PM10 and 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

Chemistry Central Journal

Pierina Ielpo²  Vincenzo Paolillo¹  Gianluigi de Gennaro¹  Paolo Rosario Dambruoso¹ 

[1] Chemistry Department, Bari University, via Orabona, 4 70126 Bari, Italy

[2] Institute of Atmospheric Sciences and Climate, Lecce division, str Lecce-Monteroni Km 1.2, 73100 Lecce, Italy

关键词: Source apportionment;    APCS;    PCA;    Air monitoring stations;    Gaseous pollutants;    PM10;   

Others  :  787797
DOI  :  10.1186/1752-153X-8-14

 received in 2013-08-13, accepted in 2014-02-12,  发布年份 2014

【 摘 要 】

Background

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, NO_x (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).

Results

Comparing 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.

Conclusions

The 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.

【 授权许可】

   
2014 Ielpo et al.; licensee Chemistry Central Ltd.

【 图 表 】

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