School Children's Personal Exposure to Ultrafine ... - ACS Publications

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School Children’s Personal Exposure to Ultrafine Particles in the Urban Environment Mandana Mazaheri,† Sam Clifford,†,§ Rohan Jayaratne,† Megat Azman Megat Mokhtar,† Fernanda Fuoco,‡ Giorgio Buonanno,‡,† and Lidia Morawska*,† †

International Laboratory for Air Quality and Health, Institute of Health and Biomedical Innovation, Queensland University of Technology, 2 George Sreet, Brisbane 4001, Australia ‡ Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, via Di Biasio 43, 03043 Cassino, Italy § Centre for Air Quality & Health Research and Evaluation, 431 Glebe Point Rd, Glebe 2037, Australia S Supporting Information *

ABSTRACT: There has been considerable scientific interest in personal exposure to ultrafine particles (UFP). In this study, the inhaled particle surface area doses and dose relative intensities in the tracheobronchial and alveolar regions of lungs were calculated using measured 24-h UFP time series of school children personal exposures. Bayesian hierarchical modeling was used to determine mean doses and dose intensities for the various microenvironments. Analysis of measured personal exposures for 137 participating children from 25 schools in the Brisbane Metropolitan Area showed similar trends for all participating children. Bayesian regression modeling was performed to calculate the daily proportion of children’s total doses in different microenvironments. The proportion of total daily alveolar doses for home, school, commuting, and other were 55.3%, 35.3%, 4.5%, and 5.0%, respectively, with the home microenvironment contributing a majority of children’s total daily dose. Children’s mean indoor dose was never higher than the outdoor’s at any of the schools, indicating there were no persistent indoor particle sources in the classrooms during the measurements. Outdoor activities, eating/cooking at home, and commuting were the three activities with the highest dose intensities. Children’s exposure during school hours was more strongly influenced by urban background particles than traffic near the school.

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INTRODUCTION Many studies have reported the levels of particle number and mass concentrations in different microenvironments, such as residential homes, schools, and workplaces. A comprehensive risk assessment study of global burden of disease (GBD), recently published in “The Lancet”, identified tobacco smoking as “one of the three leading risk factors” for GBD in 2010 and ranked ambient particle pollution as the ninth risk factor by attributable burden of disease.1 Ultrafine particles (UFP, diameter less than 100 nm) are of special concern as they can easily enter deep into the lungs and adversely affect the respiratory, central nervous and cardiovascular systems.2−5 The Health Effects Institute (HEI) recently reported that while there have been a growing number of laboratory and field studies of the effects of UFP on human health but “controlled human exposure studies and epidemiologic studies have not provided consistent findings on the effects of exposure to ambient levels of UFPs, particularly in human populations”.6 Children are generally more susceptible to air pollution than adults.7 Their exposure to UFP at school is of significant concern as they are legally mandated to attend school, spending © 2013 American Chemical Society

approximately a quarter of their day at schools during the school teaching periods (approximately 9 am to 3 pm for 40 weeks per year in Queensland, Australia), which accounts for a sizable portion of children’s total daily exposure. In general, home, school, and urban roads are the common microenvironments used by school children in urban areas of Brisbane Australia, where the main sources of urban ambient particles have been identified as traffic,8 along with household and cooking related particles.16 It should be noted that while worldwide contribution of household and cooking emissions to ambient particle concentrations was estimated as 16% in 2010,1 and cooking related particles have been reported as major contributors to daily particle exposures and respiratory health,9 direct measurements of personal exposure from household related particles are not widely available. Received: Revised: Accepted: Published: 113

August 21, 2013 November 24, 2013 November 25, 2013 November 25, 2013 dx.doi.org/10.1021/es403721w | Environ. Sci. Technol. 2014, 48, 113−120

