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Oral B, Coşgun A, Günay ME, Yıldırım R. Machine learning-based exploration of biochar for environmental management and remediation. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 360:121162. [PMID: 38749129 DOI: 10.1016/j.jenvman.2024.121162] [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: 02/26/2024] [Revised: 04/30/2024] [Accepted: 05/10/2024] [Indexed: 06/05/2024]
Abstract
Biochar has a wide range of applications, including environmental management, such as preventing soil and water pollution, removing heavy metals from water sources, and reducing air pollution. However, there are several challenges associated with the usage of biochar for these purposes, resulting in an abundance of experimental data in the literature. Accordingly, the purpose of this study is to examine the use of machine learning in biochar processes with an eye toward the potential of biochar in environmental remediation. First, recent developments in biochar utilization for the environment are summarized. Then, a bibliometric analysis is carried out to illustrate the major trends (demonstrating that the top three keywords are heavy metal, wastewater, and adsorption) and construct a comprehensive perspective for future studies. This is followed by a detailed review of machine learning applications, which reveals that adsorption efficiency and capacity are the primary utilization targets in biochar utilization. Finally, a comprehensive perspective is provided for the future. It is then concluded that machine learning can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties which can be later used for decision-making, resource allocation, and environmental management.
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Affiliation(s)
- Burcu Oral
- Department of Chemical Engineering, Boğaziçi University, 34342, Bebek, Istanbul, Turkey
| | - Ahmet Coşgun
- Department of Chemical Engineering, Boğaziçi University, 34342, Bebek, Istanbul, Turkey
| | - M Erdem Günay
- Department of Energy Systems Engineering, Istanbul Bilgi University, 34060, Eyupsultan, Istanbul, Turkey.
| | - Ramazan Yıldırım
- Department of Chemical Engineering, Boğaziçi University, 34342, Bebek, Istanbul, Turkey.
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Nguyen XC, Jang S, Noh J, Khim JS, Lee J, Kwon BO, Wang T, Hu W, Zhang X, Truong HB, Hur J. Exploring optical descriptors for rapid estimation of coastal sediment organic carbon and nearby land-use classifications via machine learning models. MARINE POLLUTION BULLETIN 2024; 202:116307. [PMID: 38564820 DOI: 10.1016/j.marpolbul.2024.116307] [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: 01/02/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/04/2024]
Abstract
This study utilizes ultraviolet and fluorescence spectroscopic indices of dissolved organic matter (DOM) from sediments, combined with machine learning (ML) models, to develop an optimized predictive model for estimating sediment total organic carbon (TOC) and identifying adjacent land-use types in coastal sediments from the Yellow and Bohai Seas. Our results indicate that ML models surpass traditional regression techniques in estimating TOC and classifying land-use types. Penalized Least Squares Regression (PLR) and Cubist models show exceptional TOC estimation capabilities, with PLR exhibiting the lowest training error and Cubist achieving a correlation coefficient 0.79. In land-use classification, Support Vector Machines achieved 85.6 % accuracy in training and 92.2 % in testing. Maximum fluorescence intensity and ultraviolet absorbance at 254 nm were crucial factors influencing TOC variations in coastal sediments. This study underscores the efficacy of ML models utilizing DOM optical indices for near real-time estimation of marine sediment TOC and land-use classification.
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Affiliation(s)
- Xuan Cuong Nguyen
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Faculty of Environmental Chemical Engineering, Duy Tan University, Da Nang 550000, Viet Nam; Department of Environment and Energy, Sejong University, Seoul 05006, South Korea
| | - Suhyeon Jang
- Department of Environment and Energy, Sejong University, Seoul 05006, South Korea
| | - Junsung Noh
- Department of Environment and Energy, Sejong University, Seoul 05006, South Korea
| | - Jong Seong Khim
- School of Earth and Environmental Sciences & Research Institute of Oceanography, Seoul National University, Seoul 08826, South Korea
| | - Junghyun Lee
- Department of Environmental Education, Kongju National University, Gongju 32588, South Korea
| | - Bong-Oh Kwon
- Department of Marine Biotechnology, Kunsan National University, Kunsan 54150, Republic of Korea
| | - Tieyu Wang
- Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Shantou University, Shantou 515063, China
| | - Wenyou Hu
- Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Xiaowei Zhang
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
| | - Hai Bang Truong
- Optical Materials Research Group, Science and Technology Advanced Institute, Van Lang University, Ho Chi Minh City 700000, Viet Nam; Faculty of Applied Technology, School of Technology, Van Lang University, Ho Chi Minh City 70000, Viet Nam
| | - Jin Hur
- Department of Environment and Energy, Sejong University, Seoul 05006, South Korea.
