Total Maximum Daily Load (TMDL) for Whiteoak Bayou in Harris County, Texas

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1 Total Maximum Daily Load (TMDL) for Whiteoak Bayou in Harris County, Texas Tina Peterson 1, Kristin White 2, and Eric Loucks 3 Background Whiteoak Bayou is an urban stream that meanders through suburban Houston and joins Buffalo Bayou in the highly urbanized, central business district. Water quality monitoring has determined that there are elevated indicator bacteria levels in the bayou, potentially posing a risk to people who could come in contact with the water by wading, swimming or boating in the Bayou. Safety of contact recreation is determined through the use of indicator bacteria, which epidemiological studies (USEPA 2004) have demonstrated are correlated to incidences of gastroenteritis in those who participate in such activities. Bacteria is measured in terms of colony forming units (cfu) which is determined from a most probable number (MPN) of bacterial colonies that grow in a cultured water sample. It is an estimate of the number of viable organisms in a specific quantity of water. The water quality is expressed in terms of concentration such as MPN per deciliter (MPN/dL or MPN/100 ml), while daily loads are expressed as totals typically in billions of MPN per day (BMPN/day). The Clean Water Act requires that all regulated water bodies be evaluated and those that do not meet water quality standards be placed on a list known as the 303(d) list. There are several mechanisms by which water bodies can be removed from the list, but perhaps the most common means is to perform a study known as a total maximum daily load (TMDL) study. A TMDL study involves assessing the assimilative capacity of a water body for a particular pollutant, identifying current loads of the pollutant into the water body, and estimating the reductions required to achieve the water quality standard for the pollutant within the water body. To date, various models have been used in TMDL studies ranging from simple load duration curves, or LDCs (Stiles 2002), to complex in-stream water quality models such as Hydrologic Simulation Program in Fortran (HSPF) (Moyer and Hyer 2003). Simple approaches such as load duration curves, while easy to use and able to estimate required reductions, do little in the way of identifying pollutant loads and developing strategies for reducing them. Furthermore, LDCs lack the meaningful spatial and temporal resolution needed during the implementation phase of the TMDL regulation. Sophisticated models such as HSPF, on the other hand, include spatial and temporal variation but are time-consuming to develop, require large data sets as input, 1 Engineer, CDM Smith, 3050 Post Oak Blvd., Houston, TX 77056, PetersenCM@cdmsmith.com 2 Engineer, U.S. Bureau of Reclamation, Sacramento, CA. 3 Senior Engineer, CDM Smith, Riata Trace Parkway, Austin TX

2 and suffer from the limitations associated with uncertainty and parameter estimation. Additionally, in-stream water quality models require inventories of point and nonpoint sources of the particular pollutant into the stream. Other tools have been developed in spreadsheets, such as the Bacteria Source Loading Calculator (Benham et al. 2006; Zeckoski et al. 2005) and the Bacteria Indicator Tool (USEPA 2000), to create input for water quality models such as HSPF. These tools differ from the Bacteria Loading Estimator Spreadsheet Tool (BLEST) because they are being used for determining model inputs, not to provide model results directly. The BLEST model was developed in Microsoft Excel to estimate indicator bacteria loads into the Buffalo and Whiteoak Bayou watersheds, two water bodies listed on the State of Texas 303(d) list for impairments related to contact recreation (Petersen et al., 2009). The model can be used to estimate load reductions and to develop loading input data for more sophisticated in-stream water quality models such as HSPF. The model can also be used to determine the variables within the source loading estimation calculations that have the most impact on developing the TMDL and estimating the required reductions. The ability to undertake such an analysis with a relatively simple tool such as BLEST is very valuable to decision makers and stakeholders as it can guide the process of sample collection, parameter estimation, and detailed model development. In BLEST, loading can be assessed for three different flow conditions: dry weather, intermediate, and wet weather. Dry weather conditions are those that are maintained primarily by point source flows to a bayou, while wet weather conditions are representative of peak storm conditions. Intermediate flow conditions represent bayou conditions several days after a rainfall event. Flow duration curves from USGS gages were used to define low, median, and wet weather flows. Assignment Using the BLEST spreadsheet tool provided, along with the Excel Solver Add-in, develop and solve a mathematical programming formulation to determine the least cost approach to meeting the Texas Commission of Environmental Quality water quality standards for bacteria in Segment 1017 of the Whiteoak Bayou. The problem can be stated as: Minimize: Cost of TMDL implementation measures. Subject to: 1. Meeting the dry weather and wet weather Bacteria standard 2. Total daily load equals current load minus amount removed by implemented removal measures plus/minus secondary effects of reduced loads 3. Secondary effects of reduced loads 4. Physical constraints and effectiveness limits on removal measures 5. Constraints to assure no duplication of stormwater measures (cannot treat same flow twice except unless Source Control, if used, is applied up front) 40

