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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 9
Atmos. Meas. Tech., 8, 3563–3575, 2015
https://doi.org/10.5194/amt-8-3563-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Meas. Tech., 8, 3563–3575, 2015
https://doi.org/10.5194/amt-8-3563-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

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.
Guyon, I. and Elisseeff, A.: An introduction to variable and feature selection, J. Mach. Learn. Res., 3, 1157–1182, 2003.
Hidy, G. M. and Pennell, W. T.: Multipollutant air quality management, J. Air Waste Manage., 60, 645–674, 2010.
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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.
Complexity of urban environments makes the problem of locating air quality monitoring stations...
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