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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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2
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Majewski G, Szeląg B, Rogula-Kozłowska W, Rogula-Kopiec P, Brandyk A, Rybak J, Radziemska M, Liniauskiene E, Klik B. Machine learning analysis of PM1 impact on visibility with comprehensive sensitivity evaluation of concentration, composition, and meteorological factors. Sci Rep 2024; 14:16732. [PMID: 39030249 PMCID: PMC11271544 DOI: 10.1038/s41598-024-67576-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 07/12/2024] [Indexed: 07/21/2024] Open
Abstract
This study introduces a novel approach to visibility modelling, focusing on PM1 concentration, its chemical composition, and meteorological conditions in two distinct Polish cities, Zabrze and Warsaw. The analysis incorporates PM1 concentration measurements as well as its chemical composition and meteorological parameters, including visibility data collected during summer and winter measurement campaigns (120 samples in each city). The developed calculation procedure encompasses several key steps: formulating a visibility prediction model through machine learning, identifying data in clusters using unsupervised learning methods, and conducting global sensitivity analysis for each cluster. The multi-layer perceptron methods developed demonstrate high accuracy in predicting visibility, with R values of 0.90 for Warsaw and an RMSE of 1.52 km for Zabrze. Key findings reveal that air temperature and relative humidity significantly impact visibility, alongside PM1 concentration and specific heavy metals such as Rb, Vi, and Cd in Warsaw and Cr, Vi, and Mo in Zabrze. Cluster analysis underscores the localized and complex nature of visibility determinants, highlighting the substantial but previously underappreciated role of heavy metals. Integrating the k-means clustering and GSA methods emerges as a powerful tool for unravelling complex mechanisms of chemical compound changes in particulate matter and air, significantly influencing visibility development.
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Affiliation(s)
- Grzegorz Majewski
- Institute of Environmental Engineering, Warsaw University of Life Sciences, 02-776, Warsaw, Poland.
| | - Bartosz Szeląg
- Institute of Environmental Engineering, Warsaw University of Life Sciences, 02-776, Warsaw, Poland
| | | | - Patrycja Rogula-Kopiec
- Institute of Environmental Engineering, Polish Academy of Sciences, 41-819, Zabrze, Poland
| | - Andrzej Brandyk
- Institute of Environmental Engineering, Warsaw University of Life Sciences, 02-776, Warsaw, Poland
| | - Justyna Rybak
- Faculty of Environmental Engineering, Wrocław University of Science and Technology, 50-370, Wrocław, Poland
| | - Maja Radziemska
- Institute of Environmental Engineering, Warsaw University of Life Sciences, 02-776, Warsaw, Poland
| | - Ernesta Liniauskiene
- Department of Hydrotechnical Engineering, Faculty Environmental Engineering, Kaunas Forestry and Environmental Engineering University of Applied Sciences, 53101, Girionys, Kaunas, Lithuania
| | - Barbara Klik
- Institute of Environmental Engineering, Warsaw University of Life Sciences, 02-776, Warsaw, Poland.
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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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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: 10] [Impact Index Per Article: 10.0] [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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5
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Uri-Carreño N, Nielsen PH, Gernaey KV, Domingo-Félez C, Flores-Alsina X. Nitrous oxide emissions from two full-scale membrane-aerated biofilm reactors. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 908:168030. [PMID: 37890634 DOI: 10.1016/j.scitotenv.2023.168030] [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: 02/11/2023] [Revised: 10/12/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023]
Abstract
The upcoming change of legislation in some European countries where wastewater treatment facilities will start to be taxed based on direct greenhouse gas (GHG) emissions will force water utilities to take a closer look at nitrous oxide (N2O) production. In this study, we report for the first time N2O emissions from two full-scale size membrane aerated biofilm reactors (MABR) (R1, R2) from two different manufacturers treating municipal wastewater. N2O was monitored continuously for 12 months in both the MABR exhaust gas and liquid phase. Multivariate analysis was used to assess process performance. Results show that emission factors (EFN2O) for both R1 and R2 (0.88 ± 1.28 and 0.82 ± 0.86 %) were very similar to each other and below the standard value from the Intergovernmental Panel on Climate Change (IPCC) 2019 (1.6 %). More specifically, N2O was predominantly emitted in the MABR exhaust gas (NTRexh) and was strongly correlated to the ammonia/um load (NHx,load). Nevertheless, the implemented Oxidation Reduction Potential (ORP) control strategy increased the bulk contribution (NTRbulk), impacting the overall EFN2O. A thorough analysis of dynamic data reveals that the changes in the external aeration (EA)/loading rate patterns suggested by ORP control substantially impacted N2O mass transfer and biological production processes. It also suggests that NTRexh is mainly caused by ammonia-oxidizing organisms (AOO) activity, while ordinary heterotrophic organisms (OHO) are responsible for NTRbulk. Different methods for calculating EFN2O were compared, and results showed EFN2O would range from 0.6 to 5.5 depending on the assumptions made. Based on existing literature, a strong correlation between EFN2O and nitrogen loading rate (R2 = 0.73) was found for different technologies. Overall, an average EFN2O of 0.86 % N2O-N per N load was found with a nitrogen loading rate >200 g N m-3 d-1, which supports the hypothesis that MABR technology can achieve intensified biological nutrient removal without increasing N2O emissions.
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Affiliation(s)
- Nerea Uri-Carreño
- Vandcenter Syd A/S, Vandværksvej 7, Odense 5000, Denmark; Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads 228A, Kgs. Lyngby 2800, Denmark.
| | - Per H Nielsen
- Vandcenter Syd A/S, Vandværksvej 7, Odense 5000, Denmark
| | - Krist V Gernaey
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads 228A, Kgs. Lyngby 2800, Denmark
| | - Carlos Domingo-Félez
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads 228A, Kgs. Lyngby 2800, Denmark
| | - Xavier Flores-Alsina
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads 228A, Kgs. Lyngby 2800, Denmark
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6
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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: 5] [Impact Index Per Article: 5.0] [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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7
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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: 0] [Impact Index Per Article: 0] [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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8
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Li Y, Cheng X, Liu K, Yu Y, Zhou Y. A new method for identifying potential hazardous areas of heavy metal pollution in sediments. WATER RESEARCH 2022; 224:119065. [PMID: 36130454 DOI: 10.1016/j.watres.2022.119065] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/31/2022] [Accepted: 09/05/2022] [Indexed: 06/15/2023]
Abstract
The combined effect of pollution source discharge and sediment adsorption leads to the rapid enrichment of heavy metals and other pollutants in lake sediments, which poses a serious threat to the lake ecosystem. Accurately identifying the risk areas of heavy metals in sediments is the key to lake sediment pollution control. Taking Taihu Lake as the study area, combined with the ecological risk status of heavy metals in sediments, the spatial clustering characteristics of pollution sources and the clustering information of sediment attributes, a potential toxic risk area identification method based on sediment source aggregation class (SLISA-SCA) was established. Through the source analysis of heavy metals in sediments, heavy metals such as Cr, Mn, Cu and Zn in Taihu Lake sediments were identified to have originated from natural sources and were subsequently disturbed by human activities to a certain extent. Cd was found to be strongly affected by human activities, and almost all Taihu Lake sediments were affected to varying degrees. In addition, the anthropogenic sources of heavy metals show high concentration clustering characteristics in the lake bay. By K-means cluster analysis of sediment attributes, three significant differences were obtained, which were determined as potential high pollution risk areas, potential medium risk areas and potential low risk areas, and the proportions were 5.6%, 27.6% and 66.8%, respectively. The SLISA-SCA model established in this study, from the perspective of source sinks, comprehensively considers the risks caused by pollution sources and sediment attributes to sediments and divides Taihu Lake into five different risk control areas (high-risk control area, potential high-risk control area, potential risk control area, potential low-risk control area and low-risk control area). This study identified areas with different levels of heavy metal pollution in Taihu Lake sediments, proposes corresponding treatment measures, and provides a scientific and systematic method and technology for the pollution management of other river and lake sediments in the world.
