are no noted impacts to the PA, composite analysis, DAS, or radioactive waste management basis documents resulting from the evaluations and screenings. Sum of fraction analysis shows that the disposed inventory meets both the concentration and inventory threshold requirements. A sum of fractions value is computed for ERDF sensitive radionuclides contributing to the all pathways and air pathway inventory limits. Computed values were 8.85E-02 and 1.78E-01, respectively. The disposed waste inventory remained well under the PA imposed limits. Required monitoring was satisfactorily completed during the fiscal year reporting period (fiscal year 2020). Compliance with performance objectives were met as each of the reported values were well below the established limit. Overall, there are no substantive changes to primary PA assumptions nor changes to the PA analysis conclusion; therefore, compliance with DOE O 435.1 and the DAS is maintained.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Replication Data for: Elucidating the composition of PtAg surface alloys with atomic-scale imaging and spectroscopy

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: The role of oxygenated species in the catalytic self-coupling of MeOH on O pre-covered Au(111)

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: Toward benchmarking theoretical computations of elementary rate constants on catalytic surfaces: formate decomposition on Au and Cu

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: Aggregated nanoparticles: Sample preparation and analysis by atom probe tomography

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: Tuning reactivity layer-by-layer: formic acid activation on Ag/Pd(111)

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: Comment on "STM study of the (111) and (100) surfaces of PdAg, Surf. Sci. 417 (1998) 292-300" and references therein

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: On the Origin of Sinter-Resistance and Catalyst Accessibility in Raspberry-Colloid-Templated Catalyst Design

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: Facilitating hydrogen atom migration via a dense phase on palladium islands to a surrounding silver surface

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: Efficient and selective carbon-carbon coupling on coke-resistant PdAu single-atom alloys

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: Mechanistic insights into carbon-carbon coupling on NiAu and PdAu single-atom alloys

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: Mechanistic and Electronic Insights into a Working NiAu Single-Atom Alloy Ethanol Dehydrogenation Catalyst

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: Entropic Control of HD Exchange Rates over Dilute Pd-in-Au Alloy Nanoparticle Catalysts

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replication Data for: Latent Representation Learning for Structural Characterization of Catalysts

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