A comprehensive review of mathematical models developed for the estimation of organic disinfection byproducts

Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction

Water/wastewater ((waste)water) disinfection, as a critical process during drinking water or wastewater treatment, can simultaneously inactivate pathogens and remove emerging organic contaminants. Due to fluctuations of (waste)water quantity and quality during the disinfection process, conventional disinfection models cannot handle intricate nonlinear situations and provide immediate responses. Artificial intelligence (AI) techniques, which can capture complex variations and accurately predict/adjust outputs on time, exhibit excellent performance for (waste)water disinfection. In this review, AI application data within the disinfection domain were searched and analyzed using CiteSpace. Then, the application of AI in the (waste)water disinfection process was comprehensively reviewed, and in addition to conventional disinfection processes, novel disinfection processes were also examined. Then, the application of AI in disinfection by-products (DBPs) formation control and disinfection residues prediction was discussed, and unregulated DBPs were also examined. Current studies have suggested that among AI techniques, fuzzy logic-based neuro systems exhibit superior control performance in (waste)water disinfection, while single AI technology is insufficient to support their applications in full-scale (waste)water treatment plants. Thus, attention should be paid to the development of hybrid AI technologies, which can give full play to the characteristics of different AI technologies and achieve a more refined effectiveness. This review provides comprehensive information for an in-depth understanding of AI application in (waste)water disinfection and reducing undesirable risks caused by disinfection processes.

Investigating bromide incorporation factor (BIF) and model development for predicting THMs in drinking water using machine learning

Many disinfection byproducts (DBPs) in drinking water can pose cancer risks to humans while several DBPs including trihalomethanes are typically regulated. Although trihalomethanes are regulated, brominated fractions (bromodichloromethane, dibromochloromethane and bromoform) are more toxic to humans than the chlorinated ones (chloroform). To date, >100 models have been reported to predict DBPs. However, models to predict individual trihalomethanes are very limited, indicating the needs of such models. Various factors including natural organic matter (NOM), bromide ions (Br-), disinfectants (e.g., chlorine dose), pH, temperature and reaction time affect the formation and distribution of trihalomethanes in drinking water. In this study, NOM was fractionated into four groups based on the molecular weight (MW) cutoff values and their respective contributions to dissolved organic carbon (DOC), trihalomethanes and bromide incorporation factors (BIF) were investigated. Models were developed for predicting chloroform, bromodichloromethane, dibromochloromethane, bromoform and trihalomethanes. Three machine learning techniques: Support Vector Regressor (SVR), Random Forest Regressor (RFR) and Artificial Neural Networks (ANN) were adopted for training and testing the models. The normalized BIFs were in the ranges of 0.08-0.16 and 0.07-0.15 per mg/L of DOC for pH 6.0 and 8.5 respectively. The BIFs were higher for lower pH and MW values while increase of bromide to chlorine ratios increased BIFs. The models showed excellent predictive performances in training (R2 = 0.889-0.998) and testing (R2 = 0.870-0.988) datasets. The SVR and RFR models showed the best performances with lower RMSE and MAE in most cases. These models can be used to better control different trihalomethanes in drinking water to maintain regulatory compliance, and to minimize the risks to humans.

Combined application of EPANET and empirical model for possible formation of trihalomethanes in water distribution network of Chattogram city to identify potential carcinogenic health risk zone

The study identifies potential carcinogenic health risk-zone of Chattogram city for the occurrence of trihalomethanes (THMs) at its water distribution network. The EPANET-THMs simulation model along with an empirical model have been adopted in the study to predict THMs content of supply water of the distribution network of the city's Karnaphuli service area. The empirical model has estimated THMs level of supply water based on influential water quality parameters, and few of these have been used as pre-set values for subsequent EPANET simulation. The simulation (R²= 0.7) shows that THMs' concentrations throughout the network vary from 33 to 486 μg/L. Around 60% of total junctions showed THMs concentrations above 150 μg/L, while that is above 50 μg/L for most (99%) of the junctions. Residual Free chlorine, one of the precursors for the THMs formation in distribution line, has also been simulated by EPANET considering varying applied chlorine dose at the water purification unit and wall (K_w) and bulk (K_b) decay constants. The simulated free residual chlorine peaks are found to be closer to the actual values with chlorine dose of 2 mg/L, and decay constants, K_w = 1 d^-1 and K_b = 1 d^-1. A mean lifetime total risk of cancer due to the presence of THMs has been found to be very high. Spatial distribution of carcinogenic risk shows that the central zone of the service area is the most vulnerable zone, followed by the western and northern zone. The first ever zone wise risk identification could be used as baseline data for operational and regulatory purposes and may raise awareness among the city's inhabitants. Furthermore, the application of EPANET in combination with an empirical model could be an effective tool for predicting THMs' concentration in water distribution networks in developing countries like Bangladesh to minimize the expenses of measuring THMs.

