Weeklong indoor PM_2.5 samples were collected using a personal exposure monitor (PEM) in school classrooms in 1 or 2 seasons during the academic school years between 2008 – 2013. PEM includes an inertial impactor designed specifically for personal or indoor sampling. PEM collects PM_2.5 on Teflon filters at a low flow rate of 1.8 L/min. In addition, concurrent daily PM_2.5 samples were collected at the central monitoring supersite, which is located within 10 km of the schools. Details on outdoor PM_2.5 measurement at the central site have been previously described by Kang et al. (34). While the central site is part of the urban community, it is located approximately 20 m above ground level and is not adjacent to local highways.

Teflon filters, including blank filters, were weighed on an electronic microbalance (MT-5 Mettler Toledo, Columbus, OH) prior to and after field measurements, after being equilibrated for a period of 48 hours in a particle-free room of controlled temperature (22±1.5°C) and relative humidity (40±5%). Following gravimetric measurement, Teflon filters were analyzed for indoor BC concentrations by measuring filter blackness using a smoke stain reflectometer (model EEL M43D, Diffusion Systems Ltd., United Kingdom). We assumed a factor of 1.0 for converting the absorption coefficient to BC mass, and then divided by each sampled volume to calculate indoor BC concentration. On the other hand, outdoor BC concentrations were measured using a single (λ=880 nm) channel aethalometer (model AE-16, Magee Scientific, Berkeley, CA). Two different BC methods were compared to establish a linear relationship (R²=0.72, Slope= 0.95). Sulfur concentration in both the indoor and outdoor PM_2.5 samples was determined using X-Ray Fluorescence (XRF) spectroscopy (model Epsilon 5, PANalytical, The Netherlands) (35). Quality assurance and control of XRF analysis was performed as previously described(36). All analyses were conducted at the Harvard T.H. Chan School of Public Health.

Particle concentrations depend on particle infiltration, exfiltration, emission and deposition. Assuming no indoor sources of sulfur, the indoor penetration of PM_2.5 emitted from outdoor sources can be approximated by the sulfur tracer method (37, 38). Accordingly, the indoor to outdoor sulfur ratio (S_IN/S_OUT) was calculated for each school classroom and classroom sampling period to approximate the infiltration fraction of fine particles.

Satellite-based exposure assessment of PM_2.5 was assessed by a hybrid approach(39, 40). This hybrid approach combined simulation outputs from chemical transport model, land-use terms, satellite measurements and meteorological fields into a neural network. The neural network was trained with monitoring PM_2.5 data from EPA Air Quality System (AQS) and made prediction of PM_2.5 at 1 km×1 km grid cell on a daily basis. Ten-fold cross-validation demonstrated that predicted PM_2.5 correlated well with monitoring data. Individual exposure levels were ascertained by matching schools geocoded location to the nearest 1 km×1 km grid cell. Estimates of ambient Black Carbon (BC) concentrations were generated using a validated nu-SVR (Support Vector Regression) prediction model trained with 24,709 observations and 408 monitors throughout the region. Ten-fold cross validation showed good correlation between observed and predicted concentrations. Daily predictions at each school from 2000 to 2011 were made by extracting temporal and spatial predictors at each location and for each date.

To examine the relationship between the indoor and outdoor concentrations of PM_2.5 and BC, linear mixed-effect models were deployed, which used indoor concentration as the dependent variable, the product outdoor concentration *sulfur ratio as the independent variable, and the random effects at the school level (intercept and slope with unstructured covariance). A random component was dropped from the model if its variance estimate was less than twice its standard error. Additional covariates to reflect time were considered such as year of study and a restricted cubic spline of days since October 1^st. indoorij=β0+β1outdoorij+uoi+u1ioutdoorij∗S_ratioij+εij for i^th school and j^th classroom measurement

To determine an infiltration factor for a given day, a model was developed to predict the sulfur ratio. The following parameters were considered as predictors: ambient temperature, cubic spline of days, school year sampled, random intercept for school, random slope for school, condition of the classroom ceiling, walls, and windows, the number of windows in the classroom, whether the windows open, whether the windows face the drop-off/pick-up area, and floor level of the classroom. The final model included ambient temperature, spline of days, and school random intercept. The predicted sulfur ratio for a given classroom sample was estimated with this final model using all data points except for the classroom sample being predicted. This predicted sulfur ratio multiplied by the outdoor concentration equals the concentration of indoor particles that are of outdoor origin, the outdoor source contribution. Subsequently, indoor concentration was predicted for a given classroom sample using the mixed effects model described above using all data points except for the classroom sample being predicted.

School and classroom exposures to particulate air pollution are likely to have significant health effects in children, since they spend the majority of their time in this environment. However, particle monitoring inside schools is challenging. Understanding the relationship between indoor and outdoor particulate matter is important to assess the origin of exposures. Both indoor generated and ambient PM elicit an inflammatory response in the respiratory system(41). Additionally, given the constraints of conducting large population studies in school classrooms, accurate predictions of indoor pollutants is critical to understanding their health effects.