Environmental Science & Technology

Article

NT was operated in Advanced mode, measuring both PNC and average particle diameter at a fixed sampling interval of 16 s. Personal Particle Measurement and Data Quality Control. The participating children were instructed to continuously carry the NT for 24-h by wearing it around the waist using a dedicated belt. The children were requested to keep the unit in close proximity when it was not being worn (e.g., during sports activities, while in the bathroom and sleeping) and to charge the unit while in the classroom, at home, and overnight. The measurements were conducted at one school at a time and the NT personal monitoring was carried out for three participating children at a time at each school. The NTs’ time stamps were synchronized to the actual local time by linear interpolation. A PNC correction factor (CF) was derived for each NT at the start of the project and periodically by running side by side with a TSI model 3787 condensation particle counter (CPC), normally once at each school, to ensure that the readings were consistent between all of the NTs and the CPC. Compared to CPC measurements, NTs underestimate the PNCs by as much as 30% at high concentrations (higher than 1.2 × 105 cm−3), but are in good agreement (within 10%) at moderate (from 2 × 104 cm−3 to 8 × 104 cm−3) and low concentrations (less than 1 × 104 cm−3).20,24 Since children in this study were mostly exposed to ambient concentrations (less than 105 cm−3), the potentially impaired performance of NT at high concentrations was not a significant concern. The correction factor for each NT (n = 1, 2, 3) was derived as

A review of studies on the topic of children’s exposure to air pollutants and traffic emissions showed that most of the previous efforts in relation to exposure in the school environment have been based on particle mass concentration, mainly PM1, PM2.5, and PM10.10−14 Very few school studies have been conducted to quantify particle number concentration in the UFP size range, most of which used fixed-site measurements,15−19 with only one recent school study using personal particle monitors (Philips Aerasense Nanotracers) to quantify children’s personal exposures in three Italian primary schools.20 The deposited particle surface area in the lung, defined here as dose, has been identified as a relevant metric for particle exposure quantifications.20 Lung deposition is known to be more efficient in the alveolar (AL) than the tracheobronchial (TB) region, excepting particles less than 6 nm in diameter.4,21,22 This paper presents the methodology and results for apportioning school children’s inhaled surface area dose and dose relative intensity (the portion of the dose that a child receives in a microenvironment divided by the portion of the day that the child spent in that microenvironment) in the TB and AL regions. The doses were calculated from the measured UFP number concentration time series obtained using a personal aerosol sampler and apportioned according to an activity diary during a typical school day.

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MATERIALS AND METHODS Study Design. This study was conducted at randomly selected state schools within the Brisbane Metropolitan Area in Australia (October 2010 to August 2012), as part of the “Ultrafine Particles from Traffic Emissions and Children’s Health (UPTECH)” cross-sectional study, Ethics approval: 1000000703 (www.ilaqh.qut.edu.au/Misc/ UPTECH%20Home.htm). The UPTECH schools admit students who live within their catchment area, defined by equidistant trafficable routes between one school and its neighboring schools. The selection criteria and locations of the total of 25 UPTECH schools (S1−S25) are available in the Supporting Information (SI) (Figure S.1). All classrooms in the study used natural ventilation all year round and no heating systems were used during winter or on cooler days. All children aged 8−11 years old within the selected schools were invited to participate in the UPTECH project, upon their parents’ consent, out of which three to six children were selected for this personal exposure monitoring study in consultation with their teachers as well as parents/guardians. An activity diary survey was developed for this study and participants were asked to record their daily activities and durations, including activities and times spent at home, travel times and mode of transport. Total traffic counts at 5-min intervals were measured at each school (SI, Figure S.4) and used as a proxy for traffic density during the commuting times. Instrumentation. Three Philips Aerasense Nanotracers (NTs), which work by diffusion charging, were used to measure particle number concentrations (PNC) within the children’s breathing zone. The NT is a portable personal sampler that measures PNC up to 1 × 106 cm−3 in the 10 to 300 size range. The NT’s design and operation characteristics as well as sensitivity and limitations are discussed in detail in Marra et al.23 Charging time and battery life are both seven hours. The

