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Performance of Storm Water Retention Ponds and Constructed Wetlands in Reducing Microbial Concentrations (PDF) (76 pp, 1.16 MB) (EPA/600/R-06/102) September 2006

Storm water runoff can transport high concentrations of pathogens to receiving waters. Bacteria indicator organisms, as surrogates for pathogens, are the most often reported cause of receiving water impairments. Storm water best management practices (BMPs) are often considered effective tools to mitigate the effects of storm water pollutants before they appear in receiving waters. However, BMP performance for pathogen removal is not well documented. Many questions remain on the transport and fate of indicator bacteria that enter and exit storm water, even with BMPs in place.

EPA’s National Risk Management Research Laboratory investigated the fate of indicator organisms in the storm water runoff entering and exiting two commonly used BMPs: constructed wetlands and retention ponds. This research used controlled-condition, pilot-scale systems that represent larger field-scale systems to determine the dominant mechanisms that influence the reduction of indicator organism concentrations. The pilot-scale work was supported by bench-scale laboratory experiments investigating the effects of single parameters (such as temperature, sunlight, and salinity) on indicator organism inactivation rates.

This document reports the results of developing techniques for creating bacterially enriched storm water bench-scale studies and the pilot-scale BMP research. Bench-scale study results show that temperature and sunlight affect the inactivation rates significantly. Results from the pilot-scale research suggest that constructed wetlands and retention ponds lower microbial concentrations in storm water runoff. Bacteria inactivation generally followed the first-order,
K-C* empirical model that acknowledges an irreducible concentration. Factors such as sunlight and temperature provide much of the inactivation in indicator bacteria, but other factors (e.g., predation, sedimentation, filtration, sorption, pH, and biochemical oxygen demand) also appear to influence indicator bacteria concentrations. Future research that validates results of the pilot-scale systems and field-scale systems should be done.

Developing microbial inactivation models to predict effluent concentrations from BMPs will help reduce the uncertainty and improve the capabilities of surface water quality models. First-order models that do not consider background concentrations or resuspension may underestimate actual bacterial concentrations.


Ariamalar Selvakumar

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