Reducing Indoor Particulate Air Pollution Improves Student Test Scores: A Randomized Double-Blind Crossover Study

Short-term exposure to air pollution is associated with a decline in cognitive function. Standardized test scores have been employed to evaluate the effects of air pollution exposure on cognitive performance. Few studies aimed to prove whether air pollution is responsible for reduced test scores; none have implemented a "gold-standard" method for assessing the association such as a randomized, double-blind intervention. This study used a "gold-standard" method─randomized, double-blind crossover─to assess whether reducing short-term indoor particle concentrations results in improved test scores in college students in Tianjin, China. Participants (n = 162) were randomly assigned to one of two similar classrooms and completed a standardized English test on two consecutive weekends. Air purifiers with active or sham (i.e., filter removed) particle filtration were placed in each classroom. The filtration mode was switched between the two test days. Linear mixed-effect models were used to evaluate the effect of the intervention mode on the test scores. The results show that air purification (i.e., reducing PM) was significantly associated with increases in the z score for combined (0.11 [95%CI: 0.02, 0.21]) and reading (0.11 [95%CI: 0.00, 0.22]) components. In conclusion, a short-term reduction in indoor particle concentration led to improved test scores in students, suggesting an improvement in cognitive function.

Blood Pressure Effect of Traffic-Related Air Pollution : A Crossover Trial of In-Vehicle Filtration

BACKGROUND

Ambient air pollution, including traffic-related air pollution (TRAP), increases cardiovascular disease risk, possibly through vascular alterations. Limited information exists about in-vehicle TRAP exposure and vascular changes.

OBJECTIVE

To determine via particle filtration the effect of on-roadway TRAP exposure on blood pressure and retinal vasculature.

DESIGN

Randomized crossover trial. (ClinicalTrials.gov: NCT05454930).

SETTING

In-vehicle scripted commutes driven through traffic in Seattle, Washington, during 2014 to 2016.

PARTICIPANTS

Normotensive persons aged 22 to 45 years (n = 16).

INTERVENTION

On 2 days, on-road air was entrained into the vehicle. On another day, the vehicle was equipped with high-efficiency particulate air (HEPA) filtration. Participants were blinded to the exposure and were randomly assigned to the sequence.

MEASUREMENTS

Fourteen 3-minute periods of blood pressure were recorded before, during, and up to 24 hours after a drive. Image-based central retinal arteriolar equivalents (CRAEs) were measured before and after. Brachial artery diameter and gene expression were also measured and will be reported separately.

RESULTS

Mean age was 29.7 years, predrive systolic blood pressure was 122.7 mm Hg, predrive diastolic blood pressure was 70.8 mm Hg, and drive duration was 122.3 minutes (IQR, 4 minutes). Filtration reduced particle count by 86%. Among persons with complete data (n = 13), at 1 hour, mean diastolic blood pressure, adjusted for predrive levels, order, and carryover, was 4.7 mm Hg higher (95% CI, 0.9 to 8.4 mm Hg) for unfiltered drives compared with filtered drives, and mean adjusted systolic blood pressure was 4.5 mm Hg higher (CI, -1.2 to 10.2 mm Hg). At 24 hours, adjusted mean diastolic blood pressure (unfiltered) was 3.8 mm Hg higher (CI, 0.02 to 7.5 mm Hg) and adjusted mean systolic blood pressure was 1.1 mm Hg higher (CI, -4.6 to 6.8 mm Hg). Adjusted mean CRAE (unfiltered) was 2.7 μm wider (CI, -1.5 to 6.8 μm).

LIMITATIONS

Imprecise estimates due to small sample size; seasonal imbalance by exposure order.

CONCLUSION

Filtration of TRAP may mitigate its adverse effects on blood pressure rapidly and at 24 hours. Validation is required in larger samples and different settings.

PRIMARY FUNDING SOURCE

U.S. Environmental Protection Agency and National Institutes of Health.

A Cross Sectional Study of Respiratory and Allergy Status in Dairy Workers

INTRODUCTION

Workers on dairy farms face exposures to organic dusts and endotoxin. At the same time, a number of studies of farmers have reported a lower prevalence of asthma in farmworkers compared to persons without farm contact. The "hygiene hypothesis" suggests that early life exposures on farms could be protective against allergic disease and asthma. Such protective relationships are less well studied in adult farm workers.

METHODS

A cross-sectional analysis of respiratory function and allergy status was performed in a sample of dairy farm workers (n = 42) and community controls (n = 40). Measures of respiratory status (spirometry, exhaled nitric oxide FeNO, self-reported symptoms) and levels of total and bovine-specific IgE were compared between the groups.

RESULTS

Prevalence of self-reported asthma and most respiratory symptoms was similar in the two groups, with the exception of increased report of dyspnea among dairy workers. In the dairy workers, level of lung function was not reduced and FeNO was not increased. In unadjusted and adjusted models, dairy work was not associated with reduced lung function or increased airway inflammation. Mean IgE levels did not differ significantly between workers and controls, but elevated bovine-specific IgE was detected only among dairy workers, with an apparent association between elevated bovine IgE and increased FeNO.

CONCLUSION

While dairy workers did not demonstrate increased asthma prevalence compared to controls, sensitization to bovine antigen in several workers appeared to be associated with airway inflammation. Occupational health programs for dairy workers should consider the risk of animal allergy as part of respiratory health protection efforts.

Long-term exposure to ambient air pollution and cognitive function in older US adults: The Multi-Ethnic Study of Atherosclerosis

Air pollution effects on cognitive function have been increasingly recognized. Little is known about the impact of different sources of fine particulate (PM2.5). We aim to evaluate the associations between long-term air pollution exposure, including source-specific components in PM2.5, and cognition in older adults.

Methods

Cognitive assessment, including the Cognitive Abilities Screening Instrument (CASI), Digit Symbol Coding (DSC), and Digit Span (DS), was completed in 4392 older participants in the United States during 2010-2012. Residence-specific air pollution exposures (i.e., oxides of nitrogen [NO2/NOx], PM2.5 and its components: elemental carbon [EC], organic carbon [OC], sulfur [S], and silicon [Si]) were estimated by geo-statistical models. Linear and logistic regression models were used to estimate the associations between each air pollutants metric and cognitive function.

Results

An interquartile range (IQR) increase in EC (0.8 μg/m3) and Si (23.1 ng/m3) was associated with -1.27 (95% confidence interval [CI]: -0.09, -2.45) and -0.88 (95% CI: -0.21, -1.54) lower CASI scores in global cognitive function. For each IQR increase in Si, the odds of low cognitive function (LCF) across domains was 1.29 times higher (95% CI: 1.04, 1.60). For other tests, NO X was associated with slower processing speed (DSC: -2.01, 95% CI: -3.50, -0.52) and worse working memory (total DS: -0.4, 95% CI: -0.78, -0.01). No associations were found for PM2.5 and two PM2.5 components (OC and S) with any cognitive function outcomes.

Conclusion

Higher exposure to traffic-related air pollutants including both tailpipe (EC and NO x ) and non-tailpipe (Si) species were associated with lower cognitive function in older adults.

Acute cardiovascular effects of traffic-related air pollution (TRAP) exposure in healthy adults: A randomized, blinded, crossover intervention study

Exposure to traffic-related air pollution (TRAP) may enhance the risk of cardiovascular disease. However, the short-term effects of TRAP components on the cardiovascular system are not well understood. We conducted a randomized, double-blinded, crossover intervention study in which 39 healthy university students spent 2 h next to a busy road. Participants wore a powered air-purifying respirator (PAPR) or an N95 mask. PAPRs were equipped with a filter for particulate matter (PM), a PM and volatile organic compounds (VOCs) filter or a sham filter. Participants were blinded to PAPR filter type and underwent randomized exposures four times, once for each intervention mode. Blood pressure (BP), heart rate (HR) and heart rate variability (HRV) were measured before, during and for 6 h after the roadside exposure. Linear mixed-effect models were used to evaluate the effects of the interventions relative to baseline controlling for other covariates. All HRV measures increased during and following exposure for all intervention modes. Some HRV measures (SDNN and rMSSD during exposure and SDNN after exposure) were marginally affected by PM filtration. Wearing the N95 mask affected VLF power and rMSSD responses to traffic exposure differently than the PAPR interventions. Both systolic and diastolic BP increased slightly during exposure, but then were generally lower than baseline after exposure for the sham and filter interventions. HR, which fell during exposure and mostly remained lower than baseline after exposure, was lower yet with all filter interventions compared to the sham mode following exposure. Therefore, short-term exposure to traffic acutely affects HRV, BP and HR, but N95 mask and PAPR interventions generally show little efficacy in reducing these effects. Removing the PM component of TRAP has some limited effects on HRV responses to exposure but exaggerates the traffic-related decrease in HR. HRV findings from N95 mask interventions need to be interpreted cautiously.

