Physical Sciences and Mathematics Commons^™

Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – Fourth Quarter 2022, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Final 2021 Unreclaimed Sites Sampling Ur-40 Site Evaluation Summary Report, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Revised Draft Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – Fourth Quarter 2021, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Dataset: Baywide Distribution Of Benthic Ecological Functions In The Past Decades In The Chesapeake Bay, Philip Ignatoff, Xun Cai, Kara Gadeken

Data

We undertook the collection and analysis of long-term benthos data from the Chesapeake Bay Benthic Monitoring Plan. Multiple ecological function traits related to feeding and disturbance were assigned to each observed benthic species based on a thorough literature review. The spatial distributions of the ecological function groups will be utilized in a 3D hydrodynamic biogeochemistry model simulation. This approach aids in estimating the contributions of benthos to estuarine hypoxia and nutrient dynamics. Furthermore, it fosters a connection between ecologists and modelers, promoting collaborative efforts in understanding and modeling the ecosystem.

Pariette Wetlands Water, Sediment And Plant Total Selenium Concentration, Colleen P. Jones, Paul R. Grossl, Astrid R. Jacobson

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We measured total Selenium in plants from July through November of 2012 and in water, macroinvertebrates, plants, and sediments from July of 2014 from Pariette Wetlands, Utah, U.S.A. to test for spatial, temporal, plant species and plant tissue distribution of Selenium.

Attachments To Reports Generated April 2022, Atlantic Richfield Company, Environmental Protection Agency, Trec Inc., A Woodard And Curran Company

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Management Practices For Urban Areas In The Hampton Roads Vicinity: Data Files, Gary F. Anderson

Data

During 1980 through 1981, the Virginia Institute of Marine Science conducted studies in the Hampton Roads Virginia vicinity to assess pollutant loading in runoff from various land use types. The 13 urban study areas also included established BMPs such as grassy swales and retention ponds to measure their effectiveness in reducing pollutant loads to the Chesapeake Bay. The focus was on nutrients, BOD and suspended solids. The studies were conducted with support of the U.S. EPA under section 208 of the Federal Clean Water Act.

Methods and results are documented in the associated publication. Data files were processed using SPSS …

First Quarter Data Sheets - Event Log, Chem Dump, Field Dump, Etc., Atlantic Richfield Company

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Data From: Yellow Air Day Advisory Study, Arthur J. Caplan

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Using a dataset consisting of daily vehicle trips, PM2.5 concentrations, along with a host of climactic control variables, we test the hypothesis that “yellow air day” advisories provided by the Utah Division of Air Quality resulted in subsequent reductions in vehicle trips taken during northern Utah’s winter-inversion seasons in the early 2000s. Winter inversions occur in northern Utah when climactic conditions are such that PM2.5 concentrations (derived mainly from vehicle emissions) become trapped in the lower atmosphere, leading to unhealthy air quality (concentrations of at least 35 µg/m3) over a span of what are called “red air days”. When concentrations …

Coastal Natural And Nature-Based Features (Nnbfs) Ranked: Co-Benefits For Coastal Buildings And Target Areas For The Creation Of New Or Restoration Of Nnbfs In Coastal Virginia, Pamela Mason, Jessica Hendricks, Julie Herman

Data

Community resilience to storm-driven coastal flooding is improved with the presence of natural and nature-based features (NNBFs) such as wetlands, wooded areas, living shorelines, and beaches. These natural and created features can provide multiple benefits for a local community, including mitigating the impacts of storm surge and sea-level rise and allowing communities to take advantage of programmatic incentive programs like FEMA’s Community Rating System and nutrient reduction crediting.

As part of a NOAA-funded project NA17NOS4730142, an exportable geospatial protocol and NNBF ranking methodology was developed with the goal of incentivizing the protection and creation of NNBFs across Chesapeake Bay localities …

Gis Data: Anne Arundel County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …

Gis Data: Calvert County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …

Gis Data: Talbot County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …

Gis Data: Dorchester County, Maryland - Shoreline Inventory Data 2020, Karinna Nunez, Marcia Berman, Sharon Killeen, Jessica Hendricks, Tamia Rudnicky, Catherine Riscassi

Data

The shoreline inventory files have been generated to support the application of the Maryland Shoreline Stabilization Model (SSM), developed by the Center for Coastal Resources Management (CCRM), Virginia Institute of Marine Science (VIMS), to enhance and streamline regulatory decision making in Maryland. This shoreline inventory includes the features needed as inputs to run the SSM.

