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Song YP, Wang WZ, Wang YQ, Yin WX, Chen JJ, Xu HR, Cheng HY, Ma F, Wang HT, Wang AJ, Wang HC. Data-driven differentiable model for dynamic prediction and control in wastewater treatment. WATER RESEARCH 2025; 282:123772. [PMID: 40334380 DOI: 10.1016/j.watres.2025.123772] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2025] [Revised: 04/02/2025] [Accepted: 05/02/2025] [Indexed: 05/09/2025]
Abstract
Wastewater treatment plants, while critical for environmental protection, face mounting challenges in operational efficiency and sustainability due to increasing urbanization and stricter environmental standards. In this study, we introduce an innovative continuous-time neural framework based on Neural Ordinary Differential Equations (Neural ODEs) to enhance the modeling of sewage treatment processes. Addressing the dual challenges of operational efficiency and sustainable development in urban wastewater treatment plants (WWTPs), our methodology marks a significant departure from traditional approaches by implementing a continuous-time neural framework that captures the inherent dynamics of wastewater treatment processes while reducing computational demands by 95 % compared to discrete-time models. We analyzed operational data from three full-scale WWTPs over a year, demonstrating that our model not only achieves superior prediction accuracy (R² > 0.95) with various input window sizes but also significantly reduces memory usage-from 111.88-12,484.59 MB to just 17.74-50.92 MB. Notably, our framework exhibits robust performance even with up to 30 % missing data, uncovering new process insights through interpretable feature attribution. Further integration with reinforcement learning has led to a 21.9 % reduction in aeration energy consumption compared to conventional open-loop control strategies while adhering to effluent quality standards. This research establishes a novel paradigm for intelligent wastewater management that optimizes operational efficiency and promotes environmental sustainability.
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Affiliation(s)
- Yun-Peng Song
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China; School of Eco-Environmental, Harbin Institute of Technology, Shenzhen 518055, PR China
| | - Wen-Zhe Wang
- School of Eco-Environmental, Harbin Institute of Technology, Shenzhen 518055, PR China
| | - Yu-Qi Wang
- School of Eco-Environmental, Harbin Institute of Technology, Shenzhen 518055, PR China
| | - Wan-Xin Yin
- College of the Environment, Liaoning University, Shenyang 110036, PR China
| | - Jia-Ji Chen
- CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Hao-Ran Xu
- School of Eco-Environmental, Harbin Institute of Technology, Shenzhen 518055, PR China
| | - Hao-Yi Cheng
- School of Eco-Environmental, Harbin Institute of Technology, Shenzhen 518055, PR China
| | - Fang Ma
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China
| | - Han-Tao Wang
- PowerChina Eco-environmental Group Co., Ltd, Guangdong, Shenzhen 518102, PR China
| | - Ai-Jie Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China; School of Eco-Environmental, Harbin Institute of Technology, Shenzhen 518055, PR China.
| | - Hong-Cheng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China; School of Eco-Environmental, Harbin Institute of Technology, Shenzhen 518055, PR China.
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2
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Zhang Z, Qi F, Liu Y, Asif MB, Ikhlaq A, Wang Z, Chen C, Li C, Chang J, Li Q, Li Y, Li Y, Jia Y, Liu Y, Xu B, Sun D. Comprehensive assessment, intelligent prediction, and precise mitigation strategies for greenhouse gas emissions in full-scale wastewater treatment plants. ENVIRONMENTAL RESEARCH 2025; 270:121052. [PMID: 39920967 DOI: 10.1016/j.envres.2025.121052] [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: 12/21/2024] [Revised: 02/03/2025] [Accepted: 02/04/2025] [Indexed: 02/10/2025]
Abstract
Wastewater treatment plants (WWTPs) are major contributors to global anthropogenic greenhouse gas (GHG) emissions, with China ranks among the leading emitters. In the context of China's "dual-carbon" journey, precision quantification and predictive forecasting of GHG fluxes, particularly methane (CH4) and nitrous oxide (N2O)-are crucial for developing advanced mitigation strategies of WWTPs. To accurately assess GHG emissions, this study firstly introduced customized emission factors (EFs) to precisely evaluate the GHG emissions of a full - scale A2O - based WWTP in Beijing. This approach addressed the overestimation of emissions when using the IPCC's standard EFs. Additionally, the study proposed machine learning (ML) techniques to predict GHG fluxes based on routine wastewater quality parameters. Specifically, Long Short-Term Memory (LSTM) and Random Forest (RF) models showed the strong performance in predicting CH4 and N2O emissions, respectively. Moreover, our findings revealed distinct spatiotemporal patterns of GHG emission: CH4 emissions peak during the summer solstice, while N2O emissions rise during the winter months. For the first time, this study identified the nitrification biofilter in the advanced treatment unit as a significant direct source of N2O emissions. Eventhough, indirect CO2 emissions account for a dominant 57%-90% of the total GHG emissions. Scenario analyses revealed a strategic mitigation approach. Energy conservation emerged as the most effective measure, capable of reducing emissions by 23.41%, followed by heat recovery, which could cut emissions by 10.15%. In practical applications, improving energy efficiency is of utmost importance in real - world mitigation strategies. This highlights the significance of integrated approaches for achieving the sustainable development of WWTPs in the "dual - carbon" background.
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Affiliation(s)
- Zitan Zhang
- Beijing Key Laboratory for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, PR China
| | - Fei Qi
- Beijing Key Laboratory for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, PR China.
| | - Yao Liu
- Beijing Drainage Group Co., LTD, 100044, PR China
| | - Muhammad Bilal Asif
- Advanced Membranes and Porous Materials Center (AMPMC), Physical Sciences and Engineering (PSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia
| | - Amir Ikhlaq
- Institute of Environment Engineering and Research, University of Engineering and Technology, GT Road, 54890, Lahore, Punjab, Pakistan
| | - Zhenbei Wang
- Beijing Key Laboratory for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, PR China
| | - Caocao Chen
- Scientific and Technological Program of Beijing Municipal Science and Technology Commission, 100012, PR China
| | - Chen Li
- Beijing Key Laboratory for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, PR China
| | - Jing Chang
- Beijing Drainage Group Co., LTD, 100044, PR China
| | - Qun Li
- Beijing Drainage Group Co., LTD, 100044, PR China
| | - Ye Li
- Beijing Drainage Group Co., LTD, 100044, PR China
| | - Yujie Li
- Beijing Key Laboratory for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, PR China
| | - Yunhan Jia
- Beijing Key Laboratory for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, PR China
| | - Yatao Liu
- Beijing Key Laboratory for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, PR China
| | - Bingbing Xu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, PR China
| | - Dezhi Sun
- Beijing Key Laboratory for Source Control Technology of Water Pollution, College of Environmental Science and Engineering, Beijing Forestry University, Beijing, 100083, PR China
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3
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Fan X, Niu G, Liu R, Qin J, Yi X, Tu J, Li X, Huang M. Effective evaluation of greenhouse gases (GHGs) emissions from anoxic/oxic (A/O) process of regenerated papermaking wastewater treatment through hybrid deep learning techniques: Leveraging the critical role of water quality indicators. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025; 380:125094. [PMID: 40174391 DOI: 10.1016/j.jenvman.2025.125094] [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: 12/03/2024] [Revised: 02/27/2025] [Accepted: 03/19/2025] [Indexed: 04/04/2025]
Abstract
Accurate accounting of greenhouse gases (GHGs) emissions from industrial wastewater treatment processes/plants with high organic concentration and fluctuating inflows is crucial for the calculation and management of carbon emissions. The impacts of water quality indicators on GHGs emissions within the biological nutrient removal process are still unclear, which deserves intensive attention. Here, a lab-scale anoxic/oxic (A/O) process was constructed for raw regenerated papermaking wastewater treatment with different low/high-concentration influent stages for about 110 days to evaluate GHGs emissions. A high-quality dataset included 295 sets of the multi-factors (including COD, suspended solid (SS), NH4+-N, NO3--N, NO2--N, and pH/DO/Temperature) was built. Moreover, the corresponding proportion of GHGs emissions were analyzed and a novel hybrid deep learning model TCNA, which integrated the Temporal Convolutional Network (TCN) and Attention Mechanism (AM), was developed to explore the trends and predictions of GHGs emissions based on the dataset. A series of comparisons with model Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and Temporal Convolutional Networks (TCN) were also conducted under the same conditions. The TCNA model showed an outstanding performance for CO2, CH4, and N2O emissions prediction, achieving the highest value of R2 score (CO2, 0.8014; CH4, 0.8839; N2O, 0.9354) and the lowest value of root mean squared error (RMSE) and mean absolute error (MAE) (CO2: 2.6137,1.9366; CH4: 1.929,0.7214; N2O: 0.8897, 0.5777) among the five models above. The results highlight the potential of the TCNA model for accurate and robust prediction of GHGs emissions from industrial wastewater treatment plants with the A/O treatment process, contributing to effective GHGs mitigation strategies and carbon management of industrial wastewater treatment.
