A methodological frame for assessing benzene induced leukemia risk mitigation due to policy measures.

The study relies on the development of a methodology for assessing the determinants that comprise the overall leukemia risk due to benzene exposure and how these are affected by outdoor and indoor air quality regulation. An integrated modeling environment was constructed comprising traffic emissions, dispersion models, human exposure models and a coupled internal dose/biology-based dose-response risk assessment model, in order to assess the benzene imposed leukemia risk, as much as the impact of traffic fleet renewal and smoking banning to these levels. Regarding traffic fleet renewal, several "what if" scenarios were tested. The detailed full-chain methodology was applied in a South-Eastern European urban setting in Greece and a limited version of the methodology in Helsinki. Non-smoking population runs an average risk equal to 4.1·10(-5) compared to 23.4·10(-5) for smokers. The estimated lifetime risk for the examined occupational groups was higher than the one estimated for the general public by 10-20%. Active smoking constitutes a dominant parameter for benzene-attributable leukemia risk, much stronger than any related activity, occupational or not. From the assessment of mitigation policies it was found that the associated leukemia risk in the optimum traffic fleet scenario could be reduced by up to 85% for non-smokers and up to 8% for smokers. On the contrary, smoking banning provided smaller gains for (7% for non-smokers, 1% for smokers), while for Helsinki, smoking policies were found to be more efficient than traffic fleet renewal. The methodology proposed above provides a general framework for assessing aggregated exposure and the consequent leukemia risk from benzene (incorporating mechanistic data), capturing exposure and internal dosimetry dynamics, translating changes in exposure determinants to actual changes in population risk, providing a valuable tool for risk management evaluation and consequently to policy support.

Measurements of benzene and formaldehyde in a medium sized urban environment. Indoor/outdoor health risk implications on special population groups.

In the present study, the results of a measurement campaign aiming to assess cancer risk among two special groups of population: policemen and laboratory technicians exposed to the toxic substances, benzene and formaldehyde are presented. The exposure is compared to general population risk. The results show that policemen working outdoor (traffic regulation, patrol on foot or in vehicles, etc.) are exposed at a significantly higher benzene concentration (3-5 times) than the general population, while the exposure to carbonyls is in general lower. The laboratory technicians appear to be highly exposed to formaldehyde while no significant variation of benzene exposure in comparison to the general population is recorded. The assessment revealed that laboratory technicians and policemen run a 20% and 1% higher cancer risk respectively compared to the general population. Indoor working place air quality is more significant in assessing cancer risk in these two categories of professionals, due to the higher Inhalation Unit Risk (IUR) of formaldehyde compared to benzene. Since the origin of the danger to laboratory technicians is clear (use of chemicals necessary for the experiments), in policemen the presence of carbonyls in indoor air concentrations due to smoking or used materials constitute a danger equal to the exposure to traffic originated air pollutants.

Bayesian algorithm implementation in a real time exposure assessment model on benzene with calculation of associated cancer risks.

The objective of the current study was the development of a reliable modeling platform to calculate in real time the personal exposure and the associated health risk for filling station employees evaluating current environmental parameters (traffic, meteorological and amount of fuel traded) determined by the appropriate sensor network. A set of Artificial Neural Networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees. Furthermore, a Physiology Based Pharmaco-Kinetic (PBPK) risk assessment model was developed in order to calculate the lifetime probability distribution of leukemia to the employees, fed by data obtained by the ANN model. Bayesian algorithm was involved in crucial points of both model sub compartments. The application was evaluated in two filling stations (one urban and one rural). Among several algorithms available for the development of the ANN exposure model, Bayesian regularization provided the best results and seemed to be a promising technique for prediction of the exposure pattern of that occupational population group. On assessing the estimated leukemia risk under the scope of providing a distribution curve based on the exposure levels and the different susceptibility of the population, the Bayesian algorithm was a prerequisite of the Monte Carlo approach, which is integrated in the PBPK-based risk model. In conclusion, the modeling system described herein is capable of exploiting the information collected by the environmental sensors in order to estimate in real time the personal exposure and the resulting health risk for employees of gasoline filling stations.

A methodology to estimate benzene concentrations in a town through a traffic model.

In this work an approach is presented that describes the changes of benzene concentration in the air, in relation with the special traffic characteristics of a road. The dominant input parameters of the model are traffic density and the vehicle's type distribution according to seven main categories characterized by different emission factors. The dispersion approach used is a semi-empirical relationship that apart from emission rates requires also wind speed and the direction, as well as the geometrical characteristics of the road. The methodology was validated for Ioannina, a Greek medium sized town with special traffic and geographic characteristics presenting high atmospheric pollution values. It is found that the benzene concentrations estimated by the methodology are in a very good agreement with the measurements.