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Gaur VK, Gautam K, Vishvakarma R, Sharma P, Pandey U, Srivastava JK, Varjani S, Chang JS, Ngo HH, Wong JWC. Integrating advanced techniques and machine learning for landfill leachate treatment: Addressing limitations and environmental concerns. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 354:124134. [PMID: 38734050 DOI: 10.1016/j.envpol.2024.124134] [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: 09/01/2023] [Revised: 05/02/2024] [Accepted: 05/08/2024] [Indexed: 05/13/2024]
Abstract
This review article explores the challenges associated with landfill leachate resulting from the increasing disposal of municipal solid waste in landfills and open areas. The composition of landfill leachate includes antibiotics (0.001-100 μg), heavy metals (0.001-1.4 g/L), dissolved organic and inorganic components, and xenobiotics including polyaromatic hydrocarbons (10-25 μg/L). Conventional treatment methods, such as biological (microbial and phytoremediation) and physicochemical (electrochemical and membrane-based) techniques, are available but face limitations in terms of cost, accuracy, and environmental risks. To surmount these challenges, this study advocates for the integration of artificial intelligence (AI) and machine learning (ML) to strengthen treatment efficacy through predictive analytics and optimized operational parameters. It critically evaluates the risks posed by recalcitrant leachate components and appraises the performance of various treatment modalities, both independently and in tandem with biological and physicochemical processes. Notably, physicochemical treatments have demonstrated pollutant removal rates of up to 90% for various contaminants, while integrated biological approaches have achieved over 95% removal efficiency. However, the heterogeneous nature of solid waste composition further complicates treatment methodologies. Consequently, the integration of advanced ML algorithms such as Support Vector Regression, Artificial Neural Networks, and Genetic Algorithms is proposed to refine leachate treatment processes. This review provides valuable insights for different stakeholders specifically researchers, policymakers and practitioners, seeking to fortify waste disposal infrastructure and foster sustainable landfill leachate management practices. By leveraging AI and ML tools in conjunction with a nuanced understanding of leachate complexities, a promising pathway emerges towards effectively addressing this environmental challenge while mitigating potential adverse impacts.
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Affiliation(s)
- Vivek Kumar Gaur
- Centre for Energy and Environmental Sustainability, Lucknow, India; School of Energy and Chemical Engineering, UNIST, Ulsan, 44919, Republic of Korea
| | - Krishna Gautam
- Centre for Energy and Environmental Sustainability, Lucknow, India
| | | | - Poonam Sharma
- Department of Bioengineering, Integral University, Lucknow, India
| | - Upasana Pandey
- Dabur Research Foundation, Ghaziabad, Uttar Pradesh, 201010, India
| | | | - Sunita Varjani
- School of Engineering, UPES, Dehradun-248 007, Uttarakhand, India; KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul 02841, Republic of Korea; School of Energy and Environment, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong.
| | - Jo-Shu Chang
- Department of Chemical and Materials Engineering, Tunghai University, Taichung, Taiwan; Department of Chemical Engineering, National Cheng Kung University, Tainan, Taiwan; Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung, Taiwan
| | - Huu Hao Ngo
- Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW - 2007, Australia
| | - Jonathan W C Wong
- Institute of Bioresource and Agriculture, Hong Kong Baptist University, Hong Kong
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Moodley T, Abunama T, Kumari S, Amoah D, Seyam M. Applications of mathematical modelling for assessing microplastic transport and fate in water environments: a comparative review. ENVIRONMENTAL MONITORING AND ASSESSMENT 2024; 196:667. [PMID: 38935176 PMCID: PMC11211188 DOI: 10.1007/s10661-024-12731-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 05/17/2024] [Indexed: 06/28/2024]
Abstract
Microplastics in the environment are considered complex pollutants as they are chemical and corrosive-resistant, non-biodegradable and ubiquitous. These microplastics may act as vectors for the dissemination of other pollutants and the transmission of microorganisms into the water environment. The currently available literature reviews focus on analysing the occurrence, environmental effects and methods of microplastic detection, however lacking a wide-scale systematic review and classification of the mathematical microplastic modelling applications. Thus, the current review provides a global overview of the modelling methodologies used for microplastic transport and fate in water environments. This review consolidates, classifies and analyses the methods, model inputs and results of 61 microplastic modelling studies in the last decade (2012-2022). It thoroughly discusses their strengths, weaknesses and common gaps in their modelling framework. Five main modelling types were classified as follows: hydrodynamic, process-based, statistical, mass-balance and machine learning models. Further, categorisations based on the water environments, location and published year of these applications were also adopted. It is concluded that addressed modelling types resulted in relatively reliable outcomes, yet each modelling framework has its strengths and weaknesses. However, common issues were found such as inputs being unrealistically assumed, especially biological processes, and the lack of sufficient field data for model calibration and validation. For future research, it is recommended to incorporate macroplastics' degradation rates, particles of different shapes and sizes and vertical mixing due to biofouling and turbulent conditions and also more experimental data to obtain precise model inputs and standardised sampling methods for surface and column waters.
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Affiliation(s)
- Tyrone Moodley
- Department of Civil Engineering and Geomatics, Durban University of Technology, Durban, 4001, South Africa
- Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa
| | - Taher Abunama
- Research Center for Treatment and Management of Water (CEBEDEAU), 4031, Liege, Belgium
| | - Sheena Kumari
- Institute for Water and Wastewater Technology, Durban University of Technology, Durban, 4001, South Africa
| | - Dennis Amoah
- Department of Environmental Science, University of Arizona, Tucson, 85721, USA
| | - Mohammed Seyam
- Department of Civil Engineering and Geomatics, Durban University of Technology, Durban, 4001, South Africa.
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Fawad M, Alabduljabbar H, Farooq F, Najeh T, Gamil Y, Ahmed B. Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches. Sci Rep 2024; 14:14252. [PMID: 38902314 PMCID: PMC11189940 DOI: 10.1038/s41598-024-64204-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 06/06/2024] [Indexed: 06/22/2024] Open
Abstract
Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.
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Affiliation(s)
- Muhammad Fawad
- Department of Structural Engineering, Faculty of Civil Engineering, Silesian University of Technology, Akademicka 2, 44-100, Gliwice, Poland.
- Budapest University of Technology and Economics Hungary, Budapest, Hungary.
| | - Hisham Alabduljabbar
- Department of Civil Engineering, College of Engineering in Al-Kharj, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia
| | - Furqan Farooq
- NUST Institute of Civil Engineering (NICE), School of Civil and Environmental Engineering (SCEE), National University of Sciences and Technology (NUST), Sector H-12, Islamabad, 44000, Pakistan
- Western Caspian University, Baku, Azerbaijan
| | - Taoufik Najeh
- Operation and Maintenance, Operation, Maintenance and Acoustics, Department of Civil, Environmental and Natural Resources Engineering, Luleå University of Technology, Lulea, Sweden.
| | - Yaser Gamil
- Department of Civil Engineering, School of Engineering, Monash University Malaysia, Jalan Lagoon Selatan, 47500, Bandar Sunway, Selangor, Malaysia
| | - Bilal Ahmed
- Department of Structural Engineering, Faculty of Civil Engineering, Silesian University of Technology, Akademicka 2, 44-100, Gliwice, Poland
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Gautam K, Pandey N, Yadav D, Parthasarathi R, Turner A, Anbumani S, Jha AN. Ecotoxicological impacts of landfill sites: Towards risk assessment, mitigation policies and the role of artificial intelligence. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:171804. [PMID: 38513865 DOI: 10.1016/j.scitotenv.2024.171804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/14/2024] [Accepted: 03/16/2024] [Indexed: 03/23/2024]
Abstract
Waste disposal in landfills remains a global concern. Despite technological developments, landfill leachate poses a hazard to ecosystems and human health since it acts as a secondary reservoir for legacy and emerging pollutants. This study provides a systematic and scientometric review of the nature and toxicity of pollutants generated by landfills and means of assessing their potential risks. Regarding human health, unregulated waste disposal and pathogens in leachate are the leading causes of diseases reported in local populations. Both in vitro and in vivo approaches have been employed in the ecotoxicological risk assessment of landfill leachate, with model organisms ranging from bacteria to birds. These studies demonstrate a wide range of toxic effects that reflect the complex composition of leachate and geographical variations in climate, resource availability and management practices. Based on bioassay (and other) evidence, categories of persistent chemicals of most concern include brominated flame retardants, per- and polyfluorinated chemicals, pharmaceuticals and alkyl phenol ethoxylates. However, the emerging and more general literature on microplastic toxicity suggests that these particles might also be problematic in leachate. Various mitigation strategies have been identified, with most focussing on improving landfill design or leachate treatment, developing alternative disposal methods and reducing waste volume through recycling or using more sustainable materials. The success of these efforts will rely on policies and practices and their enforcement, which is seen as a particular challenge in developing nations and at the international (and transboundary) level. Artificial intelligence and machine learning afford a wide range of options for evaluating and reducing the risks associated with leachates and gaseous emissions from landfills, and various approaches tested or having potential are discussed. However, addressing the limitations in data collection, model accuracy, real-time monitoring and our understanding of environmental impacts will be critical for realising this potential.
