Source apportionment for indoor air pollution: Current challenges and future directions.

Source apportionment (SA) for indoor air pollution is challenging due to the multiplicity and high variability of indoor sources, the complex physical and chemical processes that act as primary sources, sinks and sources of precursors that lead to secondary formation, and the interconnection with the outdoor environment. While the major indoor sources have been recognized, there is still a need for understanding the contribution of indoor versus outdoor-generated pollutants penetrating indoors, and how SA is influenced by the complex processes that occur in indoor environments. This paper reviews our current understanding of SA, through reviewing information on the SA techniques used, the targeted pollutants that have been studied to date, and their source apportionment, along with limitations or knowledge gaps in this research field. The majority (78 %) of SA studies to date focused on PM chemical composition/size distribution, with fewer studies covering organic compounds such as ketones, carbonyls and aldehydes. Regarding the SA method used, the majority of studies have used Positive Matrix Factorization (31 %), Principal Component Analysis (26 %) and Chemical Mass Balance (7 %) receptor models. The indoor PM sources identified to date include building materials and furniture emissions, indoor combustion-related sources, cooking-related sources, resuspension, cleaning and consumer products emissions, secondary-generated pollutants indoors and other products and activity-related emissions. The outdoor environment contribution to the measured pollutant indoors varies considerably (<10 %- 90 %) among the studies. Future challenges for this research area include the need for optimization of indoor air quality monitoring and data selection as well as the incorporation of physical and chemical processes in indoor air into source apportionment methodology.

Evaluation of the cancer risk from PAHs by inhalation: Are current methods fit for purpose?

There is ample evidence from occupational studies that exposure to a mixture of Polycyclic Aromatic Hydrocarbons (PAHs) is causally associated with an increased incidence of lung cancers. In both occupational atmospheres and ambient air, PAHs are present as a mixture of many compounds, but the composition of the mixture in ambient air differs from that in the occupational atmosphere, and varies in time and space in ambient air. Estimates of cancer risk for PAH mixtures are based upon unit risks which derive from extrapolation of occupational exposure data or animal model data, and in the case of the WHO use one compound, benzo[a]pyrene as a marker for the entire mixture, irrespective of composition. The U.S. EPA has used an animal exposure study to derive a unit risk for inhalation exposure to benzo[a]pyrene alone, and there have been a number of rankings of relative carcinogenic potency for other PAHs which many studies have used to calculate a cancer risk from the PAHs mixture, frequently incorrectly by adding the estimated relative risks of individual compounds, and applying the total "B[a]P equivalent" to the WHO unit risk, which already applies to the entire mixture. Such studies are often based upon data solely for the historic US EPA group of 16 compounds which do not include many of the apparently more potent carcinogens. There are no data for human cancer risk of individual PAHs, and conflicting evidence of additivity of PAH carcinogenicity in mixtures. This paper finds large divergences between risk estimates deriving from the WHO and U.S. EPA methods, as well as considerable sensitivity to the mixture composition, and assumed PAH relative potencies. Of the two methods, the WHO approach appears more likely to provide reliable risk estimates, but recently proposed mixture-based approaches using in vitro toxicity data may offer some advantages.

Validation of an optimised microwave-assisted acid digestion method for trace and ultra-trace elements in indoor PM2.5 by ICP-MS analysis.

Three microwave-assisted digestion procedures, followed by analysis of digestates employing inductively coupled mass spectrometry (ICP-MS) were evaluated for use in the determination of elements at trace and ultra-trace levels in PM2.5 samples. Digestion procedure 1 used 2.5 mL HNO3 (65%) at 200 °C. Procedure 2, consisted of a two-stage digestion step at 200 °C with 2.5 mL HNO3 (65%) and 3 µL HF (48%) followed by 24 µL H3BO3 (5%). A 10-fold increase in the amounts of HF and H3BO3 was used for procedure 3. The addition of HF/H3BO3 was carried out to aid the dissolution of silicate matrices and refractory compounds. The digestions were carried out using PTFE ultra-trace inserts which increased the sample throughput threefold. The addition of small quantities of HF resulted in the effective solubilisation of Na, Mg, Al, K, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Sr, Cd, Sb and Pb. The optimal method using HNO3/HF/H3BO3 digestion as in procedure 3 showed recovery efficiency greater than 70% for all elements. The validated method was applied to quantify the elemental content of indoor and outdoor PM2.5 (with samples <0.5 mg) at an urban background site in Malta.

Tobacco-specific and combustion pollutants in settled house dust in Malta.

