Modeling the formation of trihalomethanes in drinking waters of Lebanon

Environmental Monitoring and Assessment

Volumn 149, Issue 1-4, 2009, Pages 429-436

  Semerjian, Lucy ^a   Dennis, John ^b   Ayoub, George ^a  

^a AMERICAN UNIVERSITY OF BEIRUT   (Lebanon)

^b UNIVERSITY OF CALGARY   (Canada)

Author keywords

Chlorination; Lebanon; Regression modeling; Trihalo methanes

Indexed keywords

CONTACT TIME; DRINKING WATER; LEBANON; MULTIPLE REGRESSIONS; NETWORK DATABASES; ORGANICS; PREDICTIVE MODELING; REGRESSION MODELING; SIMULATED DISTRIBUTION SYSTEM (SDS); STATISTICAL ANALYSIS (IGC: E4/K7); TRIHALO METHANES;

BROMINE COMPOUNDS; CHLORINATION; DIGITAL INTEGRATED CIRCUITS; FIRE HAZARDS; LABORATORIES; METHANE; ORGANIC CARBON; POTABLE WATER; PREDICTIVE CONTROL SYSTEMS; REGRESSION ANALYSIS;

CHLORINE;

ASSESSMENT METHOD; BROMIDE; CHLORINATION; DISTRIBUTION SYSTEM; DRINKING WATER; EXPERIMENTAL STUDY; HALOGENATED HYDROCARBON; MODELING; REGRESSION ANALYSIS; TOTAL ORGANIC CARBON; WATER SUPPLY;

ENVIRONMENTAL MONITORING; HUMANS; LEBANON; MODELS, STATISTICAL; SEASONS; TRIHALOMETHANES; WATER POLLUTANTS, CHEMICAL; WATER SUPPLY;

ASIA; EURASIA; LEBANON; MIDDLE EAST;

EID: 58549113831     PISSN: 01676369     EISSN: 15732959     Source Type: Journal    
DOI: 10.1007/s10661-008-0219-4     Document Type: Article

References (25)

1. Amy, G. L., Minear, R. A., & Cooper, W. J. (1987). Testing and validation of a multiple nonlinear regression model for predicting trihalomethane formation potential. Water Research, 21 (6), 649-659.
2. Amy, G. L., Siddiqui, M., Ozekin, K., Zhu, H., Wang, C. (1998). Empirically based models for predicting chlorination and ozonation by-products: Haloacetic acids, chloral hydrate, and bromate, USEPA Report CX-819579.
3. Eaton, A. D., Clesceri L. S., Rice, E. W., Greenberg, A. E. (Eds.) (2005). Standard methods for the examination of water and wastewater. Washington, D. C: American Public Health Association, American Water Works Association, Water Environment Federation).
4. Engerholm, A., & Amy, G. L. (1983). Predictive model for chloroform formation from humic acid. Journal of American Water Works Association, 75 (8), 418-423.
5. Garcia-Villanova, R. J., Garcia, C., Gomez, A., Paz Garcia, M., & Ardanuy, R. (1997). Formation, evolution, and modeling of trihalomethanes in the drinking water of a town: I. At the municipal treatment utilities. Water Research, 31 (6), 1299-1308.
6. Golfinopoulos, S. K., & Arhonditsis, G. B. (2002a). Multiple regression models: A methodology for evaluating trihalomethane concentrations in drinking water from raw water characteristics. Chemosphere, 47, 1007-1018.
7. Golfinopoulos, S. K., & Arhonditsis, G. B. (2002b). Quantitative assessment of trihalomethane formation using simulations of reaction kinetics. Water Research, 36 (11), 2856-2868.
8. Golfinopoulos, S. K., Xilourgidis, N. K., Kostopoulou, N., & Lekkas, T. D. (1998). Use of a multiple regression model for predicting trihalomethane formation. Water Research, 32 (9), 2821-2829.
9. Ibarluzea, J. M., Goni, F., & Santamaria, J. (1994). Trihalomethanes in water supplies in the San Sebastian Area, Spain. Bulletin of Environmental Contamination and Toxicology, 52, 411-418.
10. Kavanaugh, I. C., Trussell, A. R., Cromer, J., & Rhodes, R. (1980). Empirical kinetic model of trihalomethane formation: Applications meet the proposed THM standard. Journal of American Water Works Association, 72 (10), 578-582.
11. Malcom Pirnie Inc. (1992). Water treatment plant simulation program, version 1.21, User's manual, USEPA, Office of Ground Water and Drinking Water Technology Transfer, p. 150.
12. Milot, J., Rodriguez, M. J., & Serodes, J. B. (2002). Contribution of neural networks for modeling trihalomethanes occurrence in drinking water. Journal of Water Research and Planning-ASCE, 128 (5), 370-376.
13. Montgomery Watson (1993). Final report: Mathematical modeling of the formation of THMs and HAAs in chlorinated waters. Denver, CO: American Water Works Association.
14. Morrow, C. M., & Minear, R. A. (1987). Use of regression models to link raw water characteristics to trihalomethane concentrations in drinking water. Water Research, 21 (1), 41-48.
15. Nikolaou, A. D., Lekkas, T. D., Golfinopoulos, S. K., & Kostopoulou, M. N. (2002). Application of different analytical methods for determination of volatile chlorination by-products in drinking water. Talanta, 56, 717-726.
16. Rathbun, R. E. (1996). Regression equations for disinfection by-products for the Mississippi, Ohio and Missouri rivers. Science of the Total Environment, 191, 235-244.
17. Rodriguez, M. J., Serodes, J., & Morin, M. (2000). Estimation of water utility compliance with trihalomethane regulations using a modeling approach. Journal of Water Supply: Research and Technology-AQUA, 49 (2), 57-73.
18. Sadiq, R., & Rodriguez, M. J. (2004). Disinfection by-products in drinking water and predictive models for their occurrence: A review. Science of the Total Environment, 321, 21-46.
19. Semerjian, L. A., & Dennis, J. (2007). Multipathway risk assessment of trihalomethane exposure in drinking water of Lebanon. Journal of Water and Health, 5 (4), 511-522.
20. Semerjian, L. A., Dennis, J., & Ayoub, G. M. (2007a). Spatial and seasonal evolution of trihalomethane in distribution systems of Lebanon. Journal of Water Supply: Research and Technology-AQUA, 56 (4), 263-273.
21. Semerjian, L. A., Dennis, J., & Ayoub, G. M. (2007b). Occurrence, formation, and speciation of trihalomethanes in drinking waters of Lebanon. Paper presented at the 10 th International Conference on Environmental Science and Technology, Kos, Greece.
22. SPSS (Statistical package for social studies) (2003). version 12.
23. Urano, K., Wada, H., & Takemasa, T. (1983). Empirical rate equation for trihalomethane formation with chlorination of humic substances in water. Water Research, 17, 1797.
24. USEPA (Unites States Environmental Protection Agency) (1995). Determination of chlorination DBPs, chlorinated solvents, and halogenated pesticides/herbicides in drinking water by liquid-liquid extraction and gas chromatography with electron capture detector, Method 551.1, Office of Research and Development.
25. Westerhoff, P., Reckhow, D., Amy, G., Chowdhury, Z., McClellan, J., Dundorf, S. et al. (2000). Role of five independent treatment processes NOM structure, DBP precursor removal, and DBP modeling parameters. Paper presented at the American Water Works Association Annual Conference, Denver, CO.