Summary Background Long-term exposure to fine particulate matter less than 2·5 μm in diameter (PM2·5 ) and traffic-related air pollutant concentrations are associated with cardiovascular risk. The ...disease process underlying these associations remains uncertain. We aim to assess association between long-term exposure to ambient air pollution and progression of coronary artery calcium and common carotid artery intima-media thickness. Methods In this prospective 10-year cohort study, we repeatedly measured coronary artery calcium by CT in 6795 participants aged 45–84 years enrolled in the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) in six metropolitan areas in the USA. Repeated scans were done for nearly all participants between 2002 and 2005, for a subset of participants between 2005 and 2007, and for half of all participants between 2010 and 2012. Common carotid artery intima-media thickness was measured by ultrasound in all participants at baseline and in 2010–12 for 3459 participants. Residence-specific spatio-temporal pollution concentration models, incorporating community-specific measurements, agency monitoring data, and geographical predictors, estimated concentrations of PM2·5 and nitrogen oxides (NOX ) between 1999 and 2012. The primary aim was to examine the association between both progression of coronary artery calcium and mean carotid artery intima-media thickness and long-term exposure to ambient air pollutant concentrations (PM2·5 , NOX , and black carbon) between examinations and within the six metropolitan areas, adjusting for baseline age, sex, ethnicity, socioeconomic characteristics, cardiovascular risk factors, site, and CT scanner technology. Findings In this population, coronary calcium increased on average by 24 Agatston units per year (SD 58), and intima-media thickness by 12 μm per year (10), before adjusting for risk factors or air pollutant exposures. Participant-specific pollutant concentrations averaged over the years 2000–10 ranged from 9·2–22·6 μg PM2·5 /m3 and 7·2–139·2 parts per billion (ppb) NOX . For each 5 μg PM2·5 /m3 increase, coronary calcium progressed by 4·1 Agatston units per year (95% CI 1·4–6·8) and for each 40 ppb NOX coronary calcium progressed by 4·8 Agatston units per year (0·9–8·7). Pollutant exposures were not associated with intima-media thickness change. The estimate for the effect of a 5 μg/m3 higher long-term exposure to PM2·5 in intima-media thickness was −0·9 μm per year (95% CI −3·0 to 1·3). For 40 ppb higher NOX , the estimate was 0·2 μm per year (−1·9 to 2·4). Interpretation Increased concentrations of PM2·5 and traffic-related air pollution within metropolitan areas, in ranges commonly encountered worldwide, are associated with progression in coronary calcification, consistent with acceleration of atherosclerosis. This study supports the case for global efforts of pollution reduction in prevention of cardiovascular diseases. Funding US Environmental Protection Agency and US National Institutes of Health.
•Low-cost sensors for fine particulate are appealing for use in epidemiology.•Different sensors have different performance characteristics.•With adequate calibration and caveats, one model's results ...are precise and reliable.•Calibration should be performed cautiously, preferably using region-specific model.•Low-cost sensors may be useful for exposure assessment in epidemiological studies.
Low-cost air monitoring sensors are an appealing tool for assessing pollutants in environmental studies. Portable low-cost sensors hold promise to expand temporal and spatial coverage of air quality information. However, researchers have reported challenges in these sensors′ operational quality. We evaluated the performance characteristics of two widely used sensors, the Plantower PMS A003 and Shinyei PPD42NS, for measuring fine particulate matter compared to reference methods, and developed regional calibration models for the Los Angeles, Chicago, New York, Baltimore, Minneapolis-St. Paul, Winston-Salem and Seattle metropolitan areas. Duplicate Plantower PMS A003 sensors demonstrated a high level of precision (averaged Pearson′s r = 0.99), and compared with regulatory instruments, showed good accuracy (cross-validated R2 = 0.96, RMSE = 1.15 µg/m3 for daily averaged PM2.5 estimates in the Seattle region). Shinyei PPD42NS sensor results had lower precision (Pearson′s r = 0.84) and accuracy (cross-validated R2 = 0.40, RMSE = 4.49 µg/m3). Region-specific Plantower PMS A003 models, calibrated with regulatory instruments and adjusted for temperature and relative humidity, demonstrated acceptable performance metrics for daily average measurements in the other six regions (R2 = 0.74–0.95, RMSE = 2.46–0.84 µg/m3). Applying the Seattle model to the other regions resulted in decreased performance (R2 = 0.67–0.84, RMSE = 3.41–1.67 µg/m3), likely due to differences in meteorological conditions and particle sources. We describean approach to metropolitan region-specific calibration models for low-cost sensors that can be used with cautionfor exposure measurement in epidemiological studies.
