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Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History
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Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Dec 15, 2015

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Page 1: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake

CE5504 - Surface Water Quality ModelingCase History

Page 2: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

What are Coliform Bacteria ?

Coliform bacteria are considered to be indicator organisms, i.e. those used by public health officials to indicate the potential presence of disease-causing organisms or pathogens. It is logistically impractical and prohibitively expensive to test each water sample for all of the pathogens potentially present.

The most commonly used indicator organisms are members of the coliform group, named for Escherichia coli, a normal inhabitant of the digestive tract of humans and other warm-blooded animals. Tests are made for total coliforms (an assay which also detects some soil bacteria) and fecal coliforms (a group limited to the guts of warm-blooded animals). Most strains of E. coli are not pathogenic, however, some can cause gastroenteritis, and occasionally, death. Standards vary with water use: contact recreation (200-1000), wastewater effluent (200) and drinking water (<1) colony forming units or cfu.

Page 3: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

What are Combined Sewer Overflows ?

Older cities have sewer systems which carry both sanitary wastewater and street runoff or storm water to the wastewater treatment plant. Such systems are referred to as combined sewers. Newer cities, and some renovated portions of older municipalities, have separated sewers where the sanitary wastewater is transported to the wastewater treatment plant and stormwater is discharged to receiving waters, either with or without treatment.

Combined sewer overflows or CSOs are relief structures built into combined sewer systems to prevent overloading at the treatment plant, surcharge flooding of streets and the backup of raw sewage into homes. CSOs are designed with weirs which automatically discharge a mixture of storm runoff and raw sewage to receiving waters when water levels in the sewers reach a certain level.

Page 4: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

How do CSOs impact water quality and the management of Onondaga Lake ?

CSOs discharge untreated wastewater to receiving waters. As expected, this provides a load of BOD, ammonia, and phosphorus which may stress these lakes and streams. Perhaps of greatest importance is the discharge of fecal bacteria, with pathogens which could potential endanger the human health, and sanitary detritus which is aesthetically damaging. Concentrations of fecal coliform bacteria in streams receiving CSOs can exceed 106 cells/100 mL and result in violation of the contact recreation standard over much of Onondaga Lake.

Complete elimination of CSO discharges (sewer separation, capture and treatment, treatment) is often beyond the financial resources of many municipalities. Further, the extent and magnitude of the impact of a CSO event is difficult to predict due to the interactions of loads, mass transport, and kinetics. Here, mathematical models are of great benefit in guiding management efforts seeking to minimize CSO impacts.

Page 5: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Model Development

[ ( )] ij ijii i ij j i j i i i

j j ij

E AdCV W Q C C C C V k C

dt L

Loads Advection Diffusion Kinetics

Cell i Cell j

Page 6: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Model Framework

A two-layer, 11-cell model was developed with 8 surface and 3 bottom cells. Cell dimensions were selected to accommodate spatial differences in fecal coliform levels noted through field monitoring.

Page 7: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Loading Sub-Model

Tributary loads were monitored under dry weather conditions and for two storms. Sampling was conducted on an hourly basis during storms, with the frequency reduced to twice daily as the hydrograph returned to base flow conditions. Storms 1 and 2 had return frequencies of 1 and 7 years, respectively. Lake stations were monitored 1-3 times per day over the course of the storm event.

Page 8: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Mass Transport Sub-Model

Advective mass transport was estimated as the cumulative stream flow inputs for each cell. Horizontal diffusive exchange was calculated as a function of epilimnion depth and wind speed. Vertical diffusive transport was calculated as a function of the thermal profile (vertical density gradient).

Page 9: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Kinetics Sub-Model

The overall kinetic coefficient included terms for temperature-mediated loss in the dark (kd), losses as mediated by irradiance (ki), and losses to sedimentation (ks).

d i sk k k k

Page 10: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Kinetics: Dark Death Rate

Measurements of a temperature response made for this study and those reported in the literature fail to make a strong case that differs from zero. An average dark death rate of 0.73 d-1, the intercept of the light response curve, was proposed for use in the model.

( 20), ,20

Td T dk k

Page 11: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Kinetics: Light Mediation

A strong linear correlation was noted between light intensity and fecal coliform death rate. The slope of the light response curve, = 0.008, was used to calculate ki.

ik I 0, ( )1 eavg z

e

II e

z

Page 12: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Kinetics: Sedimentation

Settling losses were determined by assessing the association of fecal coliform bacteria with particles of various size classes and then determining particle settling velocities (mean = 1.38 m·d-1) using in situ sediment traps.

s

vk

z

Page 13: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Kinetics Submodel

The overall kinetic coefficient included terms for temperature-mediated loss in the dark (kd), losses as mediated by irradiance (ki), and losses to sedimentation (ks).

0, ( )( 20),20 1 eavg zT

de e

I vk k e

z z

d i sk k k k

Page 14: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Model Evaluation

Page 15: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Model Evaluation

Page 16: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Model Evaluation

Page 17: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Model Sensitivity and Application

Environmental Effects

• Wind• Irradiance• Thermocline Depth

Tributary Loads

• South tributaries• North tributaries

Page 18: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Model Sensitivity and Application

Page 19: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Model Sensitivity and Application

Page 20: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Model Sensitivity and Application

Page 21: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Model Sensitivity and Application

Page 22: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Model Sensitivity and Application

Page 23: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

CSO Remediation Plan

There are CSO discharges to three tributaries to Onondaga Lake: Onondaga Creek, Harbor Brook, and Ley Creek. Onondaga Creek has the largest number of CSOs with 45 discharge points. Onondaga County is presently considering a plan which would reduce CSO discharges by 56% (900 MGD 392 MGD) at a cost of $65-80 million.

The plan incorporates limited sewer separation (7% of the flow reduction), activation of a dormant in-line storage system (43% of the flow reduction) and construction of two ‘regional treatment facilties’ of RTFs (50% of the flow reduction). The RTFs are composed of a wet well (~1 MG), swirl concentrators (~0.5 MG), and a disinfection tank (2 MG). Swirl concentrators remove a fraction of the solids from the combined wastewater before discharge to the receiving waters. Combined wastewater captured through in-line storage and solids captured in swirl concentrators are routed to the treatment plant as storm flows abate.

The Partnership for Onondaga Creek is contesting the County plan as an incomplete and insufficient approach which violates the principles of environmental justice.

Page 24: Modeling Fecal Coliform Bacteria Dynamics in Onondaga Lake CE5504 - Surface Water Quality Modeling Case History.

Modeling Fecal Coliform Bacteria

I. Field and Laboratory Determination of Loss Kinetics

by M.T. Auer and S.L. Niehaus

II. Model Development and Application

by R.P. Canale, M.T. Auer, E.M. Owens, T.M. Heidtke, and S.W. Effler

Based on articles published in Water Research, Volume 27, Number 4, 1993.

Contributions No. 26 and 27 of the Upstate Freshwater Institute.