The data developed for the Shoreline Inventory is based on a three-tiered shoreline assessment approach. This assessment characterizes conditions by using observations made remotely at the desktop using high resolution imagery. The three-tiered shoreline assessment approach divides the shorezone into three regions:

1) the immediate …

Gis Data: King & Queen County Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Carl Hershner, Evan Hill

Data

The Virginia Institute of Marine Science published the first Tidal marsh Inventories using data collected in the early 1970's. Using high resolution color infra-red imagery from 2009 a new Tidal Marsh Inventory has been developed for the York River Watershed in 2010. Marsh boundaries were generated using heads-up digitizing techniques at a scale of 1:1,000. Each marsh polygon was classified by morphologic type: fringe, extensive, embayed, or marsh island. Marshes were ground-truthed in the field where a community type index was assigned to each marsh based on plant community make-up. Each marsh was also coded with a marsh number which …

Gis Data: King William County Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Carl Hershner, Evan Hill

Data

The Virginia Institute of Marine Science published the first Tidal marsh Inventories using data collected in the early 1970's. Using high resolution color infra-red imagery from 2009 a new Tidal Marsh Inventory has been developed for the York River Watershed in 2010. Marsh boundaries were generated using heads-up digitizing techniques at a scale of 1:1,000. Each marsh polygon was classified by morphologic type: fringe, extensive, embayed, or marsh island. Marshes were ground-truthed in the field where a community type index was assigned to each marsh based on plant community make-up. Each marsh was also coded with a marsh number which …

King William County, Virginia Shoreline Inventory Data 2019, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Jessica Hendricks, Carl Hershner, Evan Hill

Data

The 2019 Inventory for King William County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in 2017 imagery from the Virginia Base Mapping Program (VBMP), Google Earth, and Bing high resolution oblique imagery. Five GIS shapefiles are developed. The first describes land use and bank conditions (KingWilliam_lubc_2019). The second portrays the presence of beaches (KingWilliam_beach_2019). The third reports shoreline structures that are described as arcs or lines (e.g. riprap) (KingWilliam_sstru_2019). The fourth shapefile includes all structures that are represented as points (e.g. piers) (KingWilliam_astru_2019). The Tidal Marsh Inventory is included as the fifth file …

King And Queen County, Virginia Shoreline Inventory Data 2019, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Jessica Hendricks, Carl Hershner, Evan Hill

Data

The 2019 Inventory for King and Queen County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1 while viewing conditions observed in 2017 imagery from the Virginia Base Mapping Program (VBMP), Google Earth, and Bing high resolution oblique imagery. Five GIS shapefiles are developed. The first describes land use and bank conditions (kq_lubc_2019). The second portrays the presence of beaches (kq_beach_2019). The third reports shoreline structures that are described as arcs or lines (e.g. riprap) (kq_sstru_2019). The fourth shapefile includes all structures that are represented as points (e.g. piers) (kq_astru_2019). The Tidal Marsh Inventory is included as the fifth …

Catch The King Tide 2018: All King Tide Data, Jon Derek Loftis

Data

"Catch the King" is a citizen-science GPS data collection effort centered in Hampton Roads, VA, that seeks to interactively map the King Tide's maximum inundation extents. The goal is to validate and improving predictive model accuracy for future forecasting of increasingly pervasive "nuisance" flooding.