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Affiliation(s)
- Xing Fan
- Guangdong Provincial Engineering Research Center of Intelligent, Low-carbon Pollution Prevention and Digital Technology & Guangdong Provincial, Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key, Laboratory of Theoretical Chemistry of Environment, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou, 510006, PR China
| | - Guoqiang Niu
- Guangdong Provincial Engineering Research Center of Intelligent, Low-carbon Pollution Prevention and Digital Technology & Guangdong Provincial, Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key, Laboratory of Theoretical Chemistry of Environment, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou, 510006, PR China
| | - Rui Liu
- Guangdong Provincial Engineering Research Center of Intelligent, Low-carbon Pollution Prevention and Digital Technology & Guangdong Provincial, Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key, Laboratory of Theoretical Chemistry of Environment, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou, 510006, PR China
| | - Jianwu Qin
- Guangdong Provincial Engineering Research Center of Intelligent, Low-carbon Pollution Prevention and Digital Technology & Guangdong Provincial, Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key, Laboratory of Theoretical Chemistry of Environment, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou, 510006, PR China
| | - Xiaohui Yi
- Guangdong Provincial Engineering Research Center of Intelligent, Low-carbon Pollution Prevention and Digital Technology & Guangdong Provincial, Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key, Laboratory of Theoretical Chemistry of Environment, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou, 510006, PR China; SCNU (NAN'AN) Green and Low-carbon Innovation Center & Nan'an SCNU Institute of Green and Low-carbon Research, South China Normal University, Quanzhou, 362300, PR China.
| | - Jun Tu
- Guangdong Provincial Engineering Research Center of Intelligent, Low-carbon Pollution Prevention and Digital Technology & Guangdong Provincial, Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key, Laboratory of Theoretical Chemistry of Environment, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou, 510006, PR China
| | - Xiaoyong Li
- Guangdong Provincial Engineering Research Center of Intelligent, Low-carbon Pollution Prevention and Digital Technology & Guangdong Provincial, Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key, Laboratory of Theoretical Chemistry of Environment, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou, 510006, PR China; SCNU Qingyuan Institute of Science and Technology Innovation Co., Ltd., Qingyuan, 511517, PR China
| | - Mingzhi Huang
- Guangdong Provincial Engineering Research Center of Intelligent, Low-carbon Pollution Prevention and Digital Technology & Guangdong Provincial, Key Laboratory of Chemical Pollution and Environmental Safety & MOE Key, Laboratory of Theoretical Chemistry of Environment, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou, 510006, PR China; SCNU (NAN'AN) Green and Low-carbon Innovation Center & Nan'an SCNU Institute of Green and Low-carbon Research, South China Normal University, Quanzhou, 362300, PR China; SCNU Qingyuan Institute of Science and Technology Innovation Co., Ltd., Qingyuan, 511517, PR China.
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4
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Yin WX, Chen KH, Lv JQ, Chen JJ, Liu S, Song YP, Zhao YW, Huang F, Bao HX, Wang HC, Wang AJ, Ren NQ. Deciphering and Mitigating of Dynamic Greenhouse Gas Emission in Urban Drainage Systems with Knowledge-Infused Graph Neural Network. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2025; 59:3592-3602. [PMID: 39936390 DOI: 10.1021/acs.est.4c10644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/13/2025]
Abstract
Deciphering and mitigating dynamic greenhouse gas (GHG) emissions under environmental fluctuation in urban drainage systems (UDGSs) is challenging due to the absence of a high-prediction model that accurately quantifies the contributions of biological production pathways. Here we infused biological production pathways into the graph neural network (GNN) model architecture, developing ecological knowledge-infused GNN (EcoGNN-GHG) models to evaluate methane (CH4) and nitrous oxide (N2O) production in sewers and wastewater treatment plants (WWTPs). The EcoGNN-GHG model demonstrated high predictive accuracy, achieving an R2 of 0.96 for CH4 in sewers and 0.82 for N2O in WWTPs. Model interpretability analysis revealed fluctuations in contributions of the anaerobic hydrolysis acidification pathway to CH4 production and the nitrification-denitrification pathway to N2O production under dynamic environmental conditions, guiding the formulation of a precise dissolved oxygen control strategy targeting critical water quality parameters (acetate for CH4 production and nitrite for N2O production). Implementing this strategy to control DO thereby regulating biological production pathway contributions, CH4 production in sewers and N2O production in WWTPs were reduced by 35.50% and 29.94%, respectively. Our findings offer a robust, accurate method for predicting GHG emissions, quantifying production pathway contributions, and developing effective control strategies in UDGSs.
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Affiliation(s)
- Wan-Xin Yin
- College of the Environment, Liaoning University, Shenyang 110036, China
| | - Ke-Hua Chen
- Division of Emerging Interdisciplinary Areas (EMIA), Academy of Interdisciplinary Studies, The Hong Kong University of Science and Technology, Hong Kong 999077, China
| | - Jia-Qiang Lv
- State Key Laboratory of Urban Water Resource and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
| | - Jia-Ji Chen
- CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Shuai Liu
- State Key Laboratory of Urban Water Resource and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
| | - Yun-Peng Song
- State Key Laboratory of Urban Water Resource and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
| | - Yi-Wei Zhao
- State Key Laboratory of Urban Water Resource and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
| | - Fang Huang
- State Key Laboratory of Urban Water Resource and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
| | - Hong-Xu Bao
- College of the Environment, Liaoning University, Shenyang 110036, China
| | - Hong-Cheng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
| | - Ai-Jie Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
| | - Nan-Qi Ren
- State Key Laboratory of Urban Water Resource and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
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5
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Sun Z, Li J, Meng J, Li J. Small-data-trained model for predicting nitrate accumulation in one-stage partial nitritation-anammox processes controlled by oxygen supply rate. WATER RESEARCH 2025; 269:122798. [PMID: 39581117 DOI: 10.1016/j.watres.2024.122798] [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: 07/09/2024] [Revised: 10/25/2024] [Accepted: 11/14/2024] [Indexed: 11/26/2024]
Abstract
Nitrate (NO3--N) accumulation is the biggest obstacle for wastewater treatment via partial nitritation-anammox process. Dissolved oxygen (DO) control is the most used strategy to prevent NO3--N accumulation, but the performance is usually unstable. This study proposes a novel strategy for controlling NO3--N accumulation based on oxygen supply rate (OSR). In comparison, limiting the OSR is more effective than limiting DO in controlling NO3--N accumulation through mathematical simulation. A laboratory-scale one-stage partial nitritation-anammox system was continuously operated for 135 days, which was divided into five stages with different OSRs. A novel deep learning model integrating Gated Recurrent Unit and Multilayer Perceptron was developed to predict NO3--N accumulation load. To tackle with the general obstacle of limited environmental samples, a generic evaluation was proposed to optimise the model structure by leveraging predictive performance and overfitting risk. The developed model successfully predicted the NO3--N accumulation in the system ten days in advance, showcasing its potential contribution to system design and performance enhancement.
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Affiliation(s)
- Zhenju Sun
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, 150090, PR China
| | - Jianzheng Li
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, 150090, PR China
| | - Jia Meng
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, 73 Huanghe Road, Harbin, 150090, PR China.
| | - Jiuling Li
- Australian Centre for Water and Environmental Biotechnology, The University of Queensland, Brisbane, QLD 4072, Australia.
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Yin WX, Lv JQ, Liu S, Chen JJ, Wei J, Ding C, Yuan Y, Bao HX, Wang HC, Wang AJ. Microbial-Guided prediction of methane and sulfide production in Sewers: Integrating mechanistic models with Machine learning. BIORESOURCE TECHNOLOGY 2025; 415:131640. [PMID: 39414164 DOI: 10.1016/j.biortech.2024.131640] [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: 08/28/2024] [Revised: 10/02/2024] [Accepted: 10/13/2024] [Indexed: 10/18/2024]
Abstract
Accurate modeling of methane (CH4) and sulfide (H2S) production in sewer systems was constrained by insufficient consideration of microbial processes under dynamic environmental conditions. This study introduces a microbial-guided machine learning (ML) framework (Micro-ML), which integrates microbial process representations from mechanistic models (microbial information) with ML models. Results indicate that Micro-ML model enhanced predictions of CH4 and H2S production, where microbial information provides more information for model optimization. The feature importance of microbial information performed comparable weightings for 58.12 % and 55.16 %, respectively, but their relative significance in influencing Micro-ML model performance varies considerably. The application of Micro-ML performed great potential in reducing CH4 and H2S production (decreased ∼ 80 % and 90 %). The integrated model not only improves the accuracy of CH4 and H2S predictions but also offers a valuable tool for effective management strategies for sewer systems.
