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Park J, Patel K, Lee WH. Recent advances in algal bloom detection and prediction technology using machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 938:173546. [PMID: 38810749 DOI: 10.1016/j.scitotenv.2024.173546] [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: 11/17/2023] [Revised: 05/18/2024] [Accepted: 05/24/2024] [Indexed: 05/31/2024]
Abstract
Harmful algal blooms (HAB) including red tides and cyanobacteria are a significant environmental issue that can have harmful effects on aquatic ecosystems and human health. Traditional methods of detecting and managing algal blooms have been limited by their reliance on manual observation and analysis, which can be time-consuming and costly. Recent advances in machine learning (ML) technology have shown promise in improving the accuracy and efficiency of algal bloom detection and prediction. This paper provides an overview of the latest developments in using ML for algal bloom detection and prediction using various water quality parameters and environmental factors. First, we introduced ML for algal bloom prediction using regression and classification models. Then we explored image-based ML for algae detection by utilizing satellite images, surveillance cameras, and microscopic images. This study also highlights several real-world examples of successful implementation of ML for algal bloom detection and prediction. These examples show how ML can enhance the accuracy and efficiency of detecting and predicting algal blooms, contributing to the protection of aquatic ecosystems and human health. The study also outlines recent efforts to enhance the field applicability of ML models and suggests future research directions. A recent interest in explainable artificial intelligence (XAI) was discussed in an effort to understand the most influencing environmental factors on algal blooms. XAI facilitates interpretations of ML model results, thereby enhancing the models' usability for decision-making in field management and improving their overall applicability in real-world settings. We also emphasize the significance of obtaining high-quality, field-representative data to enhance the efficiency of ML applications. The effectiveness of ML models in detecting and predicting algal blooms can be improved through management strategies for data quality, such as pre-treating missing data and integrating diverse datasets into a unified database. Overall, this paper presents a comprehensive review of the latest advancements in managing algal blooms using ML technology and proposes future research directions to enhance the utilization of ML techniques.
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Affiliation(s)
- Jungsu Park
- Department of Civil and Environmental Engineering, Hanbat National University,125, Dongseo-daero, Yuseong-gu, Daejeon 34158, Republic of Korea.
| | - Keval Patel
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Dr., Orlando, FL 32816, United States.
| | - Woo Hyoung Lee
- Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Dr., Orlando, FL 32816, United States.
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Huang S, Xia J, Wang Y, Lei J, Wang G. Water quality prediction based on sparse dataset using enhanced machine learning. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 20:100402. [PMID: 38585199 PMCID: PMC10998092 DOI: 10.1016/j.ese.2024.100402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 02/18/2024] [Accepted: 02/19/2024] [Indexed: 04/09/2024]
Abstract
Water quality in surface bodies remains a pressing issue worldwide. While some regions have rich water quality data, less attention is given to areas that lack sufficient data. Therefore, it is crucial to explore novel ways of managing source-oriented surface water pollution in scenarios with infrequent data collection such as weekly or monthly. Here we showed sparse-dataset-based prediction of water pollution using machine learning. We investigated the efficacy of a traditional Recurrent Neural Network alongside three Long Short-Term Memory (LSTM) models, integrated with the Load Estimator (LOADEST). The research was conducted at a river-lake confluence, an area with intricate hydrological patterns. We found that the Self-Attentive LSTM (SA-LSTM) model outperformed the other three machine learning models in predicting water quality, achieving Nash-Sutcliffe Efficiency (NSE) scores of 0.71 for CODMn and 0.57 for NH3N when utilizing LOADEST-augmented water quality data (referred to as the SA-LSTM-LOADEST model). The SA-LSTM-LOADEST model improved upon the standalone SA-LSTM model by reducing the Root Mean Square Error (RMSE) by 24.6% for CODMn and 21.3% for NH3N. Furthermore, the model maintained its predictive accuracy when data collection intervals were extended from weekly to monthly. Additionally, the SA-LSTM-LOADEST model demonstrated the capability to forecast pollution loads up to ten days in advance. This study shows promise for improving water quality modeling in regions with limited monitoring capabilities.