3 Data/Assumptions Current bacteria loads determined using BLEST are listed in Table 1. Bacteria sources include the following: Wastewater treatment plant (WWTP) effluent Wet weather wash-off from the WWTP biosolids handling facility Separate Sewer Overflows (SSO) Stormwater runoff Leakage from faulty on-site sewage facilities (OSSF), also known as septic tank/drainage field systems Direct deposition into the waterway by wild and domestic animals Release of bacteria from bottom sediments that accumulate during dry weather In addition, die-off and settling reduces in-stream concentrations of bacteria and thus behaves like a negative source. Note that biosolids, SSO and sediment release are sources driven by wet weather, while direct deposition is considered to be negligible during wet weather. Sediment accumulates during dry weather and is then entrained into the flow during wet weather, so the bacteria load associated with sediment depends on the dry weather concentration. Table 1. Estimated Bacteria Loads for Reach 1017 (Whiteoak Bayou) Dry Weather Wet Weather Source Flow Load Flow Load MGD 10 9 MPN/day MGD 10 9 MPN/day WWTP Discharge Biosolids Release SSO 1.2E E Stormwater Runoff ,500 Faulty Septic Tanks 5.8E E Direct Deposition Sediment Release Die off/settling ,800 Total ,300 Target 99 1,084 41

4 Removal Measures Available The following removal strategies are under consideration by the affected communities. The effectiveness and costs of these measures were adapted from Heaney and Joong (2006) and Heaney et al. (2002). 1. WWTP Discharge Improve Disinfection using Ozonation Will reduce bacteria in WW discharge by 90%. Cost = $400,000 + $10,000 per MGD treated 2. Biosolids discharge Containment improvements Will eliminate the biosolids load Cost = $1,500, SSO Eliminate SSO through system repairs Cost = $1,000 /gpd for dry weather discharges Cost = $10,000 /gpd for wet weather discharges 4. Septic Tanks Repair/replace Assume each repair will eliminate one billion MPN/day. Cost = $25,000 per repair An alternative is to extend the publically owned sewer system but this is costprohibitive as a TMDL measure. 5. Direct Deposition Animal management Can remove up to 50% of the load. Cost = $40,000 per billion MPN. 6. Stormwater Measures Any of the measures listed in Table 2 can be used to reduce the bacteria load caused by stormwater discharges. Source controls use regulations, enforcement and public education to reduce the bacteria that is exposed to stormwater. Examples of this are strict pet waste pick up laws, regulation of commercial dumpsters, and public education concerning storm drains along streets. Wet ponds, filters, wetlands and bioretention facilities remove bacteria from the water or retain bacteria to allow biological activity to reduce populations. Infiltration strips divert stormwater from the runoff thus reducing both the volume of water as well as the bacteria load. 42

