Periods of abnormally high concentrations of atmospheric pollutants, defined as air pollution episodes, can cause adverse health effects. Southern European countries experience high particulate ...matter (PM) levels originating from local and distant sources. In this study, we investigated the occurrence and nature of extreme PM
(PM with an aerodynamic diameter ≤10 μm) pollution episodes in Greece. We examined PM
concentration data from 18 monitoring stations located at five sites across the country: (1) an industrial area in northwestern Greece (Western Macedonia Lignite Area, WMLA), which includes sources such as lignite mining operations and lignite power plants that generate a high percentage of the energy in Greece; (2) the greater Athens area, the most populated area of the country; and (3) Thessaloniki, (4) Patra, and (5) Volos, three large cities in Greece. We defined extreme PM
pollution episodes (EEs) as days during which PM
concentrations at all five sites exceeded the European Union (EU) 24-hr PM
standards. For each EE, we identified the corresponding prevailing synoptic and local meteorological conditions, including wind surface data, for the period from January 2009 through December 2011. We also analyzed data from remote sensing and model simulations. We recorded 14 EEs that occurred over 49 days and could be grouped into two categories: (1) Local Source Impact (LSI; 26 days, 53%) and (2) African Dust Impact (ADI; 23 days, 47%). Our analysis suggested that the contribution of local sources to ADI EEs was relatively small. LSI EEs were observed only in the cold season, whereas ADI EEs occurred throughout the year, with a higher frequency during the cold season. The EEs with the highest intensity were recorded during African dust intrusions. ADI episodes were found to contribute more than local sources in Greece, with ADI and LSI fraction contribution ranging from 1.1 to 3.10. The EE contribution during ADI fluctuated from 41 to 83 μg/m
, whereas during LSI it varied from 14 to 67 μg/m
.
This paper examines the occurrence and nature of extreme PM
pollution episodes (EEs) in Greece during a 3-yr period (2009-2011). Fourteen EEs were found of 49 days total duration, classified into two main categories: Local Source Impact (53%) and African Dust Impact (47%). All the above extreme PM
air pollution episodes were the result of specific synoptic prevailing conditions. Specific information on the linkages between the synoptic weather patterns and PM
concentrations could be used in the development of weather/health-warning system to alert the public that a synoptic episode is imminent.
The Multicenter Ozone Study of oldEr Subjects (MOSES) was a multi-center study evaluating whether short-term controlled exposure of older, healthy individuals to low levels of ozone (O
) induced ...acute changes in cardiovascular biomarkers. In MOSES Part 1 (MOSES 1), controlled O
exposure caused concentration-related reductions in lung function with evidence of airway inflammation and injury, but without convincing evidence of effects on cardiovascular function. However, subjects' prior exposures to indoor and outdoor air pollution in the few hours and days before each MOSES controlled O
exposure may have independently affected the study biomarkers and/or modified biomarker responses to the MOSES controlled O
exposures.
MOSES 1 was conducted at three clinical centers (University of California San Francisco, University of North Carolina, and University of Rochester Medical Center) and included healthy volunteers 55 to 70 years of age. Consented participants who successfully completed the screening and training sessions were enrolled in the study. All three clinical centers adhered to common standard operating procedures and used common tracking and data forms. Each subject was scheduled to participate in a total of 11 visits: screening visit, training visit, and three sets of exposure visits consisting of the pre-exposure day, the exposure day, and the post-exposure day. After completing the pre-exposure day, subjects spent the night in a nearby hotel. On exposure days, the subjects were exposed for 3 hours in random order to 0 ppb O
(clean air), 70 ppb O
, and 120 ppm O
. During the exposure period the subjects alternated between 15 minutes of moderate exercise and 15 minutes of rest. A suite of cardiovascular and pulmonary endpoints was measured on the day before, the day of, and up to 22 hours after each exposure.