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Wang W, Chang JS, Lee DJ. Machine learning applications for biochar studies: A mini-review. BIORESOURCE TECHNOLOGY 2024; 394:130291. [PMID: 38184089 DOI: 10.1016/j.biortech.2023.130291] [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: 10/29/2023] [Revised: 12/20/2023] [Accepted: 12/31/2023] [Indexed: 01/08/2024]
Abstract
Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.
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Affiliation(s)
- Wei Wang
- Department of Chemical Engineering, National Taiwan University, Taipei 106, Taiwan
| | - Jo-Shu Chang
- Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung 407, Taiwan; Department of Chemical Engineering, National Cheng Kung University, Tainan, Taiwan; Department of Chemical and Materials Engineering, Tunghai University, Taichung 407, Taiwan
| | - Duu-Jong Lee
- Department of Chemical Engineering, National Taiwan University, Taipei 106, Taiwan; Department of Mechanical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong.
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Nguyen XC, Nguyen TP, Lam VS, Le PC, Vo TDH, Hoang THT, Chung WJ, Chang SW, Nguyen DD. Estimating ammonium changes in pilot and full-scale constructed wetlands using kinetic model, linear regression, and machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 907:168142. [PMID: 37898211 DOI: 10.1016/j.scitotenv.2023.168142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Revised: 10/16/2023] [Accepted: 10/24/2023] [Indexed: 10/30/2023]
Abstract
Constructed wetlands (CWs) are a widely utilized nature-based wastewater treatment method for various effluents. However, their application has been more focused on pilot and full-scale CWs with substantial surface areas and extended operation times, which hold greater relevance in practical scenarios. This study used kinetics, linear regression (LR), and machine learning (ML) models to estimate effluent ammonium in pilot and full-scale CWs. From screening 1476 papers, 24 pilot and full-scale CW studies were selected to extract data containing 15 features and 975 data points. Nine models were fit to this data, revealing that linear models were less effective in capturing CW effluent compared to nonlinear ML algorithms. For training data, the Monod kinetic model predicted the poorest performance with an RMSE of 41.84 mg/L and R2 of 0.34, followed by simple LR (RMSE 24.29 mg/L and R2 0.77) and multiple LR (RMSE 22.63 mg/L and R2 0.80). In contrast, Cubist and Random Forest achieved high performances, with an average RMSE of 12.01 ± 5.38 and an average R2 of 0.93 ± 0.07 for Cubist, and an average RMSE of 15.94 ± 10.69 and an average R2 of 0.91 ± 0.08 for RF. The trained Random Forest performed the best for new data, with an R2 of 0.93 and RMSE of 13.48 mg/L. This ML-based model is a valuable tool for efficiently estimating effluent ammonium concentration in pilot and full-scale CWs, thereby facilitating the design of systems.
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Affiliation(s)
- X Cuong Nguyen
- Institute of Research and Development, Duy Tan University, Da Nang 550000, Viet Nam; Faculty of Environmental and Chemical Engineering, Duy Tan University, Da Nang 550000, Viet Nam
| | - T Phuong Nguyen
- Faculty of Environmental Engineering Technology, Hue University, Quang Tri Branch, Viet Nam
| | - V Son Lam
- HUTECH Institute of Applied Sciences (HIAS), HUTECH University, 475A Dien Bien Phu Street, Binh Thanh District, Ho Chi Minh City, Viet Nam
| | - Phuoc-Cuong Le
- Department of Environmental Management, Faculty of Environment, The University of Danang-University of Science and Technology, Danang 550000, Viet Nam
| | - T Dieu Hien Vo
- Institute of Applied Technology and Sustainable Development, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Viet Nam
| | - Thu-Huong Thi Hoang
- School of Chemistry and Life Science, Hanoi University of Science and Technology, Hanoi 10000, Viet Nam
| | - W Jin Chung
- Department of Civil & Energy System Engineering, Kyonggi University, Suwon, South Korea
| | - S Woong Chang
- Department of Civil & Energy System Engineering, Kyonggi University, Suwon, South Korea.