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Affiliation(s)
- Yan Li
- Collaborative Innovation Center of Sustainable Forestry, College of forestry, Nanjing Forestry University, Nanjing, Jiangsu, China; Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai, China; Supported by State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, Jiangsu, China.
| | - Xinyu Cheng
- Collaborative Innovation Center of Sustainable Forestry, College of forestry, Nanjing Forestry University, Nanjing, Jiangsu, China
| | - Ke Liu
- School of Oceanography, Shanghai Jiao Tong University, Shanghai, China
| | - Ye Yu
- Collaborative Innovation Center of Sustainable Forestry, College of forestry, Nanjing Forestry University, Nanjing, Jiangsu, China
| | - Yujie Zhou
- School of Geography and Ocean Science, Nanjing University, 163 Xianlin Road, Nanjing, Jiangsu, China
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9
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Gutiérrez M, Ghirardini A, Borghesi M, Bonnini S, Pavlović DM, Verlicchi P. Removal of micropollutants using a membrane bioreactor coupled with powdered activated carbon - A statistical analysis approach. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 840:156557. [PMID: 35690191 DOI: 10.1016/j.scitotenv.2022.156557] [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: 04/03/2022] [Revised: 06/03/2022] [Accepted: 06/04/2022] [Indexed: 06/15/2023]
Abstract
The occurrence of micropollutants in wastewater is largely documented as well as the environmental risk posed by their residues in the aquatic environment. Many investigations have been carried out and plan to study and improve their removal efficiency in existing wastewater treatment plants. At the same time, efforts are being made to develop new technologies or upgrade existing ones to increase the removal of a selection of micropollutants. Due to the great variability in their chemical and physical properties, it would be advisable to find representative compounds or identify the factors which most influence the removal mechanisms under specific conditions. This study analyses the removal efficiencies of a great number of micropollutants in wastewater treated in a membrane bioreactor coupled with powdered activated carbon (PAC), which was the subject of a review article we have recently published. The main operational parameters (i.e. PAC dosage, PAC retention time and sludge retention time) and compound physico-chemical properties (i.e. octanol-water distribution coefficient, charge and molecular weight) were first selected on the basis of a dedicated screening step and then an attempt was carried out to clarify their influence on the removal of micropollutants from wastewater during its treatment. To this end, a statistical analysis, mainly based on exploratory methods (cluster analysis and principal component analysis) and regression analysis, was carried out to compare and discuss the different results published in the scientific literature included in the cited review article. It emerged, that, based on the collected dataset, micropollutant charge and LogDow seem to play the most important role in the removal mechanisms occurring in MBR coupled with PAC.
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Affiliation(s)
- Marina Gutiérrez
- Department of Engineering, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy; Department of Analytical Chemistry, Faculty of Chemical Engineering and Technology, University of Zagreb, Trg Marka Marulića 19, 10000 Zagreb, Croatia
| | - Andrea Ghirardini
- Department of Engineering, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy
| | - Michela Borghesi
- Department of Economics and Management, University of Ferrara, Via Voltapaletto 11, 44121 Ferrara, Italy
| | - Stefano Bonnini
- Department of Economics and Management, University of Ferrara, Via Voltapaletto 11, 44121 Ferrara, Italy
| | - Dragana Mutavdžić Pavlović
- Department of Analytical Chemistry, Faculty of Chemical Engineering and Technology, University of Zagreb, Trg Marka Marulića 19, 10000 Zagreb, Croatia
| | - Paola Verlicchi
- Department of Engineering, University of Ferrara, Via Saragat 1, 44122 Ferrara, Italy.
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10
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Abulimiti A, Wang X, Kang J, Li L, Wu D, Li Z, Piao Y, Ren N. The trade-off between N 2O emission and energy saving through aeration control based on dynamic simulation of full-scale WWTP. WATER RESEARCH 2022; 223:118961. [PMID: 35973249 DOI: 10.1016/j.watres.2022.118961] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 08/05/2022] [Accepted: 08/06/2022] [Indexed: 06/15/2023]
Abstract
This study investigated the trade-off between energy saving and N2O emission reduction of WWTP under the precise control of dissolved oxygen (DO) concentration through model simulation. A long-term dynamic model for full-scale WWTP GHG emissions was established and calibrated with monitored year-round hourly water quality data to quantify the annual GHG emissions from WWTP. Results showed that N2O dominated the direct emission, up to 76.1%, and the variability of N2O generation could better be revealed by dynamic simulation. Furthermore, GHG emissions of the WWTP were mainly contributed by electric energy, among which the blower consumes the most electricity. To reduce the electricity consumption of blowers, improve mechanical efficiency and reduce DO concentration should be considered. DO setting played a significant role in the N2O and CH4 emission, electricity consumption and effluent quality, which was challenging to balance. The ultralow-oxygen (0-1/0.2-1 mg/L) and low oxygen (1-2 mg/L) control strategies were proposed, and their effects on total GHG emissions and effluent water quality were discussed. If the anaerobic environment (DO<0.2 mg/L)could be avoided, the control frequency (high and low) of the DO set-point did not have a significant effect on the emissions of N2O and CH4 and the effluent quality. The ultralow-oxygen strategy (0.2-1 mg/L) with a high-frequency control strategy achieved the lowest GHG emissions under the current energy mix. However, by 2050, as the energy supply gets cleaner, the total GHG emissions of WWTPs with ultralow-oxygen aeration (0.2-1 mg/L) will exceed low-oxygen aeration by 3.6%-4.2%, as N2O dominates 61.6%. Therefore, considering the trade-off between N2O emission and energy saving in WWTP, ultralow-oxygen aeration is a transition scheme to cleaner energy.
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Affiliation(s)
- Aliya Abulimiti
- State key Laboratory of urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China; School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Xiuheng Wang
- State key Laboratory of urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China; School of Environment, Harbin Institute of Technology, Harbin 150090, China.
| | - Jinhao Kang
- State key Laboratory of urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China; School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Lanqing Li
- State key Laboratory of urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China; School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Dan Wu
- Longjiang Environmental Protection Group Co., Ltd, Harbin 150090, China
| | - Zhe Li
- Longjiang Environmental Protection Group Co., Ltd, Harbin 150090, China
| | - Yitong Piao
- Beijing SequoiaLibra Technology Development Co., Ltd, Beijing 100000, China
| | - Nanqi Ren
- State key Laboratory of urban Water Resource and Environment, Harbin Institute of Technology, Harbin 150090, China; School of Environment, Harbin Institute of Technology, Harbin 150090, China
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11
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Valk LC, Peces M, Singleton CM, Laursen MD, Andersen MH, Mielczarek AT, Nielsen PH. Exploring the microbial influence on seasonal nitrous oxide concentration in a full-scale wastewater treatment plant using metagenome assembled genomes. WATER RESEARCH 2022; 219:118563. [PMID: 35594748 DOI: 10.1016/j.watres.2022.118563] [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: 02/11/2022] [Revised: 05/05/2022] [Accepted: 05/06/2022] [Indexed: 06/15/2023]
Abstract
Nitrous oxide is a highly potent greenhouse gas and one of the main contributors to the greenhouse gas footprint of wastewater treatment plants (WWTP). Although nitrous oxide can be produced by abiotic reactions in these systems, biological N2O production resulting from the imbalance of nitrous oxide production and reduction by microbial populations is the dominant cause. The microbial populations responsible for the imbalance have not been clearly identified, yet they are likely responsible for strong seasonal nitrous oxide patterns. Here, we examined the seasonal nitrous oxide concentration pattern in Avedøre WWTP alongside abiotic parameters, the microbial community composition based on 16S rRNA gene sequencing and already available metagenome-assembled genomes (MAGs). We found that the WWTP parameters could not explain the observed pattern. While no distinct community changes between periods of high and low dissolved nitrous oxide concentrations were determined, we found 26 and 28 species with positive and negative correlations to the seasonal N2O concentrations, respectively. MAGs were identified for 124 species (approximately 31% mean relative abundance of the community), and analysis of their genomic nitrogen transformation potential could explain this correlation for four of the negatively correlated species. Other abundant species were also analysed for their nitrogen transformation potential. Interestingly, only one full-denitrifier (Candidatus Dechloromonas phosphorivorans) was identified. 59 species had a nosZ gene predicted, with the majority identified as a clade II nosZ gene, mainly from the phylum Bacteroidota. A correlation of MAG-derived functional guilds with the N2O concentration pattern showed that there was a small but significant negative correlation with nitrite oxidizing bacteria and species with a nosZ gene (N2O reducers (DEN)). More research is required, specifically long-term activity measurements in relation to the N2O concentration to increase the resolution of these findings.