Aim: Most of the carcinogenic pollutants coming from tobacco smoking or other combustion processes tend to accumulate in settled house dust (SHD) over time. This study evaluated the load of these pollutants in smokers and non-smokers' houses from relatively fresh SHD collected in five different districts on the island of Malta. Methods: An improved, efficient extraction method to obtain three fractions from a 200 mg of SHD was developed. It was validated for the analysis of nicotine and polycyclic aromatic hydrocarbons (PAH) by GC-MS/MS and nicotelline and TSNA by LC-MS/MS. Kruskal-Wallis H tests were used to evaluate differences across districts, while a Mann-Whitney U test was used to check differences between smokers and non-smokers' houses. Diagnostic ratios were used to evaluate the carcinogenicity of PAH in SHD in Malta. Results: For all analytes, no statistical difference was observed across different districts, but, in smokers' houses, 97.9% of the total concentration of all target analytes found in SHD is nicotine, 0.1% is TSNA, and 2.0% is PAH. In non-smokers' houses, nicotine represents 16.8% of the load, while 0.4% and 82.8% are TSNA and PAH, respectively. The carcinogenicity of the PAH mixture in Maltese SHD, expressed as the mean benzo(a)pyrene equivalent (BaPeq) is 371 ng/g. Conclusion: Indoor activities, ventilation practices, and infiltration of outdoor pollutants contribute to a complex SHD composition. Although the BaPeq is on the lower end of carcinogenicity, the effects of a mixture including tobacco-related potent carcinogens in SHD are largely unknown. In view of indoor, continuous exposure to SHD through several pathways, further research is warranted.

Determination of 4-(Methylnitrosamino)-1-(3-Pyridyl)-1-Butanone (NNK) arising from tobacco smoke in airborne particulate matter.

The most important tobacco-specific nitrosamine found in cigarette smoke and formed in ageing smoke after cigarettes are extinguished is 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). It is formed from nitrosation of nicotine, under particular conditions both in indoor and outdoor environments. NNK has been classified as a potent lung carcinogen which is expected to be found primarily in the particle-phase and to be stable in particulate matter. In this study tests have been carried out to show that a bisulfate-treated filter is more efficient than an untreated filter to collect both nicotine and NNK, and that the latter is stable in outdoor particulate matter. To characterize NNK in the outdoor environment, airborne samples were collected from 11 cities in USA, UK, Hong Kong and Malta with characteristics varying from low to high population densities and from urban to suburban to rural, and with desert characteristics and distinct climates. It has been shown that airborne particle + gas phase nicotine and particle-phase NNK behave in a linearly correlated manner. A seasonal analysis was carried out on a subset of data available from five sites in California, where the load of NNK in PM10 is driven by long range transport of the air masses passing over densely populated cities. In the winter season, the load of NNK in PM is higher than in summer in a statistically significant manner. The contamination of PM with NNK shows variability, but is observed at all sites. This paper highlights the potential risk of chronic exposure to NNK in particulate matter by the inhalation pathway.

Secondhand smoke exposure in school children in Malta assessed through urinary biomarkers.

School children may be exposed to secondhand smoke (SHS) either at home, in transit or in social gatherings permitting smoking in their presence. Questionnaires about SHS often underestimate prevalence and extent of exposure. A more accurate tool is the use of biomarkers such as cotinine (COT) and trans-3'-hydrocycotinine (3HC) as biomarkers of SHS exposure, alongside 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), a reduction product in the body of the tobacco-specific nitrosamine, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), both potent carcinogens. We measured urinary COT, 3HC and total NNAL using sensitive and specific high-performance LC-MS/MS methods. The limit of quantification (LOQ) for each assay were 0.05 ng/mL, 0.1 ng/mL and 0.25 pg/mL respectively. The aim of this study was to evaluate the exposure to SHS of school children (9-11 years), from five public schools in the island of Malta, from questionnaire information about smoking at home and verify it by urinary biomarker data of COT, 3HC and NNAL. These biomarkers were measurable in 99.4%, 95.4% and 98.3% of the participating children respectively. From the children reporting smoking at home, 11% had a history of asthma and had COT, 3HC and NNAL geometric mean concentrations double compared to the non-asthmatic group. In has been confirmed that non-smokers exposed to SHS and THS have a higher NNAL/COT ratio than the group identified as smokers according to specific and defined COT threshold levels (despite the fact that a priori, the entire study group was composed of non-smokers). The implication of high measured levels of urinary NNAL in children should be of concern given its potency. A main effects multifactor ANOVA model was developed and the children's house and school locations and the smoking frequency were statistically significant to predict the levels of the three metabolites. For 3HC only, the status of the employment of the mother was also an important predictor.

