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.

Forecasting hourly PM(10) concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management.

In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed to forecast hourly PM(10) concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies. The evaluation reveals that the MLP NN models display the best forecasting performance with R (2) values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R (2) values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality if used on an operational basis.

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.

Development of an aggregate Air Quality Index for an urban Mediterranean agglomeration: relation to potential health effects.

It is very useful for the authorities and the people to have daily easy understandable information about the levels of air pollution and the proper measures to be taken for the protection of human health. In this paper we develop an aggregate Air Quality Index (AQI) based on the combined effects of five criteria pollutants (CO, SO2, NO2, O3 and PM10) taking into account the European standards. We evaluate it for each monitoring station and for the whole area of Athens, Greece, an area with serious air pollution problems. A comparison was made with a modified version of Environmental Protection Agency/USA (USEPA) maximum value AQI model adjusted for European conditions. Hourly data of air pollutants from 4 monitoring stations, available during 1983-1999, were analysed for the development of the proposed index. The analysis reveals the Athenian population exposure reaches high levels and during last years a gradual increase of days with unhealthy conditions was detected. The proposed aggregate model estimates more effectively the exposure of citizens comparing with the modified USEPA maximum value model, because counts the impact of all the pollutants measured. Towards the informing and protection of the citizens in an urban agglomeration this model advantages as a political and administrative tool for the design of abatement strategies and effective measures of intervention.

On the association between daily mortality and air mass types in Athens, Greece during winter and summer.

In this study, we examined the short-term effects of air mass types on mortality in Athens, Greece. An objective air mass types classification was used, based on meteorological parameters measured at the surface. Mortality data were treated with generalized additive models (GAM) and extending Poisson regression, using a LOESS smoother to control for the confounding effects of seasonal patterns, adjusting also for temperature, long-term trends, day of the week, and ambient particle concentrations. The introduced air mass classification explains the daily variation of mortality to a statistically significant degree. The highest daily mortality was observed on days characterized by southerly flow conditions for both the cold (increase in relative risk for mortality 9%; with a 95% confidence interval: 3-14%), and the warm period (7%; with a 95% confidence interval: 2-13%) of the year. The northeasterly flow is associated with the lowest mortality. Effects on mortality, independent of temperature, are observed mainly for lag 0 during the cold period, but persist longer during the warm period. Not adjusting for temperature and/or ambient particle levels slightly alters the results, which then reflect the known temperature and particle effects, already reported in the literature. In conclusion, we find that air mass types have independent effects on mortality for both the cold and warm season and may be used to predict weather-related adverse health effects.

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.

Summertime measurements of benzene and toluene in Athens using a differential optical absorption spectroscopy system.

In this paper, measurements of benzene, toluene, p,m-xylene, ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2) made using the differential optical absorption spectroscopy (DOAS) technique during a 4-month period of summer 2000 (June-September) in Athens, Greece, are presented. An assessment of benzene mean value concentrations during this 4-month period exceeded 10 microg/m3, which is 2 times greater than the average yearly limit proposed by European authorities. Toluene measurements present mean values of approximately 33 microg/m3. Benzene and especially toluene measurements are highly correlated with NO2 and anticorrelated with O3. High values of benzene, NO2, and toluene are also correlated with winds from the southeast section, an area of industrial activity where emissions of volatile organic compounds (VOCs) have been recorded in previous studies. O3 is correlated with winds from the south-southwest section affected by the sea breeze circulation. Diurnal variations of O3, NO2, and SO2 concentrations are compatible with measurements from the stations of the Ministry of Environment's network. Outliers are combined with weak winds from the south-southwest. As far as p,m-xylene measurements are concerned, there is a poor correlation between gas chromatography (GC) and DOAS Opsis measurements, also observed in previous relevant campaigns and eventually a criticism in the use of the DOAS Opsis model for the measurement of p,m-xylene.

On the relation between seasonal synoptic circulation types and spatial air quality characteristics in Athens, Greece.

The impact of weather on air pollution was examined and evaluated for the city of Athens, Greece. We used an objectively defined synoptic classification scheme consisting of six summer and eight winter circulation types. This scheme was established using a combination of both factor and cluster analysis during 1954-1999. Surface and isobaric levels of 850 hPa data were used. Factor analysis combined with cluster analysis was used to derive circulation types based on surface meteorological data for the period 1954-1999 in Athens and on surface pressure grid data. The city was divided into three sectors according to the financial and social activities of the residents. To examine the spatial characteristics of pollutant concentrations over Athens for each synoptic type, the synoptic circulation types were then correlated with both gaseous and particulate pollutant concentrations measured in each sector between 1983 and 1999. Finally, extreme and severe episodic events were studied in terms of their meteorological and synoptic characteristics.