Satellite-Derived PM2.5 Composition and Its Differential Effect on Children’s Lung Function

Abstract

Studies of the association between air pollution and children’s health typically rely on fixed-site monitors to determine exposures, which have spatial and temporal limitations. Satellite observations of aerosols provide the coverage that fixed-site monitors lack, enabling more refined exposure assessments. Using aerosol optical depth (AOD) data from the Multiangle Imaging SpectroRadiometer (MISR) instrument, we predicted fine particulate matter, PM2.5, and PM2.5speciation concentrations and linked them to the residential locations of 1206 children enrolled in the Southern California Children’s Health Study. We fitted mixed-effects models to examine the relationship between the MISR-derived exposure estimates and lung function, measured as forced expiratory volume in 1 second (FEV1) and forced vital capacity (FVC), adjusting for study community and biological factors. Gradient Boosting and Support Vector Machines showed excellent predictive performance for PM2.5(testR2=0.68) and its chemical components (testR2=–0.71). In single-pollutant models, FEV1decreased by 131 mL (95% CI:−232,−35) per 10.7-µg/m3increase in PM2.5, by 158 mL (95% CI:−273,−43) per 1.2-µg/m3in sulfates (SO42−), and by 177 mL (95% CI:−306,−56) per 1.6-µg/m3increase in dust; FVC decreased by 175 mL (95% CI:−310,−29) per 1.2-µg/m3increase in SO42−and by 212 mL (95% CI:−391,−28) per 2.5-µg/m3increase in nitrates (NO3−). These results demonstrate that satellite observations can strengthen epidemiological studies investigating air pollution health effects by providing spatially and temporally resolved exposure estimates.

1. Introduction

Numerous studies have examined the association between exposure to traffic-related air pollution and children’s respiratory health [1,2,3,4]. Long-term exposure to air pollution, indicated by concentrations of nitric oxides (NO, NO₂, NO_x), ozone (O₃), particulate matter with aerodynamic diameter less than 2.5 µm (PM_2.5), and particulate matter with aerodynamic diameter less than 10 µm (PM₁₀ ), has been shown to lead to reduced lung development in children [5]. Fortunately, decreases in air pollution in Southern California over the past 17 years have led to significant reductions in these detrimental effects [6].

The air pollution exposures used in the aforementioned studies were estimated from concentrations measured at national- and state-operated fixed-site monitors [7]. For example, in a longitudinal assessment of air quality and lung development, Gauderman et al. [6] used concentrations of nitrogen dioxide (NO₂), O₃, PM_2.5, PM₁₀ from one monitor within each community to determine exposures. With this approach, the 120 to 300 study subjects residing in each community were assigned the same exposure. Spatial statistical techniques such as kriging, smoothing, and land use regression have been used to incorporate additional information (e.g., traffic, population density, elevation, land cover, and other geographic data) to characterize the spatial relationships in fixed-site monitoring data and interpolate concentrations to the unmonitored locations where there are health data [8,9]. While these approaches are valuable for generating exposures with greater spatial coverage, it has been shown that if their prediction performance is poor, subsequent epidemiological studies can yield severe biases and underestimation of standard errors in the health effects estimates [10].

Advances in using satellite observations of aerosol optical depth (AOD) to estimate ground-level concentrations of particulate matter (PM) air pollution have been extremely valuable in improving the spatial and temporal coverage of exposure estimates [11,12,13,14,15]. Among the satellite instruments most commonly used for PM estimation are the Multi-angle Implementation for Aerosols (MAIAC) of the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the NASA Earth Observing System (EOS) Terra and Aqua satellites [16], and the Multiangle Imaging SpectroRadiometer (MISR) on-board the Terra satellite [17]. Recent algorithms applied to observations from these instruments provide global, near-daily AOD at a spatially resolved grid resolutions (1 km, 4.4 km) [16,18].

Some studies have combined AOD from MODIS and MISR to derive PM_2.5 concentrations [19] and more recently PM_2.5 speciation concentrations [20]. MISR, given its configuration of nine cameras and four spectral bands, has the capability of differentiating aerosol size and type resulting in fractionated AOD [21]. In a recent study over Southern California, we reliably estimated PM_2.5with MISR 4.4-km resolution AOD small+medium, and PM₁₀ with AOD large using generalized additive models (GAMs) [22]. The MISR AOD-derived PM concentrations were well correlated (confirmed by leave-one-site-out cross validation, CV) with EPA monitoring site data (PM_2.5CVr=0.71, PM₁₀CVr=0.66). In the same region, speciated PM_2.5(sulfate, SO₄₂₋; nitrate, NO₃₋ ; organic carbon, OC; and elemental carbon, EC) were estimated using GAMs from 8 MISR component fractions combined with meteorology and geographic characteristics [23].

