Analysis of the fate and transport of nC60 nanoparticles in the subsurface using response surface methodology

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6 Citations (Scopus)

Abstract

Predicting the distribution of engineered nanomaterials (ENMs) in the environment will provide critical information for risk assessment and policy development to regulate these emerging contaminants. The fate and transport of ENMs in natural subsurface environments are complicated by various factors, such as hydraulic gradient, initial release concentration, nanoparticle size, and collision efficiency factor. Based on advanced statistical methodologies (i.e., response surface methodology (RSM)), we explore simple relationships between key factors that control ENM transport (collision efficiency factor, particle size, hydraulic gradient, and initial release concentration) and key parameters that describe the ENM concentration distribution in porous media (maximum standardized concentration, the mass percentage of injected nanoparticle attached in the aquifer, the x-centroid of aqueous phase nC60 plume, and the x-centroid of attached phase nC60 distribution). Hypothetical scenarios for the release of nanoparticles into an aquifer were simulated numerically with randomly generated permeability fields that were based on mildly and highly heterogeneous sites. RSM was used to develop polynomial regression equations based on a statistical experimental design. High R-squared values (greater than 0.9) of the regression equations were obtained for all the models developed based on the mildly heterogeneous site. On the highly heterogeneous site, the R-squared value of the regression equation for the percentage of nanoparticles attached (by mass) was more than 0.9. The ability to accurately estimate aqueous phase ENM concentration distribution using simple regression equations is particularly critical for risk assessment. Even though the correlations developed in this study were site and scenario specific, this work represents a first effort of applying RSM for predicting the distribution of engineered nanomaterials in porous media.

Original languageEnglish (US)
Pages (from-to)60-69
Number of pages10
JournalJournal of Contaminant Hydrology
Volume152
DOIs
StatePublished - Aug 1 2013

Fingerprint

Nanostructured materials
Nanoparticles
porous medium
Aquifers
risk assessment
Risk assessment
collision
Porous materials
aquifer
hydraulics
Hydraulics
policy development
experimental design
Design of experiments
plume
particle size
distribution
nanoparticle
analysis
response surface methodology

Keywords

  • Design of experiments
  • Mobility
  • Response surface methodology
  • Subsurface
  • nC nanoparticles

ASJC Scopus subject areas

  • Environmental Chemistry
  • Water Science and Technology

Cite this

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title = "Analysis of the fate and transport of nC60 nanoparticles in the subsurface using response surface methodology",
abstract = "Predicting the distribution of engineered nanomaterials (ENMs) in the environment will provide critical information for risk assessment and policy development to regulate these emerging contaminants. The fate and transport of ENMs in natural subsurface environments are complicated by various factors, such as hydraulic gradient, initial release concentration, nanoparticle size, and collision efficiency factor. Based on advanced statistical methodologies (i.e., response surface methodology (RSM)), we explore simple relationships between key factors that control ENM transport (collision efficiency factor, particle size, hydraulic gradient, and initial release concentration) and key parameters that describe the ENM concentration distribution in porous media (maximum standardized concentration, the mass percentage of injected nanoparticle attached in the aquifer, the x-centroid of aqueous phase nC60 plume, and the x-centroid of attached phase nC60 distribution). Hypothetical scenarios for the release of nanoparticles into an aquifer were simulated numerically with randomly generated permeability fields that were based on mildly and highly heterogeneous sites. RSM was used to develop polynomial regression equations based on a statistical experimental design. High R-squared values (greater than 0.9) of the regression equations were obtained for all the models developed based on the mildly heterogeneous site. On the highly heterogeneous site, the R-squared value of the regression equation for the percentage of nanoparticles attached (by mass) was more than 0.9. The ability to accurately estimate aqueous phase ENM concentration distribution using simple regression equations is particularly critical for risk assessment. Even though the correlations developed in this study were site and scenario specific, this work represents a first effort of applying RSM for predicting the distribution of engineered nanomaterials in porous media.",
keywords = "Design of experiments, Mobility, Response surface methodology, Subsurface, nC nanoparticles",
author = "Chunmei Bai and Eskridge, {Kent M} and Yusong Li",
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AU - Bai, Chunmei

AU - Eskridge, Kent M

AU - Li, Yusong

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N2 - Predicting the distribution of engineered nanomaterials (ENMs) in the environment will provide critical information for risk assessment and policy development to regulate these emerging contaminants. The fate and transport of ENMs in natural subsurface environments are complicated by various factors, such as hydraulic gradient, initial release concentration, nanoparticle size, and collision efficiency factor. Based on advanced statistical methodologies (i.e., response surface methodology (RSM)), we explore simple relationships between key factors that control ENM transport (collision efficiency factor, particle size, hydraulic gradient, and initial release concentration) and key parameters that describe the ENM concentration distribution in porous media (maximum standardized concentration, the mass percentage of injected nanoparticle attached in the aquifer, the x-centroid of aqueous phase nC60 plume, and the x-centroid of attached phase nC60 distribution). Hypothetical scenarios for the release of nanoparticles into an aquifer were simulated numerically with randomly generated permeability fields that were based on mildly and highly heterogeneous sites. RSM was used to develop polynomial regression equations based on a statistical experimental design. High R-squared values (greater than 0.9) of the regression equations were obtained for all the models developed based on the mildly heterogeneous site. On the highly heterogeneous site, the R-squared value of the regression equation for the percentage of nanoparticles attached (by mass) was more than 0.9. The ability to accurately estimate aqueous phase ENM concentration distribution using simple regression equations is particularly critical for risk assessment. Even though the correlations developed in this study were site and scenario specific, this work represents a first effort of applying RSM for predicting the distribution of engineered nanomaterials in porous media.

AB - Predicting the distribution of engineered nanomaterials (ENMs) in the environment will provide critical information for risk assessment and policy development to regulate these emerging contaminants. The fate and transport of ENMs in natural subsurface environments are complicated by various factors, such as hydraulic gradient, initial release concentration, nanoparticle size, and collision efficiency factor. Based on advanced statistical methodologies (i.e., response surface methodology (RSM)), we explore simple relationships between key factors that control ENM transport (collision efficiency factor, particle size, hydraulic gradient, and initial release concentration) and key parameters that describe the ENM concentration distribution in porous media (maximum standardized concentration, the mass percentage of injected nanoparticle attached in the aquifer, the x-centroid of aqueous phase nC60 plume, and the x-centroid of attached phase nC60 distribution). Hypothetical scenarios for the release of nanoparticles into an aquifer were simulated numerically with randomly generated permeability fields that were based on mildly and highly heterogeneous sites. RSM was used to develop polynomial regression equations based on a statistical experimental design. High R-squared values (greater than 0.9) of the regression equations were obtained for all the models developed based on the mildly heterogeneous site. On the highly heterogeneous site, the R-squared value of the regression equation for the percentage of nanoparticles attached (by mass) was more than 0.9. The ability to accurately estimate aqueous phase ENM concentration distribution using simple regression equations is particularly critical for risk assessment. Even though the correlations developed in this study were site and scenario specific, this work represents a first effort of applying RSM for predicting the distribution of engineered nanomaterials in porous media.

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