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Finding Candidate Locations for Aerosol Pollution
Monitoring at Street Level Using a Data-Driven
Methodology
Vahid Moosavi1, Gideon Aschwanden1, Erik Velasco2
[1] {Future Cities Laboratory, ETH Zurich, 8092 Zurich, Switzerland}
[2]{Singapore-MIT Alliance for Research and Technology (SMART), Center for
Environmental Sensing and Modeling (CENSAM), Singapore}
June 2014
1
2
Problem Statement
Real exposure might be different than reports
But it is hard to measure
And hard to model and simulate
3
Hypothesis: There is nonlinear relations between urban parameters
and aerosol concentrations at the ground level.
Key Idea:
So, what if we are able to capture this nonlinearity empirically using data-driven
modeling methods?
More than 80
urban parameters
And 7 aerosols
Methods and Results
Self Organizing Maps
(SOM)
Hypothesis Testing

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Finding Candidate Locations for Aerosol Pollution Monitoring at Street Level Using a Data-Driven Methodology

  • 1. svm@arch.ethz.ch SEC Finding Candidate Locations for Aerosol Pollution Monitoring at Street Level Using a Data-Driven Methodology Vahid Moosavi1, Gideon Aschwanden1, Erik Velasco2 [1] {Future Cities Laboratory, ETH Zurich, 8092 Zurich, Switzerland} [2]{Singapore-MIT Alliance for Research and Technology (SMART), Center for Environmental Sensing and Modeling (CENSAM), Singapore} June 2014 1
  • 2. 2 Problem Statement Real exposure might be different than reports But it is hard to measure And hard to model and simulate
  • 3. 3 Hypothesis: There is nonlinear relations between urban parameters and aerosol concentrations at the ground level. Key Idea: So, what if we are able to capture this nonlinearity empirically using data-driven modeling methods? More than 80 urban parameters And 7 aerosols
  • 4. Methods and Results Self Organizing Maps (SOM) Hypothesis Testing

Editor's Notes

  1. In terms of representation and inference we can go beyond idealization