Articles | Volume 8, issue 9
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, G. Aschwanden, and E. Velasco

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.

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.