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

Chiara Baldacchini, Gregorio Sgrigna, Woody Clarke, Matthew Tallis, Carlo Calfapietra

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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.
Original languageEnglish
Number of pages11
JournalEnvironmental Science and Pollution Research
Early online date4 May 2019
Publication statusEarly online - 4 May 2019


  • particulate matter
  • air quality
  • pollution monitoring
  • urban forest
  • scanning electron microscopy
  • energy-resolved X-ray spectroscopy


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