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Zhang W, Ai Z, Chen Q, Chen J, Xu D, Cao J, Kapusta K, Peng H, Leng L, Li H. Automated machine learning-aided prediction and interpretation of gaseous by-products from the hydrothermal liquefaction of biomass. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 945:173939. [PMID: 38908600 DOI: 10.1016/j.scitotenv.2024.173939] [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: 04/01/2024] [Revised: 06/04/2024] [Accepted: 06/09/2024] [Indexed: 06/24/2024]
Abstract
Hydrothermal liquefaction (HTL) is a thermochemical conversion technology that produces bio-oil from wet biomass without drying. However, by-product gases will inevitably be produced, and their formation is unclear. Therefore, an automated machine learning (AutoML) approach, automatically training without human intervention, was used to aid in predicting gaseous production and interpreting the formation mechanisms of four gases (CO2, CH4, CO, and H2). Specifically, four accurate optimal single-target models based on AutoML were developed with elemental compositions and HTL conditions as inputs for four gases. Herein, the gradient boosting machine (GBM) performed excellently with train R2 ≥ 0.99 and test R2 ≥ 0.80. Then, the screened GBM algorithm-based ML multi-target models (maximum average test R2 = 0.89 and RMSE = 0.39) were built to predict four gases simultaneously. Results indicated that biomass carbon, solid content, pressure, and biomass hydrogen were the top four factors for gas production from HTL of biomass. This study proposed an AutoML-aided prediction and interpretation framework, which could provide new insight for rapid prediction and revelation of gaseous compositions from the HTL process.
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Affiliation(s)
- Weijin Zhang
- School of Energy Science and Engineering, Central South University, Changsha 410083, China
| | - Zejian Ai
- School of Energy Science and Engineering, Central South University, Changsha 410083, China
| | - Qingyue Chen
- School of Energy Science and Engineering, Central South University, Changsha 410083, China
| | - Jiefeng Chen
- School of Energy Science and Engineering, Central South University, Changsha 410083, China
| | - Donghai Xu
- Key Laboratory of Thermo-Fluid Science·& Engineering, Ministry of Education, School of Energy and Power Engineering, Xi'an Jiao Tong University, Xi'an, Shaanxi Province 710049, China
| | - Jianbing Cao
- Research Department of Hunan eco-environmental Affairs Center, Changsha 410000, China
| | - Krzysztof Kapusta
- Główny Instytut Górnictwa (Central Mining Tnstitute), Gwarków 1, 40-166 Katowice, Poland
| | - Haoyi Peng
- School of Energy Science and Engineering, Central South University, Changsha 410083, China
| | - Lijian Leng
- School of Energy Science and Engineering, Central South University, Changsha 410083, China; Xiangjiang Laboratory, Changsha 410205, China.
| | - Hailong Li
- School of Energy Science and Engineering, Central South University, Changsha 410083, China.
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2
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Zhang Y, Wang Z, Hu J, Pu C. Intelligent management of carbon emissions of urban domestic sewage based on the Internet of Things. ENVIRONMENTAL RESEARCH 2024; 251:118594. [PMID: 38442818 DOI: 10.1016/j.envres.2024.118594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/05/2024] [Accepted: 02/28/2024] [Indexed: 03/07/2024]
Abstract
Domestic wastewater is one of the major carbon sources that cannot be ignored by human society. Against the background of carbon peaking & carbon neutrality (Double Carbon) goals, the continuous urbanization has put heavy pressure on urban drainage systems. Nevertheless, the complex subjective and objective conditions of drainage systems restrict the field monitoring, measurement, and analysis of drainage systems, which has become a great obstacle to the study of carbon emissions from drainage system. In this paper, 3389 sensor terminals of Internet of Things (IoT) are used to build a field monitoring IoT for urban domestic wastewater methane (CH4) carbon emission, with 21 main districts of Chongqing Municipality in China as the study area. Incorporating Fick's law of diffusion, this field monitoring IoT derives a measurement model for methane carbon emissions based on measured concentrations, and solves the problems of long-term and stable monitoring and measurement of methane gas in complex underground environment. With GIS spatio-temporal analysis used to analyze the spatial and temporal evolution patterns of carbon emissions from septic tanks in drainage systems, it successfully reveals the spatial and temporal distribution of methane carbon emissions from drainage systems in different seasons, as well as the relationship between methane carbon emissions from drainage systems and the latitude of direct sunlight. Applying the DTW method, it quantifies the stability of methane monitoring in drainage systems and evaluates the effects of Sampling Frequency (SF) and Number of Devices Terminal (NDT) on the stability of methane monitoring. Consequently, an intelligent management system for carbon emissions from urban domestic wastewater is constructed on the base of IoT, which integrates methane monitoring, measurement and analysis in septic tanks of drainage systems.
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Affiliation(s)
- Yanjing Zhang
- School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
| | - Zhoufeng Wang
- School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China; Chongqing Rongguan Science Technologies Co.Ltd., Chongqing 400039, China; State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610500, China.
| | - Jiaxing Hu
- School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China
| | - Chaodong Pu
- Chongqing Rongguan Science Technologies Co.Ltd., Chongqing 400039, China
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3
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Baskar G, Nashath Omer S, Saravanan P, Rajeshkannan R, Saravanan V, Rajasimman M, Shanmugam V. Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques. CHEMOSPHERE 2024; 362:142477. [PMID: 38844107 DOI: 10.1016/j.chemosphere.2024.142477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/24/2024] [Accepted: 05/27/2024] [Indexed: 06/27/2024]
Abstract
The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.