In a simulation study, high-resolution exposure estimates derived from satellite AOD were found to produce less biased acute and chronic health effects estimates with smaller standard errors than did exposure estimates derived from kriging PM_2.5 concentrations from fixed-site monitors [10]. Satellite-derived PM_2.5 concentrations have been instrumental in studies of the global burden of disease [24,25]. A few epidemiological studies of smaller cohorts have used satellite-derived PM_2.5 to estimate residential exposures in longitudinal children’s health effects [26,27,28].

In this study, we derived daily PM_2.5and PM_2.5speciation (SO₄₂₋, NO₃₋, EC, dust) exposures from 2000–2018 over the state of California by applying machine learning approaches to ground-level air quality measurements linked with the high-dimensional 4.4-km MISR AOD products and mixtures. Estimated annual average concentrations were then assigned to the residences of children in 8 Southern California communities to examine the chronic effects of exposure to PM_2.5and the aforementioned PM_2.5components on lung function. This study is unique in that it is the first of its kind to examine the differential effects of satellite-derived PM_2.5speciation on children’s respiratory health.

2. Materials and Methods 2.1. Particulate Matter Measurements

The United States Environmental Protection Agency (EPA) provides data on PM_2.5 concentrations from outdoor monitoring sites across the US through their Air Quality System (AQS) [29], and PM_2.5 speciation concentrations including ions, carbons, and metals through their Chemical Speciation Network (CSN) [30] (Figure A1). The AQS PM_2.5data we used include daily averages of hourly measurements or 24-h integrated measurements from Federal Equivalent and Reference Method (FEM/FRM) instruments. The CSN sites measure 24-h chemical composition on a 1-in-3- or 1-in-6-day sampling schedule. In this study, we used all available AQS and CSN data in California beginning in March 2000, the earliest date for which MISR AOD data are available, and ending in July 2018 for PM_2.5speciation and in December 2018 for PM_2.5mass. Among PM_2.5species, we focused on predicting SO₄₂₋ion, NO₃₋ ion, elemental carbon (EC), and dust, which was calculated as a linear combination of aluminum (Al), calcium (Ca), iron (Fe), silicon (Si), and titanium (Ti) [31]:

Dust=2.2×Al+2.49×Si+1.63×Ca+1.94×Ti+2.42×Fe.

This definition of dust pertains to fugitive geological materials, and has been shown to have a temporally stable compositional source profile over California [32].

2.2. MISR Aerosol Optical Depth Data

The MISR instrument has been collecting data from nine camera angles and four spectral bands since early 2000. Due to its narrower retrieval swaths, the instrument overpasses any given location every 3–5 days (between 10:00 and 13:00 local time) instead of daily as is the case with MODIS/MAIAC. The latest version (V23) of the retrieval algorithm re-processed the entire MISR mission at a 4.4-km spatial resolution (from 17.6-km resolution) [18] with improved retrievals over water [33]. In addition to total column AOD, MISR also characterizes the size, shape, and type of aerosol particles via these fractionated measures: small, medium, and large AOD (amount of particles of each size), nonspherical AOD (amount of nonspherical particles), and absorption AOD (amount of light-absorbing particles). These AOD features are hereafter referred to as AOD products.

In its auxiliary data products, MISR further provides 74 AOD mixtures, which are more fine-grained groupings of aerosol particles characteristics [21]. Specifically, AOD mixtures numbered 1–30 are made up of spherical, non-absorbing components, mixtures 31–50 contain spherical, absorbing components, and mixtures 51–74 contain spherical and nonspherical dust analogues. The MISR particle properties provide robust aerosol-type classification for distinguishing aerosol mass types including polluted, smoky, maritime, and dusty conditions [21,34]. Details of processing MISR data, particularly AOD mixtures, are documented in our previous paper [35].