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Affiliation(s)
- Sheng Huang
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
- Institute for Water-Carbon Cycles and Carbon Neutrality, Wuhan University, Wuhan 430072, China
- Department of Civil and Environmental Engineering, National University of Singapore, 117578 Singapore
| | - Jun Xia
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
- Institute for Water-Carbon Cycles and Carbon Neutrality, Wuhan University, Wuhan 430072, China
- Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Yueling Wang
- Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Jiarui Lei
- Department of Civil and Environmental Engineering, National University of Singapore, 117578 Singapore
| | - Gangsheng Wang
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
- Institute for Water-Carbon Cycles and Carbon Neutrality, Wuhan University, Wuhan 430072, China
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Kumar A, Mishra S, Singh NK, Yadav M, Padhiyar H, Christian J, Kumar R. Ensuring carbon neutrality via algae-based wastewater treatment systems: Progress and future perspectives. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121182. [PMID: 38772237 DOI: 10.1016/j.jenvman.2024.121182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 04/24/2024] [Accepted: 05/13/2024] [Indexed: 05/23/2024]
Abstract
The emergence of algal biorefineries has garnered considerable attention to researchers owing to their potential to ensure carbon neutrality via mitigation of atmospheric greenhouse gases. Algae-derived biofuels, characterized by their carbon-neutral nature, stand poised to play a pivotal role in advancing sustainable development initiatives aimed at enhancing environmental and societal well-being. In this context, algae-based wastewater treatment systems are greatly appreciated for their efficacy in nutrient removal and simultaneous bioenergy generation. These systems leverage the growth of algae species on wastewater nutrients-including carbon, nitrogen, and phosphorus-alongside carbon dioxide, thus facilitating a multifaceted approach to pollution remediation. This review seeks to delve into the realization of carbon neutrality through algae-mediated wastewater treatment approaches. Through a comprehensive analysis, this review scrutinizes the trajectory of algae-based wastewater treatment via bibliometric analysis. It subsequently examines the case studies and empirical insights pertaining to algae cultivation, treatment performance analysis, cost and life cycle analyses, and the implementation of optimization methodologies rooted in artificial intelligence and machine learning algorithms for algae-based wastewater treatment systems. By synthesizing these diverse perspectives, this study aims to offer valuable insights for the development of future engineering applications predicated on an in-depth understanding of carbon neutrality within the framework of circular economy paradigms.
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Affiliation(s)
- Amit Kumar
- School of Hydrology and Water Resources, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
| | - Saurabh Mishra
- Institute of Water Science and Technology, Hohai University, Nanjing China, 210098, China.
| | - Nitin Kumar Singh
- Department of Chemical Engineering, Marwadi University, Rajkot, Gujarat, India.
| | - Manish Yadav
- Central Mine Planning and Design Institute Limite, Bhubaneswar, India.
| | | | - Johnson Christian
- Environment Audit Cell, R. D. Gardi Educational Campus, Rajkot, Gujarat, India.
| | - Rupesh Kumar
- Jindal Global Business School (JGBS), O P Jindal Global University, Sonipat, 131001, Haryana, India.
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Tian Y, Yang X, Chen N, Li C, Yang W. Data-driven interpretable analysis for polysaccharide yield prediction. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 19:100321. [PMID: 38021368 PMCID: PMC10661693 DOI: 10.1016/j.ese.2023.100321] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 09/17/2023] [Accepted: 09/17/2023] [Indexed: 12/01/2023]
Abstract
Cornstalks show promise as a raw material for polysaccharide production through xylanase. Rapid and accurate prediction of polysaccharide yield can facilitate process optimization, eliminating the need for extensive experimentation in actual production to refine reaction conditions, thereby saving time and costs. However, the intricate interplay of enzymatic factors poses challenges in predicting and optimizing polysaccharide yield accurately. Here, we introduce an innovative data-driven approach leveraging multiple artificial intelligence techniques to enhance polysaccharide production. We propose a machine learning framework to identify highly accurate polysaccharide yield prediction modeling methods and uncover optimal enzymatic parameter combinations. Notably, Random Forest (RF) and eXtreme Gradient Boost (XGB) demonstrate robust performance, achieving prediction accuracies of 93.0% and 95.6%, respectively, while an independently developed deep neural network (DNN) model achieves 91.1% accuracy. A feature importance analysis of XGB reveals the enzyme solution volume's dominant role (43.7%), followed by time (20.7%), substrate concentration (15%), temperature (15%), and pH (5.6%). Further interpretability analysis unveils complex parameter interactions and potential optimization strategies. This data-driven approach, incorporating machine learning, deep learning, and interpretable analysis, offers a viable pathway for polysaccharide yield prediction and the potential recovery of various agricultural residues.