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Affiliation(s)
- Gurunathan Baskar
- Department of Biotechnology, St. Joseph's College of Engineering, Chennai, 600119. India; School of Engineering, Lebanese American University, Byblos, 1102 2801, Lebanon.
| | - Soghra Nashath Omer
- School of Bio-Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India
| | - Panchamoorthy Saravanan
- Department of Petrochemical Technology, UCE - BIT Campus, Anna University, Tiruchirappalli, Tamil Nadu, 620024, India
| | - R Rajeshkannan
- Department of Chemical Engineering, Annamalai University, Chidambaram, Tamil Nadu, 608002, India
| | - V Saravanan
- Department of Chemical Engineering, Annamalai University, Chidambaram, Tamil Nadu, 608002, India
| | - M Rajasimman
- Department of Chemical Engineering, Annamalai University, Chidambaram, Tamil Nadu, 608002, India
| | - Venkatkumar Shanmugam
- School of Bio-Sciences and Technology, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
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4
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Wang R, He Z, Chen H, Guo S, Zhang S, Wang K, Wang M, Ho SH. Enhancing biomass conversion to bioenergy with machine learning: Gains and problems. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172310. [PMID: 38599406 DOI: 10.1016/j.scitotenv.2024.172310] [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: 01/18/2024] [Revised: 03/20/2024] [Accepted: 04/06/2024] [Indexed: 04/12/2024]
Abstract
The growing concerns about environmental sustainability and energy security, such as exhaustion of traditional fossil fuels and global carbon footprint growth have led to an increasing interest in alternative energy sources, especially bioenergy. Recently, numerous scenarios have been proposed regarding the use of bioenergy from different sources in the future energy systems. In this regard, one of the biggest challenges for scientists is managing, modeling, decision-making, and future forecasting of bioenergy systems. The development of machine learning (ML) techniques can provide new opportunities for modeling, optimizing and managing the production, consumption and environmental effects of bioenergy. However, researchers in bioenergy fields have not widely utilized the ML concepts and practices. Therefore, a comparative review of the current ML techniques used for bioenergy productions is presented in this paper. This review summarizes the common issues and difficulties existing in integrating ML with bioenergy studies, and discusses and proposes the possible solutions. Additionally, a detailed discussion of the appropriate ML application scenarios is also conducted in every sector of the entire bioenergy chain. This indicates the modernized conversion processes supported by ML techniques are imperative to accurately capture process-level subtleties, and thus improving techno-economic resilience and socio-ecological integrity of bioenergy production. All the efforts are believed to help in sustainable bioenergy production with ML technologies for the future.
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Affiliation(s)
- Rupeng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Zixiang He
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Honglin Chen
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Silin Guo
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shiyu Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Ke Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Meng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China
| | - Shih-Hsin Ho
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150040, PR China.
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Deng Y, Zhang Y, Zhao Z. A data-driven approach for revealing the linkages between differences in electrochemical properties of biochar during anaerobic digestion using automated machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172291. [PMID: 38588748 DOI: 10.1016/j.scitotenv.2024.172291] [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: 02/15/2024] [Revised: 04/05/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024]
Abstract
Biochar is commonly used to enhance the anaerobic digestion of organic waste solids and wastewater, due to its electrochemical properties, which intensify the electron transfer of microorganisms attached to its large surface area. However, it is difficult to create biochar with both high conductivity and high capacitance, which makes selecting the right biochar for engineering applications challenging. To address this issue, two Auto algorithms (TPOT and H2O) were applied to model the effects of different biochar properties on anaerobic digestion processes. The results showed that the gradient boosting machine had the highest predictive accuracy (R2 = 0.96). Feature importance analysis showed that feedstock concentration, digestion time, capacitance, and conductivity of biochar were the main factors affecting methane yield. According to the two-dimensional (2D) partial dependence plots, high-capacitance biochar (0.27-0.29 V·mA) is favorable for substrates with low-solid content (< 19.6 TS%), while the high-conductivity biochar (80.82-170.58 mS/cm) is suitable for high-solids substrates (> 20.1 TS%). The software, based on the optimal model, can be used to obtain the ideal range of biochar for AD trials, aiding researchers in practical applications prior to implementation.
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Affiliation(s)
- Ying Deng
- Key Laboratory of Industrial Ecology and Environmental Engineering (Dalian University of Technology), Ministry of Education, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Yifan Zhang
- Olin Business School, Washington University in St. Louis, St. Louis 63130, United States
| | - Zhiqiang Zhao
- Key Laboratory of Industrial Ecology and Environmental Engineering (Dalian University of Technology), Ministry of Education, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
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6
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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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7
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Sun J, Rao Y, He Z. Machine learning-aided inverse design for biogas upgrading through biological CO 2 conversion. BIORESOURCE TECHNOLOGY 2024; 399:130549. [PMID: 38461869 DOI: 10.1016/j.biortech.2024.130549] [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: 01/27/2024] [Revised: 02/24/2024] [Accepted: 03/06/2024] [Indexed: 03/12/2024]
Abstract
The biogas upgrading process through bioconversion of CO2 to CH4 by hydrogenotrophic methanogens is an attractive strategy for energy decarbonation. Many studies have optimized operational parameters to improve key performance indicators such as CH4% and H2 utilization efficiency. However, inconsistent laboratory conditions make it challenging to compare results. Existing models for analyzing operating conditions can only assess the impact of individual conditions and lack the ability to simultaneously optimize multiple conditions. To address this, two XGBoost models were built with R2 of 0.779 and 0.903 with data collected from literatures and were embedded into multi-objective partitive swarm optimization algorithm to optimal operating conditions. Predictions were compared with experimental validations under optimized conditions, revealing an 8.50% and 2.95% relative error in CH4% and H2 conversion rate, respectively. This approach streamlines biogas upgrading processes, offering a data-driven solution to enhance efficiency and consistency in the pursuit of sustainable methane production.
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Affiliation(s)
- Jiasi Sun
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Yue Rao
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Zhen He
- Department of Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA.
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8
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Xiong B, Chen K, Ke C, Zhao S, Dang Z, Guo C. Prediction of heavy metal removal performance of sulfate-reducing bacteria using machine learning. BIORESOURCE TECHNOLOGY 2024; 397:130501. [PMID: 38417462 DOI: 10.1016/j.biortech.2024.130501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/24/2024] [Accepted: 02/25/2024] [Indexed: 03/01/2024]
Abstract
A robust modeling approach for predicting heavy metal removal by sulfate-reducing bacteria (SRB) is currently missing. In this study, four machine learning models were constructed and compared to predict the removal of Cd, Cu, Pb, and Zn as individual ions by SRB. The CatBoost model exhibited the best predictive performance across the four subsets, achieving R2 values of 0.83, 0.91, 0.92, and 0.83 for the Cd, Cu, Pb, and Zn models, respectively. Feature analysis revealed that temperature, pH, sulfate concentration, and C/S (the mass ratio of chemical oxygen demand to sulfate) had significant impacts on the outcomes. These features exhibited the most effective metal removal at 35 °C and sulfate concentrations of 1000-1200 mg/L, with variations observed in pH and C/S ratios. This study introduced a new modeling approach for predicting the treatment of metal-containing wastewater by SRB, offering guidance for optimizing operational parameters in the biological sulfidogenic process.
