Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

BACKGROUND: Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. OBJECTIVE: Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. METHODS: We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. RESULTS: The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV- R 2 = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and R 2 = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV- R 2 = 0.51 (with LCS). IMPACT: We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Evaluating low-cost monitoring designs for PM2.5 exposure assessment with a spatiotemporal modeling approach.

Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 µg/m3]) compared to the model with all LCM measurements (0.84 [0.9 µg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 µg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 µg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 µg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.

BACKGROUND: Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations. OBJECTIVES: To develop a daily, high-resolution ambient CO exposure prediction model at the city scale. METHODS: We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations. RESULTS: The CO model had spatial cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R2 and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads. DISCUSSION: The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889.

Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection.

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM2.5 in epidemiological studies. Using sparse regulatory PM2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models' accuracy in predicting PM2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort's residential locations). In this study, we used the PM2.5 measurements from a publicly available commercial low-cost PM2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 "gold-standard" monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R2 and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 µg/m3 to 0.92 and 1.63 µg/m3, respectively. The external spatial validation R2 and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 µg/m3 to 0.79 and 0.88 µg/m3, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model's prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.

Disparities in Air Pollution Exposure in the United States by Race/Ethnicity and Income, 1990-2010.

BACKGROUND: Few studies have investigated air pollution exposure disparities by race/ethnicity and income across criteria air pollutants, locations, or time. OBJECTIVE: The objective of this study was to quantify exposure disparities by race/ethnicity and income throughout the contiguous United States for six criteria air pollutants, during the period 1990 to 2010. METHODS: We quantified exposure disparities among racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic (any race), non-Hispanic Asian) and by income for multiple spatial units (contiguous United States, states, urban vs. rural areas) and years (1990, 2000, 2010) for carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter with aerodynamic diameter ≤2.5µm (PM2.5; excluding year-1990), particulate matter with aerodynamic diameter ≤10µm (PM10), and sulfur dioxide (SO2). We used census data for demographic information and a national empirical model for ambient air pollution levels. RESULTS: For all years and pollutants, the racial/ethnic group with the highest national average exposure was a racial/ethnic minority group. In 2010, the disparity between the racial/ethnic group with the highest vs. lowest national-average exposure was largest for NO2 [54% (4.6 ppb)], smallest for O3 [3.6% (1.6 ppb)], and intermediate for the remaining pollutants (13%-19%). The disparities varied by U.S. state; for example, for PM2.5 in 2010, exposures were at least 5% higher than average in 63% of states for non-Hispanic Black populations; in 33% and 26% of states for Hispanic and for non-Hispanic Asian populations, respectively; and in no states for non-Hispanic White populations. Absolute exposure disparities were larger among racial/ethnic groups than among income categories (range among pollutants: between 1.1 and 21 times larger). Over the period studied, national absolute racial/ethnic exposure disparities declined by between 35% (0.66µg/m3; PM2.5) and 88% (0.35 ppm; CO); relative disparities declined to between 0.99× (PM2.5; i.e., nearly zero change) and 0.71× (CO; i.e., a ∼29% reduction). DISCUSSION: As air pollution concentrations declined during the period 1990 to 2010, absolute (and to a lesser extent, relative) racial/ethnic exposure disparities also declined. However, in 2010, racial/ethnic exposure disparities remained across income levels, in urban and rural areas, and in all states, for multiple pollutants. https://doi.org/10.1289/EHP8584.

Prenatal air pollution and childhood IQ: Preliminary evidence of effect modification by folate.

OBJECTIVES: Animal studies suggest that air pollution is neurotoxic to a developing fetus, but evidence in humans is limited. We tested the hypothesis that higher air pollution is associated with lower child IQ and that effects vary by maternal and child characteristics, including prenatal nutrition. METHODS: We used prospective data collected from the Conditions Affecting Neurocognitive Development and Learning in Early Childhood study. Outdoor pollutant exposure during pregnancy was predicted at geocoded home addresses using a validated national universal kriging model that combines ground-based monitoring data with an extensive database of land-use covariates. Distance to nearest major roadway was also used as a proxy for traffic-related pollution. Our primary outcome was full-scale IQ measured at age 4-6. In regression models, we adjusted for multiple determinants of child neurodevelopment and assessed interactions between air pollutants and child sex, race, socioeconomic status, reported nutrition, and maternal plasma folate in second trimester. RESULTS: In our analytic sample (N = 1005) full-scale IQ averaged 2.5 points (95% CI: 0.1, 4.8) lower per 5 µg/m3 higher prenatal PM10, while no associations with nitrogen dioxide or road proximity were observed. Associations between PM10 and IQ were modified by maternal plasma folate (pinteraction = 0.07). In the lowest folate quartile, IQ decreased 6.8 points (95% CI: 1.4, 12.3) per 5-unit increase in PM10; no associations were observed in higher quartiles. CONCLUSIONS: Our findings strengthen evidence that air pollution impairs fetal neurodevelopment and suggest a potentially important role of maternal folate in modifying these effects.