Short-term exposure to air pollution and biomarkers of cardiovascular effect: A repeated measures study

To help understand the pathophysiologic mechanisms linking air pollutants and cardiovascular disease (CVD), we employed a repeated measures design to investigate the associations of four short-term air pollution exposures - particulate matter less than 2.5 μm in diameter (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃) and sulfur dioxide (SO₂), with two blood markers involved in vascular effects of oxidative stress, soluble lectin-like oxidized LDL receptor-1 (sLOX-1) and nitrite, using data from the Multi-Ethnic Study of Atherosclerosis (MESA). Seven hundred and forty participants with plasma sLOX-1 and nitrite measurements at three exams between 2002 and 2007 were included. Daily PM_2.5, NO₂, O₃ and SO₂ zero to seven days prior to blood draw were estimated from central monitors in six MESA regions, pre-adjusted using site-specific splines of meteorology and temporal trends, and an indicator for day of the week. Unconstrained distributed lag generalized estimating equations were used to estimate net effects over eight days with adjustment for sociodemographic and behavioral factors. The results showed that higher short-term concentrations of PM_2.5, but not other pollutants, were associated with increased sLOX-1 analyzed both as a continuous outcome (percent change per interquartile increase: 16.36%, 95%CI: 0.1-35.26%) and dichotomized at the median (odds ratio per interquartile increase: 1.21, 95%CI: 1.01-1.44). The findings were not meaningfully changed after adjustment for additional covariates or in several sensitivity analyses. Pollutant concentrations were not associated with nitrite levels. This study extends earlier experimental findings of increased sLOX-1 levels following PM inhalation to a much larger population and at ambient concentrations. In light of its known mechanistic role in promoting vascular disease, sLOX-1 may be a suitable translational biomarker linking air pollutant exposures and cardiovascular outcomes.

Improving Air Pollution Predictions of Long-Term Exposure Using Short-Term Mobile and Stationary Monitoring in Two US Metropolitan Regions

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 NO₂ 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 R² 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.

Ambient air pollution and risk of respiratory infection among adults: evidence from the multiethnic study of atherosclerosis (MESA)

BACKGROUND

Air pollution may affect the risk of respiratory infection, though research has focused on uncommon infections or infections in children. Whether ambient air pollutants increase the risk of common acute respiratory infections among adults is uncertain, yet this may help understand whether pollutants influence spread of pandemic respiratory infections like COVID-19.

OBJECTIVE

To estimate the association between ambient air pollutant exposures and respiratory infections in adults.

METHODS

During five study examinations over 12 years, 6536 participants in the multiethnic study of atherosclerosis (MESA) reported upper respiratory tract infections, bronchitis, pneumonia or febrile illness in the preceding 2 weeks. Using a validated spatiotemporal model, we estimated residential concentrations of ambient PM2.5, NOx and NO2 for the 2-6 weeks (short-term) and year (long-term) prior to each examination.

RESULTS

In this population aged 44-84 years at baseline, 10%-32% of participants reported a recent respiratory infection, depending on month of examination and study region. PM2.5, NOx and NO2 concentrations over the prior 2-6 weeks were associated with increased reporting of recent respiratory infection, with risk ratios (95% CIs) of 1.04 (1.00 to 1.09), 1.15 (1.10 to 1.20) and 1.21 (1.10 to 1.33), respectively, per increase from 25th to 75th percentile in residential pollutant concentration.

CONCLUSION

Higher short-term exposure to PM2.5 and traffic-related pollutants are associated with increased risk of symptomatic acute respiratory infections among adults. These findings may provide an insight into the epidemiology of COVID-19.

A hybrid approach to estimating long-term and short-term exposure levels of ozone at the national scale in China using land use regression and Bayesian maximum entropy

Because ambient ozone (O₃) has fine spatial scale variability in addition to a large scale regional distribution, accurate exposure predictions for population health studies need to also capture fine spatial scale differences in exposure. To address these needs, we developed a 3-year average land use regression (LUR) and combined LUR and Bayesian maximum entropy (BME) by incorporating a national area variability LUR model for China from 2015 to 2017 along with data that take into account incompleteness of O₃ monitoring data into a BME framework. Spatio-temporal kriging models that either included or did not include "soft" data were used for comparison. The final LUR model included five predictor variables: road length within a 1000 m buffer, temperature, wind speed, industrial land area within a 3000 m buffer and altitude. The 1-year predicted O₃ concentrations based on the ratio method moderately agreed with the measured concentration, and the regression R² values were 0.53, 0.57 and 0.59 in the year of 2015, 2016 and 2017, respectively. The LUR/BME model performed better (R² = 0.80, root mean squared error [RMSE] = 23.5 μg/m³) than the ordinary spatio-temporal kriging model that either included "soft" data (R² = 0.57, RMSE = 49.2 μg/m³) or did not include the "soft" data (R² = 0.52, RMSE = 58.5 μg/m³). We have demonstrated that a hybrid LUR/BME model can provide accurate predictions of O₃ concentrations with high spatio-temporal resolution at the national scale in mainland China.

Ambient PM2.5 exposures and systemic biomarkers of lipid peroxidation and total antioxidant capacity in early pregnancy

Evidence for effects of PM_2.5 on systemic oxidative stress in pregnant women is limited, especially in early pregnancy. To estimate the associations between ambient PM_2.5 exposures and biomarkers of lipid peroxidation and total antioxidant capacity (T-AOC) in women with normal early pregnancy (NEP) and women with clinically recognized early pregnancy loss (CREPL), 206 early pregnant women who had measurements of serum malondialdehyde (MDA) and T-AOC were recruited from a larger case-control study in Tianjin, China from December 2017 to July 2018. Ambient PM_2.5 concentrations of eight single-day lags exposure time windows before blood collection at the women's residential addresses were estimated using temporally-adjusted land use regression models. Effects of PM_2.5 exposures on percentage change in the biomarkers were estimated using multivariable linear regression models adjusted for month, temperature, relative humidity, gestational age and other covariates. Unconstrained distributed lag models were used to estimate net cumulative effects. Increased serum MDA and T-AOC were significantly associated with increases in PM_2.5 at several lag exposure time windows in both groups. The net effects of each interquartile range increase in PM_2.5 over the preceding 8 days on MDA were significantly higher (p < 0.001) in CREPL [52% (95% CI: 41%, 62%)] than NEP [22% (95% CI: 9%, 36%)] women. Net effects of each interquartile range increase in PM_2.5 over the preceding 5 days on T-AOC were significantly lower (p = 0.010) in CREPL [14% (95% CI: 9%, 19%)] than NEP [24% (95% CI: 18%, 29%)] women. Exposure to ambient PM_2.5 may induce systemic lipid peroxidation and antioxidant response in early pregnant women. More severe lipid peroxidation and insufficient antioxidant capacity associated with PM_2.5 was found in CREPL women than NEP women. Future studies should focus on mechanisms of individual susceptibility and interventions to reduce PM_2.5-related oxidative stress in the first trimester.

Ambient air pollution, traffic proximity and coronary atherosclerotic phenotype in China

BACKGROUND

Exposure to ambient air pollution is associated with cardiovascular risk, potentially via atherosclerosis promotion. The disease mechanisms underlying these associations remain uncertain.

OBJECTIVES

We aim to investigate the relationship of air pollution and traffic proximity with subclinical atherosclerosis, using coronary plaque phenotypes to gain insight into potential mechanisms.

METHODS

Coronary plaque total and component volumes, high-risk plaque (HRP) appearance, and luminal stenosis were characterized by coronary computed tomography angiography in 2279 patients with atherosclerosis at baseline between 2014 and 2017. Annual average exposure to air pollutants including fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃) was estimated by air pollution models for individual participants. Multiple linear regression models were used to assess the association of each exposure with plaque phenotypes and coronary stenosis, controlling for potential confounders. Multiple logistic regression models were used to estimate associations with plaque vulnerability.

RESULTS

The studied population was 60.2±9.2 years old. PM_2.5 and NO₂ concentrations were significantly associated with a 5.0% (95%CI: 0.3, 9.9%, per 15 μg/m³ increase for PM_2.5), 12.0% (95%CI: 2.5, 22.5% per 20 μg/m³ for NO₂) larger volume of non-calcified plaque, respectively. Increase in O₃ concentration was associated with a 12.2% (95%CI: 2.2, 23.2%, per 5 μg/m³ O₃) larger volume of calcified plaque and a 12.8% (95%CI: 0.9, 26.2%) greater lumen narrowing. Increased PM_2.5 and NO₂, was also associated with increase in HRP, determined by the napkin ring sign (odds ratio: 1.41 [95%CI: 1.10, 1.80] for PM_2.5 and 1.78 [95%CI: 1.20, 2.63] for NO₂) and positive remodeling index (OR: 1.11 [95%CI: 1.01, 1.21] for PM_2.5 and 1.20 [95%CI: 1.02, 1.42] for NO₂), respectively, indicating increased plaque vulnerability.

CONCLUSION

Long-term exposures to air pollution were associated with greater plaque volume and luminal stenosis, and increased plaque vulnerability with attendant risk of plaque rupture and erosion.

Estimated time-varying exposures to air emissions from animal feeding operations and childhood asthma

BACKGROUND/AIM

Industrial-scale animal feeding operations (AFOs) have adverse impacts on regional air quality. Air emissions include endotoxins and other pro-inflammatory components, and exposure may cause airway inflammation and respiratory effects in susceptible individuals residing nearby. We aimed to develop and validate metrics for estimating time-varying exposure to AFO air pollution in surrounding communities and, secondly, to determine whether exposure is associated with health effects in children with asthma.

