The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near‐ and long‐term ecological risks, developed in collaboration with climate scientists. State‐of‐the‐art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate.
This content will become publicly available on June 1, 2024
- Award ID(s):
- 2034800
- NSF-PAR ID:
- 10488117
- Publisher / Repository:
- ASEE Annual Conference Paper Repository
- Date Published:
- Format(s):
- Medium: X
- Location:
- https://nemo.asee.org/public/conferences/327/papers/37526/view
- Sponsoring Org:
- National Science Foundation
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Abstract Integr Environ Assess Manag 2024;20:367–383. © 2023 The Authors.Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). -
Given the strategic importance of the semiconductor manufacturing sector and the CHIPS Act impact on microelectronics, it is more imperative than ever to train the next generation of scientists and engineers in the field. However, this is a challenging feat since nanofabrication education uses hands-on cleanroom facilities. Since cleanrooms are expensive, have access constraints due to safety concerns, and offer limited instructional space, class sizes and outreach events are limited. To complement instruction in nanotechnology education, there is some open- or educational-access software, which is computer-based and focuses only on training for individual equipment, not on the typical workflow for device fabrication. The objective of this work was to develop an accessible virtual reality ecosystem that provides an immersive education and outreach on device nanofabrication that is user-friendly for a broad range of audiences. At the George Washington University (GWU), a virtual reality cleanroom prototype has been developed. It consists of a 45-minute gameplay module that covers the process flow for the fabrication of micro-scale resistors, from sample preparation to electrical characterization. We also performed a mixed methods study to investigate how 5 students in a nanoelectronics course utilized this virtual reality cleanroom prototype and what changes they recommend to improve its user interface and learner experience. The study population for this work-in-progress consisted of students enrolled in a nanoelectronics course at GWU during the 2022-2023 school year. Students taking this course can be undergraduate (junior or senior) or graduate (masters or PhD). The research questions for this study were 1) what is the user experience with the virtual reality cleanroom prototype, 2) what challenges, if any, did students experience, and 3) what changes did students recommend to improve the virtual reality cleanroom prototype learner experience? Preliminary results indicate that the students found the virtual reality cleanroom simulator helpful in repeatedly exploring the cleanroom space and the nanofabrication process flow in a safe way, thus developing more confidence in utilizing the actual cleanroom facility. The results of this study will provide insight on the design of future modules with more complicated levels and device process flows. Moreover, the study could inform the development of other virtual reality simulators for other lab activities. The improved usability of the proposed software could provide students in large classes or attending online programs in electrical and computer engineering, as well as K-12 students participating in nanotechnology-related outreach events, the opportunity to conduct realistic process workflows, learn first-hand about nanofabrication, and practice using a nanofabrication lab via trial and error in a safe virtual environment.more » « less
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This dataset contains three netcdf files that pertain to monthly, seasonal, and annual fields of surface wind stress, wind stress curl, and curl-derived upwelling velocities over the Northwest Atlantic (80-45W, 30-45N) covering a forty year period from 1980 to 2019. Six-hourly surface (10 m) wind speed components from the Japanese 55-year reanalysis (JRA-55; Kobayashi et al., 2015) were processed from 1980 to 2019 over a larger North Atlantic domain of 100W to 10E and 10N to 80N. Wind stress was computed using a modified step-wise formulation, originally based on (Gill, 1982) and a non-linear drag coefficient (Large and Pond, 1981), and later modified for low speeds (Trenberth et al., 1989). See Gifford (2023) for more details.
After the six-hourly zonal and meridional wind stresses were calculated, the zonal change in meridional stress (curlx) and the negative meridional change in zonal stress (curly) were found using NumPy’s gradient function in Python (Harris et al., 2020) over the larger North Atlantic domain (100W-10E, 10-80N). The curl (curlx + curly) over the study domain (80-45W, 10-80N) is then extracted, which maintain a constant order of computational accuracy in the interior and along the boundaries for the smaller domain in a centered-difference gradient calculation.
The monthly averages of the 6-hour daily stresses and curls were then computed using the command line suite climate data operators (CDO, Schulzweida, 2022) monmean function. The seasonal (3-month average) and annual averages (12-month average) were calculated in Python using the monthly fields with NumPy (NumPy, Harris et al., 2020).
Corresponding upwelling velocities at different time-scales were obtained from the respective curl fields and zonal wind stress by using the Ekman pumping equation of the study by Risien and Chelton (2008; page 2393). Please see Gifford (2023) for more details.
The files each contain nine variables that include longitude, latitude, time, zonal wind stress, meridional wind stress, zonal change in meridional wind stress (curlx), the negative meridional change in zonal wind stress (curly), total curl, and upwelling. Units of time begin in 1980 and are months, seasons (JFM etc.), and years to 2019. The longitude variable extends from 80W to 45W and latitude is 30N to 45N with uniform 1.25 degree resolution.