•High-resolution PM2.5 models were developed with low-cost PurpleAir measurements.•An independent dataset was used to validate model performance at cohort locations.•A PCA-based similarity metric was ...proposed to guide low-cost monitor deployment.•Model improvement was observed after incorporating low-cost PurpleAir measurements.•PurpleAir monitors with higher PCA similarity resulted in larger model improvement.
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.
Growing evidence shows ultrafine particles (UFPs) are detrimental to cardiovascular, cerebrovascular, and respiratory health. Historically, racialized and low-income communities are exposed to higher ...concentrations of air pollution.
Our aim was to conduct a descriptive analysis of present-day air pollution exposure disparities in the greater Seattle, Washington, area by income, race, ethnicity, and historical redlining grade. We focused on UFPs (particle number count) and compared with black carbon, nitrogen dioxide, and fine particulate matter (
) levels.
We obtained race and ethnicity data from the 2010 U.S. Census, median household income data from the 2006-2010 American Community Survey, and Home Owners' Loan Corporation (HOLC) redlining data from the University of Richmond's Mapping Inequality. We predicted pollutant concentrations at block centroids from 2019 mobile monitoring data. The study region encompassed much of urban Seattle, with redlining analyses restricted to a smaller region. To analyze disparities, we calculated population-weighted mean exposures and regression analyses using a generalized estimating equation model to account for spatial correlation.
Pollutant concentrations and disparities were largest for blocks with median household income of
, Black residents, HOLC Grade D, and ungraded industrial areas. UFP concentrations were 4% lower than average for non-Hispanic White residents and higher than average for racialized groups (Asian, 3%; Black, 15%; Hispanic, 6%; Native American, 8%; Pacific Islander, 11%). For blocks with median household incomes of
, UFP concentrations were 40% higher than average, whereas blocks with incomes of
had UFP concentrations 16% lower than average. UFP concentrations were 28% higher for Grade D and 49% higher for ungraded industrial areas compared with Grade A. Disparities were highest for UFPs and lowest for
exposure levels.
Our study is one of the first to highlight large disparities with UFP exposures compared with multiple pollutants. Higher exposures to multiple air pollutants and their cumulative effects disproportionately impact historically marginalized groups. https://doi.org/10.1289/EHP11662.
Accurately predicting ambient NO2 concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach ...incorporating traffic contribution to NO2 prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO2 models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R2 from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R2 increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO2 concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO2 exposure – especially near major roads in the U.S.
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•An advanced national-level traffic model based on point-level traffic volume data•Traffic volume estimations and meteorological parameters for RLINE input•NO2 exposure assessment incorporated RLINE into an advanced spatiotemporal model•RLINE boosted model performance, especially for roadside sites
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.
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•Addition of low-cost monitoring (LCM) sites can improve PM2.5 model accuracy.•Use of LCM designs with longer time gaps optimizes spatiotemporal PM2.5 prediction.•Deployment of LCMs in populated, road-proximate areas enhances PM2.5 modeling.
We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large ...epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO
; NO2-B43F), and oxidizing gases (OX-B431)-which refers to ozone (O
) and NO
. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R
measures (CO: RMSE = 18 ppb, R
= 0.97; NO: RMSE = 2 ppb, R
= 0.97). Performance measures for NO
and O
were somewhat lower (NO
: RMSE = 3 ppb, R
= 0.79; O
: RMSE = 4 ppb, R
= 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.
