Grantee Research Project Results
2000 Progress Report: Mechanistic-Based Disinfection and Disinfection Byproduct Models
EPA Grant Number: R826831Title: Mechanistic-Based Disinfection and Disinfection Byproduct Models
Investigators: Westerhoff, Paul , Amy, Gary , Reckhow, David A. , Chowdhury, Zaid
Institution: Arizona State University , Malcolm Pirnie , University of Colorado at Boulder , University of Massachusetts - Amherst
Current Institution: Arizona State University , Malcolm Pirnie , University of Colorado at Boulder , University of Massachusetts - Boston
EPA Project Officer: Hahn, Intaek
Project Period: December 15, 1998 through December 14, 2001
Project Period Covered by this Report: December 15, 1999 through December 14, 2000
Project Amount: $339,583
RFA: Drinking Water (1998) RFA Text | Recipients Lists
Research Category: Drinking Water , Water
Objective:
The water industry faces new challenges in understanding and controlling disinfection byproduct (DBP) formation as health concerns demonstrate a need for more stringent regulatory DBP requirements. Mechanistic tools for understanding and predicting the rate and extent of DBP formation are required to facilitate the evaluation of DBP control alternatives. Accurate predictive models for DBPs can facilitate the evaluation of treatment alternatives for disinfection and DBPs. For this reason, the EPA has developed a water treatment plant simulation model (Harrington et al., 1992) that incorporates the current state of knowledge for predicting DBP formation based upon the water quality entering a treatment plant, chemical dosages applied at various locations within the treatment process, and the detention times in these processes. This model is used for conducting regulatory impact assessments in support of developing DBP regulations. However, the current DBP modeling approach is empirical-based, rather than mechanistic-based.The goal of the proposed research is to develop and calibrate an accurate kinetic-based mechanistic model for several chlorinated DBPs of interest. The model will predict DBPs (e.g., four THM species [THM4] and nine HAA species [HAA9]) as a function of dissolved organic carbon (DOC), disinfectant level (type and dosage), reaction time, temperature, pH, and bromide concentrations. The project has the following specific objectives:
1. Compile existing databases on DBP formation experiments into a single Unified Database. Some data from the compiled database will be used to develop and/or verify mechanistic DBP prediction equations. Data deficiencies will be identified.
2. Conduct controlled batch-scale experiments with raw/untreated water targeted at transforming the amount (mg/L) and chemical structure of natural organic matter (NOM)/DBP precursors. Transformations in NOM properties will be quantified.
3. Develop and calibrate numerical models for predicting the behavior of disinfectants (free-chlorine) and the formation of DBPs (THMs and HAAs). Controlled experiments will be performed to assess inorganic reactions, disinfectant decay, DBP formation, and DBP stability. Model parameters for DBP formation will be statistically compared against NOM properties.
4. Develop an easy-to-use computer model capable of predicting DBP formation-through a combination of mechanistic subroutines-as a function of disinfectant decay and water quality conditions.
Progress Summary:
Progress on the project is excellent, and no major obstacles have been encountered. This section contains a summary of the preliminary findings and their significance.1. Several large database files have been compiled and represent our Unified Database. The Unified Database includes the following types of information: (a) water identification categories (source ID, type of treatment, and date); (b) water quality data (DOC, UVA, pH, temperature, ammonia, and bromide levels); (c) chlorination conditions (dose, reaction time, residual); and (d) byproduct formation (individual and total THM and HAAs). The Unified Database contains over 2,500 chlorination laboratory experiments and over 500 sets of data from full-scale treatment plants, representing work in the United States, Canada, and New Zealand. An electronic file (Microsoft ExcelO) will be included with the final project report.
2. Raw/untreated water from two sources (Colorado River water from the Central Arizona Project [CAP], AZ; Lake Houston water [LHW], TX; and Harwoods Mill water [HMW], VA) have been collected (~ 120 gallons) and subjected to several parallel bench-scale treatment processes. These processes included: (a) no treatment; (b) continuous-flow ozonation to achieve a CT ~ 1 mg-min/L; (c) simulated enhanced alum coagulation (10 mg alum/mg TOC); (d) simulated enhanced softening (pH 11.0 with lime and soda ash addition); (e) activated carbon adsorption to remove ~ 50 percent of the DOC; and (f) low-pressure ultrafiltration. Water chemistry and NOM characteristics were measured before and after each treatment. NOM characterization included: DOC, UV adsorption at multiple wavelengths, 3-D fluorescence, percentage DOC as hydrophobic/hydrophilic/ultrahydrophilic, and molecular weight by size exclusion chromatography. Chlorination, plus TTHM and HAA9 byproduct formation, kinetics were assessed on each of the treated water samples under variable initial pH, bromide, temperature, and chlorine dose conditions. The resulting data were combined into a large database for calibrating the mechanistic model.
3. Both empirical and mechanistic chlorine decay and DBP formation models have been developed and calibrated. This project focused on demonstrating the benefits, and increased accuracy, of the mechanistic models. Figure 1 compares data in the observed experimental dihalogenated acetic acid (DiAA) formation in the different CAP-treated waters against the predicted mechanistic model formation, using dimensionless x- and y-axes. The mechanistic model predictions are very good. Even better predicted fits of observed experimental data have been obtained for chlorine decay and THM formation. The mechanistic model has been parameterized for all waters. Currently, correlations between DOC reactive site concentrations (mM), reactive with chlorine in forming DBPs, is being correlated against measured NOM characteristics. These correlations will allow estimation of reactive site concentrations based upon easily obtainable NOM characteristics such as DOC concentration and specific UV absorbances (SUVA) at 254 nm.
4. A C+ computer language model has been written and encoded into the existing EPA water treatment plant simulation model. DOC reactive site concentrations can be inputted directly by the user, or estimated from DOC and SUVA. The user can select either an empirical model or the mechanistic model for estimation of DBP formation. The model is in its final phases of development, validation, and testing of user interfaces.
Future Activities:
Model parameterization will continue on the Unified Database, and experimental data will be collected from recently completed laboratory experiments. The Windows-based model will be completed, and correlations between NOM characteristics and DBP reactive site concentrations will be developed for the application.Journal Articles:
No journal articles submitted with this report: View all 23 publications for this projectSupplemental Keywords:
drinking water treatment, environmental chemistry, oxidation., RFA, Scientific Discipline, Water, Applied Math & Statistics, Mathematics, Environmental Chemistry, Analytical Chemistry, Drinking Water, monitoring, chlorine decay, oxidation, unified database, chemical byproducts, disinfection byproducts (DPBs), treatment, chlorine-based disinfection, chloramines, DBP risk management, water quality, drinking water contaminants, drinking water system, mechanistic-based modelsRelevant Websites:
http://www.eas.asu.edu/~civil/
http://www.ecs.umass.edu/cee/
http://civil.colorado.edu/
http://www.malcolmpirnie.com/index.html
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.