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Affiliation(s)
- Krishna Gautam
- Ecotoxicology Laboratory, REACT Division, CSIR-Indian Institute of Toxicology Research, CRK Campus, Lucknow 226008, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Namrata Pandey
- Ecotoxicology Laboratory, REACT Division, CSIR-Indian Institute of Toxicology Research, CRK Campus, Lucknow 226008, Uttar Pradesh, India
| | - Dhvani Yadav
- Computational Toxicology Facility, CSIR-Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India
| | - Ramakrishnan Parthasarathi
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India; Computational Toxicology Facility, CSIR-Indian Institute of Toxicology Research, Vishvigyan Bhawan, 31, Mahatma Gandhi Marg, Lucknow 226001, Uttar Pradesh, India
| | - Andrew Turner
- School of Geography, Earth and Environmental Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK
| | - Sadasivam Anbumani
- Ecotoxicology Laboratory, REACT Division, CSIR-Indian Institute of Toxicology Research, CRK Campus, Lucknow 226008, Uttar Pradesh, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India
| | - Awadhesh N Jha
- School of Biological and Marine Sciences, University of Plymouth, Drake Circus, Plymouth PL4 8AA, UK.
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Zhou W, Chai J, Xu Z, Qin Y, Cao J, Zhang P. A review of existing methods for predicting leachate production from municipal solid waste landfills. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:16131-16149. [PMID: 38319418 DOI: 10.1007/s11356-024-32289-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/27/2024] [Indexed: 02/07/2024]
Abstract
Landfilling is one of the predominant methods of municipal solid waste (MSW) disposal worldwide, while the generation of leachate, a kind of toxic wastewater, is among the primary factors behind landfill instability and environmental contamination problems. Precise prediction of leachate production is crucial to landfill safety evaluation and design. This paper presents a comprehensive review of methods for predicting leachate production from MSW landfills. Firstly, compositional characteristics of MSW and leachate generation mechanism are analysed. Factors influencing leachate production are summarised based on the generation mechanism, including the components of MSW, climatic conditions, landfill structure, and environmental factors. Then, we classified the existing methods for predicting leachate production into four categories: water balance formula, water balance model, empirical formula, and artificial intelligence model methods. Advantages, disadvantages, and applicability of different leachate production prediction methods are compared and analysed. Furthermore, limitations in the existing leachate production prediction methods for MSW landfills and scope for future research are discussed.
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Affiliation(s)
- Wu Zhou
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, 710048, Shaanxi, China
| | - Junrui Chai
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, 710048, Shaanxi, China.
| | - Zengguang Xu
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, 710048, Shaanxi, China
| | - Yuan Qin
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, 710048, Shaanxi, China
| | - Jing Cao
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, 710048, Shaanxi, China
| | - Pengyuan Zhang
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, 710048, Shaanxi, China
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Hoy ZX, Phuang ZX, Farooque AA, Fan YV, Woon KS. Municipal solid waste management for low-carbon transition: A systematic review of artificial neural network applications for trend prediction. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 344:123386. [PMID: 38242306 DOI: 10.1016/j.envpol.2024.123386] [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: 08/25/2023] [Revised: 11/16/2023] [Accepted: 01/16/2024] [Indexed: 01/21/2024]
Abstract
Improper municipal solid waste (MSW) management contributes to greenhouse gas emissions, necessitating emissions reduction strategies such as waste reduction, recycling, and composting to move towards a more sustainable, low-carbon future. Machine learning models are applied for MSW-related trend prediction to provide insights on future waste generation or carbon emissions trends and assist the formulation of effective low-carbon policies. Yet, the existing machine learning models are diverse and scattered. This inconsistency poses challenges for researchers in the MSW domain who seek to identify and optimize the machine learning techniques and configurations for their applications. This systematic review focuses on MSW-related trend prediction using the most frequently applied machine learning model, artificial neural network (ANN), while addressing potential methodological improvements for reducing prediction uncertainty. Thirty-two papers published from 2013 to 2023 are included in this review, all applying ANN for MSW-related trend prediction. Observing a decrease in the size of data samples used in studies from daily to annual timescales, the summarized statistics suggest that well-performing ANN models can still be developed with approximately 33 annual data samples. This indicates promising opportunities for modeling macroscale greenhouse gas emissions in future works. Existing literature commonly used the grid search (manual) technique for hyperparameter (e.g., learning rate, number of neurons) optimization and should explore more time-efficient automated optimization techniques. Since there are no one-size-fits-all performance indicators, it is crucial to report the model's predictive performance based on more than one performance indicator and examine its uncertainty. The predictive performance of newly-developed integrated models should also be benchmarked to show performance improvement clearly and promote similar applications in future works. The review analyzed the shortcomings, best practices, and prospects of ANNs for MSW-related trend predictions, supporting the realization of practical applications of ANNs to enhance waste management practices and reduce carbon emissions.
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Affiliation(s)
- Zheng Xuan Hoy
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia
| | - Zhen Xin Phuang
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia
| | - Aitazaz Ahsan Farooque
- Canadian Center for Climate Change and Adaptation, University of Prince Edward Island, St Peter's Bay, PE, Canada; Faculty of Sustainable Design Engineering, University of Prince Edward Island, Charlottetown, PE, Canada
| | - Yee Van Fan
- Sustainable Process Integration Laboratory - SPIL, NETME Centre, Faculty of Mechanical Engineering, Brno University of Technology - VUT Brno, Technická 2896/2, 61669, Brno, Czech Republic
| | - Kok Sin Woon
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia.
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Munir MT, Li B, Naqvi M, Nizami AS. Green loops and clean skies: Optimizing municipal solid waste management using data science for a circular economy. ENVIRONMENTAL RESEARCH 2024; 243:117786. [PMID: 38036215 DOI: 10.1016/j.envres.2023.117786] [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: 10/16/2023] [Revised: 11/20/2023] [Accepted: 11/23/2023] [Indexed: 12/02/2023]
Abstract
The interplay between Municipal Solid Waste (MSW) Management and data science unveils a panorama of opportunities and challenges, set against the backdrop of rising global waste and evolving technological landscapes. This article threads through the multifaceted aspects of incorporating data science into MSW management, unearthing key findings, novel knowledge, and instigating a call to action for stakeholders (e.g. policymakers, local authorities, waste management professionals, technology developers, and the general public) across the spectrum. Predominant challenges like the enigmatic nature of "black-box" models and tangible knowledge gaps in the sector are scrutinized, ushering in a narrative that emphasizes transparent, stakeholder-inclusive, and policy-adaptive approaches. Notably, a conscious shift towards "white-box" and "grey-box" data science models has been spotlighted as a pivotal response to transparency issues. Furthermore, the discourse highlights the necessity of crafting data science solutions that are specifically moulded to the nuanced challenges of MSW management, and it underscores the importance of recalibrating existing policies to be reflexive to technological advancements. A resolute call echoes for stakeholders to not just adapt but immerse themselves in a continuous learning trajectory, championing transparency, and fostering collaborations that hinge on innovative, data-driven methodologies. Thus, as the realms of data science and MSW management entwine, the article sheds light on the potential transformation awaiting waste management paradigms, contingent on the nurtured amalgamation of technological advances, policy alignment, and collaborative synergy.
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Affiliation(s)
| | - Bing Li
- Water Research Center, Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Muhammad Naqvi
- College of Engineering and Technology, American University of the Middle East, Kuwait.
| | - Abdul-Sattar Nizami
- Sustainable Development Study Center, Government College University, Lahore, 54000, Pakistan
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Choudhury AR, Singh N, Palani SG, Lalwani J. Bioremediation of reverse osmosis concentrate generated from the treatment of landfill leachate. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:93934-93951. [PMID: 37518844 DOI: 10.1007/s11356-023-28957-0] [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: 02/22/2023] [Accepted: 07/19/2023] [Indexed: 08/01/2023]
Abstract
The moisture content of municipal solid waste (MSW) and local precipitation events lead to the leachate generation from MSW landfills. The high concentration of organic pollutants in landfill leachate (LL) makes it hazardous, requiring treatment before disposal into the environment. LL is most commonly treated by reverse osmosis (RO), which generates large volumes of concentrate known as RO concentrate. This investigation aims to stabilize the RO concentrate through an inexpensive and effective bioremediation strategy. A bench-scale aerobic suspended growth reactor study was conducted using three commercial conversion agents, namely EM.1, Bokashi powder, and coir pith powder. Overall bench-scale efficiency of 63% was achieved in this study. The onsite studies were conducted in 7.5-m3 artificial ponds with 46% efficiency amid atmospheric influences and constraints. The overall efficiencies of both bench and field-scale studies were derived by ascertaining the arithmetic mean of the individual efficiency of the following parameters: chemical oxygen demand (COD), biochemical oxygen demand (BOD), and total dissolved solids (TDS). In contrast, the control pond with no conversion agents showed an increase in pollution concentration over the 100 days of retention time. The findings revealed that the investigated technology had a marginally lower evaporation rate and performed relatively well compared to traditional solar evaporation ponds. Moreover, the technology can be easily scaled-up and readily applied for RO concentrate treatment in MSW landfills.
