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Alagan M, Chandra Kishore S, Perumal S, Manoj D, Raji A, Kumar RS, Almansour AI, Lee YR. Narrative of hazardous chemicals in water: Its potential removal approach and health effects. CHEMOSPHERE 2023; 335:139178. [PMID: 37302496 DOI: 10.1016/j.chemosphere.2023.139178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/01/2023] [Accepted: 06/08/2023] [Indexed: 06/13/2023]
Abstract
H2O is essential for life to exist on earth; it is important to guarantee both the quality and supply of water to satisfy world demand. However, it became contaminated by a number of hazardous, inorganic industrial pollutants, which caused a number of issues like irrigation activities and unsafe human ingestion. Long-term exposure to harmful substances can result in respiratory, immunological, and neurological illnesses, cancer, and problems during pregnancy. Therefore, removing hazardous substances from wastewater and natural water sources is crucial. It is necessary to develop an alternate method that can effectively remove these toxins from water bodies, as conventional methods have several drawbacks. This review primarily aims to achieve the following goals: 1) to discuss the distribution of harmful chemicals: 2) to give specifics on numerous possible strategies for getting rid of hazardous chemicals, and 3) its effects on the environment and consequences for human health have been examined.
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Affiliation(s)
- Muthulakshmi Alagan
- Department of Civil and Environmental Engineering, National Institute of Technical Teachers Training and Research, Chennai, 600113, India.
| | - Somasundaram Chandra Kishore
- Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, 602105, India
| | - Suguna Perumal
- Department of Chemistry, Sejong University, Seoul, 143747, Republic of Korea
| | - Devaraj Manoj
- Department of Chemistry, Karpagam Academy of Higher Education, Coimbatore, 641021, Tamil Nadu, India; Centre for Material Chemistry, Karpagam Academy of Higher Education, Coimbatore, 641021, Tamil Nadu, India
| | - Atchudan Raji
- School of Chemical Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea.
| | - Raju Suresh Kumar
- Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Abdulrahman I Almansour
- Department of Chemistry, College of Science, King Saud University, Riyadh, 11451, Saudi Arabia
| | - Yong Rok Lee
- School of Chemical Engineering, Yeungnam University, Gyeongsan, 38541, Republic of Korea.
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Zhang W, Huang W, Tan J, Huang D, Ma J, Wu B. Modeling, optimization and understanding of adsorption process for pollutant removal via machine learning: Recent progress and future perspectives. CHEMOSPHERE 2023; 311:137044. [PMID: 36330979 DOI: 10.1016/j.chemosphere.2022.137044] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 10/22/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
It is crucial to reduce the concentration of pollutants in water environment to below safe levels. Some cost-effective pollutant removal technologies have been developed, among which adsorption technology is considered as a promising solution. However, the batch experiments and adsorption isotherms widely employed at present are inefficient and time-consuming to some extent, which limits the development of adsorption technology. As a new research paradigm, machine learning (ML) is expected to innovate traditional adsorption models. This reviews summarized the general workflow of ML and commonly employed ML algorithms for pollutant adsorption. Then, the latest progress of ML for pollutant adsorption was reviewed from the perspective of all-round regulation of adsorption process, including adsorption efficiency, operating conditions and adsorption mechanism. General guidelines of ML for pollutant adsorption were presented. Finally, the existing problems and future perspectives of ML for pollutant adsorption were put forward. We highly expect that this review will promote the application of ML in pollutant adsorption and improve the interpretability of ML.
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Affiliation(s)
- Wentao Zhang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Wenguang Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China.
| | - Jie Tan
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Dawei Huang
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Jun Ma
- South China Institute of Environmental Sciences, Ministry of Ecology and Environment of PR China, Guangzhou, 510655, People's Republic of China
| | - Bingdang Wu
- School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, People's Republic of China; Key Laboratory of Suzhou Sponge City Technology, Suzhou, 215002, People's Republic of China.
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Almalawi A, Khan AI, Alqurashi F, Abushark YB, Alam MM, Qaiyum S. Modeling of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction onto Biochar. CHEMOSPHERE 2022; 303:135065. [PMID: 35618070 DOI: 10.1016/j.chemosphere.2022.135065] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/11/2022] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
Environmental distresses linked to heavy metal (HM) impurity in the water received significant attention among research communities. Recently, advancements in industrial sectors like paper industries, mining, non-ferrous metallurgy, electroplating, mineral paint production, etc. have resulted in massive heavy metals in wastewater. In contrast to organic pollutants, HMs are not recyclable and can be simply engrossed by living organisms. Recently, different solutions have been employed for removing HMs from water and wastewater, like membrane filtration, chemical precipitation, adsorption, ion-exchange, flotation, flocculation, etc. Sorption can be considered one of the efficient solutions for eradicating HMs from waste water. With this motivation, this article concentrates on the design of Remora Optimization with Deep Learning Enabled Heavy Metal Sorption Efficiency Prediction (RODL-HMSEP) model onto Biochar. The proposed RODL-HMSEP technique intends to determine the sorption performance of HMs of various biochar features. Initially, the density based clustering (DBSCAN) technique is applied to simulating the features of metal adsorption data and splitting them into clusters of identical features. Besides, deep belief network (DBN) model was employed for prediction and the efficiency of the DBN model is optimally adjusted with utilize of RO technique. The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods The experimental validation of the RODL-HMSEP technique ensured the promising performance of the RODL-HMSEP technique on the prediction of sorption efficiency onto biochar over other methods.
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Affiliation(s)
- Abdulmohsen Almalawi
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Asif Irshad Khan
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Fahad Alqurashi
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Yoosef B Abushark
- Computer Science Department, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Md Mottahir Alam
- Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
| | - Sana Qaiyum
- Center for Research in Data Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 21 32610, 22 Perak, Malaysia.
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A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring. WATER 2022. [DOI: 10.3390/w14091384] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Artificial-intelligence methods and machine-learning models have demonstrated their ability to optimize, model, and automate critical water- and wastewater-treatment applications, natural-systems monitoring and management, and water-based agriculture such as hydroponics and aquaponics. In addition to providing computer-assisted aid to complex issues surrounding water chemistry and physical/biological processes, artificial intelligence and machine-learning (AI/ML) applications are anticipated to further optimize water-based applications and decrease capital expenses. This review offers a cross-section of peer reviewed, critical water-based applications that have been coupled with AI or ML, including chlorination, adsorption, membrane filtration, water-quality-index monitoring, water-quality-parameter modeling, river-level monitoring, and aquaponics/hydroponics automation/monitoring. Although success in control, optimization, and modeling has been achieved with the AI methods, ML models, and smart technologies (including the Internet of Things (IoT), sensors, and systems based on these technologies) that are reviewed herein, key challenges and limitations were common and pervasive throughout. Poor data management, low explainability, poor model reproducibility and standardization, as well as a lack of academic transparency are all important hurdles to overcome in order to successfully implement these intelligent applications. Recommendations to aid explainability, data management, reproducibility, and model causality are offered in order to overcome these hurdles and continue the successful implementation of these powerful tools.
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