Exposure assessment for estimation of the global burden of disease attributable to outdoor air pollution.
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.
- Environmental Health Perspectives 01/2014; 122(1):A6-7. · 7.26 Impact Factor
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ABSTRACT: The deteriorating air quality in eastern China including the Yangtze River Delta is attracting growing public concern. In this study, we measured the ambient PM10 and fine PM2.5 in the mega-city, Nanjing at four different times. The 24-h average PM2.5 and PM10 mass concentrations were 0.033-0.234 and 0.042-0.328 mg/m(3), respectively. The daily PM10 and PM2.5 concentrations were 2.9 (2.7-3.2, at 95% confidence interval) and 4.2 (3.8-4.6) times the WHO air quality guidelines of 0.025 mg/m(3) for PM2.5 and 0.050 mg/m(3) for PM10, respectively, which indicated serious air pollution in the city. There was no obvious weekend effect. The highest PM10 pollution occurred in the wintertime, with higher PM2.5 loadings in the winter and summer. PM2.5 was correlated significantly with PM10 and the average mass fraction of PM2.5 in PM10 was about 72.5%. This fraction varied during different sampling periods, with the lowest PM2.5 fraction in the spring but minor differences among the other three seasons.Journal of Environmental Science and Health Part A Toxic/Hazardous Substances & Environmental Engineering 01/2014; 49(2):171-8.
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ABSTRACT: There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 μm in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial-temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003-2005. Via cross-validation experiments, our model had an out-of-sample prediction R(2) of 0.78 and a root mean-squared error (RMSE) of 3.61 μg/m(3) between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial-temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined.Journal of Exposure Science and Environmental Epidemiology advance online publication, 25 December 2013; doi:10.1038/jes.2013.90.Journal of Exposure Science and Environmental Epidemiology 12/2013; · 3.19 Impact Factor