CFn =

CCPC C NTn

(1)

where CCPC and CNTn refer to the concurrent total PNC measured by the CPC and the NT unit. According to Marra et al.,23 particle diameters recorded by the NTs are within 10−15% of that measured with the scanning mobility particle sizer (SMPS). Therefore, the measured particle diameters were assumed to be within the acceptable range. Quantification of Dose. NT devices do not characterize the size distribution of the sampled air but report the average particle diameter, xp . Size distributions may be estimated with a log-normal density, f, with location, dp(t), and shape parameter, σ, and scaled by the number concentration, N(t). Appropriate estimates for IR and σ for each microenvironment and age group are found in SI Table S.1. The model for the total inhaled dose for a child (i) in a particular microenvironment (k) is based on the dose model of Buonanno et al.17 but it also integrates over both the reconstructed particle size distribution f(xp; xp (t),σ) and time J

dik =

∑ IR ∫ j=1

dx p dt

Tjk 2

Tjk1

∫0

∞

N (t )f (xp; xp(t ), σ )φr(IR, xp)h(xp) (2)

where Tjk1 and Tjk2 represent the start and end times of block j, IR is the inhalation rate, xp the particle diameter, and φr the deposition efficiency within the region of interest in the lung (r either alveolar or tracheo-bronchial).22 The relationship between particle diameter and the quantity of interest is described by h(xp), (i.e., 1 for particle number, πx2p for surface 114

dx.doi.org/10.1021/es403721w | Environ. Sci. Technol. 2014, 48, 113−120


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area and ((ρπx3p)/6) for mass). That is, at each time for which PNC, N(t), and mean particle size, xp(t), are measured a particle surface area distribution is reconstructed, multiplied by the deposition efficiency curve and inhalation rate and then integrated over particle diameter. The resulting time series is then integrated to determine the total inhaled surface area dose in that microenvironment. To determine which microenvironments were associated with the highest doses, the dose relative intensity for each child in each microenvironment was obtained by dividing the portion dose that child receives in that microenvironment by the portion of the day that the child spent in that microenvironment. That is, dose relative intensityik =

dik K

Differences at school j between microenvironment k1 and k2 were inferred by deriving γj = βjk2 − βjk1. If the 95% credible interval for γj was strictly positive, doses in k2 were higher than those for k1. The school-level effects, βjk, were drawn from a distribution centered around a microenvironment level mean, αk. Microenvironment Proportions. The proportion of total daily dose and time corresponding to each microenvironment were modeled as multinomial, specified as a series of binomial models.30 The percentage of each child’s dose in microenvironment number k out of the K = 8 possible microenvironments was then yik = ⌊dik/ni) × 100⌉, where ni = ΣkK= 1dik and follows a binomial distribution

J

/

∑j = 1 Tjk 2 − Tjk1 K

J

∑k = 1 dik ∑k = 1 ∑j = 1 Tjk 2 − Tjk1 (3)

k−1

yi1 ∼ Bin(p1 , ni)

2

The dose relative intensity is a ratio of dose proportion (cm / cm2) to time proportion (days/days) and is a dimensionless number greater than 0. An intensity greater than one indicates that the portion of the total daily dose received in that microenvironment was higher than the portion of the day spent there. Bayesian Regression Modeling. Bayesian regression model was utilized to understand the effect of covariates on the calculated dose. Posterior inference was performed by MCMC sampling with the rjags library25,26 in R.27 The use of Bayesian statistics necessitates the use of the credible interval, rather than the confidence interval, to quantify uncertainty in parameter estimates. The two intervals are analogous but not identical in their interpretation. The confidence interval is a random interval around an unknown but fixed true parameter, based on asymptotics, and does not incorporate prior beliefs. In contrast, the credible interval is based on treating the unknown parameter as a probability distribution (for which prior information is encoded, even if the prior is noninformative), simulating from the distribution and calculating the quantiles of the simulated parameter estimates. Linear Regression. The relationship between a dose, di, and the value of some explanatory variable, zi, was modeled with a log-Normal density, where the mean is a linear function and τd represents the precision (inverse variance):

yik |k ≠ 1 ∼ Bin(pk , ni −

∑ dim) m=1

k−1

p1 = θ1

pk |k ≠ 1 = θk(1 −

∑ θm)−1 m=1

θ ∼ Dirichlet(α) αk ∼ Γ(2, 2)

