Gastrointestinal (GI) illness risks associated with exposure to waters impacted by human and nonhuman fecal sources were estimated using quantitative microbial risk assessment (QMRA). Microbial source tracking (MST) results had identified Escherichia coli (E. coli) contributors to the waterbody as human and unidentified (10%), cattle and domestic animals (25%), and wildlife (65%) in a rural watershed. The illness risks associated with ingestion during recreation were calculated by assigning reference pathogens for each contributing source and using pathogen dose–response relationships. The risk of GI illness was calculated for a specific sampling site with a geometric mean of E. coli of 163 colony forming units (cfu) 100 mL−1, and the recreational standard of E. coli, 126 cfu 100 mL−1. While the most frequent sources of fecal indicator bacteria at the sampling site were nonhuman, the risk of illness from norovirus, the reference pathogen representing human waste, contributed the greatest risk to human health. This study serves as a preliminary review regarding the potential for incorporating results from library-dependent MST to inform a QMRA for recreational waters. The simulations indicated that identifying the sources contributing to the bacterial impairment is critical to estimate the human health risk associated with recreation in a waterbody.

Abstract:The monitoring network for a river system is designed to provide information about water quantity and quality. The development of Watershed Protection Plans and Total Maximum Daily Loads require systematic monitoring of waterbodies. In this study, optimum water quality monitoring networks were selected to assess E. coli loads in the Guadalupe River and San Antonio River basins. A Genetic Algorithm (GA) was applied to select monitoring stations using the mean annual E. coli flux from the Spatially Referenced Regression Model on Watershed Attributes (SPARROW). The objectives of the GA were to minimize the number of monitoring stations, include large values of the mean annual E. coli flux, and minimize uncertainty of the flux estimations. Constraints related to the monitoring of critical locations were included in a multi-objective optimization problem. The SPARROW model was applied to the optimized GA solution sets, which were compared using the objective values and statistical indices. The best GA-generated alternative set adequately represented the San Antonio River basin, in good agreement with a previous study conducted using only SPARROW. The application of the GA ensured the inclusion of the monitoring stations with large values of E. coli flux, which reflected high-risk areas within the watershed.

Abstract: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.

When developing a watershed protection plan (WPP) or a total maximum daily load (TMDL), it is often difficult to accurately assess pollutant loads and sources for a watershed because insufficient water quality monitoring data are available. According to the Texas Commission on Environmental Quality, there are 274 bacterial impairments in Texas water bodies out of a total of 438 impaired water bodies. Bacterial data are often sparse, which hinders the development of WPPs or TMDLs. To address this lack of data, the Spatially Explicit Load Enrichment Calculation Tool (SELECT) was used to develop WPPs for 3 rural watersheds in Texas that are impaired due to E. coli bacteria: Buck Creek, 5 subwatersheds of Little Brazos River, and Lampasas River. SELECT is an automated geographical information system tool that can assess potential bacteria sources and relative loads in watersheds using spatial factors such as land use, population density, and soil type. The results show how the SELECT methodology was applied and adapted to each watershed based on stakeholder concerns and data availability. Citation: Borel KE, Karthikeyan R, Smith PK, Gregory LF, Srinivasan R. 2012. Estimating daily potential E. coli loads in rural Texas watersheds using Spatially Explicit Load Enrichment Calculation Tool (SELECT). Texas Water Journal. 3(1):42-58. Available from: https://doi.org/10.21423/twj.v3i1.6164.

Pathogens are the principal cause of water body impairment for 303(d) listed waters in Texas and across the United States with 10,654 impairments nationally (TCEQ 2012; USEPA 2013). In Texas, 45% of 568 total impairments are caused by elevated bacteria levels (TCEQ 2012). Models such as the Soil and Water Assessment Tool (SWAT) and Hydrological Simulation Program-FORTRAN (HSPF) have been used for assessing bacterial sources and loading. Other simplistic microbial models, such as the Potential Nonpoint Pollution Index (PNPI), Spatially Explicit Delivery MODel (SEDMOD), and Spatially Explicit Load Enrichment Calculation Tool (SELECT), have been developed to rank potential pollution impacts from nonpoint sources and identify critical areas primarily using land use and geomorphology.   Citation: Borel K, Karthikeyan R, Berthold TA, Wagner K. 2014. Estimating E. coli and Enterococcus loads in a coastal Texas watershed. Texas Water Journal. 6(1):33-44. Available from: https://doi.org/10.21423/twj.v6i1.7008.