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Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model
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 micropollu...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666614/ https://www.ncbi.nlm.nih.gov/pubmed/38025695 http://dx.doi.org/10.7717/peerj.16429 |
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author | McDaniel, Emma L. Atkinson, Samuel F. Tiwari, Chetan |
author_facet | McDaniel, Emma L. Atkinson, Samuel F. Tiwari, Chetan |
author_sort | McDaniel, Emma L. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10666614 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106666142023-11-20 Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model McDaniel, Emma L. Atkinson, Samuel F. Tiwari, Chetan PeerJ Public Health 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. PeerJ Inc. 2023-11-20 /pmc/articles/PMC10666614/ /pubmed/38025695 http://dx.doi.org/10.7717/peerj.16429 Text en © 2023 McDaniel et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Public Health McDaniel, Emma L. Atkinson, Samuel F. Tiwari, Chetan Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model |
title | Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model |
title_full | Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model |
title_fullStr | Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model |
title_full_unstemmed | Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model |
title_short | Quantifying the land and population risk of sewage spills overland using a fine-scale, DEM-based GIS model |
title_sort | quantifying the land and population risk of sewage spills overland using a fine-scale, dem-based gis model |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10666614/ https://www.ncbi.nlm.nih.gov/pubmed/38025695 http://dx.doi.org/10.7717/peerj.16429 |
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