EARLY HEARING DETECTION AND INTERVENTION VIRTUAL CONFERENCE
MARCH 2-5, 2021

(Virtually the same conference, without elevators, airplane tickets, or hotel room keys)

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5/25/2021  |   2:00 PM - 3:30 PM   |  Water Quality - Inequality: A non-Targeted Hotspot Analysis for Ambient   |  Virtual Platform

Water Quality - Inequality: A non-Targeted Hotspot Analysis for Ambient

Water is an essential life sustaining resource tied to many socio-environmental needs and values. Although water has been a focal point of many environmental justice studies, ambient water quality (i.e. water quality before the tap) has been overlooked, resulting in only a partial understanding of how water quality may correlate with marginalized communities and ecosystems. In this presentation, data from the U.S. Environmental Protection Agency STORET database and the 2010 U.S. Census are analysed to identify poor ambient water quality within marginalized communities. For our purposes, final contaminants used for analysis included mercury, lead, copper, zinc, nickel, and fluoranthene and we defined marginalized communities by race and income (e.g. non-white and low-income). Hotspot analyses, Ordinary Least Squares, and the Geographically Weighted Regression, identify environmental justice cases across our target area of the Southeastern USA. High densities of hotspots in localized areas indicate that marginalized communities are significantly impacted by copper, lead, and mercury contamination. Race correlated more strongly with poor ambient water quality than community income levels. Results suggest our approach and analyses are effective for identifying environmental justice issues across large spatial scales.

  • Socio-ecological systems
  • Interdisciplinary
  • Monitoring

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Presenters/Authors

Justine Neville (), North Carolina State University, janevill@ncsu.edu;


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Jacyln Guz (), Clark University, JGuz@Clarku.edu;


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Helen Rosko (), Clark University, hmrosko@gmail.com;


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Mitchell Owens (), Indiana University - Bloomington , owensm42@gmail.com;


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