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

Bruno, P.; Caselli, M.; Gennaro, Gianluigi de; Traini, A.

doi:10.1007/s002160101084

Cited by 38 publications

(24 citation statements)

References 17 publications

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“…Among multivariate techniques, PCA is often used as an exploratory tool to identify the major sources of air pollutant emissions (Bruno et al, 2001;Guo et al, 2004;Marcazzan et al, 2003;Thurston and Spengler, 1985). The great advantage of using PCA as a receptor model is that there is no need for a priori knowledge of emission inventories (Chio et al, 2004).…”

Section: Factor Analysismentioning

confidence: 99%

Characterization and Identification of the Sources of Chromium, Zinc, Lead, Cadmium, Nickel, Manganese and Iron in Pm10 Particulates at the Two Sites of Kolkata, India

Karar

Gupta

Kumar

et al. 2006

Environ Monit Assess

128

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Monitoring of ambient PM10 (particulate matter which passes through a size selective impactor inlet with a 50% efficiency cut-off at 10 microm aerodynamic diameter) has been done at residential (Kasba) and industrial (Cossipore) sites of an urban region of Kolkata during November 2003 to November 2004. These sites were selected depending on the dominant anthropogenic activities. Metal constituents of atmospheric PM10 deposited on glass fibre filter paper were estimated using Inductively Coupled Plasma Atomic Emission Spectrometer (ICP-AES). Chromium (Cr), zinc (Zn), lead (Pb), cadmium (Cd), nickel (Ni), manganese (Mn) and iron (Fe) are the seven toxic trace metals quantified from the measured PM10 concentrations. The 24 h average concentrations of Cr, Zn, Pb, Cd, Ni, Mn and Fe from ninety PM10 particulate samples of Kolkata were found to be 6.9, 506.1, 79.1, 3.3, 7.4, 2.4 and 103.6 ng/m3, respectively. The 24 h average PM10 concentration exceeded national ambient air quality standard (NAAQS) as specified by central pollution control board, India at both residential (Kasba) and industrial (Cossipore) areas with mean concentration of 140.1 and 196.6 microg/m3, respectively. A simultaneous meteorology study was performed to assess the influence of air masses by wind speed, wind direction, rainfall, relative humidity and temperature. The measured toxic trace metals generally showed inverse relationship with wind speed, relative humidity and temperature. Factor analysis, a receptor modeling technique has been used for identification of the possible sources contributing to the PM10. Varimax rotated factor analysis identified four possible sources of measured trace metals comprising solid waste dumping, vehicular traffic with the influence of road dust, road dust and soil dust at residential site (Kasba), while vehicular traffic with the influence of soil dust, road dust, galvanizing and electroplating industry, and tanning industry at industrial site (Cossipore).

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Section: Factor Analysismentioning

confidence: 99%

Characterization and Identification of the Sources of Chromium, Zinc, Lead, Cadmium, Nickel, Manganese and Iron in Pm10 Particulates at the Two Sites of Kolkata, India

Karar

Gupta

Kumar

et al. 2006

Environ Monit Assess

128

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“…There are a few works in the literature that use in situ GC-PID to measure benzene in air (Bruno et al, 2001;Kelessis et al, 2006;Villanueva et al, 2012). Bruno et al (2001) and Villanueva et al (2012) use gas chromatographs from Syntech Spectras so a study of the influence of humidity in their measurements could be done as long as calibration details and RH data are provided.…”

Section: Influence Of Humidity On Benzene Measurementsmentioning

confidence: 99%

Section: Influence Of Humidity On Benzene Measurementsmentioning

confidence: 99%

“…Bruno et al (2001) and Villanueva et al (2012) use gas chromatographs from Syntech Spectras so a study of the influence of humidity in their measurements could be done as long as calibration details and RH data are provided. Bruno et al (2001) mainly focused on source apportionment and they do not provide information related to ambient humidity. The average concentration of benzene during the sampling period was 4 ± 1.6 µg m −3 so measurements were close to the annual limit value but, mainly, below it, so around 2 % bias is expected at 20 • C and 50 % RH if calibration was carried out with dry gases.…”

Section: Influence Of Humidity On Benzene Measurementsmentioning

confidence: 99%

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Influence of sample temperature and environmental humidity on measurements of benzene in ambient air by transportable GC-PID

et al. 2017

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Abstract. Calibration of in situ analysers of air pollutants is usually done with dry standards. In this paper, the influence of sample temperature and environmental humidity on benzene measurements by gas chromatography coupled with a photoionisation detector (GC-PID) is studied. Two reference gas mixtures (40 and 5 µg m −3 nominal concentration benzene in air) were subjected to two temperature cycles (20/5/20 • C and 20/35/20 • C) and measured with two identical GC-PIDs. The change in sample temperature did not produce any significant change in readings. Regarding ambient humidity, the chromatographs were calibrated for benzene with dry gases and subjected to measure reference standards with humidity (20 and 80 % at 20 • C). When measuring a concentration of 0.5 µg m −3 benzene in air, the levels of humidity tested did not produce any significant interference in measurements taken with any of the analysers. However, when measuring a concentration of 40 µg m −3 , biases in measurements of 18 and 21 % for each respective analyser were obtained when the relative humidity of the sample was 80 % at 20 • C. Further tests were carried out to study the nature of this interference. Results show that humidity interference depends on both the amount fractions of water vapour and benzene. If benzene concentrations in an area are close to its annual limit value (5 µg m −3 ), biases of 2.2 % can be expected when the absolute humidity is 8.6 g cm −3 -corresponding to a relative humidity of 50 % at 20 • C. This can be accounted for in the uncertainty budget of measurements with no need for corrections. If benzene concentrations are above the annual limit value, biases become higher. Thus, in these cases, actions should be taken to reduce the humidity interference, as an underestimation of benzene concentrations may cause a mismanagement of air quality in these situations.

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“…On the other hand, FA attempts to apportion the sources and to determine their composition on the basis of a series of observations at the receptor site only (Henry, 1984). Among multivariate techniques, Principal Component Analysis (PCA) is often used as an exploratory tool to identify the major sources of air pollutant emissions (Bruno et al, 2001;Guo et al, 2004;Marcazzan et al, 2003;Thurston & Spengler, 1985). The great advantage of using PCA as a receptor model is that there is no need for a priori knowledge of emission inventories (Chio et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Role of the Ionic Component and Carbon Fractions in the Fine and Coarse Fractions of Particulate Matter for the Identification of Pollution Sources: Application of Receptor Models

Ielpo¹,

Placentino²,

Cafagna³

et al. 2011

Air Quality Monitoring, Assessment and Management

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

Cited by 38 publications

References 17 publications

Characterization and Identification of the Sources of Chromium, Zinc, Lead, Cadmium, Nickel, Manganese and Iron in Pm10 Particulates at the Two Sites of Kolkata, India

Characterization and Identification of the Sources of Chromium, Zinc, Lead, Cadmium, Nickel, Manganese and Iron in Pm10 Particulates at the Two Sites of Kolkata, India

Influence of sample temperature and environmental humidity on measurements of benzene in ambient air by transportable GC-PID

Role of the Ionic Component and Carbon Fractions in the Fine and Coarse Fractions of Particulate Matter for the Identification of Pollution Sources: Application of Receptor Models

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