It is not known if ultrasound carotid plaque features are associated with cardiovascular disease (CVD) risk factors or if they predict future CVD events.
We measured total carotid plaque area (TPA) ...and grayscale plaque features (grayscale median, black areas, and discrete white areas) by B-mode carotid ultrasound among 2205 participants who participated in the first (baseline) visit of the Multi-Ethnic Study of Atherosclerosis. Multivariable linear regression was used to examine relationships between ultrasound plaque features and CVD risk factors at baseline. Cox proportional hazards models were used to assess if TPA, grayscale features, and carotid plaque score (number of arterial segments with a plaque) could predict incident coronary heart disease and cerebrovascular disease events over a mean follow-up of 13.3 years.
Participants were mean (standard deviation SD) 65.4 (9.6) years, 49% male, 39% White, 11% Chinese, 28% Black, and 22% Hispanic. Mean TPA 27.7 (24.7) mm2, but no grayscale plaque features, was associated with CVD risk factors. In fully adjusted models, TPA but no grayscale features predicted incident coronary heart disease (CHD) events (HR 1.23; 95%CI 1.11–1.36; p<0.001), however, C-statistics for CHD were similar to carotid plaque score but less than for coronary artery calcium (CAC) scoring. Neither TPA nor grayscale features independently predicted cerebrovascular events.
In middle-aged adults free of known cardiovascular disease, TPA but not grayscale plaque features was associated with CVD risk factors and predicted incident CHD events. For CHD, prediction indices for TPA were similar to carotid plaque score but less than for CAC.
In middle-aged individuals free of known cardiovascular disease;•Total plaque area was associated with CHD risk factors and predicted incident CHD events.•Grayscale carotid plaque features did not predict incident CHD events.•For CHD, predictive characteristics of TPA were similar to carotid plaque score, but less than CAC score.
Black adults have worse health outcomes compared with white adults in certain chronic diseases, including chronic obstructive pulmonary disease (COPD).
To determine to what degree disadvantage by ...individual and neighborhood socioeconomic status (SES) may contribute to racial disparities in COPD outcomes.
Individual and neighborhood-scale sociodemographic characteristics were determined in 2,649 current or former adult smokers with and without COPD at recruitment into SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study). We assessed whether racial differences in symptom, functional, and imaging outcomes (St. George's Respiratory Questionnaire, COPD Assessment Test score, modified Medical Research Council dyspnea scale, 6-minute-walk test distance, and computed tomography CT scan metrics) and severe exacerbation risk were explained by individual or neighborhood SES. Using generalized linear mixed model regression, we compared respiratory outcomes by race, adjusting for confounders and individual-level and neighborhood-level descriptors of SES both separately and sequentially.
After adjusting for COPD risk factors, Black participants had significantly worse respiratory symptoms and quality of life (modified Medical Research Council scale, COPD Assessment Test, and St. George's Respiratory Questionnaire), higher risk of severe exacerbations and higher percentage of emphysema, thicker airways (internal perimeter of 10 mm), and more air trapping on CT metrics compared with white participants. In addition, the association between Black race and respiratory outcomes was attenuated but remained statistically significant after adjusting for individual-level SES, which explained up to 12-35% of racial disparities. Further adjustment showed that neighborhood-level SES explained another 26-54% of the racial disparities in respiratory outcomes. Even after accounting for both individual and neighborhood SES factors, Black individuals continued to have increased severe exacerbation risk and persistently worse CT outcomes (emphysema, air trapping, and airway wall thickness).
Disadvantages by individual- and neighborhood-level SES each partly explain disparities in respiratory outcomes between Black individuals and white individuals. Strategies to narrow the gap in SES disadvantages may help to reduce race-related health disparities in COPD; however, further work is needed to identify additional risk factors contributing to persistent disparities.
BACKGROUNDBased 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. OBJECTIVESTo develop a daily, high-resolution ambient CO exposure prediction model at the city scale. METHODSWe 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. RESULTSThe 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. DISCUSSIONThe 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.
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