Exposure potential of marsh units to environmental health stressors in Connecticut salt marshes

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Frequently anticipated questions:

What does this data set describe?

Title:
Exposure potential of marsh units to environmental health stressors in Connecticut salt marshes
Abstract:
This data release contains coastal wetland synthesis products for the state of Connecticut. Metrics for resiliency, including the unvegetated to vegetated ratio (UVVR), marsh elevation, tidal range, wave power, and exposure potential to environmental health stressors are calculated for smaller units delineated from a digital elevation model, providing the spatial variability of physical factors that influence wetland health. The U.S. Geological Survey has been expanding national assessment of coastal change hazards and forecast products to coastal wetlands with the intent of providing federal, state, and local managers with tools to estimate the vulnerability and ecosystem service potential of these wetlands. For this purpose, the response and resilience of coastal wetlands to physical factors need to be assessed in terms of the ensuing change to their vulnerability and ecosystem services. This project has been funded in part by the United States Environmental Protection Agency under assistance agreement DW-014-92531201-1 to N. Ganju.
  1. How might this data set be cited?
    Ackerman, Kate V., Defne, Zafer, Ganju, Neil K., Jones, Daniel K., Peterson, Brennon, Gordon, Stephanie, and Williams, Brianna, 2024, Exposure potential of marsh units to environmental health stressors in Connecticut salt marshes: data release DOI:10.5066/P96QND48, U.S. Geological Survey, Coastal and Marine Hazards and Resources Program, Woods Hole Coastal and Marine Science Center, Woods Hole, MA.

    Online Links:

    This is part of the following larger work.

    Ackerman, Kate V., Defne, Zafer, and Ganju, Neil K., 2023, Geospatial characterization of salt marshes in Connecticut (ver. 2.0, April 2024: data release DOI:10.5066/P96QND48, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    Suggested citation: Ackerman, K.V., Defne, Z., and Ganju, N.K., 2023, Geospatial characterization of salt marshes in Connecticut (ver. 2.0, April 2024): U.S. Geological Survey data release, https://doi.org/10.5066/P96QND48.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -73.658477
    East_Bounding_Coordinate: -71.831904
    North_Bounding_Coordinate: 41.532199
    South_Bounding_Coordinate: 40.983874
  3. What does it look like?
    https://www.sciencebase.gov/catalog/file/get/65dcf88bd34eb64889e19e89/?name=SCoRR_CT_Browse.png&allowOpen=true (PNG)
    Graphic that shows the exposure potential to environmental health stressors in CT salt marshes.
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2024
    Currentness_Reference:
    publication date
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: vector digital dataset (polygon)
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Vector data set. It contains the following vector data types (SDTS terminology):
      • G-polygon (2499)
    2. What coordinate system is used to represent geographic features?
      The map projection used is WGS 1984 Web Mercator Auxiliary Sphere (Esri Full Name: WGS_1984_Web_Mercator_Auxiliary_Sphere).
      Projection parameters:
      Standard_Parallel: 0.0
      Longitude_of_Central_Meridian: 0.0
      False_Easting: 0.0
      False_Northing: 0.0
      Planar coordinates are encoded using coordinate pair
      Abscissae (x-coordinates) are specified to the nearest 0.6096
      Ordinates (y-coordinates) are specified to the nearest 0.6096
      Planar coordinates are specified in Meter
      The horizontal datum used is D_WGS_1984.
      The ellipsoid used is WGS_1984.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257223563.
  7. How does the data set describe geographic features?
    mu_scorr_CT
    Table containing attribute information associated with the dataset. (Source: U.S. Geological Survey)
    FID
    Internal feature number. (Source: Esri) Sequential unique whole numbers that are automatically generated.
    Shape
    Feature geometry. (Source: Esri) Feature geometry. Marsh units are polygon features.
    FID_CMU
    Unique whole numbers used to identify each conceptual marsh unit. (Source: USGS)
    Range of values
    Minimum:0
    Maximum:2498
    MIN
    Minimum value of Sediment-bound Contaminant Resiliency and Response (SCoRR) rank within a marsh unit. (Source: USGS)
    Range of values
    Minimum:0
    Maximum:33.40678
    Units:None
    MAX
    Maximum value of SCoRR rank within a marsh unit. (Source: USGS)
    Range of values
    Minimum:0
    Maximum:67.35318
    Units:None
    MEAN
    Mean value of SCoRR rank within a marsh unit. (Source: USGS)
    Range of values
    Minimum:0
    Maximum:36.12451
    Units:None
    STD
    Standard deviation of SCoRR rank within a marsh unit. (Source: USGS)
    Range of values
    Minimum:0
    Maximum:11.062928
    Units:None
    FLG
    Marsh unit flag indicating anomalous marsh units based on absence of vegetation, elevation, surface area and tidal range extrapolation. FLG values indicate: (-1) no vegetated area in the marsh unit; (-10) marsh unit elevation higher than the 99.8 percentile; (-100) marsh unit elevation less than the 0.2 percentile; (-200) greater than 25 percent of the marsh unit elevation is hydro-flattened; (-1000) marsh unit surface area less than 900 square meters; (-10000) mean tidal range of marsh unit is extrapolated; and (0) no flag. Combination of negative values indicates a combination of flags. For example, -1001 indicates no vegetated area and area less than 900 square meters. (Source: USGS)
    Range of values
    Minimum:-11200
    Maximum:0
    Units:None
    Entity_and_Attribute_Overview:
    In this dataset, exposure potential to environmental health stressors calculated with the SCoRR strategy is interpolated over the salt marsh units in Connecticut salt marshes. A higher SCoRR rank indicates higher exposure potential to environmental health stressors. Raster cells that do not have a facility within a 2000 meter proximity are assigned a SCoRR rank of zero prior to clipping to the processing extent. Decimal values in the attribute table are a result of double precision calculations while significant digits are considered in defining the attribute measurement resolutions. Therefore, the smallest unit increment for exposure potential (SCoRR) values can be assumed to be a practical value such as 0.01.
    Entity_and_Attribute_Detail_Citation: USGS

