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Objective: The U.S. Environmental Protection Agency funded five academic centers in 1999 to address the uncertainties in exposure, toxicity, and health effects of airborne particulate matter (PM) identified in the 'Research Priorities for Airborne Particulate Matter' of the National Research Council (NRC). The centers were structured to promote interdisciplinary approaches to address research priorities of the NRC. In this report, we present selected accomplishments from the first 6 years of the PM Centers, with a focus on the advantages afforded by the interdisciplinary, center-based research approach. The review highlights advances in the area of ultrafine particles and traffic-related health effects as well as cardiovascular and respiratory effects, mechanisms, susceptibility, and PM exposure and characterization issues. Data sources and synthesis: The collective publications of the centers served as the data source. To provide a concise synthesis of overall findings, authors representing each of the five centers identified a limited number of topic areas that serve to illustrate the key accomplishments of the PM Centers program, and a consensus statement was developed. Conclusions: The PM Centers program has effectively applied interdisciplinary research approaches to advance PM science. [ABSTRACT FROM AUTHOR]

Background: Ultrafine particles (UFP) are unregulated air pollutants abundant in aviation exhaust. Emerging evidence suggests that UFPs may impact lung health due to their high surface area-to-mass ratio and deep penetration into airways. This study aimed to assess long-term exposure to airport-related UFPs and lung cancer incidence in a multiethnic population in Los Angeles County., Methods: Within the California Multiethnic Cohort, we examined the association between long-term exposure to airport-related UFPs and lung cancer incidence. Multivariable Cox proportional hazards regression models were used to estimate the effect of UFP exposure on lung cancer incidence. Subgroup analyses by demographics, histology and smoking status were conducted., Results: Airport-related UFP exposure was not associated with lung cancer risk [per one IGR HR, 1.01; 95% confidence interval (CI), 0.97-1.05] overall and across race/ethnicity. A suggestive positive association was observed between a one IQR increase in UFP exposure and lung squamous cell carcinoma (SCC) risk (HR, 1.08; 95% CI, 1.00-1.17) with a Phet for histology = 0.05. Positive associations were observed in 5-year lag analysis for SCC (HR, 1.12; 95% CI, CI, 1.02-1.22) and large cell carcinoma risk (HR, 1.23; 95% CI, 1.01-1.49) with a Phet for histology = 0.01., Conclusions: This large prospective cohort analysis suggests a potential association between airport-related UFP exposure and specific lung histologies. The findings align with research indicating that UFPs found in aviation exhaust may induce inflammatory and oxidative injury leading to SCC., Impact: These results highlight the potential role of airport-related UFP exposure in the development of lung SCC., (©2024 American Association for Cancer Research.)

Background: In contrast to fine particles, less is known of the inflammatory and coagulation impacts of coarse particulate matter ( PM 10 - 2.5 , particulate matter with aerodynamic diameter ≤ 10 μ m  and > 2.5 μ m ). Toxicological research suggests that these pathways might be important processes by which PM 10 - 2.5 impacts health, but there are relatively few epidemiological studies due to a lack of a national PM 10 - 2.5 monitoring network., Objectives: We used new spatiotemporal exposure models to examine associations of both 1-y and 1-month average PM 10 - 2.5 concentrations with markers of inflammation and coagulation., Methods: We leveraged data from 7,071 Multi-Ethnic Study of Atherosclerosis and ancillary study participants 45-84 y of age who had repeated plasma measures of inflammatory and coagulation biomarkers. We estimated PM 10 - 2.5 at participant addresses 1 y and 1 month before each of up to four exams (2000-2012) using spatiotemporal models that incorporated satellite, regulatory monitoring, and local geographic data and accounted for spatial correlation. We used random effects models to estimate associations with interleukin-6 (IL-6), C-reactive protein (CRP), fibrinogen, and D-dimer, controlling for potential confounders., Results: Increases in PM 10 - 2.5 were not associated with greater levels of inflammation or coagulation. A 10 - μ g / m 3 increase in annual average PM 10 - 2.5 was associated with a 2.5% decrease in CRP [95% confidence interval (CI): - 5.5 , 0.6]. We saw no association between annual average PM 10 - 2.5 and the other markers (IL-6: - 0.7 % , 95% CI: - 2.6 , 1.2; fibrinogen: - 0.3 % , 95% CI: - 0.9 , 0.3; D-dimer: - 0.2 % , 95% CI: - 2.6 , 2.4). Associations consistently showed that a 1 0 - μ g / m 3 increase in 1-month average PM 10 - 2.5 was associated with reduced inflammation and coagulation, though none were distinguishable from no association (IL-6: - 1.2 % , 95% CI: - 3.0   , 0.5; CRP: - 2.5 % , 95% CI: - 5.3 , 0.4; fibrinogen: - 0.4 % , 95% CI: - 1.0 , 0.1; D-dimer: - 2.0 % , 95% CI: - 4.3 , 0.3)., Discussion: We found no evidence that PM 10 - 2.5 is associated with higher inflammation or coagulation levels. More research is needed to determine whether the inflammation and coagulation pathways are as important in explaining observed PM 10 - 2.5 health impacts in humans as they have been shown to be in toxicology studies or whether PM 10 - 2.5 might impact human health through alternative biological mechanisms. https://doi.org/10.1289/EHP12972.

Background: While epidemiologic evidence links higher levels of exposure to fine particulate matter (PM 2.5 ) to decreased cognitive function, fewer studies have investigated links with traffic-related air pollution (TRAP), and none have examined ultrafine particles (UFP, ≤100 nm) and late-life dementia incidence., Objective: To evaluate associations between TRAP exposures (UFP, black carbon [BC], and nitrogen dioxide [NO 2 ]) and late-life dementia incidence., Methods: We ascertained dementia incidence in the Seattle-based Adult Changes in Thought (ACT) prospective cohort study (beginning in 1994) and assessed ten-year average TRAP exposures for each participant based on prediction models derived from an extensive mobile monitoring campaign. We applied Cox proportional hazards models to investigate TRAP exposure and dementia incidence using age as the time axis and further adjusting for sex, self-reported race, calendar year, education, socioeconomic status, PM 2.5 , and APOE genotype. We ran sensitivity analyses where we did not adjust for PM 2.5 and other sensitivity and secondary analyses where we adjusted for multiple pollutants, applied alternative exposure models (including total and size-specific UFP), modified the adjustment covariates, used calendar year as the time axis, assessed different exposure periods, dementia subtypes, and others., Results: We identified 1,041 incident all-cause dementia cases in 4,283 participants over 37,102 person-years of follow-up. We did not find evidence of a greater hazard of late-life dementia incidence with elevated levels of long-term TRAP exposures. The estimated hazard ratio of all-cause dementia was 0.98 (95 % CI: 0.92-1.05) for every 2000 pt/cm 3 increment in UFP, 0.95 (0.89-1.01) for every 100 ng/m 3 increment in BC, and 0.96 (0.91-1.02) for every 2 ppb increment in NO 2 . These findings were consistent across sensitivity and secondary analyses., Discussion: We did not find evidence of a greater hazard of late-life dementia risk with elevated long-term TRAP exposures in this population-based prospective cohort study., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Inhaled particles and gases can harm health by promoting chronic inflammation in the body. Few studies have investigated the relationship between outdoor air pollution and inflammation by race and ethnicity, socioeconomic status, and lifestyle risk factors. We examined associations of particulate matter (PM) and other markers of traffic-related air pollution with circulating levels of C-reactive protein (CRP), a biomarker of systemic inflammation. CRP was measured from blood samples obtained in 1994-2016 from 7,860 California residents participating in the Multiethnic Cohort (MEC) Study. Exposure to PM (aerodynamic diameter ≤2.5 μm [PM 2.5 ], ≤10 μm [PM 10 ], and between 2.5 and 10 μm [PM 10-2.5 ]), nitrogen oxides (NO x , including nitrogen dioxide [NO 2 ]), carbon monoxide (CO), ground-level ozone (O 3 ), and benzene averaged over one or twelve months before blood draw were estimated based on participants' addresses. Percent change in geometric mean CRP levels and 95% confidence intervals (CI) per standard concentration increase of each pollutant were estimated using multivariable generalized linear regression. Among 4,305 females (55%) and 3,555 males (45%) (mean age 68.1 [SD 7.5] years at blood draw), CRP levels increased with 12-month exposure to PM 10 (11.0%, 95% CI: 4.2%, 18.2% per 10 μg/m 3 ), PM 10-2.5 (12.4%, 95% CI: 1.4%, 24.5% per 10 μg/m 3 ), NO x (10.4%, 95% CI: 2.2%, 19.2% per 50 ppb), and benzene (2.9%, 95% CI: 1.1%, 4.6% per 1 ppb). In subgroup analyses, these associations were observed in Latino participants, those who lived in low socioeconomic neighborhoods, overweight or obese participants, and never or former smokers. No consistent patterns were found for 1-month pollutant exposures. This investigation identified associations of primarily traffic-related air pollutants, including PM, NO x , and benzene, with CRP in a multiethnic population. The diversity of the MEC across demographic, socioeconomic, and lifestyle factors allowed us to explore the generalizability of the effects of air pollution on inflammation across subgroups., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology.