METHODS

We conducted a longitudinal panel study of N = 58 children with asthma in an agricultural region of Washington State with a high density of dairy AFOs. Children were followed for up to 26 months with repeated measures of respiratory health (N = 2023 interviews; N = 3853 lung function measurements); urine was collected in a subcohort (N = 16) at six-day intervals over three months and analyzed for leukotriene E4 (LTE4), a biomarker of systemic inflammation (N = 138 measurements). We developed an approach to estimate daily exposure to AFO airborne emissions based on distance to AFOs, AFO size, and daily wind speed and direction, and validated the estimates against direct measurements of ammonia, a chemical marker of AFO emissions, measured biweekly at 18 sites across the region for 14 months. Short-term relationships between AFO pollutant exposure and outcomes were assessed using regression models accounting for within-participant correlation and several potential confounders.

RESULTS

Estimates of daily AFO air pollution correlated moderately well with outdoor ammonia measurements (N = 842; r = 0.62). Forced expiratory volume in 1 s (FEV₁) as percent of predicted was 2.0% (95% CI: 0.5, 3.5) lower with each interquartile increase in previous day exposure, but no associations with asthma symptoms were observed. There was suggestive evidence that LTE4 concentrations were higher following days of elevated exposure to AFO emissions (p = 0.06).

CONCLUSIONS

A simple metric of time-varying exposure to AFO emissions was correlated with daily outdoor ammonia levels. Children with asthma may be adversely affected by exposure to AFO emissions.

Author Correction: Integrating data from multiple time-location measurement methods for use in exposure assessment: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

In addition to the acknowledgments that were included, the authors wish to add the following: MESA was supported by contracts HHSN268201500003I, N01-HC-95159, N01-HC-95160, N01-HC-95161, N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute.

Association Between Long-term Exposure to Ambient Air Pollution and Change in Quantitatively Assessed Emphysema and Lung Function

IMPORTANCE

While air pollutants at historical levels have been associated with cardiovascular and respiratory diseases, it is not known whether exposure to contemporary air pollutant concentrations is associated with progression of emphysema.

OBJECTIVE

To assess the longitudinal association of ambient ozone (O3), fine particulate matter (PM2.5), oxides of nitrogen (NOx), and black carbon exposure with change in percent emphysema assessed via computed tomographic (CT) imaging and lung function.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included participants from the Multi-Ethnic Study of Atherosclerosis (MESA) Air and Lung Studies conducted in 6 metropolitan regions of the United States, which included 6814 adults aged 45 to 84 years recruited between July 2000 and August 2002, and an additional 257 participants recruited from February 2005 to May 2007, with follow-up through November 2018.

EXPOSURES

Residence-specific air pollutant concentrations (O3, PM2.5, NOx, and black carbon) were estimated by validated spatiotemporal models incorporating cohort-specific monitoring, determined from 1999 through the end of follow-up.

MAIN OUTCOMES AND MEASURES

Percent emphysema, defined as the percent of lung pixels less than -950 Hounsfield units, was assessed up to 5 times per participant via cardiac CT scan (2000-2007) and equivalent regions on lung CT scans (2010-2018). Spirometry was performed up to 3 times per participant (2004-2018).

RESULTS

Among 7071 study participants (mean [range] age at recruitment, 60 [45-84] years; 3330 [47.1%] were men), 5780 were assigned outdoor residential air pollution concentrations in the year of their baseline examination and during the follow-up period and had at least 1 follow-up CT scan, and 2772 had at least 1 follow-up spirometric assessment, over a median of 10 years. Median percent emphysema was 3% at baseline and increased a mean of 0.58 percentage points per 10 years. Mean ambient concentrations of PM2.5 and NOx, but not O3, decreased substantially during follow-up. Ambient concentrations of O3, PM2.5, NOx, and black carbon at study baseline were significantly associated with greater increases in percent emphysema per 10 years (O3: 0.13 per 3 parts per billion [95% CI, 0.03-0.24]; PM2.5: 0.11 per 2 μg/m3 [95% CI, 0.03-0.19]; NOx: 0.06 per 10 parts per billion [95% CI, 0.01-0.12]; black carbon: 0.10 per 0.2 μg/m3 [95% CI, 0.01-0.18]). Ambient O3 and NOx concentrations, but not PM2.5 concentrations, during follow-up were also significantly associated with greater increases in percent emphysema. Ambient O3 concentrations, but not other pollutants, at baseline and during follow-up were significantly associated with a greater decline in forced expiratory volume in 1 second per 10 years (baseline: 13.41 mL per 3 parts per billion [95% CI, 0.7-26.1]; follow-up: 18.15 mL per 3 parts per billion [95% CI, 1.59-34.71]).

CONCLUSIONS AND RELEVANCE

In this cohort study conducted between 2000 and 2018 in 6 US metropolitan regions, long-term exposure to ambient air pollutants was significantly associated with increasing emphysema assessed quantitatively using CT imaging and lung function.

Association of Estimated Long-term Exposure to Air Pollution and Traffic Proximity With a Marker for Coronary Atherosclerosis in a Nationwide Study in China

IMPORTANCE

Epidemiologic evidence of the mechanisms of the association between long-term exposure to air pollution and coronary heart disease (CHD) is limited and relies heavily on studies performed in Europe and the United States, where air pollution levels are relatively low. In particular, the association between air pollution and CHD in patients with underlying risks for CHD is understudied.

OBJECTIVE

To determine whether air pollution and proximity to traffic are associated with the coronary artery calcium (CAC) score, a key atherosclerotic marker.

DESIGN, SETTING, AND PARTICIPANTS

In this prospective, population-based cross-sectional study in a large-scale setting in China, 8867 consecutive patients aged 25 to 92 years with suspected CHD were recruited between November 17, 2015, and September 13, 2017. Participants were excluded if they had previous myocardial infarction, stenting, or coronary artery bypass grafting or incomplete risk factors and exposure data. Each participant underwent assessment of CAC and CHD risk factors at baseline. Data were analyzed from December 2017 to November 2018.

EXPOSURES

Annual means of fine particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) were estimated at the participants' residences using a validated geostatistical prediction model. Exposure to a nearby roadway was also estimated.

MAIN OUTCOMES AND MEASURES

Computed tomography measurement of CAC score.

RESULTS

The mean (SD) age of the 8867 participants was 56.9 (10.4) years; 4378 (53.6%) were men. Annual mean (SD) PM2.5, NO2, and O3 measurements were 70.1 (20.0), 41.4 (14.7), and 93.9 (10.5) μg/m3, respectively. The mean (SD) CAC score was 91.4 (322.2) Agatston units. Exposure to PM2.5 and NO2, adjusting for CHD risk factors and multiple pollutants, were independently associated with increases in CAC scores of 27.2% (95% CI, 10.8% to 46.1%) per 30 μg/m3 PM2.5 and 24.5% (95% CI, 3.6% to 49.7%) per 20 μg/m3 NO2. For PM2.5, odds of both detectable CAC (Agatston score >0; odds ratio, 1.28; 95% CI, 1.13 to 1.45) and severe CAC (Agatston score >400; odds ratio, 1.59; 95% CI, 1.20 to 2.12) were increased. Associations of CAC with PM2.5 and NO2 were greater among male participants (PM2.5: 42.2%; 95% CI, 24.3% to 62.7%; NO2: 45.7%; 95% CI, 25.3% to 69.5%) and elderly participants (PM2.5: 50.1%; 95% CI, 28.8% to 75.0%; NO2: 55.5%; 95% CI, 31.8% to 83.6%) and those with diabetes (PM2.5: 62.2%; 95% CI, 30.9% to 101.0%; NO2: 31.2%; 95% CI, 13.9% to 51.0%). Independent association with CAC score was 9.0% (95% CI, -1.4% to 20.4%) for O3 per 15 μg/m3 and 2.4% (95% CI, -0.6% to 5.4%) for distance near roadway per 50% decrease.

CONCLUSIONS AND RELEVANCE

In this large Chinese study, long-term exposures to PM2.5 and NO2 were independently associated with severity of CAC. This finding may provide support for the pathophysiological role of coronary atherosclerosis through which air pollution exposure may be associated with CHD.

Ambient PM2.5 and clinically recognized early pregnancy loss: A case-control study with spatiotemporal exposure predictions

BACKGROUND

Experimental research suggests that fine particulate matter (PM_2.5) exposure might affect embryonic development. However, only few population-based studies have investigated the impact of maternal exposure to PM_2.5 on the early pregnancy loss.

OBJECTIVES

To estimate associations between clinically recognized early pregnancy loss (CREPL) and exposure to ambient PM_2.5 at individual residences during peri-conception periods, with the aim to identify susceptible exposure time windows.

