Characterization of airborne particulate matter in Thessaloniki, Greece

Samara, Constantini; Kouimtzis, Th.; Katsoulos, G.A.

doi:10.1080/02772249409357977

Cited by 18 publications

(2 citation statements)

References 19 publications

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“…[33][34][35] Multivariate analysis approaches such as cluster analysis and principal component analysis are strong tools to get maximum information as reported by many workers. [36][37][38][39] Like most other developing countries, the industrial development and urbanization in Pakistan have not gone in pace with environmental safety, resulting in numerous problems arising from atmospheric pollution. Over the years, in the capital city, Islamabad, transportation has gone up tremendously, and in addition, an industrial sector has been established to meet requirements of industrial commodities for a large population (Fig.…”

Section: Introductionmentioning

confidence: 99%

Screening of Urban Aerosol Particulate Composites for Selected Metal Distribution and Their Dependence on Meteorological Parameters

2004

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Local atmospheric aerosol particulate samples, collected as composites on daily 6-12 hour basis, at Quaid-i-Azam University campus, Islamabad, Pakistan, using high volume sampling technique, were analysed for Pb, Na, K, Fe, Mn, Cd, Cr, Ni, Zn and Co by FAAS method. The monitoring period ran from October, 2001 through March, 2002, with a total of 105 samples collected on cellulose filters, treated in part with the HNO3-based wet digestion method for metal quantification, and for particle size distribution separately. The metal content of the aerosols was examined in relation to dependence on meteorological parameters, such as temperature, relative humidity, wind speed, sun shine and pan evaporation. Statistical correlation analysis was conducted for multiple metal pairs in aerosols, and the data were examined in relation to meteorological parameters and relevant aerosol particle size fractions. The study revealed no viable strong correlation between the meteorological parameters and metal levels; in general, however, a significant positive correlation was found for temperature. A strong positive correlation was observed for PM<25 and PM2.5-10. For coarse particles (PM10-100 and PM>100), however, a negative correlation was observed. The levels of Na, K, Fe and Zn were found in the range of 1-5 microg/m3 while those for the rest of the metals in the sub microg/m3 range. Principal component analysis and cluster analysis were performed on dataset for source identification and appointment. Largest contribution (33%) was shown by the industrial emissions followed by traffic/road dust (16.7%).

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Section: Introductionmentioning

confidence: 99%

Screening of Urban Aerosol Particulate Composites for Selected Metal Distribution and Their Dependence on Meteorological Parameters

2004

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“…To perform a source apportionment it is necessary to perform an appropriate treatment of rotated scores to obtain the contribution of each source to C i . Several methods X V U = [1,2,3,8,11], and Absolute Principal Component Scores, APCS [7,17,18,19,20,21]. Since no observed compound could be considered as unique tracer for any source, the latter approach has been used for the collected data analysis.…”

Section: Receptor Modelmentioning

confidence: 99%

Source apportionment of gaseous atmospheric pollutants by means of an absolute principal component scores (APCS) receptor model

Bruno

Caselli

Gennaro

et al. 2001

Fresenius J Anal Chem

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A multivariate statistical method has been applied to apportion the atmospheric pollutant concentrations measured by automatic gas analyzers placed on a mobile laboratory for air quality monitoring in Taranto (Italy). In particular, Principal Component Analysis (PCA) followed by Absolute Principal Component Scores (APCS) technique was performed to identify the number of emission sources and their contribution to measured concentrations of CO, NOx, benzene toluene m+p-Xylene (BTX). This procedure singled out two different sources that explain about 85% of collected data variance.

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Identification of PM10 Sources in a Mediterranean Island

Bari

Baumbach

Sarachage-Ruiz

et al. 2008

Water Air Soil Pollut: Focus

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Due to the dry Mediterranean climate in Cyprus, particulate matter is resuspended from soils and other surfaces. From November 2002 to August 2003, gravimetric PM 10 measurements were carried out at three characteristic sites (traffic, residential and rural). A significant seasonal trend with high winter concentrations was observed at the traffic site. Special events, e.g. long-range transport of Sahara dust storms, were recorded over traffic, residential and rural areas in the order of six to eight events per year, with a major frequency in summer and spring periods. This contributes to the increase of 24-h EU limit value exceedances for PM 10 at the three investigated sites. The origin of the PM 10 load was determined by enrichment factors based on analyses of the local soil deposition at the investigated sites. Furthermore, positive matrix factorisation modelling was applied to find the sources of PM 10 . Results indicate that the major emission sources affecting the PM 10 load were mineral soil, sea salt, road dust, oil combustion, secondary pollutants and gasoline vehicles. The natural contribution (local mineral soil and sea salt) at the three sites was in the range of 7-9 μg m −3 in PM 10 . Besides the Sahara dust storms and natural background concentrations, the vehicular pollution was found as the largest contributor (12-14 μg m −3 ) to PM 10 load at the traffic site.

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Characterization of airborne particulate matter in Thessaloniki, Greece

Cited by 18 publications

References 19 publications

Screening of Urban Aerosol Particulate Composites for Selected Metal Distribution and Their Dependence on Meteorological Parameters

Screening of Urban Aerosol Particulate Composites for Selected Metal Distribution and Their Dependence on Meteorological Parameters

Source apportionment of gaseous atmospheric pollutants by means of an absolute principal component scores (APCS) receptor model

Identification of PM10 Sources in a Mediterranean Island

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