Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Bioremediation

Bioremediation

Bioremediation is the use of prokaryotes, fungi, or plants to remove pollutants from the environment. This process has been used to remove harmful toxins in groundwater as a byproduct of agricultural run-off and also to clean up oil spills.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Cosine Similarity Distillation Vision Mixture-of-Experts for Intelligent Housing-Dimensional Urban Physical Examinations.

Sensors (Basel, Switzerland)·2026

Same author

Comparative assessing prefabricated and conventional 3D morphology carbon emission intensity of economy chain hotel buildings in Hangzhou.

Scientific reports·2026

Same author

lncRNA-operated stem cell fate control: RPS3 mediates the in situ Gm16751-Zhx3 interactome to program spermatogonial stem cell differentiation.

Cell & bioscience·2026

Same author

Carbon reduction multidimensional ternary model for robotic 3D printing prefabricated construction across hotels star levels.

Carbon balance and management·2026

Same author

The Restorative Potential of Natural Soundscapes in Indoor Environments: EEG and Psychological Evidence.

Stress and health : journal of the International Society for the Investigation of Stress·2026

Same author

Deployable knowledge-data hybrid models for day-ahead cooling load prediction under data scarcity: a case study and performance validation.

Scientific reports·2026

Search research articles

Related Experiment Video

Updated: May 31, 2025

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

Published on: September 1, 2020

A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution.

Sheng Miao¹, Guoqing Ni¹, Guangze Kong¹

¹School of Information and Control Engineering, Qingdao University of Technology, Qingdao, China.

|January 24, 2025

Summary

This study introduces a Three-Dimensional Convolutional Neural Network (3DCNN) for precise soil hydrocarbon pollution prediction. The advanced 3DCNN model significantly outperforms traditional methods, offering a sustainable tool for soil management and remediation strategies.

More Related Videos

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Related Experiment Videos

Last Updated: May 31, 2025

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

Two-Dimensional Visualization and Quantification of Labile, Inorganic Plant Nutrients and Contaminants in Soil

Published on: September 1, 2020

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

Area of Science:

Environmental Science
Geospatial Analysis
Machine Learning

Background:

Petroleum hydrocarbon pollution severely impacts soil health, necessitating accurate prediction for effective management.
Traditional soil analysis methods and existing machine learning approaches have limitations in handling spatial data and assumptions.

Purpose of the Study:

To explore the application of Three-Dimensional Convolutional Neural Networks (3DCNN) for spatial interpolation in evaluating soil pollution.
To enhance prediction accuracy by incorporating a Channel Attention Mechanism (CAM) for weighted auxiliary variable assignment.

Main Methods:

Utilized a 3DCNN model integrated with CAM for spatial interpolation of soil hydrocarbon content.
Collected soil pollution data and validated the model using a drilling dataset.
Compared the 3DCNN method against traditional Kriging3D and support vector regression.

Main Results:

The proposed 3DCNN method achieved a high accuracy (R2 = 0.954).
This significantly outperformed Kriging3D (R2 = 0.318) and support vector regression (R2 = 0.582).
The generated spatial distribution maps accurately reflected soil pollution levels.

Conclusions:

3DCNN with CAM offers a superior and sustainable approach for soil hydrocarbon pollution assessment.
This method provides a valuable tool for decision-makers in soil remediation and environmental management.
The study advances spatial interpolation techniques within environmental science.