METHODS

CREPL cases and normal early pregnancy controls (of similar age and gravidity presenting within one week, a total of 364 pairs) were recruited between July 2017 and July 2018 among women residing in Tianjin, China. Average ambient PM_2.5 concentrations of ten exposure windows (4 weeks, 2 weeks and 1 week before conception; the first, second, third and fourth single week, the first and second 2-week periods, and the entire 4-week period after conception) at the women's residential addresses were estimated using temporally-adjusted land use regression models. Associations between PM_2.5 exposures at specific peri-conception time windows and CREPL were examined using conditional logistic regression models, adjusted for covariates.

RESULTS

Based on adjusted models, CREPL was significantly associated with a 10 μg/m³ increase in PM_2.5 exposure during the second week after conception (OR = 1.15; 95% CI: 1.04, 1.27; p = 0.005), independent of effects at other time windows. There was also an association of CREPL with PM_2.5 during the entire 4-week period after conception (OR = 1.22; 95% CI: 1.02, 1.46; p = 0.027). There was little evidence for associations with exposure during pre-conception exposure windows.

CONCLUSIONS

Maternal exposures to ambient PM_2.5 during a critical time window following conception are associated with CREPL, with the second week after conception possibly being the exposure window of most vulnerability. Future studies should focus on replicating these findings and on pathogenic mechanisms.

Long-Term Exposure to Ambient Ozone and Progression of Subclinical Arterial Disease: The Multi-Ethnic Study of Atherosclerosis and Air Pollution

BACKGROUND

Long-term ozone ([Formula: see text]) exposure is associated with cardiovascular mortality, but little is known about the associations between [Formula: see text] and subclinical arterial disease.

OBJECTIVES

We studied the longitudinal association of exposure to [Formula: see text] and progression of key subclinical arterial markers in adults: intima-media thickness of common carotid artery ([Formula: see text]), carotid plaque (CP) burden, and coronary artery calcification (CAC).

METHODS

CAC was measured one to four times at baseline and at follow-up exams (1999–2012) by computed tomography (CT) in 6,619 healthy adults, recruited at age 45-84 y without cardiovascular disease (CVD), over a mean of 6.5 y (standard deviation: 3.5 y). [Formula: see text] and CP burden were quantified in 3,392 participants using carotid artery ultrasound imaging acquired over a mean of 9 y (1.7 y). Over 91% and 89% participants had at least one follow-up [Formula: see text] and CAC measurement, respectively. Residence-specific [Formula: see text] concentrations were estimated by a validated spatiotemporal model spanning from 1999 to 2012. This model relied on comprehensive monitoring data and geographical variables to predict individualized long-term average concentrations since baseline. Linear mixed models and logistic regression model were used to evaluate relationships of long-term average exposure to [Formula: see text] with longitudinal change in [Formula: see text], CAC, and CP formation, respectively.

RESULTS

Mean progression rates of [Formula: see text] and CAC were [Formula: see text] and [Formula: see text]. CP formation was identified in 55% of the subjects. A [Formula: see text] increase in long-term average [Formula: see text] exposure was associated with a [Formula: see text] [95% confidence interval (CI): 1.4, 9.7] greater increase in [Formula: see text] over 10 y. A [Formula: see text] increase in [Formula: see text] was also associated with new CP formation [odds ratio (OR): 1.2 (95% CI: 1.1, 1.4)] but not CAC progression [[Formula: see text] (95% CI: [Formula: see text], 2)]. Associations were robust in the analysis with extended covariate adjustment, including copollutants, i.e., nitrogen oxides ([Formula: see text]) and particulate matter with diameter [Formula: see text] ([Formula: see text]).

CONCLUSION

Over almost a decade of follow-up, outdoor [Formula: see text] concentrations were associated with increased rate of carotid wall thickness progression and risk of new plaque formation, suggesting arterial injury in this cohort. https://doi.org/10.1289/EHP3325.

The Association between Long-Term Air Pollution and Urinary Catecholamines: Evidence from the Multi-Ethnic Study of Atherosclerosis

BACKGROUND

Autonomic nervous system effects have been hypothesized as a mechanism of air pollutant health effects, though scant prior epidemiologic research has examined the association between air pollutants and catecholamines.

OBJECTIVES

To examine the association of long-term air pollutants with three urinary catecholamines: dopamine (DA), epinephrine (EPI), and norepinephrine (NE). As a secondary aim, we also examined the association between short-term (or acute) exposure to fine particulate matter [particulate matter with aerodynamic diameter [Formula: see text] ([Formula: see text])] and those catecholamines.

METHODS

We used data from the Multi-Ethnic Study of Atherosclerosis (MESA) and two of its ancillary studies, the MESA Air Pollution Study and the MESA Stress Study, to provide exposure and outcome data. DA, EPI, and NE from urine samples were collected from 2004 to 2006 from 1,002 participants in the New York, New York, and Los Angeles, California, study sites. Spatiotemporal models incorporated cohort-specific monitoring and estimated annual average pollutant concentrations ([Formula: see text], [Formula: see text], [Formula: see text] and black carbon) at participants' homes the year prior to urine collection. Secondarily, short-term [Formula: see text] was evaluated (day of, day prior, and 2- to 5-d lags prior to urine collection). Several covariates were considered confounders (age, race, sex, site, socioeconomic status, cardiovascular disease risk factors, psychosocial stressors, and medication use) in linear regression models.

RESULTS

A [Formula: see text] higher annual [Formula: see text] concentration was associated with 6.3% higher mean EPI level [95% confidence interval (CI): 0.3%, 12.6%]. A 2-[Formula: see text] higher annual ambient [Formula: see text] concentration was associated with 9.1% higher mean EPI (95% CI: 3.2%, 15.3%) and 4.4% higher DA level (95% CI: 1%, 7.9%). [Formula: see text], black carbon, and short-term [Formula: see text] exposures were not significantly associated with any of the catecholamines.

CONCLUSIONS

We found an association between EPI and long-term concentrations of [Formula: see text] and [Formula: see text] and an association between DA and long-term ambient [Formula: see text]. These novel findings provide modest support for the hypothesis that air pollutant exposures are related to sympathetic nervous system activation. https://doi.org/10.1289/EHP3286.

National PM2.5 and NO2 exposure models for China based on land use regression, satellite measurements, and universal kriging

Outdoor air pollution is a major killer worldwide and the fourth largest contributor to the burden of disease in China. China is the most populous country in the world and also has the largest number of air pollution deaths per year, yet the spatial resolution of existing national air pollution estimates for China is generally relatively low. We address this knowledge gap by developing and evaluating national empirical models for China incorporating land-use regression (LUR), satellite measurements, and universal kriging (UK). Land use, traffic and meteorological variables were included for model building. We tested the resulting models in several ways, including (1) comparing models developed using forward variable selection vs. partial least squares (PLS) variable reduction, (2) comparing models developed with and without satellite measurements, and with and without UK, and (3) 10-fold cross-validation (CV), Leave-One-Province-Out CV (LOPO-CV), and Leave-One-City-Out CV (LOCO-CV). Satellite data and kriging are complementary in making predictions more accurate: kriging improved the models in well-sampled areas; satellite data substantially improved performance at locations far away from monitors. Variable-selection models performed similarly to PLS models in 10-fold CV, but better in LOPO-CV. Our best models employed forward variable selection and UK, with 10-fold CV R² of 0.89 (for both 2014 and 2015) for PM_2.5 and of 0.73 (year-2014) and 0.78 (year-2015) for NO₂. Population-weighted concentrations during 2014-2015 decreased for PM_2.5 (58.7 μg/m³ to 52.3 μg/m³) and NO₂ (29.6 μg/m³ to 26.8 μg/m³). We produced the first high resolution national LUR models for annual-average concentrations in China. Models were applied on 1 km grid to support future research. In 2015, >80% of the Chinese population lived in areas that exceeded the Chinese national PM_2.5 standard, 35 μg/m³. Results here will be publicly available and may be useful for epidemiology, risk assessment, and environmental justice research.

Estimating ambient-origin PM2.5 exposure for epidemiology: observations, prediction, and validation using personal sampling in the Multi-Ethnic Study of Atherosclerosis

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/m³. 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/m³ 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.

Spatiotemporal modeling of PM2.5 concentrations at the national scale combining land use regression and Bayesian maximum entropy in China

Concentrations of particulate matter with aerodynamic diameter <2.5 μm (PM_2.5) are relatively high in China. Estimation of PM_2.5 exposure is complex because PM_2.5 exhibits complex spatiotemporal patterns. To improve the validity of exposure predictions, several methods have been developed and applied worldwide. A hybrid approach combining a land use regression (LUR) model and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals were developed to estimate the PM_2.5 concentrations on a national scale in China. This hybrid model could potentially provide more valid predictions than a commonly-used LUR model. The LUR/BME model had good performance characteristics, with R² = 0.82 and root mean square error (RMSE) of 4.6 μg/m³. Prediction errors of the LUR/BME model were reduced by incorporating soft data accounting for data uncertainty, with the R² increasing by 6%. The performance of LUR/BME is better than OK/BME. The LUR/BME model is the most accurate fine spatial scale PM_2.5 model developed to date for China.

