ABSTRACT Numerous diffuse sources contribute in addition to industrial plants to atmospheric particulate matter (PM) concentrations causing concern regarding potential health effects and environmental impacts. Routine monitoring focuses on PM mass and determination of the total concentrations for selected elements. However, assessment of the (eco-)toxicological potential and the spatial distribution as well as the interaction in the environment requires suitable characterisation of the elemental speciation and mobility. Classical off-line sequential extraction techniques have been explored for this purpose but require high effort and were of limited suitability especially for low particle mass sampled on filters. A novel online sequential extraction (OSE) technique was recently developed using low amounts of model dust samples with transient elemental monitoring by inductively coupled plasma mass spectrometry (ICP-MS) to overcome the challenges. However, the application of this method to PM samples was not reported until now. In the current study, the OSE was adapted for application to punches of PM2.5 filter samples to achieve elemental fractionation with minimum manual input, low relative standard deviations and reduced risk of contamination or loss of residual sample material during solvent changes. Four samples from China and seven samples from Germany were exemplary investigated to identify regional and temporal changes in elemental fractionation and to highlight the potential of the OSE method for filter samples. Normalisation of the data as percentage of the aqua regia leachable fraction for each element demonstrated clear differences between the Chinese and the German samples. In particular, fireworks affected samples showed a unique characteristic mobility pattern. In addition, normalisation to the aqua regia leachable Mn content was performed to highlight the combination of the elemental fractionation pattern and the relative abundance between the various investigated samples. Thereby, a major improvement in the classification of the PM samples was achieved. Principal component analysis visualises the observed differences in 2-D plots with clusters of different regional and also partly different temporal samples. The obtained results indicate the potential of OSE for an improved understanding of chemical element patterns of PM samples in future studies.