Background: Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment., Objective: We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces., Methods: We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design's land use regression prediction model., Results: The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results., Significance: A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts., Impact Statement: Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms., Objectives: We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies., Methods: We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs., Results: Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility., Discussion: Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO 2 ), fine particulate matter (PM 2.5 ), and carbon dioxide (CO 2 ). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R 2 s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R 2 ) with ∼1000 to 3000 randomly selected stops for NO 2 , PNC, and BC, and ∼4000 to 5000 stops for PM 2.5 and CO 2 . Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.

The Healthy Air, Healthy Schools Study was established to better understand the impact of ultrafine particles (UFPs) on indoor air quality in communities surrounding Seattle-Tacoma (Sea-Tac) International Airport. The study team took multipollutant measurements of indoor and outdoor air pollution at five participating school locations to estimate infiltration indoors. The schools participating in this project were located within a 7-mile radius of Sea-Tac International Airport and within 0.5 mile of an active flight path. Based on experimental measures in an unoccupied classroom, infiltration rates of (a) UFPs of aircraft origin, (b) UFPs of traffic origin, and (c) wildfire smoke or other outdoor pollutants were characterized before and after the introduction of a portable high-efficiency particulate air (HEPA) filter intervention. The portable HEPA cleaners were an effective short-term intervention to improve the air quality in classroom environments, reducing the UFP count concentration from one-half to approximately one-tenth of that measured outside. This study is unique in focusing on UFPs in schools and demonstrating that UFPs measured in classroom spaces are primarily of outdoor origin. Although existing research suggests that reducing particulate matter in homes can significantly improve asthma outcomes, further investigation is necessary to establish the benefits to student health and academic performance of reducing UFP exposures in schools.

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 ( PM 2.5 , NO 2 , and NO x ) concentrations., Results: The CO model had spatial cross-validation (CV) R 2 and root-mean-square error (RMSE) of 0.70  and  0.02  parts per million (ppm), respectively; the model had temporal CV R 2 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.

Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We designed an innovative and extensive mobile monitoring campaign to characterize TRAP exposure levels for the Adult Changes in Thought (ACT) study, a Seattle-based cohort. The campaign measured particle number concentration (PNC) to capture ultrafine particles (UFP), black carbon (BC), nitrogen dioxide (NO 2 ), fine particulate matter (PM 2.5 ), and carbon dioxide (CO 2 ) at 309 roadside sites within a large, 1200 land km 2 (463 mi 2 ) area representative of the cohort. We collected about 29 two-minute measurements at each site during all seasons, days of the week, and most times of the day over a 1-year period. Validation showed good agreement between our BC, NO 2 , and PM 2.5 measurements and monitoring agency sites ( R 2 = 0.68-0.73). Universal kriging-partial least squares models of annual average pollutant concentrations had cross-validated mean square error-based R 2 (and root mean square error) values of 0.77 (1177 pt/cm 3 ) for PNC, 0.60 (102 ng/m 3 ) for BC, 0.77 (1.3 ppb) for NO 2 , 0.70 (0.3 μg/m 3 ) for PM 2.5 , and 0.51 (4.2 ppm) for CO 2 . Overall, we found that the design of this extensive campaign captured the spatial pollutant variations well and these were explained by sensible land use features, including those related to traffic.

Introduction: Air pollution has been linked to preterm birth (PTB) while findings for noise exposure have been mixed. Few studies - none considering airports - have investigated combined exposures. We explore the relationship between joint exposure to airport-related noise, airport ultrafine particles (UFP), and vehicle traffic-related air pollution (TRAP) on risk of PTB near Los Angeles International Airport (LAX)., Methods: We used comprehensive birth data for mothers living ≤15 km from LAX from 2008 to 2016 (n = 174,186) Noise data were generated by monitor-validated models. NO 2 was used as a TRAP proxy, estimated with a seasonally-adjusted, validated land-use regression model. We estimated the effects of exposure to airport-related noise and TRAP on PTB employing logistic regression models that adjusted for known maternal risk factors for PTB as well as aircraft-origin UFP and neighborhood characteristics., Results: The adjusted odds ratio (aOR) for PTB from high noise exposure (i.e. > 65 dB) was 1.10 (95% CI: 1.01-1.19). Relative to the first quartile, the aORs for PTB in the second, third, and fourth TRAP quartiles were 1.10 (95% CI: 1.05-1.16), 1.11 (95% CI: 1.05-1.16), and 1.15 (95% CI: 1.10-1.22), respectively. When stratifying by increasing TRAP quartiles, the aORs for PTB with high airport-related noise were 1.04 (95% CI: 0.91-1.18), 1.02 (95% CI: 0.88-1.19), 1.24 (95% CI: 1.03-1.48), and 1.44 (95% CI: 1.08-1.91) (p-interaction = 0.06)., Conclusion: Our results suggest a potential synergism between airport-related noise and TRAP exposures on increasing the risk of PTB in this metropolitan area., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM 2.5 in epidemiological studies. Using sparse regulatory PM 2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models' accuracy in predicting PM 2.5 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 PM 2.5 measurements from a publicly available commercial low-cost PM 2.5 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 R 2 and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 μg/m 3 to 0.92 and 1.63 μg/m 3 , respectively. The external spatial validation R 2 and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 μg/m 3 to 0.79 and 0.88 μg/m 3 , 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., (Copyright © 2021. Published by Elsevier Ltd.)

Ultrafine particles (UFP; diameter less than or equal to 100 nm) may reach the brain via systemic circulation or the olfactory tract and have been implicated in the risk of brain tumors. The effects of airport-related UFP on the risk of brain tumors are not known. Here we determined the association between airport-related UFP and risk of incident malignant brain cancer ( n = 155) and meningioma ( n = 420) diagnosed during 16.4 years of follow-up among 75,936 men and women residing in Los Angeles County from the Multiethnic Cohort study. UFP exposure from aircrafts was estimated for participants who lived within a 53 km × 43 km grid area around the Los Angeles International Airport (LAX) from date of cohort entry (1993-1996) through December 31, 2013. Cox proportional hazards models were used to estimate the effects of time-varying, airport-related UFP exposure on risk of malignant brain cancer and meningioma, adjusting for sex, race/ethnicity, education, and neighborhood socioeconomic status. Malignant brain cancer risk in all subjects combined increased 12% [95% confidence interval (CI), 0.98-1.27] per interquartile range (IQR) of airport-related UFP exposure (∼6,700 particles/cm 3 ) for subjects with any address in the grid area surrounding the LAX airport. In race/ethnicity-stratified analyses, African Americans, the subgroup who had the highest exposure, showed a HR of 1.32 (95% CI, 1.07-1.64) for malignant brain cancer per IQR in UFP exposure. UFP exposure was not related to risk of meningioma overall or by race/ethnicity. These results support the hypothesis that airport-related UFP exposure may be a risk factor for malignant brain cancers. SIGNIFICANCE: Malignant brain cancer risk increases with airport-related UFP exposure, particularly among African Americans, suggesting UFP exposure may be a modifiable risk factor for malignant brain cancer., (©2021 American Association for Cancer Research.)