Contribution of the in-vehicle microenvironment to individual ambient-source nitrogen dioxide exposure: the Multi-Ethnic Study of Atherosclerosis and Air Pollution

Exposure estimates that do not account for time in-transit may underestimate exposure to traffic-related air pollution, but exact contributions have not been studied directly. We conducted a 2-week monitoring, including novel in-vehicle sampling, in a subset of the Multi-Ethnic Study of Atherosclerosis and Air Pollution cohort in two cities. Participants spent the majority of their time indoors and only 4.4% of their time (63 min/day) in-vehicle, on average. The mean ambient-source NO₂ concentration was 5.1 ppb indoors and 32.3 ppb in-vehicle during drives. On average, indoor exposure contributed 69% and in-vehicle exposure contributed 24% of participants' ambient-source NO₂ exposure. For participants in the highest quartile of time in-vehicle (≥1.3 h/day), indoor and in-vehicle contributions were 60 and 31%, respectively. Incorporating infiltrated indoor and measured in-vehicle NO₂ produced exposure estimates 5.6 ppb lower, on average, than using only outdoor concentrations. The indoor microenvironment accounted for the largest proportion of ambient-source exposure in this older population, despite higher concentrations of NO₂ outdoors and in vehicles than indoors. In-vehicle exposure was more influential among participants who drove the most and for participants residing in areas with lower outdoor air pollution. Failure to characterize exposures in these microenvironments may contribute to exposure misclassification in epidemiologic studies.

Mobile and Fixed-Site Measurements To Identify Spatial Distributions of Traffic-Related Pollution Sources in Los Angeles

Mobile monitoring and fixed-site monitoring using passive sampling devices (PSD) are popular air pollutant measurement techniques with complementary strengths and weaknesses. This study investigates the utility of combining data from concurrent 2-week mobile monitoring and fixed-site PSD campaigns in Los Angeles in the summer and early spring to identify sources of traffic-related air pollutants (TRAP) and their spatial distributions. There were strong to moderate correlations between mobile and fixed-site PSD measurements of both NO2 and NO x in the summer and spring (Pearson's r between 0.43 and 0.79), suggesting that the two data sets can be reliably combined for source apportionment. PCA identified the major TRAP sources as light-duty vehicle emissions, diesel exhaust, crankcase vent emissions, and an independent source of combustion-derived ultrafine particle emissions. The component scores of those four sources at each site were significantly correlated across the two seasons (Pearson's r between 0.58 and 0.79). Spatial maps of absolute principal component scores showed all sources to be most prominent near major roadways and the central business district and the ultrafine particle source being, in addition, more prominent near the airport. Mobile monitoring combined with fixed-site PSD sampling can provide high spatial resolution estimates of TRAP and can reveal underlying sources of exposure variability.

Ambient Air Pollution and Chronic Bronchitis in a Cohort of U.S. Women

BACKGROUND

Limited evidence links air pollution exposure to chronic cough and sputum production. Few reports have investigated the association between long-term exposure to air pollution and classically defined chronic bronchitis.

OBJECTIVES

Our objective was to estimate the association between long-term exposure to particulate matter (diameter <10 μm, PM₁₀; <2.5μm, PM_2.5), nitrogen dioxide (NO₂), and both incident and prevalent chronic bronchitis.

METHODS

We estimated annual average PM_2.5, PM₁₀, and NO₂ concentrations using a national land-use regression model with spatial smoothing at home addresses of participants in a prospective nationwide U.S. cohort study of sisters of women with breast cancer. Incident chronic bronchitis and prevalent chronic bronchitis, cough and phlegm, were assessed by questionnaires.

RESULTS

Among 47,357 individuals with complete data, 1,383 had prevalent chronic bronchitis at baseline, and 647 incident cases occurred over 5.7-y average follow-up. No associations with incident chronic bronchitis were observed. Prevalent chronic bronchitis was associated with PM₁₀ [adjusted odds ratio (aOR) per interquartile range (IQR) difference (5.8μg/m³)=1.07; 95% confidence interval (CI): 1.01, 1.13]. In never-smokers, PM_2.5 was associated with prevalent chronic bronchitis (aOR=1.18 per IQR difference; 95% CI: 1.04, 1.34), and _NO2 was associated with prevalent chronic bronchitis (aOR=1.10; 95% CI=1.01, 1.20), cough (aOR=1.10; 95% CI: 1.05, 1.16), and phlegm (aOR=1.07; 95% CI: 1.01, 1.14); interaction p-values (nonsmokers vs. smokers) <0.05.

CONCLUSIONS

PM₁₀ exposure was related to chronic bronchitis prevalence. Among never-smokers, PM_2.5 and NO₂ exposure was associated with chronic bronchitis and component symptoms. Results may have policy ramifications for PM₁₀ regulation by providing evidence for respiratory health effects related to long-term PM₁₀ exposure. https://doi.org/10.1289/EHP2199.

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies

No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources.

Air pollution and subclinical interstitial lung disease: the Multi-Ethnic Study of Atherosclerosis (MESA) air-lung study

We studied whether ambient air pollution is associated with interstitial lung abnormalities (ILAs) and high attenuation areas (HAAs), which are qualitative and quantitative measurements of subclinical interstitial lung disease (ILD) on computed tomography (CT).We performed analyses of community-based dwellers enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA) study. We used cohort-specific spatio-temporal models to estimate ambient pollution (fine particulate matter (PM_2.5), nitrogen oxides (NO_x), nitrogen dioxide (NO₂) and ozone (O₃)) at each home. A total of 5495 participants underwent serial assessment of HAAs by cardiac CT; 2671 participants were assessed for ILAs using full lung CT at the 10-year follow-up. We used multivariable logistic regression and linear mixed models adjusted for age, sex, ethnicity, education, tobacco use, scanner technology and study site.The odds of ILAs increased 1.77-fold per 40 ppb increment in NO_x (95% CI 1.06 to 2.95, p = 0.03). There was an overall trend towards an association between higher exposure to NO_x and greater progression of HAAs (0.45% annual increase in HAAs per 40 ppb increment in NO_x; 95% CI -0.02 to 0.92, p = 0.06). Associations of ambient fine particulate matter (PM_2.5), NO_x and NO₂ concentrations with progression of HAAs varied by race/ethnicity (p = 0.002, 0.007, 0.04, respectively, for interaction) and were strongest among non-Hispanic white people.We conclude that ambient air pollution exposures were associated with subclinical ILD.

Integrating data from multiple time-location measurement methods for use in exposure assessment: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

Tools to assess time-location patterns related to environmental exposures have expanded from reliance on time-location diaries (TLDs) and questionnaires to use of geospatial location devices such as data-logging Global Positioning System (GPS) equipment. The Multi-Ethnic Study of Atherosclerosis and Air Pollution obtained typical time-location patterns via questionnaire for 6424 adults in six US cities. At a later time (mean 4.6 years after questionnaire), a subset (n=128) participated in high-resolution data collection for specific 2-week periods resulting in concurrent GPS and detailed TLD data, which were aggregated to estimate time spent in various microenvironments. During these 2-week periods, participants were observed to spend the most time at home indoors (mean of 78%) and a small proportion of time in-vehicle (mean of 4%). Similar overall patterns were reported by these participants on the prior questionnaire (mean home indoors: 75%; mean in-vehicle: 4%). However, individual micro-environmental time estimates measured over specific 2-week periods were not highly correlated with an individual's questionnaire report of typical behavior (Spearman's ρ of 0.43 for home indoors and 0.39 for in-vehicle). Although questionnaire data about typical time-location patterns can inform interpretation of long-term epidemiological analyses and risk assessment, they may not reliably represent an individual's short-term experience.

Occupational Exposures and Subclinical Interstitial Lung Disease. The MESA (Multi-Ethnic Study of Atherosclerosis) Air and Lung Studies

RATIONALE

The impact of a broad range of occupational exposures on subclinical interstitial lung disease (ILD) has not been studied.

OBJECTIVES

To determine whether occupational exposures to vapors, gas, dust, and fumes (VGDF) are associated with high-attenuation areas (HAA) and interstitial lung abnormalities (ILA), which are quantitative and qualitative computed tomography (CT)-based measurements of subclinical ILD, respectively.

METHODS

We performed analyses of participants enrolled in MESA (Multi-Ethnic Study of Atherosclerosis), a population-based cohort aged 45-84 years at recruitment. HAA was measured at baseline and on serial cardiac CT scans in 5,702 participants. ILA was ascertained in a subset of 2,312 participants who underwent full-lung CT scanning at 10-year follow-up. Occupational exposures were assessed by self-reported VGDF exposure and by job-exposure matrix (JEM). Linear mixed models and logistic regression were used to determine whether occupational exposures were associated with log-transformed HAA and ILA. Models were adjusted for age, sex, race/ethnicity, education, employment status, tobacco use, and scanner technology.

MEASUREMENTS AND MAIN RESULTS

Each JEM score increment in VGDF exposure was associated with 2.64% greater HAA (95% confidence interval [CI], 1.23-4.19%). Self-reported vapors/gas exposure was associated with an increased odds of ILA among those currently employed (1.76-fold; 95% CI, 1.09-2.84) and those less than 65 years old (1.97-fold; 95% CI, 1.16-3.35). There was no consistent evidence that occupational exposures were associated with progression of HAA over the follow-up period.

