Next Article in Journal
The Effect of a Spiral Gradient Magnetic Field on the Ionic Conductivity of Water
Previous Article in Journal
Coupling the Modified Linear Spectral Mixture Analysis and Pixel-Swapping Methods for Improving Subpixel Water Mapping: Application to the Pearl River Delta, China
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Water 2017, 9(9), 665;

Modeling the Dispersion of E. coli in Waterbodies Due to Urban Sources: A Spatial Approach

Biological and Agricultural Engineering Department, Texas A&M University, 2117 TAMU, College Station, TX 77843, USA
North Texas Municipal Water District, 505 E Brown St, Wylie, TX 75098, USA
Spatial Science Laboratory in the Department of Ecosystem Science and Management, Texas A&M University, College Station, TX 77845, USA
Author to whom correspondence should be addressed.
Received: 7 August 2017 / Revised: 17 August 2017 / Accepted: 29 August 2017 / Published: 2 September 2017
Full-Text   |   PDF [2420 KB, uploaded 2 September 2017]   |  


In the United States, pathogens are the leading cause for rivers and streams to exceed water quality standards. The Spatially Explicit Load Enrichment Calculation Tool (SELECT) was developed to estimate bacterially contaminated water bodies based on spatial factors such as land use, soil, and population density. SELECT was originally automated using Visual Basics for Applications (VBA), which is no longer supported by the current version of ArcGIS. The aim of this research was to develop a new SELECT interface, pySELECT, using the Python programming language and to incorporate a rainfall-runoff E. coli transport module to simulate E. coli loads resulting from urban sources, such as dogs and on-site wastewater treatment systems. The pySELECT tool was applied to Lavon Lake, a semi urban study watershed in Northeast Texas. The highest potential E. coli loads were in the areas closest to the Dallas-Fort Worth metroplex, and the highest transported loads were located downstream from those identified hotspots or where the most runoff was generated. Watershed managers can use pySELECT to develop best management practices on the specific areas and fecal sources that contribute fecal contamination into a waterbody. View Full-Text
Keywords: GIS; non-point sources; bacterial contamination; on-site wastewater treatment systems GIS; non-point sources; bacterial contamination; on-site wastewater treatment systems

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Borel, K.; Swaminathan, V.; Vance, C.; Roberts, G.; Srinivasan, R.; Karthikeyan, R. Modeling the Dispersion of E. coli in Waterbodies Due to Urban Sources: A Spatial Approach. Water 2017, 9, 665.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top