Assessing oxidative stress resulting from environmental exposure to metals (Oids) in a middle Eastern population.

Concentrations of metals and metalloids derived mainly from anthropogenic activities have increased considerably in the environment. Metals might be associated with increase reactive oxygen species (ROS) damage, potentially related to several health outcomes. This study has recruited 200 adult participants, including 110 males and 90 females in Shiraz (Iran), to investigate the relationship between chronic exposure to metals and ROS damage by analyzing malondialdehyde (MDA) and 8-Oxo-2'-deoxyguanosine (8-OHdG) concentrations, and has evaluated the associations between chronic metal exposure and ROS damage using regression analysis. Our findings showed participants are chronically exposed to elevate As, Ni, Hg, and Pb levels. The mean urinary concentrations of 8-OHdG and MDA were 3.8 ± 2.35 and 214 ± 134 µg/g creatinine, respectively. This study shows that most heavy metals are correlated with urinary ROS biomarkers (R ranges 0.19 to 0.64). In addition, regression analysis accounting for other confounding factors such as sex, age, smoking status, and teeth filling with amalgam highlights that Al, Cu, Si and Sn are associated with 8-OHdG concentrations, while an association between Cr and MDA and 8-OHdG is suggested. Smoking cigarettes and water-pipe is considered a significant contributory factor for both ROS biomarkers (MDA and 8-OHdG).

Ubiquitous atmospheric contamination by tobacco smoke: Nicotine and a new marker for tobacco smoke-derived particulate matter, nicotelline.

Second Hand Smoke (SHS) has always been primarily linked with indoor pollution. To date nicotine was the favoured marker for SHS alongside measurements of particulate matter (PM) levels. As nicotine is mainly found in the gas-phase and reactive in the outdoor environment it is not ideal as a marker for the SHS-driven particulate component in PM. Nicotelline, a minor tobacco alkaloid that is stable, found almost exclusively in the particle phase and easy to quantify even at low concentrations, is being proposed as a better marker. It is the first study using bisulfate-treated quartz fiber filters to show that airborne nicotine (gas+particle phase) is directly proportional to airborne nicotelline in countries that have different climates. The analytical method developed has been validated to show that the use of untreated filters is suitable for the quantification of nicotelline even at low concentrations. Although nicotelline exhibits a seasonal and geographical variation, this is the first comprehensive study which demonstrates the ubiquitous presence of nicotelline in PM from outdoor air samples collected in the USA (0.1-285.6 pgm-3), UK (2.3-9.1 pgm-3), Hong Kong (3.8-109.3 pgm-3) and Malta (4.2-280.8 pgm-3). From the nicotelline apportionment factor of 1589 ng/mg of tobacco smoke PM we estimate the fraction of outdoor airborne PM derived from SHS to be in the range of 0.03-0.08%. While it is unlikely for tobacco smoke-related toxics in outdoor PM to be considered a major health hazard, in heavily polluted microenvironments this marker would be useful in tracing the presence of SHS and emerging Third Hand Smoke components that form or are found in airborne and settled PM that could induce serious health effects.

Characteristics and toxicological effects of commuter exposure to black carbon and metal components of fine particles (PM2.5) in Hong Kong.

Epidemiological studies have demonstrated significant associations between traffic-related air pollution and adverse health outcomes. Personal exposure to fine particles (PM2.5) in transport microenvironments and their toxicological properties remain to be investigated. Commuter exposures were investigated in public transport systems (including the buses and Mass Transit Railway (MTR)) along two sampling routes in Hong Kong. Real-time sampling for PM2.5 and black carbon (BC), along with integrated PM2.5 sampling, were performed during the warm and cold season of 2016-2017, respectively. Commuter exposure to BC during 3-hour commuting time exhibited a wider range, from 3.4 to 4.6 µg/m3 on the bus and 5.5 to 8.7 µg/m3 in MTR cabin (p < .05). PM2.5 mass and major chemical constituents (including organic carbon (OC), elemental carbon (EC), and metals) were analyzed. Cytotoxicity, including cellular reactive oxygen species (ROS) production, was determined in addition to acellular ROS generation. PM2.5 treatment promoted the ROS generation in a concentration-dependent manner. Consistent diurnal variations were observed for commuter exposure to BC and PM2.5 components, along with cellular and acellular ROS generation, which marked with two peaks during the morning (08:00-11:00) and evening rush hours (17:30-20:30). Commuter exposures in the MTR system were characterized by higher levels of PM2.5 and elemental components (e.g., Ca, Cr, Fe, Zn, Ba) compared to riding the bus, along with higher cellular and acellular ROS production (p < .01). These metals were attributed to different sources: rail tracks, wheels, brakes, and crustal origin. Weak to moderate associations were shown for the analyzed transition metals with PM2.5-induced cell viability and cellular ROS. Multiple linear regression analysis revealed that Ni, Zn, Mn, Fe, Ti, and Co attributed to cytotoxicity and ROS generation. These findings underscore the importance of commuter exposures and their toxic effects, urging effective mitigating strategies to protect human health.