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Affiliation(s)
- Laura Christina Valk
- Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, 9220 Aalborg, Denmark
| | - Miriam Peces
- Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, 9220 Aalborg, Denmark
| | - Caitlin Margaret Singleton
- Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, 9220 Aalborg, Denmark
| | - Mads Dyring Laursen
- Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, 9220 Aalborg, Denmark
| | | | | | - Per Halkjær Nielsen
- Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Fredrik Bajers Vej 7H, 9220 Aalborg, Denmark.
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12
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Liu X, Lu D, Zhang A, Liu Q, Jiang G. Data-Driven Machine Learning in Environmental Pollution: Gains and Problems. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:2124-2133. [PMID: 35084840 DOI: 10.1021/acs.est.1c06157] [Citation(s) in RCA: 95] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The complexity and dynamics of the environment make it extremely difficult to directly predict and trace the temporal and spatial changes in pollution. In the past decade, the unprecedented accumulation of data, the development of high-performance computing power, and the rise of diverse machine learning (ML) methods provide new opportunities for environmental pollution research. The ML methodology has been used in satellite data processing to obtain ground-level concentrations of atmospheric pollutants, pollution source apportionment, and spatial distribution modeling of water pollutants. However, unlike the active practices of ML in chemical toxicity prediction, advanced algorithms such as deep neural networks in environmental process studies of pollutants are still deficient. In addition, over 40% of the environmental applications of ML go to air pollution, and its application range and acceptance in other aspects of environmental science remain to be increased. The use of ML methods to revolutionize environmental science and its problem-solving scenarios has its own challenges. Several issues should be taken into consideration, such as the tradeoff between model performance and interpretability, prerequisites of the machine learning model, model selection, and data sharing.
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Affiliation(s)
- Xian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, People's Republic of China
| | - Dawei Lu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, People's Republic of China
| | - Aiqian Zhang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, People's Republic of China
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, People's Republic of China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Institute of Environment and Health, Jianghan University, Wuhan 430056, People's Republic of China
| | - Qian Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, People's Republic of China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Institute of Environment and Health, Jianghan University, Wuhan 430056, People's Republic of China
| | - Guibin Jiang
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, People's Republic of China
- School of Environment, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310012, People's Republic of China
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13
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Gruber W, von Känel L, Vogt L, Luck M, Biolley L, Feller K, Moosmann A, Krähenbühl N, Kipf M, Loosli R, Vogel M, Morgenroth E, Braun D, Joss A. Estimation of countrywide N 2O emissions from wastewater treatment in Switzerland using long-term monitoring data. WATER RESEARCH X 2021; 13:100122. [PMID: 34661091 PMCID: PMC8503907 DOI: 10.1016/j.wroa.2021.100122] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 05/21/2023]
Abstract
Nitrous oxides (N2O) emissions contribute to climate change and stratospheric ozone depletion. Wastewater treatment is an important, yet likely underestimated, source of N2O emissions, as recent, long-term monitoring campaigns have demonstrated. However, the available data are insufficient to representatively estimate countrywide emission due to the brevity of most monitoring campaigns. This study showed that the emission estimates can be significantly improved using an advanced approach based on multiple continuous, long-term monitoring campaigns. In monitoring studies on 14 full-scale wastewater treatment plants (WWTPs), we found a strong variability in the yearly emission factors (EFs) (0.1 to 8% of the incoming nitrogen load) which exhibited a good correlation with effluent nitrite. But countrywide data on nitrite effluent concentrations is very limited and unavailable for emission estimation in many countries. Hence, we propose a countrywide emission factor calculated from the weighted EFs of three WWTP categories (carbon removal, EF: 0.1-8%, nitrification only: 1.8%, and full nitrogen removal: 0.9%). However, EF of carbon removal WWTPs are still highly uncertain given the expected variability in performance. The newly developed approach allows representative, country-specific estimations of the N2O emissions from WWTP. Applied to Switzerland, the estimations result in an average EF of 0.9 to 3.6% and total emissions of 410 to 1690 tN2O-N/year, which corresponds to 0.3-1.4% of the total greenhouse gas emissions in Switzerland. Our results demonstrate that better data availability and an improved understanding of long-term monitoring campaigns is crucial to improve current emission estimations. Finally, our results confirm several measures to mitigate N2O emissions from wastewater treatment; year-round denitrification, limiting nitrite accumulation, and stringent control of sludge age in carbon removal plants.
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Affiliation(s)
- Wenzel Gruber
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
- Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Luzia von Känel
- Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Liliane Vogt
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
- Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Manuel Luck
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
- Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Lucien Biolley
- Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Kilian Feller
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
| | - Andrin Moosmann
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
| | - Nikita Krähenbühl
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
| | - Marco Kipf
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
| | - Reto Loosli
- Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Michael Vogel
- Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Eberhard Morgenroth
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
- Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Daniel Braun
- Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Adriano Joss
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
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14
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Sheng D, Bu L, Zhu S, Wu Y, Wang J, Zhou S. Organic chloramines formation from algal organic matters: Insights from Fourier transform-ion cyclotron resonance mass spectrometry. WATER RESEARCH 2021; 206:117746. [PMID: 34678699 DOI: 10.1016/j.watres.2021.117746] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 10/04/2021] [Accepted: 10/06/2021] [Indexed: 06/13/2023]
Abstract
Release of algal organic matter (AOM) from algae poses great threats to drinking water safety. As organic nitrogen in AOM is relatively higher compared to natural organic matter (NOM), the organic chloramine formation during chlorination cause overestimation of effective chlorine, which may lead to a biological risk. This study compared the organic chloramine formation from AOM and NOM, and confirmed that AOM tend to form more organic chloramines during chlorination. Furthermore, it was found that hydrophilic fraction and high molecular weight (>100 kDa) fraction of AOM generated major organic chloramines due to a high content of protein. Based on the results of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), Spearman's rank correlation was used to analyze the relationship between molecular composition of AOM and organic chloramine formation. Notably, molecules with high correlation to organic chloramine formation located in a triangle region of van Krevelen diagram, which is a typical area of peptides. Therefore, it indicates that the precursors of organic chloramine in AOM are mainly proteins/peptides, and appropriate treatment processes (e.g., biological treatment or membrane filtration) should be addressed to effectively remove the precursors before chlorination.
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Affiliation(s)
- Da Sheng
- Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Department of Water Engineering and Science, College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Lingjun Bu
- Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Department of Water Engineering and Science, College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Shumin Zhu
- Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Department of Water Engineering and Science, College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Yangtao Wu
- Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Department of Water Engineering and Science, College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Jue Wang
- Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Department of Water Engineering and Science, College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Shiqing Zhou
- Key Laboratory of Building Safety and Energy Efficiency, Ministry of Education, Department of Water Engineering and Science, College of Civil Engineering, Hunan University, Changsha, Hunan 410082, China.
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15
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Uri-Carreño N, Nielsen PH, Gernaey KV, Flores-Alsina X. Long-term operation assessment of a full-scale membrane-aerated biofilm reactor under Nordic conditions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 779:146366. [PMID: 33752004 DOI: 10.1016/j.scitotenv.2021.146366] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 03/05/2021] [Accepted: 03/05/2021] [Indexed: 06/12/2023]
Abstract
Membrane-aerated biofilm reactor (MABR) technology is an exciting alternative to conventional activated sludge, with promising results in bench and pilot-scale systems. Nevertheless, there is still a lack of long-term and full-scale data under different operational conditions. This study aims to report the performance of a full-scale hybrid MABR located in the North of Europe. Influent, effluent, and exhaust data were collected for 1 year (September 2019 to September 2020) using online sensors/gas-analyzers and off-line laboratory analysis. Next, oxygen transfer rate (OTR), oxygen transfer efficiency (OTE), and nitrification rates (NR) were quantified as process indicators. Finally, multivariate methods were used to find patterns among monitored variables. Observations revealed that lower airflows achieved higher OTE at the same values of OTR and OTR was strongly correlated to ammonia/um concentration in the MABR tank (NHx,eff). The dynamics between oxygen concentration in the exhaust (O2,exh) and NHx,eff indicated that a nitrifying biofilm was established within 3 weeks. Average NR were calculated using four different methods and ranged between 1 and 2 g N m-2d-1. Principal component analysis (PCA) explained 81.4% of the sample variance with the first three components and cluster analysis (CA) divided the yearly data into five distinctive periods. Hence, it was possible to identify typical Nordic episodes with high frequency of heavy rain, low temperature, and high variations in pollution load. The study concludes that nitrification capacity obtained with MABR is robust during cold weather conditions, and its volumetric value is comparable to other well-established biofilm-based technologies. Moreover, the aeration efficiency (AE) obtained in this study, 5.8 kg O2 kW h-1, would suppose an average reduction in energy consumption of 55% compared to fine pore diffused aeration and 74% to the existing surface aeration at the facility.