2.3. Meteorological Data

Similar to other studies [10,19,22,26,36], we used meteorological data including temperature, relative humidity, wind speed, and wind direction to better inform the association between ground-monitored air pollutants and satellite-observed AOD. The Climatology Lab at the University of Idaho provides daily meteorological data, called gridMET, in the contiguous United States from 1979–yesterday [37]. This dataset has been extensively validated [38] and, at 4-km resolution, is particularly useful for our study. The gridMET data also provide surface shortwave radiation (SSR–the amount of shortwave radiation that reaches the earth), whose negative correlation with aerosol emissions has been documented by Smith et al. [39] and Freychet et al. [40].

2.4. Health Data

Since its inception in the early 1990s, the Southern California Children’s Health Study (CHS) has enrolled over 11,000 children in a series of five cohorts. In this study, we focused on the most recent cohort that began in 2003, enrolling approximately 3,000 children at age 6–7 years, and followed until 2012 when they were 15–16 years old. These children resided and went to school in eight communities in the greater Los Angeles, California area: Anaheim, Glendora, Long Beach, Mira Loma, Riverside, Santa Barbara, San Dimas, and Upland (Figure 1).

From 2007–2012, pulmonary function tests were conducted on each child by trained respiratory staff, measuring FEV₁ and FVC with pressure transducer-based spirometers (ScreenStar Spirometers, Morgan Scientific, Haverhill, Massachusetts, USA). A written questionnaire was also administered to obtain information including age, sex, self-identified race and ethnic background, parental education, occurrences of acute respiratory illness, exercise, tobacco-smoke exposure (personal smoking or environmental), and housing characteristics (air conditioning, age of house, presence of mildew, pets in the home). Ethnic background in the CHS specifically relates to Hispanic ancestry, identifying Caucasian subjects with Hispanic and non-Hispanic ethnicity [41]. Study protocols were approved by the Institutional Review Board at the University of Southern California (USC), and additional details of CHS community and subject selection have been previously reported [42,43].

2.5. Exposure Estimation Methods

We expand upon our previous work [22,35] to include MISR aerosol properties of absorption (absorbing or non-absorbing), shape (spherical or nonspherical), and type provided by 74 weighted aerosol optical depths (mixtures) [21] to predict PM_2.5and PM_2.5SO₄₂₋, NO₃₋, EC, and dust. We matched daily PM_2.5and PM_2.5 speciation measurements from ground monitors to the nearest available MISR pixel within 4.4 km (Figure A2) and then further matched them to the nearest gridMET pixel within 4 km. As some PM_2.5monitors are close to each other, it is possible they share the same MISR or gridMET pixel. In these instances, we used an algorithm to pair the shared MISR/gridMET pixel to the nearest monitor, and the remaining monitors were paired with the next available MISR/gridMET pixel within 4.4/4 km. If there were no other available MISR/gridMET pixels within an appropriate distance, the PM_2.5measurement on that day for that monitor was removed in order to avoid duplicating AOD and meteorological data in the dataset.

We trained the PM_2.5models on 70% of the data using 5-fold CV and assessed performance of the best model on the remaining 30%. We also trained the PM_2.5speciation models on 70% of the data with 5-fold CV and tested on 30%, but repeated the process 20 times to assess model stability due to much smaller sample sizes. Five machine learning methods were considered: Ridge regression, Least Absolute Shrinkage and Selection Operator (LASSO), Gradient Boosting (GBM), Random Forests (RF), and Support Vector Machines (SVM), all within a regression setting. Inputs to the models were meteorology and either the MISR AOD products or the 74 MISR AOD mixtures. The optimal model for each pollutant was chosen based on its test^R2as the primary metric and its test RMSE as the secondary metric. We further supplemented the model with geospatial (coordinates of MISR pixels projected to UTM zone 11) and temporal (Julian date and month) predictors. The best predicting model for each pollutant was trained on the full dataset prior to estimating exposures for the epidemiological assessment.

2.6. Epidemiological Methods

To examine the association between air pollution exposure and lung function during the children’s critical period of development, we focused on pulmonary function tests (FEV₁and FVC) taken at ages 15–16 (2011–2012). During each assessment visit, the study participants reported their current addresses, which were geocoded. We identified MISR aerosol optical depth and gridMET meteorological data within 4.4 km of each study participant’s residence for the 12 months prior to their pulmonary function test, and predicted PM_2.5, NO₃₋, SO₄₂₋, EC, and dust concentrations spatially and temporally specific to each child. The 12-month means of these air pollutant estimates were assigned as exposures of interest for each child.