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Affiliation(s)
- Atun Roy Choudhury
- Cube Bio Energy Pvt. Ltd., Madhapur, Hyderabad, Telangana, India
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad Campus, Pilani, Telangana, India
| | - Neha Singh
- Chadwick's FSM Laboratory, Banka BioLoo Limited, Shed no-37, Industrial Development Area, Mallapur, Secunderabad, India
| | - Sankar Ganesh Palani
- Department of Biological Sciences, Birla Institute of Technology and Science, Hyderabad Campus, Pilani, Telangana, India.
| | - Jitesh Lalwani
- Department of Civil Engineering, Vardhaman College of Engineering, Hyderabad, Telangana, India
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Fang B, Yu J, Chen Z, Osman AI, Farghali M, Ihara I, Hamza EH, Rooney DW, Yap PS. Artificial intelligence for waste management in smart cities: a review. ENVIRONMENTAL CHEMISTRY LETTERS 2023; 21:1-31. [PMID: 37362015 PMCID: PMC10169138 DOI: 10.1007/s10311-023-01604-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 04/24/2023] [Indexed: 06/28/2023]
Abstract
The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.
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Affiliation(s)
- Bingbing Fang
- Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123 China
| | - Jiacheng Yu
- Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123 China
| | - Zhonghao Chen
- Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123 China
| | - Ahmed I. Osman
- School of Chemistry and Chemical Engineering, Queen’s University Belfast, David Keir Building, Stranmillis Road, Belfast, BT9 5AG Northern Ireland UK
| | - Mohamed Farghali
- Department of Agricultural Engineering and Socio-Economics, Kobe University, Kobe, 657-8501 Japan
- Department of Animal and Poultry Hygiene & Environmental Sanitation, Faculty of Veterinary Medicine, Assiut University, Assiut, 71526 Egypt
| | - Ikko Ihara
- Department of Agricultural Engineering and Socio-Economics, Kobe University, Kobe, 657-8501 Japan
| | - Essam H. Hamza
- Electric and Computer Engineering Department, Aircraft Armament (A/CA), Military Technical College, Cairo, Egypt
| | - David W. Rooney
- School of Chemistry and Chemical Engineering, Queen’s University Belfast, David Keir Building, Stranmillis Road, Belfast, BT9 5AG Northern Ireland UK
| | - Pow-Seng Yap
- Department of Civil Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, 215123 China
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Teja DR, Kumar PSS, Jariwala N. Application of multi-criteria decision-making techniques to develop modify-leachate pollution index. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:41172-41186. [PMID: 36630033 DOI: 10.1007/s11356-023-25178-3] [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: 09/29/2022] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
The modify-leachate pollution index (m-LPI) was developed with the help of multi-criteria decision-making (MCDM) technique based on the landfill leachate pollution potential by considering the limitations of traditional methodologies. Across India, twenty major landfill sites (LS) were selected for which m-LPI was assessed. Twenty-five experts' opinions were taken for the determination of nine input criteria weights, such as pH, COD, TDS, Cl, Zn, Pb, Cu, annual rainfall, and landfill age with the help of a questionnaire-based survey. In this context, six MCDM techniques were investigated to develop m-LPI. Among different MCDM techniques selected, weighted aggregated sum product assessment (WASPAS) proved to be an effective one with an R2 value of 0.828 and IA value of 0.813. WASPAS gave first and last rank to Kadapa, Andhra Pradesh LS (1.677) and Turbhe, Maharashtra LS (2.193), respectively. The investigation revealed that around 90% of LS considered in the present study require leachate treatment. WASPAS sensitivity analysis showed that the least sensitive criteria were pH, followed by Cl and Zn. The m-LPI can be used by researchers and scientists to investigate and evaluate various challenges involved with solid waste management in LS.
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Affiliation(s)
- Dharmasanam Ravi Teja
- Department of Environmental Science and Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, 826004, India
| | | | - Namrata Jariwala
- Civil Engineering Department, S.V. National Institute of Technology, Surat, India.
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Ibrahim Z, Tulay P, Abdullahi J. Multi-region machine learning-based novel ensemble approaches for predicting COVID-19 pandemic in Africa. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:3621-3643. [PMID: 35948797 PMCID: PMC9365685 DOI: 10.1007/s11356-022-22373-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 07/30/2022] [Indexed: 06/15/2023]
Abstract
Coronavirus disease 2019 (COVID-19) has produced a global pandemic, which has devastating effects on health, economy and social interactions. Despite the less contraction and spread of COVID-19 in Africa compared to some other continents in the world, Africa remains amongst the most vulnerable regions due to less technology and unequipped or poor health system. Recent happenings showed that COVID-19 may stay for years owing to the discoveries of new variants (such as Omicron) and new wave of infections in several countries. Therefore, accurate prediction of new cases is vital to make informed decisions and in evaluating the measures that should be implemented. Studies on COVID-19 prediction are limited in Africa despite the risks and dangers that the virus possessed. Hence, this study was performed to predict daily COVID-19 cases in 10 African countries spread across the north, south, east, west and central Africa considering countries with few and large number of daily COVID-19 cases. Machine learning (ML) models due to their nonlinearity and accurate prediction capabilities were employed for this purpose, including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and conventional multiple linear regression (MLR) models. As any other natural process, the COVID-19 pandemic may contain both linear and nonlinear aspects. In such circumstances, neither nonlinear (ML) nor linear (MLR) models could be sufficient; hence, combining both ML and MLR models may produce better accuracy. Consequently, to improve the prediction efficiency of the ML models, novel ensemble approaches including ANN-E and SVM-E were employed. The advantage of using ensemble approaches is that they provide collective benefits of all the standalone models, thereby reducing their weaknesses and enhancing their prediction capabilities. The obtained results showed that ANFIS led to better prediction performance with MAD = 0.0106, MSE = 0.0003, RMSE = 0.0185 and R2 = 0.9059 in the validation step. The results of the proposed ensemble approaches demonstrated very high improvements in predicting the COVID-19 pandemic in Africa with MAD = 0.0073, MSE = 0.0002, RMSE = 0.0155 and R2 = 0.9616. The ANN-E improved the standalone models performance in the validation step up to 10%, 14%, 42%, 6%, 83%, 11%, 7%, 5%, 7% and 31% for Morocco, Sudan, Namibia, South Africa, Uganda, Rwanda, Nigeria, Senegal, Gabon and Cameroon, respectively. This study results offer a solid foundation in the application of ensemble approaches for predicting COVID-19 pandemic across all regions and countries in the world.
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Affiliation(s)
- Zurki Ibrahim
- Department of Medical Genetics, Near East University, Mersin 10, Lefkosa, Turkey
| | - Pinar Tulay
- Department of Medical Genetics, Near East University, Mersin 10, Lefkosa, Turkey
| | - Jazuli Abdullahi
- Department of Civil Engineering, Faculty of Engineering, Baze University, Abuja, Nigeria.
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12
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Cha GW, Choi SH, Hong WH, Park CW. Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 20:107. [PMID: 36612429 PMCID: PMC9819715 DOI: 10.3390/ijerph20010107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/14/2022] [Accepted: 12/17/2022] [Indexed: 06/17/2023]
Abstract
Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying artificial intelligence to the construction and prediction of WG information. In this study, research was conducted on the development of machine learning (ML) models for predicting the demolition waste generation rate (DWGR) of buildings in redevelopment areas in South Korea. Various ML algorithms (i.e., artificial neural network (ANN), K-nearest neighbors (KNN), linear regression (LR), random forest (RF), and support vector machine (SVM)) were applied to the development of an optimal predictive model, and the main hyper parameters (HPs) for each algorithm were optimized. The results suggest that ANN-ReLu (coefficient of determination (R2) 0.900, the ratio of percent deviation (RPD) 3.16), SVM-polynomial (R2 0.889, RPD 3.00), and ANN-logistic (R2 0.883, RPD 2.92) are the best ML models for predicting the DWGR. They showed average errors of 7.3%, 7.4%, and 7.5%, respectively, compared to the average observed values, confirming the accurate predictive performance, and in the uncertainty analysis, the d-factor of the models appeared less than 1, showing that the presented models are reliable. Through a comparison with ML algorithms and HPs applied in previous related studies, the results herein also showed that the selection of various ML algorithms and HPs is important in developing optimal ML models for WG management.