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Affiliation(s)
- Wan-Xin Yin
- College of the Environment, Liaoning University, Shenyang 110036, PR China; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, PR China
| | - Jia-Qiang Lv
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, PR China
| | - Shuai Liu
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, PR China
| | - Jia-Ji Chen
- CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Jun Wei
- PowerChina Huadong Engineering Corporation Limited, Hangzhou 311122, PR China
| | - Cheng Ding
- School of Environmental Science and Engineering, Yancheng Institute of Technology, Yancheng 224051, PR China
| | - Ye Yuan
- School of Environmental Science and Engineering, Yancheng Institute of Technology, Yancheng 224051, PR China
| | - Hong-Xu Bao
- College of the Environment, Liaoning University, Shenyang 110036, PR China
| | - Hong-Cheng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, PR China.
| | - Ai-Jie Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, PR China
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7
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Lv JQ, Yin WX, Xu JM, Cheng HY, Li ZL, Yang JX, Wang AJ, Wang HC. Augmented machine learning for sewage quality assessment with limited data. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2025; 23:100512. [PMID: 39659704 PMCID: PMC11629219 DOI: 10.1016/j.ese.2024.100512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 11/12/2024] [Accepted: 11/12/2024] [Indexed: 12/12/2024]
Abstract
Physical, chemical, and biological processes within sewers significantly alter sewage composition during conveyance. This leads to the formation of sulfide and methane-compounds that contribute to sewer corrosion and greenhouse gas emissions. Reliable modeling of these compounds is essential for effective sewer management, but the development of machine learning (ML) models is hindered by differences in data accessibility and sampling frequencies of water quality variables. Here we present a mechanistically enhanced hybrid (ME-Hybrid) model that combines mechanistic modeling with data-driven approaches. This model harmonizes datasets with varying sampling frequencies and generates synthetic samples for ML training, thereby enhancing the monitoring of methane and sulfide in sewers. The optimal ME-Hybrid model integrates the backpropagation neural network with mechanistic frequency harmonization. We demonstrate that the ME-Hybrid model outperforms pure ML and linear interpolation in capturing fluctuating trends and extremes of sulfide concentrations, achieving a coefficient of determination (R2) of 0.94. Synthetic samples generated through mechanistic augmentation closely approximate real samples in modeling performance, statistical distribution, and data structure. This enables the model to maintain high predictive accuracy (R2 > 0.76) for sulfide even when trained on only 50 % of the dataset. Additionally, the ME-Hybrid model successfully assesses sewer methane concentrations with an R2 of 0.94, validating its applicability and generalization ability. Our results provide a reliable methodological framework for modeling and prediction under data scarcity. By facilitating better monitoring and management of sewer systems, the ME-Hybrid model aids in the development of strategies that minimize environmental impacts, enhance urban resilience, and ultimately lead to sustainable urban water systems.
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Affiliation(s)
- Jia-Qiang Lv
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen, 518055, China
| | - Wan-Xin Yin
- College of the Environment, Liaoning University, Shenyang, 110036, China
| | - Jia-Min Xu
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen, 518055, China
| | - Hao-Yi Cheng
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen, 518055, China
| | - Zhi-Ling Li
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Ji-Xian Yang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Ai-Jie Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen, 518055, China
| | - Hong-Cheng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
- School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen, 518055, China
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8
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Li Y, Cai C, Liu E, Lin X, Zhang Y, Chen H, Wei Z, Huang X, Guo R, Peng K, Liu J. A novel hybrid variable cross layer-based machine learning model improves the accuracy and interpretation of energy intensity prediction of wastewater treatment plant. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 371:123209. [PMID: 39541811 DOI: 10.1016/j.jenvman.2024.123209] [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: 06/11/2024] [Revised: 10/19/2024] [Accepted: 11/01/2024] [Indexed: 11/16/2024]
Abstract
Energy intensity (EI) prediction in wastewater treatment plants (WWTPs) suffers from inaccuracy and non-interpretability due to poor data quality, complex mechanisms and various confounding variables. In this study, the novel hybrid variable cross layer-based machine learning (VCL-ML) model was devised, which generates new knowledge with monitoring indicators (e.g., COD, etc.) and then embeds both domain knowledge and monitoring indicators into the ML model. This novel hybrid VCL-ML model achieves a root-mean-square error (RMSE) of 0.021 kW h/m³ with an 8.7% improvement over the conventional ML (Con-ML) model. The Shapley additive explanation demonstrated that domain knowledge features are ranked high and have important interpretable implications for the model, such as capacity utilization (CU), which measures the efficiency of resource use, and total nitrogen remaining rate (TN_rr), which indicates the nitrogen retention in a system. Partially dependent interactions between domain knowledge (e.g., sludge yield) and monitoring indexes (e.g., influent pH) could contribute to the interpretation of reality. By comparing the feature categorization between VCL-ML and Con-ML models, temporal information (e.g., month) and removal information (e.g., TN_rr) played an important role in the model's performance improvement. This result highlights the strong correlation between wastewater treatment plant energy intensity with pollutant removal and temporal information while weakening the contribution of other redundant features. This VCL-ML model improves the predicting accuracy and interpretation of the EI of WWTPs, which can be used in the optimal operation and sustainable management of WWTPs.
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Affiliation(s)
- Yucheng Li
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai, 200092, PR China
| | - Chen Cai
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Institute of Carbon Neutrality, Tongji University, Shanghai, 200092, PR China.
| | - Erwu Liu
- College of Electronic Information and Engineering, Tongji University, Shanghai, 200092, PR China
| | - Xiaofeng Lin
- Fujian Haixia Environmental Protection Group Co., Ltd, Fujian, 350014, PR China
| | - Ying Zhang
- Fujian Haixia Environmental Protection Group Co., Ltd, Fujian, 350014, PR China
| | - Hongjing Chen
- Fuzhou Water Group Co., Ltd, Fujian, 350001, PR China
| | - Zhongqing Wei
- Fuzhou Water Group Co., Ltd, Fujian, 350001, PR China
| | - Xiangfeng Huang
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Institute of Carbon Neutrality, Tongji University, Shanghai, 200092, PR China
| | - Ru Guo
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Institute of Carbon Neutrality, Tongji University, Shanghai, 200092, PR China
| | - Kaiming Peng
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Institute of Carbon Neutrality, Tongji University, Shanghai, 200092, PR China
| | - Jia Liu
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Institute of Carbon Neutrality, Tongji University, Shanghai, 200092, PR China.
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9
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Chen Z, Cheng H, Wang X, Chen B, Chen Y, Cai R, Zhang G, Song C, He Q. Development and application of an intelligent nitrogen removal diagnosis and optimization framework for WWTPs: Low-carbon and stable operation. WATER RESEARCH 2024; 266:122337. [PMID: 39216130 DOI: 10.1016/j.watres.2024.122337] [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: 08/13/2024] [Accepted: 08/24/2024] [Indexed: 09/04/2024]
Abstract
Optimizing nitrogen removal is crucial for ensuring the efficient operation of wastewater treatment plants (WWTPs), but it is susceptible to variations in influent conditions and operational parameter constraints, and conflicts with the energy-saving and carbon emission reduction goals. To address these issues, this study proposes a hybrid framework integrating process simulation, machine learning, and multi-objective genetic algorithms for nitrogen removal diagnosis and optimization, aiming to predict the total nitrogen in effluent, diagnose nitrogen over-limit risks, and optimize the control strategies. Taking a full-scale WWTP as a case study, a process time-lag simulation-enhanced machine learning model (PTLS-ML) was developed, achieving R2 values of 0.94 and 0.79 for the training and testing sets, respectively. The proposed model successfully identified the potential reasons of nitrogen over-limit risks under different influent conditions and operational parameters, and accordingly provided optimization suggestions. In addition, the multi-objective optimization (MOO) algorithms analysis further demonstrated that maintaining 4-6 mg/L total nitrogen concentration in effluent by adjusting process operational parameters can effectively balance multiple objectives (i.e., effluent water quality, operating costs, and greenhouse gas emissions), achieving coordinated optimization. This framework can serve as a reference for stable operation, energy-saving, and emission reduction in the nitrogen removal of WWTPs.
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Affiliation(s)
- Zhichi Chen
- Key Laboratory of Eco-environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400044, China
| | - Hong Cheng
- Key Laboratory of Eco-environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400044, China.
| | - Xinge Wang
- Key Laboratory of Eco-environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400044, China
| | - Bowen Chen
- Key Laboratory of Eco-environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400044, China
| | - Yao Chen
- Key Laboratory of Eco-environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400044, China
| | - Ran Cai
- Beijing Capital Eco-Environment Protection Group Co., Ltd., Beijing 100044, China
| | - Gongliang Zhang
- Beijing Capital Eco-Environment Protection Group Co., Ltd., Beijing 100044, China
| | - Chenxin Song
- Sichuan Shuihui Ecological Environment Management Co., Ltd., Neijiang 641000, China
| | - Qiang He
- Key Laboratory of Eco-environments in Three Gorges Reservoir Region, Ministry of Education, College of Environment and Ecology, Chongqing University, Chongqing 400044, China.
