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

Yubero, Eduardo; Carratalá, Adoración; Crespo, J.; Nicolás, J.F.; Santacatalina, M.; Nava, S.; Lucarelli, F.; Chiari, M.

doi:10.1007/s11356-010-0352-9

Cited by 39 publications

(9 citation statements)

References 31 publications

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“…In total, 22 analyzed species were considered. Additionally, the insoluble fraction of Ca (Ca-ins) was also incorporated in the PMF analysis through subtraction of ionic Ca 2+ from the Ca determined by ICP-OES (Beuck et al, 2011;Yubero et al, 2011). PM 2.5 was set as a total variable and had its uncertainty tripled so that it would not overly influence solutions.…”

Section: Source Apportionmentmentioning

confidence: 99%

Multi-year chemical composition of the fine-aerosol fraction in Athens, Greece, with emphasis on the contribution of residential heating in wintertime

et al. 2018

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Abstract. In an attempt to take effective action towards mitigating pollution episodes in Athens, precise knowledge of PM2.5 composition and its sources is a prerequisite. Thus, a 2-year chemical composition dataset from aerosol samples collected in an urban background site in central Athens from December 2013 to March 2016 has been obtained and a positive matrix factorization (PMF) was applied in order to identify and apportion fine aerosols to their sources. A total of 850 aerosol samples were collected on a 12 to 24 h basis and analyzed for major ions, trace elements, and organic and elemental carbon, allowing us to further assess the impact of residential heating as a source of air pollution over Athens. The ionic and carbonaceous components were found to constitute the major fraction of the PM2.5 aerosol mass. The annual contribution of the ion mass (IM), particulate organic mass (POM), dust, elemental carbon (EC), and sea salt (SS) was calculated at 31 %, 38 %, 18 %, 8 %, and 3 %, respectively, and exhibited considerable seasonal variation. In winter, the share of IM was estimated down to 23 %, with POM + EC being the dominant component accounting for 52 % of the PM2.5 mass, while in summer, IM (42 %) and carbonaceous aerosols (41 %) contributed almost equally. Results from samples collected on a 12 h basis (day and night) during the three intensive winter campaigns indicated the impact of heating on the levels of a series of compounds. Indeed, PM2.5, EC, POM, NO3-, C2O42-, non sea salt (nss) K+ and selected trace metals including Cd and Pb were increased by up to a factor of 4 in the night compared to the day, highlighting the importance of heating on air quality in Athens. Furthermore, in order to better characterize wintertime aerosol sources and quantify the impact of biomass burning on PM2.5 levels, source apportionment was performed. The data can be interpreted on the basis of six sources, namely biomass burning (31 %), vehicular emissions (19 %), heavy oil combustion (7 %), regional secondary (21 %), marine aerosols (9 %), and dust particles (8 %). Regarding night-to-day patterns their contributions shifted from 19 %, 19 %, 8 %, 31 %, 12 %, and 10 % of the PM2.5 mass during day to 39 %, 19 %, 6 %, 14 %, 7 %, and 7 % during the night, underlining the significance of biomass burning as the main contributor to fine particle levels during nighttime in winter.

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Section: Source Apportionmentmentioning

confidence: 99%

Multi-year chemical composition of the fine-aerosol fraction in Athens, Greece, with emphasis on the contribution of residential heating in wintertime

et al. 2018

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“…Source apportionment techniques are valuable tools that aid in the design of effective emissions control programs to reduce particulate air pollution (Gupta et al, 2007). Receptor model is the application of multivariate statistical methods addressed to the identification and quantitative apportionment of air pollutants to their sources (Callén et al, 2009), mainly including chemical mass balance (CMB) Samara et al, 2003;Gupta et al, 2007;Stone et al, 2010), principal component analysis (PCA) (Querol et al, 2001;Pandolfi et al, 2008;Moreno et al, 2009), and positive matrix factorization (PMF) (Yatkin and Bayram, 2008;Pandolfi et al, 2010;Yubero et al, 2010). Source apportionment studies of urban ambient PM 10 using CMB have also been carried out in some cities of China in recent years (Senlin et al, 2007;Wu et al, 2009;Kong et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Source Apportionment of PM10 in Four Cities of Northeastern China

Tianru

Han

Zhang

2012

Aerosol Air Qual. Res.