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Affiliation(s)
- Gi-Wook Cha
- School of Science and Technology Acceleration Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Se-Hyu Choi
- School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Won-Hwa Hong
- School of Architectural, Civil, Environmental and Energy Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
| | - Choon-Wook Park
- Industry Academic Cooperation Foundation, Kyungpook National University, Daegu 41566, Republic of Korea
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13
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Cheah CG, Chia WY, Lai SF, Chew KW, Chia SR, Show PL. Innovation designs of industry 4.0 based solid waste management: Machinery and digital circular economy. ENVIRONMENTAL RESEARCH 2022; 213:113619. [PMID: 35700763 DOI: 10.1016/j.envres.2022.113619] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/23/2022] [Accepted: 06/03/2022] [Indexed: 05/21/2023]
Abstract
The Industrial Revolution 4.0 (IR 4.0) holds the opportunity to improve the efficiency of managing solid waste through digital and machinery applications, effectively eliminating, recovering, and repurposing waste. This research aims to discover and review the potential of current technologies encompassing innovative Industry 4.0 designs for solid waste management. Machinery and processes emphasizing on circular economy were summarized and evaluated. The application of IR 4.0 technologies shows promising opportunities in improving the management and efficiency in view of solid waste. Machine learning (ML), artificial intelligence (AI), and image recognition can be used to automate the segregation of waste, reducing the risk of exposing labour workers to harmful waste. Radio Frequency Identification (RFID) and wireless communications enable the traceability in materials to better understand the opportunities in circular economy. Additionally, the interconnectivity of systems and automatic transfer of data enable the creation of more complex system that houses a larger solution space that was previously not possible such as centralised cloud computing to reduce the cost by eliminating the need for individual computing systems. Through this comprehensive review-based work, innovative Industry 4.0 components of machinery and processes involving waste management which focuses on circular economy are identified with the critical ones evaluated briefly. It was found that the current research and work done is based on applying Industry 4.0 technologies on individual waste management systems, which lacks the coherency needed to capitalise on technologies such as cloud computing, interconnectivity, big data, etc on a larger scale. Therefore, a real world comprehensive end-to-end integration aimed to optimize every process within the solid waste management chain should be explored.
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Affiliation(s)
- Chor Gene Cheah
- Department of Mechanical, Materials and Manufacturing (M3) Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500, Semenyih, Selangor Darul Ehsan, Malaysia
| | - Wen Yi Chia
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500, Semenyih, Selangor Darul Ehsan, Malaysia
| | - Shuet Fen Lai
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia
| | - Kit Wayne Chew
- School of Energy and Chemical Engineering, Xiamen University Malaysia, Jalan Sunsuria, Bandar Sunsuria, 43900, Sepang, Selangor, Malaysia; College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, Fujian, China.
| | - Shir Reen Chia
- Institute of Sustainable Energy, Universiti Tenaga Nasional (UNITEN), Jalan IKRAM-UNITEN, 43000, Kajang, Selangor, Malaysia
| | - Pau Loke Show
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500, Semenyih, Selangor Darul Ehsan, Malaysia
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14
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Andeobu L, Wibowo S, Grandhi S. Artificial intelligence applications for sustainable solid waste management practices in Australia: A systematic review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 834:155389. [PMID: 35460765 DOI: 10.1016/j.scitotenv.2022.155389] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/14/2022] [Accepted: 04/15/2022] [Indexed: 05/17/2023]
Abstract
Solid waste generation and its impact on human health and the environment have long been a matter of concern for governments across the world. In recent years, there has been increasing emphasis on resource recovery (reusing, recycling and extracting energy from waste) using more advanced approaches such as artificial intelligence (AI) in Australia. AI is a powerful technology that is increasingly gaining popularity and application in various fields. The adoption of AI techniques offers alternative innovative approaches to solid waste management (SWM). Although there are previous studies on AI technologies and SWM, no study has assessed the adoption of AI applications in solving the diverse SWM problems for achieving sustainable waste management in Australia. Moreover, there are inconsistencies and a lack of awareness on how AI technologies function in relation to their application to SWM. This study examines the application of AI technologies in various areas of SWM (generation, sorting, collection, vehicle routing, treatment, disposal and waste management planning) to enhance sustainable waste management practices in Australia. To achieve the aims of this study, prior studies from 2005 to 2021 from various databases are collected and analyzed. The study focuses on the adoption of AI applications on SWM, compares the performance of AI applications, explores the benefits and challenges, and provides best practice recommendations on how resource efficiency can be optimized to improve economic, environmental and social outcomes. This study found that AI-based models have better prediction abilities when compared to other models used in forecasting solid waste generation and recycling. Findings show that waste generation in Australia has been steadily increasing and requires upgraded and improved recovery infrastructure and the appropriate adoption of AI technologies to enhance sustainable SWM. Australia's adoption of AI recycling technologies would benefit from a national approach that seeks consistency across jurisdictions, while catering for regional differences. This study will benefit researchers, governments, policy-makers, municipalities and other waste management organizations to increase current recycling rates, eliminate the need for manual labor, reduce costs, maximize efficiency, and transform the way we approach the management of solid waste.
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Affiliation(s)
- Lynda Andeobu
- Central Queensland University, 120 Spencer Street, Melbourne 3000, Australia.
| | - Santoso Wibowo
- Central Queensland University, 120 Spencer Street, Melbourne 3000, Australia.
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15
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Xia W, Jiang Y, Chen X, Zhao R. Application of machine learning algorithms in municipal solid waste management: A mini review. WASTE MANAGEMENT & RESEARCH : THE JOURNAL OF THE INTERNATIONAL SOLID WASTES AND PUBLIC CLEANSING ASSOCIATION, ISWA 2022; 40:609-624. [PMID: 34269157 PMCID: PMC9016669 DOI: 10.1177/0734242x211033716] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Population growth and the acceleration of urbanization have led to a sharp increase in municipal solid waste production, and researchers have sought to use advanced technology to solve this problem. Machine learning (ML) algorithms are good at modeling complex nonlinear processes and have been gradually adopted to promote municipal solid waste management (MSWM) and help the sustainable development of the environment in the past few years. In this study, more than 200 publications published over the last two decades (2000-2020) were reviewed and analyzed. This paper summarizes the application of ML algorithms in the whole process of MSWM, from waste generation to collection and transportation, to final disposal. Through this comprehensive review, the gaps and future directions of ML application in MSWM are discussed, providing theoretical and practical guidance for follow-up related research.
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Affiliation(s)
- Wanjun Xia
- School of Computing and
Artificial Intelligence, Southwest Jiaotong University, Chengdu, Sichuan,
China
- Library, Southwest Jiaotong
University, Chengdu, Sichuan, China
- Wanjun Xia, School of Computing and
Artificial Intelligence, Southwest Jiaotong University, West Park of
Hi-Tech Zone, Chengdu, Sichuan 611756, China.
| | - Yanping Jiang
- Library, Southwest Jiaotong
University, Chengdu, Sichuan, China
| | - Xiaohong Chen
- Library, Southwest Jiaotong
University, Chengdu, Sichuan, China
| | - Rui Zhao
- Faculty of Geosciences and
Environmental Engineering, Southwest Jiaotong University, Chengdu, Sichuan,
China
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16
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Ishii K, Sato M, Ochiai S. Prediction of leachate quantity and quality from a landfill site by the long short-term memory model. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 310:114733. [PMID: 35189557 DOI: 10.1016/j.jenvman.2022.114733] [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: 09/15/2021] [Revised: 02/02/2022] [Accepted: 02/13/2022] [Indexed: 06/14/2023]
Abstract
The long short-term memory (LSTM) model was first applied in this study for the prediction of the leachate quantity and quality at a real landfill site. In our LSTM model, in the learning phase from July 2003 to March 2018, three input data items consisting of the daily precipitation (DP), the daily average temperature (DAT), and the accumulated amount of landfilled waste presented the quantity of leachate generated with high accuracy. The DAT was important for the landfill site, particularly in a snow area because it contributes to the leachate generated during the spring thaw with low precipitation. In the testing phase from April 2018 to March 2019, our LSTM model predicted the leachate generated with a mean absolute percentage error (MAPE) of 26.2%. The concentrations of biological oxygen demand, chemical oxygen demand, total nitrogen, calcium ion and chloride ion in leachate were presented in the learning phase by six input data items: DP, DAT, and the daily amount of landfilled waste (incineration residue, incombustible waste, business waste, and combustible waste) with high R2 values. In the testing phase, the quality of leachate was predicted with the MAPE between 11.8% and 30.2%. Another year data from April 2019 to March 2020 was used to verify accuracy of our model with no overfitting. This study showed the possibility of applying the LSTM model to future predictions of leachate quantity and quality from landfill sites with an acceptable error for daily operation.
