Associations of Fine Particulate Matter Species with Mortality in the United States: A Multicity Time-Series Analysis

Background: Epidemiological studies have examined the association between PM2.5 and mortality, but uncertainty remains about the seasonal variations in PM2.5-related effects and the relative importance of species. Objectives: We estimated the effects of PM2.5 species on mortality and how infiltration rates may modify the association. Methods: Using city–season specific Poisson regression, we estimated PM2.5 effects on approximately 4.5 million deaths for all causes, cardiovascular disease (CVD), myocardial infarction (MI), stroke, and respiratory diseases in 75 U.S. cities for 2000–2006. We added interaction terms between PM2.5 and monthly average species-to-PM2.5 proportions of individual species to determine the relative toxicity of each species. We combined results across cities using multivariate meta-regression, and controlled for infiltration. Results: We estimated a 1.18% (95% CI: 0.93, 1.44%) increase in all-cause mortality, a 1.03% (95% CI: 0.65, 1.41%) increase in CVD, a 1.22% (95% CI: 0.62, 1.82%) increase in MI, a 1.76% (95% CI: 1.01, 2.52%) increase in stroke, and a 1.71% (95% CI: 1.06, 2.35%) increase in respiratory deaths in association with a 10-μg/m3 increase in 2-day averaged PM2.5 concentration. The associations were largest in the spring. Silicon, calcium, and sulfur were associated with more all-cause mortality, whereas sulfur was related to more respiratory deaths. County-level smoking and alcohol were associated with larger estimated PM2.5 effects. Conclusions: Our study showed an increased risk of mortality associated with PM2.5, which varied with seasons and species. The results suggest that mass alone might not be sufficient to evaluate the health effects of particles. Citation: Dai L, Zanobetti A, Koutrakis P, Schwartz JD. 2014. Associations of fine particulate matter species with mortality in the United States: a multicity time-series analysis. Environ Health Perspect 122:837–842; http://dx.doi.org/10.1289/ehp.130756

In-vehicle exposure to NO2 and PM2.5:A comprehensive assessment of controlling parameters and reduction strategies to minimise personal exposure

Vehicles are the third most occupied microenvironment, other than home and workplace, in developed urban areas. Vehicle cabins are confined spaces where occupants can mitigate their exposure to on-road nitrogen dioxide (NO2) and fine particulate matter (PM2.5) concentrations. Understanding which parameters exert the greatest influence on in-vehicle exposure underpins advice to drivers and vehicle occupants in general. This study assessed the in-vehicle NO2 and PM2.5 levels and developed stepwise general additive mixed models (sGAMM) to investigate comprehensively the combined and individual influences of factors that influence the in-vehicle exposures. The mean in-vehicle levels were 19 ± 18 and 6.4 ± 2.7 μg/m3 for NO2 and PM2.5, respectively. sGAMM model identified significant factors explaining a large fraction of in-vehicle NO2 and PM2.5 variability, R2 = 0.645 and 0.723, respectively. From the model's explained variability on-road air pollution was the most important predictor accounting for 22.3 and 30 % of NO2 and PM2.5 variability, respectively. Vehicle-based predictors included manufacturing year, cabin size, odometer reading, type of cabin filter, ventilation fan speed power, window setting, and use of air recirculation, and together explained 48.7 % and 61.3 % of NO2 and PM2.5 variability, respectively, with 41.4 % and 51.9 %, related to ventilation preference and type of filtration media, respectively. Driving-based parameters included driving speed, traffic conditions, traffic lights, roundabouts, and following high emitters and accounted for 22 and 7.4 % of in-vehicle NO2 and PM2.5 exposure variability, respectively. Vehicle occupants can significantly reduce their in-vehicle exposure by moderating vehicle ventilation settings and by choosing an appropriate cabin air filter

The effect of short-term changes in air pollution on respiratory and cardiovascular morbidity in Nicosia, Cyprus.

Presented at the 6th International Conference on Urban Air Quality, Limassol, March, 2007. Short-paper was submitted for peer-review and appears in proceedings of the conference.This study investigates the effect of daily changes in levels of PM10 on the daily volume of respiratory and cardiovascular admissions in Nicosia, Cyprus during 1995-2004. After controlling for long- (year and month) and short-term (day of the week) patterns as well as the effect of weather in Generalized Additive Poisson models, some positive associations were observed with all-cause and cause-specific admissions. Risk of hospitalization increased stepwise across quartiles of days with increasing levels of PM10 by 1.3% (-0.3, 2.8), 4.9% (3.3, 6.6), 5.6% (3.9, 7.3) as compared to days with the lowest concentrations. For every 10μg/m3 increase in daily average PM10 concentration, there was a 1.2% (-0.1%, 2.4%) increase in cardiovascular admissions. With respects to respiratory admissions, an effect was observed only in the warm season with a 1.8% (-0.22, 3.85) increase in admissions per 10μg/m3 increase in PM10. The effect on respiratory admissions seemed to be much stronger in women and, surprisingly, restricted to people of adult age