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Ambient particulate matter with the aerodynamics diameter less than 10 μm (PM 10 ) was sampled in four northeastern Chinese cities (Shenyang, Anshan, Fushun and Huludao) , organic and total carbon were determined. In addition, chemical source profiles consisting of the same particulate components were obtained from a number of naturally occurring geological sources (soil dust from exposed lands and marine salt) and sources of atmospheric particulates resulting from human activities (construction derived dust, coal combustion fly ash, iron and steel manufacturing dust, zinc dust, vehicle exhaust, and sulfate). Chemical mass balance modeling (CMB) was applied to determine the particulate matter (PM) sources and their contributions to PM 10 in these four cities. The results showed that soil dust and coal fly ash were the major sources of ambient PM 10 in all four cities. The construction derived dust, iron and steel manufacturing dust also was the major source of PM 10 in Huludao, Anshan, respectively. Higher contribution of iron and steel manufacturing dust (33.3 μg/m 3 , 20.9%) in Anshan was observed in this study, compared with previous studies. The zinc dust was a special PM source of Huludao, with a contribution of 19.1 μg/m 3 (7.0%) to PM 10 .

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“…Factor 4 is characterized in both fractions by Ca and S and it is likely to include contributions from a cement kiln [29,30] located on the westerly sector with respect to sampling site A (see Figure 9) and in the southerly sector with respect to sampling site B. The source polar plots (Figure 9) confirm this association.…”

Section: Source Apportionmentmentioning

confidence: 62%

Hourly Elemental Composition and Source Identification by Positive Matrix Factorization (PMF) of Fine and Coarse Particulate Matter in the High Polluted Industrial Area of Taranto (Italy)

et al. 2020

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In the framework of an extensive environmental investigation, promoted by the Italian Health Ministry, the ISPESL (Istituto Superiore per la Prevenzione e la Sicurezza del Lavoro) and the CNR (Consiglio Nazionale della Ricerca), aerosol samples were collected in Taranto (one of the most industrialized towns in southern Italy) with high time resolution and analyzed by PIXE. The samples were collected in two periods (February–March and June 2004) and in two different sites: an urban district close to the industrial area and a small town 7 km N-NW of Taranto. The use of ‘‘streaker’’ samplers (by PIXE International Corporation) allowed for the simultaneous collection of the fine (<2.5 μm) and coarse (2.5–10 μm) fractions of particulate matter. PIXE analyses were performed with a 3 MeV proton beam from the 3 MV Tandetron accelerator of the INFN-LABEC laboratory. Particulate emissions as well as their atmospheric transport and dilution processes change within a few hours, but most of the results in literature are limited to daily time resolution of the input samples that are not suitable for tracking these rapid changes. Furthermore, since source apportionment receptor models need a series of samples containing material from the same set of sources in different proportions, a higher variability between samples can be obtained by increasing the temporal resolution rather than with samples integrated over a longer time. In this study, the high time resolution of the adopted approach allowed us to follow in detail the changes in the aerosol elemental composition due to both the time evolution of the industrial emissions and the time changes in meteorological conditions, and thus, transport pathways. Moreover, the location of the sampling sites, along the prevalent wind direction and in opposite positions with respect to the industrial site, allowed us to follow the impact of the industrial plume as a function of wind direction. Positive matrix factorization (PMF) analysis on the elemental hourly concentrations identified eight sources in the fine fraction and six sources in the coarse one.

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

Cited by 39 publications

References 31 publications

Multi-year chemical composition of the fine-aerosol fraction in Athens, Greece, with emphasis on the contribution of residential heating in wintertime

Multi-year chemical composition of the fine-aerosol fraction in Athens, Greece, with emphasis on the contribution of residential heating in wintertime

Source Apportionment of PM10 in Four Cities of Northeastern China

Hourly Elemental Composition and Source Identification by Positive Matrix Factorization (PMF) of Fine and Coarse Particulate Matter in the High Polluted Industrial Area of Taranto (Italy)

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