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Affiliation(s)
- Kazuei Ishii
- Faculty of Engineering, Hokkaido University, N13, W8, Kita-ku, Sapporo, Hokkaido, 060-8628, Japan.
| | - Masahiro Sato
- Faculty of Engineering, Hokkaido University, N13, W8, Kita-ku, Sapporo, Hokkaido, 060-8628, Japan
| | - Satoru Ochiai
- Faculty of Engineering, Hokkaido University, N13, W8, Kita-ku, Sapporo, Hokkaido, 060-8628, Japan
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17
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Rubab S, Khan MM, Uddin F, Abbas Bangash Y, Taqvi SAA. A Study on AI‐based Waste Management Strategies for the COVID‐19 Pandemic. CHEMBIOENG REVIEWS 2022. [PMCID: PMC9083818 DOI: 10.1002/cben.202100044] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
COVID‐19 has swept across the globe and disrupted all vectors of social life. Every informed measure must be taken to stop its spread, bring down number of new infections and move to normalization of daily life. Contemporary research has not identified waste management as one of the critical transmission vectors for COVID‐19 virus. However, most underdeveloped countries are facing problems in waste management processes due to the general inadequacy and inability of waste management. In that context, smart intervention will be needed to contain possibility of the COVID‐19 spread due to inadequate waste management. This paper presents a comparative study of the artificial intelligence/machine learning based techniques, and potential applications in the COVID‐19 waste management cycle (WMC). A general integrated solid waste management (ISWM) strategy is mapped for both short‐term and long‐term goals of COVID‐19 WMC, making use of the techniques investigated. By aligning current health/waste‐related guidelines from health organizations and governments worldwide and contemporary, relevant research in area, the challenge of COVID‐19 waste management and, subsequently, slowing the pandemic down may be assisted.
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Affiliation(s)
- Saddaf Rubab
- National University of Sciences and Technology (NUST) 44000 Islamabad Pakistan
| | - Malik M. Khan
- National University of Sciences and Technology (NUST) 44000 Islamabad Pakistan
| | - Fahim Uddin
- NED University of Engineering and Technology Department of Chemical Engineering Karachi Pakistan
| | - Yawar Abbas Bangash
- National University of Sciences and Technology (NUST) 44000 Islamabad Pakistan
| | - Syed Ali Ammar Taqvi
- NED University of Engineering and Technology Department of Chemical Engineering Karachi Pakistan
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18
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Predicting Marshall Flow and Marshall Stability of Asphalt Pavements Using Multi Expression Programming. BUILDINGS 2022. [DOI: 10.3390/buildings12030314] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The traditional method to obtain optimum bitumen content and the relevant parameters of asphalt pavements entails time-consuming, complicated and expensive laboratory procedures and requires skilled personnel. This research study uses innovative and advanced machine learning techniques, i.e., Multi-Expression Programming (MEP), to develop empirical predictive models for the Marshall parameters, i.e., Marshall Stability (MS) and Marshall Flow (MF) for Asphalt Base Course (ABC) and Asphalt Wearing Course (AWC) of flexible pavements. A comprehensive, reliable and wide range of datasets from various road projects in Pakistan were produced. The collected datasets contain 253 and 343 results for ABC and AWC, respectively. Eight input parameters were considered for modeling MS and MF. The overall performance of the developed models was assessed using various statistical measures in conjunction with external validation. The relationship between input and output parameters was determined by performing parametric analysis, and the results of trends were found to be consistent with earlier research findings stating that the developed predicted models are well trained. The results revealed that developed models are superior and efficient in terms of prediction and generalization capability for output parameters, as evident by the correlation coefficient (R) (in this case >0.90) for both ABC and AWC.
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19
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Ebrahimzade I, Ebrahimi-Nik M, Rohani A, Tedesco S. Towards monitoring biodegradation of starch-based bioplastic in anaerobic condition: Finding a proper kinetic model. BIORESOURCE TECHNOLOGY 2022; 347:126661. [PMID: 35007734 DOI: 10.1016/j.biortech.2021.126661] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 12/27/2021] [Accepted: 12/28/2021] [Indexed: 06/14/2023]
Abstract
Bioplastic biodegradation showed varying behavior during the process of biodegradation. The First-order and Gompertz models are the most prevalent models for monitoring biodegradation in an anaerobic digestion (AD) process, which do not suit adequately bioplastics fermentation modeling. This research aimed at studying the kinetics of methane production during AD of starch-based bioplastic by using a large library of non-linear regressions (NLRs) and an artificial neural network (ANN). Although 26 NLR models (25 were outlined in the AD literature + 1 modified by authors) have been analyzed, 9 of them were proper predictors for the whole AD process for methane production. In the end M9, which has been proposed by authors, was selected owing to the simplicity of regression as well as good statistical criteria. Moreover, MLP-ANN could outperform the NLR model and has been selected as the superior model that can define the kinetics of bioplastic AD.
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Affiliation(s)
- Iman Ebrahimzade
- Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mohammadali Ebrahimi-Nik
- Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
| | - Abbas Rohani
- Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran.
| | - Silvia Tedesco
- Department of Engineering, School of Mechanical Engineering, Manchester Metropolitan University, Dalton Building, Chester Street, Manchester M1 5GD, UK
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20
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Li R, Xu A, Zhao Y, Chang H, Li X, Lin G. Genetic algorithm (GA) - Artificial neural network (ANN) modeling for the emission rates of toxic volatile organic compounds (VOCs) emitted from landfill working surface. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 305:114433. [PMID: 34995942 DOI: 10.1016/j.jenvman.2022.114433] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 11/11/2021] [Accepted: 01/02/2022] [Indexed: 06/14/2023]
Abstract
Volatile organic compounds (VOCs) emitted from the working surface of landfills have received increasing attention due to the potential risks to human health. Quantifying the emission rates of risky VOCs is important to their health risk assessment but is also challenging because of their high variation and complicated relationship between the emission rates and various influencing factors. In this study, a continuous nine-month sampling of VOCs was conducted on a landfill working surface to identify dominant VOCs that are risky to human health and to construct artificial neural network (ANN) models for their emission rates by involving 105 datasets. Among the 63 detected VOCs, ethanol presented the highest emission rate (885.28 ± 1398.10 μg·m-2·s-1), and the dominant compounds with high emission rates and detection frequencies were characterized in each category. According to the human toxicity impact scores calculated with USEtox method, carbon tetrachloride, ethanol, tetrachloroethylene, 1, 2-dichloroethane, benzene, ethylbenzene, and chloroform were identified as the dominant carcinogenic VOCs, and acrolein, carbon tetrachloride, and 1, 2-dichloropropane were the dominant noncarcinogenic VOCs. ANN models were established for the emission rates of six typical risky VOCs, with meteorological conditions and waste compositions as input parameters and emission rates as output parameters. With the structure optimization and genetic algorithm, all the ANN models achieved good performance and excellent prediction capability with high R2 and low root mean square error (RMSE) values. The emission rates under a 95% probability were predicted for each risky VOCs via the established ANN models, by randomly sampling the input parameters under their data distribution. The approach proposed and results obtained can provide scientific methodology and important information for the monitoring, prediction, and health risk assessment of the VOCs emitted from MSW landfills.
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Affiliation(s)
- Rong Li
- School of Environment, Beijing Normal University, Beijing, 100875, PR China; State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin, 300191, PR China
| | - Ankun Xu
- School of Environment, Beijing Normal University, Beijing, 100875, PR China; State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin, 300191, PR China
| | - Yan Zhao
- School of Environment, Beijing Normal University, Beijing, 100875, PR China; State Environmental Protection Key Laboratory of Odor Pollution Control, Tianjin Academy of Eco-environmental Sciences, Tianjin, 300191, PR China.
| | - Huimin Chang
- School of Environment, Beijing Normal University, Beijing, 100875, PR China
| | - Xiang Li
- School of Environment, Beijing Normal University, Beijing, 100875, PR China
| | - Guannv Lin
- School of Environment, Beijing Normal University, Beijing, 100875, PR China
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21
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Bisht TS, Kumar D, Alappat BJ. Selection of optimal aggregation function for the revised leachate pollution index (r-LPI). ENVIRONMENTAL MONITORING AND ASSESSMENT 2022; 194:187. [PMID: 35165810 DOI: 10.1007/s10661-022-09861-5] [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: 07/12/2021] [Accepted: 02/05/2022] [Indexed: 06/14/2023]
Abstract
A tool to quantify the pollution potential of leachate, termed the revised leachate pollution index (r-LPI), has been developed. It was developed using the fuzzy Delphi analytic hierarchy process (FDAHP). The formulation entails four major steps: parameter selection, weight calculation, normalization of parameters, and aggregation of the parameters. Eleven leachate parameters categorized into three criteria were selected using the fuzzy Delphi method (FDM). The relative weights of the parameters and the criteria were computed using the fuzzy analytic hierarchy process (FAHP), and rating curves were used for normalization of the parameters. The selection of an aggregation function is one of the most critical steps in the development of a composite indicator. In this study, the concept of r-LPI was briefly discussed and 14 different aggregation functions were examined to estimate the pollution potential of landfill leachate. Based on accountability and non-accountability of weights of the parameters, ambiguity, eclipsing, and constant functional behavior, 8 aggregation functions were eliminated. The remaining six aggregation functions were subjected to sensitivity analysis. Furthermore, information lost due to aggregation of parameters was quantified. Based on the findings, it was concluded that the weighted additive function effectively quantifies the pollution potential of landfill leachate and thus recommended for the r-LPI.