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Kate V. Ackerman
    • Zafer Defne
    • Neil K. Ganju
    • Daniel K. Jones
    • Brennon Peterson
    • Stephanie Gordon
    • Brianna Williams
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    Kate Ackerman
    U.S. Geological Survey
    Geologist
    384 Woods Hole Road
    Woods Hole, MA
    US

    508-548-8700 x2331 (voice)
    508-457-2310 (FAX)
    kackerman@usgs.gov

Why was the data set created?

This dataset displays the exposure potential to environmental health stressors in Connecticut salt marshes. Health stressors include natural and anthropogenic contaminants, pathogens, and viruses found in soils and sediments throughout the United States. Enhanced dispersion and concentration of these environmental health stressors in coastal regions can result from sea level rise and storm-derived disturbances. The combination of existing environmental health stressors and those mobilized by natural or anthropogenic disasters could adversely impact the health and resilience of coastal communities and ecosystems. Analysis of environmental health stressors is part of a comprehensive assessment to identify the factors and their weights in determining the vulnerability and resiliency of salt marshes.
Exposure potential in Connecticut’s salt marshes was calculated with the Sediment-bound Contaminant Resiliency and Response (SCoRR) ranking system designed to define baseline and post-event sediment-bound environmental health stressors (Reilly and others, 2015). Industrial facilities obtained from the Environmental Protection Agency's (EPA) Toxic Release Inventory (TRI) database were ranked based on their potential contaminant hazard. Ranks were based in part on previous work by Olsen and others (2013) and literature reviews. A 2000-meter search radius was used to identify nearby ranked facility locations.

How was the data set created?

  1. From what previous works were the data drawn?
    CMU_CT (source 1 of 1)
    Ackerman, Kate V., Defne, Zafer, and Ganju, Neil K., 2023, Conceptual marsh units of Connecticut salt marshes (in: Geospatial characterization of salt marshes in Connecticut): data release DOI:10.5066/P96QND48, U.S. Geological Survey, Reston, VA.

    Online Links:

    Other_Citation_Details:
    This dataset (Conceptual marsh units of Connecticut salt marshes) is within the larger data release (Geospatial characterization of salt marshes in Connecticut). The first link below is to the conceptual marsh units data and the second link is to the data release.
    Type_of_Source_Media: Digital
    Source_Contribution:
    Conceptual marsh units, generated as part of this data release, were used to delineate marsh unit boundaries.
  2. How were the data generated, processed, and modified?
    Date: 2023 (process 1 of 7)
    This process step and all subsequent process steps were performed in ArcGIS Pro (ver. 2.8.8), unless otherwise stated. For simple operations the name of the geoprocessing tool used is given in capital letters; for complex operations, the name of the geoprocessing tool used is given in capital letters and any critical parameters used are given in parentheses, separated by a semicolon, immediately after the tool name. The input and output file names are provided in [square brackets] when necessary. Units for length and area calculations are meters (m) and square meters unless otherwise stated.
    a) CREATE CONSTANT RASTER(Constant value= 1; Output cell size: 0.003 degrees) that surpasses the Connecticut marsh unit boundaries by 250 m, as defined in the conceptual marsh units layer [CMU_CT.shp].
    b) RASTER TO POINT to generate an array of points [scorr_pts.shp].
    c) Export point coordinates to an Excel table [contPts.xlsx]. Person who carried out this activity:
    Kate Ackerman
    U.S. Geological Survey
    Geologist
    384 Woods Hole Road
    Woods Hole, MA
    US