CONCLUSIONS

JEM-assigned and self-reported exposures to VGDF were associated with measurements of subclinical ILD in community-dwelling adults.

Long-term exposure to residential ambient fine and coarse particulate matter and incident hypertension in post-menopausal women

BACKGROUND

Long-term exposure to ambient particulate matter (PM) has been previously linked with higher risk of cardiovascular events. This association may be mediated, at least partly, by increasing the risk of incident hypertension, a key determinant of cardiovascular risk. However, whether long-term exposure to PM is associated with incident hypertension remains unclear.

METHODS

Using national geostatistical models incorporating geographic covariates and spatial smoothing, we estimated annual average concentrations of residential fine (PM2.5), respirable (PM10), and course (PM10-2.5) fractions of particulate matter among 44,255 post-menopausal women free of hypertension enrolled in the Women's Health Initiative (WHI) clinical trials. We used time-varying Cox proportional hazards models to evaluate the association between long-term average residential pollutant concentrations and incident hypertension, adjusting for potential confounding by sociodemographic factors, medical history, neighborhood socioeconomic measures, WHI study clinical site, clinical trial, and randomization arm.

RESULTS

During 298,383 person-years of follow-up, 14,511 participants developed incident hypertension. The adjusted hazard ratios per interquartile range (IQR) increase in PM2.5, PM10, and PM10-2.5 were 1.13 (95% CI: 1.08, 1.17), 1.06 (1.03, 1.10), and 1.01 (95% CI: 0.97, 1.04), respectively. Statistically significant concentration-response relationships were identified for PM2.5 and PM10 fractions. The association between PM2.5 and hypertension was more pronounced among non-white participants and those residing in the Northeastern United States.

CONCLUSIONS

In this cohort of post-menopausal women, ambient fine and respirable particulate matter exposures were associated with higher incidence rates of hypertension. These results suggest that particulate matter may be an important modifiable risk factor for hypertension.

Long-term exposure to residential ambient fine and coarse particulate matter and incident hypertension in post-menopausal women

BACKGROUND

Long-term exposure to ambient particulate matter (PM) has been previously linked with higher risk of cardiovascular events. This association may be mediated, at least partly, by increasing the risk of incident hypertension, a key determinant of cardiovascular risk. However, whether long-term exposure to PM is associated with incident hypertension remains unclear.

METHODS

Using national geostatistical models incorporating geographic covariates and spatial smoothing, we estimated annual average concentrations of residential fine (PM_2.5), respirable (PM₁₀), and course (PM_10-2.5) fractions of particulate matter among 44,255 post-menopausal women free of hypertension enrolled in the Women's Health Initiative (WHI) clinical trials. We used time-varying Cox proportional hazards models to evaluate the association between long-term average residential pollutant concentrations and incident hypertension, adjusting for potential confounding by sociodemographic factors, medical history, neighborhood socioeconomic measures, WHI study clinical site, clinical trial, and randomization arm.

RESULTS

During 298,383 person-years of follow-up, 14,511 participants developed incident hypertension. The adjusted hazard ratios per interquartile range (IQR) increase in PM_2.5, PM₁₀, and PM_10-2.5 were 1.13 (95% CI: 1.08, 1.17), 1.06 (1.03, 1.10), and 1.01 (95% CI: 0.97, 1.04), respectively. Statistically significant concentration-response relationships were identified for PM_2.5 and PM₁₀ fractions. The association between PM_2.5 and hypertension was more pronounced among non-white participants and those residing in the Northeastern United States.

CONCLUSIONS

In this cohort of post-menopausal women, ambient fine and respirable particulate matter exposures were associated with higher incidence rates of hypertension. These results suggest that particulate matter may be an important modifiable risk factor for hypertension.

Use of mobile and passive badge air monitoring data for NOX and ozone air pollution spatial exposure prediction models

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 (O₃) 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 O₃, with LOOCV R²s 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 R²s 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 O₃. Mobile air monitoring data can be used to successfully build well-performing LUR exposure prediction models for NO_X and O₃ and are a better source of data for these models than 2-week passive badge data.

Individual and Neighborhood Socioeconomic Status and the Association between Air Pollution and Cardiovascular Disease

BACKGROUND

Long-term fine particulate matter (PM2.5) exposure is linked with cardiovascular disease, and disadvantaged status may increase susceptibility to air pollution-related health effects. In addition, there are concerns that this association may be partially explained by confounding by socioeconomic status (SES).

OBJECTIVES

We examined the roles that individual- and neighborhood-level SES (NSES) play in the association between PM2.5 exposure and cardiovascular disease.

METHODS

The study population comprised 51,754 postmenopausal women from the Women's Health Initiative Observational Study. PM2.5 concentrations were predicted at participant residences using fine-scale regionalized universal kriging models. We assessed individual-level SES and NSES (Census-tract level) across several SES domains including education, occupation, and income/wealth, as well as through an NSES score, which captures several important dimensions of SES. Cox proportional-hazards regression adjusted for SES factors and other covariates to determine the risk of a first cardiovascular event.

RESULTS

A 5 μg/m3 higher exposure to PM2.5 was associated with a 13% increased risk of cardiovascular event [hazard ratio (HR) 1.13; 95% confidence interval (CI): 1.02, 1.26]. Adjustment for SES factors did not meaningfully affect the risk estimate. Higher risk estimates were observed among participants living in low-SES neighborhoods. The most and least disadvantaged quartiles of the NSES score had HRs of 1.39 (95% CI: 1.21, 1.61) and 0.90 (95% CI: 0.72, 1.07), respectively.

CONCLUSIONS

Women with lower NSES may be more susceptible to air pollution-related health effects. The association between air pollution and cardiovascular disease was not explained by confounding from individual-level SES or NSES. Citation: Chi GC, Hajat A, Bird CE, Cullen MR, Griffin BA, Miller KA, Shih RA, Stefanick ML, Vedal S, Whitsel EA, Kaufman JD. 2016. Individual and neighborhood socioeconomic status and the association between air pollution and cardiovascular disease. Environ Health Perspect 124:1840-1847; http://dx.doi.org/10.1289/EHP199.

Advances in Understanding Air Pollution and CVD

The MESA Air (Multi-Ethnic Study of Atherosclerosis and Air Pollution) leveraged the platform of the MESA cohort into a prospective longitudinal study of relationships between air pollution and cardiovascular health. MESA Air researchers developed fine-scale, state-of-the-art air pollution exposure models for the MESA Air communities, creating individual exposure estimates for each participant. These models combine cohort-specific exposure monitoring, existing monitoring systems, and an extensive database of geographic and meteorological information. Together with extensive phenotyping in MESA-and adding participants and health measurements to the cohort-MESA Air investigated environmental exposures on a wide range of outcomes. Advances by the MESA Air team included not only a new approach to exposure modeling, but also biostatistical advances in addressing exposure measurement error and temporal confounding. The MESA Air study advanced our understanding of the impact of air pollutants on cardiovascular disease and provided a research platform for advances in environmental epidemiology.

Prediction of fine particulate matter chemical components with a spatio-temporal model for the Multi-Ethnic Study of Atherosclerosis cohort

Although cohort studies of the health effects of PM2.5 have developed exposure prediction models to represent spatial variability across participant residences, few models exist for PM2.5 components. We aimed to develop a city-specific spatio-temporal prediction approach to estimate long-term average concentrations of four PM2.5 components including sulfur, silicon, and elemental and organic carbon for the Multi-Ethnic Study of Atherosclerosis cohort, and to compare predictions to those from a national spatial model. Using 2-week average measurements from a cohort-focused monitoring campaign, the spatio-temporal model employed selected geographic covariates in a universal kriging framework with the data-driven temporal trend. Relying on long-term means of daily measurements from regulatory monitoring networks, the national spatial model employed dimension-reduced predictors using universal kriging. For the spatio-temporal model, the cross-validated and temporally-adjusted R(2) was relatively higher for EC and OC, and in the Los Angeles and Baltimore areas. The cross-validated R(2)s for both models across the six areas were reasonably high for all components except silicon. Predicted long-term concentrations at participant homes from the two models were generally highly correlated across cities but poorly correlated within cities. The spatio-temporal model may be preferred for city-specific health analyses, whereas both models could be used for multi-city studies.

Ambient Ammonia Exposures in an Agricultural Community and Pediatric Asthma Morbidity

BACKGROUND

Large-scale animal feeding operations compromise regional air quality in the rural US through emission of pollutants, such as ammonia gas. Exposure to airborne pollution from animal feeding operations may cause pediatric asthma exacerbations in surrounding communities.

OBJECTIVES

To describe spatial and temporal patterns in ambient ammonia concentrations in an agricultural region, and to investigate associations between short-term fluctuations in ammonia and subsequent changes in respiratory health in children with asthma.

METHODS

For 13 months in the Yakima Valley of Washington State, 14 monitors sampled ammonia in outdoor air for 24-hour periods every 6 days. School-age children with asthma (n = 51) were followed for two health outcomes: biweekly reports of asthma symptoms and quick relief medication usage, and daily measurements of forced expiratory volume in 1 second. We assessed associations between each outcome and ammonia using generalized estimating equations.