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Affiliation(s)
- Tribhuwan Singh Bisht
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India.
| | - Dinesh Kumar
- North Delhi Municipal Corporation, New Delhi, 110002, India
| | - Babu J Alappat
- Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India
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22
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Amoah ID, Abunama T, Awolusi OO, Pillay L, Pillay K, Kumari S, Bux F. Effect of selected wastewater characteristics on estimation of SARS-CoV-2 viral load in wastewater. ENVIRONMENTAL RESEARCH 2022; 203:111877. [PMID: 34390718 PMCID: PMC8356757 DOI: 10.1016/j.envres.2021.111877] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 08/04/2021] [Accepted: 08/08/2021] [Indexed: 05/18/2023]
Abstract
Wastewater-based epidemiology has been used as a tool for surveillance of COVID-19 infections. This approach is dependent on the detection and quantification of SARS-CoV-2 RNA in untreated/raw wastewater. However, the quantification of the viral RNA could be influenced by the physico-chemical properties of the wastewater. This study presents the first use of Adaptive Neuro-Fuzzy Inference System (ANFIS) to determine the potential impact of physico-chemical characteristics of wastewater on the detection and concentration of SARS-CoV-2 RNA in wastewater. Raw wastewater samples from four wastewater treatment plants were investigated over four months. The physico-chemical characteristics of the raw wastewater was recorded, and the SARS-CoV-2 RNA concentration determined via amplification with droplet digital polymerase chain reaction. The wastewater characteristics considered were chemical oxygen demand, flow rate, ammonia, pH, permanganate value, and total solids. The mean SARS-CoV-2 RNA concentrations ranged from 648.1(±514.6) copies/mL to 1441.0(±1977.8) copies/mL. Among the parameters assessed using the ANFIS model, ammonia and pH showed significant association with the concentration of SARS-CoV-2 RNA measured. Increasing ammonia concentration was associated with increasing viral RNA concentration and pH between 7.1 and 7.4 were associated with the highest SARS-CoV-2 concentration. Other parameters, such as total solids, were also observed to influence the viral RNA concentration, however, this observation was not consistent across all the wastewater treatment plants. The results from this study indicate the importance of incorporating wastewater characteristic assessment into wastewater-based epidemiology for a robust and accurate COVID-19 surveillance.
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Affiliation(s)
- Isaac Dennis Amoah
- Institute for Water and Wastewater Technology, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa
| | - Taher Abunama
- Institute for Water and Wastewater Technology, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa
| | - Oluyemi Olatunji Awolusi
- Institute for Water and Wastewater Technology, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa
| | - Leanne Pillay
- Institute for Water and Wastewater Technology, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa
| | - Kriveshin Pillay
- Institute for Water and Wastewater Technology, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa
| | - Sheena Kumari
- Institute for Water and Wastewater Technology, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa.
| | - Faizal Bux
- Institute for Water and Wastewater Technology, Durban University of Technology, P.O. Box 1334, Durban, 4000, South Africa
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Ghaffariraad M, Ghanbarzadeh Lak M, Ebrahimi Sarindizaj E. Sensitivity analysis of significant parameters affecting landfill leachate generation rate. ENVIRONMENTAL MONITORING AND ASSESSMENT 2021; 194:12. [PMID: 34877632 DOI: 10.1007/s10661-021-09653-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 11/27/2021] [Indexed: 06/13/2023]
Abstract
A landfill was designed to evaluate leachate quantity's governing factors using the HELP model. As a case study, the proposed methodology was tested in the Nazlou Landfill, Urmia, Iran. Indeed, the main objective was to illuminate the efficacy of executive parameters defined in the model. Lack of the final cover layer in the pre-closure period affects leachate generation as precipitation enters directly into the waste layers and changes their moisture constituents. Concerning the occurrence of the heaviest precipitation condition in the last 30 years in the pre-closure period, leachate leakage might increase to 0.23 m3/tonne of waste. Due to changes in the layers' initial moisture, the effect of precipitation on leachate generation lasts till the landfill post-closure and would change the drained leachate from 0.083 to 0.089 m3/year-tonne of waste (this amount is generally neglected in the leachate collection system design). During pre-closure, the heaviest precipitation causes an increased generated leachate by 37.59% in the first post-closure year, which is vital especially in landfills with limited capacity treatment plants. The impact of operating conditions involves the landfill leachate generation up to different years. However, over five years of averaging, the precipitation trend was consistent with the leachate leakage. Based on the different scenarios defined regarding the meteorological conditions and design parameters, if the geomembrane layer was removed from the final cover, the leachate quantity will be increased by 79.38%. Replacing 76 cm of dense clay can overcome this challenge.
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Affiliation(s)
- Milad Ghaffariraad
- School of Engineering, Civil Engineering Department, Urmia University, Urmia, Iran
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Sales Junior SF, Costa Amaral IC, Mannarino CF, Hauser-Davis RA, Correia FV, Saggioro EM. Long-term landfill leachate exposure modulates antioxidant responses and causes cyto-genotoxic effects in Eisenia andrei earthworms. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2021; 287:117351. [PMID: 34000669 DOI: 10.1016/j.envpol.2021.117351] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 05/06/2021] [Accepted: 05/09/2021] [Indexed: 06/12/2023]
Abstract
It is estimated that approximately 0.4% of the total leachate produced in a landfill is destined for treatment plants, while the rest can reach the soil and groundwater. In this context, this study aimed to perform leachate toxicity evaluations through immune system cytotoxic assessments, genotoxic (comet assay) appraisals and antioxidant system (superoxide dismutase - SOD; catalase - CAT, glutathione-S-transferase - GST; reduced glutathione - GSH and metallothionein - MT) evaluations in Eisenia andrei earthworms exposed to a Brazilian leachate for 77 days. The leachate sample contained high organic matter (COD - 10,630 mg L-1) and ammoniacal nitrogen (2398 mg L-1), as well as several metals, including Ca, Cr, Fe, Mg, Ni and Zn. Leachate exposure resulted in SOD activity alterations and increased CAT activity and MT levels. Decreased GST activity and GSH levels were also observed. Antioxidant system alterations due to leachate exposure led to increased malondialdehyde levels as a result of lipid peroxidation after the 77 day-exposure. An inflammatory process was also observed in exposed earthworms, evidenced by increased amoebocyte density, and DNA damage was also noted. This study demonstrates for the first time that sublethal effect assessments in leachate-exposed earthworms comprise an important tool for solid waste management.
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Affiliation(s)
- Sidney Fernandes Sales Junior
- Center of Studies on Worker's Health and Human Ecology, Sergio Arouca National School of Public Health, Oswaldo Cruz Foundation, Av. Leopoldo Bulhões 1480, 21041-210, Rio de Janeiro, RJ, Brazil
| | - Isabele Campos Costa Amaral
- Center of Studies on Worker's Health and Human Ecology, Sergio Arouca National School of Public Health, Oswaldo Cruz Foundation, Av. Leopoldo Bulhões 1480, 21041-210, Rio de Janeiro, RJ, Brazil
| | - Camille Ferreira Mannarino
- Sanitation and Environment Health Department, Sergio Arouca National School of Public Health, Oswaldo Cruz Foundation, Av. Leopoldo Bulhões 1480, 21041-210, Rio de Janeiro, RJ, Brazil
| | - Rachel Ann Hauser-Davis
- Laboratório de Avaliação e Promoção da Saúde Ambiental, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, 21040-360, Rio de Janeiro, Brazil
| | - Fábio Veríssimo Correia
- UNIRIO,Departamento de Ciências Naturais, Av. Pasteur, 458, Urca, 22290-20, Rio de Janeiro, Brazil
| | - Enrico Mendes Saggioro
- Center of Studies on Worker's Health and Human Ecology, Sergio Arouca National School of Public Health, Oswaldo Cruz Foundation, Av. Leopoldo Bulhões 1480, 21041-210, Rio de Janeiro, RJ, Brazil; Sanitation and Environment Health Department, Sergio Arouca National School of Public Health, Oswaldo Cruz Foundation, Av. Leopoldo Bulhões 1480, 21041-210, Rio de Janeiro, RJ, Brazil.