(6)

The Dirichlet prior on the Multinomial proportions, θ, represents the proportion of the dose received in each microenvironment being assumed equal in the absence of data. The Gamma prior on the Dirichlet parameters has E(αj) = 1, giving a weakly informative conjugat prior for the Dirichlet parameters. The daily dose portion is a proportion of inhaled surface area dose, and is a dimensionless number between 0 and 1.

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RESULTS The personal exposure analysis in this paper involved measuring 137 children from 24 out of the 25 UPTECH schools, as no students from S11 participated in this study. No issues or concerns were raised during the measurements in relation to data collection by the participants, their parents or guardians or school authorities. The NTs performed well within their limitations, as outlined in the Instrumentation Section. Data missingness occurred due to participants not complying with the NT charging instructions or incomplete activity diaries. Therefore, the presented total daily analysis is based on data from the 89 children for whom at least 23 h of data is available, which results in no children from schools 4 and 5 being included in the total daily analysis. The measured PNCs, as well as the calculated doses and dose relative intensities corresponding to each identified microenvironment are analyzed and discussed in the following sections. Children’s Activity Diaries. More than 90 unique activities from the diaries were classified into eight mutually exclusive and exhaustive activity categories: home (cooking and eating, sleeping, other), commuting, schooling (indoors, outdoors), other (indoors, outdoors), shown by gray boxes in SI Figure S.2. The amount of time each child spent in the various activity categories were analyzed, as recorded in the activity diaries (SI, Figure S.6). Analysis of reported nonmissing activities (with eq 6) showed that on average, children spent 28% of a typical school day at the school, composed of 20% of the day indoors

Weakly informative Normal priors with mean zero and variance 106 are placed on the regression coefficients, β0,β1, and a weakly informative Gamma prior with mean 1 and variance 1000 is applied to the model precision (inverse of the variance).28 Hierarchical Linear Modeling. Bayesian hierarchical linear models29 were fit to investigate school-level comparisons of doses, assuming that the personal doses for each microenvironment were centered around an exchangeable school-level mean. The dose for observation i, for a student attending school j in microenvironment k, was modeled as follows: 115



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Figure 1. Mean daily trend of PNC as measured by students carrying the nanotracer, averaged to 24 h. The dashed lines represent the 95% credible interval of the predicted mean daily trend of PNC.

Figure 2. Calculated mean and 95% credible interval for nonzero alveolar and tracheo-bronchial surface area doses corresponding to each activity.

Figure 3. Calculated mean and 95% credible interval for nonzero alveolar and tracheo-bronchial surface area dose intensities corresponding to each activity.

and 8% outdoors within the school grounds. Commuting accounted for 3% of the day’s activity and the remaining time was spent at home (65%), categorized as cooking/eating (15%), sleeping (40%), and home other activities (10%). Complete information on the children’s commutes during the 24-h of measurements were available for 104 of the participating children (SI Table S.2). Most children either walked or were driven in a private car to get to and from school. Students listed as walking, running, or biking were classed as active transport users; students as car or truck passengers were classed as private transport users; students catching the bus or train were classed as public transport users. UFP Number Concentrations. The measured time series for all children participating in the study exhibited similar

trends, and the recorded PNCs during school hours (typically 9 am to -3 pm) were broadly comparable with the ambient concentrations for different environments worldwide in the literature: road side (4.82 × 104 cm−3), urban (1.08 × 104 cm−3) and urban background (7.29 × 103 cm−3).31 Figure 1 shows the mean daily trend of PNC and 95% credible intervals (CI), calculated using all the PNC data collected for all the children with a penalised random walk model.32 Peaks in the mean daily trend are evident around 5−7 pm and 7−8 am, corresponding to cooking and eating periods and that PNC was lowest during the times that children were sleeping. The mean and standard error of the mean for PNC in each of the four environments (home, school, commuting, other) across all children with more than 23 h of activity and PNC 116



