Characterization of PM10 sources in a coastal area near Venice (Italy): An application of factor-cluster analysis

Masiol, Mauro; Rampazzo, Giancarlo; Ceccato, D.; Pavoni, Bruno

doi:10.1016/j.chemosphere.2010.05.008

Cited by 58 publications

(52 citation statements)

References 17 publications

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“…In this study, also two different applications of cluster analysis (CA) were tested to assess the impacts of local circulation and regional-scale transports on PAHs levels. CA has been widely used in atmospheric sciences, e.g., to explore the structure of data and detect the most probable emission sources (Kavouras et al 2001;Contini et al 2010), to highlight group of samples on the basis of their chemical composition (Molinaroli et al 1999;Masiol et al 2010), to manage air quality networking (Pires et al 2008), to classify wind patterns in a region (Kaufmann and Whiteman 1999;Darby 2005), and to group similar back-trajectories and air pathways (Tamamura et al 2007;Ravindra et al 2008b).…”

Section: Responsible Editor: Leif Kronbergmentioning

confidence: 99%

“…A second site was chosen downwind from the main industrial area (IND). A semi-rural coastal site (SRC) placed in an area upwind to major local emission sources and tested in previous studies (Masiol et al 2010;Squizzato et al 2012) was selected as reference for studying the regional-scale transport of pollutants.…”

Section: Samplingmentioning

confidence: 99%

“…Fast northeasterly winds called "bora" form peculiar cold and gusty downslope windstorms blowing over the Adriatic Sea and bringing air masses from Northern Europe. Generally, in the study area, these conditions may cause increased sea-spray generation and dispersion of pollutants (Masiol et al 2010). Group 2 includes days with middle intensity winds blowing mainly from N-NE, other directions are negligible.…”

Section: Areal Distribution Of Pahs (Discriminant Analysis)mentioning

confidence: 99%

“…Group 3 includes days characterized by a high percentage of wind calm (about 20 %) or just low speeds (1/1.5 m/s) and almost no prevailing direction, except for few events of winds blowing from West. These "stagnation" conditions were associated to the rise of locally emitted pollutants (Masiol et al 2010). Group 4 includes conditions with~50 % of winds from quadrant I and~50 % of winds from the quadrant II.…”

Section: Areal Distribution Of Pahs (Discriminant Analysis)mentioning

confidence: 99%

“…Lower levels in group 1 are principally due to the enhanced changing of air masses and the raised atmospheric dispersion and air cleaning. Differently, slow winds in group 3 may prevent the atmospheric dispersion and cause locally emitted pollutants to be retained (Rampazzo et al 2008a;Masiol et al 2010).…”

Section: Areal Distribution Of Pahs (Discriminant Analysis)mentioning

confidence: 99%

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GC-MS analyses and chemometric processing to discriminate the local and long-distance sources of PAHs associated to atmospheric PM2.5

Masiol

Centanni

Hofer

et al. 2012

Environ Sci Pollut Res

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Purpose This study presents a procedure to differentiate the local and remote sources of particulate-bound polycyclic aromatic hydrocarbons (PAHs). Methods Data were collected during an extended PM 2.5 sampling campaign (2009)(2010) carried out for 1 year in Venice-Mestre, Italy, at three stations with different emissive scenarios: urban, industrial, and semirural background. Diagnostic ratios and factor analysis were initially applied to point out the most probable sources. In a second step, the areal distribution of the identified sources was studied by applying the discriminant analysis on factor scores. Third, samples collected in days with similar atmospheric circulation patterns were grouped using a cluster analysis on wind data. Local contributions to PM 2.5 and PAHs were then assessed by interpreting cluster results with chemical data.Results Results evidenced that significantly lower levels of PM 2.5 and PAHs were found when faster winds changed air masses, whereas in presence of scarce ventilation, locally emitted pollutants were trapped and concentrations increased. This way, an estimation of pollutant loads due to local sources can be derived from data collected in days with similar wind patterns. Long-range contributions were detected by a cluster analysis on the air mass back-trajectories. Results revealed that PM 2.5 concentrations were relatively high when air masses had passed over the Po Valley. However, external sources do not significantly contribute to the PAHs load. Conclusions The proposed procedure can be applied to other environments with minor modifications, and the obtained information can be useful to design local and national air pollution control strategies.

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Section: Responsible Editor: Leif Kronbergmentioning

confidence: 99%

Section: Samplingmentioning

confidence: 99%

Section: Areal Distribution Of Pahs (Discriminant Analysis)mentioning

confidence: 99%

Section: Areal Distribution Of Pahs (Discriminant Analysis)mentioning

confidence: 99%

Section: Areal Distribution Of Pahs (Discriminant Analysis)mentioning

confidence: 99%

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GC-MS analyses and chemometric processing to discriminate the local and long-distance sources of PAHs associated to atmospheric PM2.5

Masiol

Centanni

Hofer

et al. 2012

Environ Sci Pollut Res

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Source contributions and spatiotemporal characteristics of PAHs in sediments: Using three‐way source apportionment approach

Tian

Shi

Liu

et al. 2014

Enviro Toxic and Chemistry

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Polycyclic aromatic hydrocarbon (PAHs) were measured in sediments from 29 sites throughout Taihu Lake in China during 2 seasons to investigate spatiotemporal characteristics and source contributions using a 3-way source apportionment approach to positive matrix factorization (PMF3). Seasonal and spatial variations of levels and toxicity suggested higher individual carcinogenic PAH concentrations and toxic equivalent quantity (TEQ) in the flooding season. Three-way PAHs dataset (PAH species, spatial variability, and seasonal variability) was analyzed by PMF3, and its results were compared with a widely used 2-way model (PMF2). Consistent results were observed: vehicular emission was the most important contributor (67.08% by PMF2 and 61.83% by PMF3 for the flooding season; 54.21% by PMF2 and 52.94% by PMF3 for dry season), followed by coal combustion and wood combustion in both seasons. The PMF-cluster analysis was employed to investigate spatial variability of source contributions. Findings can introduce the 3-way approach to apportion sources of PAHs and other persistent organic pollutants (POPs) in sediments, offering the advantage of accounting for information on 3-way datasets.

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Road Traffic: A Major Source of Particulate Matter in Europe

Amato¹,

Schaap²,

Reche

et al. 2013

The Handbook of Environmental Chemistry

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Characterization of PM10 sources in a coastal area near Venice (Italy): An application of factor-cluster analysis

Cited by 58 publications

References 17 publications

GC-MS analyses and chemometric processing to discriminate the local and long-distance sources of PAHs associated to atmospheric PM2.5

GC-MS analyses and chemometric processing to discriminate the local and long-distance sources of PAHs associated to atmospheric PM2.5

Source contributions and spatiotemporal characteristics of PAHs in sediments: Using three‐way source apportionment approach

Road Traffic: A Major Source of Particulate Matter in Europe

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