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10
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Seshan S, Poinapen J, Zandvoort MH, van Lier JB, Kapelan Z. Forecasting nitrous oxide emissions from a full-scale wastewater treatment plant using LSTM-based deep learning models. WATER RESEARCH 2024; 268:122754. [PMID: 39522482 DOI: 10.1016/j.watres.2024.122754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 10/15/2024] [Accepted: 11/04/2024] [Indexed: 11/16/2024]
Abstract
Nitrous oxide (N2O) emissions from wastewater treatment plants (WWTPs) exhibit significant seasonal variability, making accurate predictions with conventional biokinetic models difficult due to complex and poorly understood biochemical processes. This study addresses these challenges by exploring data-driven alternatives, using long short-term memory (LSTM) based encoder-decoder models as basis. The models were developed for future integration into a model predictive control framework, aiming to reduce N2O emissions by forecasting these over varying prediction horizons. The models were trained on 12 months and tested on 3 months of data from a full-scale WWTP in Amsterdam West, the Netherlands. The dataset encompasses seasonal peaks in N2O emissions typical for winter and spring months. The best performing model, featuring a 256-256 LSTM architecture, achieved the highest accuracy with test R2 values up to 0.98 across prediction horizons spanning 0.5 to 6.0 h ahead. Feature importance analysis identified past N2O emissions, influent flowrate, NH4+, NOx, and dissolved oxygen (DO) in the aerobic tank as most significant inputs. The observed decreasing influence of historical N2O emissions over extended prediction horizons highlights the importance and significance of process variables for the model's performance. The model's ability to accurately forecast short-term N2O emissions and capture immediate trends highlights its potential for operational use in controlling emissions in WWTPs. Further research incorporating diverse datasets and biochemical process inputs related to microbial activities in the N2O production pathways could improve the model's accuracy for longer forecasting horizons. These findings advocate for hybridising deep learning models with biokinetic and mechanistic insights to enhance prediction accuracy and interpretability.
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Affiliation(s)
- Siddharth Seshan
- KWR Water Research Institute, Nieuwegein, the Netherlands; Section Sanitary Engineering, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands.
| | | | | | - Jules B van Lier
- Section Sanitary Engineering, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
| | - Zoran Kapelan
- Section Sanitary Engineering, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands
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11
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Zhao GY, Furumai H, Fujita M. Supporting data-enhanced hybrid ordinary differential equation model for phosphate dynamics in municipal wastewater treatment. BIORESOURCE TECHNOLOGY 2024; 409:131217. [PMID: 39117242 DOI: 10.1016/j.biortech.2024.131217] [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: 06/11/2024] [Revised: 08/02/2024] [Accepted: 08/03/2024] [Indexed: 08/10/2024]
Abstract
A parallel hybrid ordinary differential equation (ODE) integrating the Activated Sludge Model No. 2d (ASM2d) and an artificial neural network (ANN) was developed to simulate biological phosphorus removal (BPR) with high accuracy and interpretability. Two novelties were introduced; first, the involved supporting data (i.e., phosphate-release activity) were incorporated as an input in the ANN. Second, the outputs of the ANN were selective. Three models were implemented using different ANN outputs, and all three outperformed ASM2d in phosphate estimation for anaerobic/aerobic sequencing batch reactor operation. In particular, the incorporation of four variables responsible for BPR into the ANN enabled the highest performance (R2 = 0.93) owing to the capture of increasing phosphate-accumulating organisms (PAOs). The ANN with the supporting data worked satisfactorily to compensate for ASM2d by adding proper PAOs, resulting in improvement in phosphate estimation. The novel parallel hybrid ODE can simulate BPR while maintaining physical meaning.
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Affiliation(s)
- Guang-Yao Zhao
- Graduate School of Science and Engineering, Ibaraki University, Hitachi, Ibaraki 316-8511, Japan
| | - Hiroaki Furumai
- Research and Development Initiative, Chuo University, Bunkyo, Tokyo 112-8551, Japan
| | - Masafumi Fujita
- Global and Local Environment Co-creation Institute, Ibaraki University, Hitachi, Ibaraki 316-8511, Japan.
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12
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Lancioni N, Szelag B, Sgroi M, Barbusiński K, Fatone F, Eusebi AL. Novel extended hybrid tool for real time control and practically support decisions to reduce GHG emissions in full scale wastewater treatment plants. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 365:121502. [PMID: 38936025 DOI: 10.1016/j.jenvman.2024.121502] [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: 01/15/2024] [Revised: 04/08/2024] [Accepted: 06/15/2024] [Indexed: 06/29/2024]
Abstract
In this paper, a novel methodology and extended hybrid model for the real time control, prediction and reduction of direct emissions of greenhouse gases (GHGs) from wastewater treatment plants (WWTPs) is proposed to overcome the lack of long-term data availability in several full-scale case studies. A mechanistic model (MCM) and a machine learning (ML) model are combined to real time control, predict the emissions of nitrous oxide (N2O) and carbon dioxide (CO2) as well as effluent quality (COD - chemical oxygen demand, NH4-N - ammonia, NO3-N - nitrate) in activated sludge method. For methane (CH4), using the MCM model, predictions are performed on the input data (VFA, CODs for aerobic and anaerobic compartments) to the MLM model. Additionally, scenarios were analyzed to assess and reduce the GHGs emissions related to the biological processes. A real WWTP, with a population equivalent (PE) of 125,000, was studied for the validation of the hybrid model. A global sensitivity analysis (GSA) of the MCM and a ML model were implemented to assess GHGs emission mechanisms the biological reactor. Finally, an early warning tool for the prediction of GHGs errors was implemented to assess the accuracy and the reliability of the proposed algorithm. The results could support the wastewater treatment plant operators to evaluate possible mitigation scenarios (MS) that can reduce direct GHG emissions from WWTPs by up to 21%, while maintaining the final quality standard of the treated effluent.
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Affiliation(s)
- Nicola Lancioni
- Dipartimento SIMAU, Università Politecnica Delle Marche, Via Brecce Bianche, 60131, Ancona, Italy.
| | - Bartosz Szelag
- Dipartimento SIMAU, Università Politecnica Delle Marche, Via Brecce Bianche, 60131, Ancona, Italy; Department of Geotechnics and Water Engineering, Kielce University of Technology, Al. Tysiąclecia Pa' nstwa Polskiego 7, 25-314, Kielce, Poland.
| | - Massimiliano Sgroi
- Dipartimento SIMAU, Università Politecnica Delle Marche, Via Brecce Bianche, 60131, Ancona, Italy.
| | - Krzysztof Barbusiński
- Department of Water and Wastewater Engineering, Silesian University of Technology, Konarskiego 18 St., 44-100, Gliwice, Poland
| | - Francesco Fatone
- Dipartimento SIMAU, Università Politecnica Delle Marche, Via Brecce Bianche, 60131, Ancona, Italy
| | - Anna Laura Eusebi
- Dipartimento SIMAU, Università Politecnica Delle Marche, Via Brecce Bianche, 60131, Ancona, Italy
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13
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Zheng Y, Wei J, Zhang W, Zhang Y, Zhang T, Zhou Y. An ensemble model for accurate prediction of key water quality parameters in river based on deep learning methods. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 366:121932. [PMID: 39043087 DOI: 10.1016/j.jenvman.2024.121932] [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: 02/27/2024] [Revised: 06/10/2024] [Accepted: 07/17/2024] [Indexed: 07/25/2024]
Abstract
Deep learning models provide a more powerful method for accurate and stable prediction of water quality in rivers, which is crucial for the intelligent management and control of the water environment. To increase the accuracy of predicting the water quality parameters and learn more about the impact of complex spatial information based on deep learning models, this study proposes two ensemble models TNX (with temporal attention) and STNX (with spatio-temporal attention) based on seasonal and trend decomposition (STL) method to predict water quality using geo-sensory time series data. Dissolved oxygen, total phosphorus, and ammonia nitrogen were predicted in short-step (1 h, and 2 h) and long-step (12 h, and 24 h) with seven water quality monitoring sites in a river. The ensemble model TNX improved the performance by 2.1%-6.1% and 4.3%-22.0% relative to the best baseline deep learning model for the short-step and long-step water quality prediction, and it can capture the variation pattern of water quality parameters by only predicting the trend component of raw data after STL decomposition. The STNX model, with spatio-temporal attention, obtained 0.5%-2.4% and 2.3%-5.7% higher performance compared to the TNX model for the short-step and long-step water quality prediction, and such improvement was more effective in mitigating the prediction shift patterns of long-step prediction. Moreover, the model interpretation results consistently demonstrated positive relationship patterns across all monitoring sites. However, the significance of seven specific monitoring sites diminished as the distance between the predicted and input monitoring sites increased. This study provides an ensemble modeling approach based on STL decomposition for improving short-step and long-step prediction of river water quality parameter, and understands the impact of complex spatial information on deep learning model.
