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Leilani Arthurs and Patricia Maurice Civil Engineering and Geological Sciences

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

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Leilani Arthurs and Patricia Maurice Civil Engineering and Geological Sciences

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  1. 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

  2. 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

  3. Figure from Cabaniss et al. (2000)

  4. 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.

  5. 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

  6. 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)

  7. Adsorption & Desorption Probabilities to Fit Batch Data • High MW adsorbs slowly and desorbs slowly. • Low MW adsorbs fast and desorbs fast.

  8. NOM Input Distribution 1. Example of WEB interface: 2. Initial Molecular Distribution: (Equation Cabaniss et al. 2000)

  9. 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.

  10. Probability equations optimized from batch experiments applied to flow model (column experiment). Flow simulation will be compared to future laboratory column experiments.

  11. Visualization of Simulation Settings Legend

  12. Visualization Capture 1

  13. Visualization Capture 2

  14. Visualization Capture 3

  15. Visualization Capture 4

  16. 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.

  17. 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

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