The study has three important results. First, daily BG estimates for ambient PM2.5 and ozone concentration were comparable to data observed at monitoring sites, which suggested that inverse-distance weighing was an appropriate method to generate estimates for BGs from gridded data. The second important result was that we generated daily potential population exposure estimates, for both PM2.5 and ozone, for various CGUs from BG to state and the U.S. Such population exposure estimates for small areas such as census tracts and counties are very valuable for conducting health impact studies. Moreover, this result highlights the need for investigation and intervention in places with higher estimated daily potential population exposures (not concentrations) and/or longer duration.
The geographical patterns of PM2.5
and ozone found (especially at census tract level) were generally consistent with the ranking of most polluted cities (by year round particle pollution and ozone, respectively), provided by the American Lung Association - available at http://www.stateoftheair.org/
]. The highest daily potential population exposure to ambient PM2.5
in the west coast and northwest U.S. may be largely contributed by organic carbon due to high biomass burning such as wildfires, waste burning, and woodstoves [34
], though nitrate, sulfate, or crustal material may also represent substantial components of PM2.5
for the western U.S. [36
]. The higher daily potential population exposure to PM2.5
in other areas and ozone in general may mainly occur in those megacities or large metropolitan areas where ozone precursors such as volatile organic compounds and oxides of nitrogen produced by heavy traffic (also contribute to organic carbon and nitrite for PM2.5
) and electric utilities and industrial boilers (also contribute to sulfate and nitrite for PM2.5
) are concentrated [36
The third important result was that we generated population at risk for each CGU from BG to state and the U.S. based on the NAAQS for PM2.5 and ozone. This result provides a hierarchical structure that links hazardous pollution to population affected at different geographic levels. For example, population at risk presented at the state level could be easily traced back to specific CGUs, where information on potential population exposures to ambient air pollutants and population size is needed at smaller CGUs. Such detailed information on potential population exposure level and size of population affected could be used to facilitate communications among public health professionals and/or policy makers across different levels of jurisdiction and help them prioritize resources based on size of population affected and duration of exposures to ambient air pollutants.
There are several limitations. First, we assumed independence among the nearest four grids. This could potentially underestimate the standard errors associated with BG estimates. Second, we used the BG centroid to represent the entire BG area, which on average contains about 39 census blocks [38
]. However, in reality, ambient PM2.5
or ozone may vary within a BG. Although we thought to convert gridded concentration data to census blocks (the smallest CGU in the U.S.), we were limited to BGs because population data were not available on an annual basis at block level to allow us to generate potential population exposure estimates for larger CGUs. Third, like other studies, we could not account for net population gain or loss for a BG on a daily basis due to population movement across BGs.
An additional limitation was associated with the uncertainty of 36 km- versus 12 km-gridded data. For example, BG estimates from 36 km-grids were slightly more approximate to ground monitoring data than those estimated from 12 km-grids. This may be explained by different sets of input variables included in 36 km- versus 12 km-CMAQ modeling system [18
]. We compared 12 km- and 36 km-gridded data against values observed at the nearest monitoring site within specific grids (in an eastern portion of the U.S.) and the comparison statistics (e.g., MAD and R) showed the same pattern as in Table (data not shown): 36 km-gridded data were more approximate to the observed values than 12 km-gridded data. Thus, interpretations of results found must be considered in the context of the limitations of this study.