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Affiliation(s)
- Yue Zheng
- The Institute of Municipal Engineering, Zhejiang University, Hangzhou, China
| | - Jun Wei
- Power China Huadong Engineering Corporation Limited, Hangzhou, China
| | - Wenming Zhang
- Department of Civil and Environmental Engineering, University of Alberta, Canada
| | - Yiping Zhang
- The Institute of Municipal Engineering, Zhejiang University, Hangzhou, China
| | - Tuqiao Zhang
- The Institute of Municipal Engineering, Zhejiang University, Hangzhou, China
| | - Yongchao Zhou
- The Institute of Municipal Engineering, Zhejiang University, Hangzhou, China.
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14
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Wang Q, Sheng Y, Zhang Y, Zhong X, Liu H, Huang Z, Li D, Wu H, Ni Y, Zhang J, Lin W, Qiu K, Qian X. Complete long-term monitoring of greenhouse gas emissions from a full-scale industrial wastewater treatment plant with different cover configurations. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121206. [PMID: 38776658 DOI: 10.1016/j.jenvman.2024.121206] [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: 02/29/2024] [Revised: 05/13/2024] [Accepted: 05/18/2024] [Indexed: 05/25/2024]
Abstract
The greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs), consisting mainly of methane (CH4) and nitrous oxide (N2O), have been constantly increasing and become a non-negligible contributor towards carbon neutrality. The precise evaluation of plant-specific GHG emissions, however, remains challenging. The current assessment approach is based on the product of influent load and emission factor (EF), of which the latter is quite often a single value with huge uncertainty. In particular, the latest default Tier 1 value of N2O EF, 0.016 ± 0.012 kgN2O-N kgTN-1, is estimated based on the measurement of 30 municipal WWTPs only, without involving any industrial wastewater. Therefore, to resolve the pattern of GHG emissions from industrial WWTPs, this work conducted a 14-month monitoring campaign covering all the process units at a full-scale industrial WWTP in Shanghai, China. The total CH4 and N2O emissions from the whole plant were, on average, 447.7 ± 224.5 kgCO2-eq d-1 and 1605.3 ± 2491.0 kgCO2-eq d-1, respectively, exhibiting a 5.2- or 3.9-times more significant deviation than the influent loads of chemical oxygen demand (COD) or total nitrogen (TN). The resulting EFs, 0.00072 kgCH4 kgCOD-1 and 0.00211 kgN2O-N kgTN-1, were just 0.36% of the IPCC recommended value for CH4, and 13.2% for N2O. Besides, the parallel anoxic-oxic (A/O) lines of this industrial WWTP were covered in two configurations, allowing the comparison of GHG emissions from different odor control setup. Unit-specific analysis showed that the replacement of enclosed A/open O with enclosed A/O reduced the CH4 EF by three times, from 0.00159 to 0.00051 kgCH4 kgCOD-1, and dramatically decreased the N2O EF by an order of magnitude, from 0.00376 to 0.00032 kgN2O-N kgTN-1, which was among the lowest of all full-scale WWTPs.
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Affiliation(s)
- Qinyi Wang
- Shanghai Academy of Environmental Sciences, Shanghai, 200233, China; State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Yangyue Sheng
- Shanghai Academy of Environmental Sciences, Shanghai, 200233, China; State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Yili Zhang
- Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| | - Xinrun Zhong
- State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Hui Liu
- Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| | - Zhengfeng Huang
- State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Dan Li
- Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| | - Hao Wu
- State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Yuanzhi Ni
- Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| | - Junqi Zhang
- State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, 200237, China
| | - Weiqing Lin
- Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
| | - Kaipei Qiu
- State Environmental Protection Key Laboratory of Environmental Risk Assessment and Control on Chemical Process, School of Resources and Environmental Engineering, East China University of Science and Technology, Shanghai, 200237, China; Shanghai Environmental Protection Key Laboratory for Environmental Standard and Risk Management of Chemical Pollutants, Shanghai, 200237, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200292, China.
| | - Xiaoyong Qian
- Shanghai Academy of Environmental Sciences, Shanghai, 200233, China.
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15
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An T, Feng K, Cheng P, Li R, Zhao Z, Xu X, Zhu L. Adaptive prediction for effluent quality of wastewater treatment plant: Improvement with a dual-stage attention-based LSTM network. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 359:120887. [PMID: 38678908 DOI: 10.1016/j.jenvman.2024.120887] [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: 12/05/2023] [Revised: 04/04/2024] [Accepted: 04/10/2024] [Indexed: 05/01/2024]
Abstract
The accurate effluent prediction plays a crucial role in providing early warning for abnormal effluent and achieving the adjustment of feedforward control parameters during wastewater treatment. This study applied a dual-staged attention mechanism based on long short-term memory network (DA-LSTM) to improve the accuracy of effluent quality prediction. The results showed that input attention (IA) and temporal attention (TA) significantly enhanced the prediction performance of LSTM. Specially, IA could adaptively adjust feature weights to enhance the robustness against input noise, with R2 increased by 13.18%. To promote its long-term memory ability, TA was used to increase the memory span from 96 h to 168 h. Compared to a single LSTM model, the DA-LSTM model showed an improvement in prediction accuracy by 5.10%, 2.11%, 14.47% for COD, TP, and TN. Additionally, DA-LSTM demonstrated excellent generalization performance in new scenarios, with the R2 values for COD, TP, and TN increasing by 22.67%, 20.06%, and 17.14% respectively, while the MAPE values decreased by 56.46%, 63.08%, and 42.79%. In conclusion, the DA-LSTM model demonstrated excellent prediction performance and generalization ability due to its advantages of feature-adaptive weighting and long-term memory focusing. This has forward-looking significance for achieving efficient early warning of abnormal operating conditions and timely management of control parameters.
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Affiliation(s)
- Tong An
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Kuanliang Feng
- Zhejiang Supcon Information Technology Co., Ltd, Hangzhou, 310052, China
| | - Peijin Cheng
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Ruojia Li
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zihao Zhao
- Shanghai Municipal Engineering Design Institute (group) Co., Ltd, Shanghai, 200092, China
| | - Xiangyang Xu
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Zhejiang Provincial Engineering Laboratory of Water Pollution Control, Hangzhou, 310058, China
| | - Liang Zhu
- College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Innovation Center of Yangtze River Delta, Zhejiang University, Jiashan, 314100, China; Zhejiang Provincial Engineering Laboratory of Water Pollution Control, Hangzhou, 310058, China.
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16
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Shang Z, Cai C, Guo Y, Huang X, Peng K, Guo R, Wei Z, Wu C, Cheng S, Liao Y, Hung CY, Liu J. Direct and indirect monitoring methods for nitrous oxide emissions in full-scale wastewater treatment plants: A critical review. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 358:120842. [PMID: 38599092 DOI: 10.1016/j.jenvman.2024.120842] [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: 01/17/2024] [Revised: 03/17/2024] [Accepted: 04/02/2024] [Indexed: 04/12/2024]
Abstract
Mitigation of nitrous oxide (N2O) emissions in full-scale wastewater treatment plant (WWTP) has become an irreversible trend to adapt the climate change. Monitoring of N2O emissions plays a fundamental role in understanding and mitigating N2O emissions. This paper provides a comprehensive review of direct and indirect N2O monitoring methods. The techniques, strengths, limitations, and applicable scenarios of various methods are discussed. We conclude that the floating chamber technique is suitable for capturing and interpreting the spatiotemporal variability of real-time N2O emissions, due to its long-term in-situ monitoring capability and high data acquisition frequency. The monitoring duration, location, and frequency should be emphasized to guarantee the accuracy and comparability of acquired data. Calculation by default emission factors (EFs) is efficient when there is a need for ambiguous historical N2O emission accounts of national-scale or regional-scale WWTPs. Using process-specific EFs is beneficial in promoting mitigation pathways that are primarily focused on low-emission process upgrades. Machine learning models exhibit exemplary performance in the prediction of N2O emissions. Integrating mechanistic models with machine learning models can improve their explanatory power and sharpen their predictive precision. The implementation of the synergy of nutrient removal and N2O mitigation strategies necessitates the calibration and validation of multi-path mechanistic models, supported by long-term continuous direct monitoring campaigns.
