Estimation of E. coli Concentrations from Failing On-Site Wastewater Treatment Facilities (OWTS) Using GIS

Failing Onsite Wastewater Treatment Systems (OWTSs) have been identified as a significant threat to water quality, discharging significant amounts of inadequately treated sewage effluents. When developing a Watershed Protection Plan (WPP), OWTS has often been difficult to assess due to technological, institutional and economic constraints. In Texas, contamination from bacterial pathogens is the primary source in water quality concern. According to the 2012 Texas Water Quality Inventory, the Dickinson Bayou watershed is listed as ?impaired?, due to bacteria. Since the bacterial levels in this watershed are not meeting the State?s recreation standards, actions are needed to improve the water quality. Poorly designed and maintained OWTS, along with inappropriate site characterization are major contributors of the bacteria in this watershed. The majority of the OWTS located in Dickinson Bayou are located in poorly drained soils increasing the likelihood of contaminated runoff into the surface waters. A prediction tool was developed using Geographic Information System (GIS) to assess failing OWTS and the potential E. coli contamination to surface waters. This tool will help identify different parameters affecting E. coli concentration in streams, which include: rainfall conditions, spatial connections of OWTS to stream network, age of the OWTS, and the failure rate of the OWTS.

A spatially-explicit algorithm was developed to estimate E. coli concentrations in watersheds resulting from failing OWTS, and implemented using ArcGIS 10. Spatial analysis of accumulated E. coli concentrations in streams was made possible by GIS. The algorithm was automated using python programming language, ArcPy, to simulate E. coli concentrations in surface waters in a coastal Texas watershed for different rainfall conditions.

This automated tool simulated potential E. coli loads and concentrations from failing OWTS across the Dickinson Bayou watershed in Texas. The tool was validated using observed runoff data in the Dickinson Bayou watershed. The highest potential E. coli loads were identified and the areas of concern were highlighted to more effectively apply Best Management Practices (BMPs). Results concluded that precipitation played a significant role in routing the E. coli loads to streams in the watershed. The potential E. coli concentration in streams decreased with increasing rainfall amount. Also, the simulation results showed the number of household size and the number of OWTS plays a major role in E. coli contribution in the watershed. The age of the OWTS and the hydrologic connectivity of those failing systems should be considered while simulating the E. coli concentrations in the stream. Regulators, planners, and watershed managers to make timely management decisions can use results from this automated tool.