Project description:We evaluate fine particulate matter (PM2.5) exposure-response models to propose a consistent set of global effect factors for product and policy assessments across spatial scales and across urban and rural environments. Relationships among exposure concentrations and PM2.5-attributable health effects largely depend on location, population density, and mortality rates. Existing effect factors build mostly on an essentially linear exposure-response function with coefficients from the American Cancer Society study. In contrast, the Global Burden of Disease analysis offers a nonlinear integrated exposure-response (IER) model with coefficients derived from numerous epidemiological studies covering a wide range of exposure concentrations. We explore the IER, additionally provide a simplified regression as a function of PM2.5 level, mortality rates, and severity, and compare results with effect factors derived from the recently published global exposure mortality model (GEMM). Uncertainty in effect factors is dominated by the exposure-response shape, background mortality, and geographic variability. Our central IER-based effect factor estimates for different regions do not differ substantially from previous estimates. However, IER estimates exhibit significant variability between locations as well as between urban and rural environments, driven primarily by variability in PM2.5 concentrations and mortality rates. Using the IER as the basis for effect factors presents a consistent picture of global PM2.5-related effects for use in product and policy assessment frameworks.
Project description:Epidemiologic and health impact studies are inhibited by the paucity of global, long-term measurements of the chemical composition of fine particulate matter. We inferred PM2.5 chemical composition at 0.1° × 0.1° spatial resolution for 2004-2008 by combining aerosol optical depth retrieved from the MODIS and MISR satellite instruments, with coincident profile and composition information from the GEOS-Chem global chemical transport model. Evaluation of the satellite-model PM2.5 composition data set with North American in situ measurements indicated significant spatial agreement for secondary inorganic aerosol, particulate organic mass, black carbon, mineral dust, and sea salt. We found that global population-weighted PM2.5 concentrations were dominated by particulate organic mass (11.9 ± 7.3 ?g/m(3)), secondary inorganic aerosol (11.1 ± 5.0 ?g/m(3)), and mineral dust (11.1 ± 7.9 ?g/m(3)). Secondary inorganic PM2.5 concentrations exceeded 30 ?g/m(3) over East China. Sensitivity simulations suggested that population-weighted ambient PM2.5 from biofuel burning (11 ?g/m(3)) could be almost as large as from fossil fuel combustion sources (17 ?g/m(3)). These estimates offer information about global population exposure to the chemical components and sources of PM2.5.
Project description: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.
Project description:Several studies have pointed to fine particulate matter (PM2.5) as the main responsible for air pollution toxic effects. Indeed, PM2.5 may not only cause respiratory and cardiovascular abnormalities but it may also affect other organs such as the liver. Be that as it may, only a few studies have evaluated the PM2.5 effects on hepatic tissue. Moreover, most of them have not analyzed the relationship between particles composition and toxicological effects. In this study, healthy rats were subjected to urban levels of PM2.5 particles in order to assess their structural and functional effects on the liver. During the exposure periods, mean PM2.5 concentrations were slightly higher than the value suggested by the daily guideline of the World Health Organization. The exposed rats showed a hepatic increase of Cr, Zn, Fe, Ba, Tl and Pb levels. This group also showed leukocyte infiltration, sinusoidal dilation, hydropic inclusions and alterations in carbohydrates distribution. These histologic lesions were accompanied by serological changes, such as increase of total cholesterol and triglycerides, as well as genotoxic damage in their nuclei. We also observed significant associations between several biomarkers and PM2.5 composition. Our results show that exposure to low levels of PM2.5 might cause histologic and serological changes in liver tissue, suggesting that PM2.5 toxicity is influenced not only by their concentration but also by their composition and the exposure frequency.
Project description:BACKGROUND: More than a decade of satellite observations offers global information about the trend and magnitude of human exposure to fine particulate matter (PM2.5). OBJECTIVE: In this study, we developed improved global exposure estimates of ambient PM2.5 mass and trend using PM2.5 concentrations inferred from multiple satellite instruments. METHODS: We combined three satellite-derived PM2.5 sources to produce global PM2.5 estimates at about 10 km × 10 km from 1998 through 2012. For each source, we related total column retrievals of aerosol optical depth to near-ground PM2.5 using the GEOS-Chem chemical transport model to represent local aerosol optical properties and vertical profiles. We collected 210 global ground-based PM2.5 observations from the literature to evaluate our satellite-based estimates with values measured in areas other than North America and Europe. RESULTS: We estimated that global population-weighted ambient PM2.5 concentrations increased 0.55 ?g/m3/year (95% CI: 0.43, 0.67) (2.1%/year; 95% CI: 1.6, 2.6) from 1998 through 2012. Increasing PM2.5 in some developing regions drove this global change, despite decreasing PM2.5 in some developed regions. The estimated proportion of the population of East Asia living above the World Health Organization (WHO) Interim Target-1 of 35 ?g/m3 increased from 51% in 1998-2000 to 70% in 2010-2012. In contrast, the North American proportion above the WHO Air Quality Guideline of 10 ?g/m3 fell from 62% in 1998-2000 to 19% in 2010-2012. We found significant agreement between satellite-derived estimates and ground-based measurements outside North America and Europe (r = 0.81; n = 210; slope = 0.68). The low bias in satellite-derived estimates suggests that true global concentrations could be even greater. CONCLUSIONS: Satellite observations provide insight into global long-term changes in ambient PM2.5 concentrations. Satellite-derived estimates and ground-based PM2.5 observations from this study are available for public use.