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Affiliation(s)
- Zhenxin Shang
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China
| | - Chen Cai
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Institute of Carbon Neutrality, Tongji University, Shanghai, 200092, PR China.
| | - Yanli Guo
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China
| | - Xiangfeng Huang
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Institute of Carbon Neutrality, Tongji University, Shanghai, 200092, PR China
| | - Kaiming Peng
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Institute of Carbon Neutrality, Tongji University, Shanghai, 200092, PR China
| | - Ru Guo
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Institute of Carbon Neutrality, Tongji University, Shanghai, 200092, PR China
| | - Zhongqing Wei
- Fuzhou Water Group Co., Ltd, Fuzhou, 350000, PR China
| | - Chenyuan Wu
- Fuzhou Water Group Co., Ltd, Fuzhou, 350000, PR China
| | - Shunjian Cheng
- Fuzhou City Construction Design & Research Institute Co., Ltd, Fuzhou, 350000, PR China
| | - Youxiang Liao
- Fuzhou City Construction Design & Research Institute Co., Ltd, Fuzhou, 350000, PR China
| | - Chih-Yu Hung
- Environment and Climate Change, 351 Saint-Joseph Blvd., 9th Floor. Gatineau, Quebec, K1A 0H3, Canada
| | - Jia Liu
- College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China; Institute of Carbon Neutrality, Tongji University, Shanghai, 200092, PR China
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17
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Duarte MS, Martins G, Oliveira P, Fernandes B, Ferreira EC, Alves MM, Lopes F, Pereira MA, Novais P. A Review of Computational Modeling in Wastewater Treatment Processes. ACS ES&T WATER 2024; 4:784-804. [PMID: 38482340 PMCID: PMC10928720 DOI: 10.1021/acsestwater.3c00117] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 08/11/2023] [Accepted: 08/11/2023] [Indexed: 06/10/2024]
Abstract
Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.
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Affiliation(s)
- M. Salomé Duarte
- CEB
− Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
- LABBELS
− Associate Laboratory, 4710-057 Braga, Guimarães, Portugal
| | - Gilberto Martins
- CEB
− Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
- LABBELS
− Associate Laboratory, 4710-057 Braga, Guimarães, Portugal
| | - Pedro Oliveira
- ALGORITMI
Centre, Department of Informatics, University
of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Bruno Fernandes
- ALGORITMI
Centre, Department of Informatics, University
of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
| | - Eugénio C. Ferreira
- CEB
− Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
- LABBELS
− Associate Laboratory, 4710-057 Braga, Guimarães, Portugal
| | - M. Madalena Alves
- CEB
− Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
- LABBELS
− Associate Laboratory, 4710-057 Braga, Guimarães, Portugal
| | - Frederico Lopes
- Águas
do Norte, Rua Dr. Roberto
de Carvalho, no. 78-90, 4810-284 Guimarães, Portugal
| | - M. Alcina Pereira
- CEB
− Centre of Biological Engineering, University of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
- LABBELS
− Associate Laboratory, 4710-057 Braga, Guimarães, Portugal
| | - Paulo Novais
- ALGORITMI
Centre, Department of Informatics, University
of Minho, Campus de Gualtar, 4710-057 Braga, Portugal
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18
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Liu Z, Xu Z, Zhu X, Yin L, Yin Z, Li X, Zheng W. Calculation of carbon emissions in wastewater treatment and its neutralization measures: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169356. [PMID: 38110091 DOI: 10.1016/j.scitotenv.2023.169356] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/08/2023] [Accepted: 12/11/2023] [Indexed: 12/20/2023]
Abstract
As the pursuit of "carbon neutrality" gains momentum, the emphasis on low-carbon solutions, emphasizing energy conservation and resource reuse, has introduced fresh challenges to conventional wastewater treatment approaches. Precisely evaluating carbon emissions in urban water supply and drainage systems, wastewater treatment plants, and establishing carbon-neutral operating models has become a pivotal concern in the future of wastewater treatment. Regrettably, limited research has been devoted to carbon accounting and the development of carbon-neutral strategies for wastewater treatment. In this review, to facilitate comprehensive carbon accounting, we initially recognizes direct and indirect carbon emission sources in the wastewater treatment process. We then provide an overview of several major carbon accounting methods and propose a carbon accounting framework. Furthermore, we advocate for a systemic perspective, highlighting that achieving carbon neutrality in wastewater treatment extends beyond the boundaries of wastewater treatment plants. We assess current technical measures both within and outside the plants that contribute to achieving carbon-neutral operations. Encouraging the application of intelligent algorithms for the multifaceted monitoring and control of wastewater treatment processes is paramount. Supporting resource and energy recycling is also essential, as is recognizing the benefits of synergistic wastewater treatment technologies. We advocate a systematic, multi-level planning approach that takes into account a wide range of factors. Our goal is to offer valuable insights and support for the practical implementation of water environment management within the framework of carbon neutrality, and to advance sustainable socio-economic development and contribute to a more environmentally responsible future.
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Affiliation(s)
- Zhixin Liu
- School of Life and Environmental Science, Shaoxing University, Shaoxing 312000, China.
| | - Ziyi Xu
- School of Life and Environmental Science, Shaoxing University, Shaoxing 312000, China
| | - Xiaolei Zhu
- School of Life and Environmental Science, Shaoxing University, Shaoxing 312000, China
| | - Lirong Yin
- Department of Geography and Anthropology, Louisiana State University, Baton Rouge 70803, LA, USA.
| | - Zhengtong Yin
- College of Resource and Environment Engineering, Guizhou University, Guiyang 550025, China.
| | - Xiaolu Li
- School of Geographical Sciences, Southwest University, Chongqing 400715, China.
| | - Wenfeng Zheng
- School of Automation, University of Electronic Science and Technology of China, Chengdu 610054, China.
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19
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Xu X, Wei A, Tang S, Liu Q, Shi H, Sun W. Prediction of nitrous oxide emission of a municipal wastewater treatment plant using LSTM-based deep learning models. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:2167-2186. [PMID: 38055175 DOI: 10.1007/s11356-023-31250-9] [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: 10/04/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023]
Abstract
Accurate assessment of greenhouse gas emissions from wastewater treatment plants is crucial for mitigating climate change. N2O is a potent greenhouse gas that is emitted from wastewater treatment plants during the biological denitrification process. In this study, we developed and evaluated deep learning models for predicting N2O emissions from a WWTP in Switzerland. Six key parameters were selected to obtain the optimal LSTM model by adjusting experimental parameter conditions. The optimal parameter condition was achieved with 150 neurons, the tanh activation function, the RMSprop optimization algorithm, a learning rate of 0.001, no dropout regularization, and a batch size of 128. Under the same conditions, we compared the performance of recurrent neural networks (RNNs) and Long Short-Term Memory (LSTM) networks. We found that LSTM models outperformed RNN models in predicting N2O emissions. The optimal LSTM model achieved a 36% improvement in mean absolute error (MAE), a 19% improvement in root mean squared error (RMSE), and a 6.92% improvement in R2 score compared to the RNN model. Additionally, LSTM models demonstrated better resilience to sudden changes in the target sequence, exhibiting a 9.54% higher percentage of explained variance compared to RNNs. These results highlight the potential of LSTM models for accurate and robust prediction of N2O emissions from wastewater treatment plants, contributing to effective greenhouse gas mitigation strategies.
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Affiliation(s)
- Xiaozhen Xu
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, Shaanxi, China
| | - Anlei Wei
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, Shaanxi, China.
| | - Songjun Tang
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, Shaanxi, China
| | - Qi Liu
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, Shaanxi, China
| | - Hanxiao Shi
- Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi'an, 710127, Shaanxi, China
| | - Wei Sun
- School of Geography and Planning, Sun Yat-Sen University, Guangzhou, 510275, Guangdong, China
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20
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Wang K, Li J, Gu X, Wang H, Li X, Peng Y, Wang Y. How to Provide Nitrite Robustly for Anaerobic Ammonium Oxidation in Mainstream Nitrogen Removal. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21503-21526. [PMID: 38096379 DOI: 10.1021/acs.est.3c05600] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
Abstract
Innovation in decarbonizing wastewater treatment is urgent in response to global climate change. The practical implementation of anaerobic ammonium oxidation (anammox) treating domestic wastewater is the key to reconciling carbon-neutral management of wastewater treatment with sustainable development. Nitrite availability is the prerequisite of the anammox reaction, but how to achieve robust nitrite supply and accumulation for mainstream systems remains elusive. This work presents a state-of-the-art review on the recent advances in nitrite supply for mainstream anammox, paying special attention to available pathways (forward-going (from ammonium to nitrite) and backward-going (from nitrate to nitrite)), key controlling strategies, and physiological and ecological characteristics of functional microorganisms involved in nitrite supply. First, we comprehensively assessed the mainstream nitrite-oxidizing bacteria control methods, outlining that these technologies are transitioning to technologies possessing multiple selective pressures (such as intermittent aeration and membrane-aerated biological reactor), integrating side stream treatment (such as free ammonia/free nitrous acid suppression in recirculated sludge treatment), and maintaining high activity of ammonia-oxidizing bacteria and anammox bacteria for competing oxygen and nitrite with nitrite-oxidizing bacteria. We then highlight emerging strategies of nitrite supply, including the nitrite production driven by novel ammonia-oxidizing microbes (ammonia-oxidizing archaea and complete ammonia oxidation bacteria) and nitrate reduction pathways (partial denitrification and nitrate-dependent anaerobic methane oxidation). The resources requirement of different mainstream nitrite supply pathways is analyzed, and a hybrid nitrite supply pathway by combining partial nitrification and nitrate reduction is encouraged. Moreover, data-driven modeling of a mainstream nitrite supply process as well as proactive microbiome management is proposed in the hope of achieving mainstream nitrite supply in practical application. Finally, the existing challenges and further perspectives are highlighted, i.e., investigation of nitrite-supplying bacteria, the scaling-up of hybrid nitrite supply technologies from laboratory to practical implementation under real conditions, and the data-driven management for the stable performance of mainstream nitrite supply. The fundamental insights in this review aim to inspire and advance our understanding about how to provide nitrite robustly for mainstream anammox and shed light on important obstacles warranting further settlement.
