International expert workshop on the analysis of the economic and public health impacts of air pollution: workshop summary.

Forty-nine experts from 18 industrial and developing countries met on 6 September 2001 in Garmisch-Partenkirchen, Germany, to discuss the economic and public health impacts of air pollution, particularly with respect to assessing the public health benefits from technologies and policies that reduce greenhouse gas (GHG) emissions. Such measures would provide immediate public health benefits, such as reduced premature mortality and chronic morbidity, through improved local air quality. These mitigation strategies also allow long-term goals--for example, reducing the buildup of GHG emissions--to be achieved alongside short-term aims, such as immediate improvements in air quality, and therefore benefits to public health. The workshop aimed to foster research partnerships by improving collaboration and communication among various agencies and researchers; providing a forum for presentations by sponsoring agencies and researchers regarding research efforts and agency activities; identifying key issues, knowledge gaps, methodological shortcomings, and research needs; and recommending activities and initiatives for research, collaboration, and communication. This workshop summary briefly describes presentations made by workshop participants and the conclusions of three separate working groups: economics, benefits transfer, and policy; indoor air quality issues and susceptible populations; and development and transfer of dose-response relationships and exposure models in developing countries. Several common themes emerged from the working group sessions and subsequent discussion. Key recommendations include the need for improved communication and extended collaboration, guidance and support for researchers, advances in methods, and resource support for data collection, assessment, and research

Ambient particulate matter air pollution exposure and mortality in the NIH-AARP diet and health cohort

BACKGROUND: Outdoor fine particulate matter (≤ 2.5 μm; PM2.5) has been identified as a global health threat, but the number of large U.S. prospective cohort studies with individual participant data remains limited, especially at lower recent exposures. OBJECTIVES: We aimed to test the relationship between long-term exposure PM2.5 and death risk from all nonaccidental causes, cardiovascular (CVD), and respiratory diseases in 517,041 men and women enrolled in the National Institutes of Health-AARP cohort. METHODS: Individual participant data were linked with residence PM2.5 exposure estimates across the continental United States for a 2000–2009 follow-up period when matching census tract–level PM2.5 exposure data were available. Participants enrolled ranged from 50 to 71 years of age, residing in six U.S. states and two cities. Cox proportional hazard models yielded hazard ratio (HR) estimates per 10 μg/m3 of PM2.5 exposure. RESULTS: PM2.5 exposure was significantly associated with total mortality (HR = 1.03; 95% CI: 1.00, 1.05) and CVD mortality (HR = 1.10; 95% CI: 1.05, 1.15), but the association with respiratory mortality was not statistically significant (HR = 1.05; 95% CI: 0.98, 1.13). A significant association was found with respiratory mortality only among never smokers (HR = 1.27; 95% CI: 1.03, 1.56). Associations with 10-μg/m3 PM2.5 exposures in yearly participant residential annual mean, or in metropolitan area-wide mean, were consistent with baseline exposure model results. Associations with PM2.5 were similar when adjusted for ozone exposures. Analyses of California residents alone also yielded statistically significant PM2.5 mortality HRs for total and CVD mortality. CONCLUSIONS: Long-term exposure to PM2.5 air pollution was associated with an increased risk of total and CVD mortality, providing an independent test of the PM2.5–mortality relationship in a new large U.S. prospective cohort experiencing lower post-2000 PM2.5 exposure levels. CITATION: Thurston GD, Ahn J, Cromar KR, Shao Y, Reynolds HR, Jerrett M, Lim CC, Shanley R, Park Y, Hayes RB. 2016. Ambient particulate matter air pollution exposure and mortality in the NIH-AARP Diet and Health cohort. Environ Health Perspect 124:484–490; http://dx.doi.org/10.1289/ehp.150967

Workgroup Report: Workshop on Source Apportionment of Particulate Matter Health Effects—Intercomparison of Results and Implications

Although the association between exposure to ambient fine particulate matter with aerodynamic diameter < 2.5 μm (PM(2.5)) and human mortality is well established, the most responsible particle types/sources are not yet certain. In May 2003, the U.S. Environmental Protection Agency’s Particulate Matter Centers Program sponsored the Workshop on the Source Apportionment of PM Health Effects. The goal was to evaluate the consistency of the various source apportionment methods in assessing source contributions to daily PM(2.5) mass–mortality associations. Seven research institutions, using varying methods, participated in the estimation of source apportionments of PM(2.5) mass samples collected in Washington, DC, and Phoenix, Arizona, USA. Apportionments were evaluated for their respective associations with mortality using Poisson regressions, allowing a comparative assessment of the extent to which variations in the apportionments contributed to variability in the source-specific mortality results. The various research groups generally identified the same major source types, each with similar elemental makeups. Intergroup correlation analyses indicated that soil-, sulfate-, residual oil-, and salt-associated mass were most unambiguously identified by various methods, whereas vegetative burning and traffic were less consistent. Aggregate source-specific mortality relative risk (RR) estimate confidence intervals overlapped each other, but the sulfate-related PM(2.5) component was most consistently significant across analyses in these cities. Analyses indicated that source types were a significant predictor of RR, whereas apportionment group differences were not. Variations in the source apportionments added only some 15% to the mortality regression uncertainties. These results provide supportive evidence that existing PM(2.5) source apportionment methods can be used to derive reliable insights into the source components that contribute to PM(2.5) health effects

