Exposure Assessment for Estimation of the Global Burden of Disease Attributable to Outdoor Air Pollution

School of Population and Public Health, The University of British Columbia, 2206 East Mall, Vancouver, British Columbia V6T1Z3, Canada.
Environmental Science & Technology (Impact Factor: 5.48). 12/2011; 46(2):652-60. DOI: 10.1021/es2025752
Source: PubMed

ABSTRACT Ambient air pollution is associated with numerous adverse health impacts. Previous assessments of global attributable disease burden have been limited to urban areas or by coarse spatial resolution of concentration estimates. Recent developments in remote sensing, global chemical-transport models, and improvements in coverage of surface measurements facilitate virtually complete spatially resolved global air pollutant concentration estimates. We combined these data to generate global estimates of long-term average ambient concentrations of fine particles (PM(2.5)) and ozone at 0.1° × 0.1° spatial resolution for 1990 and 2005. In 2005, 89% of the world's population lived in areas where the World Health Organization Air Quality Guideline of 10 μg/m(3) PM(2.5) (annual average) was exceeded. Globally, 32% of the population lived in areas exceeding the WHO Level 1 Interim Target of 35 μg/m(3), driven by high proportions in East (76%) and South (26%) Asia. The highest seasonal ozone levels were found in North and Latin America, Europe, South and East Asia, and parts of Africa. Between 1990 and 2005 a 6% increase in global population-weighted PM(2.5) and a 1% decrease in global population-weighted ozone concentrations was apparent, highlighted by increased concentrations in East, South, and Southeast Asia and decreases in North America and Europe. Combined with spatially resolved population distributions, these estimates expand the evaluation of the global health burden associated with outdoor air pollution.

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Available from: Sarah B Henderson, Apr 22, 2015
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    • "The annual average ambient fine particulate matter (PM 2.5 , or particles up to 2.5 μm in diameter) concentrations in this city were measured at about 70–100 μg/m 3 (Cheng et al. 2013a; Wang et al. 2013), which were about two to three times higher than the WHO Level 1 Interim Target of 35 μg/ m 3 (WHO Press 2006). Globally, the city has been listed among the most PM 2.5 -polluted areas (Brauer et al. 2012; Van Donkelaar et al. 2010). Overexposure to PM 2.5 has been proved to be associated with a wide variety of health effects such as aggravation of existing heart and lung disease and premature mortality (Anderson et al. 2012; Dockery et al. 1993; Jerrett et al. 2005b; Pope and Dockery 2006). "
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    ABSTRACT: Fine particulate matter (PM2.5) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM2.5 by building separate LUR models in Beijing. Hourly routine PM2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m3 (SD = 13.7). PM2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R 2 of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m3. This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.
    Environmental Science and Pollution Research 12/2014; 22(9). DOI:10.1007/s11356-014-3893-5 · 2.76 Impact Factor
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    • "Including NO 3 and more accurate secondary organic aerosol representations would likely lead to a larger influence of intercontinental transport than has been calculated here since these secondary PM 2.5 components can be formed far from the precursor emission location. However, estimated total PM 2.5 concentrations in some areas are substantially higher than those estimated by the coarsely resolved global models for the three PM 2.5 components we included in our PM 2.5 definition (e.g., Brauer et al. 2012). The 20 % emission reductions may have a smaller mortality benefit if we were able to include all PM 2.5 components in the PM 2.5 definition since evidence suggests that the concentration-response curve flattens out at high concentrations (e.g., Pope et al. 2009, 2011). "
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    ABSTRACT: Fine particulate matter with diameter of 2.5 μm or less (PM 2.5) is associated with premature mortality and can travel long distances, impacting air quality and health on intercontinental scales. We estimate the mortality impacts of 20 % anthropogenic primary PM 2.5 and PM 2.5 precursor emission reductions in each of four major industrial regions (North America, Europe, East Asia, and South Asia) using an ensemble of global chemical transport model simulations coordinated by the Task Force on Hemispheric Transport of Air Pollution and epidemiologically-derived concentration-response functions. We estimate that while 93–97 % of avoided deaths from reducing emissions in all four regions occur within the source region, 3–7 % (11,500; 95 % confi-dence interval, 8,800–14,200) occur outside the source region Electronic supplementary material The online version of this article (doi:10.1007/s11869-014-0248-9) contains supplementary material, which is available to authorized users.
    Air Quality Atmosphere & Health 09/2014; 7(3). DOI:10.1007/s11869-014-0248-9 · 1.46 Impact Factor
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    • "Air pollution is recognized as a priority global health issue, affecting millions in both the developed and developing world (Brauer et al., 2012). Early studies have found that socioeconomic disparities in air pollution exposure and related health effects are prevalent (Zanobetti and Schwartz, 2000; O'Neill et al., 2003). "
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    ABSTRACT: Recent studies examining racial and ethnic inequities in exposure to urban air pollution have led to advances in understanding the nature and extent of overall concentration exposures by pollutant, demarcated by disadvantaged groups. However, the stability of inequities at various spatial units and the exposure by air pollution sources are often neglected. In this case study from the Dallas-Fort Worth (Texas, USA) area, we used Geographic Information Systems (GIS) and an air dispersion model to estimate environmental justice impacts at different spatial scales (i.e., zip code, census tract, block group) and by source (i.e., industrial pollution sources, vehicle pollution sources, industry and vehicle pollution sources combined). Using whites as a reference, blacks and other races were more likely to be exposed to higher sulfur dioxide (SO2) concentrations although the Odds Ratio (OR) varied substantially by pollution source type [e.g., industrial pollution source based: (OR=1.80; 95% CI (Confidence Interval): 1.79-1.80) vs. vehicle pollution source based: (OR=2.70; 95% CI: 2.68-2.71)] and varied less between spatial scales [for vehicle pollution sources, (OR=2.70; 95% CI: 2.68-2.71) at the census tract level but was (OR=2.54; 95% CI: 2.53-2.55) at the block group scale]. Similar to the pattern of racial inequities, people with less education (i.e., less than 12 years of education) and low income (i.e., per capital income below $20 000) were more likely to be exposed to higher SO2 concentrations, and those ORs also varied greatly with the pollution sources and slightly with spatial scales. It is concluded that the type of pollution source plays an important role in SO2 pollution exposure inequity assessment, while spatial scale variations have limited influence. Future studies should incorporate source-specific exposure assessments when conducting studies on environmental justice.
    Atmospheric Pollution Research 07/2014; 5(3). DOI:10.5094/APR.2014.058 · 1.23 Impact Factor
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