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PM10and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set

Overview of attention for article published in BMC Chemistry, February 2014
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Title
PM10and gaseous pollutants trends from air quality monitoring networks in Bari province: principal component analysis and absolute principal component scores on a two years and half data set
Published in
BMC Chemistry, February 2014
DOI 10.1186/1752-153x-8-14
Pubmed ID
Authors

Pierina Ielpo, Vincenzo Paolillo, Gianluigi de Gennaro, Paolo Rosario Dambruoso

Abstract

The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality. In particular, the exposure to finer particles can cause short and long-term effects on human health. In the present paper PM10 (particulate matter with aerodynamic diameter lower than 10 μm), CO, NOx (NO and NO2), Benzene and Toluene trends monitored in six monitoring stations of Bari province are shown. The data set used was composed by bi-hourly means for all parameters (12 bi-hourly means per day for each parameter) and it's referred to the period of time from January 2005 and May 2007. The main aim of the paper is to provide a clear illustration of how large data sets from monitoring stations can give information about the number and nature of the pollutant sources, and mainly to assess the contribution of the traffic source to PM10 concentration level by using multivariate statistical techniques such as Principal Component Analysis (PCA) and Absolute Principal Component Scores (APCS).

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The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 3%
Unknown 36 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 24%
Researcher 4 11%
Student > Bachelor 3 8%
Student > Master 3 8%
Student > Postgraduate 2 5%
Other 3 8%
Unknown 13 35%
Readers by discipline Count As %
Environmental Science 7 19%
Engineering 3 8%
Earth and Planetary Sciences 3 8%
Physics and Astronomy 2 5%
Agricultural and Biological Sciences 1 3%
Other 6 16%
Unknown 15 41%