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Jalal FE, Xu Y, Iqbal M, Javed MF, Jamhiri B. Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 289:112420. [PMID: 33831756 DOI: 10.1016/j.jenvman.2021.112420] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 03/04/2021] [Accepted: 03/17/2021] [Indexed: 06/12/2023]
Abstract
This study presents the development of new empirical prediction models to evaluate swell pressure and unconfined compression strength of expansive soils (PsUCS-ES) using three soft computing methods, namely artificial neural networks (ANNs), adaptive neuro fuzzy inference system (ANFIS), and gene expression programming (GEP). An extensive database comprising 168 Ps and 145 UCS records was established after a comprehensive literature search. The nine most influential and easily determined geotechnical parameters were taken as the predictor variables. The network was trained and tested, and the predictions of the proposed models were compared with the observed results. The performance of all the models was tested using mean absolute error (MAE), root squared error (RSE), root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), regression coefficient (R2) and relative root mean square error (RRMSE). The sensitivity analysis indicated that the increasing order of inputs importance in case of Ps followed the order: maximum dry density MDD (30.5%) > optimum moisture content OMC (28.7%) > swell percent SP (28.1%) > clay fraction CF (9.4%) > plasticity index PI (3.2%) > specific gravity Gs (0.1%), whereas, in case of UCS it followed the order: sand (44%) > PI (26.3%) > MDD (16.8%) > silt (6.8%) > CF (3%) > SP (2.9%) > Gs (0.2%) > OMC (0.03%). Parametric analysis was also performed and the resulting trends were found to be in line with findings of past literature. The comparison results reflected that GEP and ANN are efficacious and reliable techniques for estimation of PsUCS-ES. The derived mathematical GP-based equations portray the novelty of GEP model and are comparatively simple and reliable. The Roverall values for PsUCS-ES followed the order: ANN > GEP > ANFIS, with all values lying above the acceptable range of 0.80. Hence, all the proposed AI approaches exhibit superior performance, possess high generalization and prediction capability, and evaluate the relative importance of the input parameters in predicting the PsUCS-ES. The GEP model outperformed the other two models in terms of closeness of training, validation and testing data set with the ideal fit (1:1) slope. Evidently the findings of this study can help researchers, designers and practitioners to readily evaluate the swell-strength characteristics of the widespread expansive soils thus curtailing their environmental vulnerabilities which leads to faster, safer and sustainable construction from the standpoint of environment friendly waste management.
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Affiliation(s)
- Fazal E Jalal
- Department of Civil Engineering, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Yongfu Xu
- Department of Civil Engineering, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.
| | - Mudassir Iqbal
- Department of Civil Engineering, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Muhammad Faisal Javed
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, KPK, Pakistan
| | - Babak Jamhiri
- Department of Civil Engineering, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
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Modeling Surface Water Quality Using the Adaptive Neuro-Fuzzy Inference System Aided by Input Optimization. SUSTAINABILITY 2021. [DOI: 10.3390/su13084576] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Modeling surface water quality using soft computing techniques is essential for the effective management of scarce water resources and environmental protection. The development of accurate predictive models with significant input parameters and inconsistent datasets is still a challenge. Therefore, further research is needed to improve the performance of the predictive models. This study presents a methodology for dataset pre-processing and input optimization for reducing the modeling complexity. The objective of this study was achieved by employing a two-sided detection approach for outlier removal and an exhaustive search method for selecting essential modeling inputs. Thereafter, the adaptive neuro-fuzzy inference system (ANFIS) was applied for modeling electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River. A larger dataset of a 30-year historical period, measured monthly, was utilized in the modeling process. The prediction capacity of the developed models was estimated by statistical assessment indicators. Moreover, the 10-fold cross-validation method was carried out to address the modeling overfitting issue. The results of the input optimization indicate that Ca2+, Na+, and Cl− are the most relevant inputs to be used for EC. Meanwhile, Mg2+, HCO3−, and SO42− were selected to model TDS levels. The optimum ANFIS models for the EC and TDS data showed R values of 0.91 and 0.92, and the root mean squared error (RMSE) results of 30.6 µS/cm and 16.7 ppm, respectively. The optimum ANFIS structure comprises a hybrid training algorithm with 27 fuzzy rules of triangular fuzzy membership functions for EC and a Gaussian curve for TDS modeling, respectively. Evidently, the outcome of the present study reveals that the ANFIS modeling, aided with data pre-processing and input optimization, is a suitable technique for simulating the quality of surface water. It could be an effective approach in minimizing modeling complexity and elaborating proper management and mitigation measures.
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Xu A, Chang H, Xu Y, Li R, Li X, Zhao Y. Applying artificial neural networks (ANNs) to solve solid waste-related issues: A critical review. WASTE MANAGEMENT (NEW YORK, N.Y.) 2021; 124:385-402. [PMID: 33662770 DOI: 10.1016/j.wasman.2021.02.029] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 02/09/2021] [Accepted: 02/15/2021] [Indexed: 05/20/2023]
Abstract
Artificial neural networks (ANNs) have recently attracted significant attention in environmental areas because of their great self-learning capability and good accuracy in mapping complex nonlinear relationships. These properties of ANNs benefit their application in solving different solid waste-related issues. However, the configurations, including ANN framework, algorithm, data set partition, input parameters, hidden layer, and performance evaluation, vary and have not reached a consensus among relevant studies. To address the current state of the art of ANN application in the solid waste field and identify the commonalities of ANNs, this critical review was conducted by focusing on a modeling perspective and using 177 relevant papers published over the last decade (2010-2020). We classified the reviewed studies into four categories in terms of research scales. ANNs were found to be applied widely in waste generation and technological parameter prediction and proven effective in solving meso-microscale and microscale issues, including waste conversion, emissions, and microbial and dynamic processes. Given the difficulty of data collection in many solid waste-related issues, most studies included a data size of 101-150. For mathematical optimization, dividing the data into training-validation-test sets is preferable, and the training set is supposed to account for ~70%. A single hidden layer is usually sufficient, and the optimal numbers of hidden layer nodes most likely range from 4 to 20. This review is supposed to contribute basic and comprehensive knowledge to the researchers in general waste management and specialized ANN study on solid waste-related issues.
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Affiliation(s)
- Ankun Xu
- School of Environment, Beijing Normal University, Beijing 100875, PR China
| | - Huimin Chang
- School of Environment, Beijing Normal University, Beijing 100875, PR China
| | - Yingjie Xu
- School of Environment, Beijing Normal University, Beijing 100875, PR China
| | - Rong Li
- School of Environment, Beijing Normal University, Beijing 100875, PR China
| | - Xiang Li
- School of Environment, Beijing Normal University, Beijing 100875, PR China
| | - Yan Zhao
- School of Environment, Beijing Normal University, Beijing 100875, PR China.
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Shah MI, Javed MF, Abunama T. Proposed formulation of surface water quality and modelling using gene expression, machine learning, and regression techniques. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021; 28:13202-13220. [PMID: 33179185 DOI: 10.1007/s11356-020-11490-9] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 10/30/2020] [Indexed: 06/11/2023]
Abstract
The rising water pollution from anthropogenic factors motivates further research in developing water quality predicting models. The available models have certain limitations due to limited timespan data and the incapability to provide empirical expressions. This study is devoted to model and derive empirical equations for surface water quality of upper Indus river basin using a 30-year dataset with machine learning techniques and then to determine the most reliable model capable to accurately predict river water quality. Total dissolve solids (TDS) and electrical conductivity (EC) were used as dependent variables, whereas eight parameters were used as independent variables with 70 and 30% data for model training and testing, respectively. Various evaluation criteria, i.e., Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE), were used to assess the performance of models. The data is also validated with the help of k-fold cross-validation using R2 and RMSE. The results indicated a strong correlation with NSE and R2 both above 0.85 for all the developed models. Gene expression programming (GEP) outperformed both artificial neural network (ANN) and linear and non-linear regression models for TDS and EC. The sensitivity and parametric analyses revealed that bicarbonate is the most sensitive parameter influencing both TDS and EC models. Two equations were derived and formulated to represent the novel results of GEP model to help authorities in the effective monitoring of river water quality.
