Appraisal of machine learning techniques for predicting emerging disinfection byproducts in small water distribution networks

Complex impact of metals on the fate of disinfection by-products in drinking water pipelines: A systematic review

Metals in the drinking water distribution system (DWDS) play an important role on the fate of disinfection by-products (DBPs). They can increase the formation of DBPs through several mechanisms, such as enhancing the proportion of reactive halogen species (RHS), catalysing the reaction between natural organic matter (NOM) and RHS through complexation, or by increasing the conversion of NOM into DBP precursors. This review comprehensively summarizes these complex processes, focusing on the most important metals (copper, iron, manganese) in DWDS and their impact on various DBPs. It organizes the dispersed 'metals-DBPs' experimental results into an easily accessible content structure and presents their underlying common or unique mechanisms. Furthermore, the practically valuable application directions of these research findings were analysed, including the toxicity changes of DBPs in DWDS under the influence of metals and the potential enhancement of generalization in DBP model research by the introduction of metals. Overall, this review revealed that the metal environment within DWDS is a crucial factor influencing DBP levels in tap water.

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.

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These predictive insights play a crucial role in decision-making processes aimed at safeguarding water quality, such as identifying areas in need of immediate attention and implementing preventive measures to avert contamination. This paper aims to provide a comprehensive review of the current state of the art in water quality monitoring, with a specific focus on the employment of IoT wireless technologies and ML techniques. The study examines the utilization of a range of IoT wireless technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, in the context of monitoring water quality. Furthermore, it explores the application of both supervised and unsupervised ML algorithms for analyzing and interpreting the collected data. In addition to discussing the current state of the art, this survey also addresses the challenges and open research questions involved in integrating IoT wireless technologies and ML for water quality monitoring (WQM).

Prediction of antibiotic sorption in soil with machine learning and analysis of global antibiotic resistance risk

Although the sorption of antibiotics in soil has been extensively studied, their spatial distribution patterns and sorption mechanisms still need to be clarified, which hinders the assessment of antibiotic resistance risk. In this study, machine learning was employed to develop the models for predicting the soil sorption behavior of three classes of antibiotics (sulfonamides, tetracyclines, and fluoroquinolones) in 255 soils with 2203 data points. The optimal independent models obtained an accurate predictive performance with R2 of 0.942 to 0.977 and RMSE of 0.051 to 0.210 on test sets compared to combined models. Besides, a global map of the antibiotic sorption capacity of soil predicted with the optimal models revealed that the sorption potential of fluoroquinolones was the highest, followed by tetracyclines and sulfonamides. Additionally, 14.3% of regions had higher antibiotic sorption potential, mainly in East and South Asia, Central Siberia, Western Europe, South America, and Central North America. Moreover, a risk index calculated with the antibiotic sorption capacity of soil and population density indicated that about 3.6% of soils worldwide have a high risk of resistance, especially in South and East Asia with high population densities. This work has significant implications for assessing the antibiotic contamination potential and resistance risk.

A comprehensive review on artificial intelligence in water treatment for optimization. Clean water now and the future

Given the severe effects that toxic compounds present in wastewater streams have on humans, it is imperative that water and wastewater streams pollution be addressed globally. This review comprehensively examines various water and wastewater treatment methods and water quality management methods based on artificial intelligence (AI). Machine learning (ML) and AI have become a powerful tool for addressing problems in the real world and has gained a lot of interest since it can be used for a wide range of activities. The foundation of ML techniques involves training of a network with collected data, followed by application of learned network to the process simulation and prediction. The creation of ML models for process simulations requires measured data. In order to forecast and simulate chemical and physical processes such chemical reactions, heat transfer, mass transfer, energy, pharmaceutics and separation, a variety of machine-learning algorithms have recently been developed. These models have shown to be more adept at simulating and modeling processes than traditional models. Although AI offers many advantages, a number of disadvantages have kept these methods from being extensively applied in actual water treatment systems. Lack of evidence of application in actual water treatment scenarios, poor repeatability and data availability and selection are a few of the main problems that need to be resolved.

Trihalomethane prediction model for water supply system based on machine learning and Log-linear regression

Laboratory determination of trihalomethanes (THMs) is a very time-consuming task. Therefore, establishing a THMs model using easily obtainable water quality parameters would be very helpful. This study explored the modeling methods of the random forest regression (RFR) model, support vector regression (SVR) model, and Log-linear regression model to predict the concentration of total-trihalomethanes (T-THMs), bromodichloromethane (BDCM), and dibromochloromethane (DBCM), using nine water quality parameters as input variables. The models were developed and tested using a dataset of 175 samples collected from a water treatment plant. The results showed that the RFR model, with the optimal parameter combination, outperformed the Log-linear regression model in predicting the concentration of T-THMs (N25 = 82-88%, rp = 0.70-0.80), while the SVR model performed slightly better than the RFR model in predicting the concentration of BDCM (N25 = 85-98%, rp = 0.70-0.97). The RFR model exhibited superior performance compared to the other two models in predicting the concentration of T-THMs and DBCM. The study concludes that the RFR model is superior overall to the SVR model and Log-linear regression models and could be used to monitor THMs concentration in water supply systems.

Fluorescence spectroscopy in the detection and management of disinfection by-product precursors in drinking water treatment processes: A review

Monitoring and prevention of the formation of disinfection by-products (DBPs) is paramount in drinking water treatment plants (DWTP) to ensure human health safety. This review provides an overview of how fluorescence techniques are developed to predict DBP formation and to evaluate the reduction of fluorescence components and DBPs following individual DWTP processes. Evidence has shown that common DBPs, nitrogenous DBPs and specific emerging DBPs exhibit positive linear relationships with terrestrial, anthropogenic, tryptophan-like, and eutrophic humic-like fluorescence. Due to the interrelationships of both regulated and emerging DBP types with fluorescence components, the limitations arise when attempting to predict emerging DBPs solely through linear relationships. Monitoring the reduction of DBP precursors after each treatment process can be achieved by studying the relationship between fluorescence components and DBPs. During the coagulation process, highest reduction rates are observed for terrestrial humic-like fluorescence. Advanced treatments such as granular, powdered, silver-impregnated activated carbon, magnetic ion exchange resins, and reverse osmosis, have revealed a significant reduction of fluorescent DBP precursors, ranging from 53% to 100%. During chlorination, the reduction rate follows the order: terrestrial humic-like > microbial humic-like > protein/tryptophan-like fluorescence. This review provides insights into the reduction of fluorescence signatures following individual DWTP processes, which offers information regarding DBP formation. These insights could assist in optimizing the treatment process to more effectively manage DBP formation. For the identification of emerging DBP generation, the utilization of advanced models is imperative to precisely predict emerging DBPs and to more accurately trace DBP precursors within DWTPs.