In MOSES Part 2 (MOSES 2), we used a longitudinal panel study design, cardiopulmonary biomarker data from MOSES 1, passive cumulative personal exposure samples (PES) of O
and nitrogen dioxide (NO
) in the 72 hours before the pre-exposure visit, and hourly ambient air pollution and weather measurements in the 96 hours before the pre-exposure visit. We used mixed-effects linear regression and evaluated whether PES O
and NO
and these ambient pollutant concentrations in the 96 hours before the pre-exposure visit confounded the MOSES 1 controlled O
exposure effects on the pre- to post-exposure biomarker changes (Aim 1), whether they modified these pre- to post-exposure biomarker responses to the controlled O
exposures (Aim 2), whether they were associated with changes in biomarkers measured at the pre-exposure visit or morning of the exposure session (Aim 3), and whether they were associated with differences in the pre- to post-exposure biomarker changes independently of the controlled O
exposures (Aim 4).
Ambient pollutant concentrations at each site were low and were regularly below the National Ambient Air Quality Standard levels. In Aim 1, the controlled O
exposure effects on the pre- to post-exposure biomarker differences were little changed when PES or ambient pollutant concentrations in the previous 96 hours were included in the model, suggesting these were not confounders of the controlled O
exposure/biomarker difference associations. In Aim 2, effects of MOSES controlled O
exposures on forced expiratory volume in 1 second (FEV
) and forced vital capacity (FVC) were modified by ambient NO
and carbon monoxide (CO), and PES NO
, with reductions in FEV
and FVC observed only when these concentrations were "Medium" or "High" in the 72 hours before the pre-exposure visit. There was no such effect modification of the effect of controlled O
exposure on any other cardiopulmonary biomarker.
As hypothesized for Aim 3, increased ambient O
concentrations were associated with decreased pre-exposure heart rate variability (HRV). For example, high frequency (HF) HRV decreased in association with increased ambient O
concentrations in the 96 hours before the pre-exposure visit (-0.460 lnms
; 95% CI, -0.743 to -0.177 for each 10.35-ppb increase in O
;
= 0.002). However, in Aim 4 these increases in ambient O
were also associated with increases in HF and low frequency (LF) HRV from pre- to post-exposure, likely reflecting a "recovery" of HRV during the MOSES O
exposure sessions. Similar patterns across Aims 3 and 4 were observed for LF (the other primary HRV marker), and standard deviation of normal-to-normal sinus beat intervals (SDNN) and root mean square of successive differences in normal-to-normal sinus beat intervals (RMSSD) (secondary HRV markers).
Similar Aim 3 and Aim 4 patterns were observed for FEV
and FVC in association with increases in ambient PM with an aerodynamic diameter ≤ 2.5 μm (PM
), CO, and NO
in the 96 hours before the pre-exposure visit. For Aim 3, small decreases in pre-exposure FEV
were significantly associated with interquartile range (IQR) increases in PM
concentrations in the 1 hour before the pre-exposure visit (-0.022 L; 95% CI, -0.037 to -0.006;
= 0.007), CO in the 3 hours before the pre-exposure visit (-0.046 L; 95% CI, -0.076 to -0.016;
= 0.003), and NO
in the 72 hours before the pre-exposure visit (-0.030 L; 95% CI, -0.052 to -0.008;
= 0.007). However, FEV
was not associated with ambient O
or sulfur dioxide (SO
), or PES O
or NO
(Aim 3). For Aim 4, increased FEV
across the exposure session (post-exposure minus pre-exposure) was marginally significantly associated with each 4.1-ppb increase in PES O
concentration (0.010 L; 95% CI, 0.004 to 0.026;
= 0.010), as well as ambient PM
and CO at all lag times. FVC showed similar associations, with patterns of decreased pre-exposure FVC associated with increased PM
, CO, and NO
at most lag times, and increased FVC across the exposure session also associated with increased concentrations of the same pollutants, reflecting a similar recovery. However, increased pollutant concentrations were not associated with adverse changes in pre-exposure levels or pre- to post-exposure changes in biomarkers of cardiac repolarization, ST segment, vascular function, nitrotyrosine as a measure of oxidative stress, prothrombotic state, systemic inflammation, lung injury, or sputum polymorphonuclear leukocyte (PMN) percentage as a measure of airway inflammation.
