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.