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Affiliation(s)
- Yushi Tian
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Xu Yang
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Nianhua Chen
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Chunyan Li
- School of Resource and Environment, Northeast Agriculture University, Harbin, 150030, PR China
| | - Wulin Yang
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, PR China
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Wang X, Fu G, Ren NQ. Artificial intelligence is transforming the research paradigm of environmental science and engineering. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2024; 19:100346. [PMID: 38058954 PMCID: PMC10696158 DOI: 10.1016/j.ese.2023.100346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/08/2023]
Affiliation(s)
- Xu Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China
| | - Guangtao Fu
- Centre for Water Systems, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, United Kingdom
| | - Nan-Qi Ren
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, Shenzhen, 518055, China
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Syed T, Krujatz F, Ihadjadene Y, Mühlstädt G, Hamedi H, Mädler J, Urbas L. A review on machine learning approaches for microalgae cultivation systems. Comput Biol Med 2024; 172:108248. [PMID: 38493599 DOI: 10.1016/j.compbiomed.2024.108248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2023] [Revised: 02/15/2024] [Accepted: 03/06/2024] [Indexed: 03/19/2024]
Abstract
Microalgae plays a crucial role in biomass production within aquatic environments and are increasingly recognized for their potential in generating biofuels, biomaterials, bioactive compounds, and bio-based chemicals. This growing significance is driven by the need to address imminent global challenges such as food and fuel shortages. Enhancing the value chain of bio-based products necessitates the implementation of an advanced screening and monitoring system. This system is crucial for tailoring and optimizing the cultivation conditions, ensuring the lucrative and efficient production of the final desired product. This, in turn, underscores the necessity for robust predictive models to accurately emulate algae growth in different conditions during the initial cultivation phase and simulate their subsequent processing in the downstream stage. In pursuit of these objectives, diverse mechanistic and machine learning-based methods have been independently employed to model and optimize microalgae processes. This review article thoroughly examines the techniques delineated in the literature for modeling, predicting, and monitoring microalgal biomass across various applications such as bioenergy, pharmaceuticals, and the food industry. While highlighting the merits and limitations of each method, we delve into the realm of newly emerging hybrid approaches and conduct an exhaustive survey of this evolving methodology. The challenges currently impeding the practical implementation of hybrid techniques are explored, and drawing inspiration from successful applications in other machine-learning-assisted fields, we review various plausible solutions to overcome these obstacles.
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Affiliation(s)
- Tehreem Syed
- Institute of Automation, Technische Universität Dresden, 01062, Saxony, Germany
| | - Felix Krujatz
- Faculty of Natural and Environmental Sciences, University of Applied Sciences Zittau/Görlitz, 02763, Zittau, Germany; Institute of Natural Materials Technology, Technische Universität Dresden, 01069, Saxony, Germany
| | - Yob Ihadjadene
- Institute of Natural Materials Technology, Technische Universität Dresden, 01069, Saxony, Germany
| | | | - Homa Hamedi
- Institute of Process Engineering and Environmental Technology, Technische Universität Dresden, 01062, Saxony, Germany
| | - Jonathan Mädler
- Institute of Process Engineering and Environmental Technology, Technische Universität Dresden, 01062, Saxony, Germany.
| | - Leon Urbas
- Institute of Automation, Technische Universität Dresden, 01062, Saxony, Germany; Institute of Process Engineering and Environmental Technology, Technische Universität Dresden, 01062, Saxony, Germany
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Niu J, Lu Y, Xie M, Ou L, Cui L, Qiu H, Lu S. Prediction of Aureococcus anophageffens using machine learning and deep learning. MARINE POLLUTION BULLETIN 2024; 200:116148. [PMID: 38364640 DOI: 10.1016/j.marpolbul.2024.116148] [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: 11/29/2023] [Revised: 02/06/2024] [Accepted: 02/08/2024] [Indexed: 02/18/2024]
Abstract
The recurrent brown tide phenomenon, attributed to Aureococcus anophagefferens (A. anophagefferens), constitutes a significant threat to the Qinhuangdao sea area in China, leading to pronounced ecological degradation and substantial economic losses. This study utilized machine learning and deep learning techniques to predict A. anophagefferens population density, aiming to elucidate the occurrence mechanism and influencing factors of brown tide. Specifically, Random Forest (RF) algorithm was utilized to impute missing water quality data, facilitating its direct application in subsequent algal population prediction models. The results revealed that all four models-RF, Support Vector Regression (SVR), Multilayer Perceptron (MLP), and Convolutional Neural Network (CNN)-exhibited high accuracy in predicting A. anophagefferens population densities, with R2 values exceeding 0.75. RF, in particular, showed exceptional accuracy and reliability, with an R2 value surpassing 0.8. Additionally, the study ascertained five critical factors influencing A. anophagefferens population density: ammonia nitrogen, pH, total nitrogen, temperature, and silicate.
