Environmental Science and Pollution Research

, Volume 26, Issue 18, pp 18719–18729 | Cite as

An ultra-spatially resolved method to quali-quantitative monitor particulate matter in urban environment

  • Chiara BaldacchiniEmail author
  • Gregorio Sgrigna
  • Woody Clarke
  • Matthew Tallis
  • Carlo Calfapietra
Research Article


Monitoring the amount and composition of airborne particulate matter (PM) in the urban environment is a crucial aspect to guarantee citizen health. To focus the action of stakeholders in limiting air pollution, fast and highly spatially resolved methods for monitoring PM are required. Recently, the trees’ capability in capturing PM inspired the development of several methods intended to use trees as biomonitors; this results in the potential of having an ultra-spatially resolved network of low-cost PM monitoring stations throughout cities, without the needing of on-site stations. Within this context, we propose a fast and reliable method to qualitatively and quantitatively characterize the PM present in urban air based on the analysis of tree leaves by scanning electron microscopy combined with X-ray spectroscopy (SEM/EDX). We have tested our method in the Real Bosco di Capodimonte urban park (Naples, Italy), by collecting leaves from Quercus ilex trees along transects parallel to the main wind directions. The coarse (PM10–2.5) and fine (PM2.5) amounts obtained per unit leaf area have been validated by weighting the PM washed from leaves belonging to the same sample sets. PM size distribution and elemental composition match appropriately with the known pollution sources in the sample sites (i.e., traffic and marine aerosol). The proposed methodology will then allow the use of the urban forest as an ultra-spatially resolved PM monitoring network, also supporting the work of urban green planners and stakeholders.


Particulate matter Air quality Pollution monitoring Urban forest Scanning electron microscopy Energy-resolved X-ray spectroscopy 



The authors acknowledge the project PON infrastructure I-Amica (High Technology Infrastructure for Climate and Environmental Monitoring; PONa3_00363) for the availability of the SEM-EDX facility. Anna Rita Bizzarri and Salvatore Cannistraro (Università degli Studi della Tuscia, Viterbo) are acknowledged for providing the conductimeter. Alessandro Feliziani and Maria Grazia Agrimi (Università degli Studi della Tuscia, Viterbo) are acknowledged for preliminary contribution to conductivity measurements. Michele Mattioni and Gabriele Guidolotti (CNR-IRET) are acknowledged for providing the meteorological data. Martina Ristorini and Silvia Canepari (Università degli Studi di Roma “La Sapienza”) are acknowledged for scientific discussions.

Funding information

The publication was financially supported by the Ministry for Education, University and Research of Italy (PRIN 20173RRN2S), by the Ministry of Education and Science of the Russian Federation (Agreement No. 02.A03.21.0008), and by the EC Erasmus+ Capacity Building Menvipro. Woody Clarke was supported by a Faculty bursary from the University of Portsmouth and a work placement grant funded by EC Erasmus+.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Research CouncilInstitute of Research on Terrestrial EcosystemsPoranoItaly
  2. 2.Biophysics and Nanoscience Centre, DEBUniversità degli Studi della TusciaViterboItaly
  3. 3.School of Biological SciencesUniversity of PortsmouthPortsmouthUK
  4. 4.Department of Landscape Design and Sustainable Ecosystems, Agrarian-technological Institute30 RUDN UniversityMoscowRussia

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