Framework for assessment of relative pollutant loads in streams with limited data

Amin Elshorbagy; Ramesh S.V. Teegavarapu; Lindell Ormsbee

doi:10.1080/02508060508691892

Framework for assessment of relative pollutant loads in streams with limited data

Amin Elshorbagy, Ramesh S.V. Teegavarapu, Lindell Ormsbee

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

A framework that integrates two data-driven techniques is proposed and developed to assess fecal coliform loadings in natural streams. A relationship between transport medium (streamflow) and non-conservative pollutant (fecal coliform) load is first developed using conventional regression technique. The spatial distribution of the fecal load over watersheds is then captured using artificial neural networks through a disaggregation scheme. Streamflow, as a surrogate for non-conservative fecal load, has been used in the disaggregation process. The framework is applied to an area that encompasses four USGS 8-digit Hydrologic Unit Code (HUC) watersheds in the southeastern region of Kentucky, USA. The study attempts to address two major issues: (i) assessment of relative pollutant loads from watersheds and (ii) evaluation into possible reduction in the number of monitoring stations to meet the budgetary constraints. Preliminary results indicate the potential of this approach in assessing the relative fecal loading contribution from different watersheds with the help of conservative hydrological parameters, especially in data-poor conditions.

Original language	English
Pages (from-to)	477-486
Number of pages	10
Journal	Water International
Volume	30
Issue number	4
DOIs	https://doi.org/10.1080/02508060508691892
State	Published - Dec 2005

Bibliographical note

Funding Information:
The research work reported in this paper was supported by grant sponsored by the Eastern Kentucky PRIDE. The data used in the study are provided by the Kentucky Division of Water (DOW) and the Kentucky River Watershed Watch program. The authors would like to acknowledge the help of Jason Booth and L.T. Yee for their assistance with the preparation of data. The first author acknowledges the financial support of NSERC-Canada for its financial support through its Discovery Grant Program.

Keywords

Artificial neural networks (ANN)
Fecal coliform bacteria
Kentucky
Regression analysis
Spatial disaggregation

ASJC Scopus subject areas

Water Science and Technology
Management, Monitoring, Policy and Law

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10.1080/02508060508691892

Cite this

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title = "Framework for assessment of relative pollutant loads in streams with limited data",

abstract = "A framework that integrates two data-driven techniques is proposed and developed to assess fecal coliform loadings in natural streams. A relationship between transport medium (streamflow) and non-conservative pollutant (fecal coliform) load is first developed using conventional regression technique. The spatial distribution of the fecal load over watersheds is then captured using artificial neural networks through a disaggregation scheme. Streamflow, as a surrogate for non-conservative fecal load, has been used in the disaggregation process. The framework is applied to an area that encompasses four USGS 8-digit Hydrologic Unit Code (HUC) watersheds in the southeastern region of Kentucky, USA. The study attempts to address two major issues: (i) assessment of relative pollutant loads from watersheds and (ii) evaluation into possible reduction in the number of monitoring stations to meet the budgetary constraints. Preliminary results indicate the potential of this approach in assessing the relative fecal loading contribution from different watersheds with the help of conservative hydrological parameters, especially in data-poor conditions.",

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author = "Amin Elshorbagy and Teegavarapu, {Ramesh S.V.} and Lindell Ormsbee",

note = "Funding Information: The research work reported in this paper was supported by grant sponsored by the Eastern Kentucky PRIDE. The data used in the study are provided by the Kentucky Division of Water (DOW) and the Kentucky River Watershed Watch program. The authors would like to acknowledge the help of Jason Booth and L.T. Yee for their assistance with the preparation of data. The first author acknowledges the financial support of NSERC-Canada for its financial support through its Discovery Grant Program.",

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N2 - A framework that integrates two data-driven techniques is proposed and developed to assess fecal coliform loadings in natural streams. A relationship between transport medium (streamflow) and non-conservative pollutant (fecal coliform) load is first developed using conventional regression technique. The spatial distribution of the fecal load over watersheds is then captured using artificial neural networks through a disaggregation scheme. Streamflow, as a surrogate for non-conservative fecal load, has been used in the disaggregation process. The framework is applied to an area that encompasses four USGS 8-digit Hydrologic Unit Code (HUC) watersheds in the southeastern region of Kentucky, USA. The study attempts to address two major issues: (i) assessment of relative pollutant loads from watersheds and (ii) evaluation into possible reduction in the number of monitoring stations to meet the budgetary constraints. Preliminary results indicate the potential of this approach in assessing the relative fecal loading contribution from different watersheds with the help of conservative hydrological parameters, especially in data-poor conditions.

AB - A framework that integrates two data-driven techniques is proposed and developed to assess fecal coliform loadings in natural streams. A relationship between transport medium (streamflow) and non-conservative pollutant (fecal coliform) load is first developed using conventional regression technique. The spatial distribution of the fecal load over watersheds is then captured using artificial neural networks through a disaggregation scheme. Streamflow, as a surrogate for non-conservative fecal load, has been used in the disaggregation process. The framework is applied to an area that encompasses four USGS 8-digit Hydrologic Unit Code (HUC) watersheds in the southeastern region of Kentucky, USA. The study attempts to address two major issues: (i) assessment of relative pollutant loads from watersheds and (ii) evaluation into possible reduction in the number of monitoring stations to meet the budgetary constraints. Preliminary results indicate the potential of this approach in assessing the relative fecal loading contribution from different watersheds with the help of conservative hydrological parameters, especially in data-poor conditions.

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KW - Regression analysis

KW - Spatial disaggregation

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Framework for assessment of relative pollutant loads in streams with limited data

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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