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

Vivek Kumar Gaur; Krishna Gautam; Reena Vishvakarma; Poonam Sharma; Upasana Pandey; Janmejai Kumar Srivastava; Sunita Varjani; Jo-Shu Chang; Huu Hao Ngo; Jonathan W C Wong

doi:10.1016/j.envpol.2024.124134

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

Environ Pollut. 2024 May 9:354:124134. doi: 10.1016/j.envpol.2024.124134. Online ahead of print.

Authors

Affiliations

¹ Centre for Energy and Environmental Sustainability, Lucknow, India; School of Energy and Chemical Engineering, UNIST, Ulsan, 44919, Republic of Korea.
² Centre for Energy and Environmental Sustainability, Lucknow, India.
³ Department of Bioengineering, Integral University, Lucknow, India.
⁴ Dabur Research Foundation, Ghaziabad, Uttar Pradesh, 201010, India.
⁵ Amity Institute of Biotechnology, Amity University Lucknow, India.
⁶ 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. Electronic address: drsvs18@gmail.com.
⁷ 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.
⁸ Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW - 2007, Australia.
⁹ Institute of Bioresource and Agriculture, Hong Kong Baptist University, Hong Kong.

PMID: 38734050
DOI: 10.1016/j.envpol.2024.124134

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.

Keywords: Artificial neural networks; Landfill leachate; Machine learning; Municipal solid waste; Smart Dustbins; Treatment approaches.

Publication types

Review