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Figure 4. Difference between children’s mean alveolar surface area doses during nonschool and school hours, γ, from the log-Normal Bayesian hierarchical linear model.

data are home: 1.05 × 104 cm−3 ± 5.88 × 101 cm−3, school: 8.53 × 103 cm−3 ± 1.26 × 102 cm−3, commuting: 1.37 × 104 cm−3 ± 3.01 × 102 cm−3 and other: 8.66 × 103 cm−3 ± 2.77 × 102 cm−3. In this study, the mean measured indoor PNC during school hours was 8.46 × 103 cm−3, which ranges from 20 to 75% of that found in a similar personal exposure monitoring study conducted in three Italian schools;20 150−240% of that found in a study of rural residential schools in low traffic areas in the United States;19 and comparable to those found in a study of secondary science classrooms in Poland.18 Both the American and Polish studies used stationary PNC monitors, while the Italian study used NT’s. Children’s Personal Dose and Dose Relative Intensity. Figure 2 and SI Table S.3 show the calculated mean total AL and TB surface area doses for all the participating children whose activity diaries comprise at least 23 h of nonmissing information. Parameter estimates were obtained by fitting the regression model presented in eq 4 with no covariates to the nonzero doses for each activity. While most activities provide a similar contribution to the daily alveolar dose, they do so with varying intensities (eq 3, Figure 3, SI Table S.5) due to the amount of time spent in each microenvironment (SI Figure S.6). The TB dose intensities were approximately equal to those in the AL region but the absolute TB dose was approximately a ninth of the AL dose. This is due to the deposition efficiency for particles around the mode of the average particle diameter (approximately 50 nm) being lower for the TB region than for the AL.22 This paper focuses on the results for AL surface area doses from now on. The median proportion of AL doses (and 95% credible intervals) in the total daily dose (SI Table S.4), calculated using eq 6, for the four main microenvironments of home, school, commuting, and other were 55.3% (54.1, 56.4%), 35.3% (34.1, 36.4%), 4.5% (4.0, 5.0%), and 5.0% (4.5, 5.4%), respectively. Despite the prior for proportions being a weakly informative Gamma-Dirichlet prior (E(αj) = 1), the credible intervals for each of the estimated proportions was very tight, indicating that the data were highly informative and there was very little uncertainty in the estimates. The lowest AL dose intensity, 0.42 (95%CI: 0.37, 0.47), was during sleeping, a time with lowest PNC and inhalation rate (SI Table S.1). The highest dose intensity, 2.21 (95% CI: 1.98, 2.45), occurred during outdoor times at school, when PNC was moderately high and children were more active (higher inhalation rate).

Comparisons of our dose and dose intensity results with those of a similar study conducted recently in Italy16 showed that inhaled alveolar surface area doses in the Italian schools were approximately 1.5−5 times higher than in Brisbane. The daily dose portions in each of the microenvironments studied in Italy were comparable to those reported here, with the exception of the Italian home environment which contributed 70% of the total inhaled dose. The students in the Italian schools were exposed to higher PNC concentrations across all microenvironments (the mean PNC for children in the urban Italian schools was approximately 6.2 × 104 cm−3) and this was most pronounced during eating times in the home. School and Nonschool Hours. There was typically more variation in the nonschool hours doses than the school hours due to differences in the afterschool activities (Figure 2 and SI Figure S.7). School hour doses were expected to be quite homogeneous for children attending the same school, as students at each school have the same school timetable, were exposed to the same traffic conditions, and partake in the same activities at school. Employing the hierarchical linear model (eq 5), for most schools, the difference in the means of the log school dose and log nonschool dose indicated that the nonschool environments contributed more to the total daily dose than the school microenvironments (Figure 4). For schools 1, 6, 8, 9, 10, 12, 19, 20, 21, 22, and 24 (with a 95% credible interval that did not contain zero) and 13, 16, 17, 18, and 25 (with a 90% credible interval not containing zero), the mean dose received by the children outside school hours was higher than that received at school. No observations were available from schools 4, 5, and 11 and so their credible intervals were equal and were informed solely by the hierarchical prior. For the remaining schools (2, 3, 7, 14, 15, and 23), the 90% credible intervals of the differences of means contained zero. At S14 and S15, children’s mean nonschool doses were substantially lower than those received by children at other schools and were very similar to their mean school hours doses. These schools had six and four participating children, respectively, and there was very little variation in these doses compared to other schools. As a result, the 95% credible intervals for the difference in mean doses was narrower than most other schools yet still contained zero. The mean school and nonschool doses for children at schools 3 and 7 exhibited similar relationships as described for schools 14 and 15 but displayed more within-school variation. Children at schools 2 and 23 had among the highest mean school hour doses in the 117