Background: Air pollution may be associated with elevated dementia risk. Prior research has limitations that may affect reliability, and no studies have evaluated this question in a population-based cohort of men and women in the United States., Objectives: We evaluated the association between time-varying, 10-y average fine particulate matter ( PM 2.5 ) exposure and hazard of all-cause dementia. An additional goal was to understand how to adequately control for age and calendar-time-related confounding through choice of the time axis and covariate adjustment., Methods: Using the Adult Changes in Thought (ACT) population-based prospective cohort study in Seattle, we linked spatiotemporal model-based PM 2.5 exposures to participant addresses from 1978 to 2018. Dementia diagnoses were made using high-quality, standardized, consensus-based protocols at biennial follow-ups. We conducted multivariable Cox proportional hazards regression to evaluate the association between time-varying, 10-y average PM 2.5 exposure and time to event in a model with age as the time axis, stratified by apolipoprotein E (APOE) genotype, and adjusted for sex, education, race, neighborhood median household income, and calendar time. Alternative models used calendar time as the time axis., Results: We report 1,136 cases of incident dementia among 4,166 individuals with nonmissing APOE status. Mean [mean ± standard deviation (SD)] 10-y average PM 2.5 was 10.1   ( ± 2.9 )   μ g / m 3 . Each 1 - μ g / m 3 increase in the moving average of 10-y PM 2.5 was associated with a 16% greater hazard of all-cause dementia [1.16 (95% confidence interval: 1.03, 1.31)]. Results using calendar time as the time axis were similar., Discussion: In this prospective cohort study with extensive exposure data and consensus-based outcome ascertainment, elevated long-term exposure to PM 2.5 was associated with increased hazard of all-cause dementia. We found that optimal control of age and time confounding could be achieved through use of either age or calendar time as the time axis in our study. Our results strengthen evidence on the neurodegenerative effects of PM 2.5 . https://doi.org/10.1289/EHP9018.

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 2 ; NO2-B43F), and oxidizing gases (OX-B431)-which refers to ozone (O 3 ) and NO 2 . 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 2 measures (CO: RMSE = 18 ppb, R 2 = 0.97; NO: RMSE = 2 ppb, R 2 = 0.97). Performance measures for NO 2 and O 3 were somewhat lower (NO 2 : RMSE = 3 ppb, R 2 = 0.79; O 3 : RMSE = 4 ppb, R 2 = 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.

Purpose of Review: Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models., Recent Findings: Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM 2.5 ), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

Mobile monitoring is increasingly employed to measure fine spatial-scale variation in air pollutant concentrations. However, mobile measurement campaigns are typically conducted over periods much shorter than the decadal periods used for modeling chronic exposure for use in air pollution epidemiology. Using the regions of Los Angeles and Baltimore and the time period from 2005 to 2014 as our modeling domain, we investigate whether including mobile or stationary passive sampling device (PSD) monitoring data collected over a single 2-week period in one or two seasons using a unified spatio-temporal air pollution model can improve model performance in predicting NO 2 and NO x concentrations throughout the 9-year study period beyond what is possible using only routine monitoring data. In this initial study, we use data from mobile measurement campaigns conducted contemporaneously with deployments of stationary PSDs and only use mobile data collected within 300 m of a stationary PSD location for inclusion in the model. We find that including either mobile or PSD data substantially improves model performance for pollutants and locations where model performance was initially the worst (with the most-improved R 2 changing from 0.40 to 0.82) but does not meaningfully change performance in cases where performance was already very good. Results indicate that in many cases, additional spatial information from mobile monitoring and personal sampling is potentially cost-efficient inexpensive way of improving exposure predictions at both 2-week and decadal averaging periods, especially for the predictions that are located closer to features such as roadways targeted by the mobile short-term monitoring campaign.

The Mobile ObserVations of Ultrafine Particles study was a two-year project to analyze potential air quality impacts of ultrafine particles (UFPs) from aircraft traffic for communities near an international airport. The study assessed UFP concentrations within 10 miles of the airport in the directions of aircraft flight. Over the course of four seasons, this study conducted a mobile sampling scheme to collect time-resolved measures of UFP, CO 2 , and black carbon (BC) concentrations, as well as UFP size distributions. Primary findings were that UFPs were associated with both roadway traffic and aircraft sources, with the highest UFP counts found on the major roadway (I-5). Total concentrations of UFPs alone (10-1000 nm) did not distinguish roadway and aircraft features. However, key differences existed in the particle size distribution and the black carbon concentration for roadway and aircraft features. These differences can help distinguish between the spatial impact of roadway traffic and aircraft UFP emissions using a combination of mobile monitoring and standard statistical methods.

Background: Exposure to traffic-related air pollution is associated with an increased risk of cardiovascular and respiratory disease. Evidence suggests that inhaled pollutants precipitate these effects via multiple pathways involving oxidative stress., Objective: Postulating that a decrease in circulating antioxidant levels reflect an oxidative response, we investigated the effect of inhaled diesel exhaust (DE) on the ratio of reduced to oxidized glutathione (GSH/GSSG) in healthy adults, and whether pre-exposure antioxidant supplementation blunted this response. We also examined exposure-related changes in antioxidant/stress response leukocyte gene expression (GCLc, HMOX-1, IL-6, TGFβ) and plasma IL-6 levels., Methods: Nineteen nonsmoking adults participated in a double-blind, randomized, four-way crossover study. Each subject completed 120-min exposures to filtered air and DE (200 μg/m 3 ), with and without antioxidant pretreatment. Antioxidant comprised 1000 mg ascorbate for 7 days and 1200 mg N-acetylcysteine 1 day prior to exposure, with 1000 mg and 600 mg, respectively, administered 2 h prior to exposure. Whole blood glutathione was measured pre- and post-exposure; plasma IL-6 and mRNA expression were quantified pre, during and post exposure., Results: Diesel exhaust exposure was associated with significantly decreased GSH/GSSG (p = 0.001) and a 4-fold increase in IL-6 mRNA (p = 0.01) post exposure. Antioxidant pretreatment did not significantly mediate the effect of DE exposure on GSH/GSSG, though appeared to decrease the effect of exposure on IL-6 mRNA expression., Conclusions: Acute DE inhalation induced detectable oxidative effects in healthy adults, which were not significantly attenuated by the selected antioxidant pre-treatment. This finding supports the premise that oxidative stress is one mechanism underlying the adverse effects of traffic-related air pollution., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Although the exposure to PM 2.5 has serious health implications, indoor PM 2.5 monitoring is not a widely applied practice. Regulations on the indoor PM 2.5 level and measurement schemes are not well established. Compared to other indoor settings, PM 2.5 prediction models for large office buildings are particularly lacking. In response to these challenges, statistical models were developed in this paper to predict the PM 2.5 concentration in well-mixed indoor air in a commercial office building. The performances of different modeling methods, including multiple linear regression (MLR), partial least squares regression (PLS), distributed lag model (DLM), least absolute shrinkage selector operator (LASSO), simple artificial neural networks (ANN), and long-short term memory (LSTM), were compared. Various combinations of environmental and meteorological parameters were used as predictors. The root-mean-square error (RMSE) of the predicted hourly PM 2.5 was 1.73 μg/m 3 for the LSTM model and in the range of 2.20-4.71 μg/m 3 for the other models when regulatory ambient PM 2.5 data were used as predictors. The LSTM models outperformed other modeling approaches across the performance metrics used by learning the predictors' temporal patterns. Even without any ambient PM 2.5 information, the developed models still demonstrated relatively high skill in predicting the PM 2.5 levels in well-mixed indoor air.

