| Summary: | 博士 === 國立中山大學 === 環境工程研究所 === 100 === In response to organic contaminations pollutating water sources of drinking water, domestic water treatment plants (WTP) were transforming from traditional chlorination disinfecton method to advanced ozone-based disinfection processes. However, the effectiveness of water purification procedures n removing AOC (Assimilable Organic Carbon) and DBPsFP (Disinfection By-Products Formation Potential) can be improved. Additionally, the quality of clean water purified at WTP may deteriorate in the water distribution network for various reasons, primarily resulting from the regrowth of microorganisms in the water distribution pipelines.
This study investigates and researches the essential water quality items of effluent before and after the advanced water purification treatment plants and water movement to end users through water distribution networks. The investigation proceeded in four directions: (1) the efficiency of removing AOC from raw water using powdered and granular activated carbon biological systems, and the development of an AOC prediction model based on water quality monitoring items using the AutoNet (6.03) method of the artificial neural network system; (2) removal of the byproducts of disinfection from raw water using powdered activated carbon biological systems; (3) examining the relationship between ozone-based and chlorination-based water disinfection methods by comparing the number of coliform bacteria and total bacteria population in traditional and advanced processing units; (4) regarding the water distribution storage facilities for users, water reservoir towers were examined for water quality sampling and analysis and water tower cleaning frequencies. Regression analysis was performed using SPSS （Statistical Product and Service Solutions） statistical software, with the correlation coefficient denoting the closeness of relationships. We anticipate understanding the water quality situation for current users of tap water, and demands for cleaning frequencies, thereby achieving the purpose of improving drinking water safety.
Regarding the efficiency of removing AOC from raw water, the results showed powdered and granular activated carbon biological systems performed well, with the AOC removal rate reaching 53% and 54%, respectively, and the SUVA (Specific Ultraviolet Absorbance) value (showed by UV254/DOC) being reduced by 15-18% and 22-23%, respectively. The correlation analysis of the AOC prediction model shows that the GAC (Granular Activated Carbon) had high predictive and actual value R values (R2 = 0.772) after model regressing, and the PAC (Powder Activated Carbon) had higher predictive and actual value R values (R2 = 0.856) after model regressing as well. That the PAC system AOC prediction model has a slightly higher correlation that may be attributed to water contaminations resulting from domestic sewage, agricultural fertilizers, and livestock excretions.
In the use of powdered activated carbon biological systems to remove disinfection byproducts, THMsFP (Trihalomethanes Formation Potential) and HAAsFP (Haloacetic acids Formation Potential) functioned with a certain removal efficiency, with the average effluent concentrations being under the regulatory standard of 80μg/L, respectively, which reduces carcinogenic risks. Correlation analyses conducted using SUVA on the three water quality concentrations (HAA5FP, HAA9FP, and THMsFP) obtained R2 values of 0.805, 0.820, and 0.823, respectively, indicating high levels of correlation.
For the results of microbial assessment using ozone and chlorine to process drinking water, the advanced and conventional WTP achieved a removal rate greater than 99% for microbial removal (coliform bacteria and total bacteria population).
The correlation analysis between cleaning frequencies and water quality parameters showed the frequency at which the water reservoirs and towers were cleaned has a significant impact on tap water quality in residential compounds and schools that accommodated more than 100 households or less than 99 households. Higher cleaning frequency (more than four cleanings a year) results in better the water quality.
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