Source apportionment of PM10 and PM2.5 in Milan (Italy) using receptor modelling

Marcazzan, G.; Ceriani, Michela; Valli, G.; Vecchi, R.

doi:10.1016/s0048-9697(03)00368-1

Cited by 141 publications

(59 citation statements)

References 13 publications

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“…It is noteworthy that all the listed components were of primary origin while secondary components did not show any significant increase during the episode. EC is the tracer for traffic emission , as well as Fe, Cu and Cr (Marcazzan et al 2003b;Vecchi et al 2007). Mn and Zn are associated to industrial emissions in this area (Marcazzan et al 2003b).…”

Section: Local Productionmentioning

confidence: 99%

“…EC is the tracer for traffic emission , as well as Fe, Cu and Cr (Marcazzan et al 2003b;Vecchi et al 2007). Mn and Zn are associated to industrial emissions in this area (Marcazzan et al 2003b). CO and NO are typically emitted during combustion processes while Cl, Ni and Pb may be emitted by incinerators or by oils combustion (Artaxo et al 1999;Song et al 2001;Thomaidis et al 2003;Qin and Oduyemi 2003;Graney et al 2004).…”

Section: Local Productionmentioning

confidence: 99%

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4-hours resolution data to study PM10 in a “hot spot” area in Europe

Vecchi

Bernardoni

Fermo

et al. 2008

Environ Monit Assess

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Nowadays, high-time resolved aerosol studies are mandatory to better understand atmospheric processes, such as formation, removal, transport, deposition or chemical reactions. This work focuses on PM10 physical and chemical characterisation with high-time resolution: elements (from Na to Pb), ions and OC/EC fractions concentration were determined during two weeks Further measurements aimed at hourly elemental characterisation of fine and coarse fractions and at the determination of particles number concentration in the 0.25-32 μm size range in 31 bins. The chemical mass closure was carried out in both seasons, enhancing intra-day differences in PM10 composition. In Milan, the highest contribution came from organic matter (34% and 33% in summer and winter, respectively); other important contributors were secondary inorganic compounds (16% and 24% in summer and winter, respectively) and, in summer, crustal matter (14%). Temporal trends showed strong variations in PM10 composition during contiguous time-slots and diurnal variations in different components contribution were identified. Moreover, peculiar phenomena, which would have hardly been detected with 24-hours samplings, were evidenced. Particles removal due to precipitations, aerosol local production and long range transport were studied in detail.

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Section: Local Productionmentioning

confidence: 99%

Section: Local Productionmentioning

confidence: 99%

4-hours resolution data to study PM10 in a “hot spot” area in Europe

Vecchi

Bernardoni

Fermo

et al. 2008

Environ Monit Assess

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“…Several methods have been introduced to identify and quantify OC emission sources, such as the use of organic molecular tracers (Simoneit et al, 1999), receptor models (positive matrix factorization, PMF; chemical mass balance, CMB) (Singh et al, 2017;Bove et al, 2014;Marcazzan et al, 2003), and dispersion models (Colvile et al, 2003); however, their reliability is limited by their low atmospheric lifetimes, in turn due to chemical reactivity and highly variable emission factors (Fine et al, 2001(Fine et al, , 2002(Fine et al, , 2004Gao et al, 2003;Hedberg et al, 2006;Robinson et al, 2006). Recently, radiocarbon ( 14 C) analysis has been used as a powerful tool for facilitating the direct differentiation of non-fossilfuel (NF) carbon sources from fossil fuel (FF) sources, because 14 C is completely absent from FF carbon (e.g., diesel and gasoline exhaust, coal combustion), whereas NF carbon (e.g., biomass burning, cooking and biogenic emissions) shows a high contemporary 14 C level (Szidat et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Sources of non-fossil-fuel emissions in carbonaceous aerosols during early winter in Chinese cities

Liu

Cheng

et al. 2017

Atmos. Chem. Phys.