We propose a low-cost passive method for monitoring long-term average levels of light-absorbing carbon air pollution in polluted indoor environments. Building on prior work, the method here estimates the change in reflectance of a passively exposed surface through analysis of digital images. To determine reproducibility and limits of detection, we tested low-cost passive samplers with exposure to kerosene smoke in the laboratory and to environmental pollution in 20 indoor locations. Preliminary results suggest robust reproducibility ( r = 0.99) and limits of detection appropriate for longer-term (~1-3 months) monitoring in households that use solid fuels. The results here suggest high precision; further testing involving "gold standard" measurements is needed to investigate accuracy.

Accurate predictions of pollutant concentrations at new locations are often of interest in air pollution studies on fine particulate matters (PM 2.5 ), in which data is usually not measured at all study locations. PM 2.5 is also a mixture of many different chemical components. Principal component analysis (PCA) can be incorporated to obtain lower-dimensional representative scores of such multi-pollutant data. Spatial prediction can then be used to estimate these scores at new locations. Recently developed predictive PCA modifies the traditional PCA algorithm to obtain scores with spatial structures that can be well predicted at unmeasured locations. However, these approaches require complete data, whereas multi-pollutant data tends to have complex missing patterns in practice. We propose probabilistic versions of predictive PCA which allow for flexible model-based imputation that can account for spatial information and subsequently improve the overall predictive performance.

Introduction: Ambient air pollution is a known risk factor for adverse birth outcomes, but the role of ultrafine particles (UFPs) is not well understood. Aircraft-origin UFPs adversely affect air quality over large residential areas downwind of airports, but their reproductive health burden remains uninvestigated., Objectives: This analysis evaluated whether UFPs from jet aircraft emissions are associated with increased rates of preterm birth (PTB) among pregnant mothers living downwind of Los Angeles International Airport (LAX)., Methods: This population-based study used birth records, provided by the California Department of Public Health, to ascertain birth outcomes and a novel, validated geospatial UFP dispersion model approach to estimate in utero exposures. All mothers who gave birth from 2008 to 2016 while living within 15 km of LAX were included in this analysis ( N = 174,186 ; including 15,134 PTBs)., Results: In utero exposure to aircraft-origin UFPs was positively associated with PTB. The odds ratio (OR) per interquartile range (IQR) increase [9,200 particles per cubic centimeter (cc)] relative UFP exposure was 1.04 [95% confidence interval (CI): 1.02, 1.06]. When comparing the fourth quartile of UFP exposure to the first quartile, the OR for PTB was 1.14 (95% CI: 1.08, 1.20), adjusting for maternal demographic characteristics, exposure to traffic-related air pollution, and airport-related noise., Conclusion: Our results suggest that emissions from aircraft play an etiologic role in PTBs, independent of noise and traffic-related air pollution exposures. These findings are of public health concern because UFP exposures downwind of airfields are common and may affect large, densely populated residential areas. https://doi.org/10.1289/EHP5732.

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 R 2  = 0.96, RMSE = 1.15 µg/m 3 for daily averaged PM 2.5 estimates in the Seattle region). Shinyei PPD42NS sensor results had lower precision (Pearson's r = 0.84) and accuracy (cross-validated R 2  = 0.40, RMSE = 4.49 µg/m 3 ). 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 (R 2  = 0.74-0.95, RMSE = 2.46-0.84 µg/m 3 ). Applying the Seattle model to the other regions resulted in decreased performance (R 2  = 0.67-0.84, RMSE = 3.41-1.67 µg/m 3 ), 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., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: We aim to characterize the qualities of estimation approaches for individual exposure to ambient-origin fine particulate matter (PM 2.5 ), for use in epidemiological studies., Methods: The analysis incorporates personal, home indoor, and home outdoor air monitoring data and spatio-temporal model predictions for 60 participants from the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). We compared measurement-based personal PM 2.5 exposure with several measured or predicted estimates of outdoor, indoor, and personal exposures., Results: The mean personal 2-week exposure was 7.6 (standard deviation 3.7) µg/m 3 . Outdoor model predictions performed far better than outdoor concentrations estimated using a nearest-monitor approach (R = 0.63 versus R = 0.43). Incorporating infiltration indoors of ambient-derived PM 2.5 provided better estimates of the measurement-based personal exposures than outdoor concentration predictions (R = 0.81 versus R = 0.63) and better scaling of estimated exposure (mean difference 0.4 versus 5.4 µg/m 3 higher than measurements), suggesting there is value to collecting data regarding home infiltration. Incorporating individual-level time-location information into exposure predictions did not increase correlations with measurement-based personal exposures (R = 0.80) in our sample consisting primarily of retired persons., Conclusions: This analysis demonstrates the importance of incorporating infiltration when estimating individual exposure to ambient air pollution. Spatio-temporal models provide substantial improvement in exposure estimation over a nearest monitor approach.

Background: Differences in traffic-related air pollution (TRAP) composition may cause heterogeneity in associations between air pollution exposure and cardiovascular health outcomes. Clustering multi-pollutant measurements allows investigation of effect modification by TRAP profiles., Methods: We measured TRAP components with fixed-site and on-road instruments for two two-week periods in Baltimore, Maryland. We created representative TRAP profiles for cold and warm seasons using predictive k -means clustering. We predicted cluster membership for 1005 participants in the Multi-Ethnic Study of Atherosclerosis and Air Pollution with follow-up between 2000 and 2012. We estimated cluster-specific relationships between coronary artery calcification (CAC) progression and long-term exposure to fine particulate matter (PM 2.5 ) and oxides of nitrogen (NO X )., Results: We identified two clusters in the cold season, notable for higher ratios of gases and ultrafine particles, respectively. A 5 μg/m 3 difference in PM 2.5 was associated with 17.0 (95% Confidence Interval [CI]: 7.2, 26.7) and 42.6 (95% CI: 25.7, 59.4) Agatston units/year CAC progression among participants in clusters 1 and 2, respectively (effect modification p =0.006). A 40ppb difference in NO X was associated with 22.2 (95% CI: 7.7, 36.7) and 41.9 (95% CI: 23.7, 60.2) Agatston units/year CAC progression in clusters 1 and 2, respectively ( p =0.08). Similar trends occurred using clusters identified from warm season measurements. Clusters correlated highly with baseline pollution level., Conclusions: Clustering TRAP measurements identified spatial differences in composition. We found evidence of greater CAC progression rates per unit PM 2.5 exposures among people living in areas characterized by high ratios of ultrafine particle counts relative to NO X concentrations., Competing Interests: Conflict of Interest: None declared.

Background: Coarse particulate matter (P 10-2.5 ) is primarily mechanically generated and includes crustal material, brake and tire wear, and biological particles. PM 10-2.5 is associated with pulmonary disease, which can lead to right ventricular (RV) dysfunction. Although RV characteristics have been associated with combustion-related pollutants, relationships with PM 10-2.5 remain unknown., Objectives: To quantify cross-sectional associations between RV dysfunction and PM 10-2.5 mass and components among older adults and susceptible populations., Methods: We used baseline cardiac magnetic resonance images from 1,490 participants (45-84 y old) from the Multi-Ethnic Study of Atherosclerosis and assigned 5-y residential concentrations of PM 10-2.5 mass, copper, zinc, phosphorus, silicon, and endotoxin, using land-use regression models. We quantified associations with RV mass, end-diastolic volume, and ejection fraction after control for risk factors and copollutants using linear regression. We further examined personal susceptibility., Results: We found positive associations of RV mass and, to a lesser extent, end diastolic volume with PM 10-2.5 mass among susceptible populations including smokers and persons with emphysema. After adjustment for copollutants, an interquartile range increase in PM 10-2.5 mass (2.2 μg/m 3 ) was associated with 0.5 g (95% CI: 0.0, 1.0), 0.9 g (95% CI: 0.1, 1.7), and 1.4 g (95% CI: 0.4, 2.5) larger RV mass among former smokers, current smokers, and persons with emphysema, respectively. No associations were found with healthy individuals or with ejection fraction., Conclusions: Alterations to RV structure may represent a mechanism by which long-term PM 10-2.5 exposure increases risks for adverse respiratory and cardiovascular outcomes, especially among certain susceptible populations. https://doi.org/10.1289/EHP658.