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Figure 5. Difference between children’s mean alveolar surface area doses during outdoor and indoor activities in the school environments, γ, from the log-Normal Bayesian hierarchical linear model.

for both AL and TB regions (Figure 2). The AL dose intensity for commuting was 1.24 (95% CI: 1.09, 1.42), which was the fourth highest microenvironment behind schooling outdoors, other outdoors, and home cooking/eating. The relationship between calculated AL surface area dose and commute time was linear on the log−log scale with a correlation of 0.82 (SI Figure S.5b). The effect of traffic count on dose during school hours and during the commute was different (SI Figures S.5c, S.5d); during commuting, the effect size of the log of traffic count was 0.622 (95% credible interval 0.526, 0.715); during school hours the effect size was 0.171 (95% credible interval 0.032, 0.301). The effect size was higher during commuting than school hours, even though some schools were located very close to major roads. While public transport users had a longer commute time than others but the relationship between dose and commute time for these students was no different to that of active or private transport users. The results imply that children’s doses during school hours were more affected by the urban background particles than traffic near the school. The results do not take into account variation in urban background PNC arising from seasonal trends.33 The doses reported here are therefore an indication of the personal exposure of particular children during the 24 h for which they were fitted with the NTs, rather than an estimate of that child’s total annual dose. While only 24 h of data were collected for each of the 89 children included in the analysis, these represented typical school days for both the children and the classrooms. The panel design nature of the observational study meant that students of only one school were being monitored at any given time, confounding conditions unique to a given school with temporal trends. While the number of participating children at each school was quite low, six at most, the Bayesian hierarchical modeling allowed the borrowing of strength from data from other schools to determine school-level measures of mean dose for the various microenvironments. For the majority of children, outdoor activities at school was the most dose intense, due to inhalation rate being higher than other daily activities. The proximity to particle sources was responsible for the resulting high dose intensities during eating/cooking and commuting. Combining the personal sampling data with stationary monitoring data from the UPTECH project (three condensation particle counters in each school and one at a central monitoring location), spatiotemporal estimates of dose can be obtained that also take long-term trends into account.

