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svm@arch.ethz.ch
SEC
Finding Candidate Locations for Aerosol Pollution
Monitoring at Street Level Using a Data-Driven
Meth...
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 Id...
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

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Presented at MIT CENSAM Workshop on June 2014 as a part of collaborative project between ETH and MIT in Singapore.
Air pollution at street level: http://censam.mit.edu/research/Pages/Characterization%20of%20airborne%20particles%20in%20outdoor%20environments%20of%20Singapore.aspx
related publication: http://www.atmos-meas-tech.net/8/3563/2015/
poster:http://censam.mit.edu/research/CENSAM%20Publications/Urban/Characterization%20of%20airborne%20particles%20in%20outdoor%20environments%20of%20Singapore/Vahid%20poster.pdf

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

  1. 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. 2 Problem Statement Real exposure might be different than reports But it is hard to measure And hard to model and simulate
  3. 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. 4. Methods and Results Self Organizing Maps (SOM) Hypothesis Testing

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