Air pollution exposure prediction models can make use of many types of air monitoring data. Fixed location passive samples typically measure concentrations averaged over several days to weeks. Mobile monitoring data can generate near continuous concentration measurements. It is not known whether mobile monitoring data are suitable for generating well-performing exposure prediction models or how they compare with other types of monitoring data in generating exposure models. Measurements from fixed site passive samplers and mobile monitoring platform were made over a 2-week period in Baltimore in the summer and winter months in 2012. Performance of exposure prediction models for long-term nitrogen oxides (NO X ) and ozone (O 3 ) concentrations were compared using a state-of-the-art approach for model development based on land use regression (LUR) and geostatistical smoothing. Model performance was evaluated using leave-one-out cross-validation (LOOCV). Models performed well using the mobile peak traffic monitoring data for both NO X and O 3 , with LOOCV R 2 s of 0.70 and 0.71, respectively, in the summer, and 0.90 and 0.58, respectively, in the winter. Models using 2-week passive samples for NO X had LOOCV R 2 s of 0.60 and 0.65 in the summer and winter months, respectively. The passive badge sampling data were not adequate for developing models for O 3 . Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NO X and O 3 and are a better source of data for these models than 2-week passive badge data.

Introduction: Recent cohort studies have used exposure prediction models to estimate the association between long-term residential concentrations of fine particulate matter (PM2.5) and health. Because these prediction models rely on PM2.5 monitoring data, predictions for times before extensive spatial monitoring present a challenge to understanding long-term exposure effects. The U.S. Environmental Protection Agency (EPA) Federal Reference Method (FRM) network for PM2.5 was established in 1999., Objectives: We evaluated a novel statistical approach to produce high-quality exposure predictions from 1980 through 2010 in the continental United States for epidemiological applications., Methods: We developed spatio-temporal prediction models using geographic predictors and annual average PM2.5 data from 1999 through 2010 from the FRM and the Interagency Monitoring of Protected Visual Environments (IMPROVE) networks. Temporal trends before 1999 were estimated by using a) extrapolation based on PM2.5 data in FRM/IMPROVE, b) PM2.5 sulfate data in the Clean Air Status and Trends Network, and c) visibility data across the Weather Bureau Army Navy network. We validated the models using PM2.5 data collected before 1999 from IMPROVE, California Air Resources Board dichotomous sampler monitoring (CARB dichot), the Children's Health Study (CHS), and the Inhalable Particulate Network (IPN)., Results: In our validation using pre-1999 data, the prediction model performed well across three trend estimation approaches when validated using IMPROVE and CHS data (R2 = 0.84-0.91) with lower R2 values in early years. Model performance using CARB dichot and IPN data was worse (R2 = 0.00-0.85) most likely because of fewer monitoring sites and inconsistent sampling methods., Conclusions: Our prediction modeling approach will allow health effects estimation associated with long-term exposures to PM2.5 over extended time periods ≤ 30 years. Citation: Kim SY, Olives C, Sheppard L, Sampson PD, Larson TV, Keller JP, Kaufman JD. 2017. Historical prediction modeling approach for estimating long-term concentrations of PM2.5 in cohort studies before the 1999 implementation of widespread monitoring. Environ Health Perspect 125:38-46; http://dx.doi.org/10.1289/EHP131., Competing Interests: Although this publication was developed under a Science to Achieve Results (STAR) research assistance agreement (RD831697) awarded by the U.S. EPA, it has not been formally reviewed by the U.S. EPA. The views expressed in this document are solely those of the University of Washington, and the U.S. EPA does not endorse any products or commercial services mentioned in this publication. The authors declare they have no actual or potential competing financial interests.

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/m(3) and 7.2-139.2 parts per billion (ppb) NOX. For each 5 μg PM2.5/m(3) 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/m(3) 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., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Background: Reduced heart rate variability, a marker of impaired cardiac autonomic function, has been linked to short-term exposure to airborne particles. This research adds to the literature by examining associations with long-term exposures to coarse particles (PM10-2.5)., Methods: Using electrocardiogram recordings from 2,780 participants (45-84 years) from three Multi-ethnic Study of Atherosclerosis sites, we assessed the standard deviation of normal to normal intervals and root-mean square differences of successive normal to normal intervals at a baseline (2000-2002) and follow-up (2010-2012) examination (mean visits/person = 1.5). Annual average concentrations of PM10-2.5 mass, copper, zinc, phosphorus, silicon, and endotoxin were estimated using site-specific spatial prediction models. We assessed associations for baseline heart rate variability and rate of change in heart rate variability over time using multivariable mixed models adjusted for time, sociodemographic, lifestyle, health, and neighborhood confounders, including copollutants., Results: In our primary models adjusted for demographic and lifestyle factors and site, PM10-2.5 mass was associated with 1.0% (95% confidence interval [CI]: -4.1, 2.1%) lower standard deviation of normal to normal interval levels per interquartile range of 2 μg/m. Stronger associations, however, were observed before site adjustment and with increasing residential stability. Similar patterns were found for root-mean square differences of successive normal to normal intervals. We found little evidence for associations with other chemical species and with the rate of change in heart rate variability, though endotoxin was associated with increasing heart rate variability over time., Conclusion: We found only weak evidence that long-term PM10-2.5 exposures are associated with lowered heart rate variability. Stronger associations among residentially stable individuals suggest that confirmatory studies are needed.

Background: Current epidemiologic studies rely on simple ozone metrics which may not appropriately capture population ozone exposure. For understanding health effects of long-term ozone exposure in population studies, it is advantageous for exposure estimation to incorporate the complex spatiotemporal pattern of ozone concentrations at fine scales., Objective: To develop a geo-statistical exposure prediction model that predicts fine scale spatiotemporal variations of ambient ozone in six United States metropolitan regions., Methods: We developed a modeling framework that estimates temporal trends from regulatory agency and cohort-specific monitoring data from MESA Air measurement campaigns and incorporates land use regression with universal kriging using predictor variables from a large geographic database. The cohort-specific data were measured at home and community locations. The framework was applied in estimating two-week average ozone concentrations from 1999 to 2013 in models of each of the six MESA Air metropolitan regions., Results: Ozone models perform well in both spatial and temporal dimensions at the agency monitoring sites in terms of prediction accuracy. City-specific leave-one (site)-out cross-validation R 2 accounting for temporal and spatial variability ranged from 0.65 to 0.88 in the six regions. For predictions at the home sites, the R 2 is between 0.60 and 0.91 for cross-validation that left out 10% of home sites in turn. The predicted ozone concentrations vary substantially over space and time in all the metropolitan regions., Conclusion: Using the available data, our spatiotemporal models are able to accurately predict long-term ozone concentrations at fine spatial scales in multiple regions. The model predictions will allow for investigation of the long-term health effects of ambient ozone concentrations in future epidemiological studies.

