Multi-scale, multimedia modelingto 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)

[ 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 gridcells (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 mattersNeed for multi-scale grid

5,127 multiscalegrid cells

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

Potential for Grid Refinement

1 2

10 3 4 5

8 9

1 2

3 4

Background grid(static)

Multiscale grid (iterative refinement)

Potential for refinement

normalizedi

Δi

DfD

; or ; or any normalized

ii(a +b D )fi=1

ic+

6 7

n

: 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

normalized

iD

f ({Di})

ai, bi

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 AmericaWhite background covering rest of the globe

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

Selection of power plants: http://carma.org

10.5

0 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: http://sedac.ciesin.columbia.edu

(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) * (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.

[ 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: 95-50-1, half life in air: 21.1 [days])

Kg/m3

Cities > 1mio

Powerplants

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

100

101

102

103

104

0

10

20

30

40

50

60

70

80

90

100

Distance from Point Source [km]

Per

cent

age

of tot

al in

take

[%

]

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 = 9749 38521x38521, nnz = 137981

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 ]