TY - JOUR
T1 - Integrating advanced techniques and machine learning for landfill leachate treatment
T2 - Addressing limitations and environmental concerns
AU - Gaur, Vivek Kumar
AU - Gautam, Krishna
AU - Vishvakarma, Reena
AU - Sharma, Poonam
AU - Pandey, Upasana
AU - Srivastava, Janmejai Kumar
AU - Varjani, Sunita
AU - Chang, Jo Shu
AU - Ngo, Huu Hao
AU - Wong, Jonathan W.C.
N1 - Funding Information:
This paper received support from SEED funding at UPES, India (Project Code: UPES/R&D-SoHST/08042024/49) and fellowship at City University of Hong Kong.
Publisher Copyright:
© 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
PY - 2024/8/1
Y1 - 2024/8/1
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Landfill leachate
KW - Machine learning
KW - Municipal solid waste
KW - Smart Dustbins
KW - Treatment approaches
UR - http://www.scopus.com/inward/record.url?scp=85193498154&partnerID=8YFLogxK
U2 - 10.1016/j.envpol.2024.124134
DO - 10.1016/j.envpol.2024.124134
M3 - Journal article
C2 - 38734050
AN - SCOPUS:85193498154
SN - 0269-7491
VL - 354
JO - Environmental Pollution
JF - Environmental Pollution
M1 - 124134
ER -