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Affiliation(s)
- Jie Niu
- College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China
| | - Yanqun Lu
- School of Environment, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Mengyu Xie
- School of Environment, College of Life Science and Technology, Jinan University, Guangzhou 510632, China.
| | - Linjian Ou
- School of Environment, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Lei Cui
- School of Environment, College of Life Science and Technology, Jinan University, Guangzhou 510632, China
| | - Han Qiu
- Atmospheric, Climate, and Earth Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Songhui Lu
- School of Environment, College of Life Science and Technology, Jinan University, Guangzhou 510632, China; Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519000, China.
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Xue J, Yuan C, Ji X, Zhang M. Predictive modeling of nitrogen and phosphorus concentrations in rivers using a machine learning framework: A case study in an urban-rural transitional area in Wenzhou China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 910:168521. [PMID: 37981147 DOI: 10.1016/j.scitotenv.2023.168521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 11/04/2023] [Accepted: 11/10/2023] [Indexed: 11/21/2023]
Abstract
Rapid urbanization in China since 1980 generated environmental pressures of non-point source pollution (NPSP) and increased wide public concerns. Excessive quantities of nitrogen (N) and phosphorus (P) is a significant source of aquatic pollution, despite of their roles as essential nutritional elements for aquatic life processes. In this study, we present a new framework using random forest (RF) as a powerful machine learning algorithm driven by geo-datasets to estimate and map the concentration of total nitrogen (TN) and phosphorus (TP) at a spatial resolution for the Wen-Rui Tang River (WRTR) watershed, which is a typically urban-rural transitional area in east coastal region of China. A comprehensive GIS database of 26 in-house built environmental variables was adopted to build the predictive models of TN and TP in open waters over the watershed. The performances of the RF regression models were evaluated in comparison with in-situ measurements, and the results indicated the ability of RF regression models to accurately predict the spatiotemporal distribution of N and P concentration in rivers. Charactering the explanatory variable importance measures in the calibrated RF regression model defined the most significant variables impacting N and P contaminations in open waters across the urban-rural transitional area, and the results showed that these variables are aquaculture, direct domestic sewage, industrial wastewater discharges and the changing meteorological variables. Besides, mapping of the TN and TP concentrations across the continuous river at high spatiotemporal resolution (daily, 1 km × 1 km) in this study were informative. The results in this study provided the valuable data to various different stakeholders for managing water quality and pollution control where similar regions with rapid urbanization and a lack of water quality monitoring datasets.
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Affiliation(s)
- Jingyuan Xue
- Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu 610041, China; College of Water Resource and Civil Engineering, China Agricultural University, Beijing 100083, China
| | - Can Yuan
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China
| | - Xiaoliang Ji
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China
| | - Minghua Zhang
- Key Laboratory of Watershed Science and Health of Zhejiang Province, School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, China; Department of Land Air & Water Resources, University of California Davis, Davis, CA 95616, USA.
