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 Experiment Videos

Dimension reduction and source identification for multispecies groundwater contamination.

C J Duffy1, D Brandes

  • 1Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA 16802, USA. cxd11@psu.edu

Journal of Contaminant Hydrology
|April 9, 2001
PubMed
Summary

Principal component analysis (PCA) simplifies complex chemical contamination data from industrial sites. This method identifies distinct chemical groups, aiding in contaminant source area delineation and environmental remediation planning.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Optical frequency shifter technique based on stimulated Brillouin scattering in birefringent optical fiber.

Applied optics·2010
Same author

Incorporation of imaging into a temporal coherence sensor.

Optics letters·2009
Same author

Visual mechanisms of spatial disorientation in Alzheimer's disease.

Cerebral cortex (New York, N.Y. : 1991)·2001
Same author

Longitudinal MRI study of hippocampal volume in trauma survivors with PTSD.

The American journal of psychiatry·2001
Same author

Successful treatment of feline pancreatitis using an endoscopically placed gastrojejunostomy tube.

Journal of the American Animal Hospital Association·2001
Same author

Cortical motion blindness in visuospatial AD.

Neurobiology of aging·2000

Area of Science:

  • Environmental Science
  • Geochemistry
  • Data Science

Background:

  • Assessing chemical contamination at industrial sites is challenging due to complex histories and contaminant behavior.
  • Traditional mapping methods are insufficient for sites with numerous undocumented chemicals and diverse transport properties.

Purpose of the Study:

  • To apply principal component analysis (PCA) to a contaminated site with an undocumented disposal history.
  • To identify dominant chemical groups and reduce data dimensionality for better understanding of contaminant distribution.

Main Methods:

  • Utilized principal component analysis (PCA) on a dataset of 116 chemicals from a contaminated industrial site.
  • Identified dominant chemical groups based on the eigenvectors of the correlation matrix.

Related Experiment Videos

Main Results:

  • PCA identified five primary and three transition chemical groups, accounting for 61% of the total variance.
  • Each identified group represented chemicals with similar chemo-dynamic properties and environmental responses.
  • Dimensionality was significantly reduced from 116 chemicals to distinct chemical groups.

Conclusions:

  • PCA is an effective data reduction strategy for subsurface characterization of complex contaminated sites.
  • The identified chemical groups aid in inferring contaminant source areas and inform remediation planning.
  • This approach provides a preliminary step for subsurface modeling and environmental management.