1
|
Li H, Zhou B, Xu X, Huo R, Zhou T, Dong X, Ye C, Li T, Xie L, Pang W. The insightful water quality analysis and predictive model establishment via machine learning in dual-source drinking water distribution system. Environ Res 2024; 250:118474. [PMID: 38368920 DOI: 10.1016/j.envres.2024.118474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 02/02/2024] [Accepted: 02/10/2024] [Indexed: 02/20/2024]
Abstract
Dual-source drinking water distribution systems (DWDS) over single-source water supply systems are becoming more practical in providing water for megacities. However, the more complex water supply problems are also generated, especially at the hydraulic junction. Herein, we have sampled for a one-year and analyzed the water quality at the hydraulic junction of a dual-source DWDS. The results show that visible changes in drinking water quality, including turbidity, pH, UV254, DOC, residual chlorine, and trihalomethanes (TMHs), are observed at the sample point between 10 and 12 km to one drinking water plant. The average concentration of residual chlorine decreases from 0.74 ± 0.05 mg/L to 0.31 ± 0.11 mg/L during the water supplied from 0 to 10 km and then increases to 0.75 ± 0.05 mg/L at the end of 22 km. Whereas the THMs shows an opposite trend, the concentration reaches to a peak level at hydraulic junction area (10-12 km). According to parallel factor (PARAFAC) and high-performance size-exclusion chromatography (HPSEC) analysis, organic matters vary significantly during water distribution, and tryptophan-like substances and amino acids are closely related to the level of THMs. The hydraulic junction area is confirmed to be located at 10-12 km based on the water quality variation. Furthermore, data-driven models are established by machine learning (ML) with test R2 higher than 0.8 for THMs prediction. And the SHAP analysis explains the model results and identifies the positive (water temperature and water supply distance) and negative (residual chlorine and pH) key factors influencing the THMs formation. This study conducts a deep understanding of water quality at the hydraulic junction areas and establishes predictive models for THMs formation in dual-sources DWDS.
Collapse
Affiliation(s)
- Huiping Li
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Baiqin Zhou
- Gansu Academy of Eco-environmental Sciences, Lanzhou, 730030, China; School of Municipal and Environmental Engineering, Harbin Institute of Technology (Shenzhen), Shenzhen, 518055, China.
| | - Xiaoyan Xu
- Suzhou Industrial Park Qingyuan Hong Kong & China Water Co. Ltd., Suzhou, 215021, China
| | - Ranran Huo
- Suzhou Industrial Park Qingyuan Hong Kong & China Water Co. Ltd., Suzhou, 215021, China
| | - Ting Zhou
- Suzhou Industrial Park Qingyuan Hong Kong & China Water Co. Ltd., Suzhou, 215021, China
| | - Xiaochen Dong
- Suzhou Industrial Park Qingyuan Hong Kong & China Water Co. Ltd., Suzhou, 215021, China
| | - Cheng Ye
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Tian Li
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Li Xie
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Weihai Pang
- Key Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China.
| |
Collapse
|
2
|
Li M, Zhang D, Zhang R, Wang F, Song Y, Chen F, Yang J, Li C. Recent advances in the unlined cast iron pipe scale characteristics, cleaning techniques and harmless disposal methods: An overview. Chemosphere 2023; 340:139849. [PMID: 37595692 DOI: 10.1016/j.chemosphere.2023.139849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/09/2023] [Accepted: 08/15/2023] [Indexed: 08/20/2023]
Abstract
Drinking water discoloration and its potential health risks (e.g., heavy metals, pathogens, carcinogenic organics) have aroused wide public concerns around the world, and the characteristics and corresponding cleaning techniques of pipe scales are one of the most important research fields closely related to people's lives and health. This Overview Article summarizes the latest research achievements about the new insights into the unlined cast iron pipe corrosion scale characteristics as well as the advanced cleaning techniques applied in drinking water distribution systems. The typical pollutants such as heavy metal ions, pathogens and disinfection by-products (DBPs) in pipe scales and the main cleaning techniques including unidirectional flushing (UDF), air scouring, ice pigging and guided ultrasonic waves (GUW) are categorized and elaborated. In the final part, the current challenges and future opportunities are also further discussed from the viewpoint of evolution process of pipe scales as well as the widespread application of advanced cleaning techniques. Moreover, the possible technical route for the innocent treatment and resource utilization of pipe scale waste is also proposed. It is anticipated that this review will attract more attention toward the in-depth study of pipe scales and their cleaning techniques to enjoy cleaner and healthier drinking water for people.
Collapse
Affiliation(s)
- Ming Li
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
| | - Dong Zhang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
| | - Ru Zhang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China
| | - Fang Wang
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China.
| | - Yang Song
- Resources and Environment Innovation Institute, Shandong Jianzhu University, Jinan, 250101, China.
| | - Feiyong Chen
- Resources and Environment Innovation Institute, Shandong Jianzhu University, Jinan, 250101, China
| | - Juan Yang
- Nanjing Chibo Environmental Technology (China) Co., Ltd., Nanjing, 210044, Jiangsu Province, China
| | - Changming Li
- School of Ecology and Environment, Beijing Technology and Business University, Beijing, 100048, China.
| |
Collapse
|
3
|
Liao W, Yuan J, Wang X, Dai P, Feng W, Zhang Q, Fu A, Li X. Under-deposit microbial corrosion of X65 pipeline steel in the simulated shale gas production environment. INT J ELECTROCHEM SC 2023. [DOI: 10.1016/j.ijoes.2023.100069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
|