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Lu Y, Tuo Y, Zhang L, Hu X, Huang B, Chen M, Li Z. Vertical distribution rules and factors influencing phytoplankton in front of a drinking water reservoir outlet. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 902:166512. [PMID: 37619726 DOI: 10.1016/j.scitotenv.2023.166512] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023]
Abstract
The phenomenon of algal blooms caused by the excessive proliferation of phytoplankton in drinking water reservoirs is becoming increasingly frequent, seriously endangering water quality, ecosystems, water safety, and people's health. Thus, there is urgent need to conduct research on the distribution rules and factors influencing phytoplankton in drinking water reservoirs. Given that the outflows from reservoirs usually come from the middle and lower layers of the water column and the current studies on phytoplankton in drinking water reservoirs are usually carried out on the surface, an 8-month monitoring of vertical phytoplankton and the corresponding influencing factors in front of the outlet in a drinking water reservoir was conducted. Based on the monitoring results, the distribution rules of phytoplankton and the associated factors were analyzed. The results showed that phytoplankton biomass significantly decreased with increasing water depth, but the biomass near the outlet (40 m depth) still reached the WHO level 2 warning threshold for algal blooms multiple times. During the monitoring period, Cyanophyta, Chlorophyta and Bacillariophyta dominated. The selected multisource environmental factors explained 60.5 % of the spatiotemporal changes in phytoplankton, with thermal intensity (water temperature and thermal stratification intensity) being the driving factor. Meanwhile, excessive TN and TP provided necessary conditions for the growth of phytoplankton. Based on influencing factors, reducing upstream nutrient inflows and thermal stratification intensity are recommended as measures to prevent and control algal blooms. This study provides insights into the vertical distribution rules and factors influencing phytoplankton in a drinking water reservoir, which can provide a reference for the management of drinking water reservoirs and the prevention and control of algal blooms.
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Affiliation(s)
- Yongao Lu
- State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Youcai Tuo
- State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, Sichuan 610065, China.
| | - Linglei Zhang
- State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Xiangying Hu
- Chongqing Liyutang Reservoir Development Corporation Limited, Chongqing 405400, China
| | - Bin Huang
- School of Environmental Science&Engineering, Tianjin University, Tianjin 300072, China; PowerChina Huadong Engineering Corporation Limited, Hangzhou, Zhejiang 310005, China
| | - Min Chen
- State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu, Sichuan 610065, China
| | - Zhenghe Li
- Chongqing Liyutang Reservoir Development Corporation Limited, Chongqing 405400, China
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Hu J, Effiong K, Liu M, Xiao X. Broad spectrum and species specificity of plant allelochemicals 1,2-benzenediol and 3-indoleacrylic acid against marine and freshwater harmful algae. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 898:166356. [PMID: 37595905 DOI: 10.1016/j.scitotenv.2023.166356] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 08/07/2023] [Accepted: 08/15/2023] [Indexed: 08/20/2023]
Abstract
Allelochemicals derived from plants have shown great potential in mitigating harmful algal blooms (HABs), although different algal species can respond differently to these chemicals. Therefore, we first investigated the allelopathic effects of two newly identified plant-derived allelochemicals, 1,2-benzenediol (1,2-BD) and 3-indoleacrylic acid (3-IDC), on six algal species. Then we further evaluated the allelopathic responses of two bloom-forming species, Microcystis aeruginosa FACHB-905 and Heterosigma akashiwo to 1,2-BD. Results showed that 1,2-BD had a broader antialgal spectrum than 3-IDC. Allelopathic response analysis indicated that 1,2-BD consistently and stably inhibit the growth of M. aeruginosa FACHB-905, with inhibitory mechanism being disruption of photosynthetic activity, overwhelming of the antioxidant system and activation of programmed cell death (PCD). H. akashiwo displayed resistance to 1,2-BD during exposure, and the growth inhibition was mainly attributed to PCD. Therefore, the species-specific allelopathic responses provide new insights for controlling HABs using 1,2-BD and 3-IDC.
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Affiliation(s)
- Jing Hu
- Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316021, China; Key Laboratory of Marine Ecological Monitoring and Restoration Technologies of Ministry of Natural Resources, Shanghai 201206, China
| | - Kokoette Effiong
- Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316021, China; Key Laboratory of Marine Ecological Monitoring and Restoration Technologies of Ministry of Natural Resources, Shanghai 201206, China; Department of Marine Biology, Akwa Ibom State University (AKSU), P.M.B 1157, Uyo, Akwa Ibom State, Nigeria
| | - Muyuan Liu
- Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316021, China
| | - Xi Xiao
- Department of Marine Science, Ocean College, Zhejiang University, Zhoushan 316021, China; Key Laboratory of Marine Ecological Monitoring and Restoration Technologies of Ministry of Natural Resources, Shanghai 201206, China; Donghai Laboratory, Zhoushan, Zhejiang 316021, China; Key Laboratory of Watershed Non-point Source Pollution Control and Water Eco-security of Ministry of Water Resources, College of Environmental and Resources Sciences, Zhejiang University, Hangzhou 310058, China.
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