A photometric mapping of the night sky brightness of the Maltese islands.

Over the years, the Maltese Islands have seen a marked rise in the prevalence of artificial lighting at night. The most evident type of light pollution arising from this evolution in anthropogenic night-time lighting is artificial skyglow via partial back-scattering in the atmosphere, leading to an increase in the Night Sky Brightness (NSB). The importance of understanding and quantifying the geographical distribution of the NSB is underscored by the adverse impact of light pollution on various spheres, from astronomical observation to ecology and human health. For the first time, we present a detailed map of the NSB over the Maltese archipelago carried out with Unihedron Sky Quality Meters. We show that the vast majority of the area of the Maltese Islands is heavily light polluted, with 87% of the area registering a NSB < 20.39magSQM/arcsec2 (Bortle Class 5 or higher) and 37.3% < 19.09magSQM/arcsec2 (Bortle Class 6 or higher), with the Milky Way being visible for only 12.8% of the area (adopting a visibility threshold > 20.4 - 21.29magSQM/arcsec2; Bortle Class 4). Coastal Dark Sky Heritage Areas on the island of Gozo retain generally darker skies than the rest of the islands, but light pollution originating further inland is encroaching upon and adversely affecting these sites. The methodology presented in this study can be adopted for continued future studies in Malta as well as for other regions.

Trends in ambient ozone, nitrogen dioxide, and particulate matter concentrations over the Maltese Islands and the corresponding health impacts.

This study presents an overview of the air pollution levels in the Maltese Islands including trends in particulate matter (PM), ozone (O3) and nitrogen dioxide (NO2) at four monitoring stations in Malta and one in Gozo between 2008 and 2017. In addition, the health impacts associated with long-term exposure to annual mean PM2.5 and NO2 are estimated at each site. Irrespective of the site, PM10 and PM2.5 concentrations show statistically significant decreasing trends while statistically significant increasing trends are noted for the coarse fraction, PM2.5-10 and O3. Trends for the different pollutants vary for each site and differ from the overall trend both in magnitude and sign especially for NO2 concentrations. The attributable fraction (AF) associated with long-term exposure to PM2.5 and NO2 ranges from 0.67% (CI: 0.27%,1.07%) in Gharb to 11.79% (CI: 7.77,15.45) in Msida (2011). The corresponding attributable mortality is estimated to reach a maximum of 119 (CI: 78,156) attributable deaths associated with long-term exposure to PM2.5 in Msida in 2011. This paper thus highlights the importance of continuous air quality monitoring in distinctively different conurbations especially for pollutants showing increasing trends and is the first to outline the potential long-term health effects of air pollutant concentrations in the Maltese Islands.

Comparison of Machine Learning Approaches with a General Linear Model To Predict Personal Exposure to Benzene.

Machine learning techniques (MLTs) offer great power in analyzing complex data sets and have not previously been applied to non-occupational pollutant exposure. MLT models that can predict personal exposure to benzene have been developed and compared with a standard model using a linear regression approach (GLM). The models were tested against independent data sets obtained from three personal exposure measurement campaigns. A correlation-based feature subset (CFS) selection algorithm identified a reduced attribute set, with common attributes grouped under the use of paints in homes, upholstery materials, space heating, and environmental tobacco smoke as the attributes suitable to predict the personal exposure to benzene. Personal exposure was categorized as low, medium, and high, and for big data sets, both the GLM and MLTs show high variability in performance to correctly classify greater than 90 percentile concentrations, but the MLT models have a higher score when accounting for divergence of incorrectly classified cases. Overall, the MLTs perform at least as well as the GLM and avoid the need to input microenvironment concentrations.

Thirdhand Smoke: New Evidence, Challenges, and Future Directions.

