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Related Concept Videos

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...
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Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation
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Using Geospatial Data and Random Forest To Predict PFAS Contamination in Fish Tissue in the Columbia River Basin,

Nicole M DeLuca1, Ashley Mullikin1, Peter Brumm2

  • 1Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States.

Environmental Science & Technology
|September 5, 2023
PubMed
Summary

Decision makers can now better identify per- and polyfluoroalkyl substances (PFAS) contamination in the Columbia River Basin. This study developed a cost-effective method using random forest models to predict PFAS levels in fish, aiding targeted sampling efforts.

Keywords:
OregonWashingtonindustryland coversourcestribesvariable importance

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Area of Science:

  • Environmental Science
  • Toxicology
  • Ecotoxicology

Background:

  • Per- and polyfluoroalkyl substances (PFAS) pose a significant challenge for environmental monitoring and human exposure assessment in the Columbia River Basin (CRB).
  • Understanding PFAS contamination in fish is critical due to its importance in the diet of tribal and indigenous populations within the CRB.

Purpose of the Study:

  • To develop and pilot a predictive methodology for identifying and prioritizing areas of PFAS contamination in natural resources.
  • To assist decision-makers in targeting sampling investigations for PFAS in the CRB.

Main Methods:

  • Utilized random forest models to predict total PFAS (∑PFAS) concentrations in fish tissue.
  • Integrated geospatial data, including land cover and proximity to potential PFAS sources, as model predictors.
  • Developed and validated models for Washington and Oregon using limited empirical data.

Main Results:

  • Generated spatial predictions highlighting areas with potential detectable PFAS concentrations in fish tissue that require further investigation.
  • Identified key geospatial variables influencing PFAS levels in fish, providing insights into potential sources.
  • Demonstrated a cost-effective approach to address data sparsity in environmental PFAS occurrence.

Conclusions:

  • The developed methodology offers a valuable tool for environmental managers to prioritize PFAS sampling efforts in data-limited regions.
  • The study provides crucial insights into the distribution and potential drivers of PFAS contamination in fish within the CRB.
  • This approach can be adapted to other regions facing similar challenges in PFAS assessment.