We fitted single-pollutant models to examine the effects of each predicted air pollutant exposure on lung function. Multi-pollutant models were also fitted to assess whether multiple predicted exposures better informed the associations of interest. Using mixed-effects models, we adjusted for study community with a random intercept and for biological characteristics such as age, gender, height, BMI, and race/ethnicity as fixed effects. Based on previous studies of the CHS, log transformation of FEV₁ and FVC as well as quadratic terms for height and BMI were considered [1,3,4,6]. We separately fitted similar models with central-site PM_2.5to compare with MISR-derived PM_2.5models. Akaike information criterion (AIC) was used as the main metric for model comparison and Bayesian information criterion (BIC), which favors more parsimonious models, as the secondary metric. Generalized variance inflation factor was calculated to assess potential collinearity among the predictors (using GVIF≤10as the cutoff).

3. Results 3.1. Exposure Estimation

From March 2000 through December 2018, there were 2828 days where MISR had complete retrievals of AOD products that could be matched to EPA-PM_2.5stations, resulting in a dataset ofN≈56,000. In the same period, there were 2864 days where MISR had complete retrievals of AOD mixtures that could be matched to EPA-PM_2.5stations, resulting in a dataset ofN≈35,000. While there were more days with successful AOD mixtures retrieved, the AOD-products datasets was larger because AOD-products were successfully retrieved in more locations per day compared to AOD mixtures. In a similar time frame (ending in July 2018), MISR had 698 days with AOD-products data and 544 days with AOD-mixtures data that were matched to PM_2.5speciation concentrations from CSN sites in California, resulting in datasets of sizesN=1965for models using AOD products andN=1090for models using AOD mixtures.

Non-linear machine learning methods (GBM, RF, and SVM) generally predicted better than linear methods (ridge and LASSO) for all exposures of interest and for both AOD-products and AOD-mixtures models. Although the AOD-mixtures models for PM_2.5had a smaller training dataset, the increase in model complexity with 74 AOD mixtures versus 6 AOD products led to much more time-expensive model fitting. We experimented with a smaller sample size (N=8000) but the AOD-mixtures models did not perform better than the AOD-products models. The GBM model using AOD products had the best performance for PM_2.5(^R2=0.68 ) (Table 1).

The boxplots of test^R2 (Figure A3) for PM_2.5speciation models show that AOD-products models outperformed AOD-mixtures models and did so consistently (narrower boxplots) with the exception of dust, where AOD-mixtures models performed better. The best test^R2for SO₄₂₋and NO₃₋ was 0.71, EC 0.63, and dust 0.53 (Table 1). The most important variables for PM_2.5 include an interpretable mix of AOD, small and medium AOD, as well as meteorological variables (surface shortwave radiation, wind speed, and temperature) (Figure 2). Similar variables were important for SO₄₂₋and NO₃₋ , but nonspherical AOD played a larger role in both (Figure 3). Interestingly, AODs only ranked 8th and 9th for EC, with meteorology and temporal indicators playing a larger role in its prediction. Finally, most important for dust were AOD mixtures relating primarily to dust (mixtures 70 and 53) and non-absorbing (mixtures 4, 12, 13, 19, 21) particles.

MISR AOD-products were used to predict all exposures except for dust, which was predicted using MISR AOD mixtures. Figure 4 and Figure 5 show 6-year (2007–2012) means of the predicted air pollution exposures over greater Los Angeles and Santa Barbara. Over this period the correlation between annual mean MISR-derived PM_2.5and PM_2.5speciation with its central-site counterpart were positive and statistically significant, with Spearmanr=0.68andp<0.001 (Table A1).

3.2. Health Outcomes

There were 1206 children assessed in 2011–2012 with mean age 15.2 (SD=0.6 ) years, whose geocoded addresses were linked with MISR-derived exposure estimates. Summary statistics of the study population, including gender, height, weight, race/ethnicity, and study community, are shown in Table 2. Both FEV₁and FVC were significantly higher among boys compared to girls; mean FEV₁was 4111 mL among boys and 3304 mL among girls (t-testp<0.001), mean FVC was 4801 mL among boys and 3752 mL among girls (t-testp<0.001). Among race/ethnicity groups, there were significant differences in mean FEV₁and FVC with one-way ANOVA testp=0.003andp<0.001 , respectively (Table A2). The associations between biological factors and lung function remained significant when evaluated in fully adjusted models (Table A3).

