Articles | Volume 8, issue 9
Atmos. Meas. Tech., 8, 3563–3575, 2015
Atmos. Meas. Tech., 8, 3563–3575, 2015

Research article 03 Sep 2015

Research article | 03 Sep 2015

Finding candidate locations for aerosol pollution monitoring at street level using a data-driven methodology

V. Moosavi et al.

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Cited articles

Barreto, G. A. and Souza, L. G. M.: Adaptive filtering with the self-organizing map: a performance comparison, Neural Networks, 19, 785–798, 2006.
Bieringer, P. E., Longmore, S., Bieberbach, G., Rodriguez, L. M., Copeland, J., and Hannan, J.: A method for targeting air samplers for facility monitoring in an urban environment, Atmos. Environ., 80, 1–12, 2013.
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Short summary
Complexity of urban environments makes the problem of locating air quality monitoring stations at ground level challenging. In this work a data-driven methodology is proposed where using Self Organizing Maps along with several urban parameters and few direct measurements of aerosols at the street level, the concentration of those aerosols in a larger area is estimated. Finally, via clustering of areas with similar urban patterns, the potential locations of monitoring stations are identified.