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Urban environments typically exhibit large atmospheric pollution variation, in both space and time. In contrast to traditional monitoring networks suffering from a limited spatial coverage, mobile platforms enable personalized high-resolution monitoring, providing valuable insights into personal atmospheric pollution exposure, and the identification of potential pollution hotspots. This study evaluated personal cyclist exposure to UFPs, BC and heavy metals whilst commuting near Antwerp, Belgium, by performing mobile measurements with wearable black carbon (BC) and ultrafine particle (UFP) instruments. Loaded micro-aethalometer filterstrips were chemically analysed and the inhaled pollutant dose determined from the exhibited heart rate. Considerable spatial pollutant variation was observed along the travelled routes, with distinct contributions from spatial factors (e.g. traffic intersections, urban park and market) and temporary events. On average 300% higher BC, 20% higher UFP and changing elemental concentrations are observed along the road traffic route (RT), when compared to the bicycle highway route (BH). Although the overall background pollution determines a large portion of the experienced personal exposure (in this case 53% for BC and 40% for UFP), cyclists can influence their personal atmospheric pollution exposure, by selecting less exposed commuting routes. Our results, hereby, strengthen the body of evidence in favour of further policy investments in isolated bicycle infrastructure. [ABSTRACT FROM AUTHOR]

Particulate matter (PM) deposition on urban green enables the collection of particulate pollution from a diversity of contexts, and insight into the physico-chemical profiles of PM is key for identifying main polluting sources. This study reports on the morphological and elemental characterization of PM 2-10 deposited on ivy leaves from five different environments (forest, rural, roadside, train, industry) in the region of Antwerp, Belgium. Ca. 40,000 leaf-deposited particles were thoroughly investigated by particle-based analysis using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM/EDX) and their physico-chemical characteristics were explored for PM source apportionment purposes. The size distribution of all deposited particles was biased towards small-sized PM, with 32% of the particles smaller than 2.5 μm (PM 2.5 ) and median diameters of 2.80-3.09 μm. The source type influenced both the particles' size and morphology (aspect ratio and shape), with roadside particles being overall the smallest in size and the most spherical. While forest and rural elemental profiles were associated with natural PM, the industry particles revealed the highest anthropogenic metal input. PM 2-10 profiles for roadside and train sites were rather comparable and only distinguishable when evaluating the fine (2-2.5 μm) and coarse (2.5-10 μm) PM fractions separately, which enabled the identification of a larger contribution of combustion-derived particles (small, circular, Fe-enriched) at the roadside compared to the train. Random forest prediction model classified the source type correctly for 61-85% of the leaf-deposited PM. The still modest classification accuracy denotes the influence of regional background PM and demands for additional fingerprinting techniques to facilitate source apportionment. Nonetheless, the obtained results demonstrate the utility of leaf particle-based analysis to fingerprint and pinpoint source-specific PM, particularly when considering both the composition and size of leaf-deposited particles.

Understanding the accumulation behaviour of atmospheric particles inside tree leaves is of great importance for the interpretation of biomagnetic monitoring results. In this study, we evaluated the temporal variation of the saturation isothermal remanent magnetisation (SIRM) of leaves of a roadside urban Platanus × acerifolia Willd. tree in Antwerp, Belgium. We hereby examined the seasonal development of the total leaf SIRM signal as well as the leaf-encapsulated fraction of the deposited dust, by washing the leaves before biomagnetic analysis. On average 38% of the leaf SIRM signal was exhibited by the leaf-encapsulated particles. Significant correlations were found between the SIRM and the cumulative daily average atmospheric PM10 and PM2.5 measurements. Moreover, a steady increase of the SIRM throughout the in-leaf season was observed endorsing the applicability of biomagnetic monitoring as a proxy for the time-integrated PM exposure of urban tree leaves. Strongest correlations were obtained for the SIRM of the leaf-encapsulated particles which confirms the dynamic nature of the leaf surface-accumulated particles., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Biomagnetic monitoring of tree leaf deposited particles has proven to be a good indicator of the ambient particulate concentration. The objective of this study is to apply this method to validate a local-scale air quality model (ENVI-met), using 96 tree crown sampling locations in a typical urban street canyon. To the best of our knowledge, the application of biomagnetic monitoring for the validation of pollutant dispersion modeling is hereby presented for the first time. Quantitative ENVI-met validation showed significant correlations between modeled and measured results throughout the entire in-leaf period. ENVI-met performed much better at the first half of the street canyon close to the ring road (r=0.58-0.79, RMSE=44-49%), compared to second part (r=0.58-0.64, RMSE=74-102%). The spatial model behavior was evaluated by testing effects of height, azimuthal position, tree position and distance from the main pollution source on the obtained model results and magnetic measurements. Our results demonstrate that biomagnetic monitoring seems to be a valuable method to evaluate the performance of air quality models. Due to the high spatial and temporal resolution of this technique, biomagnetic monitoring can be applied anywhere in the city (where urban green is present) to evaluate model performance at different spatial scales., (Copyright © 2014 Elsevier Ltd. All rights reserved.)