Innovative and low-cost monitoring techniques for evaluating the spatial variability of PM components

The study of the spatial distribution of atmospheric PM and of its components is essential for a reliable identification of emission sources, the evaluation of particle dispersion over the territory and the assessment of personal exposure. However, the very high cost of a network based on traditional PM samplers generally prevents the achievement of these goals. A low-cost, self-powered and automatic device for PM sampling on membrane filters has been recently become available. The sampler constitutes promising possibility to build low-cost networks for atmospheric PM as well as the lichen biomonitors. The SMART SAMPLER (FAI Instruments, Fonte Nuova, Rome, Italy; Figure 1) operates at the flow rate of 0.5 l min-1. It is equipped with a small solar panel and a rechargeable battery. For the validation step, three PM10 samplers were operated side-by-side for 1 year (30- or 45-day samplings). The samples were analysed for PM mass by gravimetry, ions by IC, levoglucosan by HPAEC-PAD, PAH by HRGC-MS and elements by ICP-MS. The results were evaluated in terms of relative standard deviation of the replicates and compared with the average values obtained from daily samplings carried out by a reference sampler operating at the flow rate of 2.3 m3 h-1. In the field, 25 samplers along with 25 lichen transplants (E. prunastri) were located at different sampling sites to design an inexpensive, extended and extensive (approximately 1 km of distance between the sites) monitoring network across Terni (Figure 2). A bag of nylon containing lichen transplants was exposed at each monitoring site for measuring the pollutants’ bioaccumulation at 5-months and at 1-year of sampling. Localizations of the samplers and of the lichen biomonitors were chosen in order to evaluate the impact of different local PM10 emission sources (such as power plant, steel plant and vehicular traffic). Chemical analysis of the PM samples was focused on the elemental content, using a chemical fractioning procedure that allowed us to discriminate water-soluble and residual fractions of analyzed elements. This approach proved to be valuable for increasing selectivity of elements as source tracers. The repeatability of the samplings carried out by the low-cost samplers was about 5%. The comparison with the reference sampler was very good for stable, fine components (e.g.: sulphate, potassium, levoglucosan, elements) and satisfactory for stable coarse components (e.g.: sodium, magnesium, calcium). Spatially resolved data, obtained by monthly sampling in parallel at 25 monitoring sites of Terni, allowed to assess the spatial variability of PM10 and elemental mass concentrations. Furthermore the PM sampling at each site enabled to properly evaluate the potential of the lichens as biomonitors for spatially resolved analyses. Source tracers of the main PM10 local emission sources were identified. Chemical fractionation improved the selectivity of element as source tracers. Spatial variability of Ni, Cr, Mn (insoluble fraction) and Mo (water-soluble fraction) concentrations showed the steel plant role in the emission of PM10. Spatial variability of Fe (insoluble fraction) resulted to be correlated not only with the steel plant emission but also with vehicular traffic. The role of this emission source was also confirmed by the spatial variability of elements such as Sb and Cu. Rb (soluble fraction) was confirmed to be a good tracer of biomass combustion processes. Lichen transplants appeared good biomonitors for spatially resolved analyses of the elements emitted by the steel plant. The obtained results proved the efficiency of the innovative and low-cost experimental procedures for the evaluation of the spatial variability of PM10 and its main chemical components through the acquisition of spatially resolved data. In particular, the innovative and low-cost sampler, used for the first time in this monitoring campaign, allowed to build an extended and extensive monitoring network, with low associated costs, which was able to represent the different emission source contributes to the total PM10 in the monitored area. This experimental procedure promises to be effective for the validation of dispersion models without the high costs associated to an air quality monitoring network. The innovative sampling procedure allowed to evaluate the potential of the lichens as biomonitors for spatially resolved analyses.