Our previous MOSES 1 findings of controlled O
exposure effects on pulmonary function, but not on any cardiovascular biomarker, were not confounded by ambient or personal O
or other pollutant exposures in the 96 and 72 hours before the pre-exposure visit. Further, these MOSES 1 O
effects were generally not modified, blunted, or lessened by these same ambient and personal pollutant exposures. However, the reductions in markers of pulmonary function by the MOSES 1 controlled O
exposure were modified by ambient NO
and CO, and PES NO
, with reductions observed only when these pollutant concentrations were elevated in the few hours and days before the pre-exposure visit. Increased ambient O
concentrations were associated with reduced HRV, with "recovery" during exposure visits. Increased ambient PM
, NO
, and CO were associated with reduced pulmonary function, independent of the MOSES-controlled O
exposures. Increased pollutant concentrations were not associated with pre-exposure or pre- to post-exposure changes in other cardiopulmonary biomarkers. Future controlled exposure studies should consider the effect of ambient pollutants on pre-exposure biomarker levels and whether ambient pollutants modify any health response to a controlled pollutant exposure.
Children spend over 6 h a day in schools and have higher asthma morbidity from school environmental exposures. The present study aims to determine indoor and outdoor possible sources affecting indoor ...PM
in classrooms. Weeklong indoor PM
samples were collected from 32 inner-city schools from a Northeastern U.S. community during three seasons (fall, winter and spring) during the years 2009 to 2013. Concurrently, daily outdoor PM
samples were taken at a central monitoring site located at a median distance of 4974 m (range 1065-11,592 m) from the schools. Classroom indoor concentrations of PM
(an average of 5.2 μg/m
) were lower than outdoors (an average of 6.5 μg/m
), and these averages were in the lower range compared to the findings in other schools' studies. The USEPA PMF model was applied to the PM
components measured simultaneously from classroom indoor and outdoor to estimate the source apportionment. The major sources (contributions) identified across all seasons of indoor PM
were secondary pollution (41%) and motor vehicles (17%), followed by Calcium (Ca)-rich particles (12%), biomass burning (15%), soil dust (6%), and marine aerosols (4%). Likewise, the major sources of outdoor PM
across all seasons were secondary pollution (41%) and motor vehicles (26%), followed by biomass burning (17%), soil dust (7%), road dust (3%), and marine aerosols (1%). Secondary pollution was the greatest contributor to indoor and outdoor PM
over all three seasons, with the highest contribution during spring with 53% to indoor PM
and 45% to outdoor PM
. Lower contributions of this source during fall and winter are most likely attributed to less infiltration indoors. In contrast, the indoor contribution of motor vehicles source was highest in the fall (29%) and winter (25%), which was presumably categorized by a local source. From the relationship between indoor-to-outdoor sulfur ratios and each source contribution, we also estimated the local and regional influence on indoor PM
concentration. Overall, the observed differences to indoor PM
are related to seasonality, and the distinct characteristics and behavior of each classroom/school.
The link between daily changes in level of ambient fine particulate matter (PM) air pollution (PM <2.5 μm in diameter PM(2.5)) and cardiovascular morbidity and mortality is well established. Whether ...PM(2.5) levels below current US National Ambient Air Quality Standards also increase the risk of ischemic stroke remains uncertain.
We reviewed the medical records of 1705 Boston area patients hospitalized with neurologist-confirmed ischemic stroke and abstracted data on the time of symptom onset and clinical characteristics. The PM(2.5) concentrations were measured at a central monitoring station. We used the time-stratified case-crossover study design to assess the association between the risk of ischemic stroke onset and PM(2.5) levels in the hours and days preceding each event. We examined whether the association with PM(2.5) levels differed by presumed ischemic stroke pathophysiologic mechanism and patient characteristics.