With emerging evidence that diesel exhaust exposure poses distinct risks to human health, the need for fine-scale models of diesel exhaust pollutants is growing. We modeled the spatial distribution of several nitrated polycyclic aromatic hydrocarbons (NPAHs) to identify fine-scale gradients in diesel exhaust pollution in two Seattle, WA neighborhoods. Our modeling approach fused land-use regression, meteorological dispersion modeling, and pollutant monitoring from both fixed and mobile platforms. We applied these modeling techniques to concentrations of 1-nitropyrene (1-NP), a highly specific diesel exhaust marker, at the neighborhood scale. We developed models of two additional nitroarenes present in secondary organic aerosol: 2-nitropyrene and 2-nitrofluoranthene. Summer predictors of 1-NP, including distance to railroad, truck emissions, and mobile black carbon measurements, showed a greater specificity to diesel sources than predictors of other NPAHs. Winter sampling results did not yield stable models, likely due to regional mixing of pollutants in turbulent weather conditions. The model of summer 1-NP had an R(2) of 0.87 and cross-validated R(2) of 0.73. The synthesis of high-density sampling and hybrid modeling was successful in predicting diesel exhaust pollution at a very fine scale and identifying clear gradients in NPAH concentrations within urban neighborhoods.

Epidemiologic studies have linked diesel exhaust (DE) to cardiovascular and respiratory morbidity and mortality, as well as lung cancer. DE composition is known to vary with many factors, although it is unclear how this influences toxicity. We generated eight DE atmospheres by applying a 2×2×2 factorial design and altering three parameters in a controlled exposure facility: (1) engine load (27 vs 82 %), (2) particle aging (residence time ~5 s vs ~5 min prior to particle collection), and (3) oxidation (with or without ozonation during dilution). Selected exposure concentrations of both diesel exhaust particles (DEPs) and DE gases, DEP oxidative reactivity via DTT activity, and in vitro DEP toxicity in murine endothelial cells were measured for each DE atmosphere. Cell toxicity was assessed via measurement of cell proliferation (colony formation assay), cell viability (MTT assay), and wound healing (scratch assay). Differences in DE composition were observed as a function of engine load. The mean 1-nitropyrene concentration was 15 times higher and oxidative reactivity was two times higher for low engine load versus high load. There were no substantial differences in measured toxicity among the three DE exposure parameters. These results indicate that alteration of applied engine load shifts the composition and can modify the biological reactivity of DE. While engine conditions did not affect the selected in vitro toxicity measures, the change in oxidative reactivity suggests that toxicological studies with DE need to take into account engine conditions in characterizing biological effects.

Background: Regulatory monitoring data have been the exposure data resource most commonly applied to studies of the association between long-term PM2.5 components and health. However, data collected for regulatory purposes may not be compatible with epidemiological studies., Objectives: We studied three important features of the PM2.5 component monitoring data to determine whether it would be appropriate to combine all available data from multiple sources for developing spatiotemporal prediction models in the National Particle Component and Toxicity (NPACT) study., Methods: The NPACT monitoring data were collected in an extensive monitoring campaign targeting cohort participant residences. The regulatory monitoring data were obtained from the Chemical Speciation Network (CSN) and the Interagency Monitoring of Protected Visual Environments (IMPROVE). We performed exploratory analyses to examine features that could affect our approach to combining data: comprehensiveness of spatial coverage, comparability of analysis methods, and consistency in sampling protocols. In addition, we considered the viability of developing spatiotemporal prediction models given a) all available data, b) NPACT data only, and c) NPACT data with temporal trends estimated from other pollutants., Results: The number of CSN/IMPROVE monitors was limited in all study areas. The different laboratory analysis methods and sampling protocols resulted in incompatible measurements between networks. Given these features we determined that it was preferable to develop our spatiotemporal models using only the NPACT data and under simplifying assumptions., Conclusions: Investigators conducting epidemiological studies of long-term PM2.5 components need to be mindful of the features of the monitoring data and incorporate this understanding into the design of their monitoring campaigns and the development of their exposure prediction models.

Rationale: More than 25 million American children breathe polluted air on diesel school buses. Emission reduction policies exist, but the health impacts to individual children have not been evaluated., Methods: Using a natural experiment, we characterized the exposures and health of 275 school bus riders before, during, and after the adoption of clean technologies and fuels between 2005 and 2009. Air pollution was measured during 597 trips on 188 school buses. Repeated measures of exhaled nitric oxide (FeNO), lung function (FEV1, FVC), and absenteeism were also collected monthly (1,768 visits). Mixed-effects models longitudinally related the adoption of diesel oxidation catalysts (DOCs), closed crankcase ventilation systems (CCVs), ultralow-sulfur diesel (ULSD), or biodiesel with exposures and health., Measurements and Main Results: Fine and ultrafine particle concentrations were 10-50% lower on buses using ULSD, DOCs, and/or CCVs. ULSD adoption was also associated with reduced FeNO (-16% [95% confidence interval (CI), -21 to -10%]), greater changes in FVC and FEV1 (0.02 [95% CI, 0.003 to 0.05] and 0.01 [95% CI, -0.006 to 0.03] L/yr, respectively), and lower absenteeism (-8% [95% CI, -16.0 to -0.7%]), with stronger associations among patients with asthma. DOCs, and to a lesser extent CCVs, also were associated with improved FeNO, FVC growth, and absenteeism, but these findings were primarily restricted to patients with persistent asthma and were often sensitive to control for ULSD. No health benefits were noted for biodiesel. Extrapolating to the U.S. population, changed fuel/technologies likely reduced absenteeism by more than 14 million/yr., Conclusions: National and local diesel policies appear to have reduced children's exposures and improved health.

Background: Toxicological research suggests that coarse particles (PM10-2.5) are inflammatory, but responses are complex and may be best summarized by multiple inflammatory markers. Few human studies have investigated associations with PM10-2.5 and, of those, none have explored long-term exposures. Here we examine long-term associations with inflammation and coagulation in the Multi-Ethnic Study of Atherosclerosis., Methods: Participants included 3,295 adults (45-84 years of age) from three metropolitan areas. Site-specific spatial models were used to estimate 5-year concentrations of PM10-2.5 mass and copper, zinc, phosphorus, silicon, and endotoxin found in PM10-2.5. Outcomes included interleukin-6, C-reactive protein, fibrinogen, total homocysteine, D-dimer, factor VIII, plasmin-antiplasmin complex, and inflammation and coagulation scores. We used multivariable regression with multiply imputed data to estimate associations while controlling for potential confounders, including co-pollutants such as fine particulate matter., Results: Some limited evidence was found of relationships between inflammation and coagulation and PM10-2.5. Endotoxin was the PM10-2.5 component most strongly associated with inflammation, with an interquartile range (IQR) increase (0.08 EU/m3) associated with 0.15 (95% CI: 0.01, 0.28; p = 0.03) and 0.08 (95% CI: -0.07, 0.23; p = 0.28) higher inflammation scores before and after control for city, respectively. Copper was the component with the strongest association with coagulation, with a 4-ng/m3 increase associated with 0.19 (95% CI: 0.08, 0.30; p = 0.0008) and 0.12 (95% CI: -0.05, 0.30; p = 0.16) unit higher coagulation scores before and after city adjustment, respectively., Conclusions: Our cross-sectional analysis provided some evidence that long-term PM10-2.5 exposure was associated with inflammation and coagulation, but associations were modest and depended on particle composition.

Source contributions to total fine particle carbon predicted by a chemical transport model (CTM) were incorporated into the positive matrix factorization (PMF) receptor model to form a receptor-oriented hybrid model. The level of influence of the CTM versus traditional PMF was varied using a weighting parameter applied to an object function as implemented in the Multilinear Engine (ME-2). The methodology provides the ability to separate features that would not be identified using PMF alone, without sacrificing fit to observations. The hybrid model was applied to IMPROVE data taken from 2006 through 2008 at Monture and Sula Peak, Montana. It was able to separately identify major contributions of total carbon (TC) from wildfires and minor contributions from biogenic sources. The predictions of TC had a lower cross-validated RMSE than those from either PMF or CTM alone. Two unconstrained, minor features were identified at each site, a soil derived feature with elevated summer impacts and a feature enriched in sulfate and nitrate with significant, but sporadic contributions across the sampling period. The respective mean TC contributions from wildfires, biogenic emissions, and other sources were 1.18, 0.12, and 0.12 ugC/m(3) at Monture and 1.60, 0.44, and 0.06 ugC/m(3) at Sula Peak.