Thirdhand smoke (THS) is the contamination that persists after secondhand tobacco smoke has been emitted into air. It refers to the tobacco-related gases and particles that become embedded in materials, such as the carpet, walls, furniture, blankets, and toys. THS is not strictly smoke, but chemicals that adhere to surfaces from which they can be released back into the air, undergo chemical transformations and/or accumulate. Currently, the hazards of THS are not as well documented as the hazards of secondhand smoke (SHS). In this Perspective, we describe the distribution and chemical changes that occur as SHS is transformed into THS, studies of environmental contamination by THS, human exposure studies, toxicology studies using animal models and in vitro systems, possible approaches for avoiding exposure, remediation of THS contamination, and priorities for further research.

Relationship of personal exposure to volatile organic compounds to home, work and fixed site outdoor concentrations.

Personal exposures of 100 adult non-smokers living in the UK, as well as home and workplace microenvironment concentrations of 15 volatile organic compounds were investigated. The strength of the association between personal exposure and indoor home and workplace concentrations as well as with central site ambient air concentrations in medium to low pollution areas was assessed. Home microenvironment concentrations were strongly associated with personal exposures indicating that the home is the driving factor determining personal exposures to VOCs, explaining between 11 and 75% of the total variability. Workplace and central site ambient concentrations were less correlated with the corresponding personal concentrations, explaining up to 11-22% of the variability only at the low exposure end of the concentration range (e.g. benzene concentrations <2.5 µg m(-3)). One of the reasons for the discrepancies between personal exposures and central site data was that the latter does not account for exposure due to personal activities (e.g. commuting, painting). A moderate effect of season on the strength of the association between personal exposure and ambient concentrations was found. This needs to be taken into account when using fixed site measurements to infer exposures.

Comparative modeling approaches for personal exposure to particle-associated PAH.

Several models for simulation of personal exposure (PE) to particle-associated polycyclic aromatic hydrocarbons (PAH) have been developed and tested. The modeling approaches include linear regression models (Model 1), time activity weighted models (Models 2 and 3), a hybrid model (Model 4), a univariate linear model (Model 5), and machine learning technique models (Model 6 and 7). The hybrid model (Model 4), which utilizes microenvironment data derived from time-activity diaries (TAD) with the implementation of add-on variables to account for external factors that might affect PE, proved to be the best regression model (R(2) for B(a)P = 0.346, p < 0.01; N = 68). This model was compared with results from two machine learning techniques, namely decision trees (Model 6) and neural networks (Model 7), which represent an innovative approach to PE modeling. The neural network model was promising in giving higher correlation coefficient results for all PAH (R(2) for B(a)P = 0.567, p < 0.01; N = 68) and good performance with the smaller test data set (R(2) for B(a)P = 0.640, p < 0.01; N = 23). Decision tree accuracies (Model 6) which assess how precisely the algorithm can determine the correct classification of a PE concentration range indicate good performance, but this is not comparable to the other models through R(2) values. Using neural networks (Model 7) showed significant improvements over the performance of hybrid Model 4 and the univariate general linear Model 5 for test samples (not used in developing the models). The worst performance was given by linear regression Models 1 to 3 based solely on home and workplace concentrations and time-activity data.

Environmental and biological monitoring of exposures to PAHs and ETS in the general population.

