Effect of the Recycling Process on Drinking Water Treatment: Evaluation Based on Fluorescence EEM Analysis Using the Peak-Picking Technique and Self-Organizing Map

Impact of coexisting microalgae species and bacteria in the presence level of fishy odor-causing Uroglena sp. in surface water

The study investigated the interplay of factors influencing the occurrence of Uroglena sp. blooms in surface water, particularly during the spring season. While Uroglena sp. typically demonstrates a propensity for blooming during the spring season, diminished population density was documented, underscoring the influence of pertinent environmental factors. To study the determinants, surface water samples collected for 3 years were analyzed for general water quality parameters, coexisting microalgae species, and total bacteria. Key determinants were found to include the ratio of dissolved nitrogen to dissolved phosphorus (DN: DP), temperature, bacterial density, the presence of Dinobryon sp. (golden algae) and Microcystis sp. (cyanobacteria). The findings indicate that factors such as DN:DP ratios and temperature variations intricately modulate Uroglena sp. bloom by affecting microbial dynamics, notably competitive interactions. The findings of this study offer further scientific insight into addressing seasonal occurrences of fishy odors in water reservoirs, with particular relevance to the spring season.

Characterization of DOM released from bacteria in response to chlorine in water based on indicator bacteria E. coli

ABSTRACTIn this study, Escherichia coli (E. coli) was used as an indicator bacterium treated with five different concentrations of chlorine (0.1; 0.5; 1.0; 2.0, and 5.0 mg/L) and without chlorine (0.0 mg/L) to evaluate the changes in the DOM characteristics. The dissolved organic carbon (DOC) concentration initially increased along with the chlorine concentrations and decreased after 24 h (0.0 and 0.1 mg/L) and 168 h (0.5; 1.0; 2.0 and 5.0 mg/L). Ultra-violet absorbance at 260 nm (UV260) showed that the absorbance decreased for control without chlorine (0.0 mg/L) and 0.1 mg/L chlorine, while increased for other concentrations of chlorine within 120 h. The DOC and UV260 results indicated that the high concentrations of chlorine initiated high contents of DOM which contained more humic-like molecules than the DOM released from E. coli without chlorine. Fluorescence excitation-emission matrix (EEM) analysis suggested that the DOM released from E. coli without chlorine enriched with protein-like substances, whereas the fulvic-like and humic-like substances more intensified in the DOM for the high concentrations of chlorine (>1.0 mg/L). The molecular weight distribution of DOM showed that the intensity of high molecular weight substances and polydispersity increased along with chlorine concentration and contact time, whereas the low molecular weight substances were relatively higher in the DOM for control without chlorine. The obtained results of this study would be useful for a better understanding of the variation of DOM during treatment and could be used as an important reference for optimizing the operation condition of the water treatment plants.

Fluorescence-based indicators predict the performance of conventional drinking water treatment processes: Evaluation based on the changes in the compositions of dissolved organic matter

This study investigated the treatability of dissolved organic matter (DOM) by the selected lab-scale drinking water treatment processes using fluorescence excitation-emission matrix (EEM) analysis. The fluorescence ratio Peak 3/Peak 2 was established from well-defined fluorescence peak intensity of humic-like components (Ex/Em: 225 nm/425 nm) and protein-like components (Ex/Em: 230 nm/345 nm). Peak 3/Peak 2 predicted the aromatic characteristics of DOM and their origins in the different natural surface water feeding the different drinking water treatment plants. The drinking water treatment processes confirmed the treatability of DOM using Peak 3/Peak 2 and was well-confirmed by specific UV₂₆₀ absorbance relative to dissolved organic carbon (DOC) (SUVA) and fluorescence-based indices. Peak 3/Peak 2 was demonstrated to have a strong correlation with SUVA and DOC removal for the water after treatment by coagulation, adsorption, and chlorination. Compared to the humification index and fluorescence index, Peak 3/Peak 2 is better for indicating the DOM composition in terms of treatability. These findings can broaden the use of fluorescence spectroscopy in water treatment applications, by developing the fluorescence ratio to evaluate the performance of drinking water treatment plants.

Artificial neural network implementation for dissolved organic carbon quantification using fluorescence intensity as a predictor in wastewater treatment plants

Although spectroscopic methods provide a fast and cost-effective means of monitoring dissolved organic carbon (DOC) in natural and engineered water systems, the prediction accuracy of these methods is limited by the complex relationship between optical properties and DOC concentration. In this study, we developed DOC prediction models using multiple linear/log-linear regression and feedforward artificial neural network (ANN) and investigated the effectiveness of spectroscopic properties, such as fluorescence intensity and UV absorption at 254 nm (UV₂₅₄), as predictors. Optimum predictors were identified based on correlation analysis to construct models using single and multiple predictors. We compared the peak-picking and parallel factor analysis (PARAFAC) methods for selecting appropriate fluorescence wavelengths. Both methods had similar prediction capability (p-values >0.05), suggesting PARAFAC was not necessary for choosing fluorescence predictors. Fluorescence peak T was identified as a more accurate predictor than UV₂₅₄. Combining UV₂₅₄ and multiple fluorescence peak intensities as predictors further improved the prediction capability of the models. The ANN models outperformed the linear/log-linear regression models with multiple predictors, achieving higher prediction accuracy (peak-picking: R² = 0.8978, RMSE = 0.3105 mg/L; PARAFAC: R² = 0.9079, RMSE = 0.2989 mg/L). These findings suggest the potential to develop a real-time DOC concentration sensor based on optical properties using an ANN for signal processing.