Characterization of urban pollution in two cities of the Puglia region in Southern Italy using field measurements and air quality (AQ) model approach
2014
Estellano, Victor H. | Pozo, Karla | Silibello, Camillo | Mulder, Marie D. | Efstathiou, Christos | Tomasino, Maria P. | Funaro, Fulvia | Donadio, Ivana | Focardi, S. (Silvano)
Passive air sampling (PAS) consisting of polyurethane foam (PUF) disks were deployed simultaneously over four periods of 2–5 months at four locations in urban and sub–urban sites of Bari and San Vito Taranto in Southern Italy. The purpose of the study was to characterize the urban pollution for two groups of semi volatile organic compounds (SVOCs), polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs), by using two different approaches consisting of PAS–PUF and air quality models (Flexible Air quality Regional Model, FARM). The concentrations in the air ranged from 20 to 200pg m−3 for PCBs and from 5 to 48ng m−3 for PAHs with the highest concentrations being detected at Bari center. PCB composition was dominated by the 3–Cl congeners (periods 1 and 2) and by 5–Cl (periods 3 and 4). PCB–28 and –37 were the most abundant congeners during the four periods. The PAHs profile was dominated by the 3–ring (70±6)%, with phenanthrene alone accounting for (49±2)%. On a seasonal basis opposite patterns were observed for PCBs and PAHs showing high PCB concentrations during the warm periods, period 3: summer and 2: spring, while PAHs were found during cool periods, period 4: autumn, and 1: winter. The results obtained from the application of the FARM model, during 2010, and limited to period 4 in this study, showed similar estimated levels for PCBs indicating a good performance for PCB modeled concentrations whilst for benzo[b]fluoranthene (B[b]F) the results showed a less better agreement. This study represents one of the few efforts at characterizing PCBs and PAHs compositions in ambient air in southern Italy and also represents one of the preliminary attempts at using PAS–PUF to give more insight into a modeling prediction in Italy. These results also provide useful information for the future development of the FARM model.
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