The PE and microenvironment concentrations measured in this study are substantially lower than those found in similar studies, conducted in different locations in the United States and Europe and at earlier times (Edwards et al. 2001
; Kim et al. 2002
; Loh et al. 2006
This study presents seven different approaches used in the development of models that predict VOC concentrations for non-occupational PEs. Distinct category models have been tested, and each category has its own applicability and limitations. First, models 1–3 use the average of 5-day time-weighted PE concentration, whereas models 4–7 use daily 24-hr time-weighted PE. This difference in time integration scale will smooth the effect of short time activities in PE for models 1–3, so these models will be less affected by day-to-day variations. Second, empirical models (models 1, 2, 3, and 7) can be used for predictions as long as the conditions predicted are similar to those of the original data collection. In principle, empirical models cannot be transferred to new and different conditions, because they contain no information that is not contained in the original data. On the other hand, mechanistic models (model 4 and 5) are based on an assumed and simplified logical construct of the exposure conditions, and therefore can be applied for any conditions as long as the logical construct remains. The hybrid model (model 6) contains features of both mechanistic and empirical models, and therefore, expansion of the model to new conditions (e.g., new country, area, or time) needs specific consideration and possibly some readjustment. Even with all these considerations, valuable information can be drawn out from each of the proposed models.
The information extracted from the performance of models 1–3 gives valuable information in terms of evaluating the influence on PE by the two microenvironments where people spent most of their time. The model that correlates PE with home microenvironment concentrations (model 1) explains most of the variance of the PE. Home concentrations explain 60–75% of the PE variance for compounds such as benzene, toluene, and 3-ethenylpyridine and 40–55% for compounds such as the xylenes, trimethylbenzenes, and pyridine, whereas for compounds such as hexane, styrene, or 1,3-butadiene model 1 explains 5–15% of the variance in PE. On the other hand, workplace VOC concentrations (model 2) are not as good at predicting PE as are home concentrations. This is mainly due to the presence of high concentrations in the PE subset not related to workplace concentration as a consequence of strong VOC sources such as home indoor or personal activity sources. The observation that the home is a stronger predictor is also emphasized by the fact that the model that includes home jointly with workplace concentrations (model 3) does not improve the R2
coefficient with respect to model 1, and the inclusion of the workplace variable is not significant at the 0.01 level for most of the compounds. This is contrary to what was expected, because PE predictions should have improved when increasing the number of microenvironment concentrations included in the model. As outlined before, this might have been a consequence of a set of PE activities not reflected in workplace concentrations. Therefore, these results suggest that the microenvironment that affects PE most is the home microenvironment, where subjects spent an average of 62% of their time. PE was hence dominated by the contribution from the residential environment for all compounds, as previously reported by other authors (Adgate et al. 2004
; Kim et al. 2002
Two model approaches were based on predicting the PE integrating the time fraction spent in different microenvironments times the microenvironment concentration. The time-weighted model that used microenvironment concentrations measured directly in the homes and workplaces of the subjects (model 4) explained an average variability of 50% ranging from 12% to 80%. Similar results for benzene were reported by Adgate et al. (2004)
and for other compounds by Kim et al. (2002)
. Nevertheless, direct measurements in the subjects’ own locations are required to feed this model. On the other hand, model 5 follows the same approach but uses independent generic stratified microenvironment concentrations for all the microenvironments that the subjects visit. Therefore, no direct measurements are required to predict PE. The performance of this model is lower compared with the other tested models. This was expected as the generic stratified microenvironment concentrations used in model 5 does not contain specific information about each subject’s microenvironment as was the case in model 4. This is a consequence of the difficulties in adequately stratifying home microenvironments with the modest number of samples collected, combined with the home-to-home variation in home concentrations. Even when the total sample size for home microenvironment is large (number of home samples = 77), the large number of different strata, such as integral garage, ETS exposure, first-line (FL) properties, or location within a city, considerably reduces the sample size per stratum. In addition, the range of activities in which the subjects are engaged in their normal life was reflected in the specific home and workplace levels and therefore was well accounted for in models 1 and 4. This, however, does not occur in the same way in the stratified VOC levels representative of each different stratum of exposure proposed in model 5. Examination of the data [see Supplemental Material, Figure 2e (doi:10.1289/ehp.0900561.S1)] shows that model 5 predicts the concentrations well except in some cases where concentrations are in most instances underpredicted. Further the study showed that these cases are linked to activities such as exposure to ETS, do-it-yourself projects, photocopying, use of solvents, and so forth. Therefore, model 5 predicts the concentration well in most instances, but does not perform well when the subjects engage in an activity that implies a substantial increase in VOC exposure.
