Automated SEM/EDX imaging for the in-depth characterization of non-exhaust traffic emissions from the Munich subway system
Abstract
A SEM/EDX based automated measurement and classification algorithm was tested as a method for the in-depth analysis of micro-environments in the Munich subway using a custom build mobile measurements system. Sampling was conducted at platform stations, to investigate the personal exposure of commuters to subway particulate matter during platform stays.
EDX spectra and morphological features of all analyzed particles were automatically obtained and particles were automatically classified based on pre-defined chemical and morphological boundaries. Source apportionment for individual particles, such as abrasion processes at the wheel-brake interface, was partially possible based on the established particle classes.
An average of 98.87 +- 1.06 % of over 200,000 analyzed particles were automatically assigned to the pre-defined classes, with 84.68 +- 16.45 % of particles classified as highly ferruginous. Manual EDX analysis further revealed, that heavy metal rich particles were also present in the ultrafine size range well below 100 nm.