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Affiliation(s)
- Beiyi Xiong
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, China
| | - Kai Chen
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, China
| | - Changdong Ke
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Guangzhou 510535, China
| | - Shoushi Zhao
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, China
| | - Zhi Dang
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, China; Guangdong Provincial Key Lab of Solid Wastes Pollution Control and Recycling, South China University of Technology, Guangzhou 510006, China
| | - Chuling Guo
- School of Environment and Energy, South China University of Technology, Guangzhou 510006, China; The Key Lab of Pollution Control and Ecosystem Restoration in Industry Clusters, Ministry of Education, South China University of Technology, Guangzhou 510006, China.
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9
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Lin X, Hou J, Wu X, Lin D. Elucidating the impacts of microplastics on soil greenhouse gas emissions through automatic machine learning frameworks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 916:170308. [PMID: 38272088 DOI: 10.1016/j.scitotenv.2024.170308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 12/27/2023] [Accepted: 01/18/2024] [Indexed: 01/27/2024]
Abstract
With the rise in global plastic production and agricultural demand, the released microplastics (MPs) have increasingly influenced the elemental cycles of soils, leading to notable effects on greenhouse gas emissions. Despite initial research, there remains a gap in establishing a detailed modeling approach that comprehensively explores the impacts of MPs on GHG emissions. Herein, we utilized literature mining to assemble a comprehensive dataset examining the interplays between MPs and emissions of CO2, CH4, and N2O. Five automated machine learning frameworks were employed for predictive modeling. The GAMA framework was particularly effective in predicting CO2 (Q2 = 0.946) and CH4 (Q2 = 0.991) emissions. The Autogluon framework provided the most accurate prediction for N2O emission, though it exhibited signs of overfitting. Interpretability analysis indicated that the type of MPs significantly influenced CO2 emission. Degradable MPs (i.e., polyamide) inherently led to elevated CO2 emission, and the environmental aging further exacerbated this effect. Although both linear and nonlinear correlations between MPs and CH₄ emission were not identified, the incorporation of specific MPs that elevate soil pH, augment soil water retention, and cultivate anaerobic conditions may potentially elevate soil CH₄ emission. This research underscores the profound influence of MPs on soil GHG emissions, providing vital insights for shaping agricultural policies and soil management practices in the context of escalating plastic use.
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Affiliation(s)
- Xintong Lin
- Department of Environmental Science, Zhejiang University, Hangzhou 310058, China; School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong, China
| | - Jie Hou
- Department of Environmental Science, Zhejiang University, Hangzhou 310058, China
| | - Xinyue Wu
- Department of Environmental Science, Zhejiang University, Hangzhou 310058, China.
| | - Daohui Lin
- Department of Environmental Science, Zhejiang University, Hangzhou 310058, China
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10
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Jiang BN, Zhang YY, Zhang ZY, Yang YL, Song HL. Tree-structured parzen estimator optimized-automated machine learning assisted by meta-analysis for predicting biochar-driven N 2O mitigation effect in constructed wetlands. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 354:120335. [PMID: 38368804 DOI: 10.1016/j.jenvman.2024.120335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/29/2024] [Accepted: 02/08/2024] [Indexed: 02/20/2024]
Abstract
Biochar is a carbon-neutral tool for combating climate change. Artificial intelligence applications to estimate the biochar mitigation effect on greenhouse gases (GHGs) can assist scientists in making more informed solutions. However, there is also evidence indicating that biochar promotes, rather than reduces, N2O emissions. Thus, the effect of biochar on N2O remains uncertain in constructed wetlands (CWs), and there is not a characterization metric for this effect, which increases the difficulty and inaccuracy of biochar-driven alleviation effect projections. Here, we provide new insight by utilizing machine learning-based, tree-structured Parzen Estimator (TPE) optimization assisted by a meta-analysis to estimate the potency of biochar-driven N2O mitigation. We first synthesized datasets that contained 80 studies on global biochar-amended CWs. The mitigation effect size was then calculated and further introduced as a new metric. TPE optimization was then applied to automatically tune the hyperparameters of the built extreme gradient boosting (XGBoost) and random forest (RF), and the optimum TPE-XGBoost obtained adequately achieved a satisfactory prediction accuracy for N2O flux (R2 = 91.90%, RPD = 3.57) and the effect size (R2 = 92.61%, RPD = 3.59). Results indicated that a high influent chemical oxygen demand/total nitrogen (COD/TN) ratio and the COD removal efficiency interpreted by the Shapley value significantly enhanced the effect size contribution. COD/TN ratio made the most and the second greatest positive contributions among 22 input variables to N2O flux and to the effect size that were up to 18% and 14%, respectively. By combining with a structural equation model analysis, NH4+-N removal rate had significant negative direct effects on the N2O flux. This study implied that the application of granulated biochar derived from C-rich feedstocks would maximize the net climate benefit of N2O mitigation driven by biochar for future biochar-based CWs.
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Affiliation(s)
- Bi-Ni Jiang
- School of Environment, Nanjing Normal University, Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, Jiangsu Engineering Lab of Water and Soil Eco-remediation, Wenyuan Road 1, Nanjing 210023, China; Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Liuhe Observation and Experimental Station of National Agro-Environment, Nanjing, 210014, China
| | - Ying-Ying Zhang
- Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Liuhe Observation and Experimental Station of National Agro-Environment, Nanjing, 210014, China
| | - Zhi-Yong Zhang
- Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Liuhe Observation and Experimental Station of National Agro-Environment, Nanjing, 210014, China.
| | - Yu-Li Yang
- School of Environment, Nanjing Normal University, Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, Jiangsu Engineering Lab of Water and Soil Eco-remediation, Wenyuan Road 1, Nanjing 210023, China
| | - Hai-Liang Song
- School of Environment, Nanjing Normal University, Jiangsu Province Engineering Research Center of Environmental Risk Prevention and Emergency Response Technology, Jiangsu Engineering Lab of Water and Soil Eco-remediation, Wenyuan Road 1, Nanjing 210023, China.