Estimating Ground-Level PM2.5 in the Eastern United States Using Satellite Remote Sensing

An empirical model based on the regression between daily PM2.5 (particles with aerodynamic diameters of less than 2.5 μm) concentrations and aerosol optical thickness (AOT) measurements from the multiangle imaging spectroradiometer (MISR) was developed and tested using data from the eastern United States during the period of 2001. Overall, the empirical model explained 48% of the variability in PM2.5 concentrations. The root-mean-square error of the model was 6.2 μg/m3 with a corresponding average PM2.5 concentration of 13.8 μg/m3. When PM2.5 concentrations greater than 40 μg/m3 were removed, model results were shown to be unbiased estimators of observations. Several factors, such as planetary boundary layer height, relative humidity, season, and other geographical attributes of monitoring sites, were found to influence the association between PM2.5 and AOT. The findings of this study illustrate the strong potential of satellite remote sensing in regional ambient air quality monitoring as an extension to ground networks. With the continual advancement of remote sensing technology and global data assimilation systems, AOT measurements derived from satellite remote sensors may provide a cost-effective approach as a supplemental source of information for determining ground-level particle concentrations.Earth and Planetary Science

Exposure to sub-chronic and long-term particulate air pollution and heart rate variability in an elderly cohort: the Normative Aging Study

Background: Short-term particulate air pollution exposure is associated with reduced heart rate variability (HRV), a risk factor for cardiovascular morbidity and mortality, in many studies. Associations with sub-chronic or long-term exposures, however, have been sparsely investigated. We evaluated the effect of fine particulate matter (PM2.5) and black carbon (BC) exposures on HRV in an elderly cohort: the Normative Aging Study. Methods: We measured power in high frequency (HF) and low frequency (LF), standard deviation of normal-to-normal intervals (SDNN), and the LF:HF ratio among participants from the Greater Boston area. Residential BC exposures for 540 men (1161 study visits, 2000–2011) were estimated using a spatio-temporal land use regression model, and residential PM2.5 exposures for 475 men (992 visits, 2003–2011) were modeled using a hybrid satellite based and land-use model. We evaluated associations between moving averages of sub-chronic (3–84 day) and long-term (1 year) pollutant exposure estimates and HRV parameters using linear mixed models. Results: One-standard deviation increases in sub-chronic, but not long-term, BC were associated with reduced HF, LF, and SDNN and an increased LF:HF ratio (e.g., 28 day BC: −2.3 % HF [95 % CI:−4.6, −0.02]). Sub-chronic and long-term PM2.5 showed evidence of relations to an increased LF and LF:HF ratio (e.g., 1 year PM: 21.0 % LF:HF [8.6, 34.8]), but not to HF or SDNN, though the effect estimates were very imprecise and mostly spanned the null. Conclusions: We observed some evidence of a relation between longer-term BC and PM2.5 exposures and changes in HRV in an elderly cohort. While previous studies focused on short-term air pollution exposures, our results suggest that longer-term exposures may influence cardiac autonomic function. Electronic supplementary material The online version of this article (doi:10.1186/s12940-015-0074-z) contains supplementary material, which is available to authorized users

