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Abstract: Objective: Personal exposure to airborne benzene is influenced by various outdoor and indoor sources. The first aim of this study was to assess the benzene exposure of a sample of urban inhabitants living in an inner-city neighborhood of Florence, Italy, excluding exposure from active smoking. The secondary objective was to differentiate the personal exposures according to personal usage patterns of the vehicles. Methods: A sample of 67 healthy non-smokers was monitored by passive samplers during two 4-weekday campaigns in winter and late spring. Simultaneously, benzene measurements were also taken for a subset of participants, inside and outside their houses. A 4-day time microenvironment activity diary was completed by each subject during each sampling period. Other relevant exposure data were collected by a questionnaire before the sampling. Additional data on urban ambient air benzene levels were also available from the public air quality network. The passive samplers, after automated thermal desorption, were analyzed by GC-FID. Results: Benzene personal exposure levels averaged 6.9 (SD=2.1) and 2.3 (SD=0.7) μg/m3 in winter and spring, respectively. Outdoor and indoor levels showed high correlation in winter and poor in spring. In winter the highest benzene personal exposure levels were for people traveling by more public transport, followed by users of only car and by users of only bus respectively. Conclusions: The time spent in-transport for work or leisure makes a major contribution to benzene exposure among Florentine non-smoking citizens. Indoor pollution and transportation means contribute significantly to individual exposure levels especially in winter season. [Copyright &y& Elsevier]

Air pollution and particulate matter in urban areas have been associated with increased mortality from cardiovascular and respiratory diseases and increased cancer risk. Carcinogenic effects of particulate matter have been related to the contents of specific compounds, such as polycyclic aromatic hydrocarbons. The latter may form bulky DNA adducts, that may be considered as candidate markers of cancer risk. We have recently shown that traffic-exposed workers and the general population in Florence have divergent levels of DNA adducts, possibly related to different levels of exposure to genotoxic agents from vehicle emissions. In the current study, in a series of 214 Florence City healthy adults enrolled in a prospective study in the period 1993–1998 (152 residents / 62 traffic-exposed workers), we investigated the correlation between individual levels of DNA bulky adducts and PM10 exposure scores based on daily environmental measures provided by the local Environmental Protection Agency for the whole study period, by two types of urban monitoring stations (high- and low-traffic). We found that PM10 cumulative scores from high-traffic stations over the last 1–2 weeks prior to blood drawing significantly correlated (r =0.58, p =0.02) with DNA adduct levels among non-smoking traffic-exposed workers (but not among residents with no occupational exposure to vehicle emissions). A multivariate regression analysis adjusted for possible confounders confirmed these findings. PM10 scores from low-traffic stations did not show any correlation. These results show that DNA adduct levels in non-smoking workers reflect the average levels of exposure to PM10 in high-traffic urban areas experienced over a time period of 1–2 weeks. Since DNA adduct levels have been found predictive of lung cancer risk, our findings provide clues relevant to the reduction of genotoxic damage and possibly cancer risk among traffic-exposed urban workers. [Copyright &y& Elsevier]

Abstract: Prior to 2000 a network of conventional ozone (O3) analysers existed in the Province of Firenze (Tuscany, central Italy). Between 2000 and 2004 the network was extended to incorporate a newly designed bioindicator network of tobacco plants (Nicotiana tabacum Bel W3). The objective was to set-up an integrated monitoring system to obtain estimates of ground-level O3 concentrations over the whole study area (3513 km2) in order to fill data gaps and cover reporting requirements. The existing conventional monitors were purposefully located mainly in urban areas. A total of 45 biomonitoring sites were selected using a systematic design to cover the target area. Two to five additional biomonitoring sites were co-located with conventional O3 analysers for calibration purposes, and five more sites for independent validation of modelled O3 concentrations. Visible Leaf Injury Index (LII) on the tobacco plants was significantly correlated (P: 0.018÷0.0014) with a series of O3 exposure variables (mean of weekly 1-hour maxima, M1; mean of 7-hour means, M7; 24-hour mean, M24; and weekly AOT40). LII was found to be a significant predictor of weekly means of the O3 exposure variables with a standard error of estimates between 13.6 and 24.3 μg m−3 (absolute values). LII was mapped with an ad-hoc spatial model over the study area at a 2⁎2 km grid resolution, and mapped values were used to predict O3 concentrations by means of a first order linear model. Results showed that high estimates of O3 (up to 188 μg m−3 as mean of weekly maxima, M1) occurred more frequently in hilly and mountainous areas, with a spatial pattern changing on an annual basis. Predicted O3 concentrations were not significantly different from the measured concentrations (P: 0.34), although marked differences were observed for individual sites and years. The study provided evidence that integration of monitoring networks using different methods can be a viable option to obtain estimates of O3 concentrations over large areas. [Copyright &y& Elsevier]