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Affiliation(s)
- Kaichong Wang
- State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, P. R. China
- Shanghai Institute of Pollution Control and Ecological Security, Siping Road, Shanghai 200092, P. R. China
| | - Jia Li
- State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, P. R. China
- Shanghai Institute of Pollution Control and Ecological Security, Siping Road, Shanghai 200092, P. R. China
| | - Xin Gu
- State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, P. R. China
- Shanghai Institute of Pollution Control and Ecological Security, Siping Road, Shanghai 200092, P. R. China
| | - Han Wang
- State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, P. R. China
- Shanghai Institute of Pollution Control and Ecological Security, Siping Road, Shanghai 200092, P. R. China
| | - Xiang Li
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, P. R. China
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Engineering Research Center of Beijing, Beijing University of Technology, Beijing 100124, P. R. China
| | - Yayi Wang
- State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, P. R. China
- Shanghai Institute of Pollution Control and Ecological Security, Siping Road, Shanghai 200092, P. R. China
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21
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Khalil M, AlSayed A, Liu Y, Vanrolleghem PA. Machine learning for modeling N 2O emissions from wastewater treatment plants: Aligning model performance, complexity, and interpretability. WATER RESEARCH 2023; 245:120667. [PMID: 37778084 DOI: 10.1016/j.watres.2023.120667] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/22/2023] [Accepted: 09/23/2023] [Indexed: 10/03/2023]
Abstract
Nitrous oxide (N2O) emissions may account for up to 80 % of a wastewater treatment plant's (WWTP) total carbon footprint. Given the complexity of the pathways involved, estimating N2O emissions through mechanistic models still often fails to precisely depict process dynamics. Alternatively, data-driven methods for predicting N2O emissions hold substantial potential. However, so far, a comprehensive approach is still overlooked, impeding the advancement of full-scale application. Therefore, this study develops a comprehensive approach for using machine learning to perform online process modeling of N2O emissions. The approach is tested on a long-term N2O emission dataset from a full-scale WWTP. Uniquely, the proposed approach emphasizes not just model accuracy, but it also considers model complexity, computational speed, and interpretability, equipping operators with the insights needed for informed corrective actions. Algorithms with varying levels of complexity and interpretability including k-Nearest Neighbors (kNN), decision trees, ensemble learning models, and deep neural networks (DNN) were considered. Furthermore, a parametric multivariate outlier removal method was adjusted to account for data statistical distributions, significantly reducing data loss. By employing an effective feature selection methodology, a trade-off between data acquisition, model performance, and complexity was found, reducing the number of features by 40 % and decreasing data collection cost, model complexity and computational burden without significant effect on modeling accuracy. The best performing models are kNN (R2 = 0.88), AdaBoost (R2 = 0.94), and DNN (R2 = 0.90). Feature importance of models was analyzed and compared with process knowledge to test interpretability, guiding N2O mitigation decisions.
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Affiliation(s)
- Mostafa Khalil
- Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
| | - Ahmed AlSayed
- Department of Civil and Environmental Engineering, McCormick School of Engineering, Northwestern University, United States
| | - Yang Liu
- Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada; School of Civil and Environmental Engineering, Queensland University of Technology, Brisbane, Queensland, Australia.
| | - Peter A Vanrolleghem
- modelEAU, Département de génie civil et génie des eaux, Université Laval, 1065 av. de la Médecine, Québec, QC G1V 0A6, Canada
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22
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Alvi M, Batstone D, Mbamba CK, Keymer P, French T, Ward A, Dwyer J, Cardell-Oliver R. Deep learning in wastewater treatment: a critical review. WATER RESEARCH 2023; 245:120518. [PMID: 37716298 DOI: 10.1016/j.watres.2023.120518] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 08/19/2023] [Accepted: 08/22/2023] [Indexed: 09/18/2023]
Abstract
Modeling wastewater processes supports tasks such as process prediction, soft sensing, data analysis and computer assisted design of wastewater systems. Wastewater treatment processes are large, complex processes, with multiple controlling mechanisms, a high degree of disturbance variability and non-linear (generally stable) behavior with multiple internal recycle loops. Semi-mechanistic biochemical models currently dominate research and application, with data-driven deep learning models emerging as an alternative and supplementary approach. But these modeling approaches have grown in separate communities of research and practice, and so there is limited appreciation of the strengths, weaknesses, contrasts and similarities between the methods. This review addresses that gap by providing a detailed guide to deep learning methods and their application to wastewater process modeling. The review is aimed at wastewater modeling experts who are familiar with established mechanistic modeling approach, and are curious about the opportunities and challenges afforded by deep learning methods. We conclude with a discussion and needs analysis on the value of different ways of modeling wastewater processes and open research problems.
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Affiliation(s)
- Maira Alvi
- Department of Computer Science & Software Engineering, The University of Western Australia, Australia.
| | - Damien Batstone
- Australian Centre for Water and Environmental Biotechnology, University of Queensland, Brisbane, Australia
| | - Christian Kazadi Mbamba
- Australian Centre for Water and Environmental Biotechnology, University of Queensland, Brisbane, Australia
| | - Philip Keymer
- Australian Centre for Water and Environmental Biotechnology, University of Queensland, Brisbane, Australia
| | - Tim French
- Department of Computer Science & Software Engineering, The University of Western Australia, Australia
| | - Andrew Ward
- Australian Centre for Water and Environmental Biotechnology, University of Queensland, Brisbane, Australia
| | | | - Rachel Cardell-Oliver
- Department of Computer Science & Software Engineering, The University of Western Australia, Australia
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23
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Yu X, Chen S, Zhang X, Wu H, Guo Y, Guan J. Research progress of the artificial intelligence application in wastewater treatment during 2012-2022: a bibliometric analysis. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2023; 88:1750-1766. [PMID: 37830995 PMCID: wst_2023_296 DOI: 10.2166/wst.2023.296] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/14/2023]
Abstract
This study identified literatures from the Web of Science Core Collection on the application of artificial intelligence in wastewater treatment from 2011 to 2022, through bibliometrics, to summarize achievements and capture the scientific and technological progress. The number of papers published is on the rise, and especially, the number of papers issued after 2018 has increased sharply, with China contributing the most in this regard, followed by the US, Iran and India. The University of Tehran has the largest number of papers, WATER is the most published journal, and Nasr M has the largest number of articles. Collaborative network has been developed mainly through cooperation between European countries, China and the US. Remote sensing in developing countries needs to be further integrated with water quality monitoring programs. It is worth noting that artificial neural network is a research hotspot in recent years. Through keyword clustering analysis, 'machine learning' and 'deep learning' are hot keywords that have emerged since 2019. The use of neural networks for predicting the effectiveness of treatment of difficult to degrade wastewater is a future research trend. The rapid advancement of deep learning provides the opportunity to build automated pipeline defect detection systems through image recognition.
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Affiliation(s)
- Xiaoman Yu
- School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China E-mail:
| | - Shuai Chen
- School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China; Anhui International Joint Research Center for Nano Carbon-based Materials and Environmental Health, Huainan 232001, China
| | - Xiaojiao Zhang
- School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China
| | - Hongcheng Wu
- Shanghai Wobai Environmental Development Co. Ltd, Shanghai 201209, China
| | - Yaoguang Guo
- School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China
| | - Jie Guan
- School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China
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24
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Zou X, Guo H, Jiang C, Nguyen DV, Chen GH, Wu D. Physics-informed neural network-based serial hybrid model capturing the hidden kinetics for sulfur-driven autotrophic denitrification process. WATER RESEARCH 2023; 243:120331. [PMID: 37454462 DOI: 10.1016/j.watres.2023.120331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/04/2023] [Accepted: 07/09/2023] [Indexed: 07/18/2023]
Abstract
Sulfur-driven autotrophic denitrification (SdAD) is a biological process that can remove nitrate from low carbon/nitrogen (C/N) ratio wastewater. Although this process has been intensively researched, the mechanism whereby its intermediates (i.e., elemental sulfur and nitrite ions) are generated and accumulated remains elusive. Existing mathematical models developed for SdAD cannot accurately predict the intermediates in SdAD because of the incomplete knowledge of process kinetic resulting from changes in the environmental conditions and electron competition during SdAD. To address this limitation, we proposed a novel serial hybrid model structure based on a physics-informed neural network (PINN) to capture the dynamics of the process kinetics and predict the substrate concentrations in SdAD. In this study, we evaluated the model through numerical experiments and applied it to real case studies involving batch and continuous-flow reactor scenarios. By leveraging the PINN approach, the hybrid model yielded accurate predictions at both the state (i.e. substrate concentration) and kinetic levels in the numerical experiments and performed better than both mechanistic and purely data-driven models in the case studies. Furthermore, we used the trained hybrid model to design control strategies for SdAD and a novel integrated process involving SdAD and anammox for energy-efficient nitrogen removal. Finally, we discuss the advantages and application scope of the PINN-based hybrid model.
