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Using machine learning to predict the efficiency of biochar in pesticide remediation

Environmental Studies and Forestry

Using machine learning to predict the efficiency of biochar in pesticide remediation

A. Nighojkar, S. Pandey, et al.

Discover how innovative research by Amrita Nighojkar, Shilpa Pandey, Minoo Naebe, Balasubramanian Kandasubramanian, Winston Wole Soboyejo, Anand Plappally, and Xungai Wang is leveraging ensemble machine learning to enhance biochar's efficiency in removing pesticide pollutants from water, revolutionizing agricultural practices and environmental remediation!

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Playback language: English
Abstract
Pesticide pollution is a major environmental concern. Biochar adsorption offers a sustainable remediation method, but optimal conditions vary. This study uses ensemble machine learning (CatBoost, LightGBM, RF) on a literature database to predict biochar's pesticide adsorption efficiency. Textural properties of biochar, pesticide concentration, and dosage were significant factors. Data-driven modeling provides an empirical approach to optimizing biochar use for water purification in agriculture.
Publisher
npj Sustainable Agriculture
Published On
Oct 04, 2023
Authors
Amrita Nighojkar, Shilpa Pandey, Minoo Naebe, Balasubramanian Kandasubramanian, Winston Wole Soboyejo, Anand Plappally, Xungai Wang
Tags
Pesticide pollution
Biochar adsorption
Water purification
Machine learning
Agriculture

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