In single-pollutant mixed models adjusted for biological characteristics, MISR-derived PM_2.5, SO₄₂₋, and dust were significantly associated with decreases in FEV₁, and SO₄₂₋and NO₃₋were significantly associated with decreases in FVC. Notably, central-site PM_2.5 was not associated with either lung function measure (Table 3). Among multi-pollutant models, we did not find any grouping of two or more pollutants to be significantly associated with FEV₁or FVC. Furthermore, while certain multi-pollutant models achieved modest reductions in AIC, none outperformed single-pollutant models in BIC. Log transformation of the lung function measures, with slight improvements in normality on Q-Q plots, did not improve model fit for either outcomes. Collinearity was not an issue in any of the models (all GVIF≤3).

Effect estimates in Table 3 represent the difference in mean FEV₁and FVC between the highest and the lowest exposure level for each pollutant in single-pollutant models. After accounting for biological and community effects in single-pollutant mixed models, FEV₁on average decreased by 131 mL (95% CI:−232,−35) per 10.7-µg/m³increase in PM_2.5, by 158 mL (95% CI:−273,−43) per 1.2-µg/m³in SO₄₂₋, and by 177 mL (95% CI:−306,−56) per 1.6-µg/m³increase in dust. Meanwhile, FVC on average decreased by 175 mL (95% CI:−310,−29) per 1.2-µg/m³increase in SO₄₂₋and by 212 mL (95% CI:−391,−28) per 2.5-µg/m³increase in NO₃₋.

4. Discussion

In this study, we used satellite observations of AOD, characterized by size, shape, and absorption properties as well as fractionated into 74 mixtures, to estimate PM_2.5and select PM_2.5 chemical components. We then incorporated these estimates into an epidemiological assessment of their association with children’s lung function. In terms of exposure estimation, MISR AOD products resulted in better and more robust estimates than did AOD mixtures, except for dust. Non-linear models (GBM, RF, and SVM) performed better than linear models (Ridge and LASSO), which was consistent with previous studies where linear models were inadequate in explaining the relationship between AOD and ground-monitored PM [22,35,46]. Although MISR aerosol data have coarser temporal and spatial resolution compared to MAIAC (every 3–5 days vs. daily and 4.4 km vs. 1 km, respectively), our model achieved high prediction performance (Table 1) using MISR-specific data products on size, shape, and absorption, which proved vital in the prediction models. At least two MISR AOD products were among the five most important features for predicting PM_2.5, SO₄₂₋, and NO₃₋ , and the ten most important features for predicting dust were all AOD mixtures (Figure 2 and Figure 3).

Our PM_2.5 prediction performance was similar to those by Sorek-Hamer et al. [46], who modeled PM_2.5using AOD data from MODIS (Collection 5 Level 2) and the Ozone Monitoring Instrument (OMI) at 10-km resolution over the Central Valley in California. In Southern California, we improved upon previous work that predicted PM_2.5, SO₄₂₋, and NO₃₋ by Franklin et al. [22], whose meteorological data from NOAA weather stations did not provide the spatial coverage of gridMET data. Surface shortwave radiation provided by gridMET was also among the most important predictors for PM_2.5, NO₃₋ , and EC (Figure 2 and Figure 3). Our PM_2.5, SO₄₂₋, NO₃₋ , and EC models performed comparably to those by Meng et al. [23], who reconstructed fractional AOD using the AOD mixtures (V22) while we relied on MISR AOD products (V23).

One limitation in our PM_2.5speciation prediction models is the scarcity of data. As the number of CSN sites in California increased from 3 in 2000 to 19 in 2013 and decreased to 16 in 2018 (California PM_2.5 mass sites increased from 95 in 2000 to 157 in 2018), spatial coverage was certainly restricted (Figure A1). Furthermore, the locations of these sparsely available monitors are not necessarily representative of the population density of Southern California. We used the coordinates of MISR pixels, which lacked a fixed grid, instead of monitoring sites as geospatial predictors to help mitigate this problem by introducing additional spatial variability. We also attempted prediction models for PM₁₀and SO₂ (detailed results not reported here) to compare with our previous work over Mongolia [35]. While PM₁₀models performed about the same, i.e., average test^R2<0.10, SO₂models over California performed much worse, with average test^R2<0.10(vs. test^R2>0.45over Ulaanbaatar). Poor prediction performance for SO₂was likely due to much lower concentrations of SO₂in California (mean SO₂=2.4ppb in 2000–2018) compared to Ulaanbaatar (9.7 ppb in 2008–2017), where SO₂is the more dominant source of PM. For the epidemiological purposes of the current study, we focused on models predicting PM_2.5and its chemical components.