The estimated odds ratio (OR) of ischemic stroke onset was 1.34 (95% CI, 1.13-1.58) (P < .001) following a 24-hour period classified as moderate (PM(2.5) 15-40 μg/m(3)) by the US Environmental Protection Agency's (EPA) Air Quality Index compared with a 24-hour period classified as good (≤15 μg/m(3)). Considering PM(2.5) levels as a continuous variable, we found the estimated odds ratio of ischemic stroke onset to be 1.11 (95% CI, 1.03-1.20) (P = .006) per interquartile range increase in PM(2.5) levels (6.4 μg/m(3)). The increase in risk was greatest within 12 to 14 hours of exposure to PM(2.5) and was most strongly associated with markers of traffic-related pollution.
These results suggest that exposure to PM(2.5) levels considered generally safe by the US EPA increase the risk of ischemic stroke onset within hours of exposure.
Context: Histone modifications regulate gene expression; dysregulation has been linked with cardiovascular diseases. Associations between histone modification levels and blood pressure in humans are ...unclear.
Objective: We examine the relationship between global histone concentrations and various markers of blood pressure.
Materials and methods: Using the Beijing Truck Driver Air Pollution Study, we investigated global peripheral white blood cell histone modifications (H3K9ac, H3K9me3, H3K27me3, and H3K36me3) associations with pre- and post-work measurements of systolic (SBP) and diastolic (DBP) blood pressure, mean arterial pressure (MAP), and pulse pressure (PP) using multivariable mixed-effect models.
Results: H3K9ac was negatively associated with pre-work SBP and MAP; H3K9me3 was negatively associated with pre-work SBP, DBP, and MAP; and H3K27me3 was negatively associated with pre-work SBP. Among office workers, H3K9me3 was negatively associated with pre-work SBP, DBP, and MAP. Among truck drivers, H3K9ac and H3K27me were negatively associated with pre-work SBP, and H3K27me3 was positively associated with post-work PP.
Discussion and conclusion: Epigenome-wide H3K9ac, H3K9me3, and H3K27me3 were negatively associated with multiple pre-work blood pressure measures. These associations substantially changed during the day, suggesting an influence of daily activities. Blood-based histone modification biomarkers are potential candidates for studies requiring estimations of morning/pre-work blood pressure.
Temporally resolved assessment of residential exposure to radon is essential for investigating radon’s acute health effects. Recent studies have used large numbers of short-term radon measurements to ...model the spatiotemporal variations in radon concentrations. However, most short-term radon measurements in the northeastern and midwestern United States were conducted in the basements, which were less frequently occupied and had higher average radon concentrations than the upstairs spaces. Disproportionate usage of basement radon measurements in exposure assessment potentially introduces misclassifications. In an effort to mitigate the issue, we investigated the spatiotemporal gradients in ratios between the radon concentrations in the upstairs and basements (hereafter upstairs/basement ratio). Building-specific ratios were calculated on the basis of 10774 pairs of simultaneous short-term measurements and then aggregated by state and season. We found that upstairs/basement ratios of northeastern states are generally lower than those of Midwestern states, a pattern also found in 3508 pairs of simultaneous long-term radon measurements. Ratios in winter are higher than those in other seasons. Our results, in conjunction with behavior data, can improve the assessment of short-term residential exposure to radon and therefore facilitate future studies regarding the acute health effects of radon.
The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM
) for epidemiology studies has increased substantially over the past few years. These recent studies ...often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data.
We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM
at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM
measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site.
Our model performance was excellent (mean out-of-sample R
=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R
=0.87, R
=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99).
Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.
The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM sub(2.5)) for epidemiology studies has increased substantially over the past few years. These recent ...studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM sub(2.5) at a 1x1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1x1 km grid predictions. We used mixed models regressing PM sub(2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R super(2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R super(2) = 0.87, R super(2) = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.
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