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Finding the number and best locations of fixed air quality monitoringstations at street level is challenging because of the complexity of theurban environment and the large number of factors affecting the pollutantsconcentration. Data sets of such urban parameters as land use, buildingmorphology and street geometry in high-resolution grid cells in combinationwith direct measurements of airborne pollutants at high frequency (1–10 s)along a reasonable number of streets can be used to interpolateconcentration of pollutants in a whole gridded domain and determine theoptimum number of monitoring sites and best locations for a network of fixedmonitors at ground level. In this context, a data-driven modelingmethodology is developed based on the application of Self-Organizing Map(SOM) to approximate the nonlinear relations between urban parameters (80 inthis work) and aerosol pollution data, such as mass and numberconcentrations measured along streets of a commercial/residentialneighborhood of Singapore. Cross-validations between measured and predictedaerosol concentrations based on the urban parameters at each individual gridcell showed satisfying results. This proof of concept study showed that theselected urban parameters proved to be an appropriate indirect measure ofaerosol concentrations within the studied area. The potential locations forfixed air quality monitors are identified through clustering of areas (i.e.,group of cells) with similar urban patterns. The typological center of eachcluster corresponds to the most representative cell for all other cells inthe cluster. In the studied neighborhood four different clusters wereidentified and for each cluster potential sites for air quality monitoringat ground level are identified

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