This study is unique in estimating air pollution exposure specifically to the residence and follow-up period of each subject. Previously, exposures were assigned using annual means from one central air pollution monitoring site for each study community [5,6,42], even if the children might have lived far away from these sites. Furthermore, the follow-up period spanned about 211 days, yet the annual means of central-site air pollutants for each community were calculated using a fixed time window. Leveraging MISR aerosol and gridMET meteorological data, we improved upon these limitations by assigning exposures that were spatially within 4.4 km of where each child lived and temporally specific to the 12 months prior to each child’s assessment visit. Nevertheless, our exposure prediction models are not without unexplained residual variance; our best models had CV R²from 0.53 (dust) to 0.71 (SO₄₂₋, NO₃₋ ). As noted by Alexeeff et al. [10], there can be 1–5% upward bias in subsequent health effects estimates when exposure predictions have performance statistics in the range we observed, and their standard errors may be underestimated. It is difficult to mitigate these issues due to imperfect exposure models, but it is worth keeping in mind while interpreting our epidemiological results.

While this is not the first study using AOD-derived PM_2.5concentration in an epidemiological context, it is the first examining satellite-derived PM_2.5speciation. Previous studies of AOD-derived PM_2.5 include Rice et al. [27], who found that each 2 µg/m³increase in AOD-derived PM_2.5was associated with a 28 mL (−43.9to 0.2 mL) decrease in forced vital capacity (FVC) and higher odds of forced expiratory volume in 1 second (FEV₁) being less than 80% predicted (OR=1.41, 1.03 to 1.93). In another study, AOD-derived PM_2.5concentrations were associated with an increased rate of asthma onset (HR=1.31 , 1.28 to 1.33) in Quebec [28].

Similar to previous evaluations of the CHS [3,5,6], biological characteristics (age, gender, race/ethnicity, height, height squared, BMI, and BMI squared) were significantly associated with both measurements of lung function (Table A3). With these adjustments, several MISR-derived estimates of air pollutants were able to explain the residual differences in lung function measurements among the children. Another strength of this study is identifying the effect of specific PM_2.5chemical components on lung function. While MISR-derived PM_2.5was significantly associated with decreases in FEV₁, its effect, measured as the difference in FEV₁between the highest and lowest exposure level for each pollutant, is smaller than those of SO₄₂₋ and dust (Table 3). Similarly, although FVC was only marginally statistically significantly associated with MISR-derived PM_2.5, its associations with SO₄₂₋and NO₃₋were clinically significant. In California, secondary aerosols including nitrate and sulfate have been shown to be the most abundant contributors to ambient PM_2.5 , with nitrate accounting for as much as 55% of the total mass [47]. Geologic dust can also contribute up to 20% of the mass in summer in more arid regions of Southern California. Importantly, we were able to distinguish that these PM_2.5species had differentially stronger associations with children’s FEV₁and FVC.

Urman et al. [3], who examined the cross-sectional effect of central-site air pollution on lung function in the same cohort but at an earlier visit when the children were at ages 11–12, found central-site PM_2.5to be significant with both log-transformed FEV₁and FVC. In our study, we did not find central-site PM_2.5to be significant with either outcomes. We did find MISR-derived PM_2.5to be significantly associated with log-transformed FEV₁, with a similar effect size, but not with log-transformed FVC. Children exposed to the highest level of MISR-derived PM_2.5on average were−3.54%(95% CI:−6.24%,−0.49%) lower in FEV₁ compared to those exposed to the lowest level. In the same CHS cohort and during the same follow-up period, Franklin and Fruin [4] found a significant association between NO_xon FVC when adjusted for traffic-related noise exposure. We found a similarly significant relationship between NO₃₋and FVC where an IQR increase in NO₃₋(0.64 µg/m³) is associated with 53 mL decrease in FVC (95% CI:−98,−7).

5. Conclusions

We have shown in this study that MISR AOD observations distinguishing size, shape, absorption and mixture properties can aid in predicting PM_2.5and its chemical speciation including SO₄₂₋, NO₃₋, EC, and dust, particularly when supplemented with spatio-temporal information and high-resolution meteorological data. Machine learning methods such as Gradient Boosting and Support Vector Machines were more suitable for characterizing the non-linear relationships between air pollutants and AOD. We further showed that different MISR-derived PM_2.5composition, estimated specifically to the residence and follow-up period of CHS study participants, were able to explain clinically significant differences in lung function measurements FEV₁and FVC. This demonstrates that satellite-observed aerosol data products can be incorporated to strengthen epidemiological studies investigating the health effects of environmental pollution. Epidemiological assessments will only be made more viable, particularly as the quality of remote sensing data and estimation models continue to improve and exposure measurement error decreases.

