Use of System Specific Models to Evaluate DBP Control Options for New York City
Last Modified: Nov 19, 2009
- Steven Schindler, Salome Freud, William Meakin, P.E., Florence Mak, P.E. - New York City Department of Environmental Protection
- Julie A. Herzner, P.E, William Becker, Ph.D., P.E. Katie Hoek - Hazen and Sawyer
- David Reckhow, Ph.D., P.E. - University of Massachusetts
The United States Environmental Protection Agency (USEPA) promulgated the Stage 2 Disinfectant/Disinfection Byproduct (D/DBP) Rule in January 2006. New York City has until 2012 (2014 if capital improvements are needed) to comply with the new regulations, which are more stringent than the existing Stage 1 DBP Rule. Based on historical data, compliance with the new regulations could potentially be marginal (for haloacetic acids) in certain locations in the distribution system under current conditions. As a result, the City decided to evaluate various DBP control options for the Catskill/ Delaware water supply. This paper focuses on the development and use of an empirical DBP model for evaluating several optimization and capital intensive compliance alternatives for their effectiveness and feasibility for reducing DBP levels.
As part of a detailed Stage 2 D/DBP Compliance Study, field and bench-scale data were collected on water taken from New York City’s Kensico Reservoir and analyzed to develop NYC system-specific DBP prediction models. These system-specific empirical DBP predication models were used to investigate various DBP control options including operational changes in the distribution system to reduce chlorine dose, water age, and the effect of enhanced treatment and optimized reservoir operations to reduce DOC levels. The optimization scenarios that were investigated using the empirical model are as follows:
• Optimized chlorine dose scenarios
• Water age optimization scenarios
• Reduced DOC concentrations based on enhanced water treatment; and
• Optimized DOC concentrations based on modified reservoir operations (OASIS) modeling using precursor data.
The DBP predictions were compared to existing data to verify the model results.
This paper is important to the water industry because it demonstrates the utility of a predictive DBP formation model when evaluating compliance alternatives. This approach saves significant money and time.
For a copy of the full paper, please contact the author at email@example.com
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