Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model

Author:

McDaniel Emma L.12,Atkinson Samuel F.34,Tiwari Chetan125

Affiliation:

1. Center for Disaster Informatics and Computational Epidemiology, Georgia State University, Atlanta, Georgia, United States of America

2. Department of Computer Science, Georgia State University, Atlanta, Georgia, United States of America

3. Department of Biological Sciences, University of North Texas, Denton, Texas, United States of America

4. Advanced Environmental Research Institute, University of North Texas, Denton, Texas, United States of America

5. Department of Geosciences, Georgia State University, Atlanta, Georgia, United States of America

Abstract

Accidental releases of untreated sewage into the environment, known as sewage spills, may cause adverse gastrointestinal stress to exposed populations, especially in young, elderly, or immune-compromised individuals. In addition to human pathogens, untreated sewage contains high levels of micropollutants, organic matter, nitrogen, and phosphorus, potentially resulting in aquatic ecosystem impacts such as algal blooms, depleted oxygen, and fish kills in spill-impacted waterways. Our Geographic Information System (GIS) model, Spill Footprint Exposure Risk (SFER) integrates fine-scale elevation data (1/3 arc-second) with flowpath tracing methods to estimate the expected overland pathways of sewage spills and the locations where they are likely to pool. The SFER model can be integrated with secondary measures tailored to the unique needs of decision-makers so they can assess spatially potential exposure risk. To illustrate avenues to assess risk, we developed risk measures for land and population health. The land risk of sewage spills is calculated for subwatershed regions by computing the proportion of the subwatershed’s area that is affected by one modeled footprint. The population health risk is assessed by computing the estimated number of individuals who are within the modeled footprint using fine-scale (90 square meters) population estimates data from LandScan USA. In the results, with a focus on the Atlanta metropolitan region, potential strategies to combine these risk measures with the SFER model are outlined to identify specific areas for intervention.

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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