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.) 2 Institute of Energy Economics and the Rational Use of Energy, University of Stuttgart (Germany)

[ SPH, University of Michigan | IER, University of Stuttgart ] “Box” type Multimedia Models Main assumption: instantaneous homogeneity

[ SPH, University of Michigan | IER, University of Stuttgart ] 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

[ SPH, University of Michigan | IER, University of Stuttgart ] 12,960 fixed grid cells (2° x 2.5°) Global, high resolution, but how? Issue : “The number of grid cells grows faster than the resolution”

[ SPH, University of Michigan | IER, University of Stuttgart ] Drawbacks of Large Grid Cells (artifacts) Artificial dilution Assuming body of water with large residence time

[ SPH, University of Michigan | IER, University of Stuttgart ] Need for Multi-scale Grid Need for high resolution where it matters Need for multi-scale grid 5,127 multiscale grid cells

[ SPH, University of Michigan | IER, University of Stuttgart ] Potential for Grid Refinement Background grid (static) Multiscale grid (iterative refinement) Potential for refinement normalized i Δ i Df D    ; or ; or any normalized ii (a +b D ) f i=1    i c+ 6 7 n : spatial dataset (raster) #i. Each raster pixel indicates a local weight for refinement (0=no to 1=max) : scalars associated with D i, that allow offset + rescale : scalar, offset normalized i D f ({D i }) a i, b i c

[ SPH, University of Michigan | IER, University of Stuttgart ] Example: Potential «North America» : high interest for refinement : no interest for refinement (prevented) (A) Two polygons (countries are super-imposed): Black polygon (drawn by hand): covering North America White background covering rest of the globe

[ SPH, University of Michigan | IER, University of Stuttgart ] Selection of power plants: Power plant Example: Potential «Plant Proximity» Power plants (B) GIS operation : multiple ring buffers around plants

[ SPH, University of Michigan | IER, University of Stuttgart ] Example: Potential «Population Count» Number of capita per raster cell: (C) This potential is not hand-made, but comes directly from a dataset (raster) of population counts.

[ SPH, University of Michigan | IER, University of Stuttgart ] Example: Total Potential Total potential = 0 + (0 + 1 * raster North America) * ( * raster proximity) * (0 + 1 * raster population) Targets for refinement: North American regions with large population and close to (a selection of) power plants.

[ SPH, University of Michigan | IER, University of Stuttgart ] Resulting Multiscale Grid Step 1: Creation of a user-defined background grid

[ SPH, University of Michigan | IER, University of Stuttgart ] Resulting Multi-scale Grid Step 2: Iterative grid refinement according to potential

[ SPH, University of Michigan | IER, University of Stuttgart ] Resulting Multi-scale Grid Step 2: Iterative grid refinement according to potential  zoom in to the U.S.

[ SPH, University of Michigan | IER, University of Stuttgart ] Air Concentration [kg/m³] Example: emission from a power plant near Houston: 1,2-Dichlorobenzene (CAS: , half life in air: 21.1 [days]) Kg/m 3 Cities > 1mio Power plants

[ SPH, University of Michigan | IER, University of Stuttgart ] Local Studies Intake at Different Scales LC(I)A studies

[ SPH, University of Michigan | IER, University of Stuttgart ] 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 Conclusions for Environmental Scientists

[ SPH, University of Michigan | IER, University of Stuttgart ] 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!

[ SPH, University of Michigan | IER, University of Stuttgart ] A F.W. N.L. A.L. S A F.W. N.L. A.L. S Appendix – K matrices 1779x1779, nnz = x38521, nnz = Our basic example A more elaborate example

[ SPH, University of Michigan | IER, University of Stuttgart ] Appendix – Gridded water network WWDRII gridded water network, 0.5°x0.5°

[ SPH, University of Michigan | IER, University of Stuttgart ] Appendix – Clustering

[ SPH, University of Michigan | IER, University of Stuttgart ] Appendix – Clusters Composition

[ SPH, University of Michigan | IER, University of Stuttgart ]

Intake Fraction [kg/kg] kg/kg Population intake: Global iF = 2.98 E-5  96.6% outside of local area Local iF = 1.01 E-6  3.4% of total intake (but highest individual intake)