    508-548-8700 x2331 (voice)
    508-457-2310 (FAX)
    kackerman@usgs.gov
    Data sources produced in this process:
    • contPts.xlsx
    Date: 2024 (process 2 of 7)
    This step follows the SCoRR strategy described by Reilly and others (2015). Apply SCoRR ranks to the gridded array of points [contPts.xlsx].
    a) Generate a list of facilities of concern from EPA's Toxic Release Inventory (TRI) data.
    TRI data were downloaded from https://www.epa.gov/toxics-release-inventory-tri-program/tri-basic-data-files-calendar-years-1987-present using the drop-down menu for the year and state of interest. For this work, 2022 data for the state of Connecticut were used. Each facility was assigned a rank value from 1 to 4 indicating its 'perceived contaminant hazard' based on a modified version of ranked contaminants from Olsen and others (2013) as detailed in Reilly and others (2015; Table 1).
    b) Create a shapefile from the point coordinates [contPts.xlsx] and DEFINE PROJECTION(Coordinate System=WGS 84) as World Geodetic System 1984.
    c) GENERATE NEAR TABLE(Input Features=IN_FID; Near Features=NEAR_FID; Search radius= 2000 m) to identify facilities of concern within 2000 meters of each point.
    d) Tables are imported into R software package and joined using the fields 'IN_FID' and 'NEAR_FID' to link facility attributes with each location using near table output.
    e) SCoRR rank at each location is calculated using the inverse distance-weighted average rank equation (Reilly and others, 2015; Equation 2) within a search radius of 2000 meters, based on the potential contaminant hazard rank for each facility, and the distance to the facility location. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Daniel K. Jones
    Geographer
    2329 Orton Circle
    Salt Lake City, UT
    USA

    240-620-7822 (voice)
    801-908-5001 (FAX)
    dkjones@usgs.gov
    Data sources used in this process:
    • contPts.xlsx
    Data sources produced in this process:
    • scorr.shp
    Date: 2024 (process 3 of 7)
    Remaining process steps were performed by Kate Ackerman. Establish marsh unit boundaries. DISSOLVE(Dissolve Fields= FID_CMU) features in [CMU_CT] based on the attribute identification numbers. FID_CMU is the unique identifier for each marsh unit. Person who carried out this activity:
    Kate Ackerman
    U.S. Geological Survey
    Geologist
    384 Woods Hole Road
    Woods Hole, MA
    US

