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| Titel |
Finding candidate locations for aerosol pollution monitoring at street level using a data-driven methodology |
| VerfasserIn |
V. Moosavi, G. Aschwanden, E. Velasco |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 9 ; Nr. 8, no. 9 (2015-09-03), S.3563-3575 |
| Datensatznummer |
250116566
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| Publikation (Nr.) |
 copernicus.org/amt-8-3563-2015.pdf |
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| Zusammenfassung |
| 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. |
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