RESULTS

Twenty-four-hour ammonia concentrations varied from 0.2 to 238.1 μg/m during the study period and displayed a strong correlation with proximity to animal feeding operations. The percentage of forced expiratory volume in 1 second was 3.8% lower (95% confidence interval = 0.2, 7.3) per interquartile increase in 1-day lagged ammonia concentration and 3.0% lower (95% confidence interval = 0.5, 5.8) for 2-day lagged concentration. We observed no associations between self-reported asthma symptoms or medication usage and estimated ammonia exposure.

CONCLUSIONS

Ammonia concentrations were elevated in this community and strongly predicted by proximity to animal feeding operations. Ammonia's association with acute lung function decrements in children with asthma in the surrounding community may be causal or, alternatively, ammonia may be a marker for other pollutants from animal feeding operations associated with respiratory effects.

Combining Land-Use Regression and Chemical Transport Modeling in a Spatiotemporal Geostatistical Model for Ozone and PM2.5

Assessments of long-term air pollution exposure in population studies have commonly employed land-use regression (LUR) or chemical transport modeling (CTM) techniques. Attempts to incorporate both approaches in one modeling framework are challenging. We present a novel geostatistical modeling framework, incorporating CTM predictions into a spatiotemporal LUR model with spatial smoothing to estimate spatiotemporal variability of ozone (O3) and particulate matter with diameter less than 2.5 μm (PM2.5) from 2000 to 2008 in the Los Angeles Basin. The observations include over 9 years' data from more than 20 routine monitoring sites and specific monitoring data at over 100 locations to provide more comprehensive spatial coverage of air pollutants. Our composite modeling approach outperforms separate CTM and LUR models in terms of root-mean-square error (RMSE) assessed by 10-fold cross-validation in both temporal and spatial dimensions, with larger improvement in the accuracy of predictions for O3 (RMSE [ppb] for CTM, 6.6; LUR, 4.6; composite, 3.6) than for PM2.5 (RMSE [μg/m(3)] CTM: 13.7, LUR: 3.2, composite: 3.1). Our study highlights the opportunity for future exposure assessment to make use of readily available spatiotemporal modeling methods and auxiliary gridded data that takes chemical reaction processes into account to improve the accuracy of predictions in a single spatiotemporal modeling framework.

Characteristics of atmospheric single particles during haze periods in a typical urban area of Beijing: A case study in October, 2014

To investigate the composition and possible sources of particles, especially during heavy haze pollution, a single particle aerosol mass spectrometer (SPAMS) was deployed to measure the changes of single particle species and sizes during October of 2014, in Beijing. A total of 2,871,431 particles with both positive and negative spectra were collected and characterized in combination with the adaptive resonance theory neural network algorithm (ART-2a). Eight types of particles were classified: dust particles (dust, 8.1%), elemental carbon (EC, 29.0%), organic carbon (OC, 18.0%), EC and OC combined particles (ECOC, 9.5%), Na-K containing particles (NaK, 7.9%), K-containing particles (K, 21.8%), organic nitrogen and potassium containing particles (KCN, 2.3%), and metal-containing particles (metal, 3.6%). Three haze pollution events (P1, P2, P3) and one clean period (clean) were analyzed, based on the mass and number concentration of PM2.5 and the back trajectory results from the hybrid single particle Lagrangian integrated trajectory model (Hysplit-4 model). Results showed that EC, OC and K were the major components of single particles during the three haze pollution periods, which showed clearly increased ratios compared with those in the clean period. Results from the mixing state of secondary species of different types of particles showed that sulfate and nitrate were more readily mixed with carbon-containing particles during haze pollution episodes than in clean periods.

Development of Long-term Spatiotemporal Models for Ambient Ozone in Six Metropolitan regions of the United States: The MESA Air Study

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 R² accounting for temporal and spatial variability ranged from 0.65 to 0.88 in the six regions. For predictions at the home sites, the R² 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.

Combining PM2.5 Component Data from Multiple Sources: Data Consistency and Characteristics Relevant to Epidemiological Analyses of Predicted Long-Term Exposures

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.

The short-term association of selected components of fine particulate matter and mortality in the Denver Aerosol Sources and Health (DASH) study

BACKGROUND

Associations of short-term exposure to fine particulate matter (PM2.5) with daily mortality may be due to specific PM2.5 chemical components. Daily concentrations of PM2.5 components were measured over five years in Denver to investigate whether specific PM2.5 components are associated with daily mortality.

METHODS

Daily counts of total and cause-specific deaths were obtained for the 5-county Denver metropolitan region from 2003 through 2007. Daily 24-hour concentrations of PM2.5, elemental carbon (EC), organic carbon (OC), sulfate and nitrate were measured at a central residential monitoring site. Using generalized additive models, we estimated relative risks (RRs) of daily death counts for daily PM2.5 and four PM2.5 component concentrations at single and distributed lags between the current and three previous days, while controlling for longer-term time trend and meteorology.

RESULTS

RR of total non-accidental mortality for an inter-quartile increase of 4.55 μg/m(3) in PM2.5 distributed over 4 days was 1.012 (95 % confidence interval: 0.999, 1.025); RRs for EC and OC were larger (1.024 [1.005, 1.043] and 1.020 [1.000, 1.040] for 0.33 and 1.67 μg/m(3) increases, respectively) than those for sulfate and nitrate. We generally did not observe associations with cardiovascular and respiratory mortality except for associations with ischemic heart disease mortality at lags 3 and 0-3 depending on the component. In addition, there were associations with cancer mortality, particularly for EC and OC, possibly reflecting advanced deaths of a frail population.

CONCLUSIONS

PM2.5 components possibly from combustion-related sources are more strongly associated with daily mortality than are secondary inorganic aerosols.

A unified spatiotemporal modeling approach for predicting concentrations of multiple air pollutants in the multi-ethnic study of atherosclerosis and air pollution

BACKGROUND

Cohort studies of the relationship between air pollution exposure and chronic health effects require predictions of exposure over long periods of time.

OBJECTIVES

We developed a unified modeling approach for predicting fine particulate matter, nitrogen dioxide, oxides of nitrogen, and black carbon (as measured by light absorption coefficient) in six U.S. metropolitan regions from 1999 through early 2012 as part of the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air).

METHODS

We obtained monitoring data from regulatory networks and supplemented those data with study-specific measurements collected from MESA Air community locations and participants' homes. In each region, we applied a spatiotemporal model that included a long-term spatial mean, time trends with spatially varying coefficients, and a spatiotemporal residual. The mean structure was derived from a large set of geographic covariates that was reduced using partial least-squares regression. We estimated time trends from observed time series and used spatial smoothing methods to borrow strength between observations.

RESULTS

Prediction accuracy was high for most models, with cross-validation R2 (R2CV) > 0.80 at regulatory and fixed sites for most regions and pollutants. At home sites, overall R2CV ranged from 0.45 to 0.92, and temporally adjusted R2CV ranged from 0.23 to 0.92.

CONCLUSIONS

This novel spatiotemporal modeling approach provides accurate fine-scale predictions in multiple regions for four pollutants. We have generated participant-specific predictions for MESA Air to investigate health effects of long-term air pollution exposures. These successes highlight modeling advances that can be adopted more widely in modern cohort studies.

Ambient air pollution exposure and incident adult asthma in a nationwide cohort of U.S. women

RATIONALE

Limited prior data suggest an association between traffic-related air pollution and incident asthma in adults. No published studies assess the effect of long-term exposures to particulate matter less than 2.5 μm in diameter (PM2.5) on adult incident asthma.

OBJECTIVES

To estimate the association between ambient air pollution exposures (PM2.5 and nitrogen dioxide, NO2) and development of asthma and incident respiratory symptoms.

METHODS

The Sister Study is a U.S. cohort study of risk factors for breast cancer and other health outcomes (n = 50,884) in sisters of women with breast cancer (enrollment, 2003-2009). Annual average (2006) ambient PM2.5 and NO2 concentrations were estimated at participants' addresses, using a national land-use/kriging model incorporating roadway information. Outcomes at follow-up (2008-2012) included incident self-reported wheeze, chronic cough, and doctor-diagnosed asthma in women without baseline symptoms.

MEASUREMENTS AND MAIN RESULTS

Adjusted analyses included 254 incident cases of asthma, 1,023 of wheeze, and 1,559 of chronic cough. For an interquartile range (IQR) difference (3.6 μg/m(3)) in estimated PM2.5 exposure, the adjusted odds ratio (aOR) was 1.20 (95% confidence interval [CI] = 0.99-1.46, P = 0.063) for incident asthma and 1.14 (95% CI = 1.04-1.26, P = 0.008) for incident wheeze. For NO2, there was evidence for an association with incident wheeze (aOR = 1.08, 95% CI = 1.00-1.17, P = 0.048 per IQR of 5.8 ppb). Neither pollutant was significantly associated with incident cough (PM2.5: aOR = 0.95, 95% CI = 0.88-1.03, P = 0.194; NO2: aOR = 1.00, 95% CI = 0.93-1.07, P = 0.939).