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Affiliation(s)
- Nerea Uri-Carreño
- Vandcenter Syd A/S, Vandværksvej 7, Odense 5000, Denmark; Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads 228A, Kgs. Lyngby 2800, Denmark.
| | - Per H Nielsen
- Vandcenter Syd A/S, Vandværksvej 7, Odense 5000, Denmark
| | - Krist V Gernaey
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads 228A, Kgs. Lyngby 2800, Denmark
| | - Xavier Flores-Alsina
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads 228A, Kgs. Lyngby 2800, Denmark
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16
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Stentoft PA, Munk-Nielsen T, Møller JK, Madsen H, Valverde-Pérez B, Mikkelsen PS, Vezzaro L. Prioritize effluent quality, operational costs or global warming? - Using predictive control of wastewater aeration for flexible management of objectives in WRRFs. WATER RESEARCH 2021; 196:116960. [PMID: 33740729 DOI: 10.1016/j.watres.2021.116960] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 02/19/2021] [Accepted: 02/20/2021] [Indexed: 06/12/2023]
Abstract
This study presents a general model predictive control (MPC) algorithm for optimizing wastewater aeration in Water Resource Recovery Facilities (WRRF) under different management objectives. The flexibility of the MPC is demonstrated by controlling a WRRF under four management objectives, aiming at minimizing: (A) effluent concentrations, (B) electricity consumption, (C) total operations costs (sum electricity costs and discharge effluent tax) or (D) global warming potential (direct and indirect nitrous oxide emissions, and indirect from electricity production) . The MPC is tested with data from the alternating WRRF in Nørre Snede (Denmark) and from the Danish electricity grid. Results showed how the four control objectives resulted in important differences in aeration patterns and in the concentration dynamics over a day. Controls B and C showed similarities when looking at total costs, while similarities in global warming potential for controls A and D suggest that improving effluent quality also reduced greenhouse gasses emissions. The MPC flexibility in handling different objectives is shown by using a combined objective function, optimizing both cost and greenhouse emissions. This shows the trade-off between the two objectives, enabling the calculation of marginal costs and thus allowing WRRF operators to carefully evaluate prioritization of management objectives. The long-term MPC performance is evaluated over 51 days covering seasonal and inter-weekly variations. On a daily basis, control A was 9-30% cheaper on average compared to controls A, D and to the current rule-based control. Similarly, control D resulted on average in 35-43% lower greenhouse gasses daily emission compared to the other controls. Difference between control performance increased for days with greater inter-diurnal variations in electricity price or greenhouse emissions from electricity production, i.e. when MPC has greater possibilities for exploiting input variations. The flexibility of the proposed MPC can easily accommodate for additional control objectives, allowing WRRF operators to quickly adapt the plant operation to new management objectives and to face new performance requirements.
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Affiliation(s)
- P A Stentoft
- Krüger A/S, Veolia Water Technologies, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.
| | | | - J K Møller
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.
| | - H Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.
| | - B Valverde-Pérez
- Department of Environmental Engineering, Technical University of Denmark, Denmark.
| | - P S Mikkelsen
- Department of Environmental Engineering, Technical University of Denmark, Denmark.
| | - L Vezzaro
- Krüger A/S, Veolia Water Technologies, Denmark; Department of Environmental Engineering, Technical University of Denmark, Denmark.
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17
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Gruber W, Niederdorfer R, Ringwald J, Morgenroth E, Bürgmann H, Joss A. Linking seasonal N 2O emissions and nitrification failures to microbial dynamics in a SBR wastewater treatment plant. WATER RESEARCH X 2021; 11:100098. [PMID: 33889832 PMCID: PMC8050800 DOI: 10.1016/j.wroa.2021.100098] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 03/17/2021] [Accepted: 03/21/2021] [Indexed: 05/04/2023]
Abstract
Nitrous oxide (N2O) is a strong greenhouse gas and causal for stratospheric ozone depletion. During biological nitrogen removal in wastewater treatment plants (WWTP), high N2O fluxes to the atmosphere can occur, typically exhibiting a seasonal emission pattern. Attempts to explain the peak emission phases in winter and spring using physico-chemical process data from WWTP were so far unsuccessful and new approaches are required. The complex and diverse microbial community of activated sludge used in biological treatment systems also exhibit substantial seasonal patterns. However, a potentially causal link between the seasonal patterns of microbial diversity and N2O emissions has not yet been investigated. Here we show that in a full-scale WWTP nitrification failure and N2O peak emissions, bad settleability of the activated sludge and a turbid effluent strongly correlate with a significant reduction in the microbial community diversity and shifts in community composition. During episodes of impaired performance, we observed a significant reduction in abundance for filamentous and nitrite oxidizing bacteria in all affected reactors. In some reactors that did not exhibit nitrification and settling failures, we observed a stable microbial community and no drastic loss of species. Standard engineering approaches to stabilize nitrification, such as increasing the aerobic sludge age and oxygen availability failed to improve the plant performance on this particular WWTP and replacing the activated sludge was the only measure applied by the operators to recover treatment performance in affected reactors. Our results demonstrate that disturbances of the sludge microbiome affect key structural and functional microbial groups, which lead to seasonal N2O emission patterns. To reduce N2O emissions from WWTP, it is therefore crucial to understand the drivers that lead to the microbial population dynamics in the activated sludge.
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Affiliation(s)
- Wenzel Gruber
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
- Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Robert Niederdorfer
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 6047 Kastanienbaum, Switzerland
| | - Jörg Ringwald
- ARA Jungholz, Seestrasse 171, 8610 Uster, Switzerland
| | - Eberhard Morgenroth
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
- Institute of Environmental Engineering, ETH Zürich, 8093 Zürich, Switzerland
| | - Helmut Bürgmann
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 6047 Kastanienbaum, Switzerland
| | - Adriano Joss
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Duebendorf, Switzerland
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18
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Wang Q, Mao X, Jiang X, Pei D, Shao X. Digital image processing technology under backpropagation neural network and K-Means Clustering algorithm on nitrogen utilization rate of Chinese cabbages. PLoS One 2021; 16:e0248923. [PMID: 33788875 PMCID: PMC8011815 DOI: 10.1371/journal.pone.0248923] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 03/08/2021] [Indexed: 11/18/2022] Open
Abstract
The purposes are to monitor the nitrogen utilization efficiency of crops and intelligently evaluate the absorption of nutrients by crops during the production process. The research object is Chinese cabbage. The Chinese cabbage population with different agricultural parameters is constructed through different densities and nitrogen fertilizer application rates based on digital image processing technology, and an estimation NC (Nitrogen Content) model is established. The population is classified through the K-Means Clustering algorithm using the feature extraction method, and the Chinese cabbage population quality BPNN (Backpropagation Neural Network) model is constructed. The nonlinear mapping relationship between different agricultural parameters and population quality, and the contribution rate of each indicator, are studied. The nitrogen utilization of Chinese cabbage is monitored effectively. Results demonstrate that the proposed NC estimation model has correlation coefficients above 0.70 in different growth stages. This model can accurately estimate the NC of the Chinese cabbage population. The results of the Chinese cabbage population quality BPNN model show that the population planting density based on the seedling number is reasonable. The constructed population quality evaluation model has a high R2 value and a comparatively low RMSE (Root Mean Square Error) value for the quality evaluation of Chinese cabbage in different periods, showing that it applies to evaluate the population quality of Chinese cabbage in different growth stages. The constructed nitrogen utilization model and quality evaluation model can monitor the nutrient utilization of crops in different growth stages, ascertain the agricultural characteristics of other yield groups in different growth stages, and clarify the performance of agricultural parameters in different growth stages. The above results can provide some ideas for crop growth intelligent detection.