Long-term exposure to outdoor particulate matter with an aerodynamic diameter less than or equal to 2.5 µm (PM2.5) has been associated with cardiovascular morbidity and mortality. The chemical composition of PM2.5 that may be most responsible for producing these associations has not been identified. We assessed cross-sectional associations between long-term concentrations of PM2.5 and 4 of its chemical components (sulfur, silicon, elemental carbon, and organic carbon (OC)) and subclinical atherosclerosis, measured as carotid intima-media thickness (CIMT) and coronary artery calcium, between 2000 and 2002 among 5,488 Multi-Ethnic Study of Atherosclerosis participants residing in 6 US metropolitan areas. Long-term concentrations of PM2.5 components at participants' homes were predicted using both city-specific spatiotemporal models and a national spatial model. The estimated differences in CIMT associated with interquartile-range increases in sulfur, silicon, and OC predictions from the spatiotemporal model were 0.022 mm (95% confidence interval (CI): 0.014, 0.031), 0.006 mm (95% CI: 0.000, 0.012), and 0.026 mm (95% CI: 0.019, 0.034), respectively. Findings were generally similar using the national spatial model predictions but were often sensitive to adjustment for city. We did not find strong evidence of associations with coronary artery calcium. Long-term concentrations of sulfur and OC, and possibly silicon, were associated with CIMT using 2 distinct exposure prediction modeling approaches., (© The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The objective of this study was to use membrane introduction mass spectrometry (MIMS), implemented on a mobile platform, in order to provide real-time, fine-scale, temporally and spatially resolved measurements of several hazardous air pollutants. This work is important because there is now substantial evidence that fine-scale spatial and temporal variations of air pollutant concentrations are important determinants of exposure to air pollution and adverse health outcomes. The study took place in Tacoma, WA during periods of impaired air quality in the winter and summer of 2008 and 2009. Levels of fine particles were higher in winter compared to summer, and were spatially uniform across the study area. Concentrations of vapor phase pollutants measured by membrane introduction mass spectrometry (MIMS), notably benzene and toluene, had relatively uniform spatial distributions at night, but exhibited substantial spatial variation during the day-daytime levels were up to 3-fold higher at traffic-impacted locations compared to a reference site. Although no direct side-by-side comparison was made between the MIMS system and traditional fixed site monitors, the MIMS system typically reported higher concentrations of specific VOCs, particularly benzene, ethylbenzene and naphthalene, compared to annual average concentrations obtained from SUMA canisters and gas chromatographic analysis at the fixed sites.

The development of models that provide accurate spatio-temporal predictions of ambient air pollution at small spatial scales is of great importance for the assessment of potential health effects of air pollution. Here we present a spatio-temporal framework that predicts ambient air pollution by combining data from several different monitoring networks and deterministic air pollution model(s) with geographic information system (GIS) covariates. The model presented in this paper has been implemented in an R package, SpatioTemporal, available on CRAN. The model is used by the EPA funded Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) to produce estimates of ambient air pollution; MESA Air uses the estimates to investigate the relationship between chronic exposure to air pollution and cardiovascular disease. In this paper we use the model to predict long-term average concentrations of NO x in the Los Angeles area during a ten year period. Predictions are based on measurements from the EPA Air Quality System, MESA Air specific monitoring, and output from a source dispersion model for traffic related air pollution (Caline3QHCR). Accuracy in predicting long-term average concentrations is evaluated using an elaborate cross-validation setup that accounts for a sparse spatio-temporal sampling pattern in the data, and adjusts for temporal effects. The predictive ability of the model is good with cross-validated R 2 of approximately 0.7 at subject sites. Replacing four geographic covariate indicators of traffic density with the Caline3QHCR dispersion model output resulted in very similar prediction accuracy from a more parsimonious and more interpretable model. Adding traffic-related geographic covariates to the model that included Caline3QHCR did not further improve the prediction accuracy.

Background: The long-term health effects of coarse particular matter (PM10-2.5) are challenging to assess because of a limited understanding of the spatial variation in PM10-2.5 mass and its chemical components., Objectives: We conducted a spatially intensive field study and developed spatial prediction models for PM10-2.5 mass and four selected species (copper, zinc, phosphorus, and silicon) in three American cities., Methods: PM10-2.5 snapshot campaigns were conducted in Chicago, Illinois; St. Paul, Minnesota; and Winston-Salem, North Carolina, in 2009 for the Multi-Ethnic Study of Atherosclerosis and Coarse Airborne Particulate Matter (MESA Coarse). In each city, samples were collected simultaneously outside the homes of approximately 40 participants over 2 weeks in the winter and/or summer. City-specific and combined prediction models were developed using land use regression (LUR) and universal kriging (UK). Model performance was evaluated by cross-validation (CV)., Results: PM10-2.5 mass and species varied within and between cities in a manner that was predictable by geographic covariates. City-specific LUR models generally performed well for total mass (CV R2, 0.41-0.68), copper (CV R2, 0.51-0.86), phosphorus (CV R2, 0.50-0.76), silicon (CV R2, 0.48-0.93), and zinc (CV R2, 0.36-0.73). Models pooled across all cities inconsistently captured within-city variability. Little difference was observed between the performance of LUR and UK models in predicting concentrations., Conclusions: Characterization of fine-scale spatial variability of these often heterogeneous pollutants using geographic covariates should reduce exposure misclassification and increase the power of epidemiological studies investigating the long-term health impacts of PM10-2.5.

We measured the spatial pattern of particle number (PN) concentrations downwind from the Los Angeles International Airport (LAX) with an instrumented vehicle that enabled us to cover larger areas than allowed by traditional stationary measurements. LAX emissions adversely impacted air quality much farther than reported in previous airport studies. We measured at least a 2-fold increase in PN concentrations over unimpacted baseline PN concentrations during most hours of the day in an area of about 60 km(2) that extended to 16 km (10 miles) downwind and a 4- to 5-fold increase to 8-10 km (5-6 miles) downwind. Locations of maximum PN concentrations were aligned to eastern, downwind jet trajectories during prevailing westerly winds and to 8 km downwind concentrations exceeded 75 000 particles/cm(3), more than the average freeway PN concentration in Los Angeles. During infrequent northerly winds, the impact area remained large but shifted to south of the airport. The freeway length that would cause an impact equivalent to that measured in this study (i.e., PN concentration increases weighted by the area impacted) was estimated to be 280-790 km. The total freeway length in Los Angeles is 1500 km. These results suggest that airport emissions are a major source of PN in Los Angeles that are of the same general magnitude as the entire urban freeway network. They also indicate that the air quality impact areas of major airports may have been seriously underestimated.

PM 10-2.5 mass and trace element concentrations were measured in Winston-Salem, Chicago, and St. Paul at up to 60 sites per city during two different seasons in 2010. Positive Matrix Factorization (PMF) was used to explore the underlying sources of variability. Information on previously reported PM 10-2.5 tire and brake wear profiles was used to constrain these features in PMF by prior specification of selected species ratios. We also modified PMF to allow for combining the measurements from all three cities into a single model while preserving city-specific soil features. Relatively minor differences were observed between model predictions with and without the prior ratio constraints, increasing confidence in our ability to identify separate brake wear and tire wear features. Brake wear, tire wear, fertilized soil, and re-suspended soil were found to be important sources of copper, zinc, phosphorus, and silicon respectively across all three urban areas.