Figure 1. Map of the Children's Health Study participants who were recruited in Santa Barbara (left) and other communities (right).

Figure 2. Relative importance of the ten most important features in model predicting PM2.5using gradient boosting.

Figure 3. Relative importance of the ten most important features in models predicting SO42-(top left), NO3-(top right), EC (bottom left), and dust (bottom right). Feature importance were calculated using relative influences in GBM models (PM2.5, SO42- , and EC) [44] and global sensitivity analysis in SVM models (NO3- and dust) [45]. SSR: surface shortwave radiation; ERC: energy release component.

Figure 4. Maps of mean MISR-derived PM2.5(µg/m3) from 2007 through 2012 over Santa Barbara (left) and other communities in Southern California (right).

Figure 5. Maps of mean MISR-derived SO42-(top left), NO3-(top right), EC (bottom left), and dust (bottom right) from 2007 through 2012, all measured in µg/m3over Santa Barbara (left in each) and other communities in Southern California (right in each).

Exposure	ML Method	MISR Predictors	Best Test R2	Best Test RMSE (µg/m3)
PM2.5	GBM	AOD Products	0.68	4.75
SO42−	GBM	AOD Products	0.71	0.69
NO3−	SVM	AOD Products	0.71	2.13
EC	GBM	AOD Products	0.63	0.37
Dust	SVM	AOD Mixtures	0.53	0.60

	Mean (SD)		N (%)
N	1206	Gender
Lung function		Boys	588 (48.8)
Forced Vital Capacity (mL)	4265 (856)	Girls	618 (51.2)
Forced Expiratory Volume (mL)	3700 (703)	Race/Ethnicity
Exposures (µg/m3)		White	413 (34.2)
Central site		Asian	54 (4.5)
PM2.5	11.99 (2.9)	Black	23 (1.9)
MISR-derived		Hispanic	617 (51.2)
PM2.5	14.22 (2.6)	Others	99 (8.2)
Sulfate	1.37 (0.3)	Community
Nitrate	2.36 (0.4)	Anaheim	113 (9.4)
EC	0.82 (0.1)	Glendora	228 (18.9)
Dust	1.12 (0.3)	Long Beach	56 (4.6)
Demographics		Mira Loma	146 (12.1)
Age	15.24 (0.6)	Riverside	138 (11.4)
Height (cm)	166.20 (8.6)	San Dimas	170 (14.1)
Weight (kg)	64.41 (15.8)	Santa Barbara	186 (15.4)
BMI (kg/m2)	23.23 (5.0)	Upland	169 (14.0)

		FEV1			FVC
Source	Exposure	Estimate *	95% CI	p	Estimate *	95% CI	p
Central site	PM2.5	−41	(−161,80)	0.521	−21	(−162,119)	0.775
MISR-derived	PM2.5	−131	(−232,−35)	0.013	−122	(−260,25)	0.103
	SO42−	−158	(−273,−43)	0.008	−175	(−310,−29)	0.015
	NO3−	−75	(−265,124)	0.447	−212	(−391,−28)	0.026
	EC	−161	(−446,128)	0.289	−218	(−547,106)	0.206
	Dust	−177	(−306,−56)	0.011	−106	(−305,95)	0.316

* Effect estimates are the difference in FEV₁and FVC from the highest to the lowest concentration of each air pollutant in µg/m³ : [Central sites] PM_2.5 : 8.9; [MISR-derived] PM_2.5: 10.7, SO₄₂₋: 1.2, NO₃₋ : 2.5, EC: 0.6, dust: 1.6.

Author Contributions

Conceptualization, M.F.; methodology, K.C, M.F., W.J.G.; formal analysis, K.C.; data curation, K.C., M.F.; writing-original draft preparation, K.C., M.F.; writing-review and editing, K.C., M.F., W.J.G.; supervision, M.F.; project administration, M.F.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Aeronautics and Space Administration grant number NNN12AA01C and under contract to the Health Effects Institute (HEI), an organization jointly funded by the United States Environmental Protection Agency (EPA) (Assistance Award No. CR-83590201) and certain motor vehicle and engine manufacturers.

