Polycyclic Aromatic Hydrocarbons Pollution in a Coastal Environment: the Statistical Analysis of Dependence to Estimate the Source of Pollution

Pasquale      Sarnacchiaro; Sergi.      Diez; Paolo      Montuori

Abstract

Polycyclic Aromatic Hydrocarbons (PAHs) are a group of carcinogenic contaminants widespread in the environment. PAHs are produced by both anthropogenic and natural processes. Difficulties exist in identifying their origins. This paper reports a practical application of Principal Component Analysis (PCA) and Principal Component Regression (PCR) to identify the pyrolytic, petrogenic and diagenesis sources of PAH pollution in the Sarno River and Estuary. Nicknamed “the most polluted river in Europe”, the Sarno River originates in south-western Italy and has a watershed of about 715 km2. PCA indicated that the PAH contamination in the Sarno River and Estuary resulted from a mixed pattern. The first principal component (PC1) had significant positive loading in high molecular weight PAHs. This profile of PAH usually includes products of high temperature combustion/pyrolitic processes, reflecting the effects of traffic pyrolysis. The second principal component (PC2) had significant positive loading in two-to-four ring PAHs. So, PC2 may be considered as components from petrogenic sources. PC3 was characterized by a high loading of perylene, thought to originate from diagenetic alteration of perylenequinone pigment or some other organic matter. Therefore, this factor can be considered as natural-origin PAHs. In the PCR, the regression coefficients for components 1-3 were 66.6, 40.4 and 19.5, respectively. In this application, the PCR was a very useful statistical technique for handling the problem of multicollinearity. Results from the application of PCR have been compared with Partial Least Square (PLS) and no significant differences were reported in the prediction errors and latent variables available by PCR and PLS.

Keywords: Polycyclic aromatic hydrocarbons, Coastal environment, Contaminant transport processes, Principal component analysis, Principal Component Regression, Multicollinearity

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