Abstract

In the present study chemical fingerprinting approach (isomeric ratios), a receptor-oriented model (principal component analysis with multiple linear regression, PCA/MLR) and a probabilistic health risk framework were employed to characterization, source appointment and carcinogenic risk assessment of polycyclic aromatic hydrocarbons (PAHs) in street dusts of Karaj urban area (northern Iran). Thirty street dusts samples were collected from the different functional areas in the city of Karaj and analyzed for PAHs by gas chromatography/mass spectrometry (GS/MS). The results obtained showed that ∑16PAHs concentrations varied widely from 16.2 to 1236.2 with a mean of 624μg/kg and decreased in the following order of functional areas; traffic> residential > green/park areas. PAHs profile in the majority of dust samples were dominated by 5-6 rings PAHs, accounting for 25%-95% of the total PAHs. Qualitative source apportionment using the molecular isomeric ratios indicated mixed sources of PAHs in street dusts while PCA/MLR receptor model quantitatively identified three major sources with following relative contributions to the total dust PAH burden; 51% for pyrogenic-traffic sources, 32% for traffic-stationary sources and, 16% for petrogenic sources. The results of health risk assessment based on probabilistic model indicated that at the 95% percentiles, total cancer risks for children and adults are 8.43 × 10-4 and 3.34 × 10-5, respectively which both are higher than the acceptable baseline (10-6) indicating potential carcinogenic risk for local residents. It was also revealed that dust ingestion pathway is the most important contributor to the total carcinogenic risks of PAHs for both children and adults although the cancer risk level for adults through dermal and inhalation was 10 times greater than that for children. Based on the sensitivity analysis using the Monte Carlo simulation, benzo[a]pyrene equivalent concentration, exposure duration, dermal exposure area and ingestion rate were found to be the most sensitive exposure parameters which could introduce uncertainties into the cancer risk estimated.

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