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Affiliation(s)
- Qilin Wang
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
| | - Xinyu Mao
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
| | - Xiaosan Jiang
- Taizhou Research Institute of Nanjing Agricultural University, Taizhou, China
| | - Dandan Pei
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
| | - Xiaohou Shao
- College of Agricultural Science and Engineering, Hohai University, Nanjing, China
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19
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Hwangbo S, Al R, Chen X, Sin G. Integrated Model for Understanding N 2O Emissions from Wastewater Treatment Plants: A Deep Learning Approach. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:2143-2151. [PMID: 33432810 DOI: 10.1021/acs.est.0c05231] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
This study aims to demonstrate the application of deep learning to quantitatively describe long-term full-scale data observed from wastewater treatment plants (WWTPs) from the perspectives of process modeling, process analysis, and forecasting modeling. Approximately, 750,000 measurements including the influent flow rate, air flow rate, temperature, ammonium, nitrate, dissolved oxygen, and nitrous oxide (N2O) collected for more than a year from the Avedøre WWTP located in Denmark are utilized to develop a deep neural network (DNN) through supervised learning for process modeling, and the optimal DNN (R2 > 0.90) is selected for further evaluation. For process analysis, global sensitivity analysis based on variance decomposition is considered to identify the key parameters contributing to high N2O emission characteristics. For N2O forecasting, the proposed DNN-based model is compared with long short-term memory (LSTM), showing that the LSTM-based forecasting model performs significantly better than the DNN-based model (R2 > 0.94 and the root-mean-squared error is reduced by 64%). The results account for the feasibility of data-driven methods based on deep learning for quantitatively describing and understanding the rather complex N2O dynamics in WWTPs. Research into hybrid modeling concepts integrating mechanistic models of WWTPs (e.g., ASMs) with deep learning would be suggested as a future direction for monitoring N2O emissions from WWTPs.
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Affiliation(s)
- Soonho Hwangbo
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, 2800 Kgs. Lyngby, Denmark
- Department of Chemical Engineering, Gyeongsang National University, 501, Jinju-daero, Jinju-si, Gyeongsangnam-do 52828, South Korea
| | - Resul Al
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, 2800 Kgs. Lyngby, Denmark
| | - Xueming Chen
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, 2800 Kgs. Lyngby, Denmark
| | - Gürkan Sin
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering, Technical University of Denmark, Building 229, 2800 Kgs. Lyngby, Denmark
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Liao K, Hu H, Ren H. Combined influences of process parameters on microorganism-derived dissolved organic nitrogen (mDON) formation at low temperatures: Multivariable statistical and systematic analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 744:140732. [PMID: 32711305 DOI: 10.1016/j.scitotenv.2020.140732] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/29/2020] [Accepted: 07/02/2020] [Indexed: 06/11/2023]
Abstract
Regulation of process parameters is a cost-effective approach to control microorganism-derived dissolved organic nitrogen (mDON) formation in low-temperature biological wastewater conditions. However, the integrated influence of multiple parameters in this process is poorly defined. In this study, mathematical methodology was used to evaluate the combined effects of hydraulic retention time (HRT), solids retention time (SRT), and mixed liquor suspended solids (MLSS) on mDON formation at 8 °C. This study also systematically explored how multiple combinations of those three parameters affected mDON chemodiversity (fluorescent properties and molecular compositions), microbial compositions, and specific relationships between mDON molecules and microbial species in activated sludge systems. Results showed that combined effects significantly controlled the mDON formation at 8 °C (P < .05). The systematic analysis suggested that the multi-parameter effects modulated the distribution of different mDON compositions and shaped the microbial communities. Most bacterial phyla as the generalist and a few as the specialist were displayed in 2487 pairs of strong microbe-mDON connections (|r| ≥ 0.6, P < .05). Moreover, network analysis on microbe-mDON relationships identified the network centers as crucial media in terms of combined effects of process parameters on mDON formation. Our results provide comprehensive insight on the roles of multi-parameter covariation influences in regulating the high complexity of mDON traits and microbe-mDON linkages, thereby highlighting the necessity to focus on the combined effects of process parameters for effective and correct controlling strategies on mDON concentrations.
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Affiliation(s)
- Kewei Liao
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
| | - Haidong Hu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China
| | - Hongqiang Ren
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, China.
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21
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Feng C, Li Z, Zhu Y, Xu D, Geng J, Ren H, Xu K. Effect of magnetic powder on nitrous oxide emissions from a sequencing batch reactor for treating domestic wastewater at low temperatures. BIORESOURCE TECHNOLOGY 2020; 315:123848. [PMID: 32707505 DOI: 10.1016/j.biortech.2020.123848] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 07/10/2020] [Accepted: 07/11/2020] [Indexed: 06/11/2023]
Abstract
Low temperatures can lead to an increase of N2O generation and emission from the nitrogen removal process in wastewater treatment plants. This study investigated the effect of the addition of magnetic powder on N2O generation and emission from a sequencing batch reactor treating domestic sewage at low temperatures. The results showed that the magnetic powder simultaneously inhibited N2O generation and emission and improved the removal of NH4+, total nitrogen (TN), and chemical oxygen demand at low temperatures. Furthermore, the conversion rate of N2O (N2O generation to TN removal) was reduced. The efficacy of the magnetic powder depended on its concentration, which could be ordered as 1 mg/L > 2 mg/L > 4 mg/L. With the addition of magnetic powder, especially at the 1 mg/L level, the activities of nitrification and denitrification enzymes in activated sludge were significantly improved and the growth of ammonium and nitrite oxidizing bacteria was also promoted.
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Affiliation(s)
- Chuanwen Feng
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Zhihao Li
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Yuanmo Zhu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Dan Xu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Jinju Geng
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Hongqiang Ren
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Ke Xu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China.
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22
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Song MJ, Choi S, Bae WB, Lee J, Han H, Kim DD, Kwon M, Myung J, Kim YM, Yoon S. Identification of primary effecters of N 2O emissions from full-scale biological nitrogen removal systems using random forest approach. WATER RESEARCH 2020; 184:116144. [PMID: 32731040 DOI: 10.1016/j.watres.2020.116144] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 06/30/2020] [Accepted: 07/02/2020] [Indexed: 06/11/2023]
Abstract
Wastewater treatment plants (WWTPs) have long been recognized as point sources of N2O, a potent greenhouse gas and ozone-depleting agent. Multiple mechanisms, both biotic and abiotic, have been suggested to be responsible for N2O production from WWTPs, with basis on extrapolation from laboratory results and statistical analyses of metadata collected from operational full-scale plants. In this study, random forest (RF) analysis, a machine-learning approach for feature selection from highly multivariate datasets, was adopted to investigate N2O production mechanism in activated sludge tanks of WWTPs from a novel perspective. Standardized measurements of N2O effluxes coupled with exhaustive metadata collection were performed at activated sludge tanks of three biological nitrogen removal WWTPs at different times of the year. The multivariate datasets were used as inputs for RF analyses. Computation of the permutation variable importance measures returned biomass-normalized dissolved inorganic carbon concentration (DIC·VSS-1) and specific ammonia oxidation activity (sOURAOB) as the most influential parameters determining N2O emissions from the aerated zones (or phases) of activated sludge bioreactors. For the anoxic tanks, dissolved-organic-carbon-to-NO2-/NO3- ratio (DOC·(NO2--N + NO3--N)-1) was singled out as the most influential. These data analysis results clearly indicate disparate mechanisms for N2O generation in the oxic and anoxic activated sludge bioreactors, and provide evidences against significant contributions of N2O carryover across different zones or phases or niche-specific microbial reactions, with aerobic NH3/NH4+ oxidation to NO2- and anoxic denitrification predominantly responsible from aerated and anoxic zones or phases of activated sludge bioreactors, respectively.
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Affiliation(s)
- Min Joon Song
- Department of Civil and Environmental Engineering, KAIST, Daejeon, 34141, Republic of Korea
| | - Sangki Choi
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
| | - Wo Bin Bae
- School of Earth Sciences and Environmental Engineering, Gwangju Institute of Science and Technology, Gwangju, 61005, Republic of Korea
| | - Jaejin Lee
- Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, 50011, United states
| | - Heejoo Han
- Department of Civil and Environmental Engineering, KAIST, Daejeon, 34141, Republic of Korea
| | - Daehyun D Kim
- Department of Civil and Environmental Engineering, KAIST, Daejeon, 34141, Republic of Korea
| | - Miye Kwon
- Department of Civil and Environmental Engineering, KAIST, Daejeon, 34141, Republic of Korea
| | - Jaewook Myung
- Department of Civil and Environmental Engineering, KAIST, Daejeon, 34141, Republic of Korea
| | - Young Mo Kim
- Department of Civil and Environmental Engineering, Hanyang University, Seongdong-gu, Seoul, 04763, Republic of Korea
| | - Sukhwan Yoon
- Department of Civil and Environmental Engineering, KAIST, Daejeon, 34141, Republic of Korea.
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23
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Chen H, Zeng L, Wang D, Zhou Y, Yang X. Recent advances in nitrous oxide production and mitigation in wastewater treatment. WATER RESEARCH 2020; 184:116168. [PMID: 32683143 DOI: 10.1016/j.watres.2020.116168] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/17/2020] [Accepted: 07/10/2020] [Indexed: 05/21/2023]
Abstract
Nitrous oxide (N2O) emitted from wastewater treatment plants has caused widespread concern. Over the past decade, people have made tremendous efforts to discover the microorganisms responsible for N2O production, elucidate metabolic pathways, establish production models and formulate mitigation strategies. The ultimate goal of all these efforts is to shed new light on how N2O is produced and how to reduce it, and one of the best ways is to find key opportunities by integrating the information obtained. This review article critically evaluates the knowledge gained in the field within a decade, especially in N2O production microbiology, biochemistry, models and mitigation strategies, with a focus on denitrification. Previous research has greatly deepened the understanding of the N2O generation mechanism, but further efforts are still needed due to the lack of standardized methodology for establishing N2O mitigation strategies in full-scale systems. One of the challenges seems to be to convert the denitrification process from a net N2O source into an effective sink, which is recommended as a key opportunity to reduce N2O production in this review.
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Affiliation(s)
- Hongbo Chen
- College of Environment and Resources, Xiangtan University, Xiangtan 411105, China.
| | - Long Zeng
- College of Environment and Resources, Xiangtan University, Xiangtan 411105, China
| | - Dongbo Wang
- College of Environmental Science and Engineering, Hunan University, Changsha 410082, China.
| | - Yaoyu Zhou
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xiao Yang
- Korea Biochar Research Center, O-Jeong Eco-Resilience Institute & Division of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, South Korea
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24
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Vasilaki V, Danishvar S, Mousavi A, Katsou E. Data-driven versus conventional N2O EF quantification methods in wastewater; how can we quantify reliable annual EFs? Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.106997] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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25
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Vasilaki V, Conca V, Frison N, Eusebi AL, Fatone F, Katsou E. A knowledge discovery framework to predict the N 2O emissions in the wastewater sector. WATER RESEARCH 2020; 178:115799. [PMID: 32361289 DOI: 10.1016/j.watres.2020.115799] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 03/04/2020] [Accepted: 04/03/2020] [Indexed: 06/11/2023]
Abstract
Data Analytics is being deployed to predict the dissolved nitrous oxide (N2O) concentration in a full-scale sidestream sequence batch reactor (SBR) treating the anaerobic supernatant. On average, the N2O emissions are equal to 7.6% of the NH4-N load and can contribute up to 97% to the operational carbon footprint of the studied nitritation-denitritation and via-nitrite enhanced biological phosphorus removal process (SCENA). The analysis showed that average aerobic dissolved N2O concentration could significantly vary under similar influent loads, dissolved oxygen (DO), pH and removal efficiencies. A combination of density-based clustering, support vector machine (SVM), and support vector regression (SVR) models were deployed to estimate the dissolved N2O concentration and behaviour in the different phases of the SBR system. The results of the study reveal that the aerobic dissolved N2O concentration is correlated with the drop of average aerobic conductivity rate (spearman correlation coefficient equal to 0.7), the DO (spearman correlation coefficient equal to -0.7) and the changes of conductivity between sequential cycles. Additionally, operational conditions resulting in low aerobic N2O accumulation (<0.6 mg/L) were identified; step-feeding, control of initial NH4+ concentrations and aeration duration can mitigate the N2O peaks observed in the system. The N2O emissions during aeration shows correlation with the stripping of accumulated N2O from the previous anoxic cycle. The analysis shows that N2O is always consumed after the depletion of NO2- during denitritation (after the "nitrite knee"). Based on these findings SVM classifiers were constructed to predict whether dissolved N2O will be consumed during the anoxic and anaerobic phases and SVR models were trained to predict the N2O concentration at the end of the anaerobic phase and the average dissolved N2O concentration during aeration. The proposed approach accurately predicts the N2O emissions as a latent parameter from other low-cost sensors that are traditionally deployed in biological batch processes.
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Affiliation(s)
- V Vasilaki
- Department of Civil & Environmental Engineering, Brunel University London, Uxbridge, UB8 3PH, UK
| | - V Conca
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134, Verona, Italy
| | - N Frison
- Department of Biotechnology, University of Verona, Strada Le Grazie 15, 37134, Verona, Italy
| | - A L Eusebi
- Department SIMAU, Faculty of Engineering, Polytechnic University of Marche, Via Brecce Bianche 12, Ancona, Italy
| | - F Fatone
- Department SIMAU, Faculty of Engineering, Polytechnic University of Marche, Via Brecce Bianche 12, Ancona, Italy
| | - E Katsou
- Department of Civil & Environmental Engineering, Brunel University London, Uxbridge, UB8 3PH, UK.
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26
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Schambeck CM, Magnus BS, de Souza LCR, Leite WRM, Derlon N, Guimarães LB, da Costa RHR. Biopolymers recovery: dynamics and characterization of alginate-like exopolymers in an aerobic granular sludge system treating municipal wastewater without sludge inoculum. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2020; 263:110394. [PMID: 32174534 DOI: 10.1016/j.jenvman.2020.110394] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/04/2020] [Accepted: 03/03/2020] [Indexed: 06/10/2023]
Abstract
Alginate-like exopolymers (ALE) are present in the extracellular polymeric substances (EPS) of biological sludge such as aerobic granular sludge (AGS). The recovery of ALE from excess sludge produced by wastewater treatment plants (WWTP) is a relevant approach for the recovery of valuable products of industrial interest. However, little is known about dynamics of ALE content in sludge and associated factors. Thus, this study aimed at assessing the dynamics of EPS and ALE in terms of content, some chemical properties and influencing environmental factors along granulation in a sequencing batch reactor treating municipal wastewater. Results indicated that the EPS content was not correlated with the development of AGS, while the ALE content was higher, more stable and steadily increased after granulation achievement. Overall, 236 ± 27 mg VSALE/g VSsludge was recovered from AGS and 187 ± 94 mg VSALE/g VSsludge from flocs. However, the lower ALE content in flocs may be compensated by the higher sludge production rate in activated sludge systems. Principal component analysis (PCA) revealed that ALE content positively correlates with the nutrient and organic substrate conversion, and with the fraction of large AGS. Microbial analyses indicated that a stable microbial community composition was associated with a higher and more stable ALE content. ALE recovered from both flocs and AGS was endowed with hydrogel property, and no clear difference in their elemental composition and functional groups was observed. Therefore, our study provides insights about quantitative and qualitative aspects of ALE which are helpful for the improvement of waste biological sludge valorization.
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Affiliation(s)
- Cássio Moraes Schambeck
- Federal University of Santa Catarina, Trindade University Campus, Sanitary and Environmental Engineering Department, Florianópolis, Brazil.
| | - Bruna Scandolara Magnus
- Federal University of Santa Catarina, Trindade University Campus, Sanitary and Environmental Engineering Department, Florianópolis, Brazil
| | - Laís Cristina Rozone de Souza
- Federal University of Santa Catarina, Trindade University Campus, Sanitary and Environmental Engineering Department, Florianópolis, Brazil
| | - Wanderli Rogério Moreira Leite
- Federal University of Pernambuco, Civil and Environmental Engineering Department, Laboratory of Environmental Sanitation, Recife, Brazil
| | - Nicolas Derlon
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600, Dübendorf, Switzerland
| | - Lorena Bittencourt Guimarães
- Federal University of Santa Catarina, Trindade University Campus, Sanitary and Environmental Engineering Department, Florianópolis, Brazil
| | - Rejane Helena Ribeiro da Costa
- Federal University of Santa Catarina, Trindade University Campus, Sanitary and Environmental Engineering Department, Florianópolis, Brazil
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Andrejiová M, Grincova A, Marasová D. Study of the Percentage of Greenhouse Gas Emissions from Aviation in the EU-27 Countries by Applying Multiple-Criteria Statistical Methods. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E3759. [PMID: 32466445 PMCID: PMC7313020 DOI: 10.3390/ijerph17113759] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 05/18/2020] [Accepted: 05/22/2020] [Indexed: 11/16/2022]
Abstract
The transport sector, including air transport, represents an important source of air pollution. The present article deals with the current situation regarding greenhouse gas emissions in the air in 27 European Union (EU-27) member states. Every member state is characterized by selected parameters that determine the unique nature of a particular country (e.g., population, area, life expectancy, gross domestic product (GDP) per capita, etc.). In addition to these parameters, there were also other parameters which were monitored as they characterize the amount of greenhouse gas emissions and the impact of aviation on these emissions. The main purpose of the article is to compare the European Union member states on the basis of 15 examined parameters. The identification of similarities between the EU-27 member states with regard to the selected parameters was carried out by applying principal component analysis (PCA) and hierarchical cluster analysis. The average linkage method was applied to create a dendrogram representing the similarities between the examined member states. The value of the cophenetic correlation coefficient CC = 0.923 confirmed the correct application of the average linkage method. The cluster analysis outputs were five similarity-based homogeneous groups (clusters) into which the 27 member states were divided on the basis of the examined variables.
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Affiliation(s)
- Miriam Andrejiová
- Faculty of Mechanical Engineering, Technical University of Kosice, 042 00 Kosice, Slovakia;
| | - Anna Grincova
- Faculty of Electrical Engineering and Informatics, Technical University of Kosice, 042 00 Kosice, Slovakia
| | - Daniela Marasová
- Faculty of Mining, Ecology, Process Control and Geotechnology, Technical University of Kosice, 042 00 Kosice, Slovakia
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28
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Foglia A, Akyol Ç, Frison N, Katsou E, Eusebi AL, Fatone F. Long-term operation of a pilot-scale anaerobic membrane bioreactor (AnMBR) treating high salinity low loaded municipal wastewater in real environment. Sep Purif Technol 2020. [DOI: 10.1016/j.seppur.2019.116279] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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29
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Chen X, Mielczarek AT, Habicht K, Andersen MH, Thornberg D, Sin G. Assessment of Full-Scale N 2O Emission Characteristics and Testing of Control Concepts in an Activated Sludge Wastewater Treatment Plant with Alternating Aerobic and Anoxic Phases. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2019; 53:12485-12494. [PMID: 31593443 DOI: 10.1021/acs.est.9b04889] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
This work aims to obtain full-scale N2O emission characteristics translatable into viable N2O control strategies and conduct full-scale testing of the proposed N2O control concepts. Data of a long-term monitoring campaign was first used to quantify full-scale N2O emission and probe into the seasonal pattern. Then trends between N2O production/emission and process variables/conditions during typical operating cycles were revealed to explore the dynamic N2O emission behavior. A multivariate statistical analysis was performed to find the dependency of N2O emission on relevant process variables. The results show for the first time that relatively low/high N2O emission took place in seasons with a decreasing/increasing trend of water temperature, respectively. Aerobic phase contributed to N2O production/emission probably mainly through the hydroxylamine pathway. Comparatively, heterotrophic bacteria had a dual role in the anoxic phase and could be responsible for both net N2O production and consumption. Incomplete denitrification might contribute mainly to the N2O production/emission in the anoxic phase and the accumulation of N2O to be significantly emitted in the following cycle due to the competition between different denitrification steps for electron donors. Therefore, properly extending the length of anoxic phase could serve as a potential control means to regulate N2O accumulation in the anoxic phase. The full-scale testing not only verified the efficacy of reduced dissolved oxygen set-point in reducing N2O emission by 60%, but also confirmed the proposed concepts of control over the aerobic and anoxic phases collectively.
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Affiliation(s)
- Xueming Chen
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering , Technical University of Denmark , 2800 Kgs. Lyngby , Denmark
| | | | - Kirsten Habicht
- Unisense Environment A/S , Tueager 1 , DK-8200 Aarhus N , Denmark
| | | | | | - Gürkan Sin
- Process and Systems Engineering Center (PROSYS), Department of Chemical and Biochemical Engineering , Technical University of Denmark , 2800 Kgs. Lyngby , Denmark
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30
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Vasilaki V, Massara TM, Stanchev P, Fatone F, Katsou E. A decade of nitrous oxide (N 2O) monitoring in full-scale wastewater treatment processes: A critical review. WATER RESEARCH 2019; 161:392-412. [PMID: 31226538 DOI: 10.1016/j.watres.2019.04.022] [Citation(s) in RCA: 88] [Impact Index Per Article: 17.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 04/09/2019] [Accepted: 04/10/2019] [Indexed: 06/09/2023]
Abstract
Direct nitrous oxide (N2O) emissions during the biological nitrogen removal (BNR) processes can significantly increase the carbon footprint of wastewater treatment plant (WWTP) operations. Recent onsite measurement of N2O emissions at WWTPs have been used as an alternative to the controversial theoretical methods for the N2O calculation. The full-scale N2O monitoring campaigns help to expand our knowledge on the N2O production pathways and the triggering operational conditions of processes. The accurate N2O monitoring could help to find better process control solutions to mitigate N2O emissions of wastewater treatment systems. However, quantifying the emissions and understanding the long-term behaviour of N2O fluxes in WWTPs remains challenging and costly. A review of the recent full-scale N2O monitoring campaigns is conducted. The analysis covers the quantification and mitigation of emissions for different process groups, focusing on techniques that have been applied for the identification of dominant N2O pathways and triggering operational conditions, techniques using operational data and N2O data to identify mitigation measures and mechanistic modelling. The analysis of various studies showed that there are still difficulties in the comparison of N2O emissions and the development of emission factor (EF) databases; the N2O fluxes reported in literature vary significantly even among groups of similar processes. The results indicated that the duration of the monitoring campaigns can impact the EF range. Most N2O monitoring campaigns lasting less than one month, have reported N2O EFs less than 0.3% of the N-load, whereas studies lasting over a year have a median EF equal to 1.7% of the N-load. The findings of the current study indicate that complex feature extraction and multivariate data mining methods can efficiently convert wastewater operational and N2O data into information, determine complex relationships within the available datasets and boost the long-term understanding of the N2O fluxes behaviour. The acquisition of reliable full-scale N2O monitoring data is significant for the calibration and validation of the mechanistic models -describing the N2O emission generation in WWTPs. They can be combined with the multivariate tools to further enhance the interpretation of the complicated full-scale N2O emission patterns. Finally, a gap between the identification of effective N2O mitigation strategies and their actual implementation within the monitoring and control of WWTPs has been identified. This study concludes that there is a further need for i) long-term N2O monitoring studies, ii) development of data-driven methodological approaches for the analysis of WWTP operational and N2O data, and iii) better understanding of the trade-offs among N2O emissions, energy consumption and system performance to support the optimization of the WWTPs operation.
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Affiliation(s)
- V Vasilaki
- Department of Civil & Environmental Engineering, Brunel University London, Uxbridge Campus, Middlesex, UB8 3PH, Uxbridge, UK
| | - T M Massara
- Institute of Environment, Health and Societies, Brunel University London, Uxbridge Campus, Middlesex, UB8 3PH, Uxbridge, UK
| | - P Stanchev
- Department of Civil & Environmental Engineering, Brunel University London, Uxbridge Campus, Middlesex, UB8 3PH, Uxbridge, UK
| | - F Fatone
- Department of Science and Engineering of Materials, Environment and City Planning, Faculty of Engineering, Polytechnic University of Marche, Ancona, Italy
| | - E Katsou
- Department of Civil & Environmental Engineering, Brunel University London, Uxbridge Campus, Middlesex, UB8 3PH, Uxbridge, UK; Institute of Environment, Health and Societies, Brunel University London, Uxbridge Campus, Middlesex, UB8 3PH, Uxbridge, UK.