Acknowledgments

The authors would like to thank Olga Kalashnikova, Michael Garay, and Dave Diner from the Jet Propulsion Laboratory's MISR team for their guidance and support.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:

ANOVA	Analysis of variance
AOD	Aerosol Optical Depth
CHS	Children's Health Study
CSN	Chemical Speciation Network
FEV1	Forced expiratory volume in 1 second
FVC	Forced vital capacity
MAIAC	MultiAngle Implementation of Atmospheric Correction
MISR	Multiangle Imaging SpectroRadiometer
MODIS	MoDerate resolution Imaging Spectroradiometer
NO3-	Nitrate
NO2	Nitrogen Dioxide
NOx	Nitrogen Oxides
PM	Particulate Matter
PM2.5	Particulate Matter with aerodynamic diameter ≤ 2.5μm
PM10	Particulate Matter with aerodynamic diameter ≤ 10μm
PM	Particulate Matter
SO42-	Sulfate

Appendix A

Table

Table A1.Spearman correlation coefficients for annual means of air pollution exposures during the three CHS follow-up periods (2007-2012).

Table A1.Spearman correlation coefficients for annual means of air pollution exposures during the three CHS follow-up periods (2007-2012).

	PM2.5*	PM2.5	SO42-	NO3-	EC	Dust
PM2.5*	1	0.68	0.55	0.34	0.29	0.68
PM2.5		1	0.74	0.59	0.15	0.38
SO42-			1	0.92	0.29	0.14
NO3-				1	0.37	-0.12
EC					1	0.32
Dust						1

^*Central-site PM_2.5;MISR-derived PM_2.5.

Table

Table A2.Mean (SD) of FEV₁and FVC by gender and race/ethnicity.

Table A2.Mean (SD) of FEV₁and FVC by gender and race/ethnicity.

	FEV1	FVC
Gender
Female	3304 (453)	3752 (537)
Male	4111 (680)	4801 (797)
Race/Ethnicity
White	3746 (665)	4377 (831)
Asian	3492 (635)	3892 (746)
Black	3271 (548)	3777 (683)
Hispanic	3715 (733)	4258 (884)
Others	3634 (688)	4162 (774)

Table

Table A3.Effect estimates of biological characteristics on lung function in mixed models without air pollution exposure.

Table A3.Effect estimates of biological characteristics on lung function in mixed models without air pollution exposure.

	FEV1		FVC
Characteristic	Estimate	95% CI	Estimate	95% CI
Age (year)	121	(-81,162)	112	(67,156)
Gender
Girls	(ref.)	-	(ref.)	-
Boys	317	(-256,378)	440	(372,506)
Race/ethnicity
White	(ref.)	-	(ref.)	-
Asian	-21	(-145,102)	-188	(-323,-54)
Black	-331	(-513,-151)	-454	(-653,-254)
Hispanic	96	(-38,154)	40	(-24,103)
Others	52	(-42,146)	-12	(-115,91)
Height (cm)	47	(44,51)	59	(55,63)
Height2	0.3	(0.0,0.5)	0.5	(0.3,0.8)
BMI (kg/m2)	35.7	(29,42)	49	(42,56)
BMI2	-2.4	(-3.0,-1.8)	-2.8	(-3.5,-2.2)

Remotesensing 12 01028 g0a1 550

Figure A1.Maps of EPA PM2.5 monitoring sites (left) and CSN monitoring sites (right) from 2000 to 2018.

Figure A1.Maps of EPA PM2.5 monitoring sites (left) and CSN monitoring sites (right) from 2000 to 2018.

Remotesensing 12 01028 g0a2 550

Figure A2.Maps of EPA PM2.5 monitoring sites and the MISR pixels within 4.4 km of each site, in Santa Barbara (left) and other communities (right).

Figure A2.Maps of EPA PM2.5 monitoring sites and the MISR pixels within 4.4 km of each site, in Santa Barbara (left) and other communities (right).

Remotesensing 12 01028 g0a3 550

Figure A3.Boxplots of test R²from 20 iterations of each machine learning method applied to predicting PM_2.5speciation, in AOD-products (coral) and AOD-mixtures (teal) models.

Figure A3.Boxplots of test R²from 20 iterations of each machine learning method applied to predicting PM_2.5speciation, in AOD-products (coral) and AOD-mixtures (teal) models.

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