TOXICOLOGICAL EVALUATION OF REALISTIC EMISSIONS OF SOURCE AEROSOLS (TERESA): APPLICATION TO POWER PLANT-DERIVED PM2.5

Determining the health impacts of different sources and components of fine particulate matter (PM2.5) is an important scientific goal, because PM is a complex mixture of both inorganic and organic constituents that likely differ in their potential to cause adverse health outcomes. The TERESA (Toxicological Evaluation of Realistic Emissions of Source Aerosols) study focused on two PM sources - coal-fired power plants and mobile sources - and sought to investigate the toxicological effects of exposure to realistic emissions from these sources. The DOE-EPRI Cooperative Agreement covered the performance and analysis of field experiments at three power plants. The mobile source component consisted of experiments conducted at a traffic tunnel in Boston; these activities were funded through the Harvard-EPA Particulate Matter Research Center and will be reported separately in the peer-reviewed literature. TERESA attempted to delineate health effects of primary particles, secondary (aged) particles, and mixtures of these with common atmospheric constituents. The study involved withdrawal of emissions directly from power plant stacks, followed by aging and atmospheric transformation of emissions in a mobile laboratory in a manner that simulated downwind power plant plume processing. Secondary organic aerosol (SOA) derived from the biogenic volatile organic compound {alpha}-pinene was added in some experiments, and in others ammonia was added to neutralize strong acidity. Specifically, four scenarios were studied at each plant: primary particles (P); secondary (oxidized) particles (PO); oxidized particles + secondary organic aerosol (SOA) (POS); and oxidized and neutralized particles + SOA (PONS). Extensive exposure characterization was carried out, including gas-phase and particulate species. Male Sprague Dawley rats were exposed for 6 hours to filtered air or different atmospheric mixtures. Toxicological endpoints included (1) breathing pattern; (2) bronchoalveolar lavage (BAL) fluid cytology and biochemistry; (3) blood cytology; (4) in vivo oxidative stress in heart and lung tissue; and (5) heart and lung histopathology. In addition, at one plant, cardiac arrhythmias and heart rate variability (HRV) were evaluated in a rat model of myocardial infarction. Statistical analyses included analyses of variance (ANOVA) to determine differences between exposed and control animals in response to different scenario/plant combinations; univariate analyses to link individual scenario components to responses; and multivariate analyses (Random Forest analyses) to evaluate component effects in a multipollutant setting. Results from the power plant studies indicated some biological responses to some plant/scenario combinations. A number of significant breathing pattern changes were observed; however, significant clinical changes such as specific irritant effects were not readily apparent, and effects tended to be isolated changes in certain respiratory parameters. Some individual exposure scenario components appeared to be more strongly and consistently related to respiratory parameter changes; however, the specific scenario investigated remained a better predictor of response than individual components of that scenario. Bronchoalveolar lavage indicated some changes in cellularity of BAL fluid in response to the POS and PONS scenarios; these responses were considered toxicologically mild in magnitude. No changes in blood cytology were observed at any plant or scenario. Lung oxidative stress was increased with the POS scenario at one plant, and cardiac oxidative stress was increased with the PONS scenario also at one plant, suggesting limited oxidative stress in response to power plant emissions with added atmospheric constituents. There were some mild histological findings in lung tissue in response to the P and PONS scenarios. Finally, the MI model experiments indicated that premature ventricular beat frequency was increased at the plant studied, while no changes in heart rate, HRV, or electrocardiographic intervals were observed. Overall, the TERESA results should be interpreted as indicating toxicologically mild adverse responses to some scenarios. The varied responses among the three plants indicate heterogeneity in emissions. Ongoing studies using the TERESA approach to evaluate the toxicity of traffic-related pollution will yield valuable data for comparative toxicity assessment and will give us a better understanding of the contribution of different sources to the morbidity and mortality associated with exposure to air pollution

NO2 levels inside vehicle cabins with pollen and activated carbon filters::A real world targeted intervention to estimate NO2 exposure reduction potential

Traffic related nitrogen dioxide (NO2) poses a serious environmental and health risk factor in the urban environment. Drivers and vehicle occupants in general may have acute exposure to NO2 levels. In order to identify key controllable measures to reduce vehicle occupant's exposure, this study measures NO2 exposure inside ten different vehicles under real world driving conditions and applies a targeted intervention by replacing previously used filters with new standard pollen and new activated carbon cabin filters. The study also evaluates the efficiency of the latter as a function of duration of use. The mean in-vehicle NO2 exposure across the tested vehicles, driving the same route under comparable traffic and ambient air quality conditions, was 50.8 ± 32.7 μg/m3 for the new standard pollen filter tests and 9.2 ± 8.6 μg/m3 for the new activated carbon filter tests. When implementing the new activated carbon filters, overall we observed significant (p < 0.05) reductions by 87 % on average (range 80 - 94.2 %) in the in-vehicle NO2 levels compared to the on-road concentrations. We further found that the activated carbon filter NO2 removal efficiency drops by 6.8 ± 0.6 % per month; showing a faster decay in removal efficiency after the first 6 months of use. These results offer novel insights into how the general population can control and reduce their exposure to traffic related NO2. The use and regular replacement of activated carbon cabin air filters represents a relatively inexpensive method to significantly reduce in-vehicle NO2 exposure

Estimating Regional Spatial and Temporal Variability of PM2.5 Concentrations Using Satellite Data, Meteorology, and Land Use Information

Background: Studies of chronic health effects due to exposures to particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) are often limited by sparse measurements. Satellite aerosol remote sensing data may be used to extend PM2.5 ground networks to cover a much larger area. Objectives: In this study we examined the benefits of using aerosol optical depth (AOD) retrieved by the Geostationary Operational Environmental Satellite (GOES) in conjunction with land use and meteorologic information to estimate ground-level PM2.5 concentrations. Methods: We developed a two-stage generalized additive model (GAM) for U.S. Environmental Protection Agency PM2.5 concentrations in a domain centered in Massachusetts. The AOD model represents conditions when AOD retrieval is successful; the non-AOD model represents conditions when AOD is missing in the domain. Results: The AOD model has a higher predicting power judged by adjusted R2 (0.79) than does the non-AOD model (0.48). The predicted PM2.5 concentrations by the AOD model are, on average, 0.8–0.9 μg/m3 higher than the non-AOD model predictions, with a more smooth spatial distribution, higher concentrations in rural areas, and the highest concentrations in areas other than major urban centers. Although AOD is a highly significant predictor of PM2.5, meteorologic parameters are major contributors to the better performance of the AOD model. Conclusions: GOES aerosol/smoke product (GASP) AOD is able to summarize a set of weather and land use conditions that stratify PM2.5 concentrations into two different spatial patterns. Even if land use regression models do not include AOD as a predictor variable, two separate models should be fitted to account for different PM2.5 spatial patterns related to AOD availability