Units of stress are in Pascals, units of curl are in Pascals per meter, and upwelling velocity is described by centimeters per day. The spatial grid is a 29 x 13 longitude x latitude array.
Filenames:
monthly_windstress_wsc_upwelling.nc: 480 time steps from 80W to 45W and 30N to 45N.
seasonal_windstress_wsc_upwelling.nc: 160 time steps from 80W to 45W and 30N to 45N.
annual_windstress_wsc_upwelling.nc: 40 time steps from 80W to 45W and 30N to 45N.
Please contact igifford@earth.miami.edu for any queries. {"references": ["Gifford, I.H., 2023. The Synchronicity of the Gulf Stream Free Jet and the Wind Induced Cyclonic Vorticity Pool. MS Thesis, University of Massachusetts Dartmouth. 75pp.", "Gill, A. E. (1982). Atmosphere-ocean dynamics (Vol. 30). Academic Press.", "Harris, C.R., Millman, K.J., van der Walt, S.J. et al. Array programming with NumPy. Nature 585, 357\u2013362 (2020). DOI: 10.1038/s41586-020-2649-2.", "Japan Meteorological Agency/Japan (2013), JRA-55: Japanese 55-year Reanalysis, Daily 3-Hourly and 6-Hourly Data, https://doi.org/10.5065/D6HH6H41, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory, Boulder, Colo. (Updated monthly.)", "Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi, K., Kamahori, H., Kobayashi, C., Endo, H. and Miyaoka, K., 2015. The JRA-55 reanalysis: General specifications and basic characteristics.\u202fJournal of the Meteorological Society of Japan. Ser. II,\u202f93(1), pp.5-48.", "Large, W.G. and Pond, S., 1981. Open ocean momentum flux measurements in moderate to strong winds.\u202fJournal of physical oceanography,\u202f11(3), pp.324-336.", "Risien, C.M. and Chelton, D.B., 2008. A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data.\u202fJournal of Physical Oceanography,\u202f38(11), pp.2379-2413.", "Schulzweida, Uwe. (2022). CDO User Guide (2.1.0). Zenodo. https://doi.org/10.5281/zenodo.7112925.", "Trenberth, K.E., Large, W.G. and Olson, J.G., 1989. The effective drag coefficient for evaluating wind stress over the oceans.\u202fJournal of Climate,\u202f2(12), pp.1507-1516."]} -
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The historical settlement data compilation for Spain (HISDAC-ES) is a geospatial dataset consisting of over 240 gridded surfaces measuring the physical, functional, age-related, and evolutionary characteristics of the Spanish building stock. We scraped, harmonized, and aggregated cadastral building footprint data for Spain, covering over 12,000,000 building footprints including construction year attributes, to create a multi-faceted series of gridded surfaces (GeoTIFF format), describing the evolution of human settlements in Spain from 1900 to 2020, at 100m spatial and 5 years temporal resolution. Also, the dataset contains aggregated characteristics and completeness statistics at the municipality level, in CSV and GeoPackage format.
!!! UPDATE 08-2023 !!!: We provide a new, improved version of HISDAC-ES. Specifically, we fixed two bugs in the production code that caused an incorrect rasterization of the multitemporal BUFA layers and of the PHYS layers (BUFA, BIA, DWEL, BUNITS sum and mean). Moreover, we added decadal raster datasets measuring residential building footprint and building indoor area (1900-2020), and provide a country-wide, harmonized building footprint centroid dataset in GeoPackage vector data format.
File descriptions:
Datasets are available in three spatial reference systems:
- HISDAC-ES_All_LAEA.zip: Raster data in Lambert Azimuthal Equal Area (LAEA) covering all Spanish territory.
- HISDAC-ES_IbericPeninsula_UTM30.zip: Raster data in UTM Zone 30N covering all the Iberic Peninsula + Céuta and Melilla.
- HISDAC-ES_CanaryIslands_REGCAN.zip: Raster data in REGCAN-95, covering the Canary Islands only.
- HISDAC-ES_MunicipAggregates.zip: Municipality-level aggregates and completeness statistics (CSV, GeoPackage), in LAEA projection.
- ES_building_centroids_merged_spatjoin.gpkg: 7,000,000+ building footprint centroids in GeoPackage format, harmonized from the different cadastral systems, representing the input data for HISDAC-ES. These data can be used for sanity checks or for the creation of further, user-defined gridded surfaces.
