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Multi-scale, multimedia modeling to compare local and global life cycle impacts on human health

Multi-scale, multimedia modeling to compare local and global life cycle impacts on human health. Cédric Wannaz 1 , Peter Fantke 2 , Olivier Jolliet 1 1 School of public Health, University of Michigan (U.S.)

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Multi-scale, multimedia modeling to compare local and global life cycle impacts on human health

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  1. Multi-scale, multimedia modeling to compare local and global life cycle impacts on human health Cédric Wannaz1, Peter Fantke2, Olivier Jolliet1 1 School of public Health, University of Michigan (U.S.) 2 Institute of Energy Economics and the Rational Use of Energy, University of Stuttgart (Germany)

  2. “Box” type Multimedia Models Main assumption: instantaneous homogeneity

  3. Spatial Differentiation • Fixed number of grid cells • Months of work to parameterize • Higher resolution when reduced extend but still not “high resolution” • Need for global => low resolution

  4. Global, high resolution, but how? Issue : “The number of grid cells grows faster than the resolution” 12,960 fixed grid cells (2° x 2.5°)

  5. Drawbacks of Large Grid Cells (artifacts) Assuming body of water with large residence time Artificial dilution

  6. Need for Multi-scale Grid • Need for high resolution where it matters • Need for multi-scale grid 5,127 multiscale grid cells

  7. P = normalized f D i Î Δ D i Potential for Grid Refinement Background grid (static) Potential for refinement Multiscale grid (iterative refinement) 6 7 n normalized P c+ (a +b D ) × ; or any f ({Di}) = f ; or i i i i=1 normalized D : spatial dataset (raster) #i. Each raster pixel indicates a local weight for refinement (0=no to 1=max) : scalars associated with Di, that allow offset + rescale : scalar, offset i ai, bi c

  8. Example: Potential «North America» • (A) Two polygons (countries are super-imposed): • Black polygon (drawn by hand): covering North America • White background covering rest of the globe : high interest for refinement : no interest for refinement (prevented)

  9. Example: Potential «Plant Proximity» • (B) GIS operation : multiple ring buffers around plants Power plants 1 0.5 0 Power plant Selection of power plants: http://carma.org

  10. Example: Potential «Population Count» • (C) This potential is not hand-made, but comes directly • from a dataset (raster) of population counts. Number of capita per raster cell: http://sedac.ciesin.columbia.edu

  11. Example: Total Potential • Total potential = 0 + (0 + 1 * raster North America) * • (0.5 + 0.5 * raster proximity) * • (0 + 1 * raster population) • Targets for refinement: North American regions with • large population and close to (a selection of) power plants.

  12. Resulting Multiscale Grid • Step 1: Creation of a user-defined background grid

  13. Resulting Multi-scale Grid • Step 2: Iterative grid refinement according to potential

  14. Resulting Multi-scale Grid • Step 2: Iterative grid refinement according to potential •  zoom in to the U.S.

  15. Air Concentration [kg/m³] Example: emission from a power plant near Houston: 1,2-Dichlorobenzene (CAS: 95-50-1, half life in air: 21.1 [days]) Kg/m3 Cities > 1mio Powerplants

  16. Intake at Different Scales Local Studies LC(I)A studies

  17. Conclusions for Environmental Scientists • Global modeling with high resolution at specific places • Example: compare intake in vicinity • of emission source with global intake •  some % of intake in emission cell •  local study misses most of impacts •  global study misses adequate resolution • Grid adjustable to data availability, user interests, etc. • Evaluation of grid characteristics via sensitivity study

  18. Conclusions for SGM 2010 • Potential for refinement (PfR) is a very flexible solution for both GIS specialists and non-specialists to define the characteristics of the desired refined grid. • A PfR is a combination of multiple contributions that can be based on any dataset => unlimited possibilities. • Synergistic and antagonistic contributions can be used: some contributions can oppose to refinement. Absolute constraints can be defined => possible to limit refinement according to dataset native resolution/availability. • The full modeling chain includes coded procedures (Python+ Geoprocessor) for projecting data into the grids (scalar and vector fields), and then building the mathematical objects that describe the compartmental system => possible to perform sensitivity studies towards grid variations!

  19. Appendix – K matrices A F.W. N.L. A.L. S Our basic example A more elaborate example A F.W. N.L. A.L. S 1779x1779, nnz = 9749 38521x38521, nnz = 137981

  20. Appendix – Gridded water network WWDRII gridded water network, 0.5°x0.5°

  21. Appendix – Clustering

  22. Appendix – Clusters Composition

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