Chemical emissions from research and development (R&D) activities are difficult to estimate because of the large number of chemicals used and the potential for continual changes in processes. In this case study, stack measurements taken from R&D facilities at Pacific Northwest National Laboratory (PNNL) were examined, including extreme worst-case emissions estimates and alternate analyses using a Monte Carlo method that takes into account the full distribution of sampling results. The objective of this study was to develop techniques to estimate emissions from stack measurement data that take into account a high degree of variability in the actual emissions. The results from these analyses were then compared to emissions estimated from chemical inventories. Results showed that downwind ambient air concentrations calculated from the stack measurement data were below acceptable source impact levels (ASILs) for almost all compounds, even under extreme worst-case analyses. However for compounds with averaging periods of a year, the unrealistic but simplifying extreme worst-case analysis often resulted in calculated emissions that were above the lower level regulatory criteria used to determine modeling requirements or to define trivial releases. Compounds with 24-hr averaging periods were nearly all several orders of magnitude below all, including the trivial release, criteria. The alternate analysis supplied a more realistic basis of comparison and an ability to explore effects under different operational modes.

Background: Fine particulate air pollution (PM2.5) is a global health concern, as exposure to PM2.5 has consistently been found to be associated with increased cardiovascular morbidity and mortality. Although adult exposure to traffic related PM2.5, which is largely derived from diesel exhaust (DE), has been associated with increased cardiac hypertrophy, there are limited investigations into the potential effect of in utero and early life exposure on adult susceptibility to heart disease. In this study, we investigate the effect of in utero and early life exposure to DE on adult susceptibility to heart failure., Methods: Female C57BL/6 J mice were exposed to either filtered air (FA) or DE for 3 weeks (≈ 300 μg/m3 PM2.5 for 6 hours/day, 5 days/week) and then introduced to male breeders for timed matings. Female mice were exposed to either FA or DE throughout pregnancy and until offspring were 3 weeks of age. Offspring were then transferred to either FA or DE for an additional 8 weeks of exposure. At 12 weeks of age, male offspring underwent a baseline echocardiographic assessment, followed by a sham or transverse aortic constriction (TAC) surgery to induce pressure overload. Following sacrifice three weeks post surgery, ventricles were processed for histology to assess myocardial fibrosis and individual cardiomyocyte hypertrophy. mRNA from lung tissue was isolated to measure expression of inflammatory cytokines IL6 and TNFα., Results: We observed that mice exposed to DE during in utero and early life development have significantly increased susceptibility to cardiac hypertrophy, systolic failure, myocardial fibrosis, and pulmonary congestion following TAC surgery compared to FA control, or adult DE exposed mice. In utero and early life DE exposure also strongly modified the inflammatory cytokine response in the adult lung., Conclusions: We conclude that exposure to diesel exhaust air pollution during in utero and early life development in mice increases adult susceptibility to heart failure. The results of this study may imply that the effects of air pollution on cardiovascular disease in human populations may be strongly mediated through a 'fetal origins' of adult disease pathway. Further investigations on this potential pathway of disease are warranted.

Epidemiologic and toxicologic studies were carried out in concert to provide complementary insights into the compositional features of ambient particulate matter (PM*) that produce cardiovascular effects. In the epidemiologic studies, we made use of cohort data from two ongoing studies--the Multi-Ethnic Study of Atherosclerosis (MESA) and the Women's Health Initiative--Observational Study (WHI-OS)--to investigate subclinical markers of atherosclerosis and clinical cardiovascular events. In the toxicologic study, we used the apolipoprotein E null (ApoE(-/-)) hypercholesterolemic mouse model to assess cardiovascular effects of inhalation exposure to various atmospheres containing laboratory-generated pollutants. In the epidemiologic studies, individual-level residential concentrations of fine PM, that is, PM with an aerodynamic diameter of 2.5 microm or smaller (PM2.5), PM2.5 components (primarily elemental carbon [EC] and organic carbon [OC], silicon, and sulfur but also sulfate, nitrate, nickel, vanadium, and copper), and the gaseous pollutants sulfur dioxide and nitrogen dioxide were estimated using spatiotemporal modeling and other exposure estimation approaches. In the MESA cohort data, evidence for associations with increased carotid intima-media thickness (CIMT) was found to be strongest for PM2.5, OC, and sulfur, as well as for copper in more limited analyses; the evidence for this was found to be weaker for silicon, EC, and the other components and gases. Similarly, in the WHI-OS cohort data, evidence for associations with incidence of cardiovascular mortality and cardiovascular events was found to be good for OC and sulfur, respectively, and for PM2.5; the evidence for this was found to be weaker for EC and silicon. Source apportionment based on extensive monitoring data in the six cities in the MESA analyses indicated that OC represented secondary formation processes as well as primary gasoline and biomass emissions, that sulfur represented largely secondary inorganic aerosols, and that copper represented brake dust and diesel emissions. In the toxicologic study, hypercholesterolemic mice were exposed for 50 days to atmospheres containing mixed vehicular engine emissions (MVE) consisting of mixed gasoline and diesel engine exhaust or to MVE-derived gases only (MVEG). Mice were also exposed to atmospheres containing sulfate, nitrate, or road dust, either alone or mixed with MVE or MVEG. Sulfate alone or in combination with MVE was associated with increased aortic reactivity. All exposures to atmospheres containing MVE (including a combination of MVE with other PM) were associated with increases in plasma and aortic oxidative stress; exposures to atmospheres containing only sulfate or nitrate were not. Exposure to MVE and to MVEG combinations except those containing road dust resulted in increased monocyte/macrophage sequestration in aortic plaque (a measure of plaque inflammation). Exposure to all atmospheres except those containing nitrate was associated with enhanced aortic vasoconstriction. Exposure to the MVEG was an independent driver of lipid peroxidation, matrix metalloproteinase (MMP) activation, and vascular inflammation. The epidemiologic and toxicologic study designs were intended to complement each other. The epidemiologic studies provided evidence in real-world human settings, and the toxicologic study directly assessed the biologic effects of various pollutant mixtures (in a way that is not possible in epidemiologic studies) by examining endpoints that probably underlie the subclinical and clinical cardiovascular endpoints examined in the epidemiologic studies. The epidemiologic studies were not suited to determining whether the observed associations were caused by direct effects of individual pollutants or by the mixtures in which individual pollutants are found. These studies were consistent in finding that OC and sulfate had the strongest evidence for associations with the cardiovascular disease endpoints, with much weaker evidence for EC and silicon. Both OC and sulfate reflected a large secondary aerosol component. Results from the toxicologic study indicated, for the most part, that MVE and mixtures of MVE and MVEG with other PM pollutants were important in producing the toxic cardiovascular effects found in the study. Further work on the effects of pollutant mixtures and secondary aerosols should allow better understanding of the pollution components and sources most responsible for the adverse cardiovascular effects of air pollution exposure.

Many cohort studies in environmental epidemiology require accurate modeling and prediction of fine scale spatial variation in ambient air quality across the U.S. This modeling requires the use of small spatial scale geographic or "land use" regression covariates and some degree of spatial smoothing. Furthermore, the details of the prediction of air quality by land use regression and the spatial variation in ambient air quality not explained by this regression should be allowed to vary across the continent due to the large scale heterogeneity in topography, climate, and sources of air pollution. This paper introduces a regionalized national universal kriging model for annual average fine particulate matter (PM 2.5 ) monitoring data across the U.S. To take full advantage of an extensive database of land use covariates we chose to use the method of Partial Least Squares, rather than variable selection, for the regression component of the model (the "universal" in "universal kriging") with regression coefficients and residual variogram models allowed to vary across three regions defined as West Coast, Mountain West, and East. We demonstrate a very high level of cross-validated accuracy of prediction with an overall R 2 of 0.88 and well-calibrated predictive intervals. In accord with the spatially varying characteristics of PM 2.5 on a national scale and differing kriging smoothness parameters, the accuracy of the prediction varies by region with predictive intervals being notably wider in the West Coast and Mountain West in contrast to the East.