| | - D Duc Nguyen
- Institute of Applied Technology and Sustainable Development, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Viet Nam; Department of Civil & Energy System Engineering, Kyonggi University, Suwon, South Korea.
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Ekundayo TC, Adewoyin MA, Ijabadeniyi OA, Igbinosa EO, Okoh AI. Machine learning-guided determination of Acinetobacter density in waterbodies receiving municipal and hospital wastewater effluents. Sci Rep 2023; 13:7749. [PMID: 37173379 PMCID: PMC10177717 DOI: 10.1038/s41598-023-34963-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 05/10/2023] [Indexed: 05/15/2023] Open
Abstract
A smart artificial intelligent system (SAIS) for Acinetobacter density (AD) enumeration in waterbodies represents an invaluable strategy for avoidance of repetitive, laborious, and time-consuming routines associated with its determination. This study aimed to predict AD in waterbodies using machine learning (ML). AD and physicochemical variables (PVs) data from three rivers monitored via standard protocols in a year-long study were fitted to 18 ML algorithms. The models' performance was assayed using regression metrics. The average pH, EC, TDS, salinity, temperature, TSS, TBS, DO, BOD, and AD was 7.76 ± 0.02, 218.66 ± 4.76 µS/cm, 110.53 ± 2.36 mg/L, 0.10 ± 0.00 PSU, 17.29 ± 0.21 °C, 80.17 ± 5.09 mg/L, 87.51 ± 5.41 NTU, 8.82 ± 0.04 mg/L, 4.00 ± 0.10 mg/L, and 3.19 ± 0.03 log CFU/100 mL respectively. While the contributions of PVs differed in values, AD predicted value by XGB [3.1792 (1.1040-4.5828)] and Cubist [3.1736 (1.1012-4.5300)] outshined other algorithms. Also, XGB (MSE = 0.0059, RMSE = 0.0770; R2 = 0.9912; MAD = 0.0440) and Cubist (MSE = 0.0117, RMSE = 0.1081, R2 = 0.9827; MAD = 0.0437) ranked first and second respectively, in predicting AD. Temperature was the most important feature in predicting AD and ranked first by 10/18 ML-algorithms accounting for 43.00-83.30% mean dropout RMSE loss after 1000 permutations. The two models' partial dependence and residual diagnostics sensitivity revealed their efficient AD prognosticating accuracies in waterbodies. In conclusion, a fully developed XGB/Cubist/XGB-Cubist ensemble/web SAIS app for AD monitoring in waterbodies could be deployed to shorten turnaround time in deciding microbiological quality of waterbodies for irrigation and other purposes.
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Affiliation(s)
- Temitope C Ekundayo
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa.
- Department of Biotechnology and Food Science, Durban University of Technology, Steve Biko Campus, Steve Biko Rd, Musgrave, Berea, 4001, Durban, South Africa.
- Department of Microbiology, University of Medical Sciences Ondo, Ondo, Nigeria.