Repeated measures of inflammation, blood pressure, and heart rate variability associated with traffic exposures in healthy adults

Abstract Background Previous human exposure studies of traffic-related air pollutants have demonstrated adverse health effects in human populations by comparing areas of high and low traffic, but few studies have utilized microenvironmental monitoring of pollutants at multiple traffic locations while looking at a vast array of health endpoints in the same population. We evaluated inflammatory markers, heart rate variability (HRV), blood pressure, exhaled nitric oxide, and lung function in healthy participants after exposures to varying mixtures of traffic pollutants. Methods A repeated-measures, crossover study design was used in which 23 healthy, non-smoking adults had clinical cardiopulmonary and systemic inflammatory measurements taken prior to, immediately after, and 24 hours after intermittent walking for two hours in the summer months along three diverse roadways having unique emission characteristics. Measurements of PM2.5, PM10, black carbon (BC), elemental carbon (EC), and organic carbon (OC) were collected. Mixed effect models were used to assess changes in health effects associated with these specific pollutant classes. Results Minimal associations were observed with lung function measurements and the pollutants measured. Small decreases in BP measurements and rMSSD, and increases in IL-1β and the low frequency to high frequency ratio measured in HRV, were observed with increasing concentrations of PM2.5 EC. Conclusions Small, acute changes in cardiovascular and inflammation-related effects of microenvironmental exposures to traffic-related air pollution were observed in a group of healthy young adults. The associations were most profound with the diesel-source EC

Statistical-Thermodynamic Model for Light Scattering from Eye Lens Protein Mixtures

We model light-scattering cross sections of concentrated aqueous mixtures of the bovine eye lens proteins γB- and α-crystallin by adapting a statistical-thermodynamic model of mixtures of spheres with short-range attractions. The model reproduces measured static light scattering cross sections, or Rayleigh ratios, of γB-α mixtures from dilute concentrations where light scattering intensity depends on molecular weights and virial coefficients, to realistically high concentration protein mixtures like those of the lens. The model relates γB-γB and γB-α attraction strengths and the γB-α size ratio to the free energy curvatures that set light scattering efficiency in tandem with protein refractive index increments. The model includes (i) hard-sphere α-α interactions, which create short-range order and transparency at high protein concentrations, (ii) short-range attractive plus hard-core γ-γ interactions, which produce intense light scattering and liquid-liquid phase separation in aqueous γ-crystallin solutions, and (iii) short-range attractive plus hard-core γ-α interactions, which strongly influence highly non-additive light scattering and phase separation in concentrated γ-α mixtures. The model reveals a new lens transparency mechanism, that prominent equilibrium composition fluctuations can be perpendicular to the refractive index gradient. The model reproduces the concave-up dependence of the Rayleigh ratio on α/γ composition at high concentrations, its concave-down nature at intermediate concentrations, non-monotonic dependence of light scattering on γ-α attraction strength, and more intricate, temperature-dependent features. We analytically compute the mixed virial series for light scattering efficiency through third order for the sticky-sphere mixture, and find that the full model represents the available light scattering data at concentrations several times those where the second and third mixed virial contributions fail. The model indicates that increased γ-γ attraction can raise γ-α mixture light scattering far more than it does for solutions of γ-crystallin alone, and can produce marked turbidity tens of degrees celsius above liquid-liquid separation

Comparing the Health Effects of Ambient Particulate Matter Estimated Using Ground-Based versus Remote Sensing Exposure Estimates

BACKGROUND: Remote sensing (RS) is increasingly used for exposure assessment in epidemiological and burden of disease studies, including those investigating whether chronic exposure to ambient fine particulate matter (PM2.5) is associated with mortality. OBJECTIVES: To compare relative risk estimates of mortality from diseases of the circulatory system for PM2.5 modeled from RS with that for PM2.5 modeled using ground-level information. METHODS: We geocoded the baseline residence of 668,629 American Cancer Society Cancer Prevention Study II (CPS-II) cohort participants followed from 1982 to 2004 and assigned PM2.5 levels to all participants using seven different exposure models. Most of the exposure models were averaged for the years 2002-2004, while one RS estimate was for a longer, contemporaneous period. We used Cox proportional hazards regression to estimate relative risks (RR) for the association of PM2.5 with circulatory mortality and ischemic heart disease. RESULTS: Estimates of mortality risk differed among exposure models. The smallest relative risk was observed for the RS estimates that excluded ground-based monitors for circulatory deaths (RR = 1.02 (95% confidence interval (CI): 1.00-1.04 per 10 microg/m3 increment in PM2.5). The largest relative risk was observed for the land use regression model that included traffic information (RR = 1.14, 95% CI: 1.11-1.17 per 10 microg/m3 increment in PM2.5). CONCLUSIONS: We found significant associations between PM2.5 and mortality in every model; however, relative risks estimated from exposure models using ground-based information were generally larger than those estimated with RS alone

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations