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Dimitriadou S, Kokkinos PA, Kyzas GZ, Kalavrouziotis IK. Fit-for-purpose WWTP unmanned aerial systems: A game changer towards an integrated and sustainable management strategy. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 949:174966. [PMID: 39069181 DOI: 10.1016/j.scitotenv.2024.174966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/19/2024] [Accepted: 07/20/2024] [Indexed: 07/30/2024]
Abstract
In the ongoing Anthropocene era, air quality monitoring constitutes a primary axis of European and international policies for all sectors, including Waste Water Treatment Plants (WWTPs). Unmanned Aerial Systems (UASs) with proper sensing equipment provide an edge technology for air quality and odor monitoring. In addition, Unmanned Aerial Vehicle (UAV) photogrammetry has been used in civil engineering, environmental (water) quality assessment and lately for industrial facilities monitoring. This study constitutes a systematic review of the late advances and limitations of germane equipment and implementations. Despite their unassailable flexibility and efficiency, the employment of the aforementioned technologies in WWTP remote monitoring is yet sparse, partial, and concerns only particular aspects. The main finding of the review was the lack of a tailored UAS for WWTP monitoring in the literature. Therefore, to fill in this gap, we propose a fit-for-purpose remote monitoring system consisting of a UAS with a platform that would integrate all the required sensors for air quality (i.e., emissions of H2S, NH3, NOx, SO2, CH4, CO, CO2, VOCs, and PM) and odor monitoring, multispectral and thermal cameras for photogrammetric structural health monitoring (SHM) and wastewater/effluent properties (e.g., color, temperature, etc.) of a WWTP. It constitutes a novel, supreme and integrated approach to improve the sustainable management of WWTPs. Specifically, the developments that a fit-for-purpose WWTP UAS would launch, are fostering the decision-making of managers, administrations, and policymakers, both in operational conditions and in case of failures, accidents or natural disasters. Furthermore, it would significantly reduce the operational expenditure of a WWTP, ensuring personnel and population health standards, and local area sustainability.
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Affiliation(s)
- Stavroula Dimitriadou
- Laboratory of Sustainable Waste Management Technologies, School of Science and Technology, Hellenic Open University, Building D, 1(st) Floor, Parodos Aristotelous 18, 26335, Patras, Greece.
| | - Petros A Kokkinos
- Laboratory of Sustainable Waste Management Technologies, School of Science and Technology, Hellenic Open University, Building D, 1(st) Floor, Parodos Aristotelous 18, 26335, Patras, Greece.
| | - George Z Kyzas
- Hephaestus Laboratory, School of Chemistry, Faculty of Sciences, Democritus University of Thrace, Kavala, Greece.
| | - Ioannis K Kalavrouziotis
- Laboratory of Sustainable Waste Management Technologies, School of Science and Technology, Hellenic Open University, Building D, 1(st) Floor, Parodos Aristotelous 18, 26335, Patras, Greece.
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Ibrahim M, Haider A, Lim JW, Mainali B, Aslam M, Kumar M, Shahid MK. Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective. CHEMOSPHERE 2024; 362:142860. [PMID: 39019174 DOI: 10.1016/j.chemosphere.2024.142860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 07/03/2024] [Accepted: 07/14/2024] [Indexed: 07/19/2024]
Abstract
The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.
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Affiliation(s)
- Muhammad Ibrahim
- Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Adnan Haider
- Department of Environmental and IT Convergence Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Jun Wei Lim
- HICoE-Centre for Biofuel and Biochemical Research, Institute of Sustainable Energy and Resources, Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS, 32610, Seri Iskandar, Perak Darul Ridzuan, Malaysia; Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, 602105, Chennai, India
| | - Bandita Mainali
- School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney 2109, Australia
| | - Muhammad Aslam
- Membrane Systems Research Group, Department of Chemical Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan; Faculty of Engineering & Quantity Surveying, INTI International University (INTI-IU), Persiaran Perdana BBN, Putra Nilai, Nilai, 71800, Negeri Sembilan, Malaysia
| | - Mathava Kumar
- Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, Tamil Nadu, 600036, India
| | - Muhammad Kashif Shahid
- Department of Environmental and IT Convergence Engineering, Chungnam National University, Daejeon 34134, Republic of Korea; School of Engineering, Faculty of Science and Engineering, Macquarie University, Sydney 2109, Australia; Faculty of Civil Engineering and Architecture, National Polytechnic Institute of Cambodia (NPIC), Phnom Penh 12409, Cambodia.
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Prudenza S, Bax C, Capelli L. Implementation of an electronic nose for real -time identification of odour emission peaks at a wastewater treatment plant. Heliyon 2023; 9:e20437. [PMID: 37810808 PMCID: PMC10551564 DOI: 10.1016/j.heliyon.2023.e20437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 09/25/2023] [Accepted: 09/25/2023] [Indexed: 10/10/2023] Open
Abstract
This paper proposes a novel approach for the real-time monitoring of odour emissions from a WasteWater Treatment Plant (WWTP) using an Instrumental Odour Monitoring System (IOMS). The plant is characterized by unpredictable odour peaks at its arrival tank (AT), generating nuisance and complaints in the population living nearby the plant. Odour peaks are most likely due to the conferment of non-identified and malodorous wastewaters coming from various industrial activities. Due to the high variability of sources collecting their wastewaters to the WWTP, a new methodology to train the IOMS, based on the use of a one-class classifier (OCC), has been exploited. The OCC enables to detect deviations from a "Normal Operating Region" (NOR), defined as to include odour concentrations levels unlikely to cause nuisance in the citizenship. Such deviations from the NOR thus should be representative of the odour peaks. The results obtained prove that the IOMS is able to detect real-time alterations of odour emissions from the AT with an accuracy on independent validation data of about 90% (CI95% 55-100%). This ability of detecting anomalous conditions at the AT of the WWTP allowed the targeted withdrawal of liquid and gas samples in correspondence of the odour peaks, then subjected to further analyses that in turn enabled to investigate their origin and take proper counteractions to mitigate the WWTP odour impact.
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Affiliation(s)
- Stefano Prudenza
- Politecnico di Milano, Department of Chemistry Materials and Chemical Engineering “G. Natta”, Piazza Leonardo da Vinci 32, 20133, Milano, Italy
| | - Carmen Bax
- Politecnico di Milano, Department of Chemistry Materials and Chemical Engineering “G. Natta”, Piazza Leonardo da Vinci 32, 20133, Milano, Italy
| | - Laura Capelli
- Politecnico di Milano, Department of Chemistry Materials and Chemical Engineering “G. Natta”, Piazza Leonardo da Vinci 32, 20133, Milano, Italy
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Wang Y, Shao L, Kang X, Zhang H, Lü F, He P. A critical review on odor measurement and prediction. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 336:117651. [PMID: 36878058 DOI: 10.1016/j.jenvman.2023.117651] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 02/15/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
Odor pollution has become a global environmental issue of increasing concern in recent years. Odor measurements are the basis of assessing and solving odor problems. Olfactory and chemical analysis can be used for odor and odorant measurements. Olfactory analysis reflects the subjective perception of human, and chemical analysis reveals the chemical composition of odors. As an alternative to olfactory analysis, odor prediction methods have been developed based on chemical and olfactory analysis results. The combination of olfactory and chemical analysis is the best way to control odor pollution, evaluate the performances of the technologies, and predict odor. However, there are still some limitations and obstacles for each method, their combination, and the prediction. Here, we present an overview of odor measurement and prediction. Different olfactory analysis methods (namely, the dynamic olfactometry method and the triangle odor bag method) are compared in detail, the latest revisions of the standard olfactometry methods are summarized, and the uncertainties of olfactory measurement results (i.e., the odor thresholds) are analyzed. The researches, applications, and limitations of chemical analysis and odor prediction are introduced and discussed. Finally, the development and application of odor databases and algorithms for optimizing odor measurement and prediction methods are prospected, and a preliminary framework for an odor database is proposed. This review is expected to provide insights into odor measurement and prediction.
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Affiliation(s)
- Yujing Wang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Liming Shao
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China
| | - Xinyue Kang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China
| | - Hua Zhang
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China
| | - Fan Lü
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China
| | - Pinjing He
- Institute of Waste Treatment & Reclamation, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
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Wang B, Li X, Chen D, Weng X, Chang Z. Development of an electronic nose to characterize water quality parameters and odor concentration of wastewater emitted from different phases in a wastewater treatment plant. WATER RESEARCH 2023; 235:119878. [PMID: 36940564 DOI: 10.1016/j.watres.2023.119878] [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: 11/17/2022] [Revised: 02/17/2023] [Accepted: 03/12/2023] [Indexed: 06/18/2023]
Abstract
For public health consideration, it is important to ensure the wastewater discharged from wastewater treatment plant is within the regulatory limits. This problem can be effectively solved by improving the accuracy and rapid characterization of water quality parameters and odor concentration of wastewater. In this paper, we proposed a novel solution to realize the precisive analysis of water quality parameters and odor concentration of wastewater by the electronic nose device. The main work of this paper was divided into three steps: 1) recognizing wastewater samples qualitatively from different sampling points, 2) analyzing the correlation between electronic nose response signals and water quality parameters and odor concentration, and 3) predicting the odor concentration and water quality parameters quantitatively. Combined with different feature extraction methods, support vector machine and linear discriminant analysis were applied as classifiers to recognize samples at different sampling points, which reported the best recognition rate of 98.83%. Partial least squares regression was applied to complete the second step, and R2 was reaching 0.992. As for the third step, ridge regression was used to predict water quality parameters and odor concentration with the RMSE less than 0.9476. Thus, electronic noses can be applied to determine water quality parameters and odor concentrations in the effluent discharged from wastewater plants.
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Affiliation(s)
- Bingyang Wang
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China; College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China; Weihai Institute for Bionics, Jilin University, Weihai 264401, China
| | - Xiaodan Li
- China Northeast Municipal Engineering Design and Research Institute Co., Ltd., Changchun 130021, China
| | - Donghui Chen
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China; College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China; Weihai Institute for Bionics, Jilin University, Weihai 264401, China
| | - Xiaohui Weng
- Weihai Institute for Bionics, Jilin University, Weihai 264401, China; School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China
| | - Zhiyong Chang
- Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, China; College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China; Weihai Institute for Bionics, Jilin University, Weihai 264401, China.
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Vaishnav S, Saini T, Chauhan A, Gaur GK, Tiwari R, Dutt T, Tarafdar A. Livestock and poultry farm wastewater treatment and its valorization for generating value-added products: Recent updates and way forward. BIORESOURCE TECHNOLOGY 2023; 382:129170. [PMID: 37196748 DOI: 10.1016/j.biortech.2023.129170] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 05/07/2023] [Accepted: 05/10/2023] [Indexed: 05/19/2023]
Abstract
Livestock and poultry wastewater poses a potent risk factor for environmental pollution accelerating disease load and premature deaths. It is characterized by high chemical oxygen demand, biological oxygen demand, suspended solids, heavy metals, pathogens, and antibiotics, among other contaminants. These contaminants have a negative impact on the quality of soil, groundwater, and air, and is a potential hazard to human health. Depending on the specific characteristics of wastewater, such as the type and concentration of pollutants present; several physical, chemical and biological strategies have been developed for wastewater treatment. This review aims at providing comprehensive overview of the profiling of livestock wastewater from the dairy, swine and poultry sub-sectors along with the biological (annamox and genetically modified bacteria) and physico-chemical treatment methodologies, and valorisation for the generation of value-added products such as bioplastics, biofertilizers, biohydrogen and microalgal-microbial fuel cells. Additionally, future perspectives for efficient and sustainable wastewater treatment are contemplated.
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Affiliation(s)
- Sakshi Vaishnav
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243 122, Uttar Pradesh, India
| | - Tapendra Saini
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243 122, Uttar Pradesh, India
| | - Anuj Chauhan
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243 122, Uttar Pradesh, India
| | - Gyanendra Kumar Gaur
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243 122, Uttar Pradesh, India
| | - Rupasi Tiwari
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243 122, Uttar Pradesh, India
| | - Triveni Dutt
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243 122, Uttar Pradesh, India
| | - Ayon Tarafdar
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243 122, Uttar Pradesh, India.
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7
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Kim MC, Lee JH, Wang DH, Lee IS. Induction Motor Fault Diagnosis Using Support Vector Machine, Neural Networks, and Boosting Methods. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23052585. [PMID: 36904787 PMCID: PMC10007536 DOI: 10.3390/s23052585] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 06/12/2023]
Abstract
Induction motors are robust and cost effective; thus, they are commonly used as power sources in various industrial applications. However, due to the characteristics of induction motors, industrial processes can stop when motor failures occur. Thus, research is required to realize the quick and accurate diagnosis of faults in induction motors. In this study, we constructed an induction motor simulator with normal, rotor failure, and bearing failure states. Using this simulator, 1240 vibration datasets comprising 1024 data samples were obtained for each state. Then, failure diagnosis was performed on the acquired data using support vector machine, multilayer neural network, convolutional neural network, gradient boosting machine, and XGBoost machine learning models. The diagnostic accuracies and calculation speeds of these models were verified via stratified K-fold cross validation. In addition, a graphical user interface was designed and implemented for the proposed fault diagnosis technique. The experimental results demonstrate that the proposed fault diagnosis technique is suitable for diagnosing faults in induction motors.
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Affiliation(s)
| | | | | | - In-Soo Lee
- Correspondence: ; Tel.: +82-10-5312-5324
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Zarra T, Galang MGK, Oliva G, Belgiorno V. Smart instrumental Odour Monitoring Station for the efficient odour emission management and control in wastewater treatment plants. CHEMOSPHERE 2022; 309:136665. [PMID: 36191767 DOI: 10.1016/j.chemosphere.2022.136665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 09/08/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Odour emission assessment in wastewater treatment plants (WWTP) is a key aspect that needs to be improved in the plant management to avoid complaints and guarantee a sustainable environment. The research presents a smart instrumental odour monitoring station (SiOMS) composed of an advanced instrumental odour monitoring system (IOMS) integrated with other measurement units, for the continuous characterization and measurement of the odour emissions, with the aim of managing the potential odour annoyance causes in real time, in order to avoid negative effects. The application and on-site validation procedure of the trained IOMS is discussed. Experimental studies have been conducted at a large-scale WWTP. Fingerprint analysis has been applied to analyze and identify the principal gaseous compounds responsible for the odour annoyance. The artificial neural network has been adopted to elaborate and dynamically update the odour monitoring classification and quantification models (OMMs) of the IOMS. The results highlight the usefulness of a real-time measurement and control system to provide continuous and different information to the plant operators, thus allowing the identification of the odour sources and the most appropriate mitigation actions to be implemented. The paper provides important information for WWTP operators, as well as for the regulating bodies, authorities, manufacturers and end-users of odour monitoring systems involved in environmental odour impact management.
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Affiliation(s)
- Tiziano Zarra
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084, Fisciano, SA, Italy.
| | - Mark Gino K Galang
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084, Fisciano, SA, Italy.
| | - Giuseppina Oliva
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084, Fisciano, SA, Italy.
| | - Vincenzo Belgiorno
- Sanitary Environmental Engineering Division (SEED), Department of Civil Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084, Fisciano, SA, Italy.
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Jeon J, Cho K, Kang J, Park S, Uchenna Esther Ada O, Park J, Song M, Viet Ly Q, Bae H. Combined machine learning and biomolecular analysis for stability assessment of anaerobic ammonium oxidation under salt stress. BIORESOURCE TECHNOLOGY 2022; 355:127206. [PMID: 35477105 DOI: 10.1016/j.biortech.2022.127206] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/17/2022] [Accepted: 04/19/2022] [Indexed: 06/14/2023]
Abstract
In this study, the stability of the total nitrogen removal efficiency (TNRE) was modeled using an artificial neural network (ANN)-based binary classification model for the anaerobic ammonium oxidation (AMX) process under saline conditions. The TNRE was stabilized to 80.2 ± 11.4% at the final phase under the salinity of 1.0 ± 0.02%. The results of terminal restriction fragment length polymorphism (T-RFLP) analysis showed the predominance of Candidatus Jettenia genus. Real-time quantitative PCR analysis revealed the average abundance of Ca. Jettenia and Kuenenia spp. increased in 3.2 ± 5.4 × 108 and 2.0 ± 2.2 × 105 copies/mL, respectively. The prediction accuracy using operational parameters with data augmentation was 88.2%. However, integration with T-RFLP and real-time qPCR signals improved the prediction accuracy by 97.1%. This study revealed the feasible application of machine learning and biomolecular signals to the stability prediction of the AMX process under increased salinity.
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Affiliation(s)
- Junbeom Jeon
- Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea.
| | - Kyungjin Cho
- Center for Water Cycle Research, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; Division of Energy and Environment Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Republic of Korea.
| | - Jinkyu Kang
- Institute for Environment and Energy, Pusan National University, Busan 46241, Republic of Korea.
| | - Suin Park
- Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea.
| | - Okpete Uchenna Esther Ada
- Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea; Department of Precision Medicine, Step Forward Personalized Medicine (SPMED), Busan 46508, Republic of Korea
| | - Jihye Park
- Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea.
| | - Minsu Song
- Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea.
| | - Quang Viet Ly
- Institute of Environmental Engineering & Nano-Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
| | - Hyokwan Bae
- Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Republic of Korea; Institute for Environment and Energy, Pusan National University, Busan 46241, Republic of Korea.
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Demonstration of VOC Fenceline Sensors and Canister Grab Sampling near Chemical Facilities in Louisville, Kentucky. SENSORS 2022; 22:s22093480. [PMID: 35591173 PMCID: PMC9103096 DOI: 10.3390/s22093480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 02/04/2023]
Abstract
Experimental fenceline sensor pods (SPods) fitted with 30 s duration canister grab sampling (CGS) systems were deployed at a site near chemical facilities in Louisville, KY, from 4 June 2018 to 5 January 2020. The objective of the study was to better understand lower cost 10.6 eV photoionization detector (PID)-based volatile organic compound (VOC) sensors and investigate their utility for near-source emissions detection applications. Prototype SPods containing PID sensor elements from two different manufacturers yielded between 78% and 86% valid data over the study, producing a dataset of over 120,000 collocated pair fenceline measurements averaged into 5-min datapoints. Ten-second time-resolved SPod data from an elevated fenceline sensor signal day are presented, illustrating source emission detections from the direction of a facility 500 m west of the monitoring site. An SPod-triggered CGS acquired in the emission plume on this day contained elevated concentrations of 1,3-butadiene and cyclohexane (36 parts per billion by volume (ppbv) and 637 ppbv, respectively), compounds known to be emitted by this facility. Elevated concentrations of these compounds were observed in a subset of the 61 manual and triggered CGS grab samples acquired during the study, with winds from the west. Using novel wind-resolved visualization and normalization approaches described herein, the collocated pair SPod datasets exhibited similarity in emission source signature. With winds from the west, approximately 50% of SPod readings were above our defined theoretical detection limit indicating persistent measurable VOC signal at this site. Overall, this 19-month study demonstrated reasonable prototype SPod operational performance indicating that improved commercial forms of lower cost PID sensors could be useful for select VOC fenceline monitoring applications.
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