Here we demonstrate the strong relationship between the measured indoor classroom levels of PM_2.5 and BC with the corresponding outdoor levels, even with overall low levels of particulate concentrations compared with other studies of similar findings (9, 13, 16, 18). Moreover, mixed effects modeling allows for understanding the relative contributions of indoor sources, local outdoor sources near the schools and regional sources of particle pollution to classroom air pollution. Our study suggests that indoor sources of PM_2.5 significantly contribute to indoor levels in many schools despite the absence of smoking and cooking on premises. Previous studies suggest that re-suspension of particles already deposited on the floor by classroom activities (8, 9, 21, 42) is an important source, particularly in temperate climates where windows and doors are frequently closed(15). Others have shown that while the majority of indoor PM_2.5 is generated outdoors, a significant portion may be due to organic compounds and calcium-rich particles from chalk and building deterioration (9, 43, 44), as well as textiles, and outdoor soil components(13). Moreover, the composition of the classroom PM_2.5 has been found to be significantly different from that of outdoor particles (9) and may contain more toxic (17, 45) and biologically active products(41). Despite this, PM_2.5 of outdoor origin has been shown to have greater association with health effects in children with asthma (46).

While levels of PM_2.5 associated with indoor sources varied across schools, this variability was not explained by school characteristics such as building age, furnace, or structural conditions, similar to schools studies from Munich, Germany(16), Belgium(9), and France(23, 24) in which classroom and school characteristics were not found to associate with indoor/outdoor particle concentration ratios. In the current study, samples were collected when school was in session to investigate the effects of normal classroom activity; therefore, resuspension of fine particles is likely to be an important contributor to indoor fine particle levels, a dominant feature noted in the Primequal study of French schools(23, 24).

Indoor PM_2.5 levels were influenced by both indoor-school and outdoor particle sources. Whereas, the PM_2.5 in the urban area originates from different sources, BC is more specific to traffic-related particles and thus exhibits a greater spatial variability in the city landscape (34, 47, 48). Indoor BC had negligible indoor source emissions and its concentrations were correlated with those measured at the central monitor supersite. This finding is consistent with our assumptions that, in the absence of cooking and tobacco smoke on school grounds, BC concentrations would be a direct reflection of traffic pollutants infiltrating the classroom. Black carbon, along with gaseous byproducts of traffic, has been shown to easily penetrate schools and classrooms in urban areas (43).

We found little variability in the infiltration of outdoor particles across the school year as measured by sulfur tracer method. The sulfur ratio was consistently less than unity across classrooms with few exceptions, indicative of the lack or absence of indoor sources emitting sulfur, which is consistent with findings from previous studies (37, 49). Building conditions, ventilation, and seasonal influences have been implicated in the penetration of sulfur to the indoor environment (50, 51). We found ambient temperature, time, and the school random intercept to be significant predictors of the sulfur ratio, confirming the importance of seasonal and school building envelope attributes in PM_2.5. Specific classroom characteristics, including the physical condition, total number of windows, and number of windows facing a drop-off area, were not predictors of sulfur indoor/outdoor ratio.

Moreover, using the sulfur tracer method, we were able to capitalize on this close relationship of indoor to outdoor levels of PM_2.5 and BC to predict PM_2.5 and BC classroom levels on days when no particle samples were available. Our cross-validation model provided good estimates of predicted indoor concentrations (PM : out of sample r²_2.5 =0.68; BC: out of sample r²=0.62). These correspond to correlations between held out and modeled values of 0.79-0.82. Model performance was superior to those of indoor-home models due to the low contribution of the indoor sources (52). In a limited subgroup of schools based on available data, we found that satellite-based PM_2.5 and BC estimates to be similar, though overall less accurate in predicting indoor levels than the central monitor-based approach. The similarities are not surprising as the satellite estimates rely, in part, on the central monitor measures for calibration. The limited number of samples in the satellite-based analysis may account for part of the increased variance found in these models.

Our data analysis and interpretation of the findings presented here may be limited because of the relatively small number of samples and classroom time-activity data collected for each indoor sampling period. Additionally, weekday and weekend periods may have been included in the same sampling period. While our findings clearly show strong indoor-outdoor correlations for PM_2.5 and BC, the predictive model would likely be enhanced by greater quantity of indoor measures and discrimination of classroom activities. This belies the difficulty in population-based sampling in the inner-city school classroom where considerations of space, interference with educational activities, and sampling cost limit access. Despite these difficulties, our models were able to predict indoor PM_2.5 and BC with good accuracy. Finally, the lack of cooking, geographic characteristics, and local weather patterns affecting this school district may limit the generalizability of these findings to other school classroom based cohorts; however, the purpose here was to predict exposures within the classrooms measured at least once. The methodology may serve as a framework to determine the relative outdoor contributions to indoor pollution more generally.

Despite extensive study of the indoor and ambient pollution health effects at the home(53), few published studies have explored the health effects of classroom/school specific pollution in children with asthma(54-56). We demonstrate that outdoor PM_2.5 and BC concentrations can be used to predict indoor concentrations in the unique microenvironment of the school classroom. These models may contribute to more robust health effects analyses in future studies.