To solve this difficulty, model 6 uses the concentrations calculated in model 5, but includes a range of add-on variables that represent activities or home characteristics that could not be reflected in the stratified data and that lead to an increase of the VOC levels. This new model approach better reflects the VOC concentrations, explaining higher levels of variance: 80% for ethylbenzene, xylenes, styrene, and trimethylbenzenes and 45–50% for compounds such as benzene, toluene, and 1,3-butadiene. The amount of variance left unexplained by model 6 must be because the sources of such compounds were not well captured in the model by the proposed microenvironment concentrations or add-on variables.
The model that predicted PEs from key determinants (model 7) is the one that explains the least variability of all the seven tested models, implying that for predicting PEs it is advisable to include microenvironment concentration data into the model. This model, although not suited for prediction purposes, gives valuable information in identifying determinants of VOC exposures [see Supplemental Material, Table 9 (doi:10.1289/ ehp.0900561.S1)].
If indirect exposure estimates are to be routinely employed, then it is important that they are evaluated by comparison with an independent dataset of direct measurements. Such comparison has been conducted in the present study, with the authors concluding that there is good agreement between measured and predicted concentrations for most of the models proposed. The models that perform best in both the training and validation data sets are the empirical model 1 and the hybrid model 6, with model 7 the worst-performing model in both data sets.
The hybrid model 6, which includes time-weighted concentrations, independent stratified data, and add-on variables collected from questionnaires, is considered the best-performing model in order to predict PEs while minimizing the cost of direct measurements in the subjects’ homes or workplaces. It is, however, important to recognize that for benzene and toluene the best model is model 1, where PE is predicted directly from home exposure. It appears that for these compounds, the inclusion in the model of other microenvironments visited (models 3–5) or other activities performed during the day (model 6) does not improve the prediction of PEs, but increases the uncertainty not accounted for by the model (e.g., 74%, 44%, and 47% of variability explained for benzene by models 1, 4, and 6, respectively). Most of the variation for these components arises from the home concentration. On the other hand, the other microenvironments or activities that also contribute to the PE could not be captured by the other proposed models (models 4–6). This suggests that more detailed information is needed to fully understand other sources contributing to benzene and toluene concentration in addition to the home microenvironment, and that the estimates of home microenvironment concentrations proposed in the generic stratified data (models 5 and 6) do not fully reflect the behavior of these compounds and hence need further study.
The influence that several activities have on the VOC levels can be assessed from the information contained in model 6 [; see also Supplemental Material Table 8 (doi:10.1289/ ehp.0900561.S1)]. ETS exposure is important for 3-ethenylpyridine, pyridine, benzene, and 1,3-butadiene. Traffic is a good predictor for compounds such as benzene, toluene, and 1,3-butadiene because all these models contain traffic-related variables. The use of paints is an activity affecting the levels of ethylbenzene, the xylenes, and the trimethylbenzenes. Similarly, storing paints in an integral garage increases the levels of n-hexane, benzene, toluene, and ethylbenzene. In line with activities related to the integral garage, parking the car in the garage raises the levels of benzene, toluene, ethylbenzene, the xylenes, and the trimethyl-benzenes. The use of fuels other than natural gas for heating increases the concentrations of n-hexane, benzene, ethylbenzene, and p-xylene. The variables chosen are supported by the literature, as reported in [see also Supplemental Material, Table 8 (doi:10.1289/ ehp.0900561.S1)].
