A Review on Innovations in Soil Remediation Techniques Using Machine Learning - Soil Cleanup - Biochar and ML Approaches

Aug 4, 2026

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Wastewater discharge threatens ecosystem and human health by introducing toxins that must be removed through efficient, sustainable treatments. Biochar produced from lignocellulosic biomass after thermal degradation is highlighted for its large surface area, hollow structure, oxygen functional groups, and low cost, supporting catalytic and wastewater treatment performance. The review connects soil sampling variables—toxin characteristics, soil conditions, and context—with machine learning training to detect hidden relationships and improve the accuracy of soil-cleanup outcome predictions, while discussing challenges, limitations, and future investigation directions for biochar-based enzymatic and algorithmic approaches.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Introduction

Soil therapy and limitations of conventional remediation

Machine learning for prediction and decision support

Environmental drivers and agricultural contamination

Scope of contamination and relevance to ecological deterioration

A Review on Innovations in Soil Remediation Techniques Using Machine Learning Manu Hajari1, Gowtham Raj R2*, HPal Thethi3, Ravi Shankar Raman4, Praveen5, Mustafa Abdulhussein Al-Allak6, Yelamasetti Balram7 1Department ofCSBS, GRIET, Bachupally, Hyderabad, Telangana, India. 2Department of Mechanical Engineering, New Horizon College of Engineering, Bangalore, India. 3Lovely professional University, Phagwara, India. 4Department of Mechanical Engineering, ABES Engineering College, Ghaziabad, UP, India. 5Lloyd Institute of Engineering & Technology, Knowledge Park II, Greater Noida, Uttar Pradesh, India. 6College of Medical Technology, The Islamic University, Najaf, Iraq. 7Department of Mechanical Engineering, MLR Institute of Technology, Hyderabad, Telangana, India. Abstract. The discharge of wastewater into the ecosystem has an impact on fish and human health, therefore the toxins needs to be removed. It is sustainable to remove pollutants from wastewater by utilizing biochar made from lignocellulosic biomass (LCB) that has undergone thermal degradation. Because of it's large surface area, hollow structure, the oxygen groupings, and relatively low cost, bio Char is now known as a change rival in catalytic processes. Biochar was used in conjunction with a number of cutting-edge, creative technologies to treat wastewater efficiently. Details collected soil sampling, such as facts about the toxins current, the nature of the soil, its surrounding circumstances, and the efficacy of various rehabilitation methods, can be used to training machine learning methods. Through data analysis, machine learning models are able to spot relationships and trends which human beings might miss, which improves the accuracy of projections regarding the results of soil cleanup. The review paper outlines the challenges facing biochar-based enzymes using immediate and new technologies, along with emphasizes the application of algorithmic learning in pollution removal. Limitations and likelihoods for additional investigation are examined. Keyword-: soil, ecosystem, aquatic life, machine learning, biomass,enzymes. 1 Introduction One of the most important problems associated with ecological management pertains to soil therapy, which is the process of returning polluted soil to a safe and useful status. Conventional techniques can entail expensive and time-consuming procedures including excavating and disposal. With the incorporation of machine learning into remediation of soil, * Corresponding author: [gowthamiitdh19@gmail.com](gowthamiitdh19@gmail.com) © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)). there is hope for improved, affordable, and ecologically sound management techniques. Oneof the most important problems associated with ecological management pertains to soil therapy, which is the act of returning polluted soil to a safe and useful status . Conventional techniques can entail expensive and time-consuming procedures including excavating and disposal. To maximize clean-up strategies, ML algorithms can evaluate enormous volumes of biological information including pollutant levels, soil type, and ecological variables [1] . ML algorithms can evaluate enormous volumes of biological information including pollutant levels, soil type, and ecological variables to maximize clean-up strategies,. Research and environmental managers can create prediction models that direct choices, rank regions for rehabilitation, and maximize the use of assets by utilizing machine learning algorithms. However, there is a lot of promise for more effective and focused rehabilitation tactics with the development of ML (machine learning) strategies. Scholars and sustainability managers are able to develop forecasting models that direct choices, rank regions

FAQs

Why is soil remediation considered important in ecological management?

Soil therapy returns polluted soil to a safe and useful status. Conventional methods can be expensive and time-consuming, motivating more efficient approaches.

How does machine learning improve soil cleanup strategies?

Machine learning models analyze large volumes of biological and environmental information, such as pollutant levels, soil type, and ecological variables. This helps reveal patterns and improves accuracy of predictions for remediation outcomes.

What role does biochar play in the proposed wastewater and soil treatment approach?

Biochar made from lignocellulosic biomass is presented as a promising material due to its surface area, structure, oxygen functional groups, and low cost. It is used alongside advanced technologies to treat wastewater effectively.

Aug 4, 2026

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