study. For the two participating children at S2, the outdoor doses were high due to more time being spent outdoors (15%) than indoors (11%). At school 23, children’s nonschool hours doses were of the same order as the school hours, which could be caused by the nearby high traffic conditions on inner city streets other than the measured road. The minimum and maximum children indoor doses at schools were found at S01 (0.186 cm2) and S22 (0.450 cm2), respectively. The minimum outdoor dose during school hours was in S18 (0.107 cm2) and the maximum was in S02 (3.823 cm2). The results from the Bayesian hierarchical linear model of indoor and outdoor doses (eq 5) showed that for most schools the difference between the school-level mean indoor and outdoor doses were not significant (Figure 5). At S02, the 95% credible interval was strictly positive, as were the 90% credible intervals for S14 and S24. That the 95% credible interval was strictly greater than zero in S02 was explained by the fact that the two students at this school spent a greater proportion of their school day outdoors than indoors during the times they were equipped with the NT, in contrast to the children in the other participating schools. Similarly, the students spent between 30 and 60% as long outdoors as they did indoors at S14 and S24, resulting in higher outdoor doses than indoors. Home Activities. Since children are involved in a number of different activities at home, and hence exposed to different UFP sources, the estimates of exposure to PNC in the home are much more variable across students than the school-based doses (Figure 2). Doses in the home environment, where children spent the majority of their day, contributed more to the total daily dose than other microenvironmenst. Dose intensities for cooking and eating at home were found to have the third highest intensity (1.26, 95% CI: 1.45, 1.68) in all cases, except other outdoors, which contained only 14 nonzero values. This is due to the high PNCs associated with cooking/ eating activities (a median PNC of 7.4 × 103 cm−3 over all students), which is in agreement with results of a similar Italian study.20 The lowest dose relative intensities were during sleeping, corresponding to very low concentrations (median of 3.3 × 103 cm−3 across all students) and low inhalation rate (SI Table S.1) over the approximately 9 h per day for all the participated children. Commuting. The dominant modes of transport for children in this study were walking and riding in a private vehicle (SI Table S.2). While commuting accounts for only a small fraction of the daily activities of each child (minimum travel time was two minutes, SI Figure S.5b), the median relative intensity of surface area dose was strictly greater than 1 118



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(4) Oberdörster, G.; Sharp, Z.; Atudorei, V.; Elder, A.; Gelein, R.; Kreyling, W.; Cox, C. Translocation of inhaled ultrafine particles to the brain. Inhalation Toxicology 2004, 16, 437−445. (5) Rutherford, S.; Simpson, R.; Williams, G.; Mitchell, C.; McCall, B. Relationships between environmental factors and lung function of asthmatic subjects in South East Queensland, Australia. J. Occup. Environ. Med. 2000, 42, 882−891. (6) HEI. Understanding the Health Effects of Ambient Ultrafine Particles; HEI Review Panel on Ultrafine Particles, 2013. (7) Li, C.; Nguyen, Q.; Ryan, P. H.; LeMasters, G. K.; Spitz, H.; Lobaugh, M.; Glover, S.; Grinshpun, S. A. School bus pollution and changes in the air quality at schools: A case study. J. Environ. Monit. 2009, 11, 1037−1042. (8) Morawska, L.; Thomas, S.; Bofinger, N.; Wainwright, D.; Neale, D. Comprehensive characterization of aerosols in a subtropical urban atmosphere: Particle size distribution and correlation with gaseous pollutants. Atmos. Environ. 1998, 32, 2467−2478. (9) Buonanno, G.; Morawska, L.; Stabile, L. Particle emission factors during cooking activities. Atmos. Environ. 2009, 43, 3235−3242. (10) Mejía, J. F.; Choy, S. L.; Mengersen, K.; Morawska, L. Methodology for assessing exposure and impacts of air pollutants in school children: Data collection, analysis and health effectsA literature review. Atmos. Environ. 2011, 45, 813−823. (11) Almeida, S. M.; Canha, N.; Silva, A.; Freitas, M. D.; Pegas, P.; Alves, C.; Evtyugina, M.; Pio, C. A. Children exposure to atmospheric particles in indoor of Lisbon primary schools. Atmos. Environ. 2011, 45, 7594−7599. (12) Liu, M. M.; Wang, D.; Zhao, Y.; Liu, Y. Q.; Huang, M. M.; Liu, Y.; Sun, J.; Ren, W. H.; Zhao, Y. D.; He, Q. C.; Dong, G. H. Effects of outdoor and indoor air pollution on respiratory health of Chinese children from 50 kindergartens. J. Epidemiol. 2013, 23, 280−287. (13) Macedo, A.; Magalhães, O.; Brito, A.; Mayan, O. Characterization of indoor environmental quality in primary schools in Maia: A Portuguese case study. Hum. Ecol. Risk Assess. Int. J. 2012, 19, 126− 136. (14) Nastos, P. T.; Larissi, I. K.; Grigoropoulos, K. N.; Antoniou, A.; Paliatsos, A. G. Indoor and outdoor particulate matter variability in Athens, Greece. Indoor Built Environ. 2013, 22, 586−592. (15) Buonanno, G.; Fuoco, F. C.; Morawska, L.; Stabile, L. Airborne particle concentrations at schools measured at different spatial scales. Atmos. Environ. 2013, 67, 38−45. (16) Buonanno, G.; Morawska, L.; Stabile, L.; Wang, L.; Giovinco, G. A comparison of submicrometer particle dose between Australian and Italian people. Environ. Pollut. 2012, 169, 183−189. (17) Buonanno, G.; Giovinco, G.; Morawska, L.; Stabile, L. Tracheobronchial and alveolar dose of submicrometer particles for different population age groups in Italy. Atmos. Environ. 2011, 45, 6216−6224. (18) Polednik, B. Particulate matter and student exposure in school classrooms in Lublin, Poland. Environ. Res. 2013, 120, 134−139. (19) Zhang, Q.; Zhu, Y. Characterizing ultrafine particles and other air pollutants at five schools in South Texas. Indoor Air 2012, 22, 33− 42. (20) Buonanno, G.; Marini, S.; Morawska, L.; Fuoco, F. Individual dose and exposure of Italian children to ultrafine particles. Sci. Tot. Environ. 2012, 438, 271−277. (21) Cheng, Y. S.; Yeh, H. C.; Guilmette, R. A.; Simpson, S. Q.; Cheng, K. H.; Swift, D. L. Nasal Deposition of ultrafine particles in human volunteers and its relationship to airway geometry. Aerosol Sci. Technol. 1996, 25, 274−291. (22) ICRP. Human Respiratory Tract Model for Radiological Protection: A Report of a Task Group of the International Commission on Radiological Protection; Elsevier Science Ltd.: Oxford, U.K., 1994; pp 1−482. (23) Marra, J.; Voetz, M.; Kiesling, H.-J. Monitor for detecting and assessing exposure to airborne nanoparticles. J. Nanopart. Res. 2010, 12, 21−37.

