Particulate matter (PM) deposited on Platanus acerifolia tree leaves has been sampled in the urban areas of 28 European cities, over 20 countries, with the aim of testing leaf deposited particles as indicator of atmospheric PM concentration and composition. Leaves have been collected close to streets characterized by heavy traffic and within urban parks. Leaf surface density, dimensions, and elemental composition of leaf deposited particles have been compared with leaf magnetic content, and discussed in connection with air quality data. The PM quantity and size were mainly dependent on the regional background concentration of particles, while the percentage of iron-based particles emerged as a clear marker of traffic-related pollution in most of the sites. This indicates that Platanus acerifolia is highly suitable to be used in atmospheric PM monitoring studies and that morphological and elemental characteristics of leaf deposited particles, joined with the leaf magnetic content, may successfully allow urban PM source apportionment.
This study reports application of monitoring and characterization protocol for particulate matter (PM) deposited on tree leaves, using Quercus ilex as a case study species. The study area is located in the industrial city of Terni in central Italy, with high PM concentrations. Four trees were selected as representative of distinct pollution environments based on their proximity to a steel factory and a street. Wash off from leaves onto cellulose filters were characterized using scanning electron microscopy and energy dispersive X-ray spectroscopy, inferring the associations between particle sizes, chemical composition, and sampling location.Modeling of particle size distributions showed a tri-modal fingerprint, with the three modes centered at 0.6 (factory related), 1.2 (urban background), and 2.6 μm (traffic related). Chemical detection identified 23 elements abundant in the PM samples. Principal component analysis recognized iron and copper as sourcespecific PM markers, attributed mainly to industrial and heavy traffic pollution respectively. Upscaling these results on leaf area basis provided a useful indicator for strategic evaluation of harmful PM pollutants using tree leaves.
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