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dc.contributor.author
Moosavi, Vahid
dc.contributor.author
Aschwanden, Gideon
dc.contributor.author
Velasco, Erik
dc.date.accessioned
2019-04-25T12:20:19Z
dc.date.available
2017-06-11T20:58:35Z
dc.date.available
2019-04-25T12:20:19Z
dc.date.issued
2015-09
dc.identifier.issn
1867-1381
dc.identifier.issn
1867-8548
dc.identifier.other
10.5194/amt-8-3563-2015
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/106827
dc.identifier.doi
10.3929/ethz-b-000106827
dc.description.abstract
Finding the number and best locations of fixed air quality monitoring stations at street level is challenging because of the complexity of the urban environment and the large number of factors affecting the pollutants concentration. Data sets of such urban parameters as land use, building morphology and street geometry in high-resolution grid cells in combination with direct measurements of airborne pollutants at high frequency (1–10 s) along a reasonable number of streets can be used to interpolate concentration of pollutants in a whole gridded domain and determine the optimum number of monitoring sites and best locations for a network of fixed monitors at ground level. In this context, a data-driven modeling methodology is developed based on the application of Self-Organizing Map (SOM) to approximate the nonlinear relations between urban parameters (80 in this work) and aerosol pollution data, such as mass and number concentrations measured along streets of a commercial/residential neighborhood of Singapore. Cross-validations between measured and predicted aerosol concentrations based on the urban parameters at each individual grid cell showed satisfying results. This proof of concept study showed that the selected urban parameters proved to be an appropriate indirect measure of aerosol concentrations within the studied area. The potential locations for fixed air quality monitors are identified through clustering of areas (i.e., group of cells) with similar urban patterns. The typological center of each cluster corresponds to the most representative cell for all other cells in the cluster. In the studied neighborhood four different clusters were identified and for each cluster potential sites for air quality monitoring at ground level are identified.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Copernicus
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/3.0/
dc.title
Finding candidate locations for aerosol pollution monitoring at street level using a data-driven methodology
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 3.0 Unported
dc.date.published
2015-09-03
ethz.journal.title
Atmospheric Measurement Techniques
ethz.journal.volume
8
en_US
ethz.journal.issue
9
en_US
ethz.journal.abbreviated
Atmos. meas. tech.
ethz.pages.start
3563
en_US
ethz.pages.end
3575
en_US
ethz.size
13 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.publication.place
Katlenburg-Lindau
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-11T20:58:39Z
ethz.source
ECIT
ethz.identifier.importid
imp593653af0629066132
ethz.ecitpid
pub:167241
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-18T12:33:14Z
ethz.rosetta.lastUpdated
2019-04-25T12:20:36Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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