Particulate matter (PM) air pollution represents a major environmental and health issue which largely depends on the type and amount of local emissions in industrial and urban areas. Therefore, the evaluation of the spatial distribution of PM chemical components is fundamental for a reliable identification of emission sources and the assessment of personal exposure. Generally, due to the very high cost of a network based on traditional PM samplers, the dispersion of air pollutants is estimated through mathematical models, which may not be able to properly describe the complexity of PM transport and transformation processes. In the last few years, a new very-low volume and automatic device for PM sampling on membrane filters has been developed and from 12/2016 to 02/2018 it has been employed in a dense (23 sampling sites, about 1 km between the sites) and low-cost monitoring network across Terni, an urban and industrial hot-spot of Central Italy [1,2]. PM10 samples were monthly collected and analyzed for PM mass and water-soluble and insoluble element concentrations by ICP-MS. Principal component analysis was performed on the spatially-resolved chemical data to individuate reliable tracers of the main local PM emission sources: Cu, Sb, Sn, Zr, Bi (insoluble fraction) and Ba (water-soluble fraction) were found to be good tracers of rail network and vehicular traffic; K, Tl, Rb, Cs and Cd (water-soluble fraction) were identified as reliable tracers of biomass burning; Co, Ni, Cr, Nb, Mn, Pb (insoluble fraction) and As, W, Mo, Cr, Zn, Li, Mn, Ga (water-soluble fraction) showed the steel plant role in the emission of PM10. Spatial distribution of the elements was mapped by using ordinary kriging interpolation method [Figure 1]. The new experimental approach was found to be effective for the evaluation of the impact of PM10 emission sources and promises to be powerful for the improvement of PM dispersion models.
Spatial mapping of PM10 element concentrations in Terni (Central Italy) by using spatially-resolved chemical data / Massimi, Lorenzo; Ristorini, Martina; Canepari, Silvia. - (2019). (Intervento presentato al convegno Convegno Giovani Ricercatori - Dipartimento di Chimica tenutosi a Roma).
Spatial mapping of PM10 element concentrations in Terni (Central Italy) by using spatially-resolved chemical data
Lorenzo Massimi
Primo
;Martina RistoriniSecondo
;Silvia CanepariUltimo
2019
Abstract
Particulate matter (PM) air pollution represents a major environmental and health issue which largely depends on the type and amount of local emissions in industrial and urban areas. Therefore, the evaluation of the spatial distribution of PM chemical components is fundamental for a reliable identification of emission sources and the assessment of personal exposure. Generally, due to the very high cost of a network based on traditional PM samplers, the dispersion of air pollutants is estimated through mathematical models, which may not be able to properly describe the complexity of PM transport and transformation processes. In the last few years, a new very-low volume and automatic device for PM sampling on membrane filters has been developed and from 12/2016 to 02/2018 it has been employed in a dense (23 sampling sites, about 1 km between the sites) and low-cost monitoring network across Terni, an urban and industrial hot-spot of Central Italy [1,2]. PM10 samples were monthly collected and analyzed for PM mass and water-soluble and insoluble element concentrations by ICP-MS. Principal component analysis was performed on the spatially-resolved chemical data to individuate reliable tracers of the main local PM emission sources: Cu, Sb, Sn, Zr, Bi (insoluble fraction) and Ba (water-soluble fraction) were found to be good tracers of rail network and vehicular traffic; K, Tl, Rb, Cs and Cd (water-soluble fraction) were identified as reliable tracers of biomass burning; Co, Ni, Cr, Nb, Mn, Pb (insoluble fraction) and As, W, Mo, Cr, Zn, Li, Mn, Ga (water-soluble fraction) showed the steel plant role in the emission of PM10. Spatial distribution of the elements was mapped by using ordinary kriging interpolation method [Figure 1]. The new experimental approach was found to be effective for the evaluation of the impact of PM10 emission sources and promises to be powerful for the improvement of PM dispersion models.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
