Experiments and machine learning-based modeling for haloacetic acids rejection by nanofiltration: Influence of solute properties and operating conditions

Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system

Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution system (DWDS) remains challenging. Predicting DBPs using readily available water quality parameters can help to understand DBPs associated risks and capture the complex interrelationships between water quality and DBP occurrence. In this study, we collected drinking water samples from a distribution network throughout a year and measured the related water quality parameters (WQPs) and haloacetic acids (HAAs). 12 machine learning (ML) algorithms were evaluated. Random Forest (RF) achieved the best performance (i.e., R2 of 0.78 and RMSE of 7.74) for predicting HAAs concentration. Instead of using cytotoxicity or genotoxicity separately as the surrogate for evaluating toxicity associated with HAAs, we created a health risk index (HRI) that was calculated as the sum of cytotoxicity and genotoxicity of HAAs following the widely used Tic-Tox approach. Similarly, ML models were developed to predict the HRI, and RF model was found to perform the best, obtaining R2 of 0.69 and RMSE of 0.38. To further explore advanced ML approaches, we developed 3 models using uncertainty-based active learning. Our findings revealed that Categorical Boosting Regression (CAT) model developed through active learning substantially outperformed other models, achieving R2 of 0.87 and 0.82 for predicting concentration and the HRI, respectively. Feature importance analysis with the CAT model revealed that temperature, ions (e.g., chloride and nitrate), and DOC concentration in the distribution network had a significant impact on the occurrence of HAAs. Meanwhile, chloride ion, pH, ORP, and free chlorine were found as the most important features for HRI prediction. This study demonstrates that ML has the potential in the prediction of HAA occurrence and toxicity. By identifying key WQPs impacting HAA occurrence and toxicity, this research offers valuable insights for targeted DBP mitigation strategies.

Efficient degradation of haloacetic acids by vacuum ultraviolet-activated peroxymonosulfate: Kinetics, mechanisms and theoretical calculations

Efficient degradation of haloacetic acids (HAAs) is crucial due to their potential risks. This study firstly proposed vacuum ultraviolet - activated peroxymonosulfate (VUV/PMS) to remove HAAs (i.e., monochloroacetic acid (MCAA), monobromoacetic acid (MBAA), dichloroacetic acid (DCAA), etc). VUV/PMS achieved 99.51 % MCAA and 63.29 % TOC removal within 10 min. Electron paramagnetic resonance (EPR), quenching and probe experiments demonstrated that •OH was responsible for MCAA degradation. MCAA degradation followed pathways of dehalogenation (major) and decarboxylation (minor). VUV/PMS showed application potential under various reaction parameters. Broad spectrum of VUV/PMS on various HAAs was further explored. Chlorinated HAAs (Cl-HAAs) were primarily degraded by oxidation reactions, while brominated HAAs (Br-HAAs) by direct VUV photolysis. The density functional theory-based calculations (DFT) revealed that reaction rates of HAAs correlated with the highest occupied molecular orbital (HOMO) and energy gap (ΔE), indicating that HAAs degradation depends on their chemical structures. The Fukui function (f0 values) and bond length showed vulnerability of the halogen atom in Cl-HAAs and C-Br bond in Br-HAAs. Overall, this study provides an in-depth perspective on the oxidation performance and mechanism of HAAs using VUV/PMS. It not only demonstrates a green and efficient method but also inspires new strategies for HAAs remediation.

Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering

Predicting the efficacy of micropollutant separation through functionalized membranes is an arduous endeavor. The challenge stems from the complex interactions between the physicochemical properties of the micropollutants and the basic principles underlying membrane filtration. This study aimed to compare the effectiveness of a modest dataset on various machine learning tools (ML) tools in predicting micropollutant removal efficiency for functionalized reverse osmosis (RO) and nanofiltration (NF) membranes. The inherent attributes of both the micropollutants and the membranes are utilized as input factors. The chosen ML tools are supervised algorithm (adaptive network-based fuzzy inference system (NF), linear regression framework (linear regression (LR)), stepwise linear regression (SLR) and multivariate linear regression (MVR)), and unsupervised algorithm (support vector machine (SVM) and ensemble boosted tree (BT)). The feature engineering and parametric dependency analysis revealed that characteristics of micropollutants, such as maximum projection diameter (MaxP), minimal projection diameter (MinP), molecular weight (MW), and compound size (CS), exhibited a notably positive impact on the correlation with removal efficiency. Model combination with key variables demonstrated high prediction accuracy in both supervised and unsupervised ML for micropollutant removal efficiency. An NF-grid partitioning (NF-GP) model achieved the highest accuracy with an R 2 value of 0.965, accompanied by low error metrics, specifically an RMSE and MAE of 3.65. It is owed to the handling of the complex spatial and temporal aspects of micropollutant data through division into consistent subsets facilitating improved identification of rejection efficiency and relationships. The inclusion of inputs with both negative and positive correlations introduces variability, amplifies the system responsiveness, and impedes the precision of predictive models. This study identified key micropollutant properties, including MaxP, MinP, MW, and CS, as crucial factors for efficient micropollutant rejection during real-time filtration applications. It also allowed the design of pore size of self-prepared membranes for the enhanced separation of micropollutants from wastewater.

Molecularly imprinted polymers and porous organic frameworks based analytical methods for disinfection by-products in water and wastewater

Disinfection by-products (DBPs) with heritage toxicity, mutagenicity and carcinogenicity are one kind of important new pollutants, and their detection and removal in water and wastewater has become a common challenge facing mankind. Advanced functional materials with ideal selectivity, adsorption capacity and regeneration capacity provide hope for the determination of DBPs with low concentration levels and inherent molecular structural similarity. Among them, molecularly imprinted polymers (MIPs) are favored, owing to their predictable structure, specific recognition and wide applicability. Also, metal-organic frameworks (MOFs) and covalent-organic frameworks (COFs) with unique pore structure, large specific surface area and easy functionalization, attract increasing interest. Herein, we review recent advances in analytical methods based on the above-mentioned three functional materials for DBPs in water and wastewater. Firstly, MIPs, MOFs and COFs are briefly introduced. Secondly, MIPs, MOFs and COFs as extractants, recognition element and adsorbents, are comprehensively discussed. Combining the latest research progress of solid-phase extraction (SPE), sensor, adsorption and nanofiltration, typical examples on MIPs and MOFs/COFs based analytical and removal applications in water and wastewater are summarized. Finally, the application prospects and challenges of the three functional materials in DBPs analysis are proposed to promote the development of corresponding analytical methods.

Micropollutant rejection by nanofiltration membranes: A mini review dedicated to the critical factors and modelling prediction

Nanofiltration (NF) membranes, extensively used in advanced wastewater treatment, have broad application prospects for the removal of emerging trace organic micropollutants (MPs). The treatment performance is affected by several factors, such as the properties of NF membranes, characteristics of target MPs, and operating conditions of the NF system concerning MP rejection. However, quantitative studies on different contributors in this context are limited. To fill the knowledge gap, this study aims to assess critical impact factors controlling MP rejection and develop a feasible model for MP removal prediction. The mini-review firstly summarized membrane pore size, membrane zeta potential, and the normalized molecular size (λ = rs/rp), showeing better individual relationships with MP rejection by NF membranes. The Lindeman-Merenda-Gold model was used to quantitatively assess the relative importance of all summarized impact factors. The results showed that membrane pore size and operating pressure were the high impact factors with the highest relative contribution rates to MP rejection of 32.11% and 25.57%, respectively. Moderate impact factors included membrane zeta potential, solution pH, and molecular radius with relative contribution rates of 10.15%, 8.17%, and 7.83%, respectively. The remaining low impact factors, including MP charge, molecular weight, logKow, pKa and crossflow rate, comprised all the remaining contribution rates of 16.19% through the model calculation. Furthermore, based on the results and data availabilities from references, the machine learning-based random forest regression model was trained with a relatively low root mean squared error and mean absolute error of 12.22% and 6.92%, respectively. The developed model was then successfully applied to predict MPs' rejections by NF membranes. These findings provide valuable insights that can be applied in the future to optimize NF membrane designs, operation, and prediction in terms of removing micropollutants.