Characterisation and source apportionment of PM10 in an urban background site in Lecce

Contini, Daniele; Genga, A.; Cesari, Daniela; Siciliano, M.; Donateo, Antonio; Bove, Maria Chiara; Guascito, Maria Rachele

doi:10.1016/j.atmosres.2009.07.010

Cited by 130 publications

(54 citation statements)

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“…Thereby, the increase in the hot weather of this contribution is not able to compensate the decrease of anthropogenic emissions (traffic and domestic heating) in PM 2.5 concentrations as it does in PM 10 concentrations. The larger importance of SD events in the hot weather is in agreement with other observations reported in literature [19,28]. Long-terms datasets of atmospheric concentrations are often described with lognormal distributions [18,29,30].…”

Section: Pm 10 and Pm 25 Concentrationssupporting

confidence: 81%

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Characterization of PM10 and PM2.5 and Their Metals Content in Different Typologies of Sites in South-Eastern Italy

et al. 2014

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Samples of PM 10 and PM 2.5 were collected discontinuously between 2003 and 2010 at fifteen monitoring sites (urban, background, industrial) in the south-eastern part of Italy using a mobile laboratory. In total, 483 PM 10 and 154 PM 2.5 samples were collected and chemically analyzed for the determination of metal content. Data were used to investigate concentration differences among the typologies of sites, the seasonal patterns, and the influence of advection of Saharan dust (SD). PM 10 and PM 2.5 average concentrations increase from background to industrial and urban sites but the ratio PM 2.5 /PM 10 is significantly lower (0.61 ± 0.10) in background sites. The average metals concentrations in PM 10 and in PM 2.5 do not show a clear dependence on site typology apart an increase in crustal elements in background sites and an increase in the enrichment factors of Ni and of Cr in PM 10 in industrial sites. Urban sites show a statistically significant increase of PM 10 average concentration during the cold seasons (autumn and winter), likely associated with the anthropogenic urban emissions, instead, the background sites show a decrease in concentrations during the cold seasons. This could be due to more frequent cases of SD observed in spring and summer periods that mainly influence background sites. The seasonal difference on the average concentration for industrial sites is not statistically significant. The SD cases influence both PM 10 and PM 2.5 concentrations but their effect is significantly larger on PM 10 . Over the studied area, the effect is relatively limited on long-term average PM 10 (estimated increase of 3.2%) and PM 2.5 (estimated increase of 1.5%) concentrations but it OPEN ACCESSAtmosphere 2014, 5 436 is significant on daily concentrations. It is estimated an increase of 22% of the probability to overcome the air quality standard daily threshold for PM 10 .

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Section: Pm 10 and Pm 25 Concentrationssupporting

confidence: 81%

“…This represents the minimum concentration of the specific element detectable by the analytical methodology used. The approach followed is the same already applied in several source apportionment studies [19][20][21].…”

Section: Instruments and Methodsmentioning

confidence: 99%

Characterization of PM10 and PM2.5 and Their Metals Content in Different Typologies of Sites in South-Eastern Italy

et al. 2014

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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%

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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“…HCA is an effective statistical method for the study of atmospheric aerosol composition, and can be used to confirm the groups of variables and samples obtained with PCA, identifying the groupings not well detectable with the latter, since HCA considers all the information contained in the data set, not only a part of it, as in PCA (Contini et al, 2010). The method groups data by similarity: objects with the maximum of similarity are arranged into a single group, or cluster, and the calculation is iteratively repeated.…”

Section: Chemometric Toolsmentioning

confidence: 99%

Spatial distribution and potential sources of trace elements in PM10 monitored in urban and rural sites of Piedmont Region

et al. 2016

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The results on elemental composition of aerosol (PM10) sampled during 2011 in Piedmont region (Italy) are interpreted using meteorological data, Enrichment Factors (EF), chemometric processing by Principal Component Analysis (PCA), Factor Analysis (FA) and Hierarchical Cluster Analysis (HCA).Daily concentrations of about 30 elements were measured using HR-ICP-MS in five monitoring sites. A clear seasonal pattern, with higher concentrations in autumn and winter, was observed, particularly in the urban sites. Levels of As, Cd, Ni and Pb in most of the samples were within the limits imposed by the European legislation. Spatial differences in PM10 and metal concentrations were significant, with rural and urban sites showing different metal patterns, indicating different sources. K and Ca were used, respectively, as marker of biomass burning and industrial marker (cement plant); EFs showed that Ca was enriched just in one area and K was enriched only in the winter period considered and in some stations.Data analysis through PCA, FA and HCA allowed us to identify correlations among the investigated elements and similarities between sampling sites in order to individuate specific emission sources, such as non-exhaust vehicle emission. Keywords PiedmontTrace elements in PM10 Spatial variability Chemometric processingInductively coupled plasma-mass spectrometry

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Characterisation and source apportionment of PM10 in an urban background site in Lecce

Cited by 130 publications

References 50 publications

Characterization of PM10 and PM2.5 and Their Metals Content in Different Typologies of Sites in South-Eastern Italy

Characterization of PM10 and PM2.5 and Their Metals Content in Different Typologies of Sites in South-Eastern Italy

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

Spatial distribution and potential sources of trace elements in PM10 monitored in urban and rural sites of Piedmont Region

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