- freely available
Sensors 2009, 9(02), 731-755; doi:10.3390/s90200731
- Gathers information from crucial environmental parameters (through the proper environmental sensors), which constitute determinants of the exposure to benzene of filling employees
- Implements the proper algorithms evaluating the information obtained by the environmental sensors, in order to provide real time estimation (a) of exposure to benzene and consequently (b) of the associated health risk.
2.2. Monitoring sensor network design
- The amount of the gasoline that was traded.
- Wind speed (m/sec) and direction (degrees).
- Ambient air temperature.
- Traffic flow from two independent observers. Vehicles were registered in seven main categories (catalytic passenger vehicles, non catalytic passenger vehicles, diesel passenger vehicles, light duty passenger vehicles, heavy duty passenger vehicles, buses and motorcycles) and the traffic volume per category was recorded every half an hour. In the urban site the measurements were continuous from 8:00 to 16:00. Vehicle speed was calculated by the quotient of the distance (a part of the road) and the time needed to cover that distance.
- Urban background concentration. The most common and reliable modelling practice to define background concentration is to use data obtained from measurements at urban locations that are not directly affected by local sources . Based on this, two active samplers were placed in two different urban locations (not affected by traffic or other known benzene sources) and their values were averaged in order to exclude the urban background concentration. In the rural area also two passive samplers were placed to investigate any possible background concentration or the existence of any other benzene source beside the road and the gas station emissions that may affect the measurements results. In the operational mode of a multi-sensor fusion based monitoring system as the one outlined in this study, these parameters would be measured using automated sensors and their output data streams integrated following exactly the same algorithm as the one given in this paper.
- Varian 3900 GC gas chromatography system with a flame ionization detector (FID). The capillary column through which the chromatographic separation of the various pollutants was effected, is the 30 m long, 0.53 mm inner diameter and 0.5 μm film thickness, SBM™ -5 capillary column, by Supelco, Italy.
- MARKES Thermal Desorption Cold Trap Injector thermal desorption system
- Three low volume SKC model 222 pumps for gas sampling
- DryCal CD-Lite (Bios International, USA) flow meter with a measurement range of 0.010 to 12 L/min,
- Sampling tubes (suitable both for active and passive sampling): MARKES CARBOGRAPH 5TD tubes standard absorbing cartridges filled with 400 mg of sorbent.
- Qualimetrics model 2020 and 2032 cup anemometer and wind vane, respectively.
2.3. ANN (Artificial Neural Network) modeling development
2.4. PBPK-based risk assessment model
- benzene → benzene oxide
- phenol → hydroquinone
- phenol → catechol
- hydroquinone → trihydroxy benzene
3. Results and Discussion
3.1. Traffic data
3.2. Meteorological data
3.3. Amount of gasoline traded
3.4. Personal exposure results
3.5. Artificial Neural Network modelling results
3.6. Relative importance of the parameters constituting the exposure pattern obtained by the ANN model
3.7. Potential health risk estimation
- The exposure to benzene values obtained by the measurement campaign
- The related concentrations of benzene metabolites on target issue (in the case of leukaemia risk this is the bone marrow) through the developed PBPK/PD model and the related dose-response curve
- The variability of the population response through a Monte Carlo-Markov Chain approach
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|Light duty vehicles||7||23|
|Heavy duty vehicles||2||9|
|Employees refueling cars||Miscellaneous Employees||Cashiers|
|Urban + Rural||0.0900||0.0814||0.0556|
|Urban + Rural||0.9714||0.9919||0.9441|
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