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Affiliation(s)
- Muhammad Izhar Shah
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan.
| | - Muhammad Faisal Javed
- Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad, 22060, Pakistan
| | - Taher Abunama
- Institute for Water and Wastewater Technology, Durban University of Technology, PO Box 1334, Durban, South Africa
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Guo HN, Wu SB, Tian YJ, Zhang J, Liu HT. Application of machine learning methods for the prediction of organic solid waste treatment and recycling processes: A review. BIORESOURCE TECHNOLOGY 2021; 319:124114. [PMID: 32942236 DOI: 10.1016/j.biortech.2020.124114] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/04/2020] [Accepted: 09/07/2020] [Indexed: 05/23/2023]
Abstract
Conventional treatment and recycling methods of organic solid waste contain inherent flaws, such as low efficiency, low accuracy, high cost, and potential environmental risks. In the past decade, machine learning has gradually attracted increasing attention in solving the complex problems of organic solid waste treatment. Although significant research has been carried out, there is a lack of a systematic review of the research findings in this field. This study sorts the research studies published between 2003 and 2020, summarizes the specific application fields, characteristics, and suitability of different machine learning models, and discusses the relevant application limitations and future prospects. It can be concluded that studies mostly focused on municipal solid waste management, followed by anaerobic digestion, thermal treatment, composting, and landfill. The most widely used model is the artificial neural network, which has been successfully applied to various complicated non-linear organic solid waste related problems.
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Affiliation(s)
- Hao-Nan Guo
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shu-Biao Wu
- Aarhus Institute of Advanced Studies, Aarhus University, DK-8000 Aarhus C, Denmark
| | - Ying-Jie Tian
- CAS Research Center on Fictitious Economy & Data Science, Beijing 100190, China
| | - Jun Zhang
- Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin 541004, China
| | - Hong-Tao Liu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; Engineering Laboratory for Yellow River Delta Modern Agriculture, Chinese Academy of Sciences, Beijing 100101, China.
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Ma D, Duan H, Li W, Zhang J, Liu W, Zhou Z. Prediction of water inflow from fault by particle swarm optimization-based modified grey models. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2020; 27:42051-42063. [PMID: 32705550 DOI: 10.1007/s11356-020-10172-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 07/16/2020] [Indexed: 06/11/2023]
Abstract
Water inflow from fault (WIF) and its secondary impacts are the main environmental challenges in the mining industry. Traditional prediction methods for WIF are exceedingly challenging and costly. In this article, two exponentially weighted moving average (EWMA) modified grey models (GMs, i.e., EGM and REGM) were established to predict the WIF. Particle swarm optimization (PSO) algorithm was employed to optimize parameters of the models. Based on actual WIF data from Buliangou coal mine, the optimized models (i.e., EGM-PSO, REGM-PSO) were used to obtain the prediction equations for WIF. To investigate the validity of the proposed models, the differences between actual values and predicted values were analyzed, and comparison results were obtained by the commonly used GM and GM-PSO. Results show that, for the sample with the larger initial particle swarm and smaller inertia weight, there is a faster convergence speed of the PSO algorithm. Particle search efficiency in the PSO-optimized EWMA-GM is higher than that in the GM-PSO. Through the predicted results of WIF, it is found that the REGM-PSO is the best choice for WIF prediction, and the more historical information, the higher the predicted accuracy. Besides, the parameter optimization by the PSO, the EWMA optimization method and optimization of residuals all can improve the predicted accuracy. Predicted results also show that WIF will have a substantial growth in the future. Therefore, reasonable measures (e.g., draining and grouting) need to be taken to mitigate the damage caused by fault water inflow.
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Affiliation(s)
- Dan Ma
- School of Resources & Safety Engineering, Central South University, Changsha, 410083, Hunan, China.
- State Key Laboratory of Coal Resources & Safe Mining, School of Mines, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China.
- State Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China.
| | - Hongyu Duan
- School of Resources & Safety Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Wenxuan Li
- School of Resources & Safety Engineering, Central South University, Changsha, 410083, Hunan, China
| | - Jixiong Zhang
- State Key Laboratory of Coal Resources & Safe Mining, School of Mines, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
| | - Weitao Liu
- State Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China.
| | - Zilong Zhou
- School of Resources & Safety Engineering, Central South University, Changsha, 410083, Hunan, China
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31
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Investigation of the Influence of Excess Pumping on Groundwater Salinity in the Gaza Coastal Aquifer (Palestine) Using Three Predicted Future Scenarios. WATER 2020. [DOI: 10.3390/w12082218] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The Gaza coastal aquifer (GCA) is the only source of water for about two million citizens living in Gaza Strip, Palestine. The groundwater quality in GCA has deteriorated rapidly due to many factors. The most crucial factor is the excess pumping due to the high population density. The objective of this article was to evaluate the influence of excess pumping on GCA’s salinity using 10-year predicted future scenarios based on artificial neural networks (ANNs). The ANN-based model was generated to predict the GCA’s salinity for three future scenarios that were designed based on different pumping rates. The results showed that when the pumping rate remains at the present conditions, salinity will increase rapidly in most GCA areas, and the availability of fresh water will decrease in disquieting rates by 2030. Only about 8% of the overall GCA’s area is expected to stay within 500 mg/L of the chloride concentration. Results also indicate that salinity would be improved slightly if the pumping rate is kept at 50% of the current pumping rates while the improvement rate is much faster if the pumping is stopped completely, which is an unfeasible scenario. The results are considered as an urgent call for developing an integrated water management strategy aiming at improving GCA quality by providing other drinking water resources to secure the increasing water demand.
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32
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Ma S, Zhou C, Chi C, Liu Y, Yang G. Estimating Physical Composition of Municipal Solid Waste in China by Applying Artificial Neural Network Method. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:9609-9617. [PMID: 32628460 DOI: 10.1021/acs.est.0c01802] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Physical composition of municipal solid waste (PCMSW) is the fundamental parameter in domestic waste management; however, high fidelity, wide coverage, upscaling, and year continuous data sets of PCMSW in China are insufficient. A traceable and predictable methodology for estimating PCMSW in China is established for the first time by analyzing 503 PCMSW data sets of 135 prefecture-level cities in China. A hyperspherical transformation method was used to eliminate the constant sum constraint in statistically analyzing PCMSW data. Moreover, a back-propagation (BP) neural network methodology was applied to establish quantitative models between city-level PCMSW and its socio-economic factors, including city size, per capita gross regional product, geographical location, gas coverage rate, and year. Results show that (1) national-level PCMSW in 2017 was estimated as organic fraction (53.7%), ash and stone (8.3%), paper (16.9%), plastic and rubber (13.6%), textile (2.3%), wood (2.2%), metal (0.6%), glass (1.5%), and others (1.0%); (2) organic fraction, paper, and plastics showed an increasing trend from 1990 to 2017, while ash and stone decreased significantly; (3) organic fractions in East, North, and Central-South China were higher than those in other regions. This enables us to fill the data gap in the practice of municipal solid waste management in China.
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Affiliation(s)
- Shijun Ma
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chuanbin Zhou
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ce Chi
- High Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100089, China
| | - Yijie Liu
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Guang Yang
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Abdallah M, Abu Talib M, Feroz S, Nasir Q, Abdalla H, Mahfood B. Artificial intelligence applications in solid waste management: A systematic research review. WASTE MANAGEMENT (NEW YORK, N.Y.) 2020; 109:231-246. [PMID: 32428727 DOI: 10.1016/j.wasman.2020.04.057] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 04/20/2020] [Accepted: 04/30/2020] [Indexed: 05/21/2023]
Abstract
The waste management processes typically involve numerous technical, climatic, environmental, demographic, socio-economic, and legislative parameters. Such complex nonlinear processes are challenging to model, predict and optimize using conventional methods. Recently, artificial intelligence (AI) techniques have gained momentum in offering alternative computational approaches to solve solid waste management (SWM) problems. AI has been efficient at tackling ill-defined problems, learning from experience, and handling uncertainty and incomplete data. Although significant research was carried out in this domain, very few review studies have assessed the potential of AI in solving the diverse SWM problems. This systematic literature review compiled 85 research studies, published between 2004 and 2019, analyzing the application of AI in various SWM fields, including forecasting of waste characteristics, waste bin level detection, process parameters prediction, vehicle routing, and SWM planning. This review provides comprehensive analysis of the different AI models and techniques applied in SWM, application domains and reported performance parameters, as well as the software platforms used to implement such models. The challenges and insights of applying AI techniques in SWM are also discussed.
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Affiliation(s)
- Mohamed Abdallah
- Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates.
| | - Manar Abu Talib
- Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates
| | - Sainab Feroz
- Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates
| | - Qassim Nasir
- Department of Electrical Engineering, University of Sharjah, Sharjah, United Arab Emirates
| | - Hadeer Abdalla
- Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates
| | - Bayan Mahfood
- Department of Computer Science, University of Sharjah, Sharjah, United Arab Emirates
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