The link between particle pollution and health issues has been widely demonstrated during the last decades. This is the reason why the WHO set ambitious targets by drastically lowering the recommended limit values for particulate matter PM10 and PM2.5 in the newest Global air quality guidelines from September 2021. In view of these new recommendations, it can be expected that many measuring stations (and therefore, inhabited areas) are affected by PM10 and PM2.5 exceedances. Therefore, authorities have more than ever the need of seeking lowering PM values. The only chance to achieve lower PM10 and PM2.5 concentrations is to first obtain a better understanding on the fine dust constituents. This is crucial to implement
targeted dust mitigation measures. Unfortunately, there is no one single method that can characterize the totality of PM10 and PM2.5. Owing to the intrinsic different characteristics of particles, and hence, stability and detectability with the various available methods, a combination of techniques, which consists of a set of classical bulk analysis (e. g. TOT, IC) and a newly developed morphochemical single particle analysis method is applied to achieve a nearly complete PM10, resp. PM2.5 characterization and differentiation. Due to the increasing share of traffic-derived non-exhaust particles in airborne dust a need for a reliable, and cost-effective method to quantify and differentiate particles like tire, brake, road, road marking and bitumen wear becomes evident. However, the differentiation of such primary particles is not always straightforward based on bulk analysis. Here, we present a powerful approach to perform the differentiation of primary particles (e.g., traffic-derived non-exhaust). The method is based on single-particle analysis by Scanning Electron Kolloquium Luftqualität an Straßen 2023.

Microscopy (SEM) and Energy Dispersive X-ray Spectroscopy (EDX) coupled to a machine learning-based algorithm that classifies and quantifies the concentration of the different particle types (for methodological details see Rausch et al., 2022). The algorithm considers not only the elemental composition of single particles but also their size, morphology, and degree of heterogeneity (mixing), as they occur in real-world environmental samples.

Thanks to the detailed particle characterization, and the source-differentiated results (Figs. 1 & 2), the influence of specific human-induced activities (e.g., traffic, quarrying/mining, industry) on air quality can be monitored, allowing targeted measures to be taken by decision makers. In addition, a verification and quantification of the effectiveness of the implemented dust mitigation measures can be performed (e.g. contribution of non-exhaust particles from combustion engine vs. electric vehicles).

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