| | - Mary A Adewoyin
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa
- Department of Biological Sciences, Faculty of Natural, Applied and Health Sciences, Anchor University, Ayobo Road, Ipaja, P. M. B. 001, Lagos, Nigeria
| | - Oluwatosin A Ijabadeniyi
- Department of Biotechnology and Food Science, Durban University of Technology, Steve Biko Campus, Steve Biko Rd, Musgrave, Berea, 4001, Durban, South Africa
| | - Etinosa O Igbinosa
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa
- Department of Microbiology, Faculty of Life Sciences, University of Benin, Private Mail Bag 1154, Benin City, 300283, Nigeria
| | - Anthony I Okoh
- SAMRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice, Eastern Cape, South Africa
- Department of Environmental Health Sciences, College of Health Sciences, University of Sharjah, P.O. Box 27272, Sharjah, United Arab Emirates
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Xia C, Ye H, Wu Y, Garalleh HA, Garaleh M, Sharma A, Pugazhendhi A. Nanofibrous/biopolymeric membrane a sustainable approach to remove organic micropollutants: A review. CHEMOSPHERE 2023; 314:137663. [PMID: 36581125 DOI: 10.1016/j.chemosphere.2022.137663] [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: 10/26/2022] [Revised: 12/13/2022] [Accepted: 12/24/2022] [Indexed: 06/17/2023]
Abstract
Aquifers are severely polluted with organic and inorganic pollutants, posing a serious threat to the global ecological system's balance. While various traditional methods are available, the development of innovative methods for effluent treatment and reuse is critical. Polymers have recently been widely used in a variety of industry sectors due to their unique properties. Biopolymers are a biodegradable material that is also a viable alternative to synthetic polymers. Biopolymers are preferably obtained from cellulose and carrageenan molecules from various biological sources. While compared with conventional non-biodegradable polymeric materials, the biopolymer possesses unique characteristics such as renewability, cost-effectiveness, biodegradability, and biocompatibility. The improvements towards the biopolymeric (natural) membranes have also been thoroughly discussed. The use of nanofillers to stabilise and improve the effectiveness of biopolymeric membranes in the elimination of organic pollutants is one of the most recent developments. This was discovered that the majority of biopolymeric membranes technology consolidated on organic pollutants. More research should be directed toward against emerging organic/persistent organic pollutants (POP) and micropollutants. Furthermore, processes for regenerating and reusing utilized biopolymer-based carbon - based materials are emphasized.
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Affiliation(s)
- Changlei Xia
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Materials Science and Engineering, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China
| | - Haoran Ye
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Materials Science and Engineering, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China
| | - Yingji Wu
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Materials Science and Engineering, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China
| | - Hakim Al Garalleh
- Department of Mathematical Science, College of Engineering, University of Business and Technology-Dahban, Jeddah, 21361, Saudi Arabia
| | - Mazen Garaleh
- Department of Mathematical Science, College of Engineering, University of Business and Technology-Dahban, Jeddah, 21361, Saudi Arabia; Department of Applied Chemistry, Faculty of Science, Tafila Technical University, Tafila, 66141, Jordan
| | - Ashutosh Sharma
- Tecnologico de Monterrey, School of Engineering and Sciences, Centre of Bioengineering, Queretaro, 76130, Mexico
| | - Arivalagan Pugazhendhi
- School of Engineering, Lebanese American University, Byblos, Lebanon; University Centre for Research & Development, Department of Civil Engineering, Chandigarh University, Mohali, India.
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Zhou X, Yan Z, Zhou X, Wang C, Liu H, Zhou H. RETRACTED: An assessment of volatile organic compounds pollutant emissions from wood materials: A review. CHEMOSPHERE 2022; 308:136460. [PMID: 36116618 DOI: 10.1016/j.chemosphere.2022.136460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/29/2022] [Accepted: 09/11/2022] [Indexed: 06/15/2023]
Affiliation(s)
- Xihe Zhou
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Materials Science and Engineering, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China
| | - Zhisong Yan
- Zhejiang Shiyou Timber Co., Ltd., 1111 Shiyuan West Road, Huzhou, Zhejiang, 313009, China
| | - Xiang Zhou
- Sinomaple Furnishing (Jiangsu) Co., Ltd., 99 Fen'an Dong Lu, Wujiang District, Suzhou, Jiangsu, 215200, China
| | - Chengming Wang
- Holtrop & Jansma (Qingdao) Environmental Protection Equipment Co., Ltd., 8 Tongshun Road, High-tech District, Qingdao, Shandong, 266114, China
| | - Hailiang Liu
- Jiangsu Shenmao Plastic Products Co., Ltd., Wood Industrial District, Siyang, Jiangsu, 223798, China
| | - Handong Zhou
- Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, International Innovation Center for Forest Chemicals and Materials, College of Materials Science and Engineering, Nanjing Forestry University, Nanjing, Jiangsu, 210037, China.
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