Advances and research needs for disinfection byproducts control strategies in swimming pools

The control of disinfection byproducts (DBPs) in swimming pools is of great significance due to the non-negligible toxicity and widespread existence of DBPs. However, the management of DBPs remains challenging as the removal and regulation of DBPs is a multifactorial phenomenon in pools. This study summarized recent studies on the removal and regulation of DBPs, and further proposed some research needs. Specifically, the removal of DBPs was divided into the direct removal of the generated DBPs and the indirect removal by inhibiting DBP formation. Inhibiting DBP formation seems to be the more effective and economically practical strategy, which can be achieved mainly by reducing precursors, improving disinfection technology, and optimizing water quality parameters. Alternative disinfection technologies to chlorine disinfection have attracted increasing attention, while their applicability in pools requires further investigation. The regulation of DBPs was discussed in terms of improving the standards on DBPs and their preccursors. The development of online monitoring technology for DBPs is essential for implementing the standard. Overall, this study makes a significant contribution to the control of DBPs in pool water by updating the latest research advances and providing detailed perspectives.

Tracing microplastic (MP)-derived dissolved organic matter in the infiltration of MP-contaminated sand system and its disinfection byproducts formation

Microplastic (MP) pollution in soil/subsurface environments has been increasingly researched, given the uncertainties associated with the heterogeneous matrix of these systems. In this study, we tracked the spectroscopic signatures of MP-derived dissolved organic matter (MP-DOM) in infiltrated water from MP contaminated sandy subsurface systems and examined their potential to form trihalomethanes (THMs) and haloacetic acids (HAAs) by chlorination. Sand-packed columns with commercial MPs (expanded polystyrene and polyvinylchloride) on the upper layer were used as the model systems. Regardless of the plastic type, the addition of MPs resulted in a higher amount of DOM during infiltration compared with the clean sand system. This enhancement was more pronounced when the added MPs were UV-irradiated for 14 days. The infiltration was further characterized using FT-IR and fluorescence spectroscopy, which identified two fluorescent components (humic-like C1 and protein/phenol-like C2). Compared with pure MP-DOM, C1 was more predominant in sand infiltration than C2. Further studies have established that C2 may be more labile in terms of biodegradation and mineral adsorption that may occur within the sand column. However, both these environmental interferences were inadequate for entirely expanding the spectroscopic signatures of MP-DOM in sand infiltration. The infiltration also exhibited a higher potential in generating carbonaceous disinfection byproducts than natural groundwater and riverside bank filtrates. A significant correlation between the generated THMs and decreased C1 suggests the possibility of using humic-like components as optical precursors of carbonaceous DBPs in MP-contaminated subsurface systems. This study highlighted an overlooked contribution of MPs in terms of the infiltration of DOM levels in sandy subsurface systems and the potential environmental risk when used as drinking water sources.

Using simple and easy water quality parameters to predict trihalomethane occurrence in tap water

Monitoring of disinfection by-products (DBPs) in water supply system is important to ensure safety of drinking water. Yet it is a laborious job. Developing predictive DBPs models using simple and easy parameters is a promising way. Yet current models could not be well applied into practice because of the improper dataset (e.g. not from real tap water) they used or involving the parameters that are difficult to measure or require expensive instruments. In this study, four simple and easy water quality parameters (temperature, pH, UVA₂₅₄ and Cl₂) were used to predict trihalomethane (THMs) occurrence in tap water. Linear/log linear regression models (LRM) and radial basis function artificial neural network (RBF ANN) were adopted to develop the THMs models. 64 observations from tap water samples were used to develop and test models. Results showed that only one or two parameters entered LRMs, and their prediction ability was very limited (testing datasets: N₂₅ = 46-69%, r_p = 0.334-0.459). Different from LRM, the prediction accuracy of RBF ANNs developed with pH, temperature, UVA₂₅₄ and Cl₂ can be improved continuously by tweaking the maximum number of neuron (MN) and Gaussian function spread (S) until it reached best. The optimum RBF ANNs of T-THMs, TCM and BDCM were obtained when setting MN = 20, S = 100, 100.1 and 60, respectively, where the N₂₅ and r_p values for testing datasets reached 85-92% and 0.813-0.886, respectively. Accurate predictions of THMs by RBF ANNs with these four simple and easy parameters paved an economic and convenient way for THMs monitoring in real water supply system.