CONCLUSIONS

Results suggest that PM2.5 exposure increases the risk of developing asthma and that PM2.5 and NO2 increase the risk of developing wheeze, the cardinal symptom of asthma, in adult women.

Individual-level concentrations of fine particulate matter chemical components and subclinical atherosclerosis: a cross-sectional analysis based on 2 advanced exposure prediction models in the multi-ethnic study of atherosclerosis

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.

Urban particulate matter induces pro-remodeling factors by airway epithelial cells from healthy and asthmatic children

CONTEXT

Chronic exposure to ambient particulate matter pollution during childhood is associated with decreased lung function growth and increased prevalence of reported respiratory symptoms. The role of airway epithelium-derived factors has not been well determined.

OBJECTIVE

To determine if urban particulate matter (UPM) stimulates production of vascular endothelial growth factor (VEGF) and transforming growth factor-β2 (TGF-β2), and gene expression of mucin 5AC (MUC5AC) and interleukin-(IL)-8 by primary airway epithelial cells (AECs) obtained from carefully phenotyped healthy and atopic asthmatic school-aged children.

METHODS

Primary AECs from 9 healthy and 14 asthmatic children were differentiated in air--liquid interface (ALI) culture. The apical surface was exposed to UPM suspension or phosphate buffered saline (PBS) vehicle control for 96 h. VEGF and TGF-β2 concentrations in cell media at baseline, 48 and 96 h were measured via ELISA. MUC5AC and IL-8 expression by AECs at 96 h was measured via quantitative polymerase chain reaction.

RESULTS

Baseline concentrations of VEGF, but not TGF-β2, were significantly higher in asthmatic versus healthy cultures. UPM stimulated production of VEGF, but not TGF-β2, at 48 and 96 h; the magnitude of change was comparable across groups. At 96 h there was greater MUC5AC and IL-8 expression by UPM exposed compared to PBS exposed AECs.

CONCLUSIONS

Induction of the pro-remodeling cytokine VEGF may be a potential mechanism by which UPM influences lung function growth in children irrespective of asthma status. Respiratory morbidity associated with UPM exposure in children may be related to increased expression of MUC5AC and IL-8.

Infants suspected to have very-long chain acyl-CoA dehydrogenase deficiency from newborn screening

Very long-chain acyl-CoA dehydrogenase deficiency (VLCADD) is a fatty acid oxidation disorder with widely varying presentations that has presented a significant challenge to newborn screening (NBS). The Western States Regional Genetics Services Collaborative developed a workgroup to study infants with NBS positive for VLCADD. We performed retrospective analysis of newborns with elevated C14:1-acylcarnitine on NBS in California, Oregon, Washington, and Hawai'i including available confirmatory testing and clinical information. Overall, from 2,802,504 children screened, there were 242 cases screen-positive for VLCADD. There were 34 symptomatic true positive cases, 18 asymptomatic true positives, 112 false positives, 55 heterozygotes, 11 lost to follow-up, and 12 other disorders. One in 11,581 newborns had an abnormal NBS for suspected VLCADD. Comparison of analytes and analyte ratios from the NBS demonstrated statistically significant differences between true positive and false positive groups for C14:1, C14, C14:1/C2, and C14:1/C16. The positive predictive value for all true positive cases was 94%, 54%, and 23% when C14:1 was ≥2.0 μM, ≥1.0 μM, and ≥0.7 μM, respectively. Sequential post-analytical analysis could reduce the referral rate in 25.8% of cases. This study is the largest reported follow-up of infants with NBS screen-positive results for suspected VLCADD and demonstrates the necessity of developing comprehensive and consistent long-term follow-up NBS systems. Application of clinical information revealed differences between symptomatic and asymptomatic children with VLCADD. Comparison of NBS analytes and analyte ratios may be valuable in developing more effective diagnostic algorithms.

National Particle Component Toxicity (NPACT) initiative report on cardiovascular effects

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.

The sensitivity of health effect estimates from time-series studies to fine particulate matter component sampling schedule

The US Environmental Protection Agency air pollution monitoring data have been a valuable resource commonly used for investigating the associations between short-term exposures to PM2.5 chemical components and human health. However, the temporally sparse sampling on every third or sixth day may affect health effect estimation. We examined the impact of non-daily monitoring data on health effect estimates using daily data from the Denver Aerosol Sources and Health (DASH) study. Daily concentrations of four PM2.5 chemical components (elemental and organic carbon, sulfate, and nitrate) and hospital admission counts from 2003 through 2007 were used. Three every-third-day time series were created from the daily DASH monitoring data, imitating the US Speciation Trend Network (STN) monitoring schedule. A fourth, partly irregular, every-third-day time series was created by matching existing sampling days at a nearby STN monitor. Relative risks (RRs) of hospital admissions for PM2.5 components at lags 0-3 were estimated for each data set, adjusting for temperature, relative humidity, longer term temporal trends, and day of week using generalized additive models, and compared across different sampling schedules. The estimated RRs varied somewhat between the non-daily and daily sampling schedules and between the four non-daily schedules, and in some instances could lead to different conclusions. It was not evident which features of the data or analysis were responsible for the variation in effect estimates, although seeing similar variability in resampled data sets with relaxation of the every-third-day constraint suggests that limited power may have had a role. The use of non-daily monitoring data can influence interpretation of estimated effects of PM2.5 components on hospital admissions in time-series studies.

A national prediction model for PM2.5 component exposures and measurement error-corrected health effect inference

BACKGROUND

Studies estimating health effects of long-term air pollution exposure often use a two-stage approach: building exposure models to assign individual-level exposures, which are then used in regression analyses. This requires accurate exposure modeling and careful treatment of exposure measurement error.

OBJECTIVE

To illustrate the importance of accounting for exposure model characteristics in two-stage air pollution studies, we considered a case study based on data from the Multi-Ethnic Study of Atherosclerosis (MESA).

METHODS

We built national spatial exposure models that used partial least squares and universal kriging to estimate annual average concentrations of four PM2.5 components: elemental carbon (EC), organic carbon (OC), silicon (Si), and sulfur (S). We predicted PM2.5 component exposures for the MESA cohort and estimated cross-sectional associations with carotid intima-media thickness (CIMT), adjusting for subject-specific covariates. We corrected for measurement error using recently developed methods that account for the spatial structure of predicted exposures.

RESULTS

Our models performed well, with cross-validated R2 values ranging from 0.62 to 0.95. Naïve analyses that did not account for measurement error indicated statistically significant associations between CIMT and exposure to OC, Si, and S. EC and OC exhibited little spatial correlation, and the corrected inference was unchanged from the naïve analysis. The Si and S exposure surfaces displayed notable spatial correlation, resulting in corrected confidence intervals (CIs) that were 50% wider than the naïve CIs, but that were still statistically significant.

CONCLUSION

The impact of correcting for measurement error on health effect inference is concordant with the degree of spatial correlation in the exposure surfaces. Exposure model characteristics must be considered when performing two-stage air pollution epidemiologic analyses because naïve health effect inference may be inappropriate.

Particulate matter components and subclinical atherosclerosis: common approaches to estimating exposure in a Multi-Ethnic Study of Atherosclerosis cross-sectional study

BACKGROUND

Concentrations of outdoor fine particulate matter (PM2.5) have been associated with cardiovascular disease. PM2.5 chemical composition may be responsible for effects of exposure to PM2.5.

METHODS

Using data from the Multi-Ethnic Study of Atherosclerosis (MESA) collected in 2000-2002 on 6,256 US adults without clinical cardiovascular disease in six U.S. metropolitan areas, we investigated cross-sectional associations of estimated long-term exposure to total PM2.5 mass and PM2.5 components (elemental carbon [EC], organic carbon [OC], silicon and sulfur) with measures of subclinical atherosclerosis (coronary artery calcium [CAC] and right common carotid intima-media thickness [CIMT]). Community monitors deployed for this study from 2007 to 2008 were used to estimate exposures at baseline addresses using three commonly-used approaches: (1) nearest monitor (the primary approach), (2) inverse-distance monitor weighting and (3) city-wide average.

RESULTS

Using the exposure estimate based on nearest monitor, in single-pollutant models, increased OC (effect estimate [95% CI] per IQR: 35.1 μm [26.8, 43.3]), EC (9.6 μm [3.6,15.7]), sulfur (22.7 μm [15.0,30.4]) and total PM2.5 (14.7 μm [9.0,20.5]) but not silicon (5.2 μm [-9.8,20.1]), were associated with increased CIMT; in two-pollutant models, only the association with OC was robust to control for the other pollutants. Findings were generally consistent across the three exposure estimation approaches. None of the PM measures were positively associated with either the presence or extent of CAC. In sensitivity analyses, effect estimates for OC and silicon were particularly sensitive to control for metropolitan area.

CONCLUSION

Employing commonly-used exposure estimation approaches, all of the PM2.5 components considered, except silicon, were associated with increased CIMT, with the evidence being strongest for OC; no component was associated with increased CAC. PM2.5 chemical components, or other features of the sources that produced them, may be important in determining the effect of PM exposure on atherosclerosis. These cross-sectional findings await confirmation in future work employing longitudinal outcome measures and using more sophisticated approaches to estimating exposure.