    508-548-8700 x2331 (voice)
    508-457-2310 (FAX)
    kackerman@usgs.gov
    Data sources used in this process:
    • CMU_CT
    Data sources produced in this process:
    • mu.shp
    Date: 2024 (process 4 of 7)
    Create a raster surface dataset from the feature points.
    a) PROJECT [scorr.shp] to GCS NAD 1983.
    b) Create a raster surface with inverse distance weighting. IDW (Input point features: [scorr.shp]; Output Cell Size=0.0003 degrees; Power=2; Search Radius=Variable; Number of Points=12) to obtain [scorr_idw.tif]. Data sources used in this process:
    • scorr.shp
    Data sources produced in this process:
    • scorr_idw.tif
    Date: 2024 (process 5 of 7)
    Clip the raster to marsh unit boundaries, buffered by 250 meters. A buffer zone is created around the marsh boundaries to facilitate alternative interpolation and clipping options in future studies.
    a) Create 250 meter buffer around the marsh polygon [mu.shp] to create a clipping mask [mu_buff250m.shp]. BUFFER(Side type= Full; Method=Geodesic; Dissolve type= All).
    b) Clip the raster clipping mask. EXTRACT BY MASK to obtain [scorr_NSLI.tif]. Data sources used in this process:
    • mu.shp
    • scorr_idw.tif
    Data sources produced in this process:
    • scorr_CT.tif
    Date: 2024 (process 6 of 7)
    PROJECT RASTER(Input raster= scorr_CT.tif; Output raster= scorr_CT.tif; Input coordinate system= GCS_NAD_1983; Output coordinate system= WGS_1984_Web_Mercator_Auxiliary_Sphere; Resampling technique= Nearest; Output cell size= 30 meters) to obtain [scorr_CT.tif]. Data sources used in this process:
    • scorr_CT.tif
    Data sources produced in this process:
    • scorr_CT.tif
    Date: 2024 (process 7 of 7)
    Calculate values for each marsh unit. a) Clip the raster [scorr_CT.tif] by the marsh units polygon by using EXTRACT BY MASK(Cell Size=3 m) to obtain [mu_scorr_idw.tif].
    b) ZONAL STATISTICS AS TABLE(Feature Zone Data=[mu.shp]; Value Raster=[mu_scorr_idw.tif]; Output Table=mu_scorr_table; Statistics type=ALL)
    c) JOIN SCoRR fields (Min, Max, Mean, Std) to the marsh units (Layer name=[mu.shp]; Input join field=FID_CMU; Join Table=mu_scorr_table; Output join field=FID_CMU). Data sources used in this process:
    • mu.shp
    • scorr_CT.tif
    Data sources produced in this process:
    • mu_SCoRR_CT.shp
  3. What similar or related data should the user be aware of?
    Reilly, Timothy J., Jones, Daniel K., Focazio, Michael J., Aquino, Kimberly C., Carbo, Chelsea L., Kaufhold, Erika E., Zinecker, Elizabeth K., Benzel, William M., Fisher, Shawn C., Griffin, Dale W., Iwanowicz, Luke R., Loftin, Keith A., and Schill, William B., 2015, Strategy to evaluate persistent contaminant hazards resulting from sea-level rise and storm-derived disturbances-Study design and methodology for station prioritization: Open-File Report 2015-1188-A, U.S. Geological Survey, Reston, VA.

    Online Links:

    Olsen, Lisa D., Valder, Joshua F., Carter, Janet M., and Zogorski, John S., 2013, Prioritization of Constituents for National- and Regional-Scale Ambient Monitoring of Water and Sediment in the United States: Scientific Investigations Report 2012-5218, U.S. Geological Survey, Reston, VA.

    Online Links:

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    Water-land boundary and marsh unit boundaries are inherited from accuracy of the conceptual marsh units. Exposure potential to environmental health stressors is calculated with the SCoRR strategy for a uniform grid with 0.003 degrees (~300 meters) resolution and resampled at a higher resolution of 0.0003 degrees (~30 meters) prior to averaging over each marsh unit.
  2. How accurate are the geographic locations?
    Horizontal accuracy for the polygon boundaries is inherited from the source layer, conceptual marsh units, and is considered to be +/- 6 meters.
  3. How accurate are the heights or depths?
  4. Where are the gaps in the data? What is missing?
    The boundaries of this dataset were created by expanding the boundaries of the source data (conceptual marsh units) with a 250-meter exterior buffer.
  5. How consistent are the relationships among the observations, including topology?
    By definition, SCoRR ranks cannot be a negative real number. Final values for each marsh unit were verified to be either zero or a positive real number.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints None
Use_Constraints The SCoRR ranks in this dataset are defined for scientific research purposes and should not be used as a sole source of reference for any regulations and policy making. Public domain data from the U.S. Government are freely redistributable with proper metadata and source attribution. Please recognize the U.S. Geological Survey as the source of this information.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - ScienceBase
    Denver Federal Center, Building 810, Mail Stop 302
    Denver, CO

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? mu_SCoRR_CT.zip (ZIP file contains the shapefile mu_SCoRR_CT.shp and the additional shapefile components), a browse graphic (SCoRR_CT_Browse.png), and FGDC CSDGM metadata in xml format.
  3. What legal disclaimers am I supposed to read?
    Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data for other purposes, nor on all computer systems, nor shall the act of distribution constitute any such warranty. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Not for navigational use.
  4. How can I download or order the data?

Who wrote the metadata?

Dates:
Last modified: 02-Apr-2024
Metadata author:
Kate V. Ackerman
U.S. Geological Survey
Geologist
384 Woods Hole Rd
Woods Hole, MA
US

508-548-8700 x2331 (voice)
508-457-2310 (FAX)
whsc_data_contact@usgs.gov
Contact_Instructions:
The metadata contact email address is a generic address in the event the person is no longer with USGS.
Metadata standard:
FGDC Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/whcmsc/SB_data_release/DR_P96QND48/mu_SCoRR_CT.faq.html>
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