Modeling and elucidation the effects of iron deposits on chlorine decay and trihalomethane formation in drinking water distribution system

Iron deposits stimulate chlorine consumption and trihalomethane (THM) formation in drinking water distribution systems through distinct mechanisms. In this study, a second-order chlorine decay model with a variable reaction-rate coefficient was developed to quantitatively evaluate the influences of iron deposits on chlorine reactions by considering the characteristics of dissolved organic matter (DOM), the type and dosages of deposits, as well as the initial chlorine concentrations. Based on a reliable prediction of residual chlorine, the concept that THM formation had a linear relationship with chlorine consumption was further validated by chlorination of DOM in the presence of iron deposits. Due to the catalysis influences, the reactivity of DOM towards chlorine decay or THM formation was accelerated. Although iron deposits activated the reactivity of DOM with bromine and chlorine, THM slightly shifted toward chlorinated species. Due to the adsorption influences, the maximum chlorine demand increased with the increasing deposit dosages whereas the extent of enhancement mainly relied on the DOM properties. Low-molecular-weight DOM with a hydrophilic characteristic was prone to be elevated by iron deposits. Based on the model simulation, approximately 20% of chlorine consumption and 37% of THM formation were contributed by deposits after 168 h reaction. The data provided herein emphasize the role of iron deposits in chlorine consumption and THM formation, which assist the water quality management in drinking water distribution systems.

Predictive modeling of haloacetonitriles under uniform formation conditions

The objective of this study was to develop models to predict the formation of HANs under uniform formation conditions (UFC) in chlorinated, choraminated, and perchlorinated/chloraminated waters of different origins. Model equations were developed using multiple linear regression analysis to predict the formation of dichloroacetonitrile (DCAN), HAN4 (trichloroacetonitrile [TCAN], DCAN, bromochloroacetonitrile [BCAN], and dibromoacetonitrile [DBAN]) and HAN6 (HAN4 plus monochloroacetonitrile, monobromoacetonitrile). The independent variables covered a wide range of values, and included ultraviolet absorbance,(UV₂₅₄) dissolved organic carbon (DOC), dissolved organic nitrogen (DON), specific UV absorbance at 254 (SUVA₂₅₄), bromide (Br^-), pH, oxidant dose, contact time, and temperature. The regression coefficients (r²) of HAN4 and HAN6 models for natural organic matter (NOM), algal organic matter (AOM), and effluent organic matter (EfOM)  impacted waters were within the range of 60-88%, while the r² values of HAN4 and DCAN models for both groundwater and distribution systems were lower, in the range of 41-66%. The r² values for the DCAN model were mostly higher in the individual types as compared to the cumulative analysis of all source water data together. This was attributed to differences in HAN precursor characteristics. For chlorination, among all variables, pH was found to be the most significant descriptor in the model equations describing the formation of DCAN, HAN4, and HAN6, and it was negatively correlated with HAN formation in the distribution system, groundwater, AOM, and NOM samples, while it showed an inverse relationship with HAN6 formation in EfOM impacted waters. During chloramination, pH was the most influential model descriptor for DCAN formation in the NOM. Prechlorination dose was the most predominant parameter for prechlorination/chloramination, and it was positively correlated with HAN4 formation in AOM impacted waters.

Characteristics of DOM in 14 AAO processes of municipal wastewater treatment plants

The characteristics of dissolved organic matter (DOM) such as chemical composition, molecular weight (MW) distribution and hydrophobic/hydrophilic distribution can affect wastewater treatment efficiency, effluent quality and ecological risk. Fluorescence spectroscopy could provide a quick estimate of DOM characteristics during the monitoring of wastewater treatment plants (WWTPs). In this study, the characteristic and quantitative correlation of DOM from 14 anaerobic-anoxic-oxic (AAO) processes of WWTPs located in different provinces (municipalities) of China were investigated. The results showed that DOM of MW <1 kDa was the largest group of DOM in influent and secondary effluent, and DOM removal increased as the MW increased. Hydrophilic (HPI) fraction and hydrophobic acid (HPO-A) comprised the major portion of DOM in influent and secondary effluent and exhibited the lowest rate of removal. In addition, DOM concentrations in the northern provinces were higher than in the southern provinces, which were related to the water quality, economy and population. There were positive correlations between specific fluorescence intensity (SFI) and the MW <1 kDa, 1-5 kDa and <10 kDa fractions. The smaller the molecular weight, the better the correlation. Strong positive correlations between regional fluorescence proportion (f_i) and HPI were found. SFI and f_i may be explored as potential indicators of the MW fractions and the hydrophobic/hydrophilic distribution of DOM in AAO processes WWTPs.