Presence/Absence And Density Data For Epipelagic Tows Collected During R/V Blazing Seven Cruises Lf2016a And Lf2016b, Northern Gulf Of Mexico From 2016-06-09 To 2016-07-28, Jay R. Rooker, David Wells

DEEPEND Datasets

This dataset reports presence/absence and density data for epipelagic tows collected in the northern Gulf of Mexico during R/V Blazing Seven cruises LF2016A and LF2016B (2016-06-09 to 2016-07-28). Larval fishes were sampled from 48 stations and cruise data were collected at each site including latitude/longitude, date, time, environmental data (temperature, salinity, dissolved oxygen) and Sargassum dry weight. Larval catch data before and after the oil spill will be compared to improve our understanding of the causes of temporal variability as it relates to the Deep-Water Horizon oil spill (DWHOS). Habitat associations of selected taxa (billfishes, tunas, dolphinfishes, flyingfishes) will be …

Inventory Of Oceanic Fauna Data Including Species, Weight, And Measurements From R/V Point Sur (Cruise Dp05) In The Gulf Of Mexico From 2017-05-01 To 2017-05-11, April Cook, Tracey Sutton

DEEPEND Datasets

This dataset includes an inventory of Gulf of Mexico oceanic fauna data including species, weight, and measurements collected from R/V Point Sur (Cruise DP05) from 2017-05-01 to 2017-05-11. The main gear type used was a 10-m2 Multiple Opening Closing Net and Environmental Sensing System (MOCNESS). The MOCNESS was fitted with 6 nets which were opened according to the following depth scheme: net 0 from the surface to 1500m, net 1 from 1500-1200m, net 2 from 200-1000m, net 3 from 1000-600m, net 4 from 600-200m, and net 5 from 200m to the surface. Two trawls were conducted at each station sampled …

Section: 01 Line Frame: 01, 18 October 2017: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, Robert J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith

Data

Multispectral aerial imagery acquired in 2017 to monitor the distribution and abundance of submerged aquatic vegetation in Chesapeake Bay and coastal bays

Section: 01 Line Frame: 06, 27 August 2017: Aerial Imagery Acquired To Monitor The Distribution And Abundance Of Submerged Aquatic Vegetation In Chesapeake Bay And Coastal Bays, Robert J. Orth, David J. Wilcox, Jennifer R. Whiting, Anna K. Kenne, Erica R. Smith

Data

Multispectral aerial imagery acquired in 2017 to monitor the distribution and abundance of submerged aquatic vegetation in Chesapeake Bay and coastal bays

Gis Data: New Kent County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

The 2018 Inventory for New Kent County was generated using on-screen, digitizing techniques in ArcGIS® -ArcMap v10.4.1while viewing conditions observed in Bing high resolution oblique imagery, Google Earth, and2017imagery from the Virginia Base Mapping Program (VBMP).Four GIS shapefiles are developed. The first describes land use and bank conditions (New_Kent_lubc_2018). The second portrays the presence of beaches (New_Kent_beaches_2018). The third reports shoreline structures that are described as arcs or lines(e.g. riprap)(New_Kent_sstru_2018). The final shapefile includes all structures that are represented as points(e.g. piers)(New_Kent_astru_2018).The metadata file accompanies the shapefiles and defines attribute accuracy, data development, and any use restrictions that pertain to …

Gis Data:: Arlington County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie Bradshaw, Carl Herschner

Data

No abstract provided.

Gis Data: Caroline County, Virginia Shoreline Inventory Report, Marcia Berman, Karinna Nunez, Sharon A. Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl H. Hershner

Data

No abstract provided.

Gis Data: Caroline County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the …

Gis Data: Caroline County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Carl Hershner

Data

No abstract provided.

Gis Data: Richmond County, Virginia Tidal Marsh Inventory, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner

Data

No abstract provided.

Gis Data: Richmond County, Virginia Shoreline Management Model, Marcia Berman, Karinna Nunez, Sharon Killeen, Tamia Rudnicky, Julie G. Bradshaw, Jessica Hendricks, Kory Angstadt, Carl Hershner

Data

The Shoreline Management Model is a GIS spatial model that determines appropriate shoreline best management practices using available spatial data and decision tree logic. Available shoreline conditions used in the model include the presence or absence of tidal marshes, beaches, and forested riparian buffers, bank vegetation cover, bank height, wave exposure (fetch), nearshore water depth, and proximity of coastal development to the shoreline. The model output for shoreline best management practices is displayed in the locality Comprehensive Map Viewer. One GIS shapefile is developed that describes two arcs or lines representing practices in the upland area and practices at the …