Agent-based simulations of biocomplexity: Effects of adsorption to natural organic mobility through soils

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Agent-based simulations of biocomplexity Effects
of adsorption to natural organic mobility through
soils

• Leilani Arthurs and Patricia Maurice
• Civil Engineering and Geological Sciences
• Gregory Madey, Xiaorong Xiang, Yingping Huang,
  and Ryan Kennedy
• Computer Science and Engineering
• University of Notre Dame

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Natural Organic Matter (NOM)

• Ubiquitous in aqueous and terrestrial
  environments
• Breakdown product of decaying plant material
• Controls many biogeochemical processes
• Polydisperse mixture
• Molecular weight controls NOM reactivity

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Figure from Cabaniss et al. (2000)
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Development of NOM Simulator

• Complex interactions of NOM through porous media
  results in emergent behaviors amenable to a
  biocomplexity approach.
• Design and use an agent-based stochastic model
  for NOM interactions.
• We focus specifically on how NOM molecular weight
  affects adsorption to mineral surfaces and
  mobility through soil.
• Additional research by Cabaniss et al. focuses on
  higher order biogeochemical reactions.

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The NOM Simulator Design

• Java language, J2EE architecture
• Swarm and Repast software
• WEB interface
• Can be used interactively as part of a
  collaboratory
• Allows for data mining

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• Low surface coverages adsorbed fraction mimics
  initial
• Higher surface coverages preferential
  adsorption of intermediate to high
  molecular weight components
• Kinetic data show that smaller molecules adsorb
  fast, gradually replaced by larger molecules

Zhou et al. (2001)
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Adsorption Desorption Probabilities to Fit
Batch Data

• High MW adsorbs slowly and desorbs slowly.
• Low MW adsorbs fast and desorbs fast.

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NOM Input Distribution
1. Example of WEB interface
2. Initial Molecular Distribution
(Equation Cabaniss et al. 2000)
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• Zhou et al. showed that average MW in solution
  decreased over time, indicating replacement of
  fast adsorbing small molecules by larger
  molecules.
• The NOM Simulator captures this behavior for
  batch adsorption example.

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Probability equations optimized from batch
experiments applied to flow model (column
experiment).
Flow simulation will be compared to future
laboratory column experiments.
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Visualization of Simulation
Settings
Legend
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Visualization Capture 1
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Visualization Capture 2
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Visualization Capture 3
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Visualization Capture 4
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Results and Conclusions

• Developed an agent-based stochastic model for NOM
  adsorption.
• The simulator is accessible through the WEB.
• Promotes the use of a collaboratory for
  geographically separated interdisciplinary
  scientists.
• Allows users to set/refine parameters and
  equations.

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Acknowledgements

• Dr. Steve Cabaniss (University of New Mexico)
• Center for Environmental Science and Technology
  and Environmental Molecular Science Institute at
  the University of Notre Dame
• National Science Foundation (EMSI, ITR)
• PPG Industries