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31
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Caniani D, Caivano M, Pascale R, Bianco G, Mancini IM, Masi S, Mazzone G, Firouzian M, Rosso D. CO 2 and N 2O from water resource recovery facilities: Evaluation of emissions from biological treatment, settling, disinfection, and receiving water body. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 648:1130-1140. [PMID: 30340259 DOI: 10.1016/j.scitotenv.2018.08.150] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Revised: 08/09/2018] [Accepted: 08/11/2018] [Indexed: 06/08/2023]
Abstract
Water resource recovery facilities (WRRFs) contribute to climate change and air pollution, as they are anthropogenic potential sources of direct and indirect emission of greenhouse gases (GHGs). Studies concerning the monitoring and accounting for GHG emissions from WRRFs are of increasing interest. In this study, the floating hood technique for gas collection was coupled with the off-gas method to monitor and apportion nitrous oxide (N2O) and carbon dioxide (CO2) emissions from both aerated and non-aerated tanks in a municipal water resource recovery facility, in order to investigate its carbon footprint (CFP). To our knowledge, this is the first time that the chamber technique was applied to evaluate gas fluxes from the settler, where an emission factor (EF) of 4.71 ∗ 10-5 kgCO2,eq kgbCOD-1 was found. Interesting results were found in the disinfection unit, which was the major contributor to direct N2O emissions (with a specific emission factor of 0.008 kgCO2,eq kgbCOD-1), due to the chemical interaction between hydroxylamine and the disinfectant agent (hypochlorite). The specific emission factor of the biological aerated tank was 0.00112 kgCO2,eq kgbCOD-1. The average direct CO2 emission was equal to 0.068 kgCO2 kgbCOD-1 from the activated sludge tank and to 0.00017 kgCO2 kgbCOD-1 from the secondary clarifier. Therefore, taking into account the contribution of both direct N2O and CO2 emissions, values of 0.069 kgCO2,eq kgbCOD-1, 0.008 kgCO2,eq kgbCOD-1 and 0.00022 kgCO2,eq kgbCOD-1, were found for the net CFP of the aerated compartment, the disinfection unit and the clarifier, respectively. The plant energy Footprint (eFP) was also evaluated, confirming that the aeration system is the major contributor to energy consumption, as well as to indirect CO2 emission, with a specific eFP of 1.49 kWh kgbCOD-1.
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Affiliation(s)
- D Caniani
- Engineering School, University of Basilicata, viale dell'Ateneo Lucano 10, 85100 Potenza, Italy.
| | - M Caivano
- Engineering School, University of Basilicata, viale dell'Ateneo Lucano 10, 85100 Potenza, Italy
| | - R Pascale
- Engineering School, University of Basilicata, viale dell'Ateneo Lucano 10, 85100 Potenza, Italy
| | - G Bianco
- Dipartimento di Scienze, University of Basilicata, viale dell'Ateneo Lucano 10, 85100 Potenza, Italy
| | - I M Mancini
- Engineering School, University of Basilicata, viale dell'Ateneo Lucano 10, 85100 Potenza, Italy
| | - S Masi
- Engineering School, University of Basilicata, viale dell'Ateneo Lucano 10, 85100 Potenza, Italy
| | - G Mazzone
- Engineering School, University of Basilicata, viale dell'Ateneo Lucano 10, 85100 Potenza, Italy
| | - M Firouzian
- Civil & Environmental Engineering Department, University of California, Irvine, CA 92697-2175, USA
| | - D Rosso
- Civil & Environmental Engineering Department, University of California, Irvine, CA 92697-2175, USA; Water-Energy Nexus Center, University of California, Irvine, CA 92697-2175, USA
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Regmi P, Stewart H, Amerlinck Y, Arnell M, García PJ, Johnson B, Maere T, Miletić I, Miller M, Rieger L, Samstag R, Santoro D, Schraa O, Snowling S, Takács I, Torfs E, van Loosdrecht MCM, Vanrolleghem PA, Villez K, Volcke EIP, Weijers S, Grau P, Jimenez J, Rosso D. The future of WRRF modelling - outlook and challenges. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2019; 79:3-14. [PMID: 30816857 DOI: 10.2166/wst.2018.498] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
The wastewater industry is currently facing dramatic changes, shifting away from energy-intensive wastewater treatment towards low-energy, sustainable technologies capable of achieving energy positive operation and resource recovery. The latter will shift the focus of the wastewater industry to how one could manage and extract resources from the wastewater, as opposed to the conventional paradigm of treatment. Debatable questions arise: can the more complex models be calibrated, or will additional unknowns be introduced? After almost 30 years using well-known International Water Association (IWA) models, should the community move to other components, processes, or model structures like 'black box' models, computational fluid dynamics techniques, etc.? Can new data sources - e.g. on-line sensor data, chemical and molecular analyses, new analytical techniques, off-gas analysis - keep up with the increasing process complexity? Are different methods for data management, data reconciliation, and fault detection mature enough for coping with such a large amount of information? Are the available calibration techniques able to cope with such complex models? This paper describes the thoughts and opinions collected during the closing session of the 6th IWA/WEF Water Resource Recovery Modelling Seminar 2018. It presents a concerted and collective effort by individuals from many different sectors of the wastewater industry to offer past and present insights, as well as an outlook into the future of wastewater modelling.
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Affiliation(s)
- Pusker Regmi
- Brown and Caldwell, Walnut Creek, CA, USA E-mail: ; Contributed equally to this paper
| | | | | | - Magnus Arnell
- Department of Biomedical Engineering (BME), Division of Industrial Electrical Engineering and Automation (IEA), Lund University, P.O. Box 118, SE-221 00 Lund, Sweden andRISE Research Institutes of Sweden, Gjuterigatan 1D, SE-582 73 Linköping, Sweden
| | | | | | - Thomas Maere
- modelEAU, Université Laval, CanadaandCentrEau, Québec Water Research Center, Québec City, QC, Canada
| | | | - Mark Miller
- Brown and Caldwell, Walnut Creek, CA, USA E-mail:
| | | | | | - Domenico Santoro
- Trojan Technologies, Research and Development, 3020 Gore Rd, London, ON N5 V 4T7, Canada
| | | | - Spencer Snowling
- Hydromantis ESS, Inc., 407 King Street West, Hamilton, ON, Canada
| | | | - Elena Torfs
- modelEAU, Université Laval, CanadaandCentrEau, Québec Water Research Center, Québec City, QC, Canada
| | | | - Peter A Vanrolleghem
- modelEAU, Université Laval, CanadaandCentrEau, Québec Water Research Center, Québec City, QC, Canada
| | - Kris Villez
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland andETH Zürich, Institute of Environmental Engineering, 8093 Zürich, Switzerland
| | | | | | - Paloma Grau
- Ceit and Tecnun (University of Navarra), San Sebastián, Spain
| | - José Jimenez
- Brown and Caldwell, Walnut Creek, CA, USA E-mail:
| | - Diego Rosso
- University of California, Irvine, Civil & Environmental Engineering Dept., Water-Energy Nexus Center, Irvine, CA 92697-2175, USA
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Colzi Lopes A, Valente A, Iribarren D, González-Fernández C. Energy balance and life cycle assessment of a microalgae-based wastewater treatment plant: A focus on alternative biogas uses. BIORESOURCE TECHNOLOGY 2018; 270:138-146. [PMID: 30216923 DOI: 10.1016/j.biortech.2018.09.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 08/28/2018] [Accepted: 09/01/2018] [Indexed: 06/08/2023]
Abstract
The techno-environmental performance of a medium-scale wastewater treatment system using high-rate algal ponds was evaluated through mass and energy balances and life cycle assessment. The system involves wastewater primary treatment, microalgae-based secondary treatment, thermal hydrolysis with steam explosion of microalgae, anaerobic co-digestion of pre-treated microalgal biomass and primary sludge, and biogas cogeneration. Furthermore, two scenarios based on alternative biogas uses were considered: (i) biogas for heat and electricity, and (ii) biogas for heat, electricity, and biomethane. Pumping wastewater to the primary settler arose as the main source of electricity consumption. When compared to conventional activated sludge plants, a large decrease in the energy consumption was observed for the secondary treatment. Moreover, a favourable life-cycle performance was generally found for the microalgae-based systems when displacing conventional energy products. Finally, the preference for a specific scenario on biogas use was found to be highly conditioned by the techno-environmental aspects prioritised by decision-makers.
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