1
|
Guo X, Ji X, Liu Z, Feng Z, Zhang Z, Du S, Li X, Ma J, Sun Z. Complex impact of metals on the fate of disinfection by-products in drinking water pipelines: A systematic review. WATER RESEARCH 2024; 261:121991. [PMID: 38941679 DOI: 10.1016/j.watres.2024.121991] [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: 04/16/2024] [Revised: 06/14/2024] [Accepted: 06/21/2024] [Indexed: 06/30/2024]
Abstract
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.
Collapse
Affiliation(s)
- Xinming Guo
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150096, China
| | - Xiaoyue Ji
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150096, China
| | - Zihan Liu
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150096, China
| | - Zhuoran Feng
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150096, China
| | - ZiFeng Zhang
- International Joint Research Center for Persistent Toxic Substances (IJRC-PTS), State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Shuang Du
- Institute of NBC Defense. PLA Army, P.O.Box1048, Beijing 102205 China
| | - Xueyan Li
- Suzhou University Science & Technology, School of Environmental Science & Engineering, Suzhou 215009, China
| | - Jun Ma
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150096, China
| | - Zhiqiang Sun
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150096, China.
| |
Collapse
|
2
|
Guo Q, He Z, Wang Z. Monthly climate prediction using deep convolutional neural network and long short-term memory. Sci Rep 2024; 14:17748. [PMID: 39085577 PMCID: PMC11291741 DOI: 10.1038/s41598-024-68906-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2024] [Accepted: 07/29/2024] [Indexed: 08/02/2024] Open
Abstract
Climate change affects plant growth, food production, ecosystems, sustainable socio-economic development, and human health. The different artificial intelligence models are proposed to simulate climate parameters of Jinan city in China, include artificial neural network (ANN), recurrent NN (RNN), long short-term memory neural network (LSTM), deep convolutional NN (CNN), and CNN-LSTM. These models are used to forecast six climatic factors on a monthly ahead. The climate data for 72 years (1 January 1951-31 December 2022) used in this study include monthly average atmospheric temperature, extreme minimum atmospheric temperature, extreme maximum atmospheric temperature, precipitation, average relative humidity, and sunlight hours. The time series of 12 month delayed data are used as input signals to the models. The efficiency of the proposed models are examined utilizing diverse evaluation criteria namely mean absolute error, root mean square error (RMSE), and correlation coefficient (R). The modeling result inherits that the proposed hybrid CNN-LSTM model achieves a greater accuracy than other compared models. The hybrid CNN-LSTM model significantly reduces the forecasting error compared to the models for the one month time step ahead. For instance, the RMSE values of the ANN, RNN, LSTM, CNN, and CNN-LSTM models for monthly average atmospheric temperature in the forecasting stage are 2.0669, 1.4416, 1.3482, 0.8015 and 0.6292 °C, respectively. The findings of climate simulations shows the potential of CNN-LSTM models to improve climate forecasting. Climate prediction will contribute to meteorological disaster prevention and reduction, as well as flood control and drought resistance.
Collapse
Affiliation(s)
- Qingchun Guo
- School of Geography and Environment, Liaocheng University, Liaocheng, 252000, China.
- Institute of Huanghe Studies, Liaocheng University, Liaocheng, 252000, China.
- Key Laboratory of Atmospheric Chemistry, China Meteorological Administration, Beijing, 100081, China.
- State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710061, China.
| | - Zhenfang He
- School of Geography and Environment, Liaocheng University, Liaocheng, 252000, China
- Institute of Huanghe Studies, Liaocheng University, Liaocheng, 252000, China
| | - Zhaosheng Wang
- National Ecosystem Science Data Center, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China
| |
Collapse
|
3
|
Ding Y, Sun Q, Lin Y, Ping Q, Peng N, Wang L, Li Y. Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction. WATER RESEARCH 2024; 253:121267. [PMID: 38350192 DOI: 10.1016/j.watres.2024.121267] [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: 12/06/2023] [Revised: 01/30/2024] [Accepted: 02/04/2024] [Indexed: 02/15/2024]
Abstract
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.
Collapse
Affiliation(s)
- Yizhe Ding
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Qiya Sun
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Yuqian Lin
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Qian Ping
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China
| | - Nuo Peng
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Lin Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China.
| | - Yongmei Li
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China
| |
Collapse
|
4
|
Li H, Chu Y, Zhu Y, Han X, Shu S. Trihalomethane prediction model for water supply system based on machine learning and Log-linear regression. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:31. [PMID: 38227052 DOI: 10.1007/s10653-023-01778-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/09/2023] [Indexed: 01/17/2024]
Abstract
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.
Collapse
Affiliation(s)
- Hui Li
- College of Environmental Science and Engineering, Donghua University, No. 2999 North Renmin Road, Shanghai, 201620, China
| | - Yangyang Chu
- College of Environmental Science and Engineering, Donghua University, No. 2999 North Renmin Road, Shanghai, 201620, China
| | - Yanping Zhu
- College of Environmental Science and Engineering, Donghua University, No. 2999 North Renmin Road, Shanghai, 201620, China
| | - Xiaomeng Han
- College of Environmental Science and Engineering, Donghua University, No. 2999 North Renmin Road, Shanghai, 201620, China
| | - Shihu Shu
- College of Environmental Science and Engineering, Donghua University, No. 2999 North Renmin Road, Shanghai, 201620, China.
| |
Collapse
|