For benzene, the proposed model takes into account the existence of an integral garage where paints and the car are kept, traffic variables such as living in an urban area, use of trains, and time spent commuting by car, ETS exposure, variables related to heating, and other variables linked to activities such as working in a hospital. Storage of paints in the garage has been identified as the strongest predictor, followed by time exposed to ETS and parking of a car in the garage. Higher benzene levels have been previously related to integral garages (Batterman et al. 2006
), ETS (Heavner et al. 1995
), and traffic (Edwards et al. 2001
). As regards 1,3-butadiene, the variables suggested were use of solvents, ETS-related variables, visiting a gasoline station, time since carpet was placed in the living room, time spent commuting, location of the door communicating with the integral garage in the kitchen, use of additional heating other than natural gas and electricity, time spent using sprays for decoration (e.g., artificial snow spray) and wrapping presents, and time since mold was removed from the house. The literature reports transport, prescribed burning, residential and commercial space heating, fuel and gasoline distribution, burning of other materials (e.g., cigarettes), and exposure to ETS as 1,3-butadiene sources (Curren et al. 2006
). The information extracted from the models suggests that policies leading to the reduction in the VOC content of products used in the home and in ETS exposures would help reduce PEs to VOC compounds.
Several subjects with particularly high PEs have been identified in this study. The high exposures were attributed to activities within the home and exposures to ETS, which play a major role in determining exposure. Therefore, efforts to reduce PE in the high-exposure group would be best focused upon regulating highly emitting household products and exposure to tobacco smoke. On the other hand, most of the subjects had lower PE deriving largely from indoor concentrations, which in turn are related to outdoor sources via infiltration and air exchange (Turpin et al. 2007
). For the general population, abatement measures relating to outdoor sources will have a large relative impact. Because this group is far more numerous, the population-wide health benefits, especially for nonthreshold toxics, deriving from abatement of outdoor sources may be appreciable. Also, reduction in the VOC content of products used in the home and in ETS exposures would have a major benefit in reducing exposure of both groups.
The results of model development obtained in this study are comparable with previously developed models. Heavner et al. (1995)
predicted PE to benzene based on dichotomous variables related to ETS and home characteristics as well as continuous variables related to ETS, explaining 28% of the variance. The authors acknowledged that the total sum of all other unidentified benzene sources in the model could have exerted an overall greater effect on benzene concentrations than the variables applied in that model, which is consistent with our findings. Leung and Harrison (1998)
estimated 12-hr integrated exposures by combining activity data with measurements performed in subjects’ homes and offices as well as with independent measurements of VOCs in several microenvironments, which is an approach similar to our proposed model 4. They reported better benzene and toluene results (i.e., benzene R2
= 0.83, toluene R2
= 0.92), but worse m
-xylene results (i.e., R2
= 0.56). Payne-Sturges et al. (2004)
compared the values predicted with the U.S. Environmental Protection Agency model ASPEN (Assessment System for Population Exposure Nationwide) with 3-day average PE measured with passive samplers. For most of the VOCs, the median ratios were comparable within a factor of 2, showing good agreement between ambient measurements and model predictions across the community measured. Dodson et al. (2007)
used a series of time-weighted PE models to predict measured PE to several VOCs. The relative percent difference between measured and modeled with the fully saturated time-weighted exposure model, which is a similar approach to our proposed model 4, ranged between 5% and 15%. Perez Ballesta et al. (2008)
modeled VOC from time/microenvironment/activity diary information (i.e., model 4 approach) and reported an R2
value of 0.47 for benzene, which is similar to the value reported in this study, but the R2
values for toluene (R2
= 0.32), ethylbenzene (R2
= 0.27), and m
= 0.56) exposure concentrations were lower than those reported in this study. Edwards et al. (2001)
modeled VOC concentrations using the time/ microenvironment/activity approach generally underestimating the results between 6% and 15%. Differences between the results of our models and former reported models might also be a consequence of a different range of sources, microenvironments, and temporal and spatial representativeness of the exposed population.
Despite the fact that the previous studies have reported valuable information in terms of understanding how to predict VOC PE from measurements directly made in subject-related microenvironments and time/microenvironment/activity data, none has used independent microenvironment concentrations (i.e., not measured at the subject’s homes or workplaces) or has verified their model results with an independent data set.
From the difficulties observed and lessons learned in the present study, we can make several recommendations for future model development: using a larger set of PE data; using a larger data set of microenvironment concentrations in order to perform an accurate and detailed stratification of the microenvironments entered into the model; having larger variability in PE and in important predictors; gathering more detailed information recorded in the questionnaires about activities performed and microenvironments visited by the subjects; and including detailed meteorologic information.