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Abstract. China experiences frequent and severe haze outbreaks from the beginning of winter. Carbonaceous aerosols are regarded as an essential factor in controlling the formation and evolution of haze episodes. To elucidate the carbon sources of air pollution, source apportionment was conducted using radiocarbon ( 14 C) and unique molecular organic tracers. Daily 24 h PM 2.5 samples were collected continuously from October 2013 to November 2013 in 10 Chinese cities. The 14 C results indicated that non-fossil-fuel (NF) emissions were predominant in total carbon (TC; average = 65 ± 7 %). Approximately half of the EC was derived primarily from biomass burning (BB) (average = 46±11 %), while over half of the organic carbon (OC) fraction comprised NF (average = 68 ± 7 %). On average, the largest contributor to TC was NF-derived secondary OC (SOC nf ), which accounted for 46 ± 7 % of TC, followed by SOC derived from fossil fuels (FF) (SOC f ; 16 ± 3 %), BB-derived primary OC (POC bb ; 13 ± 5 %), POC derived from FF (POC f ; 12 ± 3 %), EC derived from FF (EC f ; 7±2 %) and EC derived from BB (EC bb ; 6 ± 2 %). The regional background carbonaceous aerosol composition was characterized by NF sources; POCs played a major role in northern China, while SOCs contributed more in other regions. However, during haze episodes, there were no dramatic changes in the carbon source or composition in the cities under study, but the contribution of POC from both FF and NF increased significantly.

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“…Receptor models are often constructed with fixed-site monitoring station data. 9,[21][22][23][24][25] Few receptor models have been constructed to apportion multisubject personal exposures to particulate matter. 26,27 Longitudinal studies present an interesting problem for receptor models of personal exposures.…”

Section: Introductionmentioning

confidence: 99%

Sources of Personal Exposure to Fine Particles in Toronto, Ontario, Canada

Kim

Sass-Kortsāk

Purdham

et al. 2005

Journal of the Air & Waste Management Association

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Individuals are exposed to particulate matter from both indoor and outdoor sources. The aim of this study was to compare the relative contributions of three sources of personal exposure to fine particles (PM 2.5 ) by using chemical tracers. The study design incorporated repeated 24-hr personal exposure measurements of air pollution from 28 cardiac-compromised residents of Toronto, Ontario, Canada. Each study participant wore the Rupprecht & Patashnick ChemPass Personal Sampling System 1 day a week for a maximum of 10 weeks. During their individual exposure measurement days the subjects reported to have spent an average of 89% of their time indoors. Particle phase elemental carbon, sulfate, and calcium personal exposure data were used in a mixed-effects model as tracers for outdoor PM 2.5 from traffic-related combustion, regional, and local crustal materials, respectively. These three sources were found to contribute 13% Ϯ 10%, 17% Ϯ 16%, and 7% Ϯ 6% of PM 2.5 exposures. The remaining fraction of the personal PM 2.5 is hypothesized to be predominantly related to indoor sources. For comparison, central site outdoor PM 2.5 measurements for the same dates as personal measurements were used to construct a receptor model using the same three tracers. In this case, traffic-related combustion, regional, and local crustal materials were found to contribute 19% Ϯ 17%, 52% Ϯ 22%, and 10% Ϯ 7%, respectively. Our results indicate that the three outdoor PM 2.5 sources considered are statistically significant contributors to personal exposure to PM 2.5 . Our results also suggest that among the Toronto subjects, who spent a considerable amount of time indoors, exposure to outdoor PM 2.5 includes a greater relative contribution from combustion sources compared with outdoor PM 2.5 measurements where regional sources are the dominant contributor.

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Source apportionment of PM10 and PM2.5 in Milan (Italy) using receptor modelling

Cited by 141 publications

References 13 publications

4-hours resolution data to study PM10 in a “hot spot” area in Europe

4-hours resolution data to study PM10 in a “hot spot” area in Europe

Sources of non-fossil-fuel emissions in carbonaceous aerosols during early winter in Chinese cities

Sources of Personal Exposure to Fine Particles in Toronto, Ontario, Canada

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