Integrating advanced techniques and machine learning for landfill leachate treatment: Addressing limitations and environmental concerns.

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.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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