The objective of this study was to analyse environmental tobacco smoke (ETS) and PAH metabolites in urine samples of non-occupationally exposed non-smoker adult subjects and to establish relationships between airborne exposures and urinary concentrations in order to (a) assess the suitability of the studied metabolites as biomarkers of PAH and ETS, (b) study the use of 3-ethenypyridine as ETS tracer and (c) link ETS scenarios with exposures to carcinogenic PAH and VOC. Urine samples from 100 subjects were collected and concentrations of monophenolic metabolites of naphthalene, fluorene, phenanthrene, and pyrene and the nicotine metabolites cotinine and trans-3'-hydroxycotinine were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to assess PAH and ETS exposures. Airborne exposures were measured using personal exposure samplers and analysed using GC-MS. These included 1,3-butadiene (BUT), 3-ethenylpyridine (3-EP) (a tobacco-specific tracer derived from nicotine pyrolysis) and PAHs. ETS was reported by the subjects in 30-min time-activity questionnaires and specific comments were collected in an ETS questionnaire each time ETS exposure occurred. The values of 3-EP (>0.25 microg/m(3) for ETS) were used to confirm the ETS exposure status of the subject. Concentrations as geometric mean, GM, and standard deviation (GSD) of personal exposures were 0.16 (5.50)microg/m(3) for 3-EP, 0.22 (4.28)microg/m(3) for BUT and 0.09 (3.03)ng/m(3) for benzo(a)pyrene. Concentrations of urinary metabolites were 0.44 (1.70)ng/mL for 1-hydroxypyrene and 0.88 (5.28)ng/mL for cotinine. Concentrations of urinary metabolites of nicotine were lower than in most previous studies, suggesting very low exposures in the ETS-exposed group. Nonetheless, concentrations were higher in the ETS population for cotinine, trans-3'hydroxycotinine, 3-EP, BUT and most high molecular weight PAH, whilst 2-hydroxyphenanthrene, 3+4-hydroxyphenanthrene and 1-hydroxyphenanthrene were only higher in the high-ETS subpopulation. There were not many significant correlations between either personal exposures to PAH and their urinary metabolites, or of the latter with ETS markers. However, it was found that the urinary log cotinine concentration showed significant correlation with log concentrations of 3-EP (R=0.75), BUT (R=0.47), and high molecular weight PAHs (MW>200), especially chrysene (R=0.55) at the p=0.01 level. On the other hand, low correlation was observed between the PAH metabolite 2-naphthol and the parent PAH, gas-phase naphthalene. These results suggest that (1) ETS is a significant source of inhalation exposure to the carcinogen 1,3-butadiene and high molecular weight PAHs, many of which are carcinogenic, and (2) that for lower molecular weight PAHs such as naphthalene, exposure by routes other than inhalation predominate, since metabolite levels correlated poorly with personal exposure air sampling.

Model development and validation of personal exposure to volatile organic compound concentrations.

BACKGROUND: Direct measurement of exposure to volatile organic compounds (VOCs) via personal monitoring is the most accurate exposure assessment method available. However, its wide-scale application to evaluating exposures at the population level is prohibitive in terms of both cost and time. Consequently, indirect measurements via a combination of microenvironment concentrations and personal activity diaries represent a potentially useful alternative. OBJECTIVE: The aim of this study was to optimize a model of personal exposures (PEs) based on microenvironment concentrations and time/activity diaries and to compare modeled with measured exposures in an independent data set. MATERIALS: VOC PEs and a range of microenvironment concentrations were collected with active samplers and sorbent tubes. Data were supplemented with information collected through questionnaires. Seven models were tested to predict PE to VOCs in 75% (n = 370) of the measured PE data set, whereas the other 25% (n = 120) was used for validation purposes. RESULTS: The best model able to predict PE with independence of measurements was based upon stratified microenvironment concentrations, lifestyle factors, and individual-level activities. The proposed model accounts for 40-85% of the variance for individual VOCs and was validated for almost all VOCs, showing normalized mean bias and mean fractional bias below 25% and predicting 60% of the values within a factor of 2. CONCLUSIONS: The models proposed identify the most important non-weather-related variables for VOC exposures; highlight the effect of personal activities, use of solvents, and exposure to environmental tobacco smoke on PE levels; and may assist in the development of specific models for other locations.

Measurement of personal exposure to volatile organic compounds and particle associated PAH in three UK regions.

Personal exposures to 15 volatile organic compounds (VOC) and 16 polycyclic aromatic hydrocarbons (PAH) of 100 adult nonsmokers living in three UK areas, namely London, West Midlands, and rural South Wales, were measured using an actively pumped sampler carried around by the volunteers for 5/1 (VOC/PAH) consecutive 24-h periods, following their normal lifestyle. Results from personal exposure measurements categorized by geographical location, type of dwelling, and exposure to environmental tobacco smoke (ETS) are presented. The average personal exposure concentration to benzene, 1,3-butadiene, and benzo(a)pyrene representing the main carcinogenic components of the VOC and PAH mixture were 2.2 +/- 2.5 microg/m3, 0.4 +/- 0.7 microg/m3, and 0.3 +/- 0.7 ng/m3 respectively. The association of a number of generic factors with personal exposure concentrations was investigated, including first-line property, traffic, the presence of an integral garage, and ETS. Only living in houses with integral garages and being exposed to ETS were identified as unequivocal contributors to VOC personal exposure, while only ETS had a clear effect upon PAH personal exposures. The measurements of personal exposures were compared with health-based European and UK air quality guidelines, with some exceedences occurring. Activities contributing to high personal exposures included the use of a fireplace in the home, ETS exposure, DIY (i.e., construction and craftwork activities), and photocopying, among others.