Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Gravimetry: Overview01:05

Gravimetry: Overview

Gravimetric analysis is a quantitative method where the analyte is isolated and weighed directly or after conversion into a substance of known composition. Gravimetric analysis can be classified as precipitation, electrogravimetry, volatilization, and particulate gravimetry, based on the method used to isolate the analyte.
In precipitation gravimetry, the analyte is converted into a precipitate and weighed. For example, the silver content in a sample can be estimated by precipitating and...
Microbial Leaching01:27

Microbial Leaching

Microbial leaching, also known as bioleaching, is an environmentally favorable method for extracting metals from low-grade ores using specific microorganisms. This biotechnological approach is particularly valuable for mining operations targeting copper, gold, and uranium, where traditional extraction methods may be economically or environmentally impractical.Copper Leaching and Microbial CatalysisIn copper bioleaching, crushed ore is arranged into heaps and irrigated with a dilute sulfuric...

You might also read

Related Articles

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

Sort by
Same author

Exploring the mechanisms of acupuncture in improving cognitive function in post-COVID-19 myalgic encephalomyelitis/chronic fatigue syndrome: study protocol for a randomized controlled trial using multimodal MRI.

Frontiers in neurology·2026
Same author

The Impact of Anthropomorphic Eco-Friendly Logos on Consumers' Green Purchase Intention: A Moderated Mediation Model.

Behavioral sciences (Basel, Switzerland)·2026
Same author

Glucokinase Regulatory Protein Gene Polymorphism as a Predictive Biomarker for Early Renal Injury in Newly Diagnosed Early-Onset Type 2 Diabetes Mellitus.

Diabetes/metabolism research and reviews·2026
Same author

Ischemia-Reperfusion Injury: Molecular Mechanisms and Therapeutic Interventions.

MedComm·2026
Same author

Interventions for Silent Brain Infarction and Perioperative Neurocognitive Disorders in Cardiovascular Surgery (INSPIRE): study protocol for a multicentre randomised controlled trial.

BMJ open·2026
Same author

Damage-associated molecular patterns in hepatic ischemia-reperfusion injury: spatiotemporal signatures, biomarker potential, and clinical translation.

Frontiers in immunology·2026

Related Experiment Video

Updated: Jun 3, 2026

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation
10:05

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation

Published on: July 4, 2014

14.7K

A general methodological framework for predicting and assessing heavy metal pollution in paddy soils using machine

Unurnyam Jugnee1,2, Le Jiao3,4,5, Sainbayar Dalantai1

  • 1Division of Environmental and Natural Resources Management, Institute of Geography and Geoecology, Mongolian Academy of Sciences, Ulaanbaatar, 15170, Mongolia.

Heliyon
|January 1, 2026
PubMed
Summary

This study developed a machine learning framework to predict heavy metal pollution in rice paddies, identifying key drivers and mapping polluted areas. Findings highlight moderate to severe contamination, worsening from west to east, crucial for soil conservation.

Keywords:
Heavy metalHunan provinceMachine learningPaddy soilPollution prediction

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K
Author Spotlight: Investigating the Tolerance of Cabbage Butterflies to Urban Pollutants
08:08

Author Spotlight: Investigating the Tolerance of Cabbage Butterflies to Urban Pollutants

Published on: August 18, 2023

5.0K

Related Experiment Videos

Last Updated: Jun 3, 2026

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation
10:05

Integrated Field Lysimetry and Porewater Sampling for Evaluation of Chemical Mobility in Soils and Established Vegetation

Published on: July 4, 2014

14.7K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.3K
Author Spotlight: Investigating the Tolerance of Cabbage Butterflies to Urban Pollutants
08:08

Author Spotlight: Investigating the Tolerance of Cabbage Butterflies to Urban Pollutants

Published on: August 18, 2023

5.0K

Area of Science:

  • Environmental Science
  • Soil Science
  • Geochemistry

Background:

  • Heavy metal contamination in rice paddies threatens ecological and human health.
  • Existing research primarily focuses on source apportionment, lacking robust spatial prediction models.
  • Accurate prediction of heavy metal spatial distribution and driving mechanisms is essential for effective management.

Purpose of the Study:

  • To develop and assess a general methodological framework for predicting and evaluating heavy metal pollution in paddies.
  • To identify the driving mechanisms behind heavy metal spatial distribution.
  • To visualize the spatial distribution of heavy metal pollution and assess the pollution load index.

Main Methods:

  • Employed machine learning algorithms: Random Forest (RF), Extra Trees Regressor (ETR), Extreme Gradient Boost Regression (XGBR), and Gradient Boosting Regression Tree (GBRT).
  • Utilized Shapley additive explanations (SHAP) to determine the contribution of various factors to the prediction models.
  • Analyzed climate variables as potential predictors of heavy metal content.

Main Results:

  • RF excelled in predicting Arsenic (As), Chromium (Cr), Copper (Cu), and Mercury (Hg) (R² > 0.70).
  • ETR showed good performance for Cadmium (Cd), Zinc (Zn), and Lead (Pb) (R² > 0.40).
  • GBRT was effective for Nickel (Ni) (R² = 0.61). Climate variables were significant predictors. Spatial analysis revealed 79.8% of the area moderately polluted and 20.2% severely polluted, with pollution increasing eastward.

Conclusions:

  • The developed machine learning framework provides accurate predictions of heavy metal spatial distribution in paddies.
  • Climate variables play a significant role in heavy metal accumulation.
  • The study identified widespread moderate to severe pollution, emphasizing the need for targeted soil pollution control and conservation efforts, particularly in eastern regions.