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Multiple regression models: a methodology for evaluating trihalomethane concentrations in drinking water from raw
Spyros K Golfinopoulos1, George B Arhonditsis
1Department of Environmental Studies, University of the Aegean, Mytilene, Greece. sgolf@env.aegean.gr
Chemosphere
|July 11, 2002
Summary
This study developed a regression model to predict trihalomethane (THM) levels in drinking water. The model accurately estimates THM concentrations, considering factors like chlorine and water temperature throughout the year.
Area of Science:
- Environmental Science
- Water Quality Analysis
- Public Health
Background:
- Trihalomethanes (THMs) are disinfection byproducts in drinking water.
- THMs pose potential risks to human health.
- Monitoring and controlling THM levels is crucial for safe drinking water.
Purpose of the Study:
- To develop a predictive model for trihalomethane (THM) concentrations in finished drinking water.
- To identify key raw water characteristics influencing THM formation.
- To establish a reliable methodology for estimating total and individual THM species.
Main Methods:
- Utilized multiple regression analysis.
- Incorporated routinely measured raw water parameters: chlorine dose, chlorophyll a, temperature, pH, and bromide.
- Accounted for seasonal variations in water quality.
Main Results:
- Developed a multiple regression model to estimate THM concentrations.
- The model demonstrated acceptable fits for predicting THM levels.
- Accurate estimation of both peak and low THM concentrations over an annual cycle was achieved.
Conclusions:
- The developed model provides a reliable method for predicting THM concentrations in drinking water.
- Key water quality parameters can be used to manage and control THM formation.
- Understanding seasonal effects is important for accurate THM prediction and water safety.