Abstract
BackgroundThe 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).ResultsComparing the night and day mean concentrations (per day) for each parameter it has been pointed out that there is a different night and day behavior for some parameters such as CO, Benzene and Toluene than PM10. This suggests that CO, Benzene and Toluene concentrations are mainly connected with transport systems, whereas PM10 is mostly influenced by different factors.The statistical techniques identified three recurrent sources, associated with vehicular traffic and particulate transport, covering over 90% of variance. The contemporaneous analysis of gas and PM10 has allowed underlining the differences between the sources of these pollutants.ConclusionsThe analysis of the pollutant trends from large data set and the application of multivariate statistical techniques such as PCA and APCS can give useful information about air quality and pollutant’s sources. These knowledge can provide useful advices to environmental policies in order to reach the WHO recommended levels.
Highlights
The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality
The main aim of this 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
The different night and daily behavior suggests that parameters such as CO, Benzene and Toluene are mainly connected with transport systems, whereas PM10 is mostly influenced by different factors
Summary
The chemical composition of aerosols and particle size distributions are the most significant factors affecting air quality. The knowledge of chemical composition and sources of air polluted is demanded in any program aimed at controlling the levels of pollutants in order to evaluate and reduce their impact on human health. The inhalation of air polluted, with particulate matter (PM10) and or irritant gases such as NO2 and SO2 is associated with both short-term and long term health effects, most of which impact on respiratory and cardiovascular system [1]. The U.S EPA identified 33 urban PAHs based on emissions and toxicities in a 1995 ranking analysis [11] and developed concurrent monitoring and modelling programs to evaluate potential exposures and risks to these top-ranked 33 PAHs. Developing effective control strategies to reduce population exposure to certain PAHs requires identifying sources and quantifying their contributions to the mixture of PAHs and the associated health risks. In other source apportionment studies that included both non-organic trace elements on PM and gaseous pollutants [17,18,19,20], the gaseous species usually were non-VOCs (such as CO, SO2, and NO)
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