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

Contaminants and Errors01:16

Contaminants and Errors

132
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
132
Sampling Plans01:23

Sampling Plans

244
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
244

You might also read

Related Articles

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

Sort by
Same authorSame journal

Python-Based Model Emulation Workflows with PEST.

Ground water·2026
Same author

ADNP regulates chromatin architecture and lineage fidelity during neural differentiation.

PLoS genetics·2026
Same author

A Novel Pipeline for Object Recognition Utilising Multi-Sensory Tactile Fusion.

IEEE transactions on haptics·2026
Same author

Neuroprotection against beta-amyloid toxicity by the novel estrogen receptor modulator STX requires convergent signaling pathways.

Frontiers in molecular neuroscience·2025
Same author

What matters most to people living with epilepsy? A rapid review of qualitative research relating to health outcomes.

Epilepsia·2025
Same author

Health-related quality of life following cranioplasty: a cross-sectional cohort study - Cranio-PRO.

Neurosurgical review·2025
Same journal

Computing Flow-Field Distortion Coefficients from Well-Construction and Formation Properties.

Ground water·2026
Same journal

Leaky Sewers Hydraulically Disconnect from Groundwater: A Proof-of-Concept.

Ground water·2026
Same journal

Hydrogeology in the Age of AI and Climate Change.

Ground water·2026
Same journal

Aquifer Thermal Energy Storage: Groundwater for Efficient Data Center Cooling in the United States.

Ground water·2026
Same journal

Simulating the Impacts of Deep Geothermal Development on Shallow Hydrothermal Resources in a Rocky Mountain Rift Valley.

Ground water·2026
See all related articles

Related Experiment Video

Updated: Aug 30, 2025

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
11:19

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

Published on: October 21, 2016

12.0K

Probabilistic Contaminant Source Assessment-Getting the Most Out of Field Measurements.

Rui Hugman, Francesca Lotti1,2, John Doherty3,4

  • 1Kataclima S.r.l., Vetralla, Italy.

Ground Water
|September 2, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a probabilistic method to pinpoint groundwater contaminant leaks. It reduces uncertainty in subsurface properties and contaminant source identification using particle tracking and history matching.

More Related Videos

An Inverse Analysis Approach to the Characterization of Chemical Transport in Paints
08:42

An Inverse Analysis Approach to the Characterization of Chemical Transport in Paints

Published on: August 29, 2014

8.5K
A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants
08:13

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

Published on: February 19, 2016

9.5K

Related Experiment Videos

Last Updated: Aug 30, 2025

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses
11:19

Measuring Carbon-based Contaminant Mineralization Using Combined CO2 Flux and Radiocarbon Analyses

Published on: October 21, 2016

12.0K
An Inverse Analysis Approach to the Characterization of Chemical Transport in Paints
08:42

An Inverse Analysis Approach to the Characterization of Chemical Transport in Paints

Published on: August 29, 2014

8.5K
A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants
08:13

A Filter-based Surface Enhanced Raman Spectroscopic Assay for Rapid Detection of Chemical Contaminants

Published on: February 19, 2016

9.5K

Area of Science:

  • Environmental Science
  • Hydrogeology
  • Geochemistry

Background:

  • Groundwater contamination poses significant environmental risks.
  • Accurate source localization is crucial for effective remediation.
  • Existing methods often struggle with uncertainty in subsurface properties.

Purpose of the Study:

  • To develop a probabilistic methodology for identifying groundwater contaminant leak locations.
  • To reduce uncertainties in subsurface hydraulic properties and contaminant source estimations.
  • To assess the value of expanding groundwater monitoring networks.

Main Methods:

  • Highly parameterized, stochastic history-matching.
  • Particle-based simulation of contaminant transport for efficiency and stability.
  • Binary classification of borehole measurements (detect/non-detect).
  • Assigning particle trajectory statuses to model cells for statistical analysis.

Main Results:

  • Generation of probability maps indicating contaminant detection likelihood.
  • Creation of maps showing the probability of a contaminant source's non-existence.
  • Demonstration of reduced uncertainty in hydraulic properties and source localization.
  • Methodology extended to evaluate the impact of new observation wells.

Conclusions:

  • The proposed methodology effectively reduces uncertainties in groundwater contaminant investigations.
  • Probabilistic mapping aids in understanding detection probabilities and source non-existence.
  • The approach provides a framework for optimizing groundwater monitoring network design.