Source data:
HISDAC-ES is derived from cadastral building footprint data, available from different authorities in Spain:
- Araba province: https://geo.araba.eus/WFS_Katastroa?SERVICE=WFS&VERSION=1.1.0&REQUEST=GetCapabilities
- Bizkaia province: https://web.bizkaia.eus/es/inspirebizkaia
- Gipuzkoa province: https://b5m.gipuzkoa.eus/web5000/es/utilidades/inspire/edificios/
- Navarra region: https://inspire.navarra.es/services/BU/wfs
- Other regions: http://www.catastro.minhap.es/INSPIRE/buildings/ES.SDGC.bu.atom.xml
- Data source of municipality polygons: Centro Nacional de Información Geográfica (https://centrodedescargas.cnig.es/CentroDescargas/index.jsp)
Technical notes:
Gridded data
File nomenclature:
./region_projection_theme/hisdac_es_theme_variable_version_resolution[m][_year].tif
Regions:
- all: complete territory of Spain
- can: Canarian Islands only
- ibe: Iberic peninsula + Céuta + Melilla
Projections:
- laea: Lambert azimuthal equal area (EPSG:3035)
- regcan: REGCAN95 / UTM zone 28N (EPSG:4083)
- utm: ETRS89 / UTM zone 30N (EPSG:25830)
Themes:
- evolution / evol: multi-temporal physical measurements
- landuse: multi-temporal building counts per land use (i.e., building function) class
- physical / phys: physical building characteristics in 2020
- temporal / temp: temporal characteristics (construction year statistics)
Variables: evolution
- budens: building density (count per grid cell area)
- bufa: building footprint area
- deva: developed area (any grid cell containing at least one building)
- resbufa: residential building footprint area
- resbia: residential building indoor area
Variables: physical
- bia: building indoor area
- bufa: building footprint area
- bunits: number of building units
- dwel: number of dwellings
Variables: temporal
- mincoy: minimum construction year per grid cell
- maxcoy: minimum construction year per grid cell
- meancoy: mean construction year per grid cell
- medcoy: median construction year per grid cell
- modecoy: mode (most frequent) construction year per grid cell
- varcoy: variety of construction years per grid cell
Variable: landuse
Counts of buildings per grid cell and land use type.
Municipality-level data
- hisdac_es_municipality_stats_multitemporal_longform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in long form. Note that a value of 0 for the year attribute denotes the statistics for records without construction year information.
- hisdac_es_municipality_stats_multitemporal_wideform_v1.csv: This CSV file contains the zonal sums of the gridded surfaces (e.g., number of buildings per year and municipality) in wide form. Note that a value of 0 for the year suffix denotes the statistics for records without construction year information.
- hisdac_es_municipality_stats_completeness_v1.csv: This CSV file contains the missingness rates (in %) of the building attribute per municipality, ranging from 0.0 (attribute exists for all buildings) to 100.0 (attribute exists for none of the buildings) in a given municipality.
Column names for the completeness statistics tables:
- NATCODE: National municipality identifier*
- num_total: number of buildings per munic
- perc_bymiss: Percentage of buildings with missing built year (construction year)
- perc_lumiss: Percentage of buildings with missing landuse attribute
- perc_luother: Percentage of buildings with landuse type "other"
- perc_num_floors_miss: Percentage of buildings without valid number of floors attribute
- perc_num_dwel_miss: Percentage of buildings without valid number of dwellings attribute
- perc_num_bunits_miss: Percentage of buildings without valid number of building units attribute
- perc_offi_area_miss: Percentage of buildings without valid official area (building indoor area, BIA) attribute
- perc_num_dwel_and_num_bunits_miss: Percentage of buildings missing both number of dwellings and number of building units attribute
The same statistics are available as geopackage file including municipality polygons in Lambert azimuthal equal area (EPSG:3035).
*From the NATCODE, other regional identifiers can be derived as follows:
- NATCODE: 34 01 04 04001
- Country: 34
- Comunidad autónoma (CA_CODE): 01
- Province (PROV_CODE): 04
- LAU code: 04001 (province + municipality code)
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Abstract Objectives Respiratory syncytial virus (RSV) is a significant cause of pediatric hospitalizations. This article aims to utilize multisource data and leverage the tensor methods to uncover distinct RSV geographic clusters and develop an accurate RSV prediction model for future seasons.
Materials and Methods This study utilizes 5-year RSV data from sources, including medical claims, CDC surveillance data, and Google search trends. We conduct spatiotemporal tensor analysis and prediction for pediatric RSV in the United States by designing (i) a nonnegative tensor factorization model for pediatric RSV diseases and location clustering; (ii) and a recurrent neural network tensor regression model for county-level trend prediction using the disease and location features.
Results We identify a clustering hierarchy of pediatric diseases: Three common geographic clusters of RSV outbreaks were identified from independent sources, showing an annual RSV trend shifting across different US regions, from the South and Southeast regions to the Central and Northeast regions and then to the West and Northwest regions, while precipitation and temperature were found as correlative factors with the coefficient of determination R2≈0.5, respectively. Our regression model accurately predicted the 2022-2023 RSV season at the county level, achieving R2≈0.3 mean absolute error MAE < 0.4 and a Pearson correlation greater than 0.75, which significantly outperforms the baselines with P-values <.05.
Conclusion Our proposed framework provides a thorough analysis of RSV disease in the United States, which enables healthcare providers to better prepare for potential outbreaks, anticipate increased demand for services and supplies, and save more lives with timely interventions.