The outcomes of this observational campaign using UFP personal samplers showed that the dose during commuting is very strongly associated with traffic density and commute time. Despite the commute only accounting for 3% of the day, on average, commuting was found as a fairly dose intense activity. The mode of transport did not play an observable role in the surface area dose or dose intensity in this study. Commuting occurred along roadways, much closer to traffic than any location within the schools. As such, the effect of traffic count on dose was more pronounced during commute times than during school hours. For most children, the indoor and outdoor doses at school were found to be quite similar even though more time was spent inside the classroom than in outdoor locations within the school (means of 4.9 and 1.9 h, respectively). At no school was the mean indoor dose higher than the mean outdoor dose. These results highlight the significance of personal ultrafine particle exposure monitoring at all different microenvironments, for example, not just at school but in the home and during commuting, to properly quantify the total daily dose as well as the significance of taking preventative actions to reduce these exposures, such as source control, ventilation, and air exchange ratios in indoor environments both at home and school.

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ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in the text. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*(L.M.) phone: +61 7 3138 2616; fax: +61 7 3138 9079; Email: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the Australian Research Council (ARC), QLD Department of Transport and Main Roads (DTMR) and QLD Department of Education, Training and Employment (DETE) through Linkage Grant LP0990134. S.C. is supported by a postdoctoral fellowship from the Centre for Air quality health Research and evaluation. We thank R. Fletcher (DTMR) and B. Robertson (DETE), G. Marks, P. Robinson, K. Mengersen, Z. Ristovski, G. Ayoko, C. He, G. Johnson, S. Low Choy, G. Williams, W. Ezz, F. Salimi, L. Crilley, N. Mishra, R. Laiman, L. Guo, C. Duchaine, H. Salonen, X. Ling, J. Davies, L. Leontjew Toms, A. Cortes, B. Toelle, A. Quinones, P. Kidd, E. Belousova, M. Falk, F. Fatokun, J. Mejia, D. Keogh, T. Salthammer, R. Appleby, and C. Labbe.

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