Using smartphones to collect time-activity data for long-term personal-level air pollution exposure assessment.

Because of the spatiotemporal variability of people and air pollutants within cities, it is important to account for a person's movements over time when estimating personal air pollution exposure. This study aimed to examine the feasibility of using smartphones to collect personal-level time-activity data. Using Skyhook Wireless's hybrid geolocation module, we developed "Apolux" (Air, Pollution, Exposure), an Android(TM) smartphone application designed to track participants' location in 5-min intervals for 3 months. From 42 participants, we compared Apolux data with contemporaneous data from two self-reported, 24-h time-activity diaries. About three-fourths of measurements were collected within 5 min of each other (mean=74.14%), and 79% of participants reporting constantly powered-on smartphones (n=38) had a daily average data collection frequency of <10 min. Apolux's degree of temporal resolution varied across manufacturers, mobile networks, and the time of day that data collection occurred. The discrepancy between diary points and corresponding Apolux data was 342.3 m (Euclidian distance) and varied across mobile networks. This study's high compliance and feasibility for data collection demonstrates the potential for integrating smartphone-based time-activity data into long-term and large-scale air pollution exposure studies.

Air Pollution and the microvasculature: a cross-sectional assessment of in vivo retinal images in the population-based multi-ethnic study of atherosclerosis (MESA).

BACKGROUND: Long- and short-term exposures to air pollution, especially fine particulate matter (PM(2.5)), have been linked to cardiovascular morbidity and mortality. One hypothesized mechanism for these associations involves microvascular effects. Retinal photography provides a novel, in vivo approach to examine the association of air pollution with changes in the human microvasculature. METHODS AND FINDINGS: Chronic and acute associations between residential air pollution concentrations and retinal vessel diameters, expressed as central retinal arteriolar equivalents (CRAE) and central retinal venular equivalents (CRVE), were examined using digital retinal images taken in Multi-Ethnic Study of Atherosclerosis (MESA) participants between 2002 and 2003. Study participants (46 to 87 years of age) were without clinical cardiovascular disease at the baseline examination (2000-2002). Long-term outdoor concentrations of PM(2.5) were estimated at each participant's home for the 2 years preceding the clinical exam using a spatio-temporal model. Short-term concentrations were assigned using outdoor measurements on the day preceding the clinical exam. Residential proximity to roadways was also used as an indicator of long-term traffic exposures. All associations were examined using linear regression models adjusted for subject-specific age, sex, race/ethnicity, education, income, smoking status, alcohol use, physical activity, body mass index, family history of cardiovascular disease, diabetes status, serum cholesterol, glucose, blood pressure, emphysema, C-reactive protein, medication use, and fellow vessel diameter. Short-term associations were further controlled for weather and seasonality. Among the 4,607 participants with complete data, CRAE were found to be narrower among persons residing in regions with increased long- and short-term levels of PM(2.5). These relationships were observed in a joint exposure model with -0.8 µm (95% confidence interval [CI] -1.1 to -0.5) and -0.4 µm (95% CI -0.8 to 0.1) decreases in CRAE per interquartile increases in long- (3 µg/m(3)) and short-term (9 µg/m(3)) PM(2.5) levels, respectively. These reductions in CRAE are equivalent to 7- and 3-year increases in age in the same cohort. Similarly, living near a major road was also associated with a -0.7 µm decrease (95% CI -1.4 to 0.1) in CRAE. Although the chronic association with CRAE was largely influenced by differences in exposure between cities, this relationship was generally robust to control for city-level covariates and no significant differences were observed between cities. Wider CRVE were associated with living in areas of higher PM(2.5) concentrations, but these findings were less robust and not supported by the presence of consistent acute associations with PM(2.5). CONCLUSIONS: Residing in regions with higher air pollution concentrations and experiencing daily increases in air pollution were each associated with narrower retinal arteriolar diameters in older individuals. These findings support the hypothesis that important vascular phenomena are associated with small increases in short-term or long-term air pollution exposures, even at current exposure levels, and further corroborate reported associations between air pollution and the development and exacerbation of clinical cardiovascular disease. Please see later in the article for the Editors' Summary.