5 Table 2. Stormwater measures to reduce bacteria loads Measure Source Control/ Public Education Removal Rate 25% (dry) 10% (wet) Cost $2,500,000 lump sum Wet Ponds 30% $100,000/MGD Limitations Bioretention 60% $1,000,000/MGD Wet weather only Filters 90% $10,000,000/MGD Wet weather only Infiltration Trenches 15% Constructed Wetlands $20,000/MGD 20% $25,000/MGD Limited to 5% of total flow Secondary effects 1. Sediment Release Channel sediments provide an excellent substrate for bacteria growth. Nutrients adhere to sediment, readily offering a source of food. Higher flow velocities indicative of wet weather conditions tend to stir up sediment, releasing the bacteria into the flow. During dry weather, sediment tends to accumulate and is populated by available bacteria. This gives rise to a situation where wet weather sediment load is dependent on dry weather bacteria load. For this problem, one could assume that sediment load in wet weather will be reduced in proportion to the reduction of the dry weather total daily load. 2. Die off and settling As discussed above, bacteria attach to sediment particles and settle out of the flow. Also, as food and oxygen are consumed, bacteria die off more rapidly than they reproduce. These processes have been shown to occur according to a firstorder decay relationship. Bacteria die off and settling is given by R = Load*(1-exp[-kt]) where: R = amount removed k = rate coefficient (1/day) t = elapsed time in days For this TMDL, k=1.5 and a time of one day are assumed. 43

6 Additional Guidance While it may be possible to formulate this problem as a single optimization model, it is probably more efficient to construct separate analyses of the dry weather and wet weather problems. Differences in the optimal policy for each case can be resolved by trial and error or by setting constraints to force the wet weather policy to use the dry weather measures. Also, there is more than one way to formulate this problem, but students may find it most natural to use binary (0-1) variables for yes/no decisions. However, there are also nonlinear features to the problem, and mixed-integer nonlinear programming problems are very difficult to solve. In this case, due to the small number of yes/no decisions, the 0-1 variables may be adjusted manually (fixed in a trial-and-error process) and the Solver used to adjust only the continuous variables. Integer variables, such as the number of septic tank repairs, may be treated as continuous and rounded up to the nearest integer value. Initially, no constraints have been set for the amount of stormwater that can be treated except for constructed wetlands. Students should consider the practical feasibility of capturing and treating large quantities of stormwater. References Benham, B.L., C. Baffaut, R.W. Zeckoski, K.R. Mankin, Y.A. Pachepsky, A.M. Sadeghi, K.M. Brannan, M.L. Soupir, and M.J. Habersack (2006). Modeling Bacteria Fate and Transport in Watersheds to Support TMDLs, Transactions of the ASABE, 49(4): Heaney, J.P., and J.G. Lee (2006). Methods for Optimizing Urban Wet-Weather Control System, EPA/600/R-06/034, USEPA, Cincinnati, OH. Heaney, J.P., D. Sample and L. Wright (2002). Costs of Urban Stormwater Control, EPA-600/R-02/021, University of Colorado, Boulder, CO. Moyer, D.L., and K.E. Hyer (2003). Use of the Hydrological Simulation Program- FORTRAN and Bacterial Source Tracking for Development of the Fecal Coliform Total Maximum Daily Load (TMDL) for Accotink Creek, Fairfax County, Virginia. U.S. Geological Survey Water-Resources Investigations Report Petersen, C.M., R.S. Hanadi S. Rifai, and R. Stein (2009). Bacteria Load Estimator Spreadsheet Tool for Modeling Spatial Escherichia coli Loads to an Urban Bayou, J. Environ. Eng., 135, doi: /(asce) (2009)135:4(203). Stiles, T.C. (2002). Incorporating Hydrology in Determining TMDL Endpoints and Allocations, Proceedings of the Water Environment Federation: National TMDL Science and Policy (13):

7 USEPA (2000). BASINS Technical Note 6: Estimating Hydrology and Hydraulic Parameters for HSPF. Document Number 823-R00-012, Washington, D.C. USEPA (2004). Impacts and Control of CSOs and SSOs, EPA 833-R , USEPA, Washington, D.C. Zeckoski, RW., B.L. Benham, S.B. Shah, M.L. Wolfe, K.M. Brannan, M. Al-Smadi, T.A. Dillaha, S. Mostaghimi, and C.D. Heatwole (2005). BSLC: A Tool for Bacteria Source Characterization for Watershed Management, Applied Engineering in Agriculture, 21(5):