Quantification of Saharan and local dust impact in an arid Mediterranean area by the positive matrix factorization (PMF) technique

Nicolás, J.F.; Chiari, M.; Crespo, J.; Orellana, I. García; Lucarelli, F.; Nava, S.; Pastor, C.; Yubero, Eduardo

doi:10.1016/j.atmosenv.2008.09.018

Cited by 90 publications

(49 citation statements)

References 27 publications

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“…Positive matrix factorization (PMF) is a new model based on a weighted non-negative least squares algorithm which overcomes this problem as it beforehand restricts the mentioned values in such a way that only positive values are allowed; moreover, experimental concentrations are weighted using their uncertainties. PMF has already been applied in numerous studies (Polissar et al 1998;Kim et al 2004;Kim and Hopke 2004b;Begum et al 2004;Nicolás et al 2008). The disadvantages of PCA (or FA) and advantages of advanced receptor models like PMF have been compared in different studies (e.g., Qin et al 2002;Viana et al 2008;Tauler et al 2009).…”

Section: Introductionmentioning

confidence: 99%

PM10 source apportionment in the surroundings of the San Vicente del Raspeig cement plant complex in southeastern Spain

Yubero

Carratalá

Crespo

et al. 2010

Environ Sci Pollut Res

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Introduction The concentrations of trace metals, ionic species, and carbonaceous components in PM 10 (particulate matter with aerodynamic diameters smaller than 10 µm) were measured from samples collected near an industrial complex, primarily composed of cement plants, in southeastern Spain, from September 2005 to August 2006. Materials and methods Positive matrix factorization and conditional probability function were applied to this data set to identify different types of sources. Results Six significant sources were identified: crustal matter, traffic, aged sea salt, industrial emissions, secondary aerosol, and sea salt. The difficulty of separating anthropogenic sources from those of natural origin is highlighted in this study; in particular, the crustal source can be connected with both natural (African outbreaks, wind resuspension) and man-made emissions, like fugitive emissions in an industrial environment.

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Section: Introductionmentioning

confidence: 99%

PM10 source apportionment in the surroundings of the San Vicente del Raspeig cement plant complex in southeastern Spain

Yubero

Carratalá

Crespo

et al. 2010

Environ Sci Pollut Res

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“…However, the contribution of fly ash to PM 10 is negligible (Gaffney and Marley, 2009) and Si, Al, and Fe elements are commonly used to identify and quantify the crustal content, particularly in the Mediterranean region (e.g. Remoundaki et al, 2011;Koçak et al, 2007;Nicolás et al, 2008).…”

Section: And Ni Enrichment With Respect To Crustal Sourcesmentioning

confidence: 99%

Evidence for heavy fuel oil combustion aerosols from chemical analyses at the island of Lampedusa: a possible large role of ships emissions in the Mediterranean

et al. 2012

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Abstract. Measurements of aerosol chemical composition made on the island of Lampedusa, south of the Sicily channel, during years [2004][2005][2006][2007][2008], are used to identify the influence of heavy fuel oil (HFO) combustion emissions on aerosol particles in the Central Mediterranean. Aerosol samples influenced by HFO are characterized by elevated Ni and V soluble fraction (about 80 % for aerosol from HFO combustion, versus about 40 % for crustal particles), high V and Ni to Si ratios, and values of V sol > 6 ng m −3 . Evidence of HFO combustion influence is found in 17 % of the daily samples. Back trajectories analysis on the selected events show that air masses prevalently come from the Sicily channel region, where an intense ship traffic occurs. This behavior suggests that single fixed sources like refineries are not the main responsible for the elevated V and Ni events, which are probably mainly due to ships emissions.V sol , Ni sol , and non-sea salt SO 2− 4 (nssSO 2− 4 ) show a marked seasonal behaviour, with an evident summer maximum. Such a pattern can be explained by several processes: (i) increased photochemical activity in summer, leading to a faster production of secondary aerosols, mainly nssSO 2− 4 , from the oxidation of SO 2 (ii) stronger marine boundary layer (MBL) stability in summer, leading to higher concentration of emitted compounds in the lowest atmospheric layers. A very intense event in spring 2008 was studied in detail, also using size segregated chemical measurements. These data show that elements arising from heavy oil combustion (V, Ni, Al, Fe) are distributed in the sub-micrometric fraction of the aerosol, and the metals are present as free metals, carbonates, oxides hydrates or labile complex with organic ligands, so that they are dissolved in mild condition (HNO 3 , pH1.5).Data suggest a characteristic nssSO 2− 4 /V ratio in the range 200-400 for HFO combustion aerosols in summer at Lampedusa. By using the value of 200 a lower limit for the HFO contribution to total sulphates is estimated. HFO combustion emissions account, as a summer average, at least for 1.2 µg m −3 , representing about 30 % of the total nssSO 2− 4 , 3.9 % of PM 10 , 8 % of PM 2.5 , and 11 % of PM1. Within the used dataset, sulphate from HFO combustion emissions reached the peak value of 6.1 µg m −3 on 26 June 2008, when it contributed by 47 % to nssSO 2− 4 , and by 15 % to PM 10 .

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“…The goodness of model fit parameter 'Q' was evaluated to identify the optimal number of factors, and the optimal solution should lie in this FPEAK range. However, in the current study, a subset of species was used for the analysis, and thus the measured PM 10 , PM 2.5 and PM 0.1 concentrations were included in the PMF runs as an independent variable to obtain mass apportionment without the usual multiple linear regression analysis (Nicolas et al, 2008). The mass fraction distribution of species was used to identify the sources, which included soil dust, vehicle emissions, sea salt, industrial emissions and secondary aerosols.…”

Section: Source Apportionmentmentioning

confidence: 99%

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

Gugamsetty

Han

Liu

et al. 2012

Aerosol Air Qual. Res.

203

117

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Ambient Particulate Matters (PM 10 , PM 2.5 and PM 0.1 ) were investigated at Shinjung station in New Taipei City, Taiwan. Samples were collected simultaneously using a dichotomous sampler (Andersen Model SA-241) and a MOUDI (MSP Model 110) over a 24-h period from May 2011 to November 2011 at Shinjung station. Samples were analyzed for metallic trace elements using ion coupled plasma mass spectroscopy (ICP-MS) and ionic compounds by ion chromatography (IC). The average concentrations of PM 10 , PM 2.5 and PM 0.1 were found to be 39.45 ± 11.58, 21.82 ± 7.50 and 1.42 ± 0.56 μg/m 3 , respectively. Based on the chemical information, positive matrix factorization (PMF) was used to identify PM sources. A total of five source types were identified, including soil dust, vehicle emissions, sea salt, industrial emissions and secondary aerosols, and their contributions were estimated using PMF. The crustal enrichment factors (EF) were calculated using Al as a reference for the trace metal species to identify the sources. Conditional probability functions (CPF) were computed using wind profiles and factor contributions. The results of CPF analysis were used to identify local point sources. The results suggest a competitive relationship between anthropogenic and natural source processes over the monitoring station.

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Quantification of Saharan and local dust impact in an arid Mediterranean area by the positive matrix factorization (PMF) technique

Cited by 90 publications

References 27 publications

PM10 source apportionment in the surroundings of the San Vicente del Raspeig cement plant complex in southeastern Spain

PM10 source apportionment in the surroundings of the San Vicente del Raspeig cement plant complex in southeastern Spain

Evidence for heavy fuel oil combustion aerosols from chemical analyses at the island of Lampedusa: a possible large role of ships emissions in the Mediterranean

Source Characterization and Apportionment of PM10, PM2.5 and PM0.1 by Using Positive Matrix Factorization

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