Project description:Few studies have explicitly explored the impacts of the extensive adjustment (with a lag period of more than one week) of temperature and humidity on the association between ambient fine particulate matter (PM2.5) and cardiovascular mortality. In a time stratified case-crossover study, we used a distributed lag nonlinear model to assess the impacts of extensive adjustments of temperature and humidity for longer lag periods (for 7, 14, 21, 28 and 40 days) on effects of PM2.5 on total cardiovascular mortality and mortality of cerebrovascular and ischemic heart disease and corresponding exposure-response relationships in Beijing, China, between 2008 and 2011. Compared with results only controlled for temperature and humidity for 2 days, the estimated effects of PM2.5 were smaller and magnitudes of exposure-response curves were decreased when longer lag periods of temperature and relative humidity were included for adjustments, but these changes varied across subpopulation, with marked decreases occurring in males and the elderly who are more susceptible to PM2.5-related mortalities. Our findings suggest that the adjustment of meteorological factors using lag periods shorter than one week may lead to overestimated effects of PM2.5. The associations of PM2.5 with cardiovascular mortality in susceptible populations were more sensitive to further adjustments for temperature and relative humidity.
Project description:BACKGROUND:Cerebrovascular diseases play an important role in dementia. Air pollution is associated with cardiovascular disease, with growing links to neurodegeneration. Prior studies demonstrate associations between fine particulate matter (PM2.5) and biomarkers of endothelial injury in the blood; however, no studies have evaluated these biomarkers in cerebrospinal fluid (CSF). OBJECTIVE:We evaluate associations between short-term and long-term PM2.5 exposure with CSF vascular cell adhesion molecule-1 (VCAM-1) and e-selectin in cognitively normal and mild cognitive impairment (MCI)/Alzheimer's disease (AD) individuals. METHODS:We collected CSF from 133 community volunteers at VA Puget Sound between 2001-2012. We assigned short-term PM2.5 from central monitors and long-term PM2.5 based on annual average exposure predictions linked to participant addresses. We performed analyses stratified by cognitive status and adjusted for key covariates with tiered models. Our primary exposure windows for the short-term and long-term analyses were 7-day and 1-year averages, respectively. RESULTS:Among cognitively normal individuals, a 5 ?g/m3 increase in 7-day and 1-year average PM2.5 was associated with elevated VCAM-1 (7-day: 35.4 (9.7, 61.1) ng/ml; 1-year: 51.8 (6.5, 97.1) ng/ml). A 5 ?g/m3 increase in 1-year average PM2.5, but not 7-day average, was associated with elevated e-selectin (53.3 (11.0, 95.5) pg/ml). We found no consistent associations among MCI/AD individuals. CONCLUSIONS:We report associations between short-term and long term PM2.5 and CSF biomarkers of vascular damage in cognitively normal adults. These results are aligned with prior research linking PM2.5 to vascular damage in other biofluids as well as emerging evidence of the role of PM2.5 in neurodegeneration.
Project description:BackgroundEstimating the burden of disease attributable to long-term exposure to fine particulate matter (PM2.5) in ambient air requires knowledge of both the shape and magnitude of the relative risk (RR) function. However, adequate direct evidence to identify the shape of the mortality RR functions at the high ambient concentrations observed in many places in the world is lacking.ObjectiveWe developed RR functions over the entire global exposure range for causes of mortality in adults: ischemic heart disease (IHD), cerebrovascular disease (stroke), chronic obstructive pulmonary disease (COPD), and lung cancer (LC). We also developed RR functions for the incidence of acute lower respiratory infection (ALRI) that can be used to estimate mortality and lost-years of healthy life in children < 5 years of age.MethodsWe fit an integrated exposure-response (IER) model by integrating available RR information from studies of ambient air pollution (AAP), second hand tobacco smoke, household solid cooking fuel, and active smoking (AS). AS exposures were converted to estimated annual PM2.5 exposure equivalents using inhaled doses of particle mass. We derived population attributable fractions (PAFs) for every country based on estimated worldwide ambient PM2.5 concentrations.ResultsThe IER model was a superior predictor of RR compared with seven other forms previously used in burden assessments. The percent PAF attributable to AAP exposure varied among countries from 2 to 41 for IHD, 1 to 43 for stroke, < 1 to 21 for COPD, < 1 to 25 for LC, and < 1 to 38 for ALRI.ConclusionsWe developed a fine particulate mass-based RR model that covered the global range of exposure by integrating RR information from different combustion types that generate emissions of particulate matter. The model can be updated as new RR information becomes available.
Project description:Exposure to fine particles (PM2.5) during pregnancy has been linked to lower birth weight; however, the chemical composition of PM2.5 varies widely. The health effects of PM2.5 constituents are unknown.We investigated whether PM2.5 mass, constituents, and sources are associated with birth weight for term births. PM2.5 filters collected in 3 Connecticut counties and 1 Massachusetts county from August 2000 through February 2004 were analyzed for more than 50 elements. Source apportionment was used to estimate daily contributions of PM2.5 sources, including traffic, road dust/crustal, oil combustion, salt, and regional (sulfur) sources. Gestational and trimester exposure to PM2.5 mass, constituents, and source contributions were examined in relation to birth weight and risk of small-at-term birth (term birth <2500 g) for 76,788 infants.Road dust and related constituents such as silicon and aluminum were associated with lower birth weight, as were the motor-vehicle-related species such as elemental carbon and zinc, and the oil-combustion-associated elements vanadium and nickel. An interquartile range increase in exposure was associated with low birthweight for zinc (12% increase in risk), elemental carbon (13%), silicon (10%), aluminum (11%), vanadium (8%), and nickel (11%). Analysis by trimester showed effects of third-trimester exposure to elemental carbon, nickel, vanadium, and oil-combustion PM2.5.Exposures of pregnant women to higher levels of certain PM2.5 chemical constituents originating from specific sources are associated with lower birth weight.