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Affiliation(s)
- Xu Zou
- Department of Civil and Environmental Engineering, Water Technology Center, Hong Kong Branch of Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Hongxiao Guo
- Department of Civil and Environmental Engineering, Water Technology Center, Hong Kong Branch of Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Chukuan Jiang
- Department of Civil and Environmental Engineering, Water Technology Center, Hong Kong Branch of Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Duc Viet Nguyen
- Centre for Environmental and Energy Research, Ghent University Global Campus, Incheon, Republic of Korea; Department of Green Chemistry and Technology, Centre for Advanced Process Technology for Urban REsource recovery (CAPTURE), Ghent University, Ghent, Belgium
| | - Guang-Hao Chen
- Department of Civil and Environmental Engineering, Water Technology Center, Hong Kong Branch of Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, The Hong Kong University of Science and Technology, Hong Kong, China.
| | - Di Wu
- Department of Civil and Environmental Engineering, Water Technology Center, Hong Kong Branch of Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, The Hong Kong University of Science and Technology, Hong Kong, China; Centre for Environmental and Energy Research, Ghent University Global Campus, Incheon, Republic of Korea; Department of Green Chemistry and Technology, Centre for Advanced Process Technology for Urban REsource recovery (CAPTURE), Ghent University, Ghent, Belgium.
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25
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Lu H, Wang H, Wu Q, Luo H, Zhao Q, Liu B, Si Q, Zheng S, Guo W, Ren N. Automatic control and optimal operation for greenhouse gas mitigation in sustainable wastewater treatment plants: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 855:158849. [PMID: 36122730 DOI: 10.1016/j.scitotenv.2022.158849] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/01/2022] [Accepted: 09/14/2022] [Indexed: 06/15/2023]
Abstract
In order to promote low-carbon sustainable operational management of the wastewater treatment plants (WWTPs), automatic control and optimal operation technologies, which devote to improving effluent quality, operational costs and greenhouse gas (GHG) emissions, have flourished in recent years. There is no consensus on the design procedure for optimal control/operation of sustainable WWTPs. In this review, we summarize recent researches on developing control and optimization strategies for GHG mitigation in WWTPs. Faced with the fact that direct carbon dioxide (CO2) emissions (considered biological origin) are generally not included in the carbon footprint of WWTPs, direct emissions (nitrous oxide (N2O), methane (CH4)) and indirect emissions are paid much attention. Firstly, the plant-wide models with GHG dynamic simulation, which are employed to design and evaluate the automatic control schemes as well as representative studies on identifying key factors affecting GHG emissions or comprehensive performance are outlined. Then, both traditional and advanced control methods commonly used in GHG mitigation are reviewed in detail, followed by the multi-objective optimization practices of control/operational parameters. Based on the mentioned control and (or) optimization strategies, a novel design framework for the optimal control/operation of sustainable WWTPs is proposed. The findings and design framework proposed in the paper will provide guidance for GHG mitigation and sustainable operation in WWTPs. It is foreseeable that more accurate and appropriate plant-wide models together with flexible control methods and intelligent optimization strategies will be developed to satisfy the upgrading requirements of WWTPs in the future.
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Affiliation(s)
- Hao Lu
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Huazhe Wang
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Qinglian Wu
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Haichao Luo
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Qi Zhao
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Banghai Liu
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Qishi Si
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Shanshan Zheng
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Wanqian Guo
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China.
| | - Nanqi Ren
- State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China
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26
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Yao H, Gao X, Guo J, Wang H, Zhang L, Fan L, Jia F, Guo J, Peng Y. Contribution of nitrous oxide to the carbon footprint of full-scale wastewater treatment plants and mitigation strategies- a critical review. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 314:120295. [PMID: 36181929 DOI: 10.1016/j.envpol.2022.120295] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 08/27/2022] [Accepted: 09/24/2022] [Indexed: 06/16/2023]
Abstract
Nitrous oxide (N2O), a potent greenhouse gas, significantly contributes to the carbon footprint of wastewater treatment plants (WWTPs) and contributes significantly to global climate change and to the deterioration of the natural environment. Our understanding of N2O generation mechanisms has significantly improved in the last decade, but the development of effective N2O emission mitigation strategies has lagged owing to the complexity of parameter regulation, substandard monitoring activities, and inadequate policy criteria. Based on critically screened published studies on N2O control in full-scale WWTPs, this review elucidates N2O generation pathway identifications and emission mechanisms and summarizes the impact of N2O on the total carbon footprint of WWTPs. In particular, a linear relationship was established between N2O emission factors and total nitrogen removal efficiencies in WWTPs located in China. Promising N2O mitigation options were proposed, which focus on optimizing operating conditions and implementation of innovative treatment processes. Furthermore, the sustainable operation of WWTPs has been anticipated to convert WWTPs into absolute greenhouse gas reducers as a result of the refinement and improvement of on-site monitoring activities, mitigation mechanisms, regulation of operational parameters, modeling, and policies.
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Affiliation(s)
- Hong Yao
- Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard, School of Environment, Beijing Jiaotong University, Beijing, 100044, China.
| | - Xinyu Gao
- Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard, School of Environment, Beijing Jiaotong University, Beijing, 100044, China
| | - Jingbo Guo
- School of Civil Engineering and Architecture, Northeast Electric Power University, Jilin, 132012, China
| | - Hui Wang
- SINOPEC Research Institute of Petroleum Processing, Beijing, 100083, China
| | - Liang Zhang
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Engineering Research Center of Beijing, Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing, 100124, China
| | - Liru Fan
- Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard, School of Environment, Beijing Jiaotong University, Beijing, 100044, China
| | - Fangxu Jia
- Beijing Key Laboratory of Aqueous Typical Pollutants Control and Water Quality Safeguard, School of Environment, Beijing Jiaotong University, Beijing, 100044, China
| | - Jianhua Guo
- Advanced Water Management Centre, The University of Queensland, St. Lucia, Brisbane, QLD, 4072, Australia
| | - Yongzhen Peng
- National Engineering Laboratory for Advanced Municipal Wastewater Treatment and Reuse Technology, Engineering Research Center of Beijing, Key Laboratory of Beijing for Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing, 100124, China
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27
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Lin Z, Ma K, Yang Y. Nitrous Oxide Emission from Full-Scale Anammox-Driven Wastewater Treatment Systems. LIFE (BASEL, SWITZERLAND) 2022; 12:life12070971. [PMID: 35888061 PMCID: PMC9317218 DOI: 10.3390/life12070971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 06/16/2022] [Accepted: 06/27/2022] [Indexed: 11/23/2022]
Abstract
Wastewater treatment plants (WWTPs) are important contributors to global greenhouse gas (GHG) emissions, partly due to their huge emission of nitrous oxide (N2O), which has a global warming potential of 298 CO2 equivalents. Anaerobic ammonium-oxidizing (anammox) bacteria provide a shortcut in the nitrogen removal pathway by directly transforming ammonium and nitrite to nitrogen gas (N2). Due to its energy efficiency, the anammox-driven treatment has been applied worldwide for the removal of inorganic nitrogen from ammonium-rich wastewater. Although direct evidence of the metabolic production of N2O by anammox bacteria is lacking, the microorganisms coexisting in anammox-driven WWTPs could produce a considerable amount of N2O and hence affect the sustainability of wastewater treatment. Thus, N2O emission is still one of the downsides of anammox-driven wastewater treatment, and efforts are required to understand the mechanisms of N2O emission from anammox-driven WWTPs using different nitrogen removal strategies and develop effective mitigation strategies. Here, three main N2O production processes, namely, hydroxylamine oxidation, nitrifier denitrification, and heterotrophic denitrification, and the unique N2O consumption process termed nosZ-dominated N2O degradation, occurring in anammox-driven wastewater treatment systems, are summarized and discussed. The key factors influencing N2O emission and mitigation strategies are discussed in detail, and areas in which further research is urgently required are identified.
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