Modeling airborne benzene in space and time with self-organizing maps and Bayesian techniques

Abstract

An assessment of personal exposure to airborne chemical contaminants demands for individual-specific registration of their concentrations, a procedure which is expensive and difficult to implement. An alternative approach is the calculation of a spatial concentration field in high resolution where exposure can be assigned to individuals according to their dwelling locations. Self-organizing maps (SOM) and Bayesian Hierarchical Models (BHM) were applied to model the spatial concentrations of benzene, an airborne volatile organic compound (VOC), in the urban area of Leipzig, Germany. Different performance measures (mean absolute error, coefficient of determination, etc.) were adopted to evaluate and compare the performance of SOM and BHM. Relevant input factors related to VOC dispersion were stepwise selected with the BHM. Both modeling techniques identified seasonal as well as spatial variations of benzene, with the highest concentrations occurring in winter and the lowest in summer. SOM and BHM showed that high concentrations of benzene are correlated with low distances to the city center and with the major traffic routes. Both SOM and BHM were suitable to model the spatial distribution of benzene concentrations, with the latter yielding a better overall performance using input factors selected by BHM. Beyond this specific application the suggested approaches have potential for statistical spatiotemporal modeling of other environmental parameters, an issue that is currently under rapid development.