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.     

Exposure to major volatile organic compounds and carbonyls in European indoor environments and associated health risk

This paper summarizes recent data on the occurrence of major organic compounds (benzene, toluene, xylenes, styrene, acetaldehyde, formaldehyde, naphthalene, limonene, α-pinene and ammonia, classified by the European Commission's INDEX strategy report as the priority pollutants to be regulated) and evaluates accordingly cancer and non-cancer risks posed by indoor exposure in dwellings and public buildings in European Union (EU) countries. The review process indicated that significant differences in indoor air quality exist within and among the countries where data were available, indicating corresponding differences in sources and emission strength of airborne chemicals, identified or not. Conservative exposure limits were not exceeded for non-carcinogenic effects, except for formaldehyde; for carcinogenic agents the estimated risks were up to three orders of magnitude higher than the one (10(-6)) proposed as acceptable by risk management bodies. However, the risk assessment evaluation process faces crucial difficulties, either due to the relative paucity of indoor air quality measurements in many EU countries, or by the lack of sampling consistency in the already existing studies, indicating the need for additional measurements of indoor air quality following a harmonized sampling and analytical protocol. Additionally, uncertainties embodied in the cancer potency factors and exposure limit values impose further difficulties in substance prioritization and risk management.     

Forecasting hourly PM<Subscript>10</Subscript> 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 PM₁₀ 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.     

Assessment and prediction of exposure to benzene of filling station employees

In the present study, the exposure to benzene of employees working in two filling stations (one urban and one rural) was estimated, through the method of passive sampling. Additional data (30′ measurements of benzene exposure through active sampling to employees dealing with different activities, meteorological and traffic data) were collected. The measurements campaign was performed in both summer and wintertime to determine the seasonal variation of the exposure pattern.In addition, a set of artificial neural networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees based on active sampling data and the parameters related to the employees’ exposure. The quantification of the contribution of each parameter to the overall exposure pattern was also attempted.The results showed that although vapour recovery technologies are installed in the refuelling systems and benzene emissions are significantly reduced compared to the past, filling station employees are still highly exposed to benzene (52–15 μg m⁻³). Benzene exposure is strongly correlated to car refuelling (exposure levels up to 85 μg m⁻³), while activities like car washing or working in cash machine inside an office contribute to lower exposure levels (up to 44 and 24 μg m⁻³ respectively). In rural filling station, exposure levels were in general lower compared to the urban ones, due to the smaller amount of gasoline that was traded and the absence of any significant traffic effect or urban background concentration. The developed ANN seemed to be a promising technique in the prediction of the exposure pattern giving very good results, and the quantification of the parameters affirmed the importance of the refueling procedure to the exposure levels.     

Exposure Modeling of Benzene Exploiting Passive–Active Sampling Data

The objective of the present study is the exploitation of active sampling personal exposure data in assessing the factors that affect exposure to benzene in combination with the widely accepted scheme of passive sampling—time microenvironment–activity diaries (TMAD). The campaign included personal exposure measurements with both passive and active sampling in several microenvironments, evaluation of TMAD kept by the volunteers, and a variety of environmental data (ambient air benzene determination, traffic and meteorological observations). Due to the relatively elevated benzene traffic emissions, average personal exposure was determined to be equal to 8.9 μg/m³, ranging between 5 and 20 μg/m³, which is a value highly related to the average urban concentration (9.2 μg/m³). The information gained from TMAD was embedded (in terms of spatial and temporal distribution) into three zones respectively, in order to draw statistically significant conclusions about the exposure levels and the activity patterns. The contribution of the activities to the overall amount of exposure was further quantified and refined by active sampling measurements. These data revealed that driving in a traffic-congested road was the main activity leading to elevated exposure levels (up to 70 μg/m³), followed by walking on the roadside of a congested road (up to 35 μg/m³). Indoor exposure to benzene was in general lower than outdoor (indicating that traffic is the dominant source of benzene emissions in the wider area), and it was significantly affected by the presence of environmental tobacco smoke. The higher significance of the regression coefficients obtained by statistical analysis of the active sampling data was fundamental for the development of a regression-based prediction exposure model. The model was evaluated through comparison with the passive sampling data, which were considered as an unknown but realistic data exposure pattern. The model performed very well in terms of expressing the variance of the exposure data with an average score of R ² equal to 0.935. All of the above indicate that active sampling is a necessary albeit more laborious tool that needs to be used as a complement to passive sampling for precise quantification of the factors determining personal exposure patterns.