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11
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Wu CD, Zhu JJ, Hsu CY, Shie RH. Quantifying source contributions to ambient NH 3 using Geo-AI with time lag and parcel tracking functions. ENVIRONMENT INTERNATIONAL 2024; 185:108520. [PMID: 38412565 DOI: 10.1016/j.envint.2024.108520] [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: 11/06/2023] [Revised: 01/26/2024] [Accepted: 02/19/2024] [Indexed: 02/29/2024]
Abstract
Ambient ammonia (NH3) plays an important compound in forming particulate matters (PMs), and therefore, it is crucial to comprehend NH3's properties in order to better reduce PMs. However, it is not easy to achieve this goal due to the limited range/real-time NH3 data monitored by the air quality stations. While there were other studies to predict NH3 and its source apportionment, this manuscript provides a novel method (i.e., GEO-AI)) to look into NH3 predictions and their contribution sources. This study represents a pioneering effort in the application of a novel geospatial-artificial intelligence (Geo-AI) base model with parcel tracking functions. This innovative approach seamlessly integrates various machine learning algorithms and geographic predictor variables to estimate NH3 concentrations, marking the first instance of such a comprehensive methodology. The Shapley additive explanation (SHAP) was used to further analyze source contribution of NH3 with domain knowledge. From 2016 to 2018, Taichung's hourly average NH3 values were predicted with total variance up to 96%. SHAP values revealed that waterbody, traffic and agriculture emissions were the most significant factors to affect NH3 concentrations in Taichung among all the characteristics. Our methodology is a vital first step for shaping future policies and regulations and is adaptable to regions with limited monitoring sites.
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Affiliation(s)
- Chih-Da Wu
- Department of Geomatics, National Cheng Kung University, Tainan, Taiwan; National Institute of Environmental Health Sciences, National Health Research Institutes, Miaoli, Taiwan; Innovation and Development Center of Sustainable Agriculture, National Chung-Hsing University, Taichung, Taiwan
| | - Jun-Jie Zhu
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ 08544, USA
| | - Chin-Yu Hsu
- Department of Safety, Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City, Taiwan; Center for Environmental Sustainability and Human Health, Ming Chi University of Technology, New Taipei City, Taiwan.
| | - Ruei-Hao Shie
- Green Energy and Environment Research Laboratories, Industrial Technology Research Institute, 321 Guangfu Road, East District, Hsinchu City 30011, Taiwan
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12
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Qian S, Qiao X, Zhang W, Yu Z, Dong S, Feng J. Machine learning-based prediction for settling velocity of microplastics with various shapes. WATER RESEARCH 2024; 249:121001. [PMID: 38113602 DOI: 10.1016/j.watres.2023.121001] [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: 09/11/2023] [Revised: 11/22/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023]
Abstract
Microplastics can easily enter the aquatic environment and be transported between water bodies. The terminal settling velocity of microplastics, which affects their transport and distribution in the aquatic environment, is mainly influenced by their size, density, and shape. Due to the difficulty in accurately predicting the terminal settling velocity of microplastics with various shapes, this study focuses on establishing high-performance prediction models and understanding the importance and effect of each feature parameter using machine learning. Based on the number of principal dimensions, the shapes of microplastics are classified into fiber, film, and fragment, and their thresholds are identified. The microplastics of different shape categories have different optimal shape parameters for predicting the terminal settling velocity: Corey shape factor, flatness, elongation, and sphericity for the fragment, film, fiber, and mixed-shape MPs, respectively. By including the dimensionless diameter, relative density and optimal shape parameter in the input parameter combination, the machine learning models can well predict the terminal settling velocity for the microplastics of different shape categories and mixed-shape with R2 > 0.867, achieving significantly higher performance than the existing theoretical and regression models. The interpretable analysis of machine learning reveals the highest importance of the microplastic size and its marginal effect when the dimensionless diameter D* = dn(g/v2)1/3 > 80, where dn is the equivalent diameter, g is the gravitational acceleration, and ν is the fluid kinematic viscosity. The effect of shape is weak for small microplastics and becomes significant when D* exceeds 65.
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Affiliation(s)
- Shangtuo Qian
- National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu 210024, China; College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China
| | - Xuyang Qiao
- National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu 210024, China; College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
| | - Wenming Zhang
- Department of Civil and Environmental Engineering, University of Alberta, Edmonton AB T6G 1H9, Canada
| | - Zijian Yu
- Department of Civil and Environmental Engineering, University of Alberta, Edmonton AB T6G 1H9, Canada
| | - Shunan Dong
- College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China
| | - Jiangang Feng
- National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, Jiangsu 210024, China; College of Agricultural Science and Engineering, Hohai University, Nanjing 211100, China.
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13
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Song C, Cai F, Yang S, Wang L, Liu G, Chen C. Machine learning-based prediction of methane production from lignocellulosic wastes. BIORESOURCE TECHNOLOGY 2024; 393:129953. [PMID: 37914053 DOI: 10.1016/j.biortech.2023.129953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 10/29/2023] [Accepted: 10/29/2023] [Indexed: 11/03/2023]
Abstract
The biochemical methane potential test is a standard method to determine the biodegradability of lignocellulosic wastes (LWs) during anaerobic digestion (AD) with disadvantages of long experiment duration and high operating expense. This paper developed a machine learning model to predict the cumulative methane yield (CMY) using the data of 157 LWs regarding physicochemical characteristics, digestion condition and methane yield, with the coefficient of determination equal to 0.869. Model interpretability analyses underscored lignin content, organic loading, and nitrogen content as pivotal attributes for CMY prediction. For the feedstocks with a cellulose content exceeding about 50%, the CMY in the early AD stage would be relatively lower than those with low cellulose content, but prolonging digestion time could promote methane production. Besides, lignin content in feedstock surpassing 15% would significantly inhibit methane production. This work contributes to valuable guidance for feedstock selection and operation optimization for AD plants.
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Affiliation(s)
- Chao Song
- College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Fanfan Cai
- College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Shuang Yang
- College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Ligong Wang
- College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Guangqing Liu
- College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Chang Chen
- College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, China.
