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Yu Y, Hossain MM, Sikder R, Qi Z, Huo L, Chen R, Dou W, Shi B, Ye T. Exploring the potential of machine learning to understand the occurrence and health risks of haloacetic acids in a drinking water distribution system. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 951:175573. [PMID: 39153609 DOI: 10.1016/j.scitotenv.2024.175573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2024] [Revised: 08/07/2024] [Accepted: 08/14/2024] [Indexed: 08/19/2024]
Abstract
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.
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Affiliation(s)
- Ying Yu
- School of Environmental Science and Engineering, Xiamen University of Technology, Xiamen 361024, China; Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; Key Laboratory of Water Resources Utilization and Protection, Xiamen city, Xiamen 361005, China
| | - Md Mahjib Hossain
- Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA
| | - Rabbi Sikder
- Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA
| | - Zhenguo Qi
- Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Lixin Huo
- Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Ruya Chen
- School of Environmental Science and Engineering, Zhejiang Gongshang University, Hangzhou 310018, Zhejiang, China.
| | - Wenyue Dou
- Key Laboratory of Industrial Pollution Control and Reuse of Jiangsu Province, College of Environmental Engineering, Xuzhou University of Technology, Xuzhou 221018, China
| | - Baoyou Shi
- Drinking Water Science and Technology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Tao Ye
- Department of Civil and Environmental Engineering, South Dakota School of Mines and Technology, Rapid City, SD 57701, USA.
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Wang F, Liu J, Zhang L, Wang H, Zhao Z, Chen Y, Li J, Zhang X, Dong W. Efficient degradation of haloacetic acids by vacuum ultraviolet-activated peroxymonosulfate: Kinetics, mechanisms and theoretical calculations. JOURNAL OF HAZARDOUS MATERIALS 2024; 478:135539. [PMID: 39180995 DOI: 10.1016/j.jhazmat.2024.135539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/19/2024] [Accepted: 08/14/2024] [Indexed: 08/27/2024]
Abstract
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.
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Affiliation(s)
- Feifei Wang
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China
| | - Jie Liu
- Shenzhen Wanmu Water Services Co., Shenzhen 518000, PR China
| | - Liang Zhang
- Shenzhen Wanmu Water Services Co., Shenzhen 518000, PR China
| | - Hongjie Wang
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China; Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, PR China
| | - Zilong Zhao
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China; Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, PR China
| | - Yihua Chen
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China
| | - Ji Li
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China; Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, PR China
| | - Xiaolei Zhang
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China.
| | - Wenyi Dong
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, PR China; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China; Shenzhen Key Laboratory of Water Resource Utilization and Environmental Pollution Control, Shenzhen 518055, PR China
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Yogarathinam LT, Abba SI, Usman J, Lawal DU, Aljundi IH. Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering. RSC Adv 2024; 14:19331-19348. [PMID: 38887641 PMCID: PMC11181297 DOI: 10.1039/d4ra02475c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 06/05/2024] [Indexed: 06/20/2024] Open
Abstract
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.
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Affiliation(s)
- Lukka Thuyavan Yogarathinam
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
| | - Sani I Abba
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
| | - Jamilu Usman
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
| | - Dahiru U Lawal
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
- Department of Mechanical Engineering, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
| | - Isam H Aljundi
- Interdisciplinary Research Centre for Membranes and Water Security, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
- Department of Chemical Engineering, King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia
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Li J, Sun D, Wen Y, Chen X, Wang H, Li S, Song Z, Liu H, Ma J, Chen L. Molecularly imprinted polymers and porous organic frameworks based analytical methods for disinfection by-products in water and wastewater. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 356:124249. [PMID: 38810677 DOI: 10.1016/j.envpol.2024.124249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 04/16/2024] [Accepted: 05/26/2024] [Indexed: 05/31/2024]
Abstract
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.
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Affiliation(s)
- Jinhua Li
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Coastal Zone Ecological Environment Monitoring Technology and Equipment Shandong Engineering Research Center, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003, China; College of Chemistry and Chemical Engineering, Yantai University, Yantai, 264005, China.
| | - Dani Sun
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Coastal Zone Ecological Environment Monitoring Technology and Equipment Shandong Engineering Research Center, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003, China; College of Chemistry and Chemical Engineering, Yantai University, Yantai, 264005, China
| | - Yuhao Wen
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Coastal Zone Ecological Environment Monitoring Technology and Equipment Shandong Engineering Research Center, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003, China
| | - Xuan Chen
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Coastal Zone Ecological Environment Monitoring Technology and Equipment Shandong Engineering Research Center, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003, China
| | - Hongdan Wang
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Coastal Zone Ecological Environment Monitoring Technology and Equipment Shandong Engineering Research Center, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003, China
| | - Shuang Li
- School of Environmental & Municipal Engineering, State-Local Joint Engineering Research Center of Urban Sewage Treatment and Resource Recovery, Qingdao University of Technology, Qingdao, 266033, China
| | - Zhihua Song
- School of Pharmacy, Collaborative Innovation Center of Advanced Drug Delivery System and Biotech Drugs in Universities of Shandong, Key Laboratory of Molecular Pharmacology and Drug Evaluation (Yantai University), Ministry of Education, Yantai University, Yantai, 264005, China
| | - Huitao Liu
- College of Chemistry and Chemical Engineering, Yantai University, Yantai, 264005, China
| | - Jiping Ma
- School of Environmental & Municipal Engineering, State-Local Joint Engineering Research Center of Urban Sewage Treatment and Resource Recovery, Qingdao University of Technology, Qingdao, 266033, China
| | - Lingxin Chen
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Coastal Zone Ecological Environment Monitoring Technology and Equipment Shandong Engineering Research Center, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003, China; Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao, 266237, China
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Xu R, Zhang Z, Deng C, Nie C, Wang L, Shi W, Lyu T, Yang Q. Micropollutant rejection by nanofiltration membranes: A mini review dedicated to the critical factors and modelling prediction. ENVIRONMENTAL RESEARCH 2024; 244:117935. [PMID: 38103781 DOI: 10.1016/j.envres.2023.117935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 11/22/2023] [Accepted: 12/11/2023] [Indexed: 12/19/2023]
Abstract
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.
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Affiliation(s)
- Rui Xu
- Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; National Joint Research Center for Yangtze River Conservation, Beijing, 100012, China
| | - Zeqian Zhang
- Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Chenning Deng
- Chinese Research Academy of Environmental Sciences, Beijing, 100012, China
| | - Chong Nie
- Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; National Joint Research Center for Yangtze River Conservation, Beijing, 100012, China
| | - Lijing Wang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Wenqing Shi
- School of Environmental Science & Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Tao Lyu
- School of Water, Energy and Environment, Cranfield University, College Road, Cranfield, Bedfordshire, MK43 0AL, United Kingdom.
| | - Queping Yang
- Chinese Research Academy of Environmental Sciences, Beijing, 100012, China; National Joint Research Center for Yangtze River Conservation, Beijing, 100012, China.
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