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14
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Zhang X, Wang Y, Jiao P, Zhang M, Deng Y, Jiang C, Liu XW, Lou L, Li Y, Zhang XX, Ma L. Microbiome-functionality in anaerobic digesters: A critical review. WATER RESEARCH 2024; 249:120891. [PMID: 38016221 DOI: 10.1016/j.watres.2023.120891] [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: 06/25/2023] [Revised: 11/08/2023] [Accepted: 11/16/2023] [Indexed: 11/30/2023]
Abstract
Microbially driven anaerobic digestion (AD) processes are of immense interest due to their role in the biovalorization of biowastes into renewable energy resources. The function-versatile microbiome, interspecies syntrophic interactions, and trophic-level metabolic pathways are important microbial components of AD. However, the lack of a comprehensive understanding of the process hampers efforts to improve AD efficiency. This study presents a holistic review of research on the microbial and metabolic "black box" of AD processes. Recent research on microbiology, functional traits, and metabolic pathways in AD, as well as the responses of functional microbiota and metabolic capabilities to optimization strategies are reviewed. The diverse ecophysiological traits and cooperation/competition interactions of the functional guilds and the biomanipulation of microbial ecology to generate valuable products other than methane during AD are outlined. The results show that AD communities prioritize cooperation to improve functional redundancy, and the dominance of specific microbes can be explained by thermodynamics, resource allocation models, and metabolic division of labor during cross-feeding. In addition, the multi-omics approaches used to decipher the ecological principles of AD consortia are summarized in detail. Lastly, future microbial research and engineering applications of AD are proposed. This review presents an in-depth understanding of microbiome-functionality mechanisms of AD and provides critical guidance for the directional and efficient bioconversion of biowastes into methane and other valuable products.
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Affiliation(s)
- Xingxing Zhang
- Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, PR China
| | - Yiwei Wang
- Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, PR China
| | - Pengbo Jiao
- Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, PR China
| | - Ming Zhang
- Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, PR China
| | - Ye Deng
- CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR China
| | - Chengying Jiang
- State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing 100101, PR China
| | - Xian-Wei Liu
- Chinese Academy of Sciences Key Laboratory of Urban Pollutant Conversion, Department of Environmental Science and Engineering, University of Science and Technology of China, Hefei 230026, PR China
| | - Liping Lou
- Department of Environmental Engineering, Zhejiang University, Hangzhou 310029, PR China
| | - Yongmei Li
- State Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Xu-Xiang Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, PR China
| | - Liping Ma
- Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai 200241, PR China; Technology Innovation Center for Land Spatial Eco-restoration in Metropolitan Area, Ministry of Natural Resources, Shanghai 200062, PR China.
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15
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Liu S, Su C, Lu Y, Xian Y, Chen Z, Wang Y, Deng X, Li X. Effects of microplastics on the properties of different types of sewage sludge and strategies to overcome the inhibition: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 902:166033. [PMID: 37543332 DOI: 10.1016/j.scitotenv.2023.166033] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/20/2023] [Accepted: 08/02/2023] [Indexed: 08/07/2023]
Abstract
Microplastics have been identified as an emerging pollutant. When microplastics enter wastewater treatment plants, the plant traps most of the microplastics in the sludge during sewage treatment. Therefore, the effects of microplastics on sludge removal performance, and on the physical and chemical properties and microbial communities in sludge, have attracted extensive attention. This review mainly describes the presence of microplastics in wastewater treatment plants, and the effects of microplastics on the decontamination efficiency and physicochemical properties of activated sludge, aerobic granular sludge, anaerobic granular sludge and anaerobic ammonium oxidation sludge. Further, the review summarizes the effects of microplastics on microbial activity and microbial community dynamics in various sludges in terms of type, concentration, and contact time. The mechanisms used to strengthen the reduction of microplastics, such as biochar and hydrochar, are also discussed. This review summarizes the mechanism by which microplastics influence the performance of different types of sludge, and proposes effective strategies to mitigate the inhibitive effect of microplastics on sludge and discusses removal technologies of microplastics in sewage. Biochar and hydrochar are one of the effective measures to overcome the inhibition of microplastics on sludge. Meanwhile, constructed wetland may be one of the important choice for the future removal of microplastics from sewage. The goal is to provide theoretical support and insights for ensuring the stable operation of wastewater treatment plants and reducing the impact of microplastics on the environment.
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Affiliation(s)
- Shengtao Liu
- Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, 15 Yucai Road, Guilin 541004, PR China
| | - Chengyuan Su
- Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, 15 Yucai Road, Guilin 541004, PR China; College of Environment and Resources, Guangxi Normal University, 15 Yucai Road, Guilin 541004, PR China.
| | - Yiying Lu
- Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, 15 Yucai Road, Guilin 541004, PR China
| | - Yunchuan Xian
- Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, 15 Yucai Road, Guilin 541004, PR China
| | - Zhengpeng Chen
- Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, 15 Yucai Road, Guilin 541004, PR China
| | - Yuchen Wang
- Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, 15 Yucai Road, Guilin 541004, PR China
| | - Xue Deng
- Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, 15 Yucai Road, Guilin 541004, PR China
| | - Xinjuan Li
- Key Laboratory of Ecology of Rare and Endangered Species and Environmental Protection (Guangxi Normal University), Ministry of Education, 15 Yucai Road, Guilin 541004, PR China
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16
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Sun Y, Zhao Z, Tong H, Sun B, Liu Y, Ren N, You S. Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17990-18000. [PMID: 37189261 DOI: 10.1021/acs.est.2c08771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
In this study, a machine learning (ML) framework is developed toward target-oriented inverse design of the electrochemical oxidation (EO) process for water purification. The XGBoost model exhibited the best performances for prediction of reaction rate (k) based on training the data set relevant to pollutant characteristics and reaction conditions, indicated by Rext2 of 0.84 and RMSEext of 0.79. Based on 315 data points collected from the literature, the current density, pollutant concentration, and gap energy (Egap) were identified to be the most impactful parameters available for the inverse design of the EO process. In particular, adding reaction conditions as model input features allowed provision of more available information and an increase in the sample size of the data set to improve the model accuracy. The feature importance analysis was performed for revealing the data pattern and feature interpretation by using Shapley additive explanations (SHAP). The ML-based inverse design for the EO process was generalized to a random case for tailoring the optimum conditions with phenol and 2,4-dichlorophenol (2,4-DCP) serving as model pollutants. The resulting predicted k values were close to the experimental k values by experimental verification, accounting for the relative error lower than 5%. This study provides a paradigm shift from conventional trial-and-error mode to data-driven mode for advancing research and development of the EO process by a time-saving, labor-effective, and environmentally friendly target-oriented strategy, which makes electrochemical water purification more efficient, more economic, and more sustainable in the context of global carbon peaking and carbon neutrality.
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Affiliation(s)
- Ye Sun
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, P. R. China
| | - Zhiyuan Zhao
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, P. R. China
| | - Hailong Tong
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, P. R. China
- State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150069, P. R. China
| | - Baiming Sun
- State Key Laboratory of Veterinary Biotechnology, Harbin Veterinary Research Institute, Chinese Academy of Agricultural Sciences, Harbin 150069, P. R. China
| | - Yanbiao Liu
- College of Environmental Science and Engineering, Textile Pollution Controlling Engineering Center of the Ministry of Ecology and Environment, Donghua University, Shanghai 201620, China
| | - Nanqi Ren
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, P. R. China
| | - Shijie You
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, P. R. China
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17
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Zhu JJ, Yang M, Ren ZJ. Machine Learning in Environmental Research: Common Pitfalls and Best Practices. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:17671-17689. [PMID: 37384597 DOI: 10.1021/acs.est.3c00026] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/01/2023]
Abstract
Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications.
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Affiliation(s)
- Jun-Jie Zhu
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
| | - Meiqi Yang
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
| | - Zhiyong Jason Ren
- Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States
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18
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Lee S, Jeong H, Hong SM, Yun D, Lee J, Kim E, Cho KH. Automatic classification of microplastics and natural organic matter mixtures using a deep learning model. WATER RESEARCH 2023; 246:120710. [PMID: 37857009 DOI: 10.1016/j.watres.2023.120710] [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: 06/19/2023] [Revised: 09/16/2023] [Accepted: 10/06/2023] [Indexed: 10/21/2023]
Abstract
Several preprocessing procedures are required for the classification of microplastics (MPs) in aquatic systems using spectroscopic analysis. Procedures such as oxidation, which are employed to remove natural organic matter (NOM) from MPs, can be time- and cost-intensive. Furthermore, the identification process is prone to errors due to the subjective judgment of the operators. Therefore, in this study, deep learning (DL) was applied to improve the classification accuracies for mixtures of microplastic and natural organic matter (MP-NOM). A convolutional neural network (CNN)-based DL model with a spatial attention mechanism was adopted to classify substances from their Raman spectra. Subsequently, the classification results were compared with those obtained using conventional Raman spectral library software to evaluate the applicability of the model. Additionally, the crucial spectral band for training the DL model was investigated by applying gradient-weighted class activation mapping (Grad-CAM) as a post-processing technique. The model achieved an accuracy of 99.54%, which is much higher than the 31.44% achieved by the Raman spectral library. The Grad-CAM approach confirmed that the DL model can effectively identify MPs based on their visually prominent peaks in the Raman spectra. Furthermore, by tracking distinctive spectra without relying solely on visually prominent peaks, we can accurately classify MPs with less prominent peaks, which are characterized by a high standard deviation of intensity. These findings demonstrate the potential for automated and objective classification of MPs without the need for NOM preprocessing, indicating a promising direction for future research in microplastic classification.
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Affiliation(s)
- Seunghyeon Lee
- Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan, 44919, Republic of Korea
| | - Heewon Jeong
- Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan, 44919, Republic of Korea
| | - Seok Min Hong
- Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan, 44919, Republic of Korea
| | - Daeun Yun
- Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Ulsan, 44919, Republic of Korea
| | - Jiye Lee
- Department of Environmental Science and Technology, University of Maryland, College Park, MD, 20742, United States
| | - Eunju Kim
- Water Cycle Research Center, Korea Institute of Science and Technology (KIST), Seoul, 02792, Republic of Korea
| | - Kyung Hwa Cho
- School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 02841, Repulic of Korea.
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Wang K, Yang S, Yu X, Liu Y, Bai M, Xu Y, Weng L, Li Y, Li X. Effect of microplastics on the degradation of tetracycline in a soil microbial electric field. JOURNAL OF HAZARDOUS MATERIALS 2023; 460:132313. [PMID: 37619277 DOI: 10.1016/j.jhazmat.2023.132313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/05/2023] [Accepted: 08/14/2023] [Indexed: 08/26/2023]
Abstract
The degradation of organic pollutants and the adsorption of organic pollutants onto microplastics (MPs) in the environment have recently been intensively studied, but the effects of biocurrents, which are widespread in various soil environments, on the environmental behavior of MPs and antibiotic pollutants have not been reported. In this study, it was found that polylactic acid (PLA) and polyvinyl chloride (PVC) MPs accelerated the mineralization of humic substances in microbial electrochemical systems (MESs). After tetracycline (TC) was introduced into the MESs, the internal resistance of the soil MESs decreased. Additionally, the presence of MPs enhanced the charge output of the soil MESs by 40% (PLA+TC) and 18% (PVC+TC) compared with a control group without MPs (424 C). The loss in MP mass decreased after TC was added, suggesting a promotion of TC degradation rather than MP degradation for charge output. MPs altered the distribution of the highest occupied molecular orbitals and lowest unoccupied molecular orbitals of TC molecules and reduced the energy barrier for the TC hydrolysis reaction. The microbial community of the plastisphere exhibited a greater ability to degrade xenobiotics than the soil microbial community, indicating that MPs were hotspots for TC degradation. This study provides the first glimpse into the influence of MPs on the degradation of TC in MESs, laying a theoretical and methodological foundation for the systematic evaluation of the potential risks of environmental pollutants in the future.
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Affiliation(s)
- Kai Wang
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs/Key Laboratory of Original Agro-Environmental Pollution Prevention and Control, MARA/Tianjin Key Laboratory of Agro-Environment and Agro-Product Safety, Tianjin 300191, China
| | - Side Yang
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs/Key Laboratory of Original Agro-Environmental Pollution Prevention and Control, MARA/Tianjin Key Laboratory of Agro-Environment and Agro-Product Safety, Tianjin 300191, China
| | - Xin Yu
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs/Key Laboratory of Original Agro-Environmental Pollution Prevention and Control, MARA/Tianjin Key Laboratory of Agro-Environment and Agro-Product Safety, Tianjin 300191, China
| | - Yonghong Liu
- College of Science, Huazhong Agricultural University, Wuhan 430070, China
| | - Mohan Bai
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs/Key Laboratory of Original Agro-Environmental Pollution Prevention and Control, MARA/Tianjin Key Laboratory of Agro-Environment and Agro-Product Safety, Tianjin 300191, China
| | - Yan Xu
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs/Key Laboratory of Original Agro-Environmental Pollution Prevention and Control, MARA/Tianjin Key Laboratory of Agro-Environment and Agro-Product Safety, Tianjin 300191, China
| | - Liping Weng
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs/Key Laboratory of Original Agro-Environmental Pollution Prevention and Control, MARA/Tianjin Key Laboratory of Agro-Environment and Agro-Product Safety, Tianjin 300191, China; Department of Soil Quality, Wageningen University, Wageningen 6700 HB, the Netherlands
| | - Yongtao Li
- College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
| | - Xiaojing Li
- Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs/Key Laboratory of Original Agro-Environmental Pollution Prevention and Control, MARA/Tianjin Key Laboratory of Agro-Environment and Agro-Product Safety, Tianjin 300191, China.
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20
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Su J, Zhang F, Yu C, Zhang Y, Wang J, Wang C, Wang H, Jiang H. Machine learning: Next promising trend for microplastics study. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 344:118756. [PMID: 37573697 DOI: 10.1016/j.jenvman.2023.118756] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/24/2023] [Accepted: 08/09/2023] [Indexed: 08/15/2023]
Abstract
Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and ecosystems. However, traditional MPs characterization methods are limited by sample requirements and characterization time. Machine Learning (ML) has emerged as a vital technology for analyzing MPs pollution due to its accuracy, broad application, and powerful feature extraction. Nevertheless, environmental scientists require threshold knowledge before using ML, restricting the ML application in MPs research. Furthermore, imbalanced development of ML in MPs research is a pressing concern. In order to achieve a wide ML application in MPs research, in this review, we comprehensively discussed the size and sources of MPs datasets in relevant literature to help environmental scientists deepen their understanding of the construction of MPs datasets. Commonly used ML algorithms are analyzed from the perspective of interpretability and the need for computer facilities. Additionally, methods for improving and evaluating ML model performance, such as dataset pre-processing, model optimization, and model assessment metrics, are discussed. According to datasets and characterization techniques, MPs identification using ML was divided into three categories in this work: spectral identification, image identification, and spectral imaging identification. Finally, other applications of ML in MPs studies, including toxicity analysis, pollutants adsorption, and microbial colonization, are comprehensively discussed, which reveals the great application potential of ML. Based on the discussion above, this review suggests an algorithm selection strategy to assist researchers in selecting the most suitable ML algorithm in different situations, improving efficiency and decreasing the costs of trial and error. We believe that this work sheds light on the application of ML in MPs study.
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Affiliation(s)
- Jiming Su
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, Hunan, PR China
| | - Fupeng Zhang
- Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, 518055, Shenzhen, PR China
| | - Chuanxiu Yu
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, Hunan, PR China
| | - Yingshuang Zhang
- School of Chemical Engineering and Technology, Xinjiang University, 830017, Urumqi, Xinjiang, PR China
| | - Jianchao Wang
- School of Chemical and Environmental Engineering, China University of Mining and Technology (Beijing), Beijing, 100083, PR China
| | - Chongqing Wang
- School of Chemical Engineering, Zhengzhou University, Zhengzhou, 450001, PR China
| | - Hui Wang
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, Hunan, PR China.
| | - Hongru Jiang
- College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, Hunan, PR China.
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21
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Bao H, Yin W, Wang H, Lu Y, Jiang S, Ajibade FO, Ouyang Q, Wang Y, Nie S, Bai Y, Gao H, Wang A. Automated machine learning-based models for predicting and evaluating antibiotic removal in constructed wetlands. BIORESOURCE TECHNOLOGY 2023; 385:129436. [PMID: 37399962 DOI: 10.1016/j.biortech.2023.129436] [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: 05/11/2023] [Revised: 06/19/2023] [Accepted: 06/30/2023] [Indexed: 07/05/2023]
Abstract
Machine learning models can improve antibiotic removal performance in constructed wetlands (CWs) by optimizing the operation process. However, robust modeling approaches for revealing the complex biochemical treatment process of antibiotics in CWs are still lacking. In this study, two automated machine learning (AutoML) models achieved good performance with different sizes of the training dataset (mean absolute error = 9.94-13.68, coefficient of determination = 0.780-0.877), demonstrating the ability to predict antibiotic removal performance without human intervention. Explainable analysis results (the variable importance and Shapley additive explanations) revealed that the variable substrate type was more influential than the variables of influent wastewater quality and plant type. This study proposed a potential approach to comprehensively understanding the complex effects of key operational variables on antibiotic removal, which serve as a reference for optimizing operational adjustments in the CW process.
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Affiliation(s)
- Hongxu Bao
- College of the Environment, Liaoning University, Shenyang 110036, China; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China
| | - Wanxin Yin
- College of the Environment, Liaoning University, Shenyang 110036, China
| | - Hongcheng Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China.
| | - Yin Lu
- College of Environment and Surveying and Mapping, China University of Mining and Technology, Xuzhou 221116, China
| | - Shijie Jiang
- College of the Environment, Liaoning University, Shenyang 110036, China
| | - Fidelis Odedishemi Ajibade
- CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Qinghua Ouyang
- Shenshui Hynar Water Group Co., Ltd., Shenzhen 518055, China
| | - Yongji Wang
- Shenshui Hynar Water Group Co., Ltd., Shenzhen 518055, China
| | - Shichen Nie
- Shandong Hynar Water Environmental Protection Co., Ltd., Caoxian, China
| | - Yu Bai
- Unicom Digital Technology Co. Ltd., Beijing 100032, China
| | - Huiliang Gao
- Shenyang Water Group Co., Ltd, Shenyang 110036 China
| | - Aijie Wang
- College of the Environment, Liaoning University, Shenyang 110036, China; State Key Laboratory of Urban Water Resource and Environment, School of Civil and Environmental Engineering, Harbin Institute of Technology Shenzhen, Shenzhen 518055, China; CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
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22
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Shang Z, Wang R, Zhang X, Tu Y, Sheng C, Yuan H, Wen L, Li Y, Zhang J, Wang X, Yang G, Feng Y, Ren G. Differential effects of petroleum-based and bio-based microplastics on anaerobic digestion: A review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 875:162674. [PMID: 36894074 DOI: 10.1016/j.scitotenv.2023.162674] [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: 11/23/2022] [Revised: 02/22/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
The number of plastics is increasing owing to the rapid development of the plastics industry. Microplastics (MPs) are formed during the use of both petroleum-based plastics and newly developed bio-based plastics. These MPs are inevitably released into the environment and are enriched in wastewater treatment plant sludge. Anaerobic digestion is a popular sludge stabilization method for wastewater treatment plants. Understanding the potential impacts of different MPs on anaerobic digestion is critical. This paper provides a comprehensive review of the mechanisms of petroleum-based MPs and bio-based MPs in anaerobic digestion methane production and compares their potential effects on biochemical pathways, key enzyme activities, and microbial communities. Finally, it identifies problems that must be solved in the future, proposes the focus of future research, and predicts the future development direction of the plastics industry.
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Affiliation(s)
- Zezhou Shang
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China
| | - Rui Wang
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China
| | - Xiyi Zhang
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China
| | - Yongle Tu
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China
| | - Chenjing Sheng
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China
| | - Huan Yuan
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China
| | - Lei Wen
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China
| | - Yulu Li
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China
| | - Jing Zhang
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China
| | - Xiaojiao Wang
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China.
| | - Gaihe Yang
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China
| | - Yongzhong Feng
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China
| | - Guangxin Ren
- College of Agronomy, Northwest A & F University, Yangling 712100, Shaanxi, China; Shaanxi Engineering Research Center of Circular Agriculture, Yangling 712100, Shaanxi, China
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23
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Manu MK, Luo L, Kumar R, Johnravindar D, Li D, Varjani S, Zhao J, Wong J. A review on mechanistic understanding of microplastic pollution on the performance of anaerobic digestion. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 325:121426. [PMID: 36907239 DOI: 10.1016/j.envpol.2023.121426] [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: 11/30/2022] [Revised: 02/24/2023] [Accepted: 03/08/2023] [Indexed: 06/18/2023]
Abstract
Anaerobic digestion (AD) has emerged as a promising technology for diverting the organic waste from the landfills along with the production of clean energy. AD is a microbial-driven biochemical process wherein the plethora of microbial communities participate in converting the putrescible organic matter into biogas. Nevertheless, the AD process is susceptible to the external environmental factors such as presence of physical (microplastics) and chemical (antibiotics, pesticides) pollutants. The microplastics (MPs) pollution has received recent attention due to the increasing plastic pollution in terrestrial ecosystems. This review was aimed for holistic assessment of impact of MPs pollution on AD process to develop efficient treatment technology. First, the possible pathways of MPs entry into the AD systems were critically evaluated. Further, the recent literature on the experimental studies pertaining to the impact of different types of MPs at different concentrations on the AD process was reviewed. In addition, several mechanisms such as direct exposure of MPs on the microbial cells, indirect impact of MPs through the leaching of toxic chemicals and reactive oxygen species (ROS) formation on AD process were elucidated. Besides, the risk possessed by the increase of antibiotic resistance genes (ARGs) after the AD process due to the MPs stress on microbial communities were discussed. Overall, this review deciphered the severity of MPs pollution on AD process at different levels.
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Affiliation(s)
- M K Manu
- Institute of Bioresource and Agriculture, Sino-Forest Applied Research Centre for Pearl River Delta Environment and Department of Biology, Hong Kong Baptist University, Hong Kong
| | - Liwen Luo
- Institute of Bioresource and Agriculture, Sino-Forest Applied Research Centre for Pearl River Delta Environment and Department of Biology, Hong Kong Baptist University, Hong Kong
| | - Reeti Kumar
- Institute of Bioresource and Agriculture, Sino-Forest Applied Research Centre for Pearl River Delta Environment and Department of Biology, Hong Kong Baptist University, Hong Kong
| | - Davidraj Johnravindar
- Institute of Bioresource and Agriculture, Sino-Forest Applied Research Centre for Pearl River Delta Environment and Department of Biology, Hong Kong Baptist University, Hong Kong
| | - Dongyi Li
- Institute of Bioresource and Agriculture, Sino-Forest Applied Research Centre for Pearl River Delta Environment and Department of Biology, Hong Kong Baptist University, Hong Kong
| | - Sunita Varjani
- School of Energy and Environment, City University of Hong Kong, Tat Chee Avenue, Kowloon, 999077, Hong Kong; Sustainability Cluster, School of Engineering, University of Petroleum and Energy Studies, Dehradun, 248 007, Uttarakhand, India
| | - Jun Zhao
- Institute of Bioresource and Agriculture, Sino-Forest Applied Research Centre for Pearl River Delta Environment and Department of Biology, Hong Kong Baptist University, Hong Kong
| | - Jonathan Wong
- Institute of Bioresource and Agriculture, Sino-Forest Applied Research Centre for Pearl River Delta Environment and Department of Biology, Hong Kong Baptist University, Hong Kong.
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24
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Chen F, Zhou B, Yang L, Chen X, Zhuang J. Predicting bacterial transport through saturated porous media using an automated machine learning model. Front Microbiol 2023; 14:1152059. [PMID: 37234532 PMCID: PMC10206036 DOI: 10.3389/fmicb.2023.1152059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/25/2023] [Indexed: 05/28/2023] Open
Abstract
Escherichia coli, as an indicator of fecal contamination, can move from manure-amended soil to groundwater under rainfall or irrigation events. Predicting its vertical transport in the subsurface is essential for the development of engineering solutions to reduce the risk of microbiological contamination. In this study, we collected 377 datasets from 61 published papers addressing E. coli transport through saturated porous media and trained six types of machine learning algorithms to predict bacterial transport. Eight variables, including bacterial concentration, porous medium type, median grain size, ionic strength, pore water velocity, column length, saturated hydraulic conductivity, and organic matter content were used as input variables while the first-order attachment coefficient and spatial removal rate were set as target variables. The eight input variables have low correlations with the target variables, namely, they cannot predict target variables independently. However, using the predictive models, input variables can effectively predict the target variables. For scenarios with higher bacterial retention, such as smaller median grain size, the predictive models showed better performance. Among six types of machine learning algorithms, Gradient Boosting Machine and Extreme Gradient Boosting outperformed other algorithms. In most predictive models, pore water velocity, ionic strength, median grain size, and column length showed higher importance than other input variables. This study provided a valuable tool to evaluate the transport risk of E.coli in the subsurface under saturated water flow conditions. It also proved the feasibility of data-driven methods that could be used for predicting other contaminants' transport in the environment.
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Affiliation(s)
- Fengxian Chen
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning, China
| | - Bin Zhou
- Faculty of Medicine, University of Augsburg, Augsburg, Germany
| | - Liqiong Yang
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning, China
| | - Xijuan Chen
- Key Laboratory of Pollution Ecology and Environmental Engineering, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, Liaoning, China
| | - Jie Zhuang
- Department of Biosystems Engineering and Soil Science, Center for Environmental Biotechnology, The University of Tennessee, Knoxville, TN, United States
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25
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Affiliation(s)
- Bing-Jie Ni
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, NSW 2007, Australia.
| | - Kevin V Thomas
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, 20 Cornwall Street